Models that accurately predict forest interception are essential both for water balance studies and for assessing watershed responses to changes in land use and the long-term climate variability. This paper compares the performance of four rainfall interception models-the sparse Gash (1995), Rutter et al. (1975), Liu (1997) and two new models (NvMxa and NvMxb)-using data from four spatially extensive, structurally diverse forest ecosystems in Mexico. Ninety-eight case studies measuring interception in tropical dry (25), arid/semi-arid (29), temperate (26), and tropical montane cloud forests (18) were compiled and analyzed. Coefficients derived from raw data or published statistical relationships were used as model input to evaluate multi-storm forest interception at the case study scale. On average empirical data showed that, tropical montane cloud, temperate, arid/semi-arid and tropical dry forests intercepted 14%, 18%, 22% and 26% of total precipitation, respectively. The models performed well in predicting interception, with mean deviations between measured and modeled interception as a function of total precipitation (ME) generally 0.66. Model fitting precision was dependent on the forest ecosystem. Arid/semi-arid forests exhibited the smallest, while tropical montane cloud forest displayed the largest ME deviations. Improved agreement between measured and modeled data requires modification of in-storm evaporation rate in the Liu; the canopy storage in the sparse Gash model; and the throughfall coefficient in the Rutter and the NvMx models. This research concludes on recommending the wide application of rainfall interception models with some caution as they provide mixed results. The extensive forest interception data source, the fitting and testing of four models, the introduction of a new model, and the availability of coefficient values for all four forest ecosystems are an important source of information and a benchmark for future investigations in this
J. Schellekensa; F.N. Scatenab; L.A. Bruijnzeela; A.J. \\t Wickela
Recent surveys of tropical forest water use suggest that rainfall interception by the canopy is largest in wet maritime locations. To investigate the underlying processes at one such locationâthe Luquillo Experimental Forest in eastern Puerto Ricoâ66 days of detailed throughfall and above-canopy climatic data were collected in 1996 and analysed using the Rutter and...
Qingfu Xiao; E. Gregory McPherson
A rainfall interception study was conducted in Oakland, California to determine the partitioning of rainfall and the chemical composition of precipitation, throughfall, and stemflow. Rainfall interception measurements were conducted on a gingko (Ginkgo biloba) (13.5 m tall deciduous tree), sweet gum (Liquidambar styraciflua) (8...
Klaassen, W; Bosveld, F; de Water, E
Intercepted rainfall may be evaporated during or after the rain event. Intercepted rain is generally determined as the difference between rainfall measurements outside and inside the forest. Such measurements are often used to discriminate between water storage and evaporation during rain as well.
Fernandes, Rafael Pires; Silva, Robson Willians da Costa; Salemi, Luiz Felippe; Andrade, Tatiana Morgan Berteli de; Moraes, Jorge Marcos de; Dijk, Albert I. J. M. Van; Martinelli, Luiz Antonio
The expansion of sugarcane plantations in Brazil has raised concerns regarding its hydrological impacts. One of these impacts is related to rainfall interception, which can be expected to vary in response to substantial changes in canopy structure throughout the cropping cycle. We collected field measurements to determine interception losses and interpreted the observations using an adapted Gash model during different stages of a sugarcane ratoon cropping cycle. Cumulative gross rainfall (PG), throughfall (TF) and stemflow (SF) were measured biweekly, along with vegetation structure measurements of leaf area index (LAI) and plant height. For the first 300 days after the first harvest, the cumulative PG of 1095 mm was partitioned into 635 mm TF (58%) and 263 mm SF (24%). The inferred interception loss (IL) was 263 mm (24%). There was a gradual and clear increase in IL from 3% to 46% while partitioning between TF and SF also changed during ratoon regrowth. After model parameter optimisation, observed IL was simulated satisfactorily. Model estimates suggested that evaporation from the saturated canopy is the main IL pathway, followed by evaporation after storms. Plant architecture, LAI and meteorological conditions during the cropping cycle appeared the main factors determining IL.
Timothy E. Link; Mike Unsworth; Danny. Marks
Net canopy interception (Inet) during rainfall in an old-growth Douglas-fir-western hemlock ecosystem was 22.8 and 25.0% of the gross rainfall (PG) for 1999 and 2000, respectively. The average direct throughfall proportion (p) and canopy storage capacity (
Xiang Li; Qingfu Xiao; Jianzhi Niu; Salli Dymond; E. Gregory McPherson; Natalie van Doorn; Xinxiao Yu; Baoyuan Xie; Kebin Zhang; Jiao Li
Rainfall interception research in forest ecosystems usually focuses on interception by either tree crown or leaf litter, although the 2 components interact when rainfall occurs. A process-based study was conducted to jointly measure rainfall interception by crown and litter and the interaction between the 2 interception processes for 4 tree species (...
Tarso Oliveira, Paulo; Wendland, Edson; Nearing, Mark; Perea Martins, João
The Brazilian Cerrado plays a fundamental role in water resources dynamics because it distributes fresh water to the largest basins in Brazil and South America. In recent decades, the native Cerrado vegetation has increasingly been replaced by agricultural crops and pasture. These land cover and land use changes have altered the hydrological processes. Meanwhile, little is known about the components of the water balance in the Brazilian Cerrado, mainly because the experimental field studies in this region are scarce or nonexistent. The objective of this study was to evaluate two hydrological processes under native Cerrado vegetation, the canopy interception (CI) and the surface runoff (R). The Cerrado physiognomy was classified as "cerrado sensu stricto denso" with an absolute density of 15,278 trees ha-1, and a basal area of 11.44 m2 ha-1. We measured the gross rainfall (P) from an automated tipping bucket rain gauge (model TB4) located in a tower with 11 m of height on the Cerrado. Throughfall (TF) was obtained from 15 automated tipping bucket rain gauges (model Davis) spread below the Cerrado vegetation and randomly relocated every month during the wet season. Stemflow (SF) was measured on 12 trees using a plastic hose wrapped around the trees trunks, sealed with neutral silicone sealant, and a bucket to store the water. The canopy interception was computed by the difference between P and the sum of TF and SF. Surface runoff under undisturbed Cerrado was collected in three plots of 100 m2(5 x 20 m) in size and slope steepness of approximately 0.09 m m-1. The experimental study was conducted between January 2012 and November 2013. We found TF of 81.0% of P and SF of 1.6% of P, i.e. the canopy interception was calculated at 17.4% of P. There was a statistically significant correlation (p 0.8. Our results suggest that the rainfall intensity, the characteristics of the trees trunks (crooked and twisted) and stand structure are the main factors that have influenced
Moreno-Pérez, M. F.; Roldán-Cañas, J.; Cienfuegos, I.
The study of rainfall interception by the canopy of the vegetation is of great importance in the basin water balance, because a large part returns to the atmosphere as evaporation. The presence or absence of vegetation not only affects the amount of rainfall that reaches the ground level also affects the moisture content in soil and surface runoff. In arid or semiarid regions there are few studies related to the Mediterranean vegetation and its relationship to hydrological processes. Furthermore, most studies have characterized the interception by rainfall simulators in the laboratory. The aim of this study was to evaluate in situ the amount and distribution of rainfall through the process of interception by the canopy of trees and shrubs present in the hydrologic watershed of "The Cabril" (Córdoba, southern Spain). The predominant vegetation is scrub, composed mostly of rockrose (Cistus ladanifer), and arboreal formations of tree pines (Pinus pinea). The record of precipitation was performed using a rain gauge tipping bowl Eijkelkamp mark during periods of rain occurred in 2010 and 2011. The amount of precipitation intercepted by the canopy has been determined indirectly from the difference between incident precipitation and rain that passes through the canopy of vegetation, which is divided into the flow of throughfall and cortical flow. To measure the throughfall the soil surface was waterproofed. Throughfall volume that is generated after each rain event is collected in four tanks of 200 liters capacity interconnected. For measurement of cortical flow a spiral hose previously cut lengthwise was placed around the trunk in the case of tree pines. In rockrose, a container was installed around it at its base. Monitoring soil moisture was determined by moisture probes 6 Delta-T SM200 randomly distributed, which records the water content of the topsoil. Compared with rockrose, there is a higher percentage of interception in pine and lowest percentage of cortical
Luzia Ferreira da Silva
Full Text Available Rainfall interception by the two most usual species in forest urban spaces was analysed by measuring of interception (I or interception losses, through fall (Th, stem flow (St and gross precipitation (Pg. The chosen species were Caesalpinia pluviosa DC. (Fabaceae: Caesalpinoideae or sibipiruna, and Tipuana tipu O. Kuntze (Fabaceae: Faboideae or tipuana. The individuals analysed were more than 50 years old, with three separate individuals and three individuals in each studied group of species at the campus of ”Luiz de Queiroz” College of Agriculture (University of Sao Paulo, Piracicaba. The experiments were carried out from January to February 2007. Water was collected using seven-litre pails, in the edges and in the centre of the canopies. A high correlation of Th with Pg was observed on the centre of the crow of tipuana and by the edges of sibipiruna. St and I had low correlation with Pg for both species. The average of rain interception was greater in the edges of the crow of sibipiruna individuals, 60.6%, and in the centre of tipuana crow, 59.40%. Thus, both species intercepted up to 60% of the water rainfall, which indicates a great potential of both species for arborisation in urban environments.
Xiang Li; Qingfu Xiao; Jianzhi Niu; Salli Dymond; Natalie S. van Doorn; Xinxiao Yu; Baoyuan Xie; Xizhi Lv; Kebin Zhang; Jiao Li
Rainfall interception by a tree's crown is one of the most important hydrological processes in an ecosystem, yet the mechanisms of interception are not well understood. A process-based experiment was conducted under five simulated rainfall intensities (from 10 to 150Â mmÂ hâ1) to directly quantify tree crown interception and examine the effect...
Full Text Available The interception was recognized as an important part of the catchment water balance in temperate climate. The mountainous forest ecosystem at experimental headwater catchment Liz has been subject of long-term monitoring. Unique dataset in terms of time resolution serves to determine canopy storage capacity and free throughfall. Spatial variability of throughfall was studied using one weighing and five tipping bucket rain gauges. The basic characteristics of forest affecting interception process were determined for the Norway spruce stand at the experimental area - the leaf area index was 5.66 - 6.00 m2 m-2, the basal area was 55.7 m2 ha-1, and the crown closure above individual rain gauges was between 19 and 95%. The total interception loss in both growing seasons analyzed was 34.5%. The mean value of the interception capacity determined was about 2 mm. Throughfall exhibited high variability from place to place and it was strongly affected by character of rainfall. On the other hand, spatial pattern of throughfall in average showed low variability.
Evaporation of canopy interception can be divided into three phases: evaporation during rainfall IR, storm break time when it stops raining temporarily ISbt, and after cessation of rainfall IAft. In this study, IR, ISbt, and IAft were measured using model forests, i.e. plastic Christmas tree stands. The method and preliminary results are described in Murakami and Toba (2013). Christmas trees with original height of 65 cm (small tree) and 150 cm (large tree) were placed on three trays. Small trees were set on Tray #1. The same trees with height of 110 cm (extended using plastic rod) were placed on Tray #2, and large trees with height of 240 cm (raised using iron pipe) were set on Tray #3. The dimension of Tray #1 and #2 were a 180-cm square, and Tray #3 was a 360-cm square. Measurement was conducted under natural rainfall. Gross rainfall and net rainfall of each tray (discharge from each tray), in addition to single tree weight on Tray #1 and #3 were measured. Initial tree density of each tray was 41 trees per tray. Thinning was conducted in the middle of the experiment period and it was reduced to 25 trees per tray on Tray #2 and #3, but Tray #1 was unthinned. Total rainfall for pre-thinning period was 204.2 mm with 16 rain events and canopy interception CI was 10.8% (22.0 mm), 13.9% (28.3 mm) and 16.3% (33.4 mm) of rainfall for Tray #1, #2 and #3, respectively. Amount of rainfall for after thinning period was 291.5 mm with 24 rain events and canopy interception was 12.7% (40.0 mm), 21.7% (63.3 mm) and 13.6% (39.7 mm) of rainfall for Tray #1, #2 and #3, respectively. It is noteworthy that canopy interception increased on Tray #2 after thinning. IR, ISbt, and IAft were calculated for each tray using gross rainfall, net rainfall and the weight of single tree. Before thinning the value of IR/CI was 67.3% to74.9% and IAft occupied the remaining part of CI with ISbt/CI being nearly equal to zero. After thinning, IR/CI ranged from 65.3% to 93.8%. Both before and after
Moura, A. E.; Montenegro, S. M.; Silva, B. B.; Bartlett, M. S.; Porporato, A. M.; Antonino, A. C.
Quantifying the effects of land use change and rainfall variability in seasonal, dry ecosystems is crucial to sustainable management of soil and water resources. In particular, changes in rainfall interception effects on hydrologic partitioning and soil erosion due to land use change are among the least known processes, despite their importance for water resource managements, in terms of water availability for ecosystem and society and water quality and erosion problems. In this work we quantify the interception losses in different types of vegetation (coffee, lemon and vegetation of natural forest) found in the Tapacurá basin in the Pernambuco state of NE Brazil, coupling field experiments and analytical models. The interception losses were measured with rain gauges installed in three types of vegetation along with stemflow collectors. Close to the coffee plantation, a meteorological station was also installed for measurement of the necessary variables for the model calibrations. As expected, the results show that rainfall events of smaller magnitude proportionally have larger relative interception losses, with larger differences in the wet season. The model results also allow us to quantify the nonlinear behavior of the interception process, at the same time providing a valuable tool to estimate the interception loss due to changes in vegetation and rainfall regime and thus to improve water resource management in seasonally dry tropics .
Full Text Available The factors controlling seasonal rainfall interception loss are investigated by using a double-mass curve analysis, based on direct measurements of high-temporal resolution gross rainfall, throughfall and stemflow from 43 rainfall events that occurred in central Taiwan from April 2008 to April 2009. The canopy water storage capacity for the wet season was estimated to be 1.86 mm, about twice that for the dry season (0.91 mm, likely due to the large reduction in the leaf area index (LAI from 4.63 to 2.23 (m2·m−2. Changes in seasonal canopy structure and micro-meteorological conditions resulted in temporal variations in the amount of interception components, and rainfall partitioning into stemflow and throughfall. Wet canopy evaporation after rainfall contributed 41.8% of the wet season interception loss, but only 17.1% of the dry season interception loss. Wet canopy evaporation during rainfall accounted for 82.9% of the dry season interception loss, but only 58.2% of the wet season interception loss. Throughfall accounted for over 79.7% of the dry season precipitation and 76.1% of the wet season precipitation, possibly due to the change in gap fraction from 64.2% in the dry season to 50.0% in the wet season. The reduced canopy cover in the dry season also produced less stemflow than that of the wet season. The rainfall stemflow ratio ( P s f / P g was reduced from 12.6% to 8.9%. Despite relatively large changes in canopy structure, seasonal variation of the ratio of rainfall partitioned to interception was quite small. Rainfall interception loss accounted for nearly 12% of gross precipitation for both dry and wet seasons.
Hölscher, Dirk; Köhler, Lars; van Dijk, Albert I. J. M.; Bruijnzeel, L. A.(Sampurno)
The abundant epiphyte vegetation of upper montane tropical rain forests, which in terms of biomass is mainly composed of non-vascular plants (mosses, liverworts and lichens), can be expected to influence the magnitude of canopy water fluxes such as rainfall interception. The objects of this study were to: (i) estimate stand canopy water storage characteristics, (ii) determine rainfall interception by the canopy as a whole, and (iii) adapt an analytical model of rainfall interception, to enable the quantification of the contribution by non-vascular epiphytes to total interception. The studied old-growth forest in the Cordillera de Talamanca, Costa Rica, was 35 m tall, dominated by oaks, and little affected by fog. The estimated leaf area index of the trees was 7.7 m 2 m -2, which combined with results from a leaf wetting experiment gave a tree leaf water storage capacity of 1.08 mm at the stand level. The biomass of non-vascular epiphytes amounted to 1.9 t ha -1 dry weight. Monthly moss water contents measured in situ ranged between 24 and 406% of moss dry weight, corresponding to a maximum moss water storage of 0.81 mm at stand level. Seasonal variation in moss water contents was reproduced satisfactorily by a running water balance model. A modified analytical interception model, which incorporated the moss water balance model, was applied. Weekly sums of observed throughfall, stemflow and interception measurements were available for comparison and amounted to 70, 2 and 28% of the associated 2150 mm of rain. The model predicted the observed values quite well and suggested that mosses contributed about 6% to the modelled interception total. Hence, the hydrological importance of epiphytes in the studied forest was rather limited despite their considerable maximum water storage capacity. This is thought to reflect the fact that under the prevailing rainfall conditions only a fraction of the potential storage is actually available.
Dohnal, M.; Černý, T.; Votrubová, J.; Tesař, Miroslav
Roč. 62, č. 4 ( 2014 ), s. 277-284 ISSN 0042-790X R&D Projects: GA TA ČR TA02021451 Institutional support: RVO:67985874 Keywords : Interception loss * Interception capacity * Free throughfall * Evaporation * Hydrological balance of vegetation cover Subject RIV: DA - Hydrology ; Limnology Impact factor: 1.486, year: 2014
Dohnal, M.; Černý, T.; Votrubová, J.; Tesař, Miroslav
Roč. 62, č. 4 (2014), s. 277-284 ISSN 0042-790X R&D Projects: GA TA ČR TA02021451 Institutional support: RVO:67985874 Keywords : Interception loss * Interception capacity * Free throughfall * Evaporation * Hydrological balance of vegetation cover Subject RIV: DA - Hydrology ; Limnology Impact factor: 1.486, year: 2014
Belmonte Serrato, F.; Romero Diaz, A.
In this paper a simple technique for field measurement of rain water loss arising from interception and water flows associated with species of small Mediterranean shrub is described: the interception flow collection box. This technique solves the problem of installing devices to control stemflow in species with a multiple trunk and demonstrates its efficiency through the results obtained from the data observed for three species of semi-arid Mediterranean shrub: Juniperus oxycedrus, Rosmarinus officinalis and Thymus vulgaris. Finally, the empirical equations for the prediction of throughfall, stemflow and rain water loss through interception are presented for the three selected species and the validity of the technique employed is established.
Van Dijk, A.I.J.M.; et al., et al.; Moors, E.J.
Evaporation from wet canopies (. E) can return up to half of incident rainfall back into the atmosphere and is a major cause of the difference in water use between forests and short vegetation. Canopy water budget measurements often suggest values of E during rainfall that are several times greater
Guevara-Escobar, A.; González-Sosa, E.; Véliz-Chávez, C.; Ventura-Ramos, E.; Ramos-Salinas, M.
SummaryInterception of rainfall by urban trees can be an important component of urban landscapes. This work evaluated rainfall interception and distribution patterns of gross precipitation around the canopy of a single evergreen tree Ficus benjamina (L.). Nineteen individual storms occurring from July to October, 2005, were analyzed. Total precipitation for the studied period was 152 mm representing 46% of the annual precipitation. Rainfall was partitioned as follows: 38.1% throughfall, 2.4% stemflow, and 59.5% interception by the tree canopy. Canopy saturation was estimated at 1.5 mm using a linear relationship between throughfall and stemflow. Average time for saturation of canopy was 19.5 min. The screen effect was important and accounted for 18.7% of the interception losses by the tree canopy alone. A kriging model was used to explore spatial distribution patterns of rainfall and the screen effect around the projected crown. The results indicated that the tree modifies the precipitation pattern around the tree and suggested that these patterns were similar among events.
Gonzalez-Sosa, Enrique; Mastachi-Loza, Carlos Alberto; Braud, Isabelle; Guevara-Eescobar, Aurelio
The strong pressure over natural ressources and the accelerated population growth modify natural ecosystems and decrease the canopy cover. The ecosystems of central Mexico plateau are not an exception. Although it is a natural semi-arid region induced by the continental screen effect of the Sierra mountains that regulates the humidity entrance from the Gulf and the Pacific, the semi-arid ecosystems are degraded day after day, showing a clear tendency to desertification. The aim of the study is to show the importance of rainfall interceptionby the vegetation of the semiarid of central plateau of Mexico, EI, on the annual water balance. This work was carried out during 2006 in three sites: one located in the Guanajuato state, "El Carmen", and two in the Queretaro state, "Amazcala" and "Cadereyta". The experimental sites are separated by at least 60 km. In each site two isolated trees representative of the dominant species Prosopis laevigata and Acacia farnesiana were selected. The methodology developed by Guevara Escobar et al. (J. Hydrology, 2007) was used to instrument the trees to measure EI. The data were modeled using the models described by Rutter et al. (1971), Gash (1979) and multiple linear regressions in order to better understand the interception process in the semi-arid ecosystems. Precipitation in 2006 in Carmen and Cadereyta was 770 and 732 mm respectively while Amazcala reached 451 mm of precipitation during the August-November period. On the measurement period, interception by Acacia farnesiana was 30%, 20% and 15% for Cadereyta, El Carmen and Amazcala, respectively. The figures were 27%, 21% and 14%. for Prosopis laevigata. The performance of the three models in simulated the measured data was satisfactory, with efficiencies ranging from 0,74 to 0.99 and RMSE ranging from 0,83 to 2,0 mm. The results show that the rainfall interception impact on the water balance at catchment scale would be considerable in case of a total cover by the studied
Pereira, F.L.; Gash, J.H.C.; David, J.S.; Valente, F,
A new approach is suggested for estimating evaporation of intercepted rainfall from single trees in sparse forests. It is shown that, theoretically, the surface temperature of a wet tree crown will depend on the available energy and windspeed. But for a fully saturated canopy under rainy conditions,
Zhang, Zhi-Shan; Zhao, Yang; Li, Xin-Rong; Huang, Lei; Tan, Hui-Juan
In water-limited regions, rainfall interception is influenced by rainfall properties and crown characteristics. Rainfall properties, aside from gross rainfall amount and duration (GR and RD), maximum rainfall intensity and rainless gap (RG), within rain events may heavily affect throughfall and interception by plants. From 2004 to 2014 (except for 2007), individual shrubs of Caragana korshinskii and Artemisia ordosica were selected to measure throughfall during 210 rain events. Various rainfall properties were auto-measured and crown characteristics, i.e., height, branch and leaf area index, crown area and volume of two shrubs were also measured. The relative interceptions of C. korshinskii and A. ordosica were 29.1% and 17.1%, respectively. Rainfall properties have more contributions than crown characteristics to throughfall and interception of shrubs. Throughfall and interception of shrubs can be explained by GR, RI60 (maximum rainfall intensities during 60 min), RD and RG in deceasing importance. However, relative throughfall and interception of two shrubs have different responses to rainfall properties and crown characteristics, those of C. korshinskii were closely related to rainfall properties, while those of A. ordosica were more dependent on crown characteristics. We highlight long-term monitoring is very necessary to determine the relationships between throughfall and interception with crown characteristics.
Dekker, Stefan; Staal, Arie; Tuinenburg, Obbe
In the Amazon, deep-rooted trees increase local transpiration and high tree cover increase local interception evaporation. These increased local evapotranspiration fluxes to the atmosphere have both positive effects on forests down-wind, as they stimulate rainfall. Although important for the functioning of the Amazon, we have an inadequate assessment on the strength and the timing of these forest-rainfall feedbacks. In this study we (i) estimate local forest transpiration and local interception evaporation, (ii) simulate the trajectories of these moisture flows through the atmosphere and (iii) quantify their contributions to the forest-rainfall feedback for the whole Amazon basin. To determine the atmospheric moisture flows in tropical South America we use a Lagrangian moisture tracking algorithm on 0.25° (c. 25 km) resolution with eight atmospheric layers on a monthly basis for the period 2003-2015. With our approach we account for multiple re-evaporation cycles of this moisture. We also calculate for each month the potential effects of forest loss on evapotranspiration. Combined, these calculations allow us to simulate the effects of land-cover changes on rainfall in downwind areas and estimate the effect on the forest. We found large regional and temporal differences in the importance how forest contribute to rainfall. The transpiration-rainfall feedback is highly important during the dry season. Between September-November, when large parts of the Amazon are at the end of the dry season, more than 50% of the rainfall is caused by the forests upstream. This means that droughts in the Amazon are alleviated by the forest. Furthermore, we found that much moisture cycles several times during its trajectory over the Amazon. After one evapotranspiration-rainfall cycle, more than 40% of the moisture is re-evaporated again. The interception-evaporation feedback is less important during droughts. Finally from our analysis, we show that the forest-rainfall feedback is
Maurer, Thomas; Schapp, Andrea; Büchner, Steffen; Menzel, Hannes; Hinz, Christoph
Variability of rainfall and throughfall is an essential characteristic of the water balance at spatial scales ranging from meters to hundreds of meters or even kilometers. The amount of throughfall is governed by the characteristics of the vegetation canopy and the involved interception and stemflow effects. In initial, developing ecosystems, distinct patterns of the growing vegetation (e.g. patchiness) supposedly govern the spatial distribution of water in the system, thereby initiating and supporting hydro-ecological feedback processes. Questions are i) is the spatial variability of vegetation relevant for the system as a whole, and ii) how does the distribution of the effective precipitation (i.e. the infiltration) change over time in dependency of vegetation succession? We present the first results of a spatially distributed measurement approach of surface-near precipitation on the constructed catchment "Hühnerwasser" ("Chicken Creek"). The 6-ha site is located in the recultivation area of the lignite open-cast mine "Welzow-Süd" in Lower Lusatia, Brandenburg, Germany. Here, the free development of an initial ecosystem is investigated since September 2005. After eight years of succession, the spatial distribution of plant species is highly heterogeneous, and gains increasing influence on throughfall patterns, thus impacting the distribution of soil humidity and possibly even surface runoff. For spatially distributed precipitation measurement, 47 tipping bucket rain gauges were installed in heights of 0.5 m and 1.0 m along two transects on the catchment. Rain gauge data were collected by a wireless sensor node network provided by the Sens4U joint research project. The transects run NW-SE and NE-SW and cover the range of plant communities presently existing in the ecosystem: locust copses, dense sallow thorn bushes and reeds, base herbaceous and medium-rise small-reed vegetation, and open areas covered by moss and lichens. The raw measurement data were
Nóbrega, Cristina; Pereira, Fernando L.; Valente, Fernanda
Water losses associated to the rainfall interception process by trees can be an important component of the local hydrologic balance and must be accounted for when implementing any sustainable water management programme. In many dry areas of the Mediterranean region where agro-forestry systems are common, those programmes are crucial to foster adequate water conservation measures. Recent studies have shown that the evaluation of interception loss in sparse forests or tree plantations should be made for individual trees, being the total value determined as the sum of the individual contributions. Following this approach, rainfall interception was measured and modelled over two years, in an isolated Olea europeaea L. tree, in a traditional low-density olive grove in Castelo Branco, central Portugal. Total interception loss over the experimental period was 243.5 mm, on a tree crown projected area basis, corresponding to 18.0% of gross rainfall (Pg). Modelling made for each rainfall event using the sparse version of the Gash model, slightly underestimated interception loss with a value of 240.5 mm, i.e., 17.8 % ofPg. Modelling quality, evaluated according to a number of criteria, was good, allowing the conclusion that the methodology used was adequate. Modelling was also made on a daily basis, i.e., assuming a single storm per rainday. In this case, interception loss was overestimated by 12%, mostly because 72% of all rainfall events lasted for more than a day.
Pereira, Fernando L.; Gash, John H. C.; David, Jorge S.; David, Teresa S.; Monteiro, Paulo R.; Valente, Fernanda
Evaporation of rainfall intercepted by tree canopies is usually an important part of the overall water balance of forested catchments and there have been many studies dedicated to measuring and modelling rainfall interception loss. These studies have mainly been conducted in dense forests; there have been few studies on the very sparse forests which are common in dry and semi-arid areas. Water resources are scarce in these areas making sparse forests particularly important. Methods for modelling interception loss are thus required to support sustainable water management in those areas. In very sparse forests, trees occur as widely spaced individuals rather than as a continuous forest canopy. We therefore suggest that interception loss for this vegetation type can be more adequately modelled if the overall forest evaporation is derived by scaling up the evaporation from individual trees. The evaporation rate for a single tree can be estimated using a simple Dalton-type diffusion equation for water vapour as long as its surface temperature is known. From theory, this temperature is shown to be dependent upon the available energy and windspeed. However, the surface temperature of a fully saturated tree crown, under rainy conditions, should approach the wet bulb temperature as the radiative energy input to the tree reduces to zero. This was experimentally confirmed from measurements of the radiation balance and surface temperature of an isolated tree crown. Thus, evaporation of intercepted rainfall can be estimated using an equation which only requires knowledge of the air dry and wet bulb temperatures and of the bulk tree-crown aerodynamic conductance. This was taken as the basis of a new approach for modelling interception loss from savanna-type woodland, i.e. by combining the Dalton-type equation with the Gash's analytical model to estimate interception loss from isolated trees. This modelling approach was tested using data from two Mediterranean savanna-type oak
Holwerda, F; Bruijnzeel, L.A.; Muñoz-Villers, L.E.; Equihua, M.; Asbjornsen, H.
Rainfall and cloud water interception (CWI) were determined for a mature and a 19-year old secondary lower montane cloud forest in central Veracruz, Mexico. Cloud water was measured using a passive fog gauge, and consisted most likely of a mixture of fog and wind-driven drizzle. CWI by the canopy
Heilbrun, Kirk; Goldstein, Naomi E S; DeMatteo, David; Newsham, Rebecca; Gale-Bentz, Elizabeth; Cole, Lindsay; Arnold, Shelby
Behavioral health needs in justice-involved adolescents are an increasing concern, as it has been estimated that two-thirds of youths in the juvenile justice system now meet the criteria for one or more psychological disorders. This article describes the application of the Sequential Intercept Model (SIM), developed to describe five "points of interception" from standard prosecution into rehabilitation-oriented alternatives for adults (Munetz & Griffin, 2006), to juvenile justice. The five SIM intercepts are: (1) first contact with law enforcement or emergency services; (2) initial hearings and detention following arrest; (3) jails and courts (including problem-solving courts); (4) re-entry from jails, prisons and forensic hospitals; and (5) community corrections and community support, including probation and parole. Modifying the SIM for application with justice-involved adolescents, this article describes three examples of interventions at different intercepts: Intercept 1 (the Philadelphia Police School Diversion Program), Intercept 3 (problem-solving courts for juveniles), and Intercept 5 (juvenile probation). Relevant research evidence for each example is reviewed, and the further application of this model to juveniles is described. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
von Boetticher, Albrecht; Volkwein, Axel; Wendeler, Corinna
The design and certification of effective rockfall protection barriers is mainly achieved through 1:1 prototype testing. In order to reduce development costs of a prototype it is recommended that pre-studies using numerical simulations are performed. A large component to modelling rockfall protection systems is the numerical simulation of the nets. To date there exist several approaches to model the different mesh types such as ring nets or diagonal meshes (Nicot 1999, Cazzani et al. 2002, Volkwein 2004). However, the consideration of chain link meshes has not yet been realised. Chain link meshes are normally found as standard fence structures. However, they also exist in setups using high-strength steel and wire bundles. These variants show an enormous capacity to retain loads e.g. rockfalls, and at the same time are very efficient due to their low demand of steel material. The increasing application of chain link mesh in barrier systems requires an accurate model is available to complete prototype studies. A new approach now aims to perform a Finite Element simulation of such chain link meshes. The main challenge herein is to achieve the net deformation behaviour that is observed in field tests also in the simulation. A simulation using simple truss elements would not work since it neglects the out-of-plane-height of the mesh construction providing important reserves for local and global high deformations. Thus addressing this, a specially developed Discrete Element is able to reconstruct the mechanical behaviour of the single chain wire (bundles). As input parameters it utilises typical properties such as longitudinal and transversal mesh widths, and break loads resulting from in-plane-tension tests and steel strength. The single chain elements then can be combined to a complete mesh (e.g. 130 x 65 mm, 3 - 4 mm wire with a strength of 1770 N-mm2). Combining these elements with a supporting structure consisting of posts, ropes and energy absorbers, enables the
Siles, Pablo; Vaast, Philippe; Dreyer, Erwin; Harmand, Jean-Michel
Partitioning of gross rainfall into throughfall, stemflow and rainfall interception was assessed in Costa Rica during two rainy seasons (mean annual rainfall of 2900 mm) in two coffee systems: (1) a monoculture (MC) and (2) an agroforestry system (AFS) including Inga densiflora as the associated shade tree species. Coffee architecture, not LAI, appeared to be the main driver of stemflow as stemflow was higher for shaded coffee plants (10.6% of incident rainfall) than for coffee plants in MC (...
Leslie M. Reid; Jack Lewis
Rainfall, throughfall, and stemflow were monitored at 5-min intervals for 3 years in a 120-year-old forest dominated by redwood (Sequoia sempervirens) and Douglas-fir (Pseudotsuga menziesii) at the Caspar Creek Experimental Watersheds, located in northwest California, USA. About 2.5% of annual rainfall reaches the ground as...
Siles, Pablo; Vaast, Philippe; Dreyer, Erwin; Harmand, Jean-Michel
SummaryPartitioning of gross rainfall into throughfall, stemflow and rainfall interception was assessed in Costa Rica during two rainy seasons (mean annual rainfall of 2900 mm) in two coffee systems: (1) a monoculture (MC) and (2) an agroforestry system (AFS) including Inga densiflora as the associated shade tree species. Coffee architecture, not LAI, appeared to be the main driver of stemflow as stemflow was higher for shaded coffee plants (10.6% of incident rainfall) than for coffee plants in MC (7.2%), despite the fact that these shaded plants had lower LAI. The presence of Inga trees modified coffee architecture with shaded coffee plants presenting larger stems and branches resulting in higher coffee funneling ratio under shade. In AFS, coffee plants and trees accounted respectively for 88% and 12% of total stemflow which represented 11.8% of incident rainfall. AFS displayed larger cumulative stemflow and smaller total throughfall compared to MC. Cumulative throughfall expressed in % of the gross rainfall, differed between systems and monitoring periods and the trend showed a decrease with increasing LAI. Nevertheless, as stemflow measurement and interception loss estimation were done only during the second year of the study, the shade tree showed a low influence in increasing interception loss, as the combined LAI of coffee plants and shade trees was rather similar in AFS as that of coffee in MC. Furthermore, coffee plants accounted for the largest fraction of the interception loss in AFS as the coffee LAI was more than 3-fold that of shade trees.
Carlyle-Moses, D. E.; Lishman, C. E.
In the central interior of British Columbia (BC), Canada, the mountain pine beetle (Dendroctonus ponderosae Hopkins) (MPB) has severely affected the majority of pine species in the region, especially lodgepole pine (Pinus contorta Douglas ex Louden var. latifolia Engelm. ex S. Watson). The loss of mature lodgepole pine stands, including those lost to salvage logging, has resulted in an increase in the number of juvenile pine stands in the interior of BC through planting and natural regrowth. With this change from mature forests to juvenile forests at such a large spatial scale, the water balance of impacted areas may be altered, although the magnitude of such change is uncertain. Previous studies of rainfall partitioning by lodgepole pine and lodgepole pine dominated canopies have focused on mature stands. Thus, rainfall, throughfall and stemflow were measured and canopy interception loss was derived during the growing season of 2010 in a juvenile lodgepole pine dominated stand located approximately 60 km NNW of Kamloops, BC at 51°12'49" N 120°23'43" W, 1290 m above mean sea level. Scaling up from measurements for nine trees, throughfall, stemflow and canopy interception loss accounted for 87.7, 1.8 and 10.5 percent of the 252.9 mm of rain that fell over 38 events during the study period, respectively. The reformulated versions of the Gash and Liu analytical interception loss models estimated cumulative canopy interception loss at 24.7 and 24.6 mm, respectively, compared with the observed 26.5 mm; an underestimate of 1.8 and 1.9 mm or 6.8 and 7.2% of the observed value, respectively. Our results suggest that canopy interception loss is reduced in juvenile stands compared to their mature counterparts and that this reduction is due to the decreased storage capacity offered by these younger canopies. Evaporation during rainfall from juvenile canopies is still appreciable and may be a consequence of the increased proportion of the canopy exposed to wind during events.
Long, Jeffrey D.; Loeber, Rolf; Farrington, David P.
Two models for the analysis of longitudinal binary data are discussed: the marginal model and the random intercepts model. In contrast to the linear mixed model (LMM), the two models for binary data are not subsumed under a single hierarchical model. The marginal model provides group-level information whereas the random intercepts model provides…
Sarlikioti, V.; Marcelis, L.F.M.; Visser, de P.H.B.
A number of physiological tomato models have been proposed the last decades, their main challenge being the correct simulation of fruit yield. For this, an accurate simulation of light interception, and thus photosynthesis, is of primary importance. Light interception is highly dependent of the
L-F. Chu (Lan-Fen); M.J. McAleer (Michael); C-C. Chang (Ching-Chung)
textabstractIn this paper, the annual maximum daily rainfall data from 1961 to 2010 are modelled for 18 stations in Taiwan. We fit the rainfall data with stationary and non-stationary generalized extreme value distributions (GEV), and estimate their future behaviour based on the best fitting model.
L. Chu (LanFen); M.J. McAleer (Michael); C-H. Chang (Chu-Hsiang)
textabstractIn this paper, the annual maximum daily rainfall data from 1961 to 2010 are modelled for 18 stations in Taiwan. We fit the rainfall data with stationary and non-stationary generalized extreme value distributions (GEV), and estimate their future behaviour based on the best fitting model.
yield models should be used for planning and forecasting the yield of millet and sorghum in the study area. Key words: modelling, rainfall, yields, millet, sorghum. INTRODUCTION. Meteorological variables, such as rainfall parameters, temperature, sunshine hours, relative humidity, and wind velocity and soil moisture are.
J. P. Whiting
Full Text Available Annual rainfall time series for Sydney from 1859 to 1999 is analysed. Clear evidence of nonstationarity is presented, but substantial evidence for persistence or hidden states is more elusive. A test of the hypothesis that a hidden state Markov model reduces to a mixture distribution is presented. There is strong evidence of a correlation between the annual rainfall and climate indices. Strong evidence of persistence of one of these indices, the Pacific Decadal Oscillation (PDO, is presented together with a demonstration that this is better modelled by fractional differencing than by a hidden state Markov model. It is shown that conditioning the logarithm of rainfall on PDO, the Southern Oscillation index (SOI, and their interaction provides realistic simulation of rainfall that matches observed statistics. Similar simulation models are presented for Brisbane, Melbourne and Perth. Keywords: Hydrological persistence,hidden state Markov models, fractional differencing, PDO, SOI, Australian rainfall
Long, Jeffrey D; Loeber, Rolf; Farrington, David P
Two models for the analysis of longitudinal binary data are discussed: the marginal model and the random intercepts model. In contrast to the linear mixed model (LMM), the two models for binary data are not subsumed under a single hierarchical model. The marginal model provides group-level information whereas the random intercepts model provides individual-level information including information about heterogeneity of growth. It is shown how a type of numerical averaging can be used with the random intercepts model to obtain group-level information, thus approximating individual and marginal aspects of the LMM. The types of inferences associated with each model are illustrated with longitudinal criminal offending data based on N = 506 males followed over a 22-year period. Violent offending indexed by official records and self-report were analyzed, with the marginal model estimated using generalized estimating equations and the random intercepts model estimated using maximum likelihood. The results show that the numerical averaging based on the random intercepts can produce prediction curves almost identical to those obtained directly from the marginal model parameter estimates. The results provide a basis for contrasting the models and the estimation procedures and key features are discussed to aid in selecting a method for empirical analysis.
Modeling has been created for a Space-to-Surface system defined for an optimal trajectory for targeting in terminal phase with avoids an intercepting process. The modeling includes models for simulation atmosphere, speed of sound, aerodynamic flight and navigation by an infrared system. The modeling and simulation includes statistical analysis of the modeling results.
S. J. Sutanto
Full Text Available Knowledge of the water fluxes within the soil-vegetation-atmosphere system is crucial to improve water use efficiency in irrigated land. Many studies have tried to quantify these fluxes, but they encountered difficulties in quantifying the relative contribution of evaporation and transpiration. In this study, we compared three different methods to estimate evaporation fluxes during simulated summer conditions in a grass-covered lysimeter in the laboratory. Only two of these methods can be used to partition total evaporation into transpiration, soil evaporation and interception. A water balance calculation (whereby rainfall, soil moisture and percolation were measured was used for comparison as a benchmark. A HYDRUS-1D model and isotope measurements were used for the partitioning of total evaporation. The isotope mass balance method partitions total evaporation of 3.4 mm d−1 into 0.4 mm d−1 for soil evaporation, 0.3 mm d−1 for interception and 2.6 mm d−1 for transpiration, while the HYDRUS-1D partitions total evaporation of 3.7 mm d−1 into 1 mm d−1 for soil evaporation, 0.3 mm d−1 for interception and 2.3 mm d−1 for transpiration. From the comparison, we concluded that the isotope mass balance is better for low temporal resolution analysis than the HYDRUS-1D. On the other hand, HYDRUS-1D is better for high temporal resolution analysis than the isotope mass balance.
excavating) three open channel sections, namely, the circular, parabolic and trapezoidal sections using the conditions for best hydraulic performance for the channels. Applying the model to a numerical example, new dimensions of the new channel ...
Guillén Climent, M. Luz
The light energy absorbed by plant leaves drives fundamental physiological processes such as photosynthesis. The absorption of light occurs within the 400-700 nm spectral region, so it is called Photosynthetic Active Radiation, PAR. Thus, the fraction of intercepted PAR is called fIPAR. This thesis studies the estimation of fIPAR with high spatial resolution sensors and radiative transfer models in heterogeneous orchards. The objective is to obtain maps showing the spatial v...
Cuevas, Jaime; Granato, Italo; Fritsche-Neto, Roberto; Montesinos-Lopez, Osval A.; Burgueño, Juan; Bandeira e Sousa, Massaine; Crossa, José
In this study, we compared the prediction accuracy of the main genotypic effect model (MM) without G×E interactions, the multi-environment single variance G×E deviation model (MDs), and the multi-environment environment-specific variance G×E deviation model (MDe) where the random genetic effects of the lines are modeled with the markers (or pedigree). With the objective of further modeling the genetic residual of the lines, we incorporated the random intercepts of the lines (l) and generated another three models. Each of these 6 models were fitted with a linear kernel method (Genomic Best Linear Unbiased Predictor, GB) and a Gaussian Kernel (GK) method. We compared these 12 model-method combinations with another two multi-environment G×E interactions models with unstructured variance-covariances (MUC) using GB and GK kernels (4 model-method). Thus, we compared the genomic-enabled prediction accuracy of a total of 16 model-method combinations on two maize data sets with positive phenotypic correlations among environments, and on two wheat data sets with complex G×E that includes some negative and close to zero phenotypic correlations among environments. The two models (MDs and MDE with the random intercept of the lines and the GK method) were computationally efficient and gave high prediction accuracy in the two maize data sets. Regarding the more complex G×E wheat data sets, the prediction accuracy of the model-method combination with G×E, MDs and MDe, including the random intercepts of the lines with GK method had important savings in computing time as compared with the G×E interaction multi-environment models with unstructured variance-covariances but with lower genomic prediction accuracy. PMID:29476023
Wright, A. J.; Vrugt, J. A.; Walker, J. P.; Pauwels, V. R. N.
Floods are devastating natural hazards. To provide accurate, precise and timely flood forecasts there is a need to understand the uncertainties associated with temporal rainfall and model parameters. The estimation of temporal rainfall and model parameter distributions from streamflow observations in complex dynamic catchments adds skill to current areal rainfall estimation methods, allows for the uncertainty of rainfall input to be considered when estimating model parameters and provides the ability to estimate rainfall from poorly gauged catchments. Current methods to estimate temporal rainfall distributions from streamflow are unable to adequately explain and invert complex non-linear hydrologic systems. This study uses the Discrete Wavelet Transform (DWT) to reduce rainfall dimensionality for the catchment of Warwick, Queensland, Australia. The reduction of rainfall to DWT coefficients allows the input rainfall time series to be simultaneously estimated along with model parameters. The estimation process is conducted using multi-chain Markov chain Monte Carlo simulation with the DREAMZS algorithm. The use of a likelihood function that considers both rainfall and streamflow error allows for model parameter and temporal rainfall distributions to be estimated. Estimation of the wavelet approximation coefficients of lower order decomposition structures was able to estimate the most realistic temporal rainfall distributions. These rainfall estimates were all able to simulate streamflow that was superior to the results of a traditional calibration approach. It is shown that the choice of wavelet has a considerable impact on the robustness of the inversion. The results demonstrate that streamflow data contains sufficient information to estimate temporal rainfall and model parameter distributions. The extent and variance of rainfall time series that are able to simulate streamflow that is superior to that simulated by a traditional calibration approach is a
Shi, Zhongwen; Gu, Chongshi; Qin, Dong
This study determines dam deformation similarity indexes based on an analysis of deformation zoning features and panel data clustering theory, with comprehensive consideration to the actual deformation law of super-high arch dams and the spatial-temporal features of dam deformation. Measurement methods of these indexes are studied. Based on the established deformation similarity criteria, the principle used to determine the number of dam deformation zones is constructed through entropy weight method. This study proposes the deformation zoning method for super-high arch dams and the implementation steps, analyzes the effect of special influencing factors of different dam zones on the deformation, introduces dummy variables that represent the special effect of dam deformation, and establishes a variable-intercept panel model for deformation zoning of super-high arch dams. Based on different patterns of the special effect in the variable-intercept panel model, two panel analysis models were established to monitor fixed and random effects of dam deformation. Hausman test method of model selection and model effectiveness assessment method are discussed. Finally, the effectiveness of established models is verified through a case study.
Bulcock, H. H.; Jewitt, G. P. W.
There remains a gap in the knowledge of both canopy and litter interception processes in forest hydrology and limitations in the models used to represent them. In South Africa, interception is typically considered to constitute only a small portion of the total evaporation and in some models is disregarded. Interception is a threshold process, as a certain amount of water is required before successive processes can take place. Therefore an error or false assumption introduced in modelling interception will automatically introduce errors in the calibration of subsequent models/processes. Field experiments to assess these processes, viz. canopy and litter interception were established for the three main commercial forestry genera in South Africa, namely Pinus, Acacia and Eucalyptus, which are described in a companion paper. Drawing on both field and laboratory data, the "Variable Storage Gash" model for canopy interception and an idealised drying curve litter interception model were developed to represent these processes for South African conditions. The Variable Storage Gash model was compared with the original Gash model and it was found that it performed better than the original model in forests with high storage capacities yet was similar to the original model in stands with a low storage capacity. Thus, the models developed here were shown to adequately represent the interception processes and provide a way forward for more representative water resources planning modelling. It was found that canopy and litter interception can account for as much as 26.6% and 13.4% of gross precipitation, respectively, and are therefore important hydrological processes to consider in forested catchments in South Africa. Despite the limitation of both the Variable Storage Gash model and the idealised drying curve litter interception model being reliant on empirical relationships, their application highlights the importance of considering canopy and litter interception in water
Mianabadi, A.; Coenders, Miriam; Shirazi, P.; Ghahraman, B.; Alizadeh, Amin
Evaporation is a very important flux in the hydrological cycle and links the water and energy balance of a catchment. The Budyko framework is often used to provide a first order estimate of evaporation, since it is a simple model where only rainfall and potential evaporation is required as input.
1977-01-01Rainfall series of different climatic regions were analysed with the aim of generating daily rainfall sequences. A survey of the data is given in I, 1. When analysing daily rainfall sequences one must be aware of the following points:
Full Text Available The main objective of this paper is to present a case of value interception as a result of restructuring the business model of a manufacturing company that operates in the agricultural machinery sector. A company that focuses on core activities in the value chain and commissions the manufacturing of most components to specialised suppliers – as a result of restructuring – becomes an integrator that controls all parts of the supply chain; from obtaining a raw material, through its own production of a possibly large number of components, to the distribution of a finished composite product. The framework of the conducted research featured the identification of the relationships occurring between own production of components comprising a given product, and an alternative solution, i.e. possibility of acquiring them by way of co-operation. The authors assumed that a derivative of the value intercepted in the finished product implementation process is the number of components manufactured using own production resources.
Emmanuel, I.; Andrieu, H.; Leblois, E.; Janey, N.; Payrastre, O.
No consensus has yet been reached regarding the influence of rainfall spatial variability on runoff modelling at catchment outlets. To eliminate modelling and measurement errors, in addition to controlling rainfall variability and both the characteristics and hydrological behaviour of catchments, we propose to proceed by simulation. We have developed a simulation chain that combines a stream network model, a rainfall simulator and a distributed hydrological model (with four production functions and a distributed transfer function). Our objective here is to use this simulation chain as a simplified test bed in order to better understand the impact of the spatial variability of rainfall forcing. We applied the chain to contrasted situations involving catchments ranging from a few tens to several hundreds of square km2, thus corresponding to urban and peri-urban catchments for which surface runoff constitutes the dominant process. The results obtained confirm that the proposed simulation approach is helpful to better understand the influence of rainfall spatial variability on the catchment response. We have shown that significant dispersion exists not only between the various simulation scenarios (defined by a rainfall configuration and a catchment configuration), but also within each simulation scenario. These results show that the organisation of rainfall during the study event over the study catchment plays an important role, leading us to examine rainfall variability indexes capable of summarising the influence of rainfall spatial organisation on the catchment response. Thanks to the simulation chain, we have tested the variability indexes of Zoccatelli et al. (2010) and improved them by proposing two other indexes.
M. Sayedur Rahman
Full Text Available A rainfall simulation model based on a first-order Markov chain has been developed to simulate the annual variation in rainfall amount that is observed in Bangladesh. The model has been tested in the Barind Tract of Bangladesh. Few significant differences were found between the actual and simulated seasonal, annual and average monthly. The distribution of number of success is asymptotic normal distribution. When actual and simulated daily rainfall data were used to drive a crop simulation model, there was no significant difference of rice yield response. The results suggest that the rainfall simulation model perform adequately for many applications.
A hidden Markov model to predict annual rainfall pattern has been presented in this paper. The model is developed to provide necessary information for the farmers, agronomists, water resource management scientists and policy makers to enable them plan for the uncertainty of annual rainfall. The model classified annual ...
Pinder, J.E. III; McLeod, K.W.; Adriano, D.C.
The accuracy of three radionuclide transfer models for predicting the interception and retention of airborne particles by agricultural crops was tested using Pu-bearing aerosols released to the atmosphere from nuclear fuel facilities on the U.S. Department of Energy's Savannah River Plant, near Aiken, SC. The models evaluated were: (1) NRC, the model defined in U.S. Nuclear Regulatory Guide 1.109; (2) FOOD, a model similar to the NRC model that also predicts concentrations in grains; and (3) AGNS, a model developed from the NRC model for the southeastern United States. Plutonium concentrations in vegetation and grain were predicted from measured deposition rates and compared to concentrations observed in the field. Crops included wheat, soybeans, corn and cabbage. Although predictions of the three models differed by less than a factor of 4, they showed different abilities to predict concentrations observed in the field. The NRC and FOOD models consistently underpredicted the observed Pu concentrations for vegetation. The AGNS model was a more accurate predictor of Pu concentrations for vegetation. Both the FOOD and AGNS models accurately predicted the Pu concentrations for grains
This study developed a neurofuzzy-based rainfall-runoff forecast model for river basin and evaluated the performance of the model. This was with a view to capturing the behaviour of hydrological and meterological variables involved in rainfall-runoff process to improve forecast accuracy of rainfallrunoff. Three hydrological ...
Rainfall-runoff models can be used for forecasting flow from catchments. Flow forecasting from a catchment has great use for proper water resources development and operational management. Countless models have been produced m different parts of the world to simulate this transformation of rainfall over the catchment ...
Sarlikioti, V.; Visser, de P.H.B.; Marcelis, L.F.M.
Background and Aims - At present most process-based models and the majority of three-dimensional models include simplifications of plant architecture that can compromise the accuracy of light interception simulations and, accordingly, canopy photosynthesis. The aim of this paper is to analyse canopy
Chamberlain, A.C.; Garland, J.A.
A review has been carried out of information on the fraction of radioactive material, deposited by dry or wet deposition processes, that is intercepted by vegetation. The amount of information available is limited, but it is clear that a substantial fraction may be intercepted in some circumstances. In dry deposition, the results of measurements indicate that interception decreases with increasing particle size for particles larger than about 40 μm. In low volume water sprays, interception fractions for 7 Be, 89 Sr and microspheres of 3 to 25 μm diameter were similar, but that for periodate was lower. The fraction intercepted decreased with an increase in the amount of simulated rainfall. The data are particularly sparse for dry deposition of particles smaller than 30 μm diameter. In addition, there is no information on interception at the moderate rates of rainfall common in Britain, and little is known of the differences between various species of plants. (author)
Murakami, Shigeki; Hattori, Shohei; Uemura, Ryu
Some papers proved that canopy interception is proportional to rainfall not only on a rain event basis but also on an hourly basis (e.g. Murakami, 2006, J. Hydrol.; Saito et al., 2013, J. Hydrol.). However, theoretically, evaporation does not depend on rainfall amount. These results are enigmatic and we need to reevaluate wet canopy evaporation. We measured gross rainfall and net rainfall in a plastic Christmas tree stand with a height of 165 cm placed on a 180-cm square tray as described in Murakami and Toba (2013, Hydrol. Res. Lett.). The measurement was conducted outside under natural rainfall. We also estimated wet canopy evaporation using stable isotope ratios of water. During a rain event, we manually sampled gross and net rainwater on an hourly basis. Evaporation was calculated using the difference between the δ18O (or δ2H) values in gross and net rainfall using isotope fractionation factor. Total gross rainfall in a target rain event in October, 2014, was 28.0 mm and net rainfall (discharge from the tray) was 22.7 mm, i.e. canopy interception was 5.3 mm (18.9% of gross rainfall). The δ18O (or δ2H) value in net rainfall was higher than that in gross rainfall because of fractionation by evaporation on wet canopy surface. Hourly evaporation calculated by the values of δ18O varied from 2% to 24% of gross rainfall, and the weighted average by hourly gross rainfall was 5.2% of gross rainfall. Further, we estimated rainfall interception using a tank model (Yoshida et al., 1993) assuming constant evaporation rate, i.e. 20% of gross rainfall. Total net rainfall calculated by the model was 23.1 mm, i.e. calculated canopy interception was 4.9 mm (17.5% of gross rainfall). Then, keeping the parameters of the model, we simulated net rainfall using hourly surface evaporation obtained by the δ18O values. Calculated net rainfall was 25.6 mm, i.e. wet canopy evaporation was only 2.4 mm (8.6% of gross rainfall). So far, possible explanation of the discrepancy between
Application of the rainfall infiltration breakthrough (RIB) model for groundwater recharge estimation in west coastal South Africa. ... the data from Oudebosch with different rainfall and groundwater abstraction inputs are simulated to explore individual effects on water levels as well as recharge rate estimated on a daily basis.
May 23, 2012 ... rec har ge. (m m. ) W ater lev el fluc tuation. (m. ) Months of simulation rainfall. Recharge amount. WLF dh(crd) dh(rib). (c) TMG544. Figure 4. Daily/monthly rainfall, observed. WLF as well as calculated WLF and groundwater recharge in Riverlands and Oudebosch obtained from the. RIB model and CRD.
Srikanthan, Ratnasingham; Pegram, Geoffrey G. S.
SummaryThis paper describes a nested multisite daily rainfall generation model which preserves the statistics at daily, monthly and annual levels of aggregation. A multisite two-part daily model is nested in multisite monthly, then annual models. A multivariate set of fourth order Markov chains is used to model the daily occurrence of rainfall; the daily spatial correlation in the occurrence process is handled by using suitably correlated uniformly distributed variates via a Normal Scores Transform (NST) obtained from a set of matched multinormal pseudo-random variates, following Wilks [Wilks, D.S., 1998. Multisite generalisation of a daily stochastic precipitation generation model. Journal of Hydrology 210, 178-191]; we call it a hidden covariance model. A spatially correlated two parameter gamma distribution is used to obtain the rainfall depths; these values are also correlated via a specially matched hidden multinormal process. For nesting, the generated daily rainfall sequences at all the sites are aggregated to monthly rainfall values and these values are modified by a set of lag-1 autoregressive multisite monthly rainfall models. The modified monthly rainfall values are aggregated to annual rainfall and these are then modified by a lag-1 autoregressive multisite annual model. This nesting process ensures that the daily, monthly and annual means and covariances are preserved. The model was applied to a region with 30 rainfall sites, one of the five sets reported by Srikanthan [Srikanthan, R., 2005. Stochastic Generation of Daily Rainfall Data at a Number of Sites. Technical Report 05/7, CRC for Catchment Hydrology. Monash University, 66p]. A comparison of the historical and generated statistics shows that the model preserves all the important characteristics of rainfall at the daily, monthly and annual time scales, including the spatial structure. There are some outstanding features that need to be improved: depths of rainfall on isolated wet days and
Full Text Available This paper introduces and evaluates DryMOD, a dynamic water balance model of the key hydrological process in drylands that is based on free, public-domain datasets. The rainfall model of DryMOD makes optimal use of spatially disaggregated Tropical Rainfall Measuring Mission (TRMM datasets to simulate hourly rainfall intensities at a spatial resolution of 1-km. Regional-scale applications of the model in seasonal catchments in Tunisia and Senegal characterize runoff and soil moisture distribution and dynamics in response to varying rainfall data inputs and soil properties. The results highlight the need for hourly-based rainfall simulation and for correcting TRMM 3B42 rainfall intensities for the fractional cover of rainfall (FCR. Without FCR correction and disaggregation to 1 km, TRMM 3B42 based rainfall intensities are too low to generate surface runoff and to induce substantial changes to soil moisture storage. The outcomes from the sensitivity analysis show that topsoil porosity is the most important soil property for simulation of runoff and soil moisture. Thus, we demonstrate the benefit of hydrological investigations at a scale, for which reliable information on soil profile characteristics exists and which is sufficiently fine to account for the heterogeneities of these. Where such information is available, application of DryMOD can assist in the spatial and temporal planning of water harvesting according to runoff-generating areas and the runoff ratio, as well as in the optimization of agricultural activities based on realistic representation of soil moisture conditions.
Gleixner, Stephanie; Keenlyside, Noel S.; Demissie, Teferi D.; Counillon, François; Wang, Yiguo; Viste, Ellen
The Ethiopian economy and population is strongly dependent on rainfall. Operational seasonal predictions for the main rainy season (Kiremt, June-September) are based on statistical approaches with Pacific sea surface temperatures (SST) as the main predictor. Here we analyse dynamical predictions from 11 coupled general circulation models for the Kiremt seasons from 1985-2005 with the forecasts starting from the beginning of May. We find skillful predictions from three of the 11 models, but no model beats a simple linear prediction model based on the predicted Niño3.4 indices. The skill of the individual models for dynamically predicting Kiremt rainfall depends on the strength of the teleconnection between Kiremt rainfall and concurrent Pacific SST in the models. Models that do not simulate this teleconnection fail to capture the observed relationship between Kiremt rainfall and the large-scale Walker circulation.
Herceg, András; Kalicz, Péter; Kisfaludi, Balázs
The hydrological impacts of the climate change can be dramatic. Our main purpose is the methodical improvement of a previously established Thornthwaite-type monthly step water balance model, which takes the interception item into account, and compare the results of the evapotranspiration and the soil moisture projections for the 21st century of the original and the upgraded models. Both of the models will be calibrated and validated (using remote-sensed actual evapotranspiration data, called CREMAP) and requires only temperature and precipitation time series as inputs. The projections based on 4 bias-corrected regional climate models databases (FORESEE), and the 3 investigation periods are: 2015-2045, 2045-2075, and 2070-2100. The key parameter is the water storage capacity of the soil, which can be also calibrated using the actual evapotranspiration data. The maximal rooting depth is determinable if the physical properties of the soil are available. The interception can be ranges from 5-40% of gross precipitation, which rate are differing in the various plant communities. Generally, the forests canopy intercepts considerable amounts of rainfall and evaporates back into the atmosphere during and after precipitation event. Leaf area index (LAI) is one of the most significant factor, which determine the canopies storage capacity. Here, MODIS sensor based LAI time series are applied to estimate the storage capacity. A forest covered experimental catchment is utilized for testing the models near to Sopron, Hungary. The projections will expected to demonstrate increasing actual evapotranspiration values, but decreasing trends for the 10 percentile minimum soil moisture values at the end of the 21st century in both model runs. The seasonal periodicity of evapotranspiration may demonstrates the maximums in June or July, while in case of the soil moisture it may shows minimum values in autumn. With the comparison of the two model runs, we expect lower soil water storage
Giorgio, M.; Greco, R.
Occurrence of flowslides and floods in small catchments is uneasy to predict, since it is affected by a number of variables, such as mechanical and hydraulic soil properties, slope morphology, vegetation coverage, rainfall spatial and temporal variability. Consequently, landslide risk assessment procedures and early warning systems still rely on simple empirical models based on correlation between recorded rainfall data and observed landslides and/or river discharges. Effectiveness of such systems could be improved by reliable quantitative rainfall prediction, which can allow gaining larger lead-times. Analysis of on-site recorded rainfall height time series represents the most effective approach for a reliable prediction of local temporal evolution of rainfall. Hydrological time series analysis is a widely studied field in hydrology, often carried out by means of autoregressive models, such as AR, ARMA, ARX, ARMAX (e.g. Salas ). Such models gave the best results when applied to the analysis of autocorrelated hydrological time series, like river flow or level time series. Conversely, they are not able to model the behaviour of intermittent time series, like point rainfall height series usually are, especially when recorded with short sampling time intervals. More useful for this issue are the so-called DRIP (Disaggregated Rectangular Intensity Pulse) and NSRP (Neymann-Scott Rectangular Pulse) model [Heneker et al., 2001; Cowpertwait et al., 2002], usually adopted to generate synthetic point rainfall series. In this paper, the DRIP model approach is adopted, in which the sequence of rain storms and dry intervals constituting the structure of rainfall time series is modeled as an alternating renewal process. Final aim of the study is to provide a useful tool to implement an early warning system for hydrogeological risk management. Model calibration has been carried out with hourly rainfall hieght data provided by the rain gauges of Campania Region civil
Full Text Available This paper deals with a stochastic representation of the rainfall process. The analysis of a rainfall time series shows that cumulative representation of a rainfall time series can be modeled as a non-Gaussian random walk with a log-normal jump distribution and a time-waiting distribution following a tempered α-stable probability law. Based on the random walk model, a fractional Fokker-Planck equation (FFPE with tempered α-stable waiting times was obtained. Through the comparison of observed data and simulated results from the random walk model and FFPE model with tempered á-stable waiting times, it can be concluded that the behavior of the rainfall process is globally reproduced, and the FFPE model with tempered α-stable waiting times is more efficient in reproducing the observed behavior.
Stockinger, Michael P.; Lücke, Andreas; Vereecken, Harry; Bogena, Heye R.
Forest canopy interception alters the isotopic tracer signal of precipitation leading to significant isotopic differences between open precipitation (δOP) and throughfall (δTF). This has important consequences for the tracer-based modeling of streamwater transit times. Some studies have suggested using a simple static correction to δOP by uniformly increasing it because δTF is rarely available for hydrological modeling. Here, we used data from a 38.5 ha spruce forested headwater catchment where three years of δOP and δTF were available to develop a data driven method that accounts for canopy effects on δOP. Changes in isotopic composition, defined as the difference δTF-δOP, varied seasonally with higher values during winter and lower values during summer. We used this pattern to derive a corrected δOP time series and analyzed the impact of using (1) δOP, (2) reference throughfall data (δTFref) and (3) the corrected δOP time series (δOPSine) in estimating the fraction of young water (Fyw), i.e., the percentage of streamflow younger than two to three months. We found that Fyw derived from δOPSine came closer to δTFref in comparison to δOP. Thus, a seasonally-varying correction for δOP can be successfully used to infer δTF where it is not available and is superior to the method of using a fixed correction factor. Seasonal isotopic enrichment patterns should be accounted for when estimating Fyw and more generally in catchment hydrology studies using other tracer methods to reduce uncertainty.
G. B. Crosta
Full Text Available Hazard assessment of shallow landslides represents an important aspect of land management in mountainous areas. Among all the methods proposed in the literature, physically based methods are the only ones that explicitly includes the dynamic factors that control landslide triggering (rainfall pattern, land-use. For this reason, they allow forecasting both the temporal and the spatial distribution of shallow landslides. Physically based methods for shallow landslides are based on the coupling of the infinite slope stability analysis with hydrological models. Three different grid-based distributed hydrological models are presented in this paper: a steady state model, a transient "piston-flow" wetting front model, and a transient diffusive model. A comparative test of these models was performed to simulate landslide occurred during a rainfall event (27–28 June 1997 that triggered hundreds of shallow landslides within Lecco province (central Southern Alps, Italy. In order to test the potential for a completely distributed model for rainfall-triggered landslides, radar detected rainfall intensity has been used. A new procedure for quantitative evaluation of distributed model performance is presented and used in this paper. The diffusive model results in the best model for the simulation of shallow landslide triggering after a rainfall event like the one that we have analysed. Finally, radar data available for the June 1997 event permitted greatly improving the simulation. In particular, radar data allowed to explain the non-uniform distribution of landslides within the study area.
Being an agricultural country, the inhabitants of dry land in cultivated areas mainly rely on the daily rainfall for watering their fields. A stochastic model based on first order Markov Chain was developed to simulate daily rainfall data for Multan, D. I. Khan, Nawabshah, Chilas and Barkhan for the period 1981-2010. Transitional probability matrices of first order Markov Chain was utilized to generate the daily rainfall occurrence while gamma distribution was used to generate the daily rainfall amount. In order to achieve the parametric values of mentioned cities, method of moments is used to estimate the shape and scale parameters which lead to synthetic sequence generation as per gamma distribution. In this study, unconditional and conditional probabilities of wet and dry days in sum with means and standard deviations are considered as the essential parameters for the simulated stochastic generation of daily rainfalls. It has been found that the computerized synthetic rainfall series concurred pretty well with the actual observed rainfall series. (author)
Interception is the part of the rainfall that is intercepted by the earth’s surface and which subsequently evaporates. In this definition the earth’s surface includes everything that becomes wet after a rainfall event and that dries out soon after. It includes: vegetation, soil surface, litter,
Full Text Available This study is focused on the modification of a typhoon rainfall climatological model, by using the dataset up to 2006 and including data collected from rain gauge stations established after the 921 earthquake (1999. Subsequently, the climatology rainfall models for westward- and northward-moving typhoons are established by using the typhoon track classification from the Central Weather Bureau. These models are also evaluated and examined using dependent cases collected between 1989 and 2006 and independent cases collected from 2007 to 2011. For the dependent cases, the average total rainfall at all rain gauge stations forecasted using the climatology rainfall models for westward- (W-TRCM12 and northward-moving (N-TRCM12 typhoons is superior to that obtained using the original climatological model (TRCM06. Model W-TRCM12 significantly improves the precipitation underestimation of model TRCM06. The independent cases show that model W-TRCM12 provides better accumulated rainfall forecasts and distributions than model TRCM06. A climatological model for accompanied northeastern monsoons (A-TRCM12 for special typhoon types has also been established. The current A-TRCM12 model only contains five historical cases and various typhoon combinations can cause precipitation in different regions. Therefore, precipitation is likely to be significantly overestimated and high false alarm ratios are likely to occur in specific regions. For example, model A-TRCM12 significantly overestimates the rainfall forecast for Typhoon Mitag, an independent case from 2007. However, it has a higher probability of detection than model TRCM06. From a disaster prevention perspective, a high probability of detection is much more important than a high false alarm ratio. The modified models can contribute significantly to operational forecast.
Funk, Chris; Michaelsen, Joel; Verdin, Jim; Artan, Guleid; Husak, Greg; Senay, Gabriel; Gadain, Hussein; Magadazire, Tamuka
In Africa the variability of rainfall in space and time is high, and the general availability of historical gauge data is low. This makes many food security and hydrologic preparedness activities difficult. In order to help overcome this limitation, we have created the Collaborative Historical African Rainfall Model (CHARM). CHARM combines three sources of information: climatologically aided interpolated (CAI) rainfall grids (monthly/0.5° ), National Centers for Environmental Prediction reanalysis precipitation fields (daily/1.875° ) and orographic enhancement estimates (daily/0.1° ). The first set of weights scales the daily reanalysis precipitation fields to match the gridded CAI monthly rainfall time series. This produces data with a daily/0.5° resolution. A diagnostic model of orographic precipitation, VDELB - based on the dot-product of the surface wind V and terrain gradient (DEL) and atmospheric buoyancy B - is then used to estimate the precipitation enhancement produced by complex terrain. Although the data are produced on 0.1° grids to facilitate integration with satellite-based rainfall estimates, the true resolution of the data will be less than this value, and varies with station density, topography, and precipitation dynamics. The CHARM is best suited, therefore, to applications that integrate rainfall or rainfall-driven model results over large regions.The CHARM time series is compared with three independent datasets: dekadal satellite-based rainfall estimates across the continent, dekadal interpolated gauge data in Mali, and daily interpolated gauge data in western Kenya. These comparisons suggest reasonable accuracies (standard errors of about half a standard deviation) when data are aggregated to regional scales, even at daily time steps. Thus constrained, numerical weather prediction precipitation fields do a reasonable job of representing large-scale diurnal variations. Published in 2003 by John Wiley & Sons, Ltd.
Thorndahl, Søren Liedtke; Johansen, C.; Schaarup-Jensen, Kjeld
recommended literary values for residential areas. It is proven by comparing rainfall-runoff measurements from four different residential catchments that the literary values of the hydrological reduction factor are over-estimated for this type of catchments. In addition, different catchment descriptions......In numerical modelling of rainfall caused runoff in urban sewer systems an essential parameter is the hydrological reduction factor which defines the percentage of the impervious area contributing to the surface flow towards the sewer. As the hydrological processes during a rainfall are difficult...... to determine with significant precision the hydrological reduction factor is implemented to account all hydrological losses except the initial loss. This paper presents an inconsistency between calculations of the hydrological reduction factor, based on measurements of rainfall and runoff, and till now...
Thorndahl, Søren; Johansen, C.; Schaarup-Jensen, Kjeld
recommended literature values for residential areas. It is proven by comparing rainfall-runoff measurements from four different residential catchments that the literature values of the hydrological reduction factor are over-estimated for this type of catchment. In addition, different catchment descriptions......In numerical modelling of rainfall caused runoff in urban sewer systems an essential parameter is the hydrological reduction factor which defines the percentage of the impervious area contributing to the surface flow towards the sewer. As the hydrological processes during a rainfall are difficult...... to determine with significant precision the hydrological reduction factor is implemented to account all hydrological losses except the initial loss. This paper presents an inconsistency between calculations of the hydrological reduction factor, based on measurements of rainfall and runoff, and till now...
Bergin, E.; Buytaert, W.; Onof, C.; Wheater, H.
The assessment of water resources in the Peruvian Andes is particularly important because the Peruvian economy relies heavily on agriculture. Much of the agricultural land is situated near to the coast and relies on large quantities of water for irrigation. The simulation of synthetic rainfall series is thus important to evaluate the reliability of water supplies for current and future scenarios of climate change. In addition to water resources concerns, there is also a need to understand extreme heavy rainfall events, as there was significant flooding in Machu Picchu in 2010. The region exhibits a reduction of rainfall in 1983, associated with El Nino Southern Oscillation (SOI). NCEP Reanalysis 1 data was used to provide weather variable data. Correlations were calculated for several weather variables using raingauge data in the Andes. These were used to evaluate teleconnections and provide suggested covariates for the downscaling model. External covariates used in the model include sea level pressure and sea surface temperature over the region of the Humboldt Current. Relative humidity and temperature data over the region are also included. The SOI teleconnection is also used. Covariates are standardised using observations for 1960-1990. The GlimClim downscaling model was used to fit a stochastic daily rainfall model to 13 sites in the Peruvian Andes. Results indicate that the model is able to reproduce rainfall statistics well, despite the large area used. Although the correlation between individual rain gauges is generally quite low, all sites are affected by similar weather patterns. This is an assumption of the GlimClim downscaling model. Climate change scenarios are considered using several GCM outputs for the A1B scenario. GCM data was corrected for bias using 1960-1990 outputs from the 20C3M scenario. Rainfall statistics for current and future scenarios are compared. The region shows an overall decrease in mean rainfall but with an increase in variance.
Tobar, Vladimiro; Wyseure, Guido
It is known that rainfall is affected by terrain characteristics and some studies had focussed on its distribution over complex terrain. Ecuador's temporal and spatial rainfall distribution is affected by its location on the ITCZ, the marine currents in the Pacific, the Amazon rainforest, and the Andes mountain range. Although all these factors are important, we think that the latter one may hold a key for modelling spatial and temporal distribution of rainfall. The study considered 30 years of monthly data from 319 rainfall stations having at least 10 years of data available. The relatively low density of stations and their location in accessible sites near to main roads or rivers, leave large and important areas ungauged, making it not appropriate to rely on traditional interpolation techniques to estimate regional rainfall for water balance. The aim of this research was to come up with a useful model for seasonal rainfall distribution in Ecuador based on geographical characteristics to allow its spatial generalization. The target for modelling was the seasonal rainfall, characterized by nine percentiles for each one of the 12 months of the year that results in 108 response variables, later on reduced to four principal components comprising 94% of the total variability. Predictor variables for the model were: geographic coordinates, elevation, main wind effects from the Amazon and Coast, Valley and Hill indexes, and average and maximum elevation above the selected rainfall station to the east and to the west, for each one of 18 directions (50-135°, by 5°) adding up to 79 predictors. A multiple linear regression model by the Elastic-net algorithm with cross-validation was applied for each one of the PC as response to select the most important ones from the 79 predictor variables. The Elastic-net algorithm deals well with collinearity problems, while allowing variable selection in a blended approach between the Ridge and Lasso regression. The model fitting
Full Text Available The development and the application of rainfall-runoff models have been a corner-stone of hydrological research for many decades. The amount of rainfall and its intensity and variability control the generation of runoff and the erosional processes operating at different scales. These interactions can be greatly variable in Mediterranean catchments with marked hydrological fluctuations. The aim of the study was to evaluate the performance of rainfall-runoff model, for rainfall-runoff simulation in a Mediterranean subcatchment. The Pan-European Soil Erosion Risk Assessment (PESERA, a simplified hydrological process-based approach, was used in this study to combine hydrological surface runoff factors. In total 128 input layers derived from data set includes; climate, topography, land use, crop type, planting date, and soil characteristics, are required to run the model. Initial ground cover was estimated from the Landsat ETM data provided by ESA. This hydrological model was evaluated in terms of their performance in Goksu River Watershed, Turkey. It is located at the Central Eastern Mediterranean Basin of Turkey. The area is approximately 2000 km2. The landscape is dominated by bare ground, agricultural and forests. The average annual rainfall is 636.4mm. This study has a significant importance to evaluate different model performances in a complex Mediterranean basin. The results provided comprehensive insight including advantages and limitations of modelling approaches in the Mediterranean environment.
Taschetto, A.; Haarsma, R. D.; Sen Gupta, A.
Australian climate is projected to change with increases in greenhouse gases. The IPCC reports an increase in extreme daily rainfall across the country. At the same time, mean rainfall over southeast Australia is projected to reduce during austral winter, but to increase during austral summer, mainly associated with changes in the surrounding oceans. Climate models agree better on the future reduction of average rainfall over the southern regions of Australia compared to the increase in extreme rainfall events. One of the reasons for this disagreement may be related to climate model limitations in simulating the observed mechanisms associated with the mid-latitude weather systems, in particular due to coarse model resolutions. In this study we investigate how changes in sea surface temperature (SST) affect Australian mean and extreme rainfall under global warming, using a suite of numerical experiments at two model resolutions: about 126km (T159) and 25km (T799). The numerical experiments are performed with the earth system model EC-EARTH. Two 6-member ensembles are produced for the present day conditions and a future scenario. The present day ensemble is forced with the observed daily SST from the NOAA National Climatic Data Center from 2002 to 2006. The future scenario simulation is integrated from 2094 to 2098 using the present day SST field added onto the future SST change created from a 17-member ensemble based on the RCP4.5 scenario. Preliminary results show an increase in extreme rainfall events over Tasmania associated with enhanced convection driven by the Tasman Sea warming. We will further discuss how the projected changes in SST will impact the southern mid-latitude weather systems that ultimately affect Australian rainfall.
Van Stan, John; Porada, Philipp; Kleidon, Axel
Interception of precipitation by forest canopies plays an important role in its partitioning to evaporation, transpiration and runoff. Field observations show arboreal lichens and bryophytes can substantially enhance forests' precipitation storage and evaporation. However, representations of canopy interception in global land surface models currently ignore arboreal lichen and bryophyte contributions. This study uses the lichen and bryophyte model (LiBry) to provide the first process-based modelling approach estimating these organisms' contributions to canopy water storage and evaporation. The global mean value of forest water storage capacity increased significantly from 0.87 mm to 1.33 mm by the inclusion of arboreal poikilohydric organisms. Global forest canopy evaporation of intercepted precipitation was also greatly enhanced by 44%. Ratio of total versus bare canopy global evaporation exceeded 2 in many forested regions. This altered global patterns in canopy water storage, evaporation, and ultimately the proportion of rainfall evaporated. A sensitivity analysis was also performed. Results indicate rainfall interception is of larger magnitude than previously reported by global land surface modelling work because of the important role of lichen and bryophytes in rainfall interception.
Thayakaran, R; Ramesh, N I
Time series of rainfall bucket tip times at the Beaufort Park station, Bracknell, in the UK are modelled by a class of Markov modulated Poisson processes (MMPP) which may be thought of as a generalization of the Poisson process. Our main focus in this paper is to investigate the effects of including covariate information into the MMPP model framework on statistical properties. In particular, we look at three types of time-varying covariates namely temperature, sea level pressure, and relative humidity that are thought to be affecting the rainfall arrival process. Maximum likelihood estimation is used to obtain the parameter estimates, and likelihood ratio tests are employed in model comparison. Simulated data from the fitted model are used to make statistical inferences about the accumulated rainfall in the discrete time interval. Variability of the daily Poisson arrival rates is studied.
Hwang, W. T.; Han, M. H.; Choi, Y. H.; Lee, H. S.; Lee, C. W.
For the consideration of the effects on radioactive contamination of agricultural products by rainfall during a nuclear accident, the wet interception coefficients for the plants were derived, and the previous dynamic food chain model was also modified. From the results, radioactive contamination of agricultural products was greatly decreased by rainfall, and it decreased dramatically according to increase of rainfall amount. It means that the predictive contamination in agricultural products using the previous dynamic food chain model, in which dry interception to the plants is only considered, can be overestimated. Influence of rainfall on the contamination of agricultural products was the most sensitive for 131 I, and the least sensitive for 90 Sr
Asyiqotur Rohmah, Nabila; Apriliani, Erna
Rainfall fluctuation can affect condition of other environment, correlated with economic activity and public health. The increasing of global average temperature is influenced by the increasing of CO2 in the atmosphere, which caused climate change. Meanwhile, the forests as carbon sinks that help keep the carbon cycle and climate change mitigation. Climate change caused by rainfall intensity deviations can affect the economy of a region, and even countries. It encourages research on rainfall associated with an area of forest. In this study, the mathematics model that used is a model which describes the global temperatures, forest cover, and seasonal rainfall called the TFR (temperature, forest cover, and rainfall) model. The model will be discretized first, and then it will be estimated by the method of Ensemble Kalman Filter (EnKF). The result shows that the more ensembles used in estimation, the better the result is. Also, the accurateness of simulation result is influenced by measurement variable. If a variable is measurement data, the result of simulation is better.
Hassan, S. M. Tanvir; Ghimire, Chandra Prasad; Lubczynski, Maciek W.
Tree interception loss from two Mediterranean oak species, Quercus ilex (Q.i.) and Quercus pyrenaica (Q.p.), was estimated during 2-year period (1 October 2011 to 30 September 2013) in sparsely vegetated Sardon catchment (∼80 km2, Spain) by: i) rainfall, throughfall and stemflow measurements; ii) Gash model temporal extrapolation; and iii) remote-sensing spatial upscaling. The annual, measured tree interception losses (Im) of Q.i. and Q.p. in the first year were 51% and 16% of P (335 mm) and in the second, 46% and 10% of P (672 mm), respectively. The revised Gash analytical model of rainfall interception loss, extrapolated well the Im temporal variability of Q.i. and Q.p., provided the throughfall-based, and not Pennman-Monteith-based, average wet canopy evaporation rates were used. Finally, a novel method of spatial upscaling of a tree-based interception loss into plot- and catchment-scale, using per-species, reference tree interception loss and object-attributes derived from satellite imagery, was proposed. The interception losses from Q.i. and Q.p. were upscaled first into two homogeneous plots (1-ha each and both with ∼20% canopy cover), one with Q.i. and the other with Q.p. oaks and then into the entire Sardon catchment with ∼7% canopy cover. The obtained annual-mean, plot interception losses were 9.5% of P in evergreen Q.i. and 2.5% of P in deciduous Q.p. plot. The annual-mean catchment interception loss was 1.4% of P. The proposed upscaling method is expected to improve catchment water balances, replacing common arbitrary or literature based tree interception loss estimates.
Tellez Guio, Patricia; Boschell Villamarin, Francisco; Tobon Marin, Conrado
Hydrologic simulation is a technique, which allows us to understand the relationships among hydrological, biological and ecological variables in an ecosystem. In this research, the FORHYD model is used to simulate the net precipitation and the water intercepted by the canopies of a mature forest, a 30-year old secondary forest, an 18-year old secondary forest, a 5-year old secondary forest, and a shifting cultivation plot, all located in Colombia's amazonia. The model calculates the water budget of the canopy by using the precipitation rates, canopy drainage and evaporation of the water intercepted by the canopy. This paper is the second one in a series of papers reporting the results of the research on the simulation of the hydrological fluxes in three different land use types of Colombian amazonia. The research was carried out in middle Caqueta of Colombian amazonia (northwest amazon basin). The FORHYD model was calibrated and validated by using field observations of the climate, net precipitation (PT), thoughtful (TH) and stem flow (ST), which were monitored during a period of 15 months from March 2001 to June 2002. These observations were used as both input variables and diagnostic variables to probe the model's precision to simulate field observations. Results showed that FORHYD simulates with a good precision the net precipitation and the evaporation of the water intercepted by the canopy. However, the model's precision depends on a good parameterization, which in turn depends on a good database of field observations. The model is a good tool for simulating the hydrological cycle and can be used to simulate critical scenarios of climate variability
the stochastic processes is an underlying Markov chain, the other stochastic process is an observable stochastic ... Keywords: Markov model, Hidden Markov model, Transition probability, Observation probability, Crop. Production, Annual Rainfall .... with highest value of the forward probability at time. T+1 is taken as ...
... systems (GIS), a mathematical model to estimate very accurately the values of rainfall based only on the geographical coordinates. To achieve this objective, the basins of the Hydrographic Demarcation of Manabí have been chosen to develop the indicated mathematical model, which can be applied to other basins in the ...
... present mathematical models that incorporate ideas from complex systems theory to integrate several strands of rangeland theory in a hierarchical framework. Compared with observed data from South Africa, the model successfully predicted the relationship between rainfall, vegetation composition and animal numbers ...
Design of drainage and dam structures involves a full understanding of the duration, magnitude and volume of peak flood flows anticipated. For gauged catchments a number of established flood frequency models and rainfall-runoff models are used widely. However, most planned developments for bridge or dam or any ...
Rainfall simulations over southern and tropical Africa in the form of low-resolution Atmospheric Model Intercomparison Project (AMIP) simulations and higher resolution National Centre for Environmental Prediction (NCEP) reanalysis downscalings are presented and evaluated in this paper. The model used is the ...
Full Text Available A good knowledge of rainfall is essential for hydrological operational purposes such as flood forecasting. The objective of this paper was to analyze, on a relatively large sample of flood events, how rainfall-runoff modeling using an event-based model can be sensitive to the use of spatial rainfall compared to mean areal rainfall over the watershed. This comparison was based not only on the model's efficiency in reproducing the flood events but also through the estimation of the initial conditions by the model, using different rainfall inputs. The initial conditions of soil moisture are indeed a key factor for flood modeling in the Mediterranean region. In order to provide a soil moisture index that could be related to the initial condition of the model, the soil moisture output of the Safran-Isba-Modcou (SIM model developed by Météo-France was used. This study was done in the Gardon catchment (545 km2 in South France, using uniform or spatial rainfall data derived from rain gauge and radar for 16 flood events. The event-based model considered combines the SCS runoff production model and the Lag and Route routing model. Results show that spatial rainfall increases the efficiency of the model. The advantage of using spatial rainfall is marked for some of the largest flood events. In addition, the relationship between the model's initial condition and the external predictor of soil moisture provided by the SIM model is better when using spatial rainfall, in particular when using spatial radar data with R2 values increasing from 0.61 to 0.72.
The design of hydraulic structures such as road culverts, road bridges and dam spillways requires the determination of the design food peak. Two approaches are available in the determination of the design flood peak and these are: flood frequency analysis and rainfall runoff models. Flood frequency analysis requires a ...
This report explores rainfall-runoff models, their generation methods, and the categories under which they fall. Runoff plays an important role in the hydrological cycle by returning excess precipitation to the oceans and controlling how much water flows into stream systems. Mode...
Militino, A. F.
Accumulated daily rainfall in non-observed locations on a particular day is frequently required as input to decision-making tools in precision agriculture or for hydrological or meteorological studies. Various solutions and estimation procedures have been proposed in the literature depending on the auxiliary information and the availability of data, but most such solutions are oriented to interpolating spatial data without incorporating temporal dependence. When data are available in space and time, spatiotemporal models usually provide better solutions. Here, we analyse the performance of three spatiotemporal models fitted to the whole sampled set and to clusters within the sampled set. The data consists of daily observations collected from 87 manual rainfall gauges from 1990 to 2010 in Navarre, Spain. The accuracy and precision of the interpolated data are compared with real data from 33 automated rainfall gauges in the same region, but placed in different locations than the manual rainfall gauges. Root mean squared error by months and by year are also provided. To illustrate these models, we also map interpolated daily precipitations and standard errors on a 1km2 grid in the whole region. © 2014 Royal Meteorological Society.
Sinoquet, H.; Bonhomme, R.
A radiative transfer model applied to a row crop has previously been described and tested on homogeneous canopies. To validate this model for row crops, measurements of reflected and transmitted radiation were made on two maize canopies : one orientated East-West, and the other North-South. The geometrical structure, measured with the plant profile method, differs according to row orientation. The plant azimuth distribution is not uniform. That of leaf inclination is globally uniform, but it presents spatial variations. The leaf area density shows large variations in the horizontal plane, depending on the distance from the center of the row, even in the case of a well developed crop. Linear regressions show a good agreement between calculated and measured values, and are quite similar for both row orientations. The mean quadratic errors are from 10 - 20%, depending on the nature of the radiation. Optimized values of leaf dispersion index (Nilson, 1971) indicate a clumped behaviour which decreases with the development of the canopy (mainly for the North-South orientation), with however a more clumped arrangement in the North-South rows [fr
Wang, Xiaojing; Gebremichael, Mekonnen; Yan, Jun
SummaryCopulas have recently emerged as a practical method for multivariate modeling. To date, only limited amount of work has been done to apply copula-based modeling in the context of extreme rainfall analysis, and no work exists on modeling multiple characteristics of rainfall events from data at resolutions finer than hourly. In this study, trivariate copula-based modeling is applied to annual extreme rainfall events constructed from 15-min time series precipitation data at 12 stations within the state of Connecticut. Three characteristics (volume, duration, and peak intensity) are modeled by a multivariate distribution specified by three marginal distributions and a dependence structure via copula. A major issue in this application is that, because the 15-min precipitation data are only available fairly recently, the sample size at most stations is small, ranging from 10 to 33 years. For each station, we estimate the model parameters by maximizing a weighted likelihood, which assigns weight to data at stations nearby, borrowing strengths from them. The weights are assigned by a kernel function whose bandwidth is chosen by cross-validation in terms of predictive loglikelihood. The fitted model and sampling algorithms provide new knowledge on design storms and risk assessment in Connecticut.
Full Text Available Interception losses are a major influence in the water yield of vegetated areas. For most storms, rain interception results in less water reaching the ground. However, fog interception can increase the overall water storage capacity of the vegetation and once the storage is exceeded, fog drip is a common hydrological input. Fog interception is disregarded in water budgets of semiarid regions, but for some plant communities, it could be a mechanism offsetting evaporation losses. Tillandsia recurvata is a cosmopolitan epiphyte adapted to arid habitats where fog may be an important water source. Therefore, the interception storage capacity by T. recurvata was measured in controlled conditions and applying simulated rain or fog. Juvenile, vegetative specimens were used to determine the potential upperbound storage capacities. The storage capacity was proportional to dry weight mass. Interception storage capacity (Cmin was 0.19 and 0.56 mm for rainfall and fog respectively. The coefficients obtained in the laboratory were used together with biomass measurements for T. recurvata in a xeric scrub to calculate the depth of water intercepted by rain. T. recurvata contributed 20 % to the rain interception capacity of their shrub hosts: Acacia farnesiana and Prosopis laevigata and; also potentially intercepted 4.8 % of the annual rainfall. Nocturnal stomatic opening in T. recurvata is not only relevant for CO2 but for water vapor, as suggested by the higher weight change of specimens wetted with fog for 1 h at dark in comparison to those wetted during daylight (543 ± 77 vs. 325 ± 56 mg, p = 0.048. The storage capacity of T. recurvata leaf surfaces could increase the amount of water available for evaporation, but as this species colonise montane forests, the effect could be negative on water recharge, because potential storage capacity is very high, in the laboratory experiments it took up to 12 h at a rate of 0.26 l h−1 to reach saturation conditions
Full Text Available Instead of Fourier smoothing, this study applied wavelet denoising to acquire the smooth seasonal mean and corresponding perturbation term from daily rainfall and runoff data in traditional seasonal models, which use seasonal means for hydrological time series forecasting. The denoised rainfall and runoff time series data were regarded as the smooth seasonal mean. The probability distribution of the percentage coefficients can be obtained from calibrated daily rainfall and runoff data. For validated daily rainfall and runoff data, percentage coefficients were randomly generated according to the probability distribution and the law of linear proportion. Multiplying the generated percentage coefficient by the smooth seasonal mean resulted in the corresponding perturbation term. Random modeling of daily rainfall and runoff can be obtained by adding the perturbation term to the smooth seasonal mean. To verify the accuracy of the proposed method, daily rainfall and runoff data for the Wu-Tu watershed were analyzed. The analytical results demonstrate that wavelet denoising enhances the precision of daily rainfall and runoff modeling of the seasonal model. In addition, the wavelet denoising technique proposed in this study can obtain the smooth seasonal mean of rainfall and runoff processes and is suitable for modeling actual daily rainfall and runoff processes.
Full Text Available In this study, the geophysical properties of the landslide-prone catchment of the Gaoping River in Taiwan were investigated using zones based on landslide history in conjunction with landslide analysis using a deterministic approach based on the TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope-Stability model. Typhoon Morakot in 2009 was selected as a simulation scenario to calibrate the combination of geophysical parameters in each zone before analyzing changes in the factor of safety (FS. Considering the amount of response time required for typhoons, suitable FS thresholds for landslide warnings are proposed for each town in the catchment area. Typhoon Fanapi of 2010 was used as a test scenario to verify the applicability of the FS as well as the efficacy of the cumulative rainfall thresholds derived in this study. Finally, the amount of response time provided by the FS thresholds in cases of yellow and red alerts was determined. All five of the landslide events reported by the Soil and Water Conservation Bureau were listed among the unstable sites identified in the proposed model, thereby demonstrating its effectiveness and accuracy in determining unstable areas and areas that require evacuation. These cumulative rainfall thresholds provide a valuable reference to guide disaster prevention authorities in the issuance of yellow and red alerts with the ability to reduce losses and save lives.
Zakaria, Roslinazairimah; Moslim, Nor Hafizah
An efficient design of flood mitigation and construction of crop growth models depend upon good understanding of the rainfall process and characteristics. Gamma distribution is usually used to model nonzero rainfall amounts. In this study, the mixed-gamma model is applied to accommodate both zero and nonzero rainfall amounts. The mixed-gamma model presented is for the independent case. The formulae of mean and variance are derived for the sum of two and three independent mixed-gamma variables, respectively. Firstly, the gamma distribution is used to model the nonzero rainfall amounts and the parameters of the distribution (shape and scale) are estimated using the maximum likelihood estimation method. Then, the mixed-gamma model is defined for both zero and nonzero rainfall amounts simultaneously. The formulae of mean and variance for the sum of two and three independent mixed-gamma variables derived are tested using the monthly rainfall amounts from rainfall stations within Kuantan district in Pahang Malaysia. Based on the Kolmogorov-Smirnov goodness of fit test, the results demonstrate that the descriptive statistics of the observed sum of rainfall amounts is not significantly different at 5% significance level from the generated sum of independent mixed-gamma variables. The methodology and formulae demonstrated can be applied to find the sum of more than three independent mixed-gamma variables.
Kazmi, Dildar Hussain; Li, Jianping; Ruan, Chengqing; Zhao, Sen; Li, Yanjie
A statistical approach is utilized to construct an interannual model for summer (July-August) rainfall over the western parts of South Asian Monsoon. Observed monthly rainfall data for selected stations of Pakistan for the last 55 years (1960-2014) is taken as predictand. Recommended climate indices along with the oceanic and atmospheric data on global scales, for the period April-June are employed as predictors. First 40 years data has been taken as training period and the rest as validation period. Cross-validation stepwise regression approach adopted to select the robust predictors. Upper tropospheric zonal wind at 200 hPa over the northeastern Atlantic is finally selected as the best predictor for interannual model. Besides, the next possible candidate `geopotential height at upper troposphere' is taken as the indirect predictor for being a source of energy transportation from core region (northeast Atlantic/western Europe) to the study area. The model performed well for both the training as well as validation period with correlation coefficient of 0.71 and tolerable root mean square errors. Cross-validation of the model has been processed by incorporating JRA-55 data for potential predictors in addition to NCEP and fragmentation of study period to five non-overlapping test samples. Subsequently, to verify the outcome of the model on physical grounds, observational analyses as well as the model simulations are incorporated. It is revealed that originating from the jet exit region through large vorticity gradients, zonally dominating waves may transport energy and momentum to the downstream areas of west-central Asia, that ultimately affect interannual variability of the specific rainfall. It has been detected that both the circumglobal teleconnection and Rossby wave propagation play vital roles in modulating the proposed mechanism.
de Boer-Euser, Tanja; Hrachowitz, Markus; Winsemius, Hessel; Savenije, Hubert
Incorporating spatially variable information is a frequently discussed option to increase the performance of (semi-)distributed conceptual rainfall-runoff models. One of the methods to do this is by using this spatially variable information to delineate Hydrological Response Units (HRUs) within a catchment. In large parts of Europe the original forested land cover is replaced by an agricultural land cover. This change in land cover probably affects the dominant runoff processes in the area, for example by increasing the Hortonian overland flow component, especially on the flatter and higher elevated parts of the catchment. A change in runoff processes implies a change in HRUs as well. A previous version of our model distinguished wetlands (areas close to the stream) from the remainder of the catchment. However, this configuration was not able to reproduce all fast runoff processes, both in summer as in winter. Therefore, this study tests whether the reproduction of fast runoff processes can be improved by incorporating a HRU which explicitly accounts for the effect of agriculture. A case study is carried out in the Ourthe catchment in Belgium. For this case study the relevance of different process conceptualisations is tested stepwise. Among the conceptualisations are Hortonian overland flow in summer and winter, reduced infiltration capacity due to a partly frozen soil and the relative effect of rainfall and snow smelt in case of this frozen soil. The results show that the named processes can make a large difference on event basis, especially the Hortonian overland flow in summer and the combination of rainfall and snow melt on (partly) frozen soil in winter. However, differences diminish when the modelled period of several years is evaluated based on standard metrics like Nash-Sutcliffe Efficiency. These results emphasise on one hand the importance of incorporating the effects of agricultural in conceptual models and on the other hand the importance of more event
Jonsdottir, Harpa; Madsen, Henrik; Palsson, Olafur Petur
A parameter estimation method for stochastic rainfall-runoff models is presented. The model considered in the paper is a conceptual stochastic model, formulated in continuous-discrete state space form. The model is small and a fully automatic optimization is, therefore, possible for estimating all....... For a comparison the parameters are also estimated by an output error method, where the sum of squared simulation error is minimized. The former methodology is optimal for short-term prediction whereas the latter is optimal for simulations. Hence, depending on the purpose it is possible to select whether...... the parameter values are optimal for simulation or prediction. The data originates from Iceland and the model is designed for Icelandic conditions, including a snow routine for mountainous areas. The model demands only two input data series, precipitation and temperature and one output data series...
Collis, Scott; Tao, Wei-Kuo; Giangrande, Scott; Fridlind, Ann; Theisen, Adam; Jensen, Michael
Precipitation processes play a significant role in the energy balance of convective systems for example, through latent heating and evaporative cooling. Heavy precipitation "cores" can also be a proxy for vigorous convection and vertical motions. However, comparisons between rainfall rate retrievals from volumetric remote sensors with forecast rain fields from high-resolution numerical weather prediction simulations are complicated by differences in the location and timing of storm morphological features. This presentation will outline a series of metrics for diagnosing the spatial variability and statistical properties of precipitation maps produced both from models and retrievals. We include existing metrics such as Contoured by Frequency Altitude Diagrams (Yuter and Houze 1995) and Statistical Coverage Products (May and Lane 2009) and propose new metrics based on morphology, cell and feature based statistics. Work presented focuses on observations from the ARM Southern Great Plains radar network consisting of three agile X-Band radar systems with a very dense coverage pattern and a C Band system providing site wide coverage. By combining multiple sensors resolutions of 250m2 can be achieved, allowing improved characterization of fine-scale features. Analyses compare data collected during the Midlattitude Continental Convective Clouds Experiment (MC3E) with simulations of observed systems using the NASA Unified Weather Research and Forecasting model. May, P. T., and T. P. Lane, 2009: A method for using weather radar data to test cloud resolving models. Meteorological Applications, 16, 425-425, doi:10.1002/met.150, 10.1002/met.150. Yuter, S. E., and R. A. Houze, 1995: Three-Dimensional Kinematic and Microphysical Evolution of Florida Cumulonimbus. Part II: Frequency Distributions of Vertical Velocity, Reflectivity, and Differential Reflectivity. Mon. Wea. Rev., 123, 1941-1963, doi:10.1175/1520-0493(1995)1232.0.CO;2.
Campos-Aranda Daniel Francisco
Full Text Available To solve the problems associated with the assessment of water resources of a river, the modeling of the rainfall-runoff process (RRP allows the deduction of runoff missing data and to extend its record, since generally the information available on precipitation is larger. It also enables the estimation of inputs to reservoirs, when their building led to the suppression of the gauging station. The simplest mathematical model that can be set for the RRP is the linear regression or curve on a monthly basis. Such a model is described in detail and is calibrated with the simultaneous record of monthly rainfall and runoff in Ballesmi hydrometric station, which covers 35 years. Since the runoff of this station has an important contribution from the spring discharge, the record is corrected first by removing that contribution. In order to do this a procedure was developed based either on the monthly average regional runoff coefficients or on nearby and similar watershed; in this case the Tancuilín gauging station was used. Both stations belong to the Partial Hydrologic Region No. 26 (Lower Rio Panuco and are located within the state of San Luis Potosi, México. The study performed indicates that the monthly regression model, due to its conceptual approach, faithfully reproduces monthly average runoff volumes and achieves an excellent approximation in relation to the dispersion, proved by calculation of the means and standard deviations.
Manz, Bastian Johann; Rodríguez, Juan Pablo; Maksimović, Cedo; McIntyre, Neil
A key control on the response of an urban drainage model is how well the observed rainfall records represent the real rainfall variability. Particularly in urban catchments with fast response flow regimes, the selection of temporal resolution in rainfall data collection is critical. Furthermore, the impact of the rainfall variability on the model response is amplified for water quality estimates, as uncertainty in rainfall intensity affects both the rainfall-runoff and pollutant wash-off sub-models, thus compounding uncertainties. A modelling study was designed to investigate the impact of altering rainfall temporal resolution on the magnitude and behaviour of uncertainties associated with the hydrological modelling compared with water quality modelling. The case study was an 85-ha combined sewer sub-catchment in Bogotá (Colombia). Water quality estimates showed greater sensitivity to the inter-event variability in rainfall hyetograph characteristics than to changes in the rainfall input temporal resolution. Overall, uncertainties from the water quality model were two- to five-fold those of the hydrological model. However, owing to the intrinsic scarcity of observations in urban water quality modelling, total model output uncertainties, especially from the water quality model, were too large to make recommendations for particular model structures or parameter values with respect to rainfall temporal resolution.
Brantley, S. T.; Bolstad, P. V.; Sobek, C.; Laseter, S.; Novick, K. A.; Vose, J. M.; Miniat, C. F.
Variations in evapotranspiration (ET) have been well documented across a variety of forest types and climates in recent decades; however, most of these data have focused on mature, second-growth stands. Here we present data on two important fluxes of water, canopy interception (Ic) and forest floor litter interception (Iff), across a chronosequence of forest age classes in the southern Appalachian Mountains. We used climate stations and throughfall collectors to measure gross rainfall and estimate Ic at each site and used a non-linear mixed model to determine the effects of forest age and precipitation on stand Ic. We also collected forest floor biomass monthly at each site and used these data in a model of litter wetting and drying to determine the quantity of water lost to Iff. Precipitation varied from 1690 to 2002 mm yr-1 across sites and across years (2011-2013). Canopy interception increased rapidly as forests aged to a maximum of 190 mm yr-1 in an 85 yr old forest. Despite higher leaf area in older stands, forest floor biomass did not vary significantly among sites (p = 0.47), suggesting lower decomposition rates in younger sites or effects of residual material from logging activity. At all sites, Iff accounted for 88-104 mm year-1 of total ET. Unlike Ic, modeled estimates of interannual variation in Iff were insensitive to annual rainfall amount and were dependent primarily on forest floor biomass. Additional measurements are currently underway to validate the litter interception model using litter moisture probes and forest floor wet and dry weights. Improved estimates of interception will contribute to our understanding of how forest structure and climate variability affect forest water use and help improve models of rainfall partitioning across the broader matrix of forest age classes.
Jan 15, 2015 ... a logical relationship with one and two days ago flow rate and one, two and three days ago rainfall values. ... back propagation artificial neural network (BPANN) to simulate rainfall-runoff process for two sub-basins of ...  used ANN and fuzzy logic for predicting event based rainfall runoff and tested these.
Vipul K. Dabhi
Full Text Available An accurate prediction of rainfall is crucial for national economy and management of water resources. The variability of rainfall in both time and space makes the rainfall prediction a challenging task. The present work investigates the applicability of a hybrid wavelet-postfix-GP model for daily rainfall prediction of Anand region using meteorological variables. The wavelet analysis is used as a data preprocessing technique to remove the stochastic (noise component from the original time series of each meteorological variable. The Postfix-GP, a GP variant, and ANN are then employed to develop models for rainfall using newly generated subseries of meteorological variables. The developed models are then used for rainfall prediction. The out-of-sample prediction performance of Postfix-GP and ANN models is compared using statistical measures. The results are comparable and suggest that Postfix-GP could be explored as an alternative tool for rainfall prediction.
Costa, Veber; Fernandes, Wilson
Extreme flood estimation has been a key research topic in hydrological sciences. Reliable estimates of such events are necessary as structures for flood conveyance are continuously evolving in size and complexity and, as a result, their failure-associated hazards become more and more pronounced. Due to this fact, several estimation techniques intended to improve flood frequency analysis and reducing uncertainty in extreme quantile estimation have been addressed in the literature in the last decades. In this paper, we develop a Bayesian framework for the indirect estimation of extreme flood quantiles from rainfall-runoff models. In the proposed approach, an ensemble of long daily rainfall series is simulated with a stochastic generator, which models extreme rainfall amounts with an upper-bounded distribution function, namely, the 4-parameter lognormal model. The rationale behind the generation model is that physical limits for rainfall amounts, and consequently for floods, exist and, by imposing an appropriate upper bound for the probabilistic model, more plausible estimates can be obtained for those rainfall quantiles with very low exceedance probabilities. Daily rainfall time series are converted into streamflows by routing each realization of the synthetic ensemble through a conceptual hydrologic model, the Rio Grande rainfall-runoff model. Calibration of parameters is performed through a nonlinear regression model, by means of the specification of a statistical model for the residuals that is able to accommodate autocorrelation, heteroscedasticity and nonnormality. By combining the outlined steps in a Bayesian structure of analysis, one is able to properly summarize the resulting uncertainty and estimating more accurate credible intervals for a set of flood quantiles of interest. The method for extreme flood indirect estimation was applied to the American river catchment, at the Folsom dam, in the state of California, USA. Results show that most floods
Borup, Morten; Madsen, Henrik
are equipped with basins and automated structures that allow for a large degree of control of the systems, but in order to do this optimally it is required to know what is happening throughout the system. For this task models are needed, due to the large scale and complex nature of the systems. The physically...... that are being updated from system measurements was studied. The results showed that the fact alone that it takes time for rainfall data to travel the distance between gauges and catchments has such a big negative effect on the forecast skill of updated models, that it can justify the choice of even very...... when it was used to update the water level in multiple upstream basins. This method is, however, not capable of utilising the spatial correlations in the errors to correct larger parts of the models. To accommodate this a method was developed for correcting the slow changing inflows to urban drainage...
Gao, S.; Fang, N. Z.
A previously developed Dynamic Moving Storm (DMS) generator is a multivariate rainfall model simulating the complex nature of precipitation field: spatial variability, temporal variability, and storm movement. Previous effort by the authors has investigated the sensitivity of DMS parameters on corresponding hydrologic responses by using synthetic storms. In this study, the DMS generator has been upgraded to generate more realistic precipitation field. The dependence of hydrologic responses on rainfall features was investigated by dissecting the precipitation field into rain cells and modifying their spatio-temporal specification individually. To retrieve DMS parameters from radar rainfall data, rain cell segmentation and tracking algorithms were respectively developed and applied on high resolution radar rainfall data (1) to spatially determine the rain cells within individual radar image and (2) to temporally analyze their dynamic behavior. Statistics of DMS parameters were established by processing a long record of rainfall data (10 years) to keep the modification on real storms within the limit of regional climatology. Empirical distributions of the DMS parameters were calculated to reveal any preferential pattern and seasonality. Subsequently, the WRF-Hydro model forced by the remodeled and modified precipitation was used for hydrologic simulation. The study area was the Upper Trinity River Basin (UTRB) watershed, Texas; and two kinds of high resolution radar data i.e. the Next-Generation Radar (NEXRAD) level III Digital Hybrid Reflectivity (DHR) product and Multi-Radar Multi-Sensor (MRMS) precipitation rate product, were utilized to establish parameter statistics and to recreate/remodel historical events respectively. The results demonstrated that rainfall duration is a significant linkage between DMS parameters and their hydrologic impacts—any combination of spatiotemporal characteristics that keep rain cells longer over the catchment will produce higher
Huang, Y. F.; Tsang, Y. P.
Rainfall in Hawaii is characterized with high spatial and temporal variability. In the south side of Oahu, the Manoa watershed, with an area of 11 km2, has the annual maximum rainfall of 3900mm and the minimum rainfall of 1000 mm. Despite this high spatial heterogeneity, the rain gage network seems insufficiently capture this pattern. When simulating stream flow and predicting floods with hydrological models in Hawaii, the model performance is often unsatisfactory because of inadequate representation of rainfall data. Longman et al. (in prep.) have developed the spatially distributed daily rainfall across the Hawaiian Islands by applying ordinary kriging, yet these data have not been applied to hydrological models. In this study, we used the Soil and Water Assessment Tool (SWAT) model to assess the streamflow simulation by applying spatially-distributed rainfall in the Manoa watershed. We first used point daily-rainfall at Lyon Arboretum from National Center of Environmental Information (NCEI) as the uniform rainfall input. Secondly, we summarized sub-watershed mean rainfall from the daily spatial-statistical rainfall. Both rainfall data are available from 1999 to 2014. The SWAT was set up for five-year warm-up, nine-year calibration, and two-year validation. The model parameters were calibrated and validated with four U.S. Geological Survey stream gages. We compared the calibrated watershed parameters, characteristics, and assess the streamflow hydrographs from these two rainfall inputs. The differences and improvement of using spatially distributed rainfall input in SWAT were discussed. In addition to improving the model by the representation of rainfall, this study helped us having a better understanding of the watershed hydrological response in Hawaii.
Herceg, Deborah [Rutgers University, Institute of Marine and Coastal Sciences (IMCS), New Brunswick, NJ (United States); Sobel, Adam H. [Columbia University, Department of Applied Physics and Applied Mathematics, Department of Earth and Environmental Sciences, New York, NY (United States); Columbia University, International Research Institute for Climate and Society (IRI), Palisades, NY (United States); Sun, Liqiang [Columbia University, International Research Institute for Climate and Society (IRI), Palisades, NY (United States)
A regional climate model is used to investigate the mechanism of interdecadal rainfall variability, specifically the drought of the 1970s and 1980s, in the Sahel region of Africa. The model is the National Center for Environmental Prediction's (NCEPs) Regional Spectral Model (RSM97), with a horizontal resolution of approximately equivalent to a grid spacing of 50 km, nested within the ECHAM4.5 atmospheric general circulation model (AGCM), which in turn was forced by observed sea surface temperature (SST). Simulations for the July-September season of the individual years 1955 and 1986 produced wet conditions in 1955 and dry conditions in 1986 in the Sahel, as observed. Additional July-September simulations were run forced by SSTs averaged for each month over the periods 1950-1959 and the 1978-1987. These simulations yielded wet conditions in the 1950-1959 case and dry conditions in the 1978-1987 case, confirming the role of SST forcing in decadal variability in particular. To test the hypothesis that the SST influences Sahel rainfall via stabilization of the tropospheric sounding, simulations were performed in which the temperature field from the AGCM was artificially modified before it was used to force the regional model. We modified the original 1955 ECHAM4.5 temperature profiles by adding a horizontally uniform, vertically varying temperature increase, taken from the 1986-1955 tropical mean warming in either the AGCM or the NCEP/National Center for Atmospheric Research Reanalysis. When compared to the 1955 simulations without the added tropospheric warming, these simulations show a drying in the Sahel similar to that in the 1986-1955 difference and to the decadal difference between the 1980s and 1950s. This suggests that the tropospheric warming may have been, at least in part, the agent by which the SST increases led to the Sahel drought of the 1970s and 1980s. (orig.)
Euser, Tanja; Hrachowitz, Markus; Winsemius, Hessel; Savenije, Hubert
Incorporating spatially variable information is a frequently discussed option to increase the performance of (semi) distributed conceptual rainfall runoff models. One of the methods to do this is by using these spatially variable information to delineate Hydrological Response Units (HRUs) within a catchment. This study tests whether the incorporation of an additional agricultural HRU in a conceptual hydrological model can better reflect the spatial differences in runoff generation and therefore improve the simulation of the wetting phase in autumn. The study area is the meso-scale Ourthe catchment in Belgium. A previous study in this area showed that spatial patterns in runoff generation were already better represented by incorporation of a wetland and a hillslope HRU, compared to a lumped model structure. The influences which are considered by including an agriculture HRU are increased drainage speed due to roads, plough pans and increased infiltration excess overland flow (drainage pipes area only limited present), and variable vegetation patterns due to sowing and harvesting. In addition, the vegetation is not modelled as a static resistance towards evaporation, but the Jarvis stress functions are used to increase the realism of the modelled transpiration; in land-surface models the Jarvis stress functions are already often used for modelling transpiration. The results show that an agricultural conceptualisation in addition to wetland and hillslope conceptualisations leads to small improvements in the modelled discharge. However, the influence is larger on the representation of spatial patterns and the modelled contributions of different HRUs to the total discharge.
National Oceanic and Atmospheric Administration, Department of Commerce — The Large Pelagics Intercept Survey (LPIS) is a dockside survey of private and charterboat captains who have just completed fishing trips directed at large pelagic...
Duangdai, Eakkapong; Likasiri, Chulin
In this work, 4 models for predicting rainfall amounts are investigated and compared using Northern Thailand's seasonal rainfall data for 1973-2008. Two models, global temperature, forest area and seasonal rainfall (TFR) and modified TFR based on a system of differential equations, give the relationships between global temperature, Northern Thailand's forest cover and seasonal rainfalls in the region. The other two models studied are time series and Autoregressive Moving Average (ARMA) models. All models are validated using the k-fold cross validation method with the resulting errors being 0.971233, 0.740891, 2.376415 and 2.430891 for time series, ARMA, TFR and modified TFR models, respectively. Under Business as Usual (BaU) scenario, seasonal rainfalls in Northern Thailand are projected through the year 2020 using all 4 models. TFR and modified TFR models are also used to further analyze how global temperature rise and government reforestation policy affect seasonal rainfalls in the region. Rainfall projections obtained via the two models are also compared with those from the International Panel on Climate Change (IPCC) under IS92a scenario. Results obtained through a mathematical model for global temperature, forest area and seasonal rainfall show that the higher the forest cover, the less fluctuation there is between rainy-season and summer rainfalls. Moreover, growth in forest cover also correlates with an increase in summer rainfalls. An investigation into the relationship between main crop productions and rainfalls in dry and rainy seasons indicates that if the rainy-season rainfall is high, that year's main-crop rice production will decrease but the second-crop rice, maize, sugarcane and soybean productions will increase in the following year.
Skinner, Christopher; Peleg, Nadav; Quinn, Niall
The use of Landscape Evolution Models often requires a timeseries of rainfall to drive the model. The spatial and temporal resolution of the driving data has an impact on several model outputs, including the shape of the landscape itself. Attempts to compensate for the spatiotemporal smoothing of local rainfall intensities are insufficient and may exacerbate these issues, meaning that to produce the best results the model needs to be run with data of highest spatial and temporal resolutions available. Some rainfall generators are able to produce timeseries with high spatial and temporal resolution. Observed data is used for the calibration of these generators. However, rainfall observations are highly uncertain and vary between different products (e.g. raingauges, weather radar) which may cascade through the Landscape Evolution Model. Here, we used the STREAP rainfall generator to produce high spatial (1km) and temporal (hourly) resolution ensembles of rainfall for a 50-year period, and used these to drive the CAESAR-Lisflood Landscape Evolution Model for a test catchment. Three different calibrations of STREAP were used against different products: gridded raingauge (TBR), weather radar (NIMROD), and a merged of the two. Analysis of the discharge and sediment yields from the model runs showed that the models run by STREAP calibrated by the different products were statistically significantly different, with the raingauge calibration producing 12.4 % more sediment on average over the 50-year period. The merged product produced results which were between the raingauge and radar products. The results demonstrate the importance of considering the selection of rainfall driving data on Landscape Evolution Modelling. Rainfall products are highly uncertain, different instruments will observe rainfall differently, and these uncertainties are clearly shown to cascade through the calibration of the rainfall generator and the Landscape Evolution Model. Merging raingauge and
L-F. Chu (Lan-Fen); M.J. McAleer (Michael); S-H. Wang (Szu-Hua)
textabstractThis paper has two primary purposes. First, we fit the annual maximum daily rainfall data for 6 rainfall stations, both with stationary and non-stationary generalized extreme value (GEV) distributions for the periods 1911-2010 and 1960-2010 in Taiwan, and detect the changes between the
Full Text Available The objective of this study is to develop artificial neural network (ANN models, including multilayer perceptron (MLP and Kohonen self-organizing feature map (KSOFM, for spatial disaggregation of areal rainfall in the Wi-stream catchment, an International Hydrological Program (IHP representative catchment, in South Korea. A three-layer MLP model, using three training algorithms, was used to estimate areal rainfall. The Levenberg–Marquardt training algorithm was found to be more sensitive to the number of hidden nodes than were the conjugate gradient and quickprop training algorithms using the MLP model. Results showed that the networks structures of 11-5-1 (conjugate gradient and quickprop and 11-3-1 (Levenberg-Marquardt were the best for estimating areal rainfall using the MLP model. The networks structures of 1-5-11 (conjugate gradient and quickprop and 1-3-11 (Levenberg–Marquardt, which are the inverse networks for estimating areal rainfall using the best MLP model, were identified for spatial disaggregation of areal rainfall using the MLP model. The KSOFM model was compared with the MLP model for spatial disaggregation of areal rainfall. The MLP and KSOFM models could disaggregate areal rainfall into individual point rainfall with spatial concepts.
V. A. Bell
Full Text Available The sensitivity of catchment runoff models to rainfall is investigated at a variety of spatial scales using data from a dense raingauge network and weather radar. These data form part of the HYREX (HYdrological Radar EXperiment dataset. They encompass records from 49 raingauges over the 135 km2 Brue catchment in south-west England together with 2 and 5 km grid-square radar data. Separate rainfall time-series for the radar and raingauge data are constructed on 2, 5 and 10 km grids, and as catchment average values, at a 15 minute time-step. The sensitivity of the catchment runoff models to these grid scales of input data is evaluated on selected convective and stratiform rainfall events. Each rainfall time-series is used to produce an ensemble of modelled hydrographs in order to investigate this sensitivity. The distributed model is shown to be sensitive to the locations of the raingauges within the catchment and hence to the spatial variability of rainfall over the catchment. Runoff sensitivity is strongest during convective rainfall when a broader spread of modelled hydrographs results, with twice the variability of that arising from stratiform rain. Sensitivity to rainfall data and model resolution is explored and, surprisingly, best performance is obtained using a lower resolution of rainfall data and model. Results from the distributed catchment model, the Simple Grid Model, are compared with those obtained from a lumped model, the PDM. Performance from the distributed model is found to be only marginally better during stratiform rain (R2 of 0.922 compared to 0.911 but significantly better during convective rain (R2 of 0.953 compared to 0.909. The improved performance from the distributed model can, in part, be accredited to the excellence of the dense raingauge network which would not be the norm for operational flood warning systems. In the final part of the paper, the effect of rainfall resolution on the performance of the 2 km distributed
Salih, Abubakr A. M.; Elagib, Nadir Ahmed; Tjernström, Michael; Zhang, Qiong
The African Sahel region is known to be highly vulnerable to climate variability and change. We analyze rainfall in the Sahelian Sudan in terms of distribution of rain-days and amounts, and examine whether regional climate models can capture these rainfall features. Three regional models namely, Regional Model (REMO), Rossby Center Atmospheric Model (RCA) and Regional Climate Model (RegCM4), are evaluated against gridded observations (Climate Research Unit, Tropical Rainfall Measuring Mission, and ERA-interim reanalysis) and rain-gauge data from six arid and semi-arid weather stations across Sahelian Sudan over the period 1989 to 2008. Most of the observed rain-days are characterized by weak (0.1-1.0 mm/day) to moderate (> 1.0-10.0 mm/day) rainfall, with average frequencies of 18.5% and 48.0% of the total annual rain-days, respectively. Although very strong rainfall events (> 30.0 mm/day) occur rarely, they account for a large fraction of the total annual rainfall (28-42% across the stations). The performance of the models varies both spatially and temporally. RegCM4 most closely reproduces the observed annual rainfall cycle, especially for the more arid locations, but all of the three models fail to capture the strong rainfall events and hence underestimate its contribution to the total annual number of rain-days and rainfall amount. However, excessive moderate rainfall compensates this underestimation in the models in an annual average sense. The present study uncovers some of the models' limitations in skillfully reproducing the observed climate over dry regions, will aid model users in recognizing the uncertainties in the model output and will help climate and hydrological modeling communities in improving models.
Jung, J.; Kim, H. S.; Joo, H. J.; Han, D.
Markov chain is an easy method to handle when we compare it with other ones for the rainfall simulation. However, it also has limitations in reflecting seasonal variability of rainfall or change on rainfall patterns caused by climate change. This study applied a Nonhomogeneous Hidden Markov Model(NHMM) to consider these problems. The NHMM compared with a Hidden Markov Model(HMM) for the evaluation of a goodness of the model. First, we chose Gum river basin in Korea to apply the models and collected daily rainfall data from the stations. Also, the climate variables of geopotential height, temperature, zonal wind, and meridional wind date were collected from NCEP/NCAR reanalysis data to consider external factors affecting the rainfall event. We conducted a correlation analysis between rainfall and climate variables then developed a linear regression equation using the climate variables which have high correlation with rainfall. The monthly rainfall was obtained by the regression equation and it became input data of NHMM. Finally, the daily rainfall by NHMM was simulated and we evaluated the goodness of fit and prediction capability of NHMM by comparing with those of HMM. As a result of simulation by HMM, the correlation coefficient and root mean square error of daily/monthly rainfall were 0.2076 and 10.8243/131.1304mm each. In case of NHMM, the correlation coefficient and root mean square error of daily/monthly rainfall were 0.6652 and 10.5112/100.9865mm each. We could verify that the error of daily and monthly rainfall simulated by NHMM was improved by 2.89% and 22.99% compared with HMM. Therefore, it is expected that the results of the study could provide more accurate data for hydrologic analysis. Acknowledgements This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning(2017R1A2B3005695)
Eisenberg, Marisa C; Kujbida, Gregory; Tuite, Ashleigh R; Fisman, David N; Tien, Joseph H
Haiti has been in the midst of a cholera epidemic since October 2010. Rainfall is thought to be associated with cholera here, but this relationship has only begun to be quantitatively examined. In this paper, we quantitatively examine the link between rainfall and cholera in Haiti for several different settings (including urban, rural, and displaced person camps) and spatial scales, using a combination of statistical and dynamic models. Statistical analysis of the lagged relationship between rainfall and cholera incidence was conducted using case crossover analysis and distributed lag nonlinear models. Dynamic models consisted of compartmental differential equation models including direct (fast) and indirect (delayed) disease transmission, where indirect transmission was forced by empirical rainfall data. Data sources include cholera case and hospitalization time series from the Haitian Ministry of Public Health, the United Nations Water, Sanitation and Health Cluster, International Organization for Migration, and Hôpital Albert Schweitzer. Rainfall data was obtained from rain gauges from the U.S. Geological Survey and Haiti Regeneration Initiative, and remote sensing rainfall data from the National Aeronautics and Space Administration Tropical Rainfall Measuring Mission. A strong relationship between rainfall and cholera was found for all spatial scales and locations examined. Increased rainfall was significantly correlated with increased cholera incidence 4-7 days later. Forcing the dynamic models with rainfall data resulted in good fits to the cholera case data, and rainfall-based predictions from the dynamic models closely matched observed cholera cases. These models provide a tool for planning and managing the epidemic as it continues. Copyright © 2013 Elsevier B.V. All rights reserved.
N. Q. Hung
Full Text Available This paper presents a new approach using an Artificial Neural Network technique to improve rainfall forecast performance. A real world case study was set up in Bangkok; 4 years of hourly data from 75 rain gauge stations in the area were used to develop the ANN model. The developed ANN model is being applied for real time rainfall forecasting and flood management in Bangkok, Thailand. Aimed at providing forecasts in a near real time schedule, different network types were tested with different kinds of input information. Preliminary tests showed that a generalized feedforward ANN model using hyperbolic tangent transfer function achieved the best generalization of rainfall. Especially, the use of a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness, the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input data, advanced ANN model to apply with continuous data containing rainy and non-rainy period, allowed model to issue forecast at any moment. Additionally, forecasts by ANN model were compared to the convenient approach namely simple persistent method. Results show that ANN forecasts have superiority over the ones obtained by the persistent model. Rainfall forecasts for Bangkok from 1 to 3 h ahead were highly satisfactory. Sensitivity analysis indicated that the most important input parameter besides rainfall itself is the wet bulb temperature in forecasting rainfall.
Tan, H.; Chandra, C. V.; Chen, H.
Rainfall estimation based on radar measurements has been an important topic for a few decades. Generally, radar rainfall estimation is conducted through parametric algorisms such as reflectivity-rainfall relation (i.e., Z-R relation). On the other hand, neural networks are developed for ground rainfall estimation based on radar measurements. This nonparametric method, which takes into account of both radar observations and rainfall measurements from ground rain gauges, has been demonstrated successfully for rainfall rate estimation. However, the neural network-based rainfall estimation is limited in practice due to the model complexity and structure, data quality, as well as different rainfall microphysics. Recently, the deep learning approach has been introduced in pattern recognition and machine learning areas. Compared to traditional neural networks, the deep learning based methodologies have larger number of hidden layers and more complex structure for data representation. Through a hierarchical learning process, the high level structured information and knowledge can be extracted automatically from low level features of the data. In this paper, we introduce a novel deep neural network model for rainfall estimation based on ground polarimetric radar measurements .The model is designed to capture the complex abstractions of radar measurements at different levels using multiple layers feature identification and extraction. The abstractions at different levels can be used independently or fused with other data resource such as satellite-based rainfall products and/or topographic data to represent the rain characteristics at certain location. In particular, the WSR-88DP radar and rain gauge data collected in Dallas - Fort Worth Metroplex and Florida are used extensively to train the model, and for demonstration purposes. Quantitative evaluation of the deep neural network based rainfall products will also be presented, which is based on an independent rain gauge
Naufan, Ihsan; Sivakumar, Bellie; Woldemeskel, Fitsum M.; Raghavan, Srivatsan V.; Vu, Minh Tue; Liong, Shie-Yui
Understanding the spatial and temporal variability of rainfall has always been a great challenge, and the impacts of climate change further complicate this issue. The present study employs the concepts of complex networks to study the spatial connections in rainfall, with emphasis on climate change and rainfall scaling. Rainfall outputs (during 1961-1990) from a regional climate model (i.e. Weather Research and Forecasting (WRF) model that downscaled the European Centre for Medium-range Weather Forecasts, ECMWF ERA-40 reanalyses) over Southeast Asia are studied, and data corresponding to eight different temporal scales (6-hr, 12-hr, daily, 2-day, 4-day, weekly, biweekly, and monthly) are analyzed. Two network-based methods are applied to examine the connections in rainfall: clustering coefficient (a measure of the network's local density) and degree distribution (a measure of the network's spread). The influence of rainfall correlation threshold (T) on spatial connections is also investigated by considering seven different threshold levels (ranging from 0.5 to 0.8). The results indicate that: (1) rainfall networks corresponding to much coarser temporal scales exhibit properties similar to that of small-world networks, regardless of the threshold; (2) rainfall networks corresponding to much finer temporal scales may be classified as either small-world networks or scale-free networks, depending upon the threshold; and (3) rainfall spatial connections exhibit a transition phase at intermediate temporal scales, especially at high thresholds. These results suggest that the most appropriate model for studying spatial connections may often be different at different temporal scales, and that a combination of small-world and scale-free network models might be more appropriate for rainfall upscaling/downscaling across all scales, in the strict sense of scale-invariance. The results also suggest that spatial connections in the studied rainfall networks in Southeast Asia are
Panthou, G.; Vischel, T.; Lebel, T.; Blanchet, J.; Quantin, G.; Ali, A.
In a world of increasing exposure of populations to natural hazards, the mapping of extreme rainfall remains a key subject of study. Such maps are required for both flood risk management and civil engineering structure design, the challenge being to take into account the local information provided by point rainfall series as well as the necessity of some regional coherency. Two approaches based on the extreme value theory are compared here, with an application to extreme rainfall mapping in West Africa. The first approach is a local fit and interpolation (LFI) consisting of a spatial interpolation of the generalized extreme value (GEV) distribution parameters estimated independently at each station. The second approach is a spatial maximum likelihood estimation (SMLE); it directly estimates the GEV distribution over the entire region by a single maximum likelihood fit using jointly all measurements combined with spatial covariates. Five LFI and three SMLE methods are considered, using the information provided by 126 daily rainfall series covering the period 1950-1990. The methods are first evaluated in calibration. Then the predictive skills and the robustness are assessed through a cross validation and an independent network validation process. The SMLE approach, especially when using the mean annual rainfall as covariate, appears to perform better for most of the scores computed. Using the Niamey 104 year time series, it is also shown that the SMLE approach has the capacity to deal more efficiently with the effect of local outliers by using the spatial information provided by nearby stations.
Priska Arindya Purnama
Full Text Available The aim of this research is to model and forecast the rainfall in Batu City using multi input transfer function model based on air temperature, humidity, wind speed and cloud. Transfer function model is a multivariate time series model which consists of an output series (Yt sequence expected to be effected by an input series (Xt and other inputs in a group called a noise series (Nt. Multi input transfer function model obtained is (b1,s1,r1 (b2,s2,r2 (b3,s3,r3 (b4,s4,r4(pn,qn = (0,0,0 (23,0,0 (1,2,0 (0,0,0 ([5,8],2 and shows that air temperature on t-day affects rainfall on t-day, rainfall on t-day is influenced by air humidity in the previous 23 days, rainfall on t-day is affected by wind speed in the previous day , and rainfall on day t is affected by clouds on day t. The results of rainfall forecasting in Batu City with multi input transfer function model can be said to be accurate, because it produces relatively small RMSE value. The value of RMSE data forecasting training is 7.7921 while forecasting data testing is 4.2184. Multi-input transfer function model is suitable for rainfall in Batu City.
Thorndahl, Søren; Poulsen, Troels Sander; Bøvith, Thomas
Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....
Thorndahl, Søren Liedtke; Poulsen, Troels Sander; Bøvith, Thomas
Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....
Full Text Available The scope of this study is to develop a rainfall intensity-duration-frequency (IDF equation for some return periods at Erzurum rainfall station. The maximum annual rainfall values for 5, 10, 15, 30 and 60 minutes are statistically analyzed for the period 1956 – 2004 by using some statistical distributions such as the Generalized Extreme Values (GEV, Gumbel, Normal, Two-parameter Lognormal, Three-parameter Lognormal, Gamma, Pearson type III and Log-Pearson type III distributions. ?2 goodness-of-fit test was used to choose the best statistical distribution among all distributions. IDF equation constants and coefficients of correlation (R for each emprical functions are calculated using nonlinear estimation method for each return periods (T = 2, 5, 10, 25, 50, 75 and 100 years. The most suitable IDF equation is observed that ( B max i (t = A/ t + C , except for T=100 years, because of the highest coefficients of correlation.
Lusby, G.C.; Lichty, R.W.
Results of a study using a rainfall simulator to define infiltration parameters for use in watershed modeling are presented. A total of 23 rainfall-simulation runs were made on five small plots representing four representative soil-vegetation types of the study watershed in eastern Colorado. Data for three observed rainfall-runoff events were recorded by gages on four of the plots. Data from all events were used to develop best-fit parameters of the Green and Ampt infiltration equation. The hydraulic conductivity of the transmission zone, KSAT, grossly controlled the goodness of fit of all modeling attempts. Results of fitting KSAT to reproduce runoff from rainfall simulator runs and results of fitting KSAT to reproduce runoff from observed rainfall-runoff events are inconsistent. Variations in results from site to site and at different times of the year were observed. (USGS)
Bennett, Bree; Thyer, Mark; Leonard, Michael; Lambert, Martin; Bates, Bryson
The spatial distribution of rainfall has a significant influence on catchment dynamics and the generation of streamflow time series. However, there are few stochastic models that can simulate long sequences of stochastic rainfall fields continuously in time and space. To address this issue, the first goal of this study was to present a new parsimonious stochastic model that produces daily rainfall fields across the catchment. To achieve parsimony, the model used the latent-variable approach (because this parsimoniously simulates rainfall occurrences as well as amounts) and several other assumptions (including contemporaneous and separable spatiotemporal covariance structures). The second goal was to develop a comprehensive and systematic evaluation (CASE) framework to identify model strengths and weaknesses. This included quantitative performance categorisation that provided a systematic, succinct and transparent method to assess and summarise model performance over a range of statistics, sites, scales and seasons. The model is demonstrated using a case study from the Onkaparinga catchment in South Australia. The model showed many strengths in reproducing the observed rainfall characteristics with the majority of statistics classified as either statistically indistinguishable from the observed or within 5% of the observed across the majority of sites and seasons. These included rainfall occurrences/amounts, wet/dry spell distributions, annual volumes/extremes and spatial patterns, which are important from a hydrological perspective. One of the few weaknesses of the model was that the total annual rainfall in dry years (lower 5%) was overestimated by 15% on average over all sites. An advantage of the CASE framework was that it was able to identify the source of this overestimation was poor representation of the annual variability of rainfall occurrences. Given the strengths of this continuous daily rainfall field model it has a range of potential hydrological
Gouveia-Reis, Délia; Guerreiro Lopes, Luiz; Mendonça, Sandra
The dependence between variables plays a central role in multivariate extremes. In this paper, spatial dependence of Madeira Island's rainfall data is addressed within an extreme value copula approach through an analysis of maximum annual data. The impact of altitude, slope orientation, distance between rain gauge stations and distance from the stations to the sea are investigated for two different periods of time. The results obtained highlight the influence of the island's complex topography on the spatial distribution of extreme rainfall in Madeira Island.
Full Text Available Rainfall-runoff model using tank model founded by Sugawara has been widely used in Asia. Many researchers use the tank model to predict water availability and flooding in a watershed. This paper describes the concept of rainfall-runoff model using tank model, discuss the problems and challenges in using of the model, especially in Province of Aceh, Indonesia and how to improve the outcome of simulation of tank model. Many factors affect the rainfall-runoff phenomena of a wide range of watershed include: soil types, land use types, rainfall, morphometry, geology and geomorphology, caused the tank model usefull only for concerning watershed. It is necessary to adjust some parameters of tank model for other watershed by recalibrating the parameters of the model. Rainfall runoff model using the tank model for a watershed scale is more reasonable focused on each sub-watershed by considering soil types, land use types and rainfall of the concerning watershed. Land use data can be enhanced by using landsat imagery or aerial photographs to support the validation the existing of land use type. Long term of observed discharges and rainfall data should be increased by set up the AWLR (Automatic Water Level Recorder and rainfall stations for each of sub-watersheds. The reasonable tank model can be resulted not only by calibrating the parameters, but also by considering the observed and simulated infiltration for each soil and land use types of the concerning watershed
Full Text Available Information on seasonal Kiremet and seasonal Belg rainfall amount is important in the rain fed agriculture of Ethiopia since more than 85% of the population is dependent on agriculture particularly on rain fed farming practices. The distribution pattern of rainfall rather than the total amount of rainfall within the entire period of time is more important for studying the pattern of rainfall occurrence. A two-state Markov chain was used to describe the characteristics of rainfall occurrences in this woreda. The states, as considered were; dry (d and rainy (r. The overall chance of rain and the fitted curve tells us that the chance of getting rain in the main rainy season is about twice as compared to the small rainy season. The first order Markov chain model indicates that the probability of getting rain in the small rainy season is significantly dependent on whether the earlier date was dry or wet. While the second order Marko chain indicates that the main rainy season the dependence of the probability of rain on the previous two dates’ conditions is less as compared with the small rainy season. Rainfall amounts are very variable and are usually modeled by a gamma distribution. Therefore, the pattern of rainfall is somewhat unimodial having only one extreme value in August. Onset, cessation and length of growing season of rainfall for the main rainy season show medium variation compared to the small rainy season.
Serinaldi, F.; Kilsby, C. G.
While extensive research has been devoted to rainfall-runoff modelling for risk assessment in small and medium size watersheds, less attention has been paid, so far, to large scale trans-national basins, where flood events have severe societal and economic impacts with magnitudes quantified in billions of Euros. As an example, in the April 2006 flood events along the Danube basin at least 10 people lost their lives and up to 30 000 people were displaced, with overall damages estimated at more than half a billion Euros. In this context, refined analytical methods are fundamental to improve the risk assessment and, then, the design of structural and non structural measures of protection, such as hydraulic works and insurance/reinsurance policies. Since flood events are mainly driven by exceptional rainfall events, suitable characterization and modelling of space-time properties of rainfall fields is a key issue to perform a reliable flood risk analysis based on alternative precipitation scenarios to be fed in a new generation of large scale rainfall-runoff models. Ultimately, this approach should be extended to a global flood risk model. However, as the need of rainfall models able to account for and simulate spatio-temporal properties of rainfall fields over large areas is rather new, the development of new rainfall simulation frameworks is a challenging task involving that faces with the problem of overcoming the drawbacks of the existing modelling schemes (devised for smaller spatial scales), but keeping the desirable properties. In this study, we critically summarize the most widely used approaches for rainfall simulation. Focusing on stochastic approaches, we stress the importance of introducing suitable climate forcings in these simulation schemes in order to account for the physical coherence of rainfall fields over wide areas. Based on preliminary considerations, we suggest a modelling framework relying on the Generalized Additive Models for Location, Scale
neural network architecture for precipitation prediction of Myanmar, World Academy of. Science, Engineering and Technology, 48, pp. 130 – 134. Kumarasiri, A.D. and Sonnadara, D.U.J. (2006). Rainfall forecasting: an artificial neural network approach, Proceedings of the Technical Sessions,. 22, pp. 1-13 Institute of Physics ...
In a preliminary step, in order to identify the most influential rainfall predictor, a correlation matrix and step-wise regression of 10 predictors with different lags were analysed. The influence of the southern Indian Ocean Sea Surface Temperature was identified as the most influential predictor for the highland of Eritrea.
Herwitz, Stanley R.
The rainwater interception storage capacities of mature canopy trees in a tropical rainforest site in northeast Queensland, Australia, were approximated using a combination of field and laboratory measurements. The above-ground vegetative surfaces of five selected species (three flaky-barked; two smooth-barked) were saturated under laboratory conditions in order to establish their maximum interception storage capacities. Average leaf surface interception storages ranged from 112 to 161 ml m -2. The interception storages of bark ranged from 0.51 to 0.97 ml cm -3. These standardized interception storages were applied to estimates of leaf surface area and bark volume for 51 mature canopy trees representing the selected species in the field site. The average whole tree interception storage capacities of the five species ranged from 110 to 5281 per tree and 2.2 to 8.3 mm per unit projected crown area. The highly significant interspecific differences in interception storage capacity suggest that both floristic and demographic data are needed in order to accurately calculate a forest-wide interception storage capacity for species-rich tropical rainforest vegetation. Species with large woody surface areas and small projected crown areas are capable of storing the greatest depth equivalents of rainwater under heavy rainfall conditions. In the case of both the flaky-barked and the smooth-barked species, bark accounted for > 50% of the total interception storage capacity under still-air conditions, and > 80% under turbulent air conditions. The emphasis in past interception studies on the role of leaf surfaces in determining the interception storage capacity of a vegetative cover must be modified for tropical rainforests to include the storage capacity provided by the bark tissue on canopy trees.
Wang-Erlandsson, Lan; van der Ent, Ruud; Fetzer, Ingo; Keys, Patrick; Savenije, Hubert; Gordon, Line
Forests play a major role in hydrology. Not only by immediate control of soil moisture and streamflow, but also by regulating climate through evaporation (i.e., transpiration, interception, and soil evaporation). The process of evaporation travelling through the atmosphere and returning as precipitation on land is known as moisture recycling. Whether evaporation is recycled depends on wind direction and geography. Moisture recycling and forest change studies have primarily focused on either one region (e.g. the Amazon), or one biome type (e.g. tropical humid forests). We will advance this via a systematic global inter-comparison of forest change impacts on precipitation depending on both biome type and geographic location. The rainfall effects are studied for three contemporary forest changes: afforestation, deforestation, and replacement of mature forest by forest plantations. Furthermore, as there are indications in the literature that moisture recycling in some places intensifies during dry years, we will also compare the rainfall impacts of forest change between wet and dry years. We model forest change effects on evaporation using the global hydrological model STEAM and trace precipitation changes using the atmospheric moisture tracking scheme WAM-2layers. This research elucidates the role of geographical location of forest change driven modifications on rainfall as a function of the type of forest change and climatic conditions. These knowledge gains are important at a time of both rapid forest and climate change. Our conclusions nuance our understanding of how forests regulate climate and pinpoint hotspot regions for forest-rainfall coupling.
Full Text Available Landslides can be triggered by intense or prolonged rainfall. Rain gauge measurements are commonly used to predict landslides even if satellite rainfall estimates are available. Recent research focuses on the comparison of satellite estimates and gauge measurements. The rain gauge data from the Italian network (collected in the system database “Verifica Rischio Frana”, VRF are compared with the National Aeronautics and Space Administration (NASA Tropical Rainfall Measuring Mission (TRMM products. For the purpose, we couple point gauge and satellite rainfall estimates at individual grid cells, evaluating the correlation between gauge and satellite data in different morpho-climatological conditions. We then analyze the statistical distributions of both rainfall data types and the rainfall events derived from them. Results show that satellite data underestimates ground data, with the largest differences in mountainous areas. Power-law models, are more appropriate to correlate gauge and satellite data. The gauge and satellite-based products exhibit different statistical distributions and the rainfall events derived from them differ. In conclusion, satellite rainfall cannot be directly compared with ground data, requiring local investigation to account for specific morpho-climatological settings. Results suggest that satellite data can be used for forecasting landslides, only performing a local scaling between satellite and ground data.
Full Text Available In this research parameterization of the precipitation process in Ogura & Takahashi (O-T thunderstorm model was improved in microphysical processes, specially in the autoconversion process to form raindrops, in the glaciation process and in the terminal velocities of rain and hail. The rainfall intensity became much heavier with Kesslers parameterization, the second peak of the rainfall intensity disappeared with Biggs freezing probability, and the rainfall intensity became much heavier and sharper with Lin et als terminal velocities of rain and hail than in the O-T original model. Finally, the derived rainfall pattern based on the improved model has much similarities to the observation data. This paper expresses the basic research for studying the physical treatment in clouds. The modified O-T model has different applications in analyzing radar observation data, estimate the potential of soil erosion, parameteriztion of shower in mesoscale numerical weather prediction and eta.
Full Text Available The use of artificial neural networks (ANNs in modelling the hydrological processes has become a common approach in the last two decades, among side the traditional methods. In regard to the rainfall-runoff modelling, in both traditional and ANN models the use of ground rainfall measurements is prevalent, which can be challenging in areas with low rain gauging station density, especially in catchments where strong focused rainfall can generate flash-floods. The weather radar technology can prove to be a solution for such areas by providing rain estimates with good time and space resolution. This paper presents a comparison between different ANN setups using as input both ground and radar observations for modelling the rainfall-runoff process for Bahluet catchment, with focus on a flash-flood observed in the catchment.
Full Text Available This research investigates an alternative approach to automatically evolve the hidden temporal distribution of onset of rainfall directly from multivariate time series (MTS) data in the absence of domain experts. Temporal probabilistic modelling...
Chowdhury, A. F. M. K.; Lockart, N.; Willgoose, G. R.; Kuczera, G. A.; Kiem, A.; Nadeeka, P. M.
One of the key objectives of stochastic rainfall modelling is to capture the full variability of climate system for future drought and flood risk assessment. However, it is not clear how well these models can capture the future climate variability when they are calibrated to Global/Regional Climate Model data (GCM/RCM) as these datasets are usually available for very short future period/s (e.g. 20 years). This study has assessed the ability of two stochastic daily rainfall models to capture climate variability by calibrating them to a dynamically downscaled RCM dataset in an east Australian catchment for 1990-2010, 2020-2040, and 2060-2080 epochs. The two stochastic models are: (1) a hierarchical Markov Chain (MC) model, which we developed in a previous study and (2) a semi-parametric MC model developed by Mehrotra and Sharma (2007). Our hierarchical model uses stochastic parameters of MC and Gamma distribution, while the semi-parametric model uses a modified MC process with memory of past periods and kernel density estimation. This study has generated multiple realizations of rainfall series by using parameters of each model calibrated to the RCM dataset for each epoch. The generated rainfall series are used to generate synthetic streamflow by using a SimHyd hydrology model. Assessing the synthetic rainfall and streamflow series, this study has found that both stochastic models can incorporate a range of variability in rainfall as well as streamflow generation for both current and future periods. However, the hierarchical model tends to overestimate the multiyear variability of wet spell lengths (therefore, is less likely to simulate long periods of drought and flood), while the semi-parametric model tends to overestimate the mean annual rainfall depths and streamflow volumes (hence, simulated droughts are likely to be less severe). Sensitivity of these limitations of both stochastic models in terms of future drought and flood risk assessment will be discussed.
Muhammadh, K. M.; Mafas, M. M. M.; Weerakoon, S. B.
The Upper Mahaweli basin is the upper most sub basin of 788 km2 in size above Polgolla barrage in the Mahaweli River, the longest river in Sri Lanka which starts from the central hills of the island and drains to the sea at the North-east coast. Rainfall forecast in the Upper Mahaweli basin is important for issuing flood warning in the river downstream of the reservoirs, landslide warning in the settlements in hilly areas. Anticipatory water management in the basin including reservoir operations, barrage gate operation for releasing water for irrigation and flood control also require reliable rainfall and runoff prediction in the sub basin. In this study, the Regional Climate Model (RegCM V188.8.131.52) is calibrated for the basin to dynamically downscale reanalysis weather data of Global Climate Model (GCM) to forecast the rainfall in the basin. Observed rainfalls at gauging stations within the basin were used for model calibration and validation. The observed rainfall data was analysed using ARC GIS and the output of RegCM was analysed using GrADS tool. The output of the model and the observed precipitation were obtained on grids of size 0.1 degrees and the accuracy of the predictions were analysed using RMSE and Mean Model Absolute Error percentage (MAME %). The predictions by the calibrated RegCM model for the basin is shown to be satisfactory. The model is a useful tool for rainfall forecast in the Upper Mahaweli River basin.
World's urban centers are growing rapidly causing the impact of extreme rainfall events felt much more severely due to relatively well unerstood phenomena like decreased infiltration and flow resistance. However, an increasing set of evidence (e.g. heavy rainfall event observed at Nerima, central part of Tokyo metropolitan area, on 21 July 1999) suggest that the extreme rainfall, the driving force itself increases as a result of the microclimatic changes due to urban growth. Urban heat islands(UHI) due to heat anomalies of urban sprawl act as virtual mountains resulting in a local atmosphere more conducive for heavy rainfall. In this study, we employ a popular mesoscale atmoshperic model to numerically simulate the UHI induced rainfall enhancement. Initial idealized experiments conducted under trophical atmospheric conditions indicated that the changes in landuse due to significant urban growth will indeed cause more intense rainfall events. This is largely due to increased convective breakup, causing a favourable situation for convective cloud systems. Five historical heavy rainfall events that caused floods in five urban centres (Dhaka, Mumbai, Colombo, Lyon and Taipei) were selected from historical records. Numerical simulations were setup to assertain what would be the amount of rainfall if the same large-scale atmospheric situations (forcings) occured under a hypothetical situation of doubled urbanization level these events. Significant increases (upto 50%) of extreme rainfall was indicated for many of the events. Under major assumptions, these simulations were used to estimate the anticipated changes in the Intensity-Duration-Frequency (IDF). The magnitude of the 30min event with 25 year return period increased by about 20 percent. Without considering any changes in the external forcing the urban growth alone could cause very significant increase in local rainfall.
Kamal Chowdhury, A. F. M.; Lockart, Natalie; Willgoose, Garry; Kuczera, George; Kiem, Anthony S.; Parana Manage, Nadeeka
The primary objective of this study is to develop a stochastic rainfall generation model that can match not only the short resolution (daily) variability but also the longer resolution (monthly to multiyear) variability of observed rainfall. This study has developed a Markov chain (MC) model, which uses a two-state MC process with two parameters (wet-to-wet and dry-to-dry transition probabilities) to simulate rainfall occurrence and a gamma distribution with two parameters (mean and standard deviation of wet day rainfall) to simulate wet day rainfall depths. Starting with the traditional MC-gamma model with deterministic parameters, this study has developed and assessed four other variants of the MC-gamma model with different parameterisations. The key finding is that if the parameters of the gamma distribution are randomly sampled each year from fitted distributions rather than fixed parameters with time, the variability of rainfall depths at both short and longer temporal resolutions can be preserved, while the variability of wet periods (i.e. number of wet days and mean length of wet spell) can be preserved by decadally varied MC parameters. This is a straightforward enhancement to the traditional simplest MC model and is both objective and parsimonious.
Juan David Montoya-Domínguez
Full Text Available This paper presents experimental results obtained from silty sand slope models subjected to an artificial rainfall. Four models were constructed to evaluate the effect of initial water content and rainfall intensity on the hydraulic behavior and failure mechanisms of the slopes. The models were instrumented with volumetric water content sensors to monitor the advance of the water front, and inclinometers to measure lateral movements of the slope. The models were subjected to rainfall intensities ranging from 25 to 50 mm/h, and durations from 19 to 152 minutes. The influence of low intensity rainfall events before a high intensity rainfall is discussed herein. The results showed that the time the slope models required to reach failure was influenced by the soil initial water content, being shorter at high initial water contents. These results are useful to understand the behavior of unsaturated natural slopes and embankments exposed to rainfall infiltration, and to complement the existing laboratory database existing in this subject.
Akinsanola, A. A.; Ajayi, V. O.; Adejare, A. T.; Adeyeri, O. E.; Gbode, I. E.; Ogunjobi, K. O.; Nikulin, G.; Abolude, A. T.
This study presents evaluation of the ability of Rossby Centre Regional Climate Model (RCA4) driven by nine global circulation models (GCMs), to skilfully reproduce the key features of rainfall climatology over West Africa for the period of 1980-2005. The seasonal climatology and annual cycle of the RCA4 simulations were assessed over three homogenous subregions of West Africa (Guinea coast, Savannah, and Sahel) and evaluated using observed precipitation data from the Global Precipitation Climatology Project (GPCP). Furthermore, the model output was evaluated using a wide range of statistical measures. The interseasonal and interannual variability of the RCA4 were further assessed over the subregions and the whole of the West Africa domain. Results indicate that the RCA4 captures the spatial and interseasonal rainfall pattern adequately but exhibits a weak performance over the Guinea coast. Findings from the interannual rainfall variability indicate that the model performance is better over the larger West Africa domain than the subregions. The largest difference across the RCA4 simulated annual rainfall was found in the Sahel. Result from the Mann-Kendall test showed no significant trend for the 1980-2005 period in annual rainfall either in GPCP observation data or in the model simulations over West Africa. In many aspects, the RCA4 simulation driven by the HadGEM2-ES perform best over the region. The use of the multimodel ensemble mean has resulted to the improved representation of rainfall characteristics over the study domain.
Kang, Suchul; Im, Eun-Soon; Eltahir, Elfatih A. B.
In this study, future changes in rainfall due to global climate change are investigated over the western Maritime Continent based on dynamically downscaled climate projections using the MIT Regional Climate Model (MRCM) with 12 km horizontal resolution. A total of nine 30-year regional climate projections driven by multi-GCMs projections (CCSM4, MPI-ESM-MR and ACCESS1.0) under multi-scenarios of greenhouse gases emissions (Historical: 1976-2005, RCP4.5 and RCP8.5: 2071-2100) from phase 5 of the Coupled Model Inter-comparison Project (CMIP5) are analyzed. Focusing on dynamically downscaled rainfall fields, the associated systematic biases originating from GCM and MRCM are removed based on observations using Parametric Quantile Mapping method in order to enhance the reliability of future projections. The MRCM simulations with bias correction capture the spatial patterns of seasonal rainfall as well as the frequency distribution of daily rainfall. Based on projected rainfall changes under both RCP4.5 and RCP8.5 scenarios, the ensemble of MRCM simulations project a significant decrease in rainfall over the western Maritime Continent during the inter-monsoon periods while the change in rainfall is not relevant during wet season. The main mechanism behind the simulated decrease in rainfall is rooted in asymmetries of the projected changes in seasonal dynamics of the meridional circulation along different latitudes. The sinking motion, which is marginally positioned in the reference simulation, is enhanced and expanded under global climate change, particularly in RCP8.5 scenario during boreal fall season. The projected enhancement of rainfall seasonality over the western Maritime Continent suggests increased risk of water stress for natural ecosystems as well as man-made water resources reservoirs.
A. H. Syafrina
Full Text Available Weather generator is a numerical tool that uses existing meteorological records to generate series of synthetic weather data. The AWE-GEN (Advanced Weather Generator model has been successful in producing a broad range of temporal scale weather variables, ranging from the high-frequency hourly values to the low-frequency inter-annual variability. In Malaysia, AWE-GEN has produced reliable projections of extreme rainfall events for some parts of Peninsular Malaysia. This study focuses on the use of AWE-GEN model to assess rainfall distribution in Kelantan. Kelantan is situated on the north east of the Peninsular, a region which is highly susceptible to flood. Embedded within the AWE-GEN model is the Neyman Scott process which employs parameters to represent physical rainfall characteristics. The use of correct probability distributions to represent the parameters is imperative to allow reliable results to be produced. This study compares the performance of two probability distributions, Weibull and Gamma to represent rainfall intensity and the better distribution found was used subsequently to simulate hourly scaled rainfall series. Thirty years of hourly scaled meteorological data from two stations in Kelantan were used in model construction. Results indicate that both probability distributions are capable of replicating the rainfall series at both stations very well, however numerical evaluations suggested that Gamma performs better. Despite Gamma not being a heavy tailed distribution, it is able to replicate the key characteristics of rainfall series and particularly extreme values. The overall simulation results showed that the AWE-GEN model is capable of generating tropical rainfall series which could be beneficial in flood preparedness studies in areas vulnerable to flood.
Xiang Li; Jianzhi Niu; Linus Zhang; Qingfu Xiao; Gregory E. McPherson; Natalie van Doorn; Xinxiao Yu; Baoyuan Xie; Salli Dymond; Jiao Li; Chen Meng; Ziteng Luo
An experiment was conducted to concentrate on the rainfall interception process of individual trees for four common species in Beijing, China, which included needle species (Platycladus orientalis and Pinus tabulaeformis) and broadleaf species (Quercus variabilis and Acer truncatum)....
Ibsen Chivatá Cárdenas
Full Text Available This article presents a rainfall model constructed by applying non-parametric modelling and imprecise probabilities; these tools were used because there was not enough homogeneous information in the study area. The area’s hydro-logical information regarding rainfall was scarce and existing hydrological time series were not uniform. A distributed extended rainfall model was constructed from so-called probability boxes (p-boxes, multinomial probability distribu-tion and confidence intervals (a friendly algorithm was constructed for non-parametric modelling by combining the last two tools. This model confirmed the high level of uncertainty involved in local rainfall modelling. Uncertainty en-compassed the whole range (domain of probability values thereby showing the severe limitations on information, leading to the conclusion that a detailed estimation of probability would lead to significant error. Nevertheless, rele-vant information was extracted; it was estimated that maximum daily rainfall threshold (70 mm would be surpassed at least once every three years and the magnitude of uncertainty affecting hydrological parameter estimation. This paper’s conclusions may be of interest to non-parametric modellers and decisions-makers as such modelling and imprecise probability represents an alternative for hydrological variable assessment and maybe an obligatory proce-dure in the future. Its potential lies in treating scarce information and represents a robust modelling strategy for non-seasonal stochastic modelling conditions
Kavetski, D.; Franks, S. W.; Kuczera, G.
A major unresolved issue in the identification and use of conceptual hydrologic models is realistic description of uncertainty in the data and model structure. In particular, hydrologic parameters often cannot be measured directly and must be inferred (calibrated) from observed forcing/response data (typically, rainfall and runoff). However, rainfall varies significantly in space and time, yet is often estimated from sparse gauge networks. Recent work showed that current calibration methods (e.g., standard least squares, multi-objective calibration, generalized likelihood uncertainty estimation) ignore forcing uncertainty and assume that the rainfall is known exactly. Consequently, they can yield strongly biased and misleading parameter estimates. This deficiency confounds attempts to reliably test model hypotheses, to generalize results across catchments (the regionalization problem) and to quantify predictive uncertainty when the hydrologic model is extrapolated. This paper continues the development of a Bayesian total error analysis (BATEA) methodology for the calibration and identification of hydrologic models, which explicitly incorporates the uncertainty in both the forcing and response data, and allows systematic model comparison based on residual model errors and formal Bayesian hypothesis testing (e.g., using Bayes factors). BATEA is based on explicit stochastic models for both forcing and response uncertainty, whereas current techniques focus solely on response errors. Hence, unlike existing methods, the BATEA parameter equations directly reflect the modeler's confidence in all the data. We compare several approaches to approximating the parameter distributions: a) full Markov Chain Monte Carlo methods and b) simplified approaches based on linear approximations. Studies using synthetic and real data from the US and Australia show that BATEA systematically reduces the parameter bias, leads to more meaningful model fits and allows model comparison taking
Gamal El Afandi
Full Text Available Heavy rainfall is one of major severe weather over Sinai Peninsula and causes many flash floods over the region. The good forecasting of rainfall is very much necessary for providing early warning before the flash flood events to avoid or minimize disasters. In the present study using the Weather Research and Forecasting (WRF Model, heavy rainfall events that occurred over Sinai Peninsula and caused flash flood have been investigated. The flash flood that occurred on January 18, 2010, over different parts of Sinai Peninsula has been predicted and analyzed using the Advanced Weather Research and Forecast (WRF-ARW Model. The predicted rainfall in four dimensions (space and time has been calibrated with the measurements recorded at rain gauge stations. The results show that the WRF model was able to capture the heavy rainfall events over different regions of Sinai. It is also observed that WRF model was able to predict rainfall in a significant consistency with real measurements. In this study, several synoptic characteristics of the depressions that developed during the course of study have been investigated. Also, several dynamic characteristics during the evolution of the depressions were studied: relative vorticity, thermal advection, and geopotential height.
Basarudin, Z.; Adnan, N. A.; Latif, A. R. A.; Tahir, W.; Syafiqah, N.
Flood is one of the most common natural disasters in Malaysia. According to hydrologists there are many causes that contribute to flood events. The two most dominant factors are the meteorology factor (i.e climate change) and change in land use. These two factors contributed to floods in recent decade especially in the monsoonal catchment such as Malaysia. This paper intends to quantify the influence of rainfall during extreme rainfall events on the hydrological model in the Kelantan River catchment. Therefore, two dynamic inputs were used in the study: rainfall and river discharge. The extreme flood events in 2008 and 2004 were compared based on rainfall data for both years. The events were modeled via a semi-distributed HEC-HMS hydrological model. Land use change was not incorporated in the study because the study only tries to quantify rainfall changes during these two events to simulate the discharge and runoff value. Therefore, the land use data representing the year 2004 were used as inputs in the 2008 runoff model. The study managed to demonstrate that rainfall change has a significant impact to determine the peak discharge and runoff depth for the study area.
Basarudin, Z; Adnan, N A; Latif, A R A; Syafiqah, N; Tahir, W
Flood is one of the most common natural disasters in Malaysia. According to hydrologists there are many causes that contribute to flood events. The two most dominant factors are the meteorology factor (i.e climate change) and change in land use. These two factors contributed to floods in recent decade especially in the monsoonal catchment such as Malaysia. This paper intends to quantify the influence of rainfall during extreme rainfall events on the hydrological model in the Kelantan River catchment. Therefore, two dynamic inputs were used in the study: rainfall and river discharge. The extreme flood events in 2008 and 2004 were compared based on rainfall data for both years. The events were modeled via a semi-distributed HEC-HMS hydrological model. Land use change was not incorporated in the study because the study only tries to quantify rainfall changes during these two events to simulate the discharge and runoff value. Therefore, the land use data representing the year 2004 were used as inputs in the 2008 runoff model. The study managed to demonstrate that rainfall change has a significant impact to determine the peak discharge and runoff depth for the study area
Matziaris, Vasileios; Marshall, Alec; Yu, Hai-Sui
Rainfall-induced landslides are very common natural disasters which cause damage to properties and infrastructure and may result in the loss of human lives. These phenomena often take place in unsaturated soil slopes and are triggered by the saturation of the soil profile, due to rain infiltration, which leads to a loss of shear strength. The aim of this study is to determine rainfall thresholds for the initiation of landslides under different initial conditions. Model tests of rainfall-induced landslides are conducted in the Nottingham Centre for Geomechanics 50g-T geotechnical centrifuge. Initially unsaturated plane-strain slope models made with fine silica sand are prepared at varying densities at 1g and accommodated within a climatic chamber which provides controlled environmental conditions. During the centrifuge flight at 60g, rainfall events of varying intensity and duration are applied to the slope models causing the initiation of slope failure. The impact of soil state properties and rainfall characteristics on the landslide initiation process are discussed. The variation of pore water pressures within the slope before, during and after simulated rainfall events is recorded using miniature pore pressure transducers buried in the soil model. Slope deformation is determined by using a high-speed camera and digital image analysis techniques.
Kangsabanik, Subhadip; Murmu, Sneha
The present study is based on SWAT (Soil and Water Assessment Tool) Model which integrates the GIS information with attribute database to estimate the runoff of Ajay River catchment. Soil and Water Assessment Tool (SWAT) is a physically based distributed parameter model which has been developed to predict runoff, erosion, sediment and nutrient transport from agricultural watersheds under different management practices. The SWAT Model works in conjunction with Arc GIS. In the present study the catchment area has been delineated using the DEM (Digital Elevation Model) and then divided into 19 sub-basins. For preparation of landuse map the IRS-P6 LISS-III image has been used and the soil map is extracted from HWSD (Harmonized World Soil Database) Raster world soil map. The sub basins are further divided into 223 HRUs which stands for Hydrological Response Unit. Then by using 30 years of daily rainfall data and daily maximum and minimum temperature data SWAT simulation is done for daily, monthly and yearly basis to find out Runoff for corresponding Rainfall. The coefficient of correlation (r) for rainfall in a period and the corresponding runoff is found to be 0.9419.
Capparelli, G.; Giorgio, M.; Greco, R.; Versace, P.
Occurrence of landslides is uneasy to predict, since it is affected by a number of variables, such as mechanical and hydraulic soil properties, slope morphology, vegetation coverage, rainfall spatial and temporal variability. Although heavy landslides frequently occurred in Campania, southern Italy, during the last decade, no complete data sets are available for natural slopes where landslides occurred. As a consequence, landslide risk assessment procedures and early warning systems in Campania still rely on simple empirical models based on correlation between daily rainfall records and observed landslides, like FLAIR model [Versace et al., 2003]. Effectiveness of such systems could be improved by reliable quantitative rainfall prediction. In mountainous areas, rainfall spatial and temporal variability are very pronounced due to orographic effects, making predictions even more complicated. Existing rain gauge networks are not dense enough to resolve the small scale spatial variability, and the same limitation of spatial resolution affects rainfall height maps provided by radar sensors as well as by meteorological physically based models. Therefore, analysis of on-site recorded rainfall height time series still represents the most effective approach for a reliable prediction of local temporal evolution of rainfall. Hydrological time series analysis is a widely studied field in hydrology, often carried out by means of autoregressive models, such as AR and ARMA [Box and Jenkins, 1976]. Sometimes exogenous information coming from additional series of observations is also taken into account, and the models are called ARX and ARMAX (e.g. Salas ). Such models gave the best results when applied to the analysis of autocorrelated hydrological time series, like river flow or level time series. Conversely, they are not able to model the behaviour of intermittent time series, like point rainfall height series usually are, especially when recorded with short sampling time
Maggioni, V.; Anagnostou, E. N.; Reichle, R. H.
The contribution of rainfall forcing errors relative to model (structural and parameter) uncertainty in the prediction of soil moisture is investigated by integrating the NASA Catchment Land Surface Model (CLSM), forced with hydro-meteorological data, in the Oklahoma region. Rainfall-forcing uncertainty is introduced using a stochastic error model that generates ensemble rainfall fields from satellite rainfall products. The ensemble satellite rain fields are propagated through CLSM to produce soil moisture ensembles. Errors in CLSM are modeled with two different approaches: either by perturbing model parameters (representing model parameter uncertainty) or by adding randomly generated noise (representing model structure and parameter uncertainty) to the model prognostic variables. Our findings highlight that the method currently used in the NASA GEOS-5 Land Data Assimilation System to perturb CLSM variables poorly describes the uncertainty in the predicted soil moisture, even when combined with rainfall model perturbations. On the other hand, by adding model parameter perturbations to rainfall forcing perturbations, a better characterization of uncertainty in soil moisture simulations is observed. Specifically, an analysis of the rank histograms shows that the most consistent ensemble of soil moisture is obtained by combining rainfall and model parameter perturbations. When rainfall forcing and model prognostic perturbations are added, the rank histogram shows a U-shape at the domain average scale, which corresponds to a lack of variability in the forecast ensemble. The more accurate estimation of the soil moisture prediction uncertainty obtained by combining rainfall and parameter perturbations is encouraging for the application of this approach in ensemble data assimilation systems.
Lau, William K. M.; Wu, Jenny, H.-T.; Kim, Kyu-Myong
Changes in rainfall characteristic induced by global warming are examined from outputs of IPCC AR5 models. Different scenarios of climate warming including a high emissions scenario (RCP 8.5), a medium mitigation scenario (RCP 4.5), and 1% per year CO2 increase are compared to 20th century simulations (historical). Results show that even though the spatial distribution of monthly rainfall anomalies vary greatly among models, the ensemble mean from a sizable sample (about 10) of AR5 models show a robust signal attributable to GHG warming featuring a shift in the global rainfall probability distribution function (PDF) with significant increase (>100%) in very heavy rain, reduction (10-20% ) in moderate rain and increase in light to very light rains. Changes in extreme rainfall as a function of seasons and latitudes are also examined, and are similar to the non-seasonal stratified data, but with more specific spatial dependence. These results are consistent from TRMM and GPCP rainfall observations suggesting that extreme rainfall events are occurring more frequently with wet areas getting wetter and dry-area-getting drier in a GHG induced warmer climate.
Full Text Available We use the principle of maximum entropy to propose a parsimonious model for the generation of simulated rainfall during the wettest three-month season at a typical location on the east coast of Australia. The model uses a checkerboard copula of maximum entropy to model the joint probability distribution for total seasonal rainfall and a set of two-parameter gamma distributions to model each of the marginal monthly rainfall totals. The model allows us to match the grade correlation coefficients for the checkerboard copula to the observed Spearman rank correlation coefficients for the monthly rainfalls and, hence, provides a model that correctly describes the mean and variance for each of the monthly totals and also for the overall seasonal total. Thus, we avoid the need for a posteriori adjustment of simulated monthly totals in order to correctly simulate the observed seasonal statistics. Detailed results are presented for the modelling and simulation of seasonal rainfall in the town of Kempsey on the mid-north coast of New South Wales. Empirical evidence from extensive simulations is used to validate this application of the model. A similar analysis for Sydney is also described.
Nowak, Gen; Welsh, A. H.; O'Neill, T. J.; Feng, Lingbing
The Murray-Darling Basin (MDB) is a large geographical region in southeastern Australia that contains many rivers and creeks, including Australia's three longest rivers, the Murray, the Murrumbidgee and the Darling. Understanding rainfall patterns in the MDB is very important due to the significant impact major events such as droughts and floods have on agricultural and resource productivity. We propose a model for modelling a set of monthly rainfall data obtained from stations in the MDB and for producing predictions in both the spatial and temporal dimensions. The model is a hierarchical spatio-temporal model fitted to geographical data that utilises both deterministic and data-derived components. Specifically, rainfall data at a given location are modelled as a linear combination of these deterministic and data-derived components. A key advantage of the model is that it is fitted in a step-by-step fashion, enabling appropriate empirical choices to be made at each step.
Williams, C. J. R.; Kniveton, D. R.; Layberry, R.
It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The sub-continent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite-derived rainfall data from the Microwave Infrared Rainfall Algorithm (MIRA). This dataset covers the period from 1993 to 2002 and the whole of southern Africa at a spatial resolution of 0.1° longitude/latitude. This paper concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of present-day rainfall variability over southern Africa and is not intended to discuss possible future changes in climate as these have been documented elsewhere. Simulations of current climate from the UK Meteorological Office Hadley Centre's climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. Secondly, the ability of the model to reproduce daily rainfall extremes is assessed, again by a comparison with
Williams, C.; Kniveton, D.; Layberry, R.
It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. In this research, satellite-derived rainfall data are used as a basis for undertaking model experiments using a state-of-the-art climate model, run at both high and low spatial resolution. Once the model's ability to reproduce extremes has been assessed, idealised regions of sea surface temperature (SST) anomalies are used to force the model, with the overall aim of investigating the ways in which SST anomalies influence rainfall extremes over southern Africa. In this paper, a brief overview is given of the authors' research to date, pertaining to southern African rainfall. This covers (i) a description of present-day rainfall variability over southern Africa; (ii) a comparison of model simulated daily rainfall with the satellite-derived dataset; (iii) results from sensitivity testing of the model's domain size; and (iv) results from the idealised SST experiments.
Langousis, Andreas; Kaleris, Vassilios
Due to its intermittent and highly variable character, daily precipitation is the least well reproduced hydrologic variable by both General Circulation Models (GCMs) and Limited Area Models (LAMs). To that extent, several statistical procedures (usually referred to as downscaling schemes) have been suggested to generate synthetic rainfall time series conditional on predictor variables that are descriptive of the atmospheric circulation at the mesoscale. In addition to be more accurately simulated by GCMs and LAMs, large-scale atmospheric predictors are important indicators of the local weather. Currently used downscaling methods simulate rainfall series using either stable statistical relationships (usually referred to as transfer functions) between certain characteristics of the rainfall process and mesoscale atmospheric predictor variables, or simple stochastic schemes (e.g. properly transformed autoregressive models) with parameters that depend on the large-scale atmospheric conditions. The latter are determined by classifying large-scale circulation patterns into broad categories of weather states, using empirical or theoretically based classification schemes, and modeled by resampling from those categories; a process usually referred to as weather generation. In this work we propose a statistical framework to generate synthetic rainfall timeseries at a daily level, conditional on large scale atmospheric predictors. The latter include the mean sea level pressure (MSLP), the magnitude and direction of upper level geostrophic winds, and the 500 hPa geopotential height, relative vorticity and divergence. The suggested framework operates in continuous time, avoiding the use of transfer functions, and weather classification schemes. The suggested downscaling approach is validated using atmospheric data from the ERA-Interim archive (see http://www.ecmwf.int/research/era/do/get/index), and daily rainfall data from Western Greece, for the 14-year period from 01 October
Wang, Jie; Chen, Li; Yu, Zhongbo
Rainfall infiltration on hillslopes is an important issue in hydrology, which is related to many environmental problems, such as flood, soil erosion, and nutrient and contaminant transport. This study aimed to improve the quantification of infiltration on hillslopes under both steady and unsteady rainfalls. Starting from Darcy's law, an analytical integral infiltrability equation was derived for hillslope infiltration by use of the flux-concentration relation. Based on this equation, a simple scaling relation linking the infiltration times on hillslopes and horizontal planes was obtained which is applicable for both small and large times and can be used to simplify the solution procedure of hillslope infiltration. The infiltrability equation also improved the estimation of ponding time for infiltration under rainfall conditions. For infiltration after ponding, the time compression approximation (TCA) was applied together with the infiltrability equation. To improve the computational efficiency, the analytical integral infiltrability equation was approximated with a two-term power-like function by nonlinear regression. Procedures of applying this approach to both steady and unsteady rainfall conditions were proposed. To evaluate the performance of the new approach, it was compared with the Green-Ampt model for sloping surfaces by Chen and Young (2006) and Richards' equation. The proposed model outperformed the sloping Green-Ampt, and both ponding time and infiltration predictions agreed well with the solutions of Richards' equation for various soil textures, slope angles, initial water contents, and rainfall intensities for both steady and unsteady rainfalls.
Full Text Available We modelled the mean annual rainfall for data recorded in Zimbabwe from 1901 to 2009. Extreme value theory was used to estimate the probabilities of meteorological droughts. Droughts can be viewed as extreme events which go beyond and/or below normal rainfall occurrences, such as exceptionally low mean annual rainfall. The duality between the distribution of the minima and maxima was exploited and used to fit the generalised extreme value distribution (GEVD to the data and hence find probabilities of extreme low levels of mean annual rainfall. The augmented Dickey Fuller test confirmed that rainfall data were stationary, while the normal quantile-quantile plot indicated that rainfall data deviated from the normality assumption at both ends of the tails of the distribution. The maximum likelihood estimation method and the Bayesian approach were used to find the parameters of the GEVD. The Kolmogorov-Smirnov and Anderson-Darling goodnessof- fit tests showed that the Weibull class of distributions was a good fit to the minima mean annual rainfall using the maximum likelihood estimation method. The mean return period estimate of a meteorological drought using the threshold value of mean annual rainfall of 473 mm was 8 years. This implies that if in the year there is a meteorological drought then another drought of the same intensity or greater is expected after 8 years. It is expected that the use of Bayesian inference may better quantify the level of uncertainty associated with the GEVD parameter estimates than with the maximum likelihood estimation method. The Markov chain Monte Carlo algorithm for the GEVD was applied to construct the model parameter estimates using the Bayesian approach. These findings are significant because results based on non-informative priors (Bayesian method and the maximum likelihood method approach are expected to be similar.
Rabiei, Ehsan; Haberlandt, Uwe; Sester, Monika; Fitzner, Daniel; Wallner, Markus
The need for high temporal and spatial resolution precipitation data for hydrological analyses has been discussed in several studies. Although rain gauges provide valuable information, a very dense rain gauge network is costly. As a result, several new ideas have emerged to help estimating areal rainfall with higher temporal and spatial resolution. Rabiei et al. (2013) observed that moving cars, called RainCars (RCs), can potentially be a new source of data for measuring rain rate. The optical sensors used in that study are designed for operating the windscreen wipers and showed promising results for rainfall measurement purposes. Their measurement accuracy has been quantified in laboratory experiments. Considering explicitly those errors, the main objective of this study is to investigate the benefit of using RCs for estimating areal rainfall. For that, computer experiments are carried out, where radar rainfall is considered as the reference and the other sources of data, i.e., RCs and rain gauges, are extracted from radar data. Comparing the quality of areal rainfall estimation by RCs with rain gauges and reference data helps to investigate the benefit of the RCs. The value of this additional source of data is not only assessed for areal rainfall estimation performance but also for use in hydrological modeling. Considering measurement errors derived from laboratory experiments, the result shows that the RCs provide useful additional information for areal rainfall estimation as well as for hydrological modeling. Moreover, by testing larger uncertainties for RCs, they observed to be useful up to a certain level for areal rainfall estimation and discharge simulation.
Tahir Saeed Laghari
Full Text Available A study was carried out to define the analysis of rainfall data in order to estimate its contribution towards crop water requirements to overcome these problems. Rainfall and climatic data was collected from metrological stations, C.P UAF rain gauge (A, (AARI, (B, (CAA, (C and (WAPDA, (D, Faisalabad of given region and this data was reserved for cross validation. The test station’s (A rainfall data was subjected to double mass curve technique to check its consistency with respect to other rainfall stations (B, C and D in that area. The results derived by double curve technique were accurate for interested gauge station because there was no any break in curve. Then this consistent data was used to determine effective rainfall. The ETo was established by using penman-monteith method in the course of CROPWAT model and its effect with respect to other parameters like sun shine hour, wind speed, maximum & minimum temperature and rainfall humidity were determined. It was founded that the reference evapotranspiration (ETo is more during April to September due to increase in temperature and low in remaining months. After that data was placed in the model to acquire crop water requirement and irrigation of illustrative crops (wheat & maize from the district. Through which we estimated that 7.5% rainfall for wheat and 15.5% rainfall for maize can contribute in actual irrigation per year. Through which we determined that 92.5 % and 84.5 % irrigation is required for wheat and maize crop respectively.
Müller, Hannes; Haberlandt, Uwe
High-resolution rainfall data are needed in many fields of hydrology and water resources management. For analyzes of future rainfall condition climate scenarios exist with hourly values of rainfall. However, the direct usage of these data is associated with uncertainties which can be indicated by comparisons of observations and C20 control runs. An alternative is the derivation of changes of rainfall behavior over the time from climate simulations. Conclusions about future rainfall conditions can be drawn by adding these changes to observed time series. A multiplicative cascade model is used in this investigation for the disaggregation of daily rainfall amounts to hourly values. Model parameters can be estimated by REMO rainfall time series (UBA-, BfG- and ENS-realization), based on ECHAM5. Parameter estimation is carried out for C20 period as well as near term and long term future (2021-2050 and 2071-2100). Change factors for both future periods are derived by parameter comparisons and added to the parameters estimated from observed time series. This enables the generation of hourly rainfall time series from observed daily values with respect to future changes. The investigation is carried out for rain gauges in Lower Saxony. Generated Time series are analyzed regarding statistical characteristics, e.g. extreme values, event-based (wet spell duration and amounts, dry spell duration, …) and continuum characteristics (average intensity, fraction of dry intervals,…). The generation of the time series is validated by comparing the changes in the statistical characteristics from the REMO data and from the disaggregated data.
Llanes-Estrada, Felipe J; Bicudo, Pedro; Cotanch, Stephen R
We report an odderon Regge trajectory emerging from a field theoretical Coulomb gauge QCD model for the odd signature J(PC) (P = C = -1) glueball states. The trajectory intercept is clearly smaller than the Pomeron and even the omega trajectory's intercept which provides an explanation for the nonobservation of the odderon in high energy scattering data. To further support this result we compare to glueball lattice data and also perform calculations with an alternative model based upon an exact Hamiltonian diagonalization for three constituent gluons.
Nourani, Vahid; Komasi, Mehdi
This paper demonstrates the potential use of Artificial Intelligence (AI) techniques for predicting daily runoff at multiple gauging stations. Uncertainty and complexity of the rainfall-runoff process due to its variability in space and time in one hand and lack of historical data on the other hand, cause difficulties in the spatiotemporal modeling of the process. In this paper, an Integrated Geomorphological Adaptive Neuro-Fuzzy Inference System (IGANFIS) model conjugated with C-means clustering algorithm was used for rainfall-runoff modeling at multiple stations of the Eel River watershed, California. The proposed model could be used for predicting runoff in the stations with lack of data or any sub-basin within the watershed because of employing the spatial and temporal variables of the sub-basins as the model inputs. This ability of the integrated model for spatiotemporal modeling of the process was examined through the cross validation technique for a station. In this way, different ANFIS structures were trained using Sugeno algorithm in order to estimate daily discharge values at different stations. In order to improve the model efficiency, the input data were then classified into some clusters by the means of fuzzy C-means (FCMs) method. The goodness-of-fit measures support the gainful use of the IGANFIS and FCM methods in spatiotemporal modeling of hydrological processes.
Villarini, Gabriele; Smith, James A.; Napolitano, Francesco
A long record (1862-2004) of seasonal rainfall and temperature from the Rome observatory of Collegio Romano are modeled in a nonstationary framework by means of the Generalized Additive Models in Location, Scale and Shape (GAMLSS). Modeling analyses are used to characterize nonstationarities in rainfall and related climate variables. It is shown that the GAMLSS models are able to represent the magnitude and spread in the seasonal time series with parameters which are a smooth function of time. Covariate analyses highlight the role of seasonal and interannual variability of large-scale climate forcing, as reflected in three teleconnection indexes (Atlantic Multidecadal Oscillation, North Atlantic Oscillation, and Mediterranean Index), for modeling seasonal rainfall and temperature over Rome. In particular, the North Atlantic Oscillation is a significant predictor during the winter, while the Mediterranean Index is a significant predictor for almost all seasons.
C. V. Srinivas
Full Text Available This study examines the ability of the Advanced Research WRF (ARW regional model to simulate Indian summer monsoon (ISM rainfall climatology in different climate zones during the monsoon onset phase in the decade 2000–2009. The initial and boundary conditions for ARW are provided from the NCEP/NCAR Reanalysis Project (NNRP global reanalysis. Seasonal onset-phase rainfall is compared with corresponding values from 0.25° IMD (India Meteorological Department rainfall and NNRP precipitation data over seven climate zones (perhumid, humid, dry/moist, subhumid, dry/moist, semiarid and arid of India to see whether dynamical downscaling using a regional model yields advantages over just using large-scale model predictions. Results show that the model could simulate the onset phase in terms of progression and distribution of rainfall in most zones (except over the northeast with good correlations and low error metrics. The observed mean onset dates and their variability over different zones are well reproduced by the regional model over most climate zones. It has been found that the ARW performed similarly to the reanalysis in most zones and improves the onset time by 1 to 3 days in zones 4 and 7, in which the NNRP shows a delayed onset compared to the actual IMD onset times. The variations in the onset-phase rainfall during the below-normal onset (June negative and above-normal onset (June positive phases are well simulated. The slight underestimation of onset-phase rainfall in the northeast zone could be due to failure in resolving the wide extent of topographic variations and the associated multiscale interactions in that zone. Spatial comparisons showed improvement of pentad rainfall in both space and quantity in ARW simulations over NNRP data, as evident from a wider eastward distribution of pentad rainfall over the Western Ghats, central and eastern India, as in IMD observations. While NNRP under-represented the high pentad rainfall over
Srinivas, C. V.
This study examines the ability of the Advanced Research WRF (ARW) regional model to simulate Indian summer monsoon (ISM) rainfall climatology in different climate zones during the monsoon onset phase in the decade 2000–2009. The initial and boundary conditions for ARW are provided from the NCEP/NCAR Reanalysis Project (NNRP) global reanalysis. Seasonal onset-phase rainfall is compared with corresponding values from 0.25° IMD (India Meteorological Department) rainfall and NNRP precipitation data over seven climate zones (perhumid, humid, dry/moist, subhumid, dry/moist, semiarid and arid) of India to see whether dynamical downscaling using a regional model yields advantages over just using large-scale model predictions. Results show that the model could simulate the onset phase in terms of progression and distribution of rainfall in most zones (except over the northeast) with good correlations and low error metrics. The observed mean onset dates and their variability over different zones are well reproduced by the regional model over most climate zones. It has been found that the ARW performed similarly to the reanalysis in most zones and improves the onset time by 1 to 3 days in zones 4 and 7, in which the NNRP shows a delayed onset compared to the actual IMD onset times. The variations in the onset-phase rainfall during the below-normal onset (June negative) and above-normal onset (June positive) phases are well simulated. The slight underestimation of onset-phase rainfall in the northeast zone could be due to failure in resolving the wide extent of topographic variations and the associated multiscale interactions in that zone. Spatial comparisons showed improvement of pentad rainfall in both space and quantity in ARW simulations over NNRP data, as evident from a wider eastward distribution of pentad rainfall over the Western Ghats, central and eastern India, as in IMD observations. While NNRP under-represented the high pentad rainfall over northeast, east and
This research work will allow checking efficiency of formal neural networks for flows' modelling of wadi Ouahrane's basin from rainfall-runoff relation which is non-linear. Two models of neural networks were optimized through supervised learning and compared in order to achieve this goal, the first model with input rain, and ...
Gregersen, Ida Bülow; Madsen, Henrik; Rosbjerg, Dan
of extreme rainfall events, a statistical model is tested for this purpose. The model is built on the theory of generalized linear models and uses Poisson regression solved by generalized estimation equations. Spatial and temporal explanatory variables can be included simultaneously, and their relative...
van Ootegem, Luc; van Herck, K.; Creten, T.
In Europe, floods are among the natural catastrophes that cause the largest economic damage. This article explores the potential of two distinct types of multivariate flood damage models: ‘depth-damage’ models and ‘rainfall-damage’ models. We use survey data of 346 Flemish households that were vi...
In this research, two numerical models including ANN and ANFIS were used to model the rainfall-runoff process and the best model was chosen. Also, by using SPSS software, the regression equations were developed and then the best equation was selected from regression analysis. The obtained results from the ...
Van Delft, G.; El Serafy, G.Y.; Heemink, A.W.
Rainfall-runoff models play a very important role in flood forecasting. However, these models contain large uncertainties caused by errors in both the model itself and the input data. Data assimilation techniques are being used to reduce these uncertainties. The ensemble Kalman filter (EnKF) and the
van der Merwe, M. R.; Du Preez, M.
Cholera has become endemic in coastal and inland areas within the tropics as well as areas outside of the tropics in Africa. Climate conditions and weather patterns differ between areas reporting cholera cases in Africa. Some areas experience two rainfall seasons compared to areas with only one rainfall season in a year. Further, climate variability or ENSO events affect local weather conditions differently. La Niña, i.e. cold events lead to higher than normal rainfall in areas in southern Africa compared to areas close to the equator in eastern Africa which report less than normal rainfall. Time series analysis of cholera cases and rainfall data at different spatial resolutions highlight the overlap of the rainfall season with the reporting of cholera cases. Cholera cases are also reported in between rainy seasons in different areas but the incidence is significantly less compared to the rainy season. An increase in the intensity of outbreaks is also noted during the rainy season following a drier than normal 'dry' season. This necessitates the understanding of the reasons for the observed correlation between rainfall season and cholera outbreaks in order to develop a prediction model which can accurately predict the likelihood of an outbreak. Due to the complexities associated with accurately predicting weather data more than seven days ahead of time it is necessary to identify global drivers with a lagged effect on local rainfall patterns. Climate variability, i.e. ENSO is investigated at different temporal scales; spatial locations and time lags. Sea surface temperature anomalies (SSTa) measured closed to the equator and in the southern parts of the Indian Ocean are more closely associated with rainfall anomalies at specific time lags in equatorial, East African, south East African and central African areas compared to SSTa measured in different regions in the Pacific Ocean. An explanatory prediction model is developed for conditions in Mozambique (coastal
Full Text Available Parameters of the Modified Bartlett-Lewis Rectangular Pulse (MBLRP stochastic rainfall simulation model were regionalized across the contiguous United States. Three thousand four hundred forty-four National Climate Data Center (NCDC rain gauges were used to obtain spatial and seasonal patterns of the model parameters. The MBLRP model was calibrated to minimize the discrepancy between the precipitation depth statistics between the observed and MBLRP-generated precipitation time series. These statistics included the mean, variance, probability of zero rainfall and autocorrelation at 1-, 3-, 12- and 24-hour accumulation intervals. The Ordinary Kriging interpolation technique was used to generate maps of the six MBLRP model parameters for each of the 12 months of the year. All parameters had clear to discernible regional tendencies; except for one related to rain cell duration distribution. Parameter seasonality was not obvious and it was more apparent in some locations than in others, depending on the seasonality of the rainfall statistics. Cross-validation was used to assess the validity of the parameter maps. The results indicate that the suggested maps reproduce well the observed rainfall statistics for different accumulation intervals, except for the lag-1 autocorrelation coefficient. The boundaries of the expected residual, with 95% confidence, between the observed rainfall statistics and the simulated rainfall statistics based on the map parameters were approximately ±0.064 mm hr-1, ±1.63 mm2 hr-2, ±0.16, and ±0.030 for the mean, variance, lag-1 autocorrelation and probability of zero rainfall at hourly accumulation levels, respectively. The estimated parameter values were also used to estimate the storm and rain cell characteristics.
Ibrahim Suliman Hanaish
Full Text Available Three versions of Bartlett Lewis rectangular pulse rainfall models, namely, the Original Bartlett Lewis (OBL, Modified Bartlett Lewis (MBL, and 2N-cell-type Bartlett Lewis model (BL2n, are considered. These models are fitted to the hourly rainfall data from 1970 to 2008 obtained from Petaling Jaya rain gauge station, located in Peninsular Malaysia. The generalized method of moments is used to estimate the model parameters. Under this method, minimization of two different objective functions which involve different weight functions, one weight is inversely proportional to the variance and another one is inversely proportional to the mean squared, is carried out using Nelder-Mead optimization technique. For the purpose of comparison of the performance of the three different models, the results found for the months of July and November are used for illustration. This performance is assessed based on the goodness of fit of the models. In addition, the sensitivity of the parameter estimates to the choice of the objective function is also investigated. It is found that BL2n slightly outperforms OBL. However, the best model is the Modified Bartlett Lewis MBL, particularly when the objective function considered involves weight which is inversely proportional to the variance.
Full Text Available The Green-Ampt (GA model is widely used in hydrologic studies as a simple, physically-based method to estimate infiltration processes. The accuracy of the model for applications under rainfall conditions (as opposed to initially ponded situations has not been studied extensively. We compared calculated rainfall infiltration results for various soils obtained using existing GA parameterizations with those obtained by solving the Richards equation for variably saturated flow. Results provided an overview of GA model performance evaluated by means of a root-meansquare- error-based objective function across a large region in GA parameter space as compared to the Richards equation, which showed a need for seeking optimal GA parameters. Subsequent analysis enabled the identification of optimal GA parameters that provided a close fit with the Richards equation. The optimal parameters were found to substantially outperform the standard theoretical parameters, thus improving the utility and accuracy of the GA model for infiltration simulations under rainfall conditions. A sensitivity analyses indicated that the optimal parameters may change for some rainfall scenarios, but are relatively stable for high-intensity rainfall events.
Proehl, G.; Hoffman, F.O.
Data available since the Chernobyl accident have strengthened the view that the transfer of radionuclides from air to vegetation is a primary area of uncertainty in the estimation of the contamination of food chains leading to human exposure. The processes affecting the overall transfer from air to vegetation involve wet and dry deposition, interception and initial retention, and post-deposition retention of radioactive substances by vegetation. During the growing season, the time-integrated concentrations of radionuclides on vegetation in the first few months after initial deposition are dominated by the direct foliar interception of deposited material. Chapter 2 contains a review of data for modelling the direct foliar interception and initial retention of radioactivity deposited by dry and wet processes, together with data on the factors affecting post-deposition retention of radioactivity on the vegetation. 82 refs, 9 figs, 11 tabs
For planning of urban drainage systems using hydrological models, long, continuous precipitation series with high temporal resolution are needed. Since observed time series are often too short or not available everywhere, the use of synthetic precipitation is a common alternative. This contribution compares three precipitation models regarding their suitability to provide 5 minute continuous rainfall time series for a) sizing of drainage networks for urban flood protection and b) dimensioning of combined sewage systems for pollution reduction. The rainfall models are a parametric stochastic model (Haberlandt et al., 2008), a non-parametric probabilistic approach (Bárdossy, 1998) and a stochastic downscaling of dynamically simulated rainfall (Berg et al., 2013); all models are operated both as single site and multi-site generators. The models are applied with regionalised parameters assuming that there is no station at the target location. Rainfall and discharge characteristics are utilised for evaluation of the model performance. The simulation results are compared against results obtained from reference rainfall stations not used for parameter estimation. The rainfall simulations are carried out for the federal states of Baden-Württemberg and Lower Saxony in Germany and the discharge simulations for the drainage networks of the cities of Hamburg, Brunswick and Freiburg. Altogether, the results show comparable simulation performance for the three models, good capabilities for single site simulations but low skills for multi-site simulations. Remarkably, there is no significant difference in simulation performance comparing the tasks flood protection with pollution reduction, so the models are finally able to simulate both the extremes and the long term characteristics of rainfall equally well. Bárdossy, A., 1998. Generating precipitation time series using simulated annealing. Wat. Resour. Res., 34(7): 1737-1744. Berg, P., Wagner, S., Kunstmann, H., Schädler, G
Full Text Available Rain intensity data are necessary to increase security of hydraulic projects. The objective of this study was to determine the relationships among intensity-duration-frequency (IDF and Bell’s model for the State of Mato Grosso, Brazil. The equations were obtained by disaggregation of 24 h rainfall data from 136 rain stations available in the National Water Agency (ANA data base. Employing Gumbel distribution, the rainfalls were estimated for each time duration and for the return periods of 2, 5, 10, 25, 50 and 100 years, and thereafter for each season. The coefficients of IDF relationships and Bell’s models were adjusted by the minimum square method, for all seasons evaluated. The coefficients of determination and Willmott agreement index exceeded 0.98 and 0.85, respectively, for all stations, which classifies the adjustment of the rainfall models as great.
Full Text Available The SEIR (Susceptible-Exposed-Infected-Recovered model is used to describe the transmission of dengue virus. The main contribution is determining the role of the rainfall in Thailand in the model. The transmission of dengue disease is assumed to depend on the nature of the rainfall in Thailand. We analyze the dynamic transmission of dengue disease. The stability of the solution of the model is analyzed. It is investigated by using the Routh-Hurwitz criteria. We find two equilibrium states: a disease-free state and an endemic equilibrium state. The basic reproductive number (R0 is obtained, which indicates the stability of each equilibrium state. Numerical results taking into account the rainfall are obtained and they are seen to correspond to the analytical results.
Full Text Available Optimal spatial assessment of short-time step precipitation for hydrological modelling is still an important research question considering the poor observation networks for high time resolution data. The main objective of this paper is to present a new approach for rainfall observation. The idea is to consider motorcars as moving rain gauges with windscreen wipers as sensors to detect precipitation. This idea is easily technically feasible if the cars are provided with GPS and a small memory chip for recording the coordinates, car speed and wiper frequency. This study explores theoretically the benefits of such an approach. For that a valid relationship between wiper speed and rainfall rate considering uncertainty was assumed here. A simple traffic model is applied to generate motorcars on roads in a river basin. Radar data are used as reference rainfall fields. Rainfall from these fields is sampled with a conventional rain gauge network and with several dynamic networks consisting of moving motorcars, using different assumptions such as accuracy levels for measurements and sensor equipment rates for the car networks. Those observed point rainfall data from the different networks are then used to calculate areal rainfall for different scales. Ordinary kriging and indicator kriging are applied for interpolation of the point data with the latter considering uncertain rainfall observation by cars e.g. according to a discrete number of windscreen wiper operation classes. The results are compared with the values from the radar observations. The study is carried out for the 3300 km2 Bode river basin located in the Harz Mountains in Northern Germany. The results show, that the idea is theoretically feasible and motivate practical experiments. Only a small portion of the cars needed to be equipped with sensors for sufficient areal rainfall estimation. Regarding the required sensitivity of the potential rain sensors in cars it could be shown
Full Text Available This paper presents a probabilistic model for daily rainfall, using sub-sampling based on meteorological circulation. We classified eight typical but contrasted synoptic situations (weather patterns for France and surrounding areas, using a "bottom-up" approach, i.e. from the shape of the rain field to the synoptic situations described by geopotential fields. These weather patterns (WP provide a discriminating variable that is consistent with French climatology, and allows seasonal rainfall records to be split into more homogeneous sub-samples, in term of meteorological genesis.
First results show how the combination of seasonal and WP sub-sampling strongly influences the identification of the asymptotic behaviour of rainfall probabilistic models. Furthermore, with this level of stratification, an asymptotic exponential behaviour of each sub-sample appears as a reasonable hypothesis. This first part is illustrated with two daily rainfall records from SE of France.
The distribution of the multi-exponential weather patterns (MEWP is then defined as the composition, for a given season, of all WP sub-sample marginal distributions, weighted by the relative frequency of occurrence of each WP. This model is finally compared to Exponential and Generalized Pareto distributions, showing good features in terms of robustness and accuracy. These final statistical results are computed from a wide dataset of 478 rainfall chronicles spread on the southern half of France. All these data cover the 1953–2005 period.
Serinaldi, F.; Kilsby, C. G.
Central and Eastern Europe are prone to severe floods due to heavy rainfall that cause societal and economic damages, ranging from agriculture to water resources, from the insurance/reinsurance sector to the energy industry. To improve the flood risk analysis, a better characterisation and modelling of the rainfall patterns over this area, which involves the Danube river watershed, is strategically important. In this study, we analyse the spatio-temporal properties of a large data set of daily rainfall time series from 15 countries in the Central Eastern Europe through different lagged and non-lagged indices of associations that quantify both the overall dependence and extreme dependence of pairwise observations. We also show that these measures are linked to each other and can be written in a unique and coherent notation within the copula framework. Moreover, the lagged version of these measures allows exploring some important spatio-temporal properties of the rainfall fields. The exploratory analysis is complemented by the preliminary results of a spatio-temporal rainfall simulation performed via a compound model based upon the Generalized Additive Models for Location, Scale and Shape (GAMLSS) and meta-elliptical multivariate distributions.
Full Text Available Water flow and hydromechanical coupling process in fractured rocks is more different from that in general porous media because of heterogeneous spatial fractures and possible fracture-dominated flow; a saturated-unsaturated hydromechanical coupling model using a discontinuous deformation analysis (DDA similar to FEM and DEM was employed to analyze water movement in saturated-unsaturated deformed rocks, in which the Van-Genuchten model differently treated the rock and fractures permeable properties to describe the constitutive relationships. The calibrating results for the dam foundation indicated the validation and feasibility of the proposed model and are also in good agreement with the calculations based on DEM still demonstrating its superiority. And then, the rainfall infiltration in a reservoir rock slope was detailedly investigated to describe the water pressure on the fault surface and inside the rocks, displacement, and stress distribution under hydromechanical coupling conditions and uncoupling conditions. It was observed that greater rainfall intensity and longer rainfall time resulted in lower stability of the rock slope, and larger difference was very obvious between the hydromechanical coupling condition and uncoupling condition, demonstrating that rainfall intensity, rainfall time, and hydromechanical coupling effect had great influence on the saturated-unsaturated water flow behavior and mechanical response of the fractured rock slopes.
An assessment of Box-Jenkins models: Forcados monthly rainfall as case study. ... Journal of Applied Science, Engineering and Technology ... The goodness of fit of the model was assessed by estimating the autocorrelations of the residuals of the historical data (from January 1931 to December 1960) for lags one to twelve.
Talebi, A.; Uijlenhoet, R.; Troch, P.A.
Recently, D'Odorico and Fagherazzi (2003) proposed "A probabilistic model of rainfall-triggered shallow landslides in hollows" (Water Resour. Res., 39, 2003). Their model describes the long-term evolution of colluvial deposits through a probabilistic soil mass balance at a point. Further building
Full Text Available Very short-term rainfall forecasting models designed for runoff analysis of catchments, particularly those subject to flash-floods, typically include one or more variables deduced from weather radars. Useful variables for defining the state and evolution of a rain system include rainfall rate, vertically integrated rainwater content and advection velocity. The forecast model proposed in this work complements recent dynamical formulations by focusing on a formulation incorporating these variables using volumetric radar data to define the model state variables, determining the rainfall source term directly from multi-scan radar data, explicitly accounting for orographic enhancement, and explicitly incorporating the dynamical model components in an advection-diffusion scheme. An evaluation of this model is presented for four rain events collected in the South of France and in the North-East of Italy. Model forecasts are compared with two simple methods: persistence and extrapolation. An additional analysis is performed using an existing mono-dimensional microphysical meteorological model to produce simulated rain events and provide initialization data. Forecasted rainfall produced by the proposed model and the extrapolation method are compared to the simulated events. The results show that the forecast model performance is influenced by rainfall temporal variability and performance is better for less variable rain events. The comparison with the extrapolation method shows that the proposed model performs better than extrapolation in the initial period of the forecast lead-time. It is shown that the performance of the proposed model over the extrapolation method depends essentially on the additional vertical information available from voluminal radar.
Raia, S.; Alvioli, M.; Rossi, M.; Baum, R.L.; Godt, J.W.; Guzzetti, F.
Distributed models to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides are deterministic. These models extend spatially the static stability models adopted in geotechnical engineering and adopt an infinite-slope geometry to balance the resisting and the driving forces acting on the sliding mass. An infiltration model is used to determine how rainfall changes pore-water conditions, modulating the local stability/instability conditions. A problem with the existing models is the difficulty in obtaining accurate values for the several variables that describe the material properties of the slopes. The problem is particularly severe when the models are applied over large areas, for which sufficient information on the geotechnical and hydrological conditions of the slopes is not generally available. To help solve the problem, we propose a probabilistic Monte Carlo approach to the distributed modeling of shallow rainfall-induced landslides. For the purpose, we have modified the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Analysis (TRIGRS) code. The new code (TRIGRS-P) adopts a stochastic approach to compute, on a cell-by-cell basis, transient pore-pressure changes and related changes in the factor of safety due to rainfall infiltration. Infiltration is modeled using analytical solutions of partial differential equations describing one-dimensional vertical flow in isotropic, homogeneous materials. Both saturated and unsaturated soil conditions can be considered. TRIGRS-P copes with the natural variability inherent to the mechanical and hydrological properties of the slope materials by allowing values of the TRIGRS model input parameters to be sampled randomly from a given probability distribution. The range of variation and the mean value of the parameters can be determined by the usual methods used for preparing the TRIGRS input parameters. The outputs of several model runs obtained varying the input parameters
Full Text Available Distributed models to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides are based on deterministic laws. These models extend spatially the static stability models adopted in geotechnical engineering, and adopt an infinite-slope geometry to balance the resisting and the driving forces acting on the sliding mass. An infiltration model is used to determine how rainfall changes pore-water conditions, modulating the local stability/instability conditions. A problem with the operation of the existing models lays in the difficulty in obtaining accurate values for the several variables that describe the material properties of the slopes. The problem is particularly severe when the models are applied over large areas, for which sufficient information on the geotechnical and hydrological conditions of the slopes is not generally available. To help solve the problem, we propose a probabilistic Monte Carlo approach to the distributed modeling of rainfall-induced shallow landslides. For this purpose, we have modified the transient rainfall infiltration and grid-based regional slope-stability analysis (TRIGRS code. The new code (TRIGRS-P adopts a probabilistic approach to compute, on a cell-by-cell basis, transient pore-pressure changes and related changes in the factor of safety due to rainfall infiltration. Infiltration is modeled using analytical solutions of partial differential equations describing one-dimensional vertical flow in isotropic, homogeneous materials. Both saturated and unsaturated soil conditions can be considered. TRIGRS-P copes with the natural variability inherent to the mechanical and hydrological properties of the slope materials by allowing values of the TRIGRS model input parameters to be sampled randomly from a given probability distribution. The range of variation and the mean value of the parameters can be determined by the usual methods used for preparing the TRIGRS input parameters. The outputs
Zahmatkesh, Zahra; Karamouz, Mohammad; Nazif, Sara
Simulation of rainfall-runoff process in urban areas is of great importance considering the consequences and damages of extreme runoff events and floods. The first issue in flood hazard analysis is rainfall simulation. Large scale climate signals have been proved to be effective in rainfall simulation and prediction. In this study, an integrated scheme is developed for rainfall-runoff modeling considering different sources of uncertainty. This scheme includes three main steps of rainfall forecasting, rainfall-runoff simulation and future runoff prediction. In the first step, data driven models are developed and used to forecast rainfall using large scale climate signals as rainfall predictors. Due to high effect of different sources of uncertainty on the output of hydrologic models, in the second step uncertainty associated with input data, model parameters and model structure is incorporated in rainfall-runoff modeling and simulation. Three rainfall-runoff simulation models are developed for consideration of model conceptual (structural) uncertainty in real time runoff forecasting. To analyze the uncertainty of the model structure, streamflows generated by alternative rainfall-runoff models are combined, through developing a weighting method based on K-means clustering. Model parameters and input uncertainty are investigated using an adaptive Markov Chain Monte Carlo method. Finally, calibrated rainfall-runoff models are driven using the forecasted rainfall to predict future runoff for the watershed. The proposed scheme is employed in the case study of the Bronx River watershed, New York City. Results of uncertainty analysis of rainfall-runoff modeling reveal that simultaneous estimation of model parameters and input uncertainty significantly changes the probability distribution of the model parameters. It is also observed that by combining the outputs of the hydrological models using the proposed clustering scheme, the accuracy of runoff simulation in the
Johann G. Zaller
Full Text Available Climate change scenarios for Central Europe predict less frequent but heavier rainfalls and longer drought periods during the growing season. This is expected to alter arthropods in agroecosystems that are important as biocontrol agents, herbivores or food for predators (e.g. farmland birds. In a lysimeter facility (totally 18 3-m2-plots, we experimentally tested the effects of long-term past vs. prognosticated future rainfall variations (15% increased rainfall per event, 25% more dry days according to regionalized climate change models from the Intergovernmental Panel on Climate Change (IPCC on aboveground arthropods in winter wheat (Triticum aestivum L. cultivated at three different soil types (calcaric phaeozem, calcic chernozem and gleyic phaeozem. Soil types were established 17 years and rainfall treatments one month before arthropod sampling; treatments were fully crossed and replicated three times. Aboveground arthropods were assessed by suction sampling, their mean abundances (± SD differed between April, May and June with 20 ± 3 m-2, 90 ± 35 m-2 and 289 ± 93 individuals m-2, respectively. Averaged across sampling dates, future rainfall reduced the abundance of spiders (Araneae, -47%, cicadas and leafhoppers (Auchenorrhyncha, -39%, beetles (Coleoptera, -52%, ground beetles (Carabidae, -41%, leaf beetles (Chrysomelidae, -64%, spring tails (Collembola, -58%, flies (Diptera, -73% and lacewings (Neuroptera, -73% but increased the abundance of snails (Gastropoda, +69%. Across sampling dates, soil types had no effects on arthropod abundances. Arthropod diversity was neither affected by rainfall nor soil types. Arthropod abundance was positively correlated with weed biomass for almost all taxa; abundance of Hemiptera and of total arthropods was positively correlated with weed density. These detrimental effects of future rainfall varieties on arthropod taxa in wheat fields can potentially alter arthropod-associated agroecosystem services.
da Silva, Felipe das Neves Roque; Alves, José Luis Drummond; Cataldi, Marcio
This paper aims to validate inflow simulations concerning the present-day climate at Água Vermelha Hydroelectric Plant (AVHP—located on the Grande River Basin) based on the Soil Moisture Accounting Procedure (SMAP) hydrological model. In order to provide rainfall data to the SMAP model, the RegCM regional climate model was also used working with boundary conditions from the MIROC model. Initially, present-day climate simulation performed by RegCM model was analyzed. It was found that, in terms of rainfall, the model was able to simulate the main patterns observed over South America. A bias correction technique was also used and it was essential to reduce mistakes related to rainfall simulation. Comparison between rainfall simulations from RegCM and MIROC showed improvements when the dynamical downscaling was performed. Then, SMAP, a rainfall-runoff hydrological model, was used to simulate inflows at Água Vermelha Hydroelectric Plant. After calibration with observed rainfall, SMAP simulations were evaluated in two different periods from the one used in calibration. During calibration, SMAP captures the inflow variability observed at AVHP. During validation periods, the hydrological model obtained better results and statistics with observed rainfall. However, in spite of some discrepancies, the use of simulated rainfall without bias correction captured the interannual flow variability. However, the use of bias removal in the simulated rainfall performed by RegCM brought significant improvements to the simulation of natural inflows performed by SMAP. Not only the curve of simulated inflow became more similar to the observed inflow, but also the statistics improved their values. Improvements were also noticed in the inflow simulation when the rainfall was provided by the regional climate model compared to the global model. In general, results obtained so far prove that there was an added value in rainfall when regional climate model was compared to global climate
Tolessa Leta, Olkeba; Nossent, Jiri; van Griensven, Ann; Bauwens, Willy
The European Union Water Framework Directive (EU-WFD) called its member countries to achieve a good ecological status for all inland and coastal water bodies by 2015. According to recent studies, the river Zenne (Belgium) is far from this objective. Therefore, an interuniversity and multidisciplinary project "Towards a Good Ecological Status in the river Zenne (GESZ)" was launched to evaluate the effects of wastewater management plans on the river. In this project, different models have been developed and integrated using the Open Modelling Interface (OpenMI). The hydrologic, semi-distributed Soil and Water Assessment Tool (SWAT) is hereby used as one of the model components in the integrated modelling chain in order to model the upland catchment processes. The assessment of the uncertainty of SWAT is an essential aspect of the decision making process, in order to design robust management strategies that take the predicted uncertainties into account. Model uncertainty stems from the uncertainties on the model parameters, the input data (e.g, rainfall), the calibration data (e.g., stream flows) and on the model structure itself. The objective of this paper is to assess the first three sources of uncertainty in a SWAT model of the river Zenne basin. For the assessment of rainfall measurement uncertainty, first, we identified independent rainfall periods, based on the daily precipitation and stream flow observations and using the Water Engineering Time Series PROcessing tool (WETSPRO). Secondly, we assigned a rainfall multiplier parameter for each of the independent rainfall periods, which serves as a multiplicative input error corruption. Finally, we treated these multipliers as latent parameters in the model optimization and uncertainty analysis (UA). For parameter uncertainty assessment, due to the high number of parameters of the SWAT model, first, we screened out its most sensitive parameters using the Latin Hypercube One-factor-At-a-Time (LH-OAT) technique
Thyregod, Peter; Carstensen, Niels Jacob; Madsen, Henrik
A new method for modelling the dynamics of rain sampled by a tipping bucket rain gauge is proposed. The considered models belong to the class of integer valued autoregressive processes. The models take the autocorelation and discrete nature of the data into account. A first order, a second order...... and a threshold model are presented together with methods to estimate the parameters of each model. The models are demonstrated to provide a good description of dt from actual rain events requiring only two to four parameters....
Lau, William K. M.; Wu, H. T.; Kim, K. M.
Changes in rainfall characteristics induced by global warming are examined based on probability distribution function (PDF) analysis, from outputs of 14 IPCC (Intergovernmental Panel on Climate Change), CMIP (5th Coupled Model Intercomparison Project) models under various scenarios of increased CO2 emissions. Results show that collectively CMIP5 models project a robust and consistent global and regional rainfall response to CO2 warming. Globally, the models show a 1-3% increase in rainfall per degree rise in temperature, with a canonical response featuring large increase (100-250 %) in frequency of occurrence of very heavy rain, a reduction (5-10%) of moderate rain, and an increase (10-15%) of light rain events. Regionally, even though details vary among models, a majority of the models (>10 out of 14) project a consistent large scale response with more heavy rain events in climatologically wet regions, most pronounced in the Pacific ITCZ and the Asian monsoon. Moderate rain events are found to decrease over extensive regions of the subtropical and extratropical oceans, but increases over the extratropical land regions, and the Southern Oceans. The spatial distribution of light rain resembles that of moderate rain, but mostly with opposite polarity. The majority of the models also show increase in the number of dry events (absence or only trace amount of rain) over subtropical and tropical land regions in both hemispheres. These results suggest that rainfall characteristics are changing and that increased extreme rainfall events and droughts occurrences are connected, as a consequent of a global adjustment of the large scale circulation to global warming.
Thyregod, Peter; Arnbjerg-Nielsen, Karsten; Madsen, Henrik
A new method for modelling the dynamics of rain measurement processes is suggested. The method takes the discrete nature and autocorrelation of measurements from the tipping bucket rain gauge into consideration. The considered model is a state space model with a Poisson marginal distribution. In ...
Williams, C. J. R.; Kniveton, D. R.; Layberry, R.
It is increasingly accepted that that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA). This dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. The ability of a climate model to simulate current climate provides some indication of how much confidence can be applied to its future predictions. In this paper, simulations of current climate from the UK Meteorological Office Hadley Centre's climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. This concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of rainfall variability over southern Africa. Secondly, the ability of the model to reproduce daily rainfall extremes will
De Vos, N.J.; Rientjes, T.H.M.
This paper presents results on the application of various optimization algorithms for the training of artificial neural network rainfall-runoff models. Multilayered feed-forward networks for forecasting discharge from two mesoscale catchments in different climatic regions have been developed for
R. H. Hawkins; A. Barreto-Munoz
Wildcat5 for Windows (Wildcat5) is an interactive Windows Excel-based software package designed to assist watershed specialists in analyzing rainfall runoff events to predict peak flow and runoff volumes generated by single-event rainstorms for a variety of watershed soil and vegetation conditions. Model inputs are: (1) rainstorm characteristics, (2) parameters related...
Nair, Archana; Singh, Gurjeet; Mohanty, U. C.
The monthly prediction of summer monsoon rainfall is very challenging because of its complex and chaotic nature. In this study, a non-linear technique known as Artificial Neural Network (ANN) has been employed on the outputs of Global Climate Models (GCMs) to bring out the vagaries inherent in monthly rainfall prediction. The GCMs that are considered in the study are from the International Research Institute (IRI) (2-tier CCM3v6) and the National Centre for Environmental Prediction (Coupled-CFSv2). The ANN technique is applied on different ensemble members of the individual GCMs to obtain monthly scale prediction over India as a whole and over its spatial grid points. In the present study, a double-cross-validation and simple randomization technique was used to avoid the over-fitting during training process of the ANN model. The performance of the ANN-predicted rainfall from GCMs is judged by analysing the absolute error, box plots, percentile and difference in linear error in probability space. Results suggest that there is significant improvement in prediction skill of these GCMs after applying the ANN technique. The performance analysis reveals that the ANN model is able to capture the year to year variations in monsoon months with fairly good accuracy in extreme years as well. ANN model is also able to simulate the correct signs of rainfall anomalies over different spatial points of the Indian domain.
Cowden, Joshua R.; Watkins, David W., Jr.; Mihelcic, James R.
SummarySeveral parsimonious stochastic rainfall models are developed and compared for application to domestic rainwater harvesting (DRWH) assessment in West Africa. Worldwide, improved water access rates are lowest for Sub-Saharan Africa, including the West African region, and these low rates have important implications on the health and economy of the region. Domestic rainwater harvesting (DRWH) is proposed as a potential mechanism for water supply enhancement, especially for the poor urban households in the region, which is essential for development planning and poverty alleviation initiatives. The stochastic rainfall models examined are Markov models and LARS-WG, selected due to availability and ease of use for water planners in the developing world. A first-order Markov occurrence model with a mixed exponential amount model is selected as the best option for unconditioned Markov models. However, there is no clear advantage in selecting Markov models over the LARS-WG model for DRWH in West Africa, with each model having distinct strengths and weaknesses. A multi-model approach is used in assessing DRWH in the region to illustrate the variability associated with the rainfall models. It is clear DRWH can be successfully used as a water enhancement mechanism in West Africa for certain times of the year. A 200 L drum storage capacity could potentially optimize these simple, small roof area systems for many locations in the region.
Otieno, Hesbon; Han, Dawei; Woods, Ross
Sustainable water resources management requires reliable methods for quantification of hydrological variables. This is a big challenge in developing countries, due to the problem of inadequate data as a result of sparse gauge networks. Successive occurrence of both abundance and shortage of water can arise in a catchment within the same year, with deficit situations becoming an increasingly occurring phenomenon in Kenya. This work compares the performance of two models in the Tana River catchment in Kenya, in generation of synthetic flow data. One of the models is the simpler USGS Thornthwaite monthly water balance model that uses a monthly time step and has three parameters. In order to explore alternative modelling schemes, the more complex Pitman model with 19 parameters was also applied in the catchment. It is uncertain whether the complex model (Pitman) will do better than the simple model, because a model with a large number of parameters may do well in the current system but poorly in future. To check this we have used old data (1970-1985) to calibrate the models and to validate with recent data (after 1985) to see which model is robust over time. This study is relevant and useful to water resources managers in scenario analysis for water resources management, planning and development in African countries with similar climates and catchment conditions.
José Ruy Porto de Carvalho
Full Text Available Abstract Modeling by multiple enchained imputation is an area of growing importance. However, its models and methods are frequently developed for specific applications. In this study the model for multiple imputation was used to estimate daily rainfall data. Daily precipitation records from several meteorological stations were used, obtained from system AGRITEMPO for two homogenous climatic zones. The precipitation values obtained for two dates (Jan. 20th 2005 and May 2nd 2005 using the multiple imputation model were compared with geo-statistics techniques ordinary Kriging and Co-kriging with the altitude as an auxiliary variable. The multiple imputation model was 16% better for the first zone and over 23% for the second one, compared to the rainfall estimation obtained by geo-statistical techniques. The model proved to be a versatile technique, presenting coherent results with the conditions of different zones and times.
Lewis, Sophie C.; Karoly, David J.
Australia experienced much above average rainfall in association with strong, extended La Niña conditions during 2010-2012. Was the heavy Australian rainfall influenced by La Niña conditions and/or anthropogenic greenhouse gases? We investigate the relative contributions of anthropogenic climate change and natural climatic variability to the likelihood of heavy Australian rainfall using three distinct model datasets. Area-average rainfall anomalies for model simulations with natural forcings only were compared to simulations with both anthropogenic and natural forcings using 16 models participating in the Coupled Model Intercomparison Project Phase 5. Using fraction of attributable risk to compare the likelihood of unusual rainfall between the parallel experiments, we find attribution statements are uncertain, with FAR values sensitive to the attribution parameters considered, including thresholds, regions and seasons. When heavy rainfall probabilities were next investigated in ensembles of two atmospheric general circulation models, run with and without anthropogenically-induced sea surface temperature changes, results were model-dependent. Overall, the attribution of seasonal-scale heavy Australia rainfall to a particular cause is likely more complicated than for temperature extremes. As estimates of the greenhouse gas attributable change in rainfall risk may depend on the model datasets considered, it is also useful to consider model outputs from several datasets and using various estimates of counterfactual surface conditions to establish robust attribution statements for extreme rainfall events. In contrast, comparing the likelihoods of heavy rainfall during simulated La Niña years with El Niño/neutral years reveals a substantial La Niña influence on Australian rainfall during 2010-2012 that is robust to changes in the attribution framework.
Full Text Available Hydrological processes are complex to compute in hilly areas when compared to plain areas. The governing processes behind runoff generation on hillslopes are subsurface storm flow, saturation excess flow, overland flow, return flow and pipe storage. The simulations of the above processes in the soil matrix require detailed hillslope hydrological modelling. In the present study, a hillslope experimental plot has been designed to study the runoff generation processes on the plot scale. The setup is designed keeping in view the natural hillslope conditions prevailing in the Northwestern Himalayas, India where high intensity rainfall events occur frequently. A rainfall simulator was installed over the experimental hillslope plot to generate rainfall with an intensity of 100 mm/h, which represents the dominating rainfall intensity range in the region. Soil moisture sensors were also installed at variable depths from 100 to 1000 mm at different locations of the plot to observe the soil moisture regime. From the experimental observations it was found that once the soil is saturated, it remains at field capacity for the next 24–36 h. Such antecedent moisture conditions are most favorable for the generation of rapid stormflow from hillslopes. A dye infiltration test was performed on the undisturbed soil column to observe the macropore fraction variability over the vegetated hillslopes. The estimated macropore fractions are used as essential input for the hillslope hydrological model. The main objective of the present study was to develop and test a method for estimating runoff responses from natural rainfall over hillslopes of the Northwestern Himalayas using a portable rainfall simulator. Using the experimental data and the developed conceptual model, the overland flow and the subsurface flow through a macropore-dominated area have been estimated/analyzed. The surface and subsurface runoff estimated using the developed hillslope hydrological model
Ly, S.; Sohier, C.; Charles, C.; Degré, A.
Precipitation traditionally observed using raingages or weather stations, is one of the main parameters that have direct impact on runoff production. Precipitation data require a preliminary spatial interpolation prior to hydrological modeling. The accuracy of modelling result depends on the accuracy of the interpolated spatial rainfall which differs according to different interpolation methods. The accuracy of the interpolated spatial rainfall is usually determined by cross-validation method. The objective of this study is to assess the different interpolation methods of daily rainfall at the watershed scale through hydrological modelling and to explore the best methods that provide a good long term simulation. Four versions of geostatistics: Ordinary Kriging (ORK), Universal Kriging (UNK), Kriging with External Dridft (KED) and Ordinary Cokriging (OCK) and two types of deterministic methods: Thiessen polygon (THI) and Inverse Distance Weighting (IDW) are used to produce 30-year daily rainfall inputs for a distributed physically-based hydrological model (EPIC-GRID). This work is conducted in the Ourthe and Ambleve nested catchments, located in the Ardennes hilly landscape in the Walloon region, Belgium. The total catchment area is 2908 km2, lies between 67 and 693 m in elevation. The multivariate geostatistics (KED and OCK) are also used by incorporating elevation as external data to improve the rainfall prediction. This work also aims at analysing the effect of different raingage densities and position used for interpolation, on the stream flow modelled to get insight in terms of the capability and limitation of the geostatistical methods. The number of raingage varies from 70, 60, 50, 40, 30, 20, 8 to 4 stations located in and surrounding the catchment area. In the latter case, we try to use different positions: around the catchment and only a part of the catchment. The result shows that the simple method like THI fails to capture the rainfall and to produce
K. Benzineb, M. Remaoun
Sep 1, 2016 ... The hydrologic behaviour modelling of w. Journal of Fundamental and Applied Scienc. ISSN 1112-9867. Available online at http://www.jfas.inf. Journal of Fundamental and Applied S. International License. Libraries Resource Directory. We a. INFALL-RUNOFF MODELLING BY NEURAL NETWORKS IN.
Roč. 278, - (2003), s. 144-152 ISSN 0022-1694 R&D Projects: GA ČR GA205/01/1066; GA AV ČR IBS3042101 Institutional research plan: CEZ:AV0Z3042911 Keywords : rainfall * radar * rain gauge * regression Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.354, year: 2003
Chlumecký, Martin; Buchtele, Josef; Richta, Karel
The efficient calibration of rainfall-runoff models is a difficult issue, even for experienced hydrologists. Therefore, fast and high-quality model calibration is a valuable improvement. This paper describes a novel methodology and software for the optimisation of a rainfall-runoff modelling using a genetic algorithm (GA) with a newly prepared concept of a random number generator (HRNG), which is the core of the optimisation. The GA estimates model parameters using evolutionary principles, which requires a quality number generator. The new HRNG generates random numbers based on hydrological information and it provides better numbers compared to pure software generators. The GA enhances the model calibration very well and the goal is to optimise the calibration of the model with a minimum of user interaction. This article focuses on improving the internal structure of the GA, which is shielded from the user. The results that we obtained indicate that the HRNG provides a stable trend in the output quality of the model, despite various configurations of the GA. In contrast to previous research, the HRNG speeds up the calibration of the model and offers an improvement of rainfall-runoff modelling.
Open Access DOWNLOAD FULL TEXT ... In this paper, we present mathematical models that incorporate ideas from complex systems theory to integrate several strands of rangeland theory in a hierarchical framework. ... Keywords: catastrophe theory; complexity theory; disequilibrium; hysteresis; moving attractors
Pattnayak, K. C.; Kar, S. C.; Dalal, Mamta; Pattnayak, R. K.
Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) comprising Bangladesh, Bhutan, India, Myanmar, Nepal, Sri Lanka and Thailand brings together 21% of the world population. Thus the impact of climate change in this region is a major concern for all. To study the climate change, fifth phase of Climate Model Inter-comparison Project (CMIP5) models have been used to project the climate for the 21st century under the Representative Concentration Pathways (RCPs) 4.5 and 8.5 over the BIMSTEC countries for the period 1901 to 2100 (initial 105 years are historical period and the later 95 years are projected period). Climate change in the projected period has been examined with respect to the historical period. In order to validate the models, the mean annual rainfall has been compared with observations from multiple sources and temperature has been compared with the data from Climatic Research Unit (CRU) during the historical period. Comparison reveals that ensemble mean of the models is able to represent the observed spatial distribution of rainfall and temperature over the BIMSTEC countries. Therefore, data from these models may be used to study the future changes in the 21st century. Four out of six models show that the rainfall over India, Thailand and Myanmar has decreasing trend and Bangladesh, Bhutan, Nepal and Sri Lanka show an increasing trend in both the RCP scenarios. In case of temperature, all the models show an increasing trend over all the BIMSTEC countries in both the scenarios, however, the rate of increase is relatively less over Sri Lanka than the other countries. The rate of increase/decrease in rainfall and temperature are relatively more in RCP8.5 than RCP4.5 over all these countries. Inter-model comparison show that there are uncertainties within the CMIP5 model projections. More similar studies are required to be done for better understanding the model uncertainties in climate projections over this region.
Callau Poduje, A. C.; Haberlandt, U.
The design, planning, operation and overall assessment of urban drainage systems require long and continuous rain series in a high temporal resolution. Unfortunately, the availability of this data is usually short. Nevertheless a precipitation model could be used to tackle this shortcoming; therefore it is in the aim of this study to present a stochastic point precipitation model to reproduce average rainfall event properties along with extreme values. For this purpose a model is proposed to generate long synthetic series of rainfall for a temporal resolution of 5 min. It is based on an alternating renewal framework and events are characterized by variables describing durations, amounts and peaks. A group of 24 stations located in the north of Germany is used to set up and test the model. The adequate modeling of joint behaviour of rainfall amount and duration is found to be essential for reproducing the observed properties, especially for the extreme events. Copulas are advantageous tools for modeling these variables jointly; however caution must be taken in the selection of the proper copula. The inclusion of seasonality and small events is as well tested and found to be useful. The model is directly validated by generating long synthetic time series and comparing them with observed ones. An indirect validation is as well performed based on a fictional urban hydrological system. The proposed model is capable of reproducing seasonal behaviour and main characteristics of the rainfall events including extremes along with urban flooding and overflow behaviour. Overall the performance of the model is acceptable compared to the design practice. The proposed model is simple to interpret, fast to implement and to transfer to other regions, whilst showing acceptable results.
Full Text Available Surface precipitation budget equation in a three-dimensional (3D WRF model framework is derived. By applying the convective-stratiform partition method to the surface precipitation budget equation in the 3D model, this study separated convective and stratiform rainfall of typhoon Fitow (2013. The separations are further verified by examining statistics of vertical velocity, surface precipitation budget, and cloud microphysical budget. Results show that water vapor convergence moistens local atmosphere and offsets hydrometeor divergence, and producing convective rainfall, while hydrometeor convergence primarily supports stratiform rainfall, since water vapor divergence and local atmospheric drying generally cancelled out. Mean ascending motions are prevailing in the entire troposphere in the convective region, whereas mean descending motions occur below 5 km and mean ascending motions occur above in the stratiform region. The frequency distribution of vertical velocity shows vertical velocity has wide distribution with the maximum values up to 13 m s-1 in the convective regions, whereas it has narrow distribution with absolute values confined within 7 m s-1 in the stratiform region. Liquid cloud microphysics is dominant in convective regions and ice cloud microphysics is dominant in stratiform regions. These indicate that the statistics results are generally consistent with the corresponding physical characteristics of the convective-stratiform rainfall structures generalized by previous studies.
Manivasagam, V. S.; Nagarajan, R.
Water stress due to uneven rainfall distribution causes a significant impact on the agricultural production of monsoon-dependent peninsular India. In the present study, water stress assessment for rainfed maize crop is carried out for kharif (June-October) and rabi (October-February) cropping seasons which coincide with two major Indian monsoons. Rainfall analysis (1976-2010) shows that the kharif season receives sufficient weekly rainfall (28 ± 32 mm) during 26th-39th standard meteorological weeks (SMWs) from southwest monsoon, whereas the rabi season experiences a major portion of its weekly rainfall due to northeast monsoon between the 42nd and 51st SMW (31 ± 42 mm). The later weeks experience minimal rainfall (5.5 ± 15 mm) and thus expose the late sown maize crops to a severe water stress during its maturity stage. Wet and dry spell analyses reveal a substantial increase in the rainfall intensity over the last few decades. However, the distribution of rainfall shows a striking decrease in the number of wet spells, with prolonged dry spells in both seasons. Weekly rainfall classification shows that the flowering and maturity stages of kharif maize (33rd-39th SMWs) can suffer around 30-40% of the total water stress. In the case of rabi maize, the analysis reveals that a shift in the sowing time from the existing 42nd SMW (16-22 October) to the 40th SMW (1-7 October) can avoid terminal water stress. Further, AquaCrop modeling results show that one or two minimal irrigations during the flowering and maturity stages (33rd-39th SMWs) of kharif maize positively avoid the mild water stress exposure. Similarly, rabi maize requires an additional two or three lifesaving irrigations during its flowering and maturity stages (48th-53rd SMWs) to improve productivity. Effective crop planning with appropriate sowing time, short duration crop, and high yielding drought-resistant varieties will allow for better utilization of the monsoon rain, thus reducing water stress with
Fuller, Daniel; Pabayo, Roman
To examine associations between utilitarian walking, utilitarian cycling, leisure time physical activity and body mass index (BMI). Participants from the National Population Health Survey (NPHS) of Statistics Canada were interviewed by telephone every two years from 1994 to 2010. Analysis includes data from 6894 living participants aged 18-64years. Fixed effects and random intercepts models examined the association between BMI, utilitarian walking, and utilitarian cycling, controlling for behavioral and sociodemographic factors. The final adjusted fixed effects models showed no significant relationship between utilitarian walking and BMI. In the unbalanced sample utilitarian cycling for 1 to 5h per week (b=-0.15, 95% CI: -0.28 to -0.02), and more than 5h per week (b=-0.22, 95% CI: -0.44 to 0.00) was significantly associated with BMI over time. In the fully balanced sample utilitarian cycling for 1 to 5h per week (b=-0.12, 95% CI: -0.27 to 0.03), more than 5h per week (b=-0.16, 95% CI: -0.45 to 0.13) was not significantly associated with BMI over time. The results suggest that utilitarian walking is not related to BMI. The relationship between utilitarian cycling and BMI is less clear. Copyright © 2014 Elsevier Inc. All rights reserved.
Jan 15, 2015 ... Comparison of observed and simulated data. 3.2. Regression modeling. Equations 4 to 9 show the results of linear regression using different input data. These equations were obtained by using the SPSS software. The released statistical parameters due to regression analysis have been showed in table 5.
Sunyer Pinya, Maria Antonia; Madsen, H.; Rosbjerg, Dan
are independent. The effects of this assumption on the uncertainty quantification of extreme rainfall projections are addressed in this study. First, the interdependency of the 95% quantile of wet days in the ENSEMBLES RCMs is estimated. For this statistic and the region studied, the RCMs cannot be assumed......Changes in rainfall extremes under climate change conditions are subject to numerous uncertainties. One of the most important uncertainties arises from the inherent uncertainty in climate models. In recent years, many efforts have been made in creating large multi-model ensembles of both Regional...... independent. Then, a Bayesian approach that accounts for the interdependency of the climate models is developed in order to quantify the uncertainty. The results of the Bayesian approach show that the uncertainty is narrower when the models are considered independent. These results highlight the importance...
Zhang, X.; Anagnostou, E. N.; Astitha, M.; Vergara, H. J.; Gourley, J. J.; Hong, Y.
This study aims to investigate the use of high-resolution Numerical Weather Prediction (NWP) for evaluating biases of satellite rainfall estimates of flood-inducing storms in mountainous areas and associated improvements in flood modeling. Satellite-retrieved precipitation has been considered as a feasible data source for global-scale flood modeling, given that satellite has the spatial coverage advantage over in situ (rain gauges and radar) observations particularly over mountainous areas. However, orographically induced heavy precipitation events tend to be underestimated and spatially smoothed by satellite products, which error propagates non-linearly in flood simulations.We apply a recently developed retrieval error and resolution effect correction method (Zhang et al. 2013*) on the NOAA Climate Prediction Center morphing technique (CMORPH) product based on NWP analysis (or forecasting in the case of real-time satellite products). The NWP rainfall is derived from the Weather Research and Forecasting Model (WRF) set up with high spatial resolution (1-2 km) and explicit treatment of precipitation microphysics.In this study we will show results on NWP-adjusted CMORPH rain rates based on tropical cyclones and a convective precipitation event measured during NASA's IPHEX experiment in the South Appalachian region. We will use hydrologic simulations over different basins in the region to evaluate propagation of bias correction in flood simulations. We show that the adjustment reduced the underestimation of high rain rates thus moderating the strong rainfall magnitude dependence of CMORPH rainfall bias, which results in significant improvement in flood peak simulations. Further study over Blue Nile Basin (western Ethiopia) will be investigated and included in the presentation. *Zhang, X. et al. 2013: Using NWP Simulations in Satellite Rainfall Estimation of Heavy Precipitation Events over Mountainous Areas. J. Hydrometeor, 14, 1844-1858.
Gires, A.; Tchiguirinskaia, I.; Schertzer, D. J.; Lovejoy, S.
In large urban areas, storm water management is a challenge with enlarging impervious areas. Many cities have implemented real time control (RTC) of their urban drainage system to either reduce overflow or limit urban contamination. A basic component of RTC is hydraulic/hydrologic model. In this paper we use the multifractal framework to suggest an innovative way to test the sensitivity of such a model to the spatio-temporal variability of its rainfall input. Indeed the rainfall variability is often neglected in urban context, being considered as a non-relevant issue at the scales involve. Our results show that on the contrary the rainfall variability should be taken into account. Universal multifractals (UM) rely on the concept of multiplicative cascade and are a standard tool to analyze and simulate with a reduced number of parameters geophysical processes that are extremely variable over a wide range of scales. This study is conducted on a 3 400 ha urban area located in Seine-Saint-Denis, in the North of Paris (France). We use the operational semi-distributed model that was calibrated by the local authority (Direction Eau et Assainnissement du 93) that is in charge of urban drainage. The rainfall data comes from the C-Band radar of Trappes operated by Météo-France. The rainfall event of February 9th, 2009 was used. A stochastic ensemble approach was implemented to quantify the uncertainty on discharge associated to the rainfall variability occurring at scales smaller than 1 km x 1 km x 5 min that is usually available with C-band radar networks. An analysis of the quantiles of the simulated peak flow showed that the uncertainty exceeds 20 % for upstream links. To evaluate a potential gain from a direct use of the rainfall data available at the resolution of X-band radar, we performed similar analysis of the rainfall fields of the degraded resolution of 9 km x 9 km x 20 min. The results show a clear decrease in uncertainty when the original resolution of C
Vernieuwe, Hilde; Georgieva, Olga; De Baets, Bernard; Pauwels, Valentijn R. N.; Verhoest, Niko E. C.; De Troch, François P.
Over the last decades, several data-driven techniques have been applied to model the rainfall-discharge dynamics of catchments. Among these techniques are fuzzy rule-based models, which attempt to describe the catchment response to rainfall input through fuzzy relationships. In this paper, we demonstrate three different methods for constructing fuzzy rule-based models of the Takagi-Sugeno type relating rainfall to catchment discharge. They correspond to the grid partitioning, subtractive clustering, and Gustafson-Kessel (GK) clustering identification methods. The data set used to parametrize and validate the models consists of hourly precipitation and discharge records. The models are parametrized using a 1-year identification data set and are then applied to a 4-year data set. Although the models show a similar performance, the best results are obtained for the GK method. A real-time flood forecasting algorithm is then developed, in which discharge measurements are assimilated into the model at either an hourly or a daily time step. The results suggest that the GK method can potentially be used as an operational flood forecasting tool with a low computational cost.
Hutchinson, M. F.; Xu, T.; Kesteven, J.
Reliable assessment of spatio-temporal variability of observed rainfall is difficult in the current climate because of the complex spatial variability displayed by daily and shorter time scale rainfall data. As demonstrated in a recent analysis of Canadian daily precipitation data by Hutchinson et al. (2009), direct interpolation of short time scale precipitation data is a poor way to address spatial patterns of rainfall extremes. Addressing the behaviour of projected future precipitation extremes is made even more difficult by the limited temporal and spatial resolution of precipitation as simulated by global climate models. The “uniform drizzle” that tends to be produced by these models makes the assessment of even straightforward statistics, such as daily rainfall occurrence, problematic. Putting aside significant inter-model variability, the more reliable outputs of global models include mean fluxes, such as monthly rainfall amounts, and associated insight into the nature of the modelled precipitation in relation to forcing synoptic systems. The truncated power of normal distribution, as described by Hutchinson (1995), offers a relatively simple way to make progress. Two of the three model parameters are simply calibrated in terms of monthly mean fluxes and the model is able to accurately describe precipitation extremes. These model parameters can also be robustly determined from serially incomplete data. It can be argued that the model has a broad physical process basis by modelling rainfall as an event that occurs as an appropriate threshold is exceeded. This analysis extends the approach of Stidd (1954, 1973) who suggested the cube root as a universal normalising power. We show that the power parameter, once robustly calibrated, displays a broadly spatially varying distribution of around 0.5. This corresponds well with the two dimensional synoptic convergence that is required to produce precipitation. The power parameter appears to be related to the
Madsen, Henrik; Rosbjerg, Dan; Harremoës, Poul
Since 1979 a country-wide system of raingauges has been operated in Denmark in order to obtain a better basis for design and analysis of urban drainage systems. As an alternative to the traditional non-parametric approach the Partial Duration Series method is employed in the modelling of extreme ....... The application of the Bayesian approach is derived in case of both exponential and generalized Pareto distributed exceedances. Finally, the aspect of including economic perspectives in the estimation of the design events is briefly discussed....... in Denmark cannot be justified. In order to obtain an estimation procedure at non-monitored sites and to improve at-site estimates a regional Bayesian approach is adopted. The empirical regional distributions of the parameters in the Partial Duration Series model are used as prior information...
Wu, Chunhung; Huang, Jyuntai
Most of the landslide cases in Taiwan were triggered by rainfall or earthquake events. The heavy rainfall in the typhoon seasons, from June to October, causes the landslide hazard more serious. Renai Towhship is of the most large landslide cases after 2009 Typhoon Morakot (from Aug. 5 to Aug. 10, 2009) in Taiwan. Around 2,744 landslides cases with the total landslide area of 21.5 km2 (landslide ratio =1.8%), including 26 large landslide cases, induced after 2009 Typhoon Morakot in Renai Towhship. The area of each large landslides case is more than 0.1 km2, and the area of the largest case is around 0.96 km2. 58% of large landslide cases locate in the area with metamorphosed sandstone. The mean slope of 26 large landslide cases ranges from 15 degree to 56 degree, and the accumulated rainfall during 2009 Typhoon Morakot ranges from 530 mm to 937 mm. Three methods, including frequency ratio method (abbreviated as FR), weights of evidence method (abbreviated as WOE), and logistic regression method (abbreviated as LR), are used in this study to establish the landslides susceptibility in the Renai Township, Nantou County, Taiwan. Eight landslide related-factors, including elevation, slope, aspect, geology, land use, distance to drainage, distance to fault, accumulation rainfall during 2009 Typhoon Morakot, are used to establish the landslide susceptibility models in this study. The landslide inventory after 2009 Typhoon Morakot is also used to test the model performance in this study. The mean accumulated rainfall in Renai Township during 2009 typhoon Morakot was around 735 mm with the maximum 1-hr, 3-hrs, and 6-hrs rainfall intensity of 44 mm/1-hr, 106 mm/3-hrs and 204 mm/6-hrs, respectively. The range of original susceptibility values established by three methods are 4.0 to 20.9 for FR, -33.8 to -16.1 for WOE, and -41.7 to 5.7 for LR, and the mean landslide susceptibility value are 8.0, -24.6 and 0.38, respectively. The AUC values are 0.815 for FR, 0.816 for WOE, and 0
Zhang, Kai [Jackson School of Geosciences, University of Texas at Austin, Austin TX USA; Fu, Rong [Jackson School of Geosciences, University of Texas at Austin, Austin TX USA; Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles CA USA; Shaikh, Muhammad J. [Jackson School of Geosciences, University of Texas at Austin, Austin TX USA; Ghan, Steven [Pacific Northwest National Laboratory, Richland WA USA; Wang, Minghuai [Institute for Climate and Global Change Research and School of Atmospheric Sciences, Nanjing University, Nanjing China; Collaborative Innovation Center of Climate Change, Nanjing China; Leung, L. Ruby [Pacific Northwest National Laboratory, Richland WA USA; Dickinson, Robert E. [Jackson School of Geosciences, University of Texas at Austin, Austin TX USA; Marengo, Jose [Centro Nacional de Monitoramento e Alertas aos Desastres Naturais, São Jose dos Campos Brazil
We evaluate the Community Atmosphere Model Version 5 (CAM5) with a higher-order turbulence closure scheme, named Cloud Layers Unified By Binomials (CLUBB), and a Multiscale Modeling Framework (MMF) with two different microphysics configurations to investigate their influences on rainfall simulations over Southern Amazonia. The two different microphysics configurations in MMF are the one-moment cloud microphysics without aerosol treatment (SAM1MOM) and two-moment cloud microphysics coupled with aerosol treatment (SAM2MOM). Results show that both MMF-SAM2MOM and CLUBB effectively reduce the low biases of rainfall, mainly during the wet season. The CLUBB reduces low biases of humidity in the lower troposphere with further reduced shallow clouds. The latter enables more surface solar flux, leading to stronger convection and more rainfall. MMF, especially MMF-SAM2MOM, unstablizes the atmosphere with more moisture and higher atmospheric temperatures in the atmospheric boundary layer, allowing the growth of more extreme convection and further generating more deep convection. MMF-SAM2MOM significantly increases rainfall in the afternoon, but it does not reduce the early bias of the diurnal rainfall peak; LUBB, on the other hand, delays the afternoon peak time and produces more precipitation in the early morning, due to more realistic gradual transition between shallow and deep convection. MMF appears to be able to realistically capture the observed increase of relative humidity prior to deep convection, especially with its two-moment configuration. In contrast, in CAM5 and CAM5 with CLUBB, occurrence of deep convection in these models appears to be a result of stronger heating rather than higher relative humidity.
Wallace, Jim; McJannet, Dave
SummaryThe lower and upper montane rainforests in northern Queensland receive significant amounts of cloud interception that affect both in situ canopy wetness and downstream runoff. Cloud interception contributes 5-30% of the annual water input to the canopy and this increases to 40-70% of the monthly water input during the dry season. This occult water is therefore an important input to the canopy, sustaining the epiphytes, mosses and other species that depend on wet canopy conditions. The potential effect of climate change on cloud interception was examined using the relationship between cloud interception and cloud frequency derived from measurements made at four different rainforest locations. Any given change in cloud frequency produces a greater change in cloud interception and this 'amplification' increases from 1.1 to 1.7 as cloud frequency increases from 5% to 70%. This means that any changes in cloud frequency will have the greatest relative effects at the higher altitude sites where cloud interception is greatest. As cloud frequency is also a major factor affecting canopy wetness, any given change in cloud frequency will therefore have a greater impact on canopy wetness at the higher altitude sites. These changes in wetness duration will augment those due to changes in rainfall and may have important implications for the fauna and flora that depend on wet canopy conditions. We also found that the Australian rainforests may be more efficient (by ˜50% on average) in intercepting cloud water than American coniferous forests, which may be due to differences in canopy structure and exposure at the different sites.
Karanjekar, Richa V; Bhatt, Arpita; Altouqui, Said; Jangikhatoonabad, Neda; Durai, Vennila; Sattler, Melanie L; Hossain, M D Sahadat; Chen, Victoria
Accurately estimating landfill methane emissions is important for quantifying a landfill's greenhouse gas emissions and power generation potential. Current models, including LandGEM and IPCC, often greatly simplify treatment of factors like rainfall and ambient temperature, which can substantially impact gas production. The newly developed Capturing Landfill Emissions for Energy Needs (CLEEN) model aims to improve landfill methane generation estimates, but still require inputs that are fairly easy to obtain: waste composition, annual rainfall, and ambient temperature. To develop the model, methane generation was measured from 27 laboratory scale landfill reactors, with varying waste compositions (ranging from 0% to 100%); average rainfall rates of 2, 6, and 12 mm/day; and temperatures of 20, 30, and 37°C, according to a statistical experimental design. Refuse components considered were the major biodegradable wastes, food, paper, yard/wood, and textile, as well as inert inorganic waste. Based on the data collected, a multiple linear regression equation (R(2)=0.75) was developed to predict first-order methane generation rate constant values k as functions of waste composition, annual rainfall, and temperature. Because, laboratory methane generation rates exceed field rates, a second scale-up regression equation for k was developed using actual gas-recovery data from 11 landfills in high-income countries with conventional operation. The Capturing Landfill Emissions for Energy Needs (CLEEN) model was developed by incorporating both regression equations into the first-order decay based model for estimating methane generation rates from landfills. CLEEN model values were compared to actual field data from 6 US landfills, and to estimates from LandGEM and IPCC. For 4 of the 6 cases, CLEEN model estimates were the closest to actual. Copyright © 2015 Elsevier Ltd. All rights reserved.
Okuneye, Kamaldeen; Gumel, Abba B
A new non-autonomous model is designed and used to assess the impact of variability in temperature and rainfall on the transmission dynamics of malaria in a population. In addition to adding age-structure in the host population and the dynamics of immature malaria mosquitoes, a notable feature of the new model is that recovered individuals do not revert to wholly-susceptible class (that is, recovered individuals enjoy reduced susceptibility to new malaria infection). In the absence of disease-induced mortality, the disease-free solution of the model is shown to be globally-asymptotically stable when the associated reproduction ratio is less than unity. The model has at least one positive periodic solution when the reproduction ratio exceeds unity (and the disease persists in the community in this case). Detailed uncertainty and sensitivity analysis, using mean monthly temperature and rainfall data from KwaZulu-Natal province of South Africa, shows that the top three parameters of the model that have the most influence on the disease transmission dynamics are the mosquito carrying capacity, transmission probability per contact for susceptible mosquitoes and human recovery rate. Numerical simulations of the model show that, for the KwaZulu-Natal province, malaria burden increases with increasing mean monthly temperature and rainfall in the ranges ([17-25]°C and [32-110] mm), respectively (and decreases with decreasing mean monthly temperature and rainfall values). In particular, transmission is maximized for mean monthly temperature and rainfall in the ranges [21-25]°C and [95-125] mm. This occurs for a six-month period in KwaZulu-Natal (hence, this study suggests that anti-malaria control efforts should be intensified during this period). It is shown, for the fixed mean monthly temperature of KwaZulu-Natal, that malaria burden decreases whenever the amount of rainfall exceeds a certain threshold value. It is further shown (through sensitivity analysis and
Murla Tuyls, Damian; Thorndahl, Søren
Intense rainfall can often cause severe floods, especially in urbanized areas, where population density or large impermeable areas are found. In this context, floods can generate a direct impact in a social-environmental-economic viewpoint. Traditionally, in design of Urban Drainage Systems (UDS), correlation between return period (RP) of a given rainfall and RP of its consequent flood has been assumed to be linear (e.g. DS/EN752 (2008)). However, this is not always the case. Complex UDS, where diverse hydraulic infrastructures are often found, increase the heterogeneity of system response, which may cause an alteration of the mentioned correlation. Consequently, reliability on future urban planning, design and resilience against floods may be also affected by this misassumption. In this study, an assessment of surface flood RP across rainfall RP has been carried out at Lystrup, a urbanized catchment area of 440ha and 10.400inhab. located in Jutland (Denmark), which has received the impact of several pluvial flooding in the last recent years. A historical rainfall dataset from the last 35 years from two different rain gauges located at 2 and 10 km from the study area has been provided by the Danish Wastewater Pollution Committee and the Danish Meteorological Institute (DMI). The most extreme 25 rainfall events have been selected through a two-step multi-criteria procedure, ensuring an adequate variability of rainfall, from extreme high peak storms with a short duration to moderate rainfall with longer duration. In addition, a coupled 1D/2D surface and network UDS model of the catchment area developed in an integrated MIKE URBAN and MIKE Flood model (DHI 2014), considering both permeable and impermeable areas, in combination with a DTM (2x2m res.) has been used to study and assess in detail flood RP. Results show an ambiguous relation between rainfall RP and flood response. Local flood levels, flood area and volume RP estimates should therefore not be neglected in
Alvioli, M.; Baum, R.L.
We describe a parallel implementation of TRIGRS, the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model for the timing and distribution of rainfall-induced shallow landslides. We have parallelized the four time-demanding execution modes of TRIGRS, namely both the saturated and unsaturated model with finite and infinite soil depth options, within the Message Passing Interface framework. In addition to new features of the code, we outline details of the parallel implementation and show the performance gain with respect to the serial code. Results are obtained both on commercial hardware and on a high-performance multi-node machine, showing the different limits of applicability of the new code. We also discuss the implications for the application of the model on large-scale areas and as a tool for real-time landslide hazard monitoring.
Fouladi Osgouei, Hojjatollah; Zarghami, Mahdi; Ashouri, Hamed
The availability of spatial, high-resolution rainfall data is one of the most essential needs in the study of water resources. These data are extremely valuable in providing flood awareness for dense urban and industrial areas. The first part of this paper applies an optimization-based method to the calibration of radar data based on ground rainfall gauges. Then, the climatological Z-R relationship for the Sahand radar, located in the East Azarbaijan province of Iran, with the help of three adjacent rainfall stations, is obtained. The new climatological Z-R relationship with a power-law form shows acceptable statistical performance, making it suitable for radar-rainfall estimation by the Sahand radar outputs. The second part of the study develops a new heterogeneous random-cascade model for spatially disaggregating the rainfall data resulting from the power-law model. This model is applied to the radar-rainfall image data to disaggregate rainfall data with coverage area of 512 × 512 km2 to a resolution of 32 × 32 km2. Results show that the proposed model has a good ability to disaggregate rainfall data, which may lead to improvement in precipitation forecasting, and ultimately better water-resources management in this arid region, including Urmia Lake.
Deal, E.; Dralle, D.; Braun, J.; Botter, G.
The response of river basins to rainfall changes significantly from one basin to another and one storm to the next. This is because many time-varying processes influence how rainfall makes its way into streams, such as soil moisture dynamics, antecedent conditions as well as subsurface flow and storage. Using an established, parsimonious stochastic model of catchment hydrology simplifies the problem enough to handle this complexity and search for a general response of river basins to predicted changes in rainfall resulting from climate change. The model is applicable in basins without significant snowfall. Included are simplified representations of rainfall, soil moisture dynamics and evapotranspiration. Additionally, deep water storage and subsurface hydrology are parameterized with a simple model for streamflow recessions. The model suggests a general relationship between rainfall timing and intensity and the variability of streamflow. Specifically, we predict that basins with intense rainfall or rainfall that is correlated in time (rainfall over several consecutive days) will have streamflow with a higher coefficient of variation than basins with less intense or less correlated rainfall, all else equal. We test this prediction using a database of USGS gauged rivers minimally impacted by human activity. In basins without significant snowfall, the observed relationship between rainfall and streamflow variability matches the theory well. Further, the manner in which this relationship is modulated by streamflow recession characteristics agrees with our theory. Most importantly, we find the effect of rainfall intensity is sensitive to the nonlinearity of streamflow recessions. We use our new understanding of the effect of rainfall intensity and timing on streamflow variability and the controls on this relationship to quantify the sensitivity of streamflow variability to changes in rainfall. It has been predicted that rainfall intensity will increase in many places in
Sharma, Kuldeep; Ashrit, Raghavendra; Bhatla, R.; Mitra, A. K.; Iyengar, G. R.; Rajagopal, E. N.
The quantitative precipitation forecast (QPF) performance for heavy rains is still a challenge, even for the most advanced state-of-art high-resolution Numerical Weather Prediction (NWP) modeling systems. This study aims to evaluate the performance of UK Met Office Unified Model (UKMO) over India for prediction of high rainfall amounts (>2 and >5 cm/day) during the monsoon period (JJAS) from 2007 to 2015 in short range forecast up to Day 3. Among the various modeling upgrades and improvements in the parameterizations during this period, the model horizontal resolution has seen an improvement from 40 km in 2007 to 17 km in 2015. Skill of short range rainfall forecast has improved in UKMO model in recent years mainly due to increased horizontal and vertical resolution along with improved physics schemes. Categorical verification carried out using the four verification metrics, namely, probability of detection (POD), false alarm ratio (FAR), frequency bias (Bias) and Critical Success Index, indicates that QPF has improved by >29 and >24% in case of POD and FAR. Additionally, verification scores like EDS (Extreme Dependency Score), EDI (Extremal Dependence Index) and SEDI (Symmetric EDI) are used with special emphasis on verification of extreme and rare rainfall events. These scores also show an improvement by 60% (EDS) and >34% (EDI and SEDI) during the period of study, suggesting an improved skill of predicting heavy rains.
Pham, Minh Tu; Vernieuwe, Hilde; De Baets, Bernard; Verhoest, Niko E. C.
A hydrological impact analysis concerns the study of the consequences of certain scenarios on one or more variables or fluxes in the hydrological cycle. In such an exercise, discharge is often considered, as floods originating from extremely high discharges often cause damage. Investigating the impact of extreme discharges generally requires long time series of precipitation and evapotranspiration to be used to force a rainfall-runoff model. However, such kinds of data may not be available and one should resort to stochastically generated time series, even though the impact of using such data on the overall discharge, and especially on the extreme discharge events, is not well studied. In this paper, stochastically generated rainfall and corresponding evapotranspiration time series, generated by means of vine copulas, are used to force a simple conceptual hydrological model. The results obtained are comparable to the modelled discharge using observed forcing data. Yet, uncertainties in the modelled discharge increase with an increasing number of stochastically generated time series used. Notwithstanding this finding, it can be concluded that using a coupled stochastic rainfall-evapotranspiration model has great potential for hydrological impact analysis.
Hernán D. Salas
Full Text Available We study spatial scaling and complexity properties of Amazonian radar rainfall fields using the Beta-Lognormal Model (BL-Model with the aim to characterize and model the process at a broad range of spatial scales. The Generalized Space q-Entropy Function (GSEF, an entropic measure defined as a continuous set of power laws covering a broad range of spatial scales, S q ( λ ∼ λ Ω ( q , is used as a tool to check the ability of the BL-Model to represent observed 2-D radar rainfall fields. In addition, we evaluate the effect of the amount of zeros, the variability of rainfall intensity, the number of bins used to estimate the probability mass function, and the record length on the GSFE estimation. Our results show that: (i the BL-Model adequately represents the scaling properties of the q-entropy, S q, for Amazonian rainfall fields across a range of spatial scales λ from 2 km to 64 km; (ii the q-entropy in rainfall fields can be characterized by a non-additivity value, q s a t, at which rainfall reaches a maximum scaling exponent, Ω s a t; (iii the maximum scaling exponent Ω s a t is directly related to the amount of zeros in rainfall fields and is not sensitive to either the number of bins to estimate the probability mass function or the variability of rainfall intensity; and (iv for small-samples, the GSEF of rainfall fields may incur in considerable bias. Finally, for synthetic 2-D rainfall fields from the BL-Model, we look for a connection between intermittency using a metric based on generalized Hurst exponents, M ( q 1 , q 2 , and the non-extensive order (q-order of a system, Θ q, which relates to the GSEF. Our results do not exhibit evidence of such relationship.
S. Babaei Hessar
Full Text Available Introduction: Precipitation is one of the most important and sensitive parameters of the tropical climate that influence the catchments hydrological regime. The prediction of rainfall is vital for strategic planning and water resources management. Despite its importance, statistical rainfall forecasting, especially for long-term, has been proven to be a great challenge due to the dynamic nature of climate phenomena and random fluctuations involved in the process. Various methods, such as time series and artificial neural network models, have been proposed to predict the level of rainfall. But there is not enough attention to global warming and climate change issues. The main aim of this study is to investigate the conformity of artificial neural network and time series models with climate scenarios. Materials and Methods: For this study, 50 years of daily rainfall data (1961 to 2010 of the synoptic station of Urmia, Tabriz and Khoy was investigated. Data was obtained from Meteorological Organization of Iran. In the present study, the results of two Artificial Neural Network (ANN and Time Seri (TS methods were compared with the result of the Emission Scenarios (A2 & B1. HadCM3 model in LARS-WG software was used to generate rainfall for the next 18 years (2011-2029. The results of models were compared with climate scenarios over the next 18 years in the three synoptic stations located in the basin of the Lake Urmia. At the first stage, the best model of time series method was selected. The precipitation was estimated for the next 18 years using these models. For the same period, precipitation was forecast using artificial neural networks. Finally, the results of two models were compared with data generated under two scenarios (B1 and A2 in LARS-WG. Results and Discussion: Different order of AR, MA and ARMA was examined to select the best model of TS The results show that AR(1 was suitable for Tabriz and Khoy stations .In the Urmia station MA(1 was
Full Text Available Reliable estimates of extreme rainfall events are necessary for an accurate prediction of floods. Most of the global rainfall products are available at a coarse resolution, rendering them less desirable for extreme rainfall analysis. Therefore, regional mesoscale models such as the advanced research version of the Weather Research and Forecasting (WRF model are often used to provide rainfall estimates at fine grid spacing. Modelling heavy rainfall events is an enduring challenge, as such events depend on multi-scale interactions, and the model configurations such as grid spacing, physical parameterization and initialization. With this background, the WRF model is implemented in this study to investigate the impact of different processes on extreme rainfall simulation, by considering a representative event that occurred during 15–18 June 2013 over the Ganga Basin in India, which is located at the foothills of the Himalayas. This event is simulated with ensembles involving four different microphysics (MP, two cumulus (CU parameterizations, two planetary boundary layers (PBLs and two land surface physics options, as well as different resolutions (grid spacing within the WRF model. The simulated rainfall is evaluated against the observations from 18 rain gauges and the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA 3B42RT version 7 data. From the analysis, it should be noted that the choice of MP scheme influences the spatial pattern of rainfall, while the choice of PBL and CU parameterizations influences the magnitude of rainfall in the model simulations. Further, the WRF run with Goddard MP, Mellor–Yamada–Janjic PBL and Betts–Miller–Janjic CU scheme is found to perform best in simulating this heavy rain event. The selected configuration is evaluated for several heavy to extremely heavy rainfall events that occurred across different months of the monsoon season in the region. The model performance
Chawla, Ila; Osuri, Krishna K.; Mujumdar, Pradeep P.; Niyogi, Dev
Reliable estimates of extreme rainfall events are necessary for an accurate prediction of floods. Most of the global rainfall products are available at a coarse resolution, rendering them less desirable for extreme rainfall analysis. Therefore, regional mesoscale models such as the advanced research version of the Weather Research and Forecasting (WRF) model are often used to provide rainfall estimates at fine grid spacing. Modelling heavy rainfall events is an enduring challenge, as such events depend on multi-scale interactions, and the model configurations such as grid spacing, physical parameterization and initialization. With this background, the WRF model is implemented in this study to investigate the impact of different processes on extreme rainfall simulation, by considering a representative event that occurred during 15-18 June 2013 over the Ganga Basin in India, which is located at the foothills of the Himalayas. This event is simulated with ensembles involving four different microphysics (MP), two cumulus (CU) parameterizations, two planetary boundary layers (PBLs) and two land surface physics options, as well as different resolutions (grid spacing) within the WRF model. The simulated rainfall is evaluated against the observations from 18 rain gauges and the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) 3B42RT version 7 data. From the analysis, it should be noted that the choice of MP scheme influences the spatial pattern of rainfall, while the choice of PBL and CU parameterizations influences the magnitude of rainfall in the model simulations. Further, the WRF run with Goddard MP, Mellor-Yamada-Janjic PBL and Betts-Miller-Janjic CU scheme is found to perform best in simulating this heavy rain event. The selected configuration is evaluated for several heavy to extremely heavy rainfall events that occurred across different months of the monsoon season in the region. The model performance improved through incorporation
Garcia Urquia, Elias; Axelsson, K.
Central America is constantly being affected by natural hazards. Among these events are hurricanes and earthquakes, capable of triggering landslides that can alter the natural landscape, destroy infrastructure and cause the death of people in the most important settlements of the region. Hurricane Mitch in October of 1998 was of particular interest for the region, since it provoked hundreds of rainfall-induced landslides, mainly in 4 different countries. Studies carried out after Hurricane Mitch have allowed researchers to identify the factors that contribute to slope instability in many vulnerable areas. As Tegucigalpa, Honduras was partially destroyed due to the various landslide and flooding events triggered by this devastating hurricane, various research teams have deepened in their investigations and have proposed measures to mitigate the effects of similar future incidents. A model coupling an infinite-slope analysis and a simple groundwater flow approach can serve as a basis to predict the occurrence of landslides in Tegucigalpa, Honduras as a function of topographic, hydrological and soil variables. A safety map showing the rainfall-triggered landslide risk zones for Tegucigalpa, Honduras is to be created. As opposed to previous safety maps in which only steady-state conditions are studied, this analysis is extended and different steady-state and quasi-dynamic scenarios are considered for comparison. For the purpose of the latter settings, a hydrological analysis that determines the rainfall extreme values and their return periods in Tegucigalpa will account for the influence of rainfall on the groundwater flow and strength of soils. It is known that the spatial distribution of various factors that contribute to the risk of landslides (i.e. soil thickness, conductivity and strength properties; rainfall intensity and duration; root strength; subsurface flow orientation) is hard to determine. However, an effort is done to derive correlations for these
Jung, Soyoung; Qin, Xiao; Noyce, David A
As part of the Wisconsin road weather safety initiative, the objective of this study is to assess the effects of rainfall on the severity of single-vehicle crashes on Wisconsin interstate highways utilizing polychotomous response models. Weather-related factors considered in this study include estimated rainfall intensity for 15 min prior to a crash occurrence, water film depth, temperature, wind speed/direction, stopping sight distance and deficiency of car-following distance at the crash moment. For locations with unknown weather information, data were interpolated using the inverse squared distance method. Non-weather factors such as road geometrics, traffic conditions, collision types, vehicle types, and driver and temporal attributes were also considered. Two types of polychotomous response models were compared: ordinal logistic and sequential logistic regressions. The sequential logistic regression was tested with forward and backward formats. Comparative models were also developed for single vehicle crash severity during clear weather. In conclusion, the backward sequential logistic regression model produced the best results for predicting crash severities in rainy weather where rainfall intensity, wind speed, roadway terrain, driver's gender, and safety belt were found to be statistically significant. Our study also found that the seasonal factor was significant in clear weather. The seasonal factor is a predictor suggesting that inclement weather may affect crash severity. These findings can be used to determine the probabilities of single vehicle crash severity in rainy weather and provide quantitative support on improving road weather safety via weather warning systems, highway facility improvements, and speed limit management.
Hou, Arthur Y.; Zhang, Sara Q.
Given the large temporal and spatial variability of precipitation processes, errors in rainfall observations are difficult to quantify yet crucial to making effective use of rainfall data for improving atmospheric analysis, weather forecasting, and climate modeling. We highlight the need for developing a quantitative understanding of systematic and random errors in precipitation observations by examining explicit examples of how each type of errors can affect forecasts and analyses in global data assimilation. We characterize the error information needed from the precipitation measurement community and how it may be used to improve data usage within the general framework of analysis techniques, as well as accuracy requirements from the perspective of climate modeling and global data assimilation.
planners. 14. SUBJECT TERMS quadrotor drone, micro interceptor, trajectory planner, optimal control, missile guidance law , intercept trajectory, optimal...operator of a registered but rogue UAV to be identified so that a fine or other penalty may be assessed. Companies such as Guard From Above use trained...explained in Chapter IV. The point mass model and the guidance law used for each planner to create a trajectory are discussed. The Simulink and MATLAB
Full Text Available Multi-site optimization of two adapted event-based geomorphologic rainfall-runoff models was presented using Non-dominated Sorting Genetic Algorithm (NSGA-II method for the South Fork Eel River watershed, California. The first model was developed based on Unequal Cascade of Reservoirs (UECR and the second model was presented as a modified version of Geomorphological Unit Hydrograph based on Nash’s model (GUHN. Two calibration strategies were considered as semi-lumped and semi-distributed for imposing (or unimposing the geomorphology relations in the models. The results of models were compared with Nash’s model. Obtained results using the observed data of two stations in the multi-site optimization framework showed reasonable efficiency values in both the calibration and the verification steps. The outcomes also showed that semi-distributed calibration of the modified GUHN model slightly outperformed other models in both upstream and downstream stations during calibration. Both calibration strategies for the developed UECR model during the verification phase showed slightly better performance in the downstream station, but in the upstream station, the modified GUHN model in the semi-lumped strategy slightly outperformed the other models. The semi-lumped calibration strategy could lead to logical lag time parameters related to the basin geomorphology and may be more suitable for data-based statistical analyses of the rainfall-runoff process.
Gregersen, Ida Bülow; Madsen, Henrik; Rosbjerg, Dan
of extreme rainfall. The framework is built on a partial duration series approach with a nonstationary, regional threshold value. The model is based on generalized linear regression solved by generalized estimation equations. It allows a spatial correlation between the stations in the network and accounts...... furthermore for variable observation periods at each station and in each year. Marginal regional and temporal regression models solved by generalized least squares are used to validate and discuss the results of the full spatiotemporal model. The model is applied on data from a large Danish rain gauge network...
The rainfall-runoff conceptual model as a cascade of submerged linear reservoirs with particular outflows depending on storages of adjoining reservoirs is developed. The model output contains different exponential functions with roots of Chebyshev polynomials of the first kind as exponents. The model is applied to instantaneous unit hydrograph (IUH) and recession curve problems and compared with the analogous results of the Nash cascade. A case study is performed on a basis of 46 recession periods. Obtained results show the usefulness of the model as an alternative concept to the Nash cascade.
Full Text Available Watershed management and hydrological modeling require data related to the very important matter of precipitation, often measured using raingages or weather stations. Hydrological models often require a preliminary spatial interpolation as part of the modeling process. The success of spatial interpolation varies according to the type of model chosen, its mode of geographical management and the resolution used. The quality of a result is determined by the quality of the continuous spatial rainfall, which ensues from the interpolation method used. The objective of this article is to review the existing methods for interpolation of rainfall data that are usually required in hydrological modeling. We review the basis for the application of certain common methods and geostatistical approaches used in interpolation of rainfall. Previous studies have highlighted the need for new research to investigate ways of improving the quality of rainfall data and ultimately, the quality of hydrological modeling.
Full Text Available Stochastic weather simulation, or weather generators (WGs, have gained a wide acceptance and been used for a variety of purposes, including climate change studies and the evaluation of climate variability and uncertainty effects. The two major challenges in WGs are improving the estimation of interannual variability and reducing overdispersion in the synthetic series of simulated weather. The objective of this work is to develop a WG model of daily rainfall, incorporating a covariable that accounts for interannual variability, and apply it in three climate regions (arid, Mediterranean, and temperate of Chile. Precipitation occurrence was modeled using a two-stage, first-order Markov chain, whose parameters are fitted with a generalized lineal model (GLM using a logistic function. This function considers monthly values of the observed Sea Surface Temperature Anomalies of the Region 3.4 of El Niño-Southern Oscillation (ENSO index as a covariable. Precipitation intensity was simulated with a mixed exponential distribution, fitted using a maximum likelihood approach. The stochastic simulation shows that the application of the approach to Mediterranean and arid climates largely eliminates the overdispersion problem, resulting in a much improved interannual variability in the simulated values.
Full Text Available This study quantitatively estimated the precipitation associated with a typhoon in the northwestern Pacific Ocean by using a physical algorithm which included the Weather Research and Forecasting model, Radiative Transfer for TIROS Operational Vertical Sounder model, and data from the Tropical Rainfall Measuring Mission (TRMM/TRMM Microwave Imager (TMI and TRMM/Precipitation Radar (PR. First, a prior probability distribution function (PDF was constructed using over three million rain rate retrievals from the TRMM/PR data for the period 2002–2010 over the northwestern Pacific Ocean. Subsequently, brightness temperatures for 15 typhoons that occurred over the northwestern Pacific Ocean were simulated using a microwave radiative transfer model and a conditional PDF was obtained for these typhoons. The aforementioned physical algorithm involved using a posterior PDF. A posterior PDF was obtained by combining the prior and conditional PDFs. Finally, the rain rate associated with a typhoon was estimated by inputting the observations of the TMI (attenuation indices at 10, 19, 37 GHz into the posterior PDF (lookup table. Results based on rain rate retrievals indicated that rainband locations with the heaviest rainfall showed qualitatively similar horizontal distributions. The correlation coefficient and root-mean-square error of the rain rate estimation were 0.63 and 4.45 mm·h−1, respectively. Furthermore, the correlation coefficient and root-mean-square error for convective rainfall were 0.78 and 7.25 mm·h−1, respectively, and those for stratiform rainfall were 0.58 and 9.60 mm·h−1, respectively. The main contribution of this study is introducing an approach to quickly and accurately estimate the typhoon precipitation, and remove the need for complex calculations.
Panagos, Panos; Ballabio, Cristiano; Borrelli, Pasquale; Meusburger, Katrin; Alewell, Christine
Rainfall erosivity (R-factor) is among the 6 input factors in estimating soil erosion risk by using the empirical Revised Universal Soil Loss Equation (RUSLE). R-factor is a driving force for soil erosion modelling and potentially can be used in flood risk assessments, landslides susceptibility, post-fire damage assessment, application of agricultural management practices and climate change modelling. The rainfall erosivity is extremely difficult to model at large scale (national, European) due to lack of high temporal resolution precipitation data which cover long-time series. In most cases, R-factor is estimated based on empirical equations which take into account precipitation volume. The Rainfall Erosivity Database on the European Scale (REDES) is the output of an extensive data collection of high resolution precipitation data in the 28 Member States of the European Union plus Switzerland taking place during 2013-2014 in collaboration with national meteorological/environmental services. Due to different temporal resolutions of the data (5, 10, 15, 30, 60 minutes), conversion equations have been applied in order to homogenise the database at 30-minutes interval. The 1,541 stations included in REDES have been interpolated using the Gaussian Process Regression (GPR) model using as covariates the climatic data (monthly precipitation, monthly temperature, wettest/driest month) from WorldClim Database, Digital Elevation Model and latitude/longitude. GPR has been selected among other candidate models (GAM, Regression Kriging) due the best performance both in cross validation (R2=0.63) and in fitting dataset (R2=0.72). The highest uncertainty has been noticed in North-western Scotland, North Sweden and Finland due to limited number of stations in REDES. Also, in highlands such as Alpine arch and Pyrenees the diversity of environmental features forced relatively high uncertainty. The rainfall erosivity map of Europe available at 500m resolution plus the standard error
Adler, R. F.; Wu, H.; Tian, Y.
A real-time experimental system to estimate and forecast floods over the globe, the Global Flood Monitoring System (GFMS), has been significantly improved to provide flood detection, streamflow and inundation mapping information at higher resolution (as fine as 1 km) and nowcasts and forecasts (out to five days). Images and output data are available for use by the community with updates available every three hours (http://flood.umd.edu). The system uses satellite-based rainfall information, currently the TRMM Multi-satellite Precipitation Analysis [TMPA]), other satellite and conventional information and a newly-developed hydrological and routing combination model. The improved combined model, the Dominant river Routing Integrated with VIC Environment (DRIVE) system, is based on the VIC (Variable Infiltration Capacity) land surface model (U. of Washington) and the Dominant River Tracing Routing (DRTR) method. Within the DRIVE system the surface hydrological calculations are carried out at 0.125° latitude-longitude resolution with routing, streamflow and other calculations done at that resolution and at 1km resolution. Flood detection and intensity estimates are based on water depth and streamflow thresholds calculated from a 15-year retrospective run using the satellite rainfall and model. This period is also used for testing and evaluation with results indicating improved streamflow estimation and flood detection statistics. The satellite rainfall data are integrated with global model NASA GEOS-5 Numerical Weather Prediction (NWP) rainfall predictions (adjusted to the satellite data) to extend the flood calculations out to five days. Examples of results for recent flood events are presented along with validation statistics and comparison with other flood observations (e.g., inundation calculations vs. MODIS and/or SAR flood maps). The outlook for further development in this area in terms of increased utility for national and international disaster management
Zhang, Bo; Zhao, Bin; Niu, Ruoyun
The heavy rainfall forecast in North China is the focus and difficulty in middle range numerical weather forecast. 70 typical heavy precipitation cases in North China in summer from 2010 to 2016 are selected, which are divided into vortex type, the west trough and shear line type according to the atmospheric circulation. Based on ECMWF model and the Chinese operational model T639, a spatial verification method MODE is used, the middle range precipitation forecast abilities for heavy rain in summer in North China are evaluated according to contrast the difference of centroidal distance, axis angel and aspect ratios. It is found that the ECMWF model and the T639 model all show weak predictive ability for the low-vortex-type heavy rainfall in Northern China from all the similarities. When the area of rainfall is larger, the precipitation patterns of the two models are mostly northeast-southwest. It is consistent with the actual situation. For a large area of precipitation area, both models predict the precipitation area aspect ratio is less than 1. It shows that precipitation drop area is long and narrow, and the forecast is also consistent with the actual situation. However, as far as T639 and ECMWF models are concerned, there are systematic deviations in the precipitation area, and the predicted precipitation area is located on the southwestern side of the field. For smaller/larger areas of precipitation, the predicted precipitation area is larger/smaller than the actual situation. In addition, a sensitive test for the regional heavy precipitation process in North China (such as Huanghuai and other regions) from July 18 to 20, 2016 is also done and the results show that each numerical model of the process prediction is not successful. Therefore, further research is needed on the future correction of systematic bias of numerical models of regional heavy precipitation in medium-term forecasters.
Decker, P.; Cohen, M. J.; Jawitz, J. W.
Previous hydrologic studies primarily focus on processes related to montane catchments with significant runoff ratios, low evapotranspiration rates, and reasonably short travel times. There is a significant lack of research for hydrologic processes occurring within the United States Southeastern Coastal Plain landscape where low-relief and high rates of evapotranspiration impact water fluxes. Hydrologic modeling efforts within this region may elucidate possible interactions and timescales of solute travel where much of the landscape is managed for agricultural crops, namely plantation forestry. A long-term paired watershed study carried out in northern Florida monitored two second-order blackwater streams for five years. Rainfall-runoff models for both catchments were created using daily discharge, precipitation, and modeled evapotranspiration as input parameters. Best fit occurred (NSE = 0.8) when the catchments were modeled as two-storage (shallow and deep) reservoirs in parallel and overland flow was allowed to contribute to streamflow in periods were shallow groundwater storage was at capacity. In addition, streamflow and rainfall chloride concentrations were used to model in-variable transit time distributions using spectral methods. In both catchments this transit time was unresolvable because output spectral power exceeded input spectral power, a result assumed to be driven by the evaporative demand of the region. A modeled chloride time series from random input concentration and modeled output through the rainfall-runoff model was used to alter the evaporation ratio. Once evaporation rates equaled known rates found in cool, high-relief catchments, spectral analysis illustrated higher input spectral power and therefore resolvable transit times. Findings from this study illustrate significant effects from evaporation within the catchment - often exceeding the signal from the background catchment process itself. Calculations illustrate a proposed mean transit
Sui, C.; Sun, J.
The purpose of the study is to examine the water cycling processes responsible for rainfall changes with climate forcing, using a high-resolution version of the Weather Research and Forecasting Model with cloud system resolving physics. In this study, we perform a series of simulations for May and June in 2008 (cold climate as a reference) and 2004 (warm climate). Evaluated against observed diurnal cycle, dropsonde measurements (T, q), CloudSat retrieved cloud contents, and TRMM rainfall, the WRF experiments with explicit cloud schemes reasonably simulate diurnal cycle, temperature and humidity fields, climatologic mean cloud distribution, and rainfall statistic in the MJ season in NW Pacific. Between 2004 and 2008, the intensity of heavy rain (top 5% freq) in NW Pacific increased by 6% (TRMM observations). The model simulated an increase of 8%. This is significantly larger than the observed increasing trend of extreme rain in NW Pacific associated with decadal and low-frequency climate change (about 0.1% per decade by SSMI measurements). The change of rain intensity is decided by cloud microphysics that affects the location and strength of atmospheric latent heating and then the large-scale circulation.
Pallardy, Quinn; Fox, Neil I.
Implementation of dual-polarization radar should allow for improvements in quantitative precipitation estimates due to dual-polarization capability allowing for the retrieval of the second moment of the gamma drop size distribution. Knowledge of the shape of the DSD can then be used in combination with mesoscale model data to estimate the motion and evaporation of each size of drop falling from the height at which precipitation is observed by the radar to the surface. Using data from Central Missouri at a range between 130 and 140 km from the operational National Weather Service radar a rain drop tracing scheme was developed to account for the effects of evaporation, where individual raindrops hitting the ground were traced to the point in space and time where they interacted with the radar beam. The results indicated evaporation played a significant role in radar rainfall estimation in situations where the atmosphere was relatively dry. Improvements in radar estimated rainfall were also found in these situations by accounting for evaporation. The conclusion was made that the effects of raindrop evaporation were significant enough to warrant further research into the inclusion high resolution model data in the radar rainfall estimation process for appropriate locations.
Full Text Available The spatial distribution of precipitation is an important aspect of water-related research. The use of different interpolation schemes in the same catchment may cause large differences and deviations from the actual spatial distribution of rainfall. Our study analyzes different methods of spatial rainfall interpolation at annual, daily, and hourly time scales to provide a comprehensive evaluation. An improved regression-based scheme is proposed using principal component regression with residual correction (PCRR and is compared with inverse distance weighting (IDW and multiple linear regression (MLR interpolation methods. In this study, the meso-scale catchment of the Fuhe River in southeastern China was selected as a typical region. Furthermore, a hydrological model HEC-HMS was used to calculate streamflow and to evaluate the impact of rainfall interpolation methods on the results of the hydrological model. Results show that the PCRR method performed better than the other methods tested in the study and can effectively eliminate the interpolation anomalies caused by terrain differences between observation points and surrounding areas. Simulated streamflow showed different characteristics based on the mean, maximum, minimum, and peak flows. The results simulated by PCRR exhibited the lowest streamflow error and highest correlation with measured values at the daily time scale. The application of the PCRR method is found to be promising because it considers multicollinearity among variables.
Zhang, Shaojie; Zhao, Luqiang; Delgado-Tellez, Ricardo; Bao, Hongjun
Conventional outputs of physics-based landslide forecasting models are presented as deterministic warnings by calculating the safety factor (Fs) of potentially dangerous slopes. However, these models are highly dependent on variables such as cohesion force and internal friction angle which are affected by a high degree of uncertainty especially at a regional scale, resulting in unacceptable uncertainties of Fs. Under such circumstances, the outputs of physical models are more suitable if presented in the form of landslide probability values. In order to develop such models, a method to link the uncertainty of soil parameter values with landslide probability is devised. This paper proposes the use of Monte Carlo methods to quantitatively express uncertainty by assigning random values to physical variables inside a defined interval. The inequality Fs forecasting model for rainfall-induced shallow landslides. The prediction ability of this model was tested in a case study, in which simulated forecasting of landslide disasters associated with heavy rainfalls on 9 July 2013 in the Wenchuan earthquake region of Sichuan province, China, was performed. The proposed model successfully forecasted landslides in 159 of the 176 disaster points registered by the geo-environmental monitoring station of Sichuan province. Such testing results indicate that the new model can be operated in a highly efficient way and show more reliable results, attributable to its high prediction accuracy. Accordingly, the new model can be potentially packaged into a forecasting system for shallow landslides providing technological support for the mitigation of these disasters at regional scale.
Full Text Available Most hydrological models use continuous daily precipitation and potential evapotranspiration for streamflow estimation. With the projected increase in mean surface temperature, hydrological processes are set to intensify irrespective of the underlying changes to the mean precipitation. The effect of an increase in rainfall intensity on the long-term water balance is, however, not adequately accounted for in the commonly used hydrological models. This study follows from a previous comparative analysis of a non-stationary daily series of stream flow of a forested watershed (River Rimbaud in the French Alps (area = 1.478 km2 (1966–2006. Non-stationarity in the recorded stream flow occurred as a result of a severe wild fire in 1990. Two daily models (AWBM and SimHyd were initially calibrated for each of three distinct phases in relation to the well documented land disturbance. At the daily and monthly time scales, both models performed satisfactorily with the Nash–Sutcliffe coefficient of efficiency (NSE varying from 0.77 to 0.92. When aggregated to the annual time scale, both models underestimated the flow by about 22% with a reduced NSE at about 0.71. Exploratory data analysis was undertaken to relate daily peak hourly rainfall intensity to the discrepancy between the observed and modelled daily runoff amount. Preliminary results show that the effect of peak hourly rainfall intensity on runoff prediction is insignificant, and model performance is unlikely to improve when peak daily precipitation is included. Trend analysis indicated that the large decrease of precipitation when daily precipitation amount exceeded 10–20 mm may have contributed greatly to the decrease in stream flow of this forested watershed.
Full Text Available In this study we propose a comprehensive multi-criteria validation test for rainfall-runoff modeling by artificial neural networks. This study applies 17 global statistics and 3 additional non-parametric tests to evaluate the ANNs. The weakness of global statistics for validation of ANN is demonstrated by rainfall-runoff modeling of the Plasjan Basin in the western region of the Zayandehrud watershed, Iran. Although the global statistics showed that the multi layer perceptron with 4 hidden layers (MLP4 is the best ANN for the basin comparing with other MLP networks and empirical regression model, the non-parametric tests illustrate that neither the ANNs nor the regression model are able to reproduce the probability distribution of observed runoff in validation phase. However, the MLP4 network is the best network to reproduce the mean and variance of the observed runoff based on non-parametric tests. The performance of ANNs and empirical model was also demonstrated for low, medium and high flows. Although the MLP4 network gives the best performance among ANNs for low, medium and high flows based on different statistics, the empirical model shows better results. However, none of the models is able to simulate the frequency distribution of low, medium and high flows according to non-parametric tests. This study illustrates that the modelers should select appropriate and relevant evaluation measures from the set of existing metrics based on the particular requirements of each individual applications.
M. P. Mittermaier
Full Text Available A simple measure of the uncertainty associated with using radar-derived rainfall estimates as "truth" has been introduced to the Numerical Weather Prediction (NWP verification process to assess the effect on forecast skill and errors. Deterministic precipitation forecasts from the mesoscale version of the UK Met Office Unified Model for a two-day high-impact event and for a month were verified at the daily and six-hourly time scale using a spatially-based intensity-scale method and various traditional skill scores such as the Equitable Threat Score (ETS and log-odds ratio. Radar-rainfall accumulations from the UK Nimrod radar-composite were used.
The results show that the inclusion of uncertainty has some effect, shifting the forecast errors and skill. The study also allowed for the comparison of results from the intensity-scale method and traditional skill scores. It showed that the two methods complement each other, one detailing the scale and rainfall accumulation thresholds where the errors occur, the other showing how skillful the forecast is. It was also found that for the six-hourly forecasts the error distributions remain similar with forecast lead time but skill decreases. This highlights the difference between forecast error and forecast skill, and that they are not necessarily the same.
Md. Abu Syed
Full Text Available Bangladesh has been experiencing increased temperature and change in precipitation regime, which might adversely affect the important ecosystems in the country differentially. The river flows and groundwater recharge over space and time are determined by changes in temperature, evaporation and crucially precipitation. These again have a spatio-temporal dimension. This geospatial modeling research aimed at investigating spatial patterns and changing trends of temperature and rainfall within the geographical boundary of Bangladesh. This would facilitate better understanding the change pattern and their probable impacts on the ecosystem. The southeastern region, which is one of the most important forest ecosystem zones in the country, is experiencing early onset and withdrawal of rain but increasing trends in total rainfall except in the Monsoon season. This means that the region is experiencing a lower number of rainy days. However, total rainfall has not changed significantly. The differential between maximum and minimum showed an increasing trend. This changing pattern in average max and min temperature along with precipitation might cause a situation in which the species that are growing now may shift to suitable habitats elsewhere in the future. Consequently, the biodiversity, watersheds and fisheries, productivity of land, agriculture and food security in the region will be affected by these observed changes in climate.
Breinl, Korbinian; Di Baldassarre, Giuliano; Girons Lopez, Marc
We assess uncertainties of multi-site rainfall generation across spatial scales and different climatic conditions. Many research subjects in earth sciences such as floods, droughts or water balance simulations require the generation of long rainfall time series. In large study areas the simulation at multiple sites becomes indispensable to account for the spatial rainfall variability, but becomes more complex compared to a single site due to the intermittent nature of rainfall. Weather generators can be used for extrapolating rainfall time series, and various models have been presented in the literature. Even though the large majority of multi-site rainfall generators is based on similar methods, such as resampling techniques or Markovian processes, they often become too complex. We think that this complexity has been a limit for the application of such tools. Furthermore, the majority of multi-site rainfall generators found in the literature are either not publicly available or intended for being applied at small geographical scales, often only in temperate climates. Here we present a revised, and now publicly available, version of a multi-site rainfall generation code first applied in 2014 in Austria and France, which we call TripleM (Multisite Markov Model). We test this fast and robust code with daily rainfall observations from the United States, in a subtropical, tropical and temperate climate, using rain gauge networks with a maximum site distance above 1,000km, thereby generating one million years of synthetic time series. The modelling of these one million years takes one night on a recent desktop computer. In this research, we first start the simulations with a small station network of three sites and progressively increase the number of sites and the spatial extent, and analyze the changing uncertainties for multiple statistical metrics such as dry and wet spells, rainfall autocorrelation, lagged cross correlations and the inter-annual rainfall
Joshi, Sneh; Kar, Sarat C.
Coupled ocean atmosphere global climate models are increasingly being used for seasonal scale simulation of the South Asian monsoon. In these models, sea surface temperatures (SSTs) evolve as coupled air-sea interaction process. However, sensitivity experiments with various SST forcing can only be done in an atmosphere-only model. In this study, the Global Forecast System (GFS) model at T126 horizontal resolution has been used to examine the mechanism of El Niño-Southern Oscillation (ENSO) forcing on the monsoon circulation and rainfall. The model has been integrated (ensemble) with observed, climatological and ENSO SST forcing to document the mechanism on how the South Asian monsoon responds to basin-wide SST variations in the Indian and Pacific Oceans. The model simulations indicate that the internal variability gets modulated by the SSTs with warming in the Pacific enhancing the ensemble spread over the monsoon region as compared to cooling conditions. Anomalous easterly wind anomalies cover the Indian region both at 850 and 200 hPa levels during El Niño years. The locations and intensity of Walker and Hadley circulations are altered due to ENSO SST forcing. These lead to reduction of monsoon rainfall over most parts of India during El Niño events compared to La Niña conditions. However, internally generated variability is a major source of uncertainty in the model-simulated climate.
Stisen, Simon; Sandholt, Inge
were similar. The results showed that the Climate Prediction Center/Famine Early Warning System (CPC-FEWS) and cold cloud duration (CCD) products, which are partly based on rain gauge data and produced specifically for the African continent, performed better in the modelling context than the global......The emergence of regional and global satellite-based rainfall products with high spatial and temporal resolution has opened up new large-scale hydrological applications in data-sparse or ungauged catchments. Particularly, distributed hydrological models can benefit from the good spatial coverage...
Mateos, Vidal L.; Garcia, Jose A.; Serrano, Antonio; De la Cruz Gallego, Maria [Departamento de Fisica, Universidad de Extremadura, Badajoz (Spain)
In order to improve the results given by Autoregressive Moving-Average (ARMA) modeling for the monthly accumulated rainfall series taken at 19 observatories of the Iberian Peninsula, a Discrete Linear Transfer Function Noise (DLTFN) model was applied taking the local pressure series (LP), North Atlantic sea level pressure series (SLP) and North Atlantic sea surface temperature (SST) as input variables, and the rainfall series as the output series. In all cases, the performance of the DLTFN models, measured by the explained variance of the rainfall series, is better than the performance given by the ARMA modeling. The best performance is given by the models that take the local pressure as the input variable, followed by the sea level pressure models and the sea surface temperature models. Geographically speaking, the models fitted to those observatories located in the west of the Iberian Peninsula work better than those on the north and east of the Peninsula. Also, it was found that there is a region located between 0 N and 20 N, which shows the highest cross-correlation between SST and the peninsula rainfalls. This region moves to the west and northwest off the Peninsula when the SLP series are used. [Spanish] Con el objeto de mejorar los resultados porporcionados por los modelos Autorregresivo Media Movil (ARMA) ajustados a las precipitaciones mensuales acumuladas registradas en 19 observatorios de la Peninsula Iberica se han usado modelos de funcion de transferencia (DLTFN) en los que se han empleado como variable independiente la presion local (LP), la presion a nivel del mar (SLP) o la temperatura de agua del mar (SST) en el Atlantico Norte. En todos los casos analizados, los resultados obtenidos con los modelos DLTFN, medidos mediante la varianza explicada por el modelo, han sido mejores que los resultados proporcionados por los modelos ARMA. Los mejores resultados han sido dados por aquellos modelos que usan la presion local como variable de entrada, seguidos
Kossieris, Panagiotis; Makropoulos, Christos; Onof, Christian; Koutsoyiannis, Demetris
Many hydrological applications, such as flood studies, require the use of long rainfall data at fine time scales varying from daily down to 1 min time step. However, in the real world there is limited availability of data at sub-hourly scales. To cope with this issue, stochastic disaggregation techniques are typically employed to produce possible, statistically consistent, rainfall events that aggregate up to the field data collected at coarser scales. A methodology for the stochastic disaggregation of rainfall at fine time scales was recently introduced, combining the Bartlett-Lewis process to generate rainfall events along with adjusting procedures to modify the lower-level variables (i.e., hourly) so as to be consistent with the higher-level one (i.e., daily). In the present paper, we extend the aforementioned scheme, initially designed and tested for the disaggregation of daily rainfall into hourly depths, for any sub-hourly time scale. In addition, we take advantage of the recent developments in Poisson-cluster processes incorporating in the methodology a Bartlett-Lewis model variant that introduces dependence between cell intensity and duration in order to capture the variability of rainfall at sub-hourly time scales. The disaggregation scheme is implemented in an R package, named HyetosMinute, to support disaggregation from daily down to 1-min time scale. The applicability of the methodology was assessed on a 5-min rainfall records collected in Bochum, Germany, comparing the performance of the above mentioned model variant against the original Bartlett-Lewis process (non-random with 5 parameters). The analysis shows that the disaggregation process reproduces adequately the most important statistical characteristics of rainfall at wide range of time scales, while the introduction of the model with dependent intensity-duration results in a better performance in terms of skewness, rainfall extremes and dry proportions.
Narasimha Murthy, K. V.; Saravana, R.; Vijaya Kumar, K.
Weather forecasting is an important issue in the field of meteorology all over the world. The pattern and amount of rainfall are the essential factors that affect agricultural systems. India experiences the precious Southwest monsoon season for four months from June to September. The present paper describes an empirical study for modeling and forecasting the time series of Southwest monsoon rainfall patterns in the North-East India. The Box-Jenkins Seasonal Autoregressive Integrated Moving Average (SARIMA) methodology has been adopted for model identification, diagnostic checking and forecasting for this region. The study has shown that the SARIMA (0, 1, 1) (1, 0, 1)4 model is appropriate for analyzing and forecasting the future rainfall patterns. The Analysis of Means (ANOM) is a useful alternative to the analysis of variance (ANOVA) for comparing the group of treatments to study the variations and critical comparisons of rainfall patterns in different months of the season.
García-Estringana, Pablo; Nieves Alonso-Blazquez, M.; Alegre, Jesús; Cerdà, Artemi
Desertification can be triggered by the lost of vegetation (Izzo et al., 2013). One of the impacts of the lack of vegetation is the increase in the effective rainfall and then higher soil and water losses. Vegetation can reduce the effective rainfall by interception. To recover the land that is affected by Desertification we must select plant species that will intercept the rainfall, but will not avoid the rainfall to reach the soil. This is why, studies on the plant rainfall interception are relevant to flight Land Degradation processes. Soil erosion is highly dependent on the effective rainfall (Cerdà and Lasanta, 2005; Haile and Fetene; 2012; Miao et al., 2012, Prokop and Poręba, 2012). The amount of rainfall that reaches the soil surface and can contribute to detach and transport material is determined by the interception of plants. Interception is also a key factor of the watershed hydrology (Zema et al., 2012). The importance of the rainfall partitioning fluxes is related to the climatic conditions, as climate control the plant cover and the soil properties, and then the soil losses (Cerdà, 1998). Although the shrubs has been seen as a key vegetation cover in semiarid lands to control the soil and water losses (Cerdà and Doerr, 2007) little information is available about rainfall interception in Mediterranean shrub vegetation, due to technical difficulties to measure them in such small-sized vegetation (Belmonte Serrato and Romero Diaz, 1998). The aim of this work was to assess the influence of different Mediterranean shrubs (Retama sphaerocarpa, Colutea arborescens, Dorycnium pentaphyllum, Medicago strasseri, Pistacia Lentiscus and Quercus coccifera) on rainfall partitioning fluxes (interception losses, throughfall and stemflow) in semiarid environments. The experiment was carried out under natural rainfall conditions with live specimens during two years, with automatic measurement of rainfall partitioning fluxes. In order to assess the influence of
A. J. Pitman
Full Text Available The impact of historical land use induced land cover change (LULCC on regional-scale climate extremes is examined using four climate models within the Land Use and Climate, IDentification of robust impacts project. To assess those impacts, multiple indices based on daily maximum and minimum temperatures and daily precipitation were used. We contrast the impact of LULCC on extremes with the impact of an increase in atmospheric CO2 from 280 ppmv to 375 ppmv. In general, consistent changes in both high and low temperature extremes are similar to the simulated change in mean temperature caused by LULCC and are restricted to regions of intense modification. The impact of LULCC on both means and on most temperature extremes is statistically significant. While the magnitude of the LULCC-induced change in the extremes can be of similar magnitude to the response to the change in CO2, the impacts of LULCC are much more geographically isolated. For most models, the impacts of LULCC oppose the impact of the increase in CO2 except for one model where the CO2-caused changes in the extremes are amplified. While we find some evidence that individual models respond consistently to LULCC in the simulation of changes in rainfall and rainfall extremes, LULCC's role in affecting rainfall is much less clear and less commonly statistically significant, with the exception of a consistent impact over South East Asia. Since the simulated response of mean and extreme temperatures to LULCC is relatively large, we conclude that unless this forcing is included, we risk erroneous conclusions regarding the drivers of temperature changes over regions of intense LULCC.
Buerger, C.M.; Kolditz, O.; Fowler, H.J.; Blenkinsop, S.
Global climate change may have large impacts on water supplies, drought or flood frequencies and magnitudes in local and regional hydrologic systems. Water authorities therefore rely on computer models for quantitative impact prediction. In this study we present kernel-based learning machine river flow models for the Upper Gallego catchment of the Ebro basin. Different learning machines were calibrated using daily gauge data. The models posed two major challenges: (1) estimation of the rainfall-runoff transfer function from the available time series is complicated by anthropogenic regulation and mountainous terrain and (2) the river flow model is weak when only climate data are used, but additional antecedent flow data seemed to lead to delayed peak flow estimation. These types of models, together with the presented downscaled climate scenarios, can be used for climate change impact assessment in the Gallego, which is important for the future management of the system. - Future climate change and data-based rainfall-runoff predictions are presented for the Upper Gallego
Sagero Obaigwa Philip
Full Text Available This paper focuses on one of the high resolution models used for weather forecasting at Kenya Meteorological Department (KMD. It reviews the skill and accuracy of the Weather Research and Forecasting (WRF - Environmental Modeling System (EMS model, in simulating weather over Kenya. The study period was March to May 2011, during the rainy season over Kenya. The model output was compared with the observed data from 27 synoptic stations spread over the study area, to determine the performance of the model in terms of its skill and accuracy in forecasting. The spatial distribution of rainfall and temperature showed that the WRF model was capable of reproducing the observed general pattern especially for temperature. The model has skill in forecasting both rainfall and temperature over the study area. However, the model may underestimate rainfall of more than 10 mm/day and displace its location and overestimate rainfall of less than 1 mm/day. Therefore, during the period of enhanced rainfall especially in the month of April and part of May the model forecast needs to be complemented by other models or forecasting methods before giving a forecast. There is need to improve its performance over the domain through review of the parameterization of small scale physical processes and more observed data need to be simulated into the model.
Full Text Available Merging satellite and rain gauge data by combining accurate quantitative rainfall from stations with spatial continuous information from remote sensing observations provides a practical method of estimating rainfall. However, generating high spatiotemporal rainfall fields for catchment-distributed hydrological modeling is a problem when only a sparse rain gauge network and coarse spatial resolution of satellite data are available. The objective of the study is to present a satellite and rain gauge data-merging framework adapting for coarse resolution and data-sparse designs. In the framework, a statistical spatial downscaling method based on the relationships among precipitation, topographical features, and weather conditions was used to downscale the 0.25° daily rainfall field derived from the Tropical Rainfall Measuring Mission (TRMM Multisatellite Precipitation Analysis (TMPA precipitation product version 7. The nonparametric merging technique of double kernel smoothing, adapting for data-sparse design, was combined with the global optimization method of shuffled complex evolution, to merge the downscaled TRMM and gauged rainfall with minimum cross-validation error. An indicator field representing the presence and absence of rainfall was generated using the indicator kriging technique and applied to the previously merged result to consider the spatial intermittency of daily rainfall. The framework was applied to estimate daily precipitation at a 1 km resolution in the Qinghai Lake Basin, a data-scarce area in the northeast of the Qinghai-Tibet Plateau. The final estimates not only captured the spatial pattern of daily and annual precipitation with a relatively small estimation error, but also performed very well in stream flow simulation when applied to force the geomorphology-based hydrological model (GBHM. The proposed framework thus appears feasible for rainfall estimation at high spatiotemporal resolution in data-scarce areas.
Li, Dan; Christakos, George; Ding, Xinxin; Wu, Jiaping
Spatial rainfall data is an essential input to Distributed Hydrological Models (DHM), and a significant contributor to hydrological model uncertainty. Model uncertainty is higher when rain gauges are sparse, as is often the case in practice. Currently, satellite-based precipitation products increasingly provide an alternative means to ground-based rainfall estimates, in which case a rigorous product assessment is required before implementation. Accordingly, the twofold objective of this work paper was the real-world assessment of both (a) the Tropical Rainfall Measuring Mission (TRMM) rainfall product using gauge data, and (b) the TRMM product's role in forcing data for hydrologic simulations in the area of the Tiaoxi catchment (Taihu lake basin, China). The TRMM rainfall products used in this study are the Version-7 real-time 3B42RT and the post-real-time 3B42. It was found that the TRMM rainfall data showed a superior performance at the monthly and annual scales, fitting well with surface observation-based frequency rainfall distributions. The Nash-Sutcliffe Coefficient of Efficiency (NSCE) and the relative bias ratio (BIAS) were used to evaluate hydrologic model performance. The satisfactory performance of the monthly runoff simulations in the Tiaoxi study supports the view that the implementation of real-time 3B42RT allows considerable room for improvement. At the same time, post-real-time 3B42 can be a valuable tool of hydrologic modeling, water balance analysis, and basin water resource management, especially in developing countries or at remote locations in which rainfall gauges are scarce.
von Ruette, Jonas; Papritz, Andreas; Lehmann, Peter; Rickli, Christian; Or, Dani
Statistical models that exploit the correlation between landslide occurrence and geomorphic properties are often used to map the spatial occurrence of shallow landslides triggered by heavy rainfalls. In many landslide susceptibility studies, the true predictive power of the statistical model remains unknown because the predictions are not validated with independent data from other events or areas. This study validates statistical susceptibility predictions with independent test data. The spatial incidence of landslides, triggered by an extreme rainfall in a study area, was modeled by logistic regression. The fitted model was then used to generate susceptibility maps for another three study areas, for which event-based landslide inventories were also available. All the study areas lie in the northern foothills of the Swiss Alps. The landslides had been triggered by heavy rainfall either in 2002 or 2005. The validation was designed such that the first validation study area shared the geomorphology and the second the triggering rainfall event with the calibration study area. For the third validation study area, both geomorphology and rainfall were different. All explanatory variables were extracted for the logistic regression analysis from high-resolution digital elevation and surface models (2.5 m grid). The model fitted to the calibration data comprised four explanatory variables: (i) slope angle (effect of gravitational driving forces), (ii) vegetation type (grassland and forest; root reinforcement), (iii) planform curvature (convergent water flow paths), and (iv) contributing area (potential supply of water). The area under the Receiver Operating Characteristic (ROC) curve ( AUC) was used to quantify the predictive performance of the logistic regression model. The AUC values were computed for the susceptibility maps of the three validation study areas (validation AUC), the fitted susceptibility map of the calibration study area (apparent AUC: 0.80) and another
Yang, Pan; Ng, Tze Ling
Accurate rainfall measurement at high spatial and temporal resolutions is critical for the modeling and management of urban storm water. In this study, we conduct computer simulation experiments to test the potential of a crowd-sourcing approach, where smartphones, surveillance cameras, and other devices act as precipitation sensors, as an alternative to the traditional approach of using rain gauges to monitor urban rainfall. The crowd-sourcing approach is promising as it has the potential to provide high-density measurements, albeit with relatively large individual errors. We explore the potential of this approach for urban rainfall monitoring and the subsequent implications for storm water modeling through a series of simulation experiments involving synthetically generated crowd-sourced rainfall data and a storm water model. The results show that even under conservative assumptions, crowd-sourced rainfall data lead to more accurate modeling of storm water flows as compared to rain gauge data. We observe the relative superiority of the crowd-sourcing approach to vary depending on crowd participation rate, measurement accuracy, drainage area, choice of performance statistic, and crowd-sourced observation type. A possible reason for our findings is the differences between the error structures of crowd-sourced and rain gauge rainfall fields resulting from the differences between the errors and densities of the raw measurement data underlying the two field types.
The water resources satisfaction index (WRSI) model is widely used in drought early warning and food security analyses, as well as in agro-meteorological risk management through weather index-based insurance. Key driving data for the model is provided from satellite-based rainfall estimates such as ARC2 and TAMSAT over Africa and CHIRPS globally. We evaluate the performance of these rainfall datasets for detecting onset and cessation of rainfall and estimating crop production conditions for the WRSI model. We also examine the sensitivity of the WRSI model to different satellite-based rainfall products over maize growing regions in Tanzania. Our study considers planting scenarios for short-, medium-, and long-growing cycle maize, and we apply these for 'regular' and drought-resistant maize, as well as with two different methods for defining the start of season (SOS). Simulated maize production estimates are compared against available reported production figures at the national and sub-national (province) levels. Strengths and weaknesses of the driving rainfall data, insights into the role of the SOS definition method, and phenology-based crop yield coefficient and crop yield reduction functions are discussed in the context of space-time drought characteristics. We propose a way forward for selecting skilled rainfall datasets and discuss their implication for crop production monitoring and the design and structure of weather index-based insurance products as risk transfer mechanisms implemented across scales for smallholder farmers to national programmes.
Chua, L. H. C.
Mathematical models require the estimation of rainfall abstractions for accurate predictions of runoff. Although loss models such as the constant loss and exponential loss models are commonly used, these methods are based on simplified assumptions of the physical process. A new approach based on the data driven paradigm to estimate rainfall abstractions is proposed in this paper. The proposed data driven model, based on the artificial neural network (ANN) does not make any assumptions on the loss behavior. The estimated discharge from a physically-based model, obtained from the kinematic wave (KW) model assuming zero losses, was used as the only input to the ANN. The output is the measured discharge. Thus, the ANN functions as a black-box loss model. Two sets of data were analyzed for this study. The first dataset consists of rainfall and runoff data, measured from an artificial catchment (area = 25 m2) comprising two overland planes (slope = 11%), 25m long, transversely inclined towards a rectangular channel (slope = 2%) which conveyed the flow, recorded using calibrated weigh tanks, to the outlet. Two rain gauges, each placed 6.25 m from either ends of the channel, were used to record rainfall. Data for six storm events over the period between October 2002 and December 2002 were analyzed. The second dataset was obtained from the Upper Bukit Timah catchment (area = 6.4 km2) instrumented with two rain gauges and a flow measuring station. A total of six events recorded between November 1987 and July 1988 were selected for this study. The runoff predicted by the ANN was compared with the measured runoff. In addition, results from KW models developed for both the catchments were used as a benchmark. The KW models were calibrated assuming the loss rate for an average event for each of the datasets. The results from both the ANN and KW models agreed well with the runoff measured from the artificial catchment. The KW model is expected to perform well since the catchment
Full Text Available This study presented a method to estimate areal mean rainfall (AMR using a Biased Sentinel Hospital Based Area Disease Estimation (B-SHADE model, together with biased rain gauge observations and Tropical Rainfall Measuring Mission (TRMM data, for remote areas with a sparse and uneven distribution of rain gauges. Based on the B-SHADE model, the best linear unbiased estimation of AMR could be obtained. A case study was conducted for the Three-River Headwaters region in the Tibetan Plateau of China, and its performance was compared with traditional methods. The results indicated that B-SHADE obtained the least estimation biases, with a mean error and root mean square error of −0.63 and 3.48 mm, respectively. For the traditional methods including arithmetic average, Thiessen polygon, and ordinary kriging, the mean errors were 7.11, −1.43, and 2.89 mm, which were up to 1027.1%, 127.0%, and 358.3%, respectively, greater than for the B-SHADE model. The root mean square errors were 10.31, 4.02, and 6.27 mm, which were up to 196.1%, 15.5%, and 80.0%, respectively, higher than for the B-SHADE model. The proposed technique can be used to extend the AMR record to the presatellite observation period, when only the gauge data are available.
Flynn, D. P.; O'Kane, J. P.
Multiphase porous media such as soils are known to exhibit hysteresis, e.g. in soils there is a strong hysteretic relationship between the moisture content and the matric potential and to date the Preisach model has been successful in modelling this relationship. Subsequently ODEs with Preisach hysteresis have been developed, such as a hysteretic version of Darcy's law and a hysteretic version of the linear reservoir known as the Preisach reservoir. In this paper we combine the above Hysteretic Differential Equations (HDEs) with three linear reservoirs so as to develop a simple rainfall runoff model. The model can be represented by a block diagram: Rainfall q(t) enters the soil component and either infiltrates and/or runs off when it exceeds the maximum rate of infiltration. The runoff part is fed into two linear reservoirs in series. Next, the drainage from the soil to groundwater is represented by a single linear reservoir, where the output from the soil becomes the input to the ground reservoir and vice-versa for capillary rise. Finally the groundwater and surface runoff are combined at some point and contribute to the total outflow from the catchment. Finally we investigate the effects of hysteresis in this system and compare it to the non-hysteretic case.
Full Text Available Watershed characteristics such as patterns of land use and land cover (LULC, soil structure and river systems, have substantially changed due to natural and anthropogenic factors. To adapt hydrological models to the changing characteristics of watersheds, one of the feasible strategies is to explicitly estimate the changed parameters. However, few approaches have been dedicated to these non-stationary conditions. In this study, we employ an ensemble Kalman filter (EnKF technique with a constrained parameter evolution scheme to trace the parameter changes. This technique is coupled to a rainfall-runoff model, i.e., the Xinanjiang (XAJ model. In addition to a stationary condition, we designed three typical non-stationary conditions, including sudden, gradual and rotational changes with respect to two behavioral parameters of the XAJ. Synthetic experiments demonstrated that the EnKF-based method can trace the three types of parameter changes in real time. This method shows robust performance even for the scenarios of high-level uncertainties within rainfall input, modeling and observations, and it holds an implication for detecting changes in watershed characteristics. Coupling this method with a rainfall-runoff model is useful to adapt the model to non-stationary conditions, thereby improving flood simulations and predictions.
Full Text Available Recently, D'Odorico and Fagherazzi (2003 proposed "A probabilistic model of rainfall-triggered shallow landslides in hollows" (Water Resour. Res., 39, 2003. Their model describes the long-term evolution of colluvial deposits through a probabilistic soil mass balance at a point. Further building blocks of the model are: an infinite-slope stability analysis; a steady-state kinematic wave model (KW of hollow groundwater hydrology; and a statistical model relating intensity, duration, and frequency of extreme precipitation. Here we extend the work of D'Odorico and Fagherazzi (2003 by incorporating a more realistic description of hollow hydrology (hillslope storage Boussinesq model, HSB such that this model can also be applied to more gentle slopes and hollows with different plan shapes. We show that results obtained using the KW and HSB models are significantly different as in the KW model the diffusion term is ignored. We generalize our results by examining the stability of several hollow types with different plan shapes (different convergence degree. For each hollow type, the minimum value of the landslide-triggering saturated depth corresponding to the triggering precipitation (critical recharge rate is computed for steep and gentle hollows. Long term analysis of shallow landslides by the presented model illustrates that all hollows show a quite different behavior from the stability view point. In hollows with more convergence, landslide occurrence is limited by the supply of deposits (supply limited regime or rainfall events (event limited regime while hollows with low convergence degree are unconditionally stable regardless of the soil thickness or rainfall intensity. Overall, our results show that in addition to the effect of slope angle, plan shape (convergence degree also controls the subsurface flow and this process affects the probability distribution of landslide occurrence in different hollows. Finally, we conclude that
Martinez, Gonzalo; Pachepsky, Yakov A; Whelan, Gene; Yakirevich, Alexander M; Guber, Andrey; Gish, Timothy J
Microbial quality of surface waters attracts attention due to food- and waterborne disease outbreaks. Fecal indicator organisms (FIOs) are commonly used for the microbial pollution level evaluation. Models predicting the fate and transport of FIOs are required to design and evaluate best management practices that reduce the microbial pollution in ecosystems and water sources and thus help to predict the risk of food and waterborne diseases. In this study we performed a sensitivity analysis for the KINEROS/STWIR model developed to predict the FIOs transport out of manured fields to other fields and water bodies in order to identify input variables that control the transport uncertainty. The distributions of model input parameters were set to encompass values found from three-year experiments at the USDA-ARS OPE3 experimental site in Beltsville and publicly available information. Sobol' indices and complementary regression trees were used to perform the global sensitivity analysis of the model and to explore the interactions between model input parameters on the proportion of FIO removed from fields. Regression trees provided a useful visualization of the differences in sensitivity of the model output in different parts of the input variable domain. Environmental controls such as soil saturation, rainfall duration and rainfall intensity had the largest influence in the model behavior, whereas soil and manure properties ranked lower. The field length had only moderate effect on the model output sensitivity to the model inputs. Among the manure-related properties the parameter determining the shape of the FIO release kinetic curve had the largest influence on the removal of FIOs from the fields. That underscored the need to better characterize the FIO release kinetics. Since the most sensitive model inputs are available in soil and weather databases or can be obtained using soil water models, results indicate the opportunity of obtaining large-scale estimates of FIO
Chlumecký, M.; Buchtele, Josef; Richta, K.
Roč. 553, October (2017), s. 350-355 ISSN 0022-1694 Institutional support: RVO:67985874 Keywords : genetic algorithm * optimisation * rainfall-runoff modeling * random generator Subject RIV: DA - Hydrology ; Limnology OBOR OECD: Hydrology Impact factor: 3.483, year: 2016 https://ac.els-cdn.com/S0022169417305516/1-s2.0-S0022169417305516-main.pdf?_tid=fa1bad8a-bd6a-11e7-8567-00000aab0f27&acdnat=1509365462_a1335d3d997e9eab19e23b1eee977705
Hazenberg, P.; Leijnse, H.; Uijlenhoet, R.; Delobbe, L.; Weerts, A.; Reggiani, P.
In the current study half a year of volumetric radar data for the period October 1, 2002 until March 31, 2003 is being analyzed which was sampled at 5 minutes intervals by C-band Doppler radar situated at an elevation of 600 m in the southern Ardennes region, Belgium. During this winter half year most of the rainfall has a stratiform character. Though radar and raingauge will never sample the same amount of rainfall due to differences in sampling strategies, for these stratiform situations differences between both measuring devices become even larger due to the occurrence of a bright band (the point where ice particles start to melt intensifying the radar reflectivity measurement). For these circumstances the radar overestimates the amount of precipitation and because in the Ardennes bright bands occur within 1000 meter from the surface, it's detrimental effects on the performance of the radar can already be observed at relatively close range (e.g. within 50 km). Although the radar is situated at one of the highest points in the region, very close to the radar clutter is a serious problem. As a result both nearby and farther away, using uncorrected radar results in serious errors when estimating the amount of precipitation. This study shows the effect of carefully correcting for these radar errors using volumetric radar data, taking into account the vertical reflectivity profile of the atmosphere, the effects of attenuation and trying to limit the amount of clutter. After applying these correction algorithms, the overall differences between radar and raingauge are much smaller which emphasizes the importance of carefully correcting radar rainfall measurements. The next step is to assess the effect of using uncorrected and corrected radar measurements on rainfall-runoff modeling. The 1597 km2 Ourthe catchment lies within 60 km of the radar. Using a lumped hydrological model serious improvement in simulating observed discharges is found when using corrected radar
Chlumecký, M.; Buchtele, Josef; Richta, K.
Roč. 553, October (2017), s. 350-355 ISSN 0022-1694 Institutional support: RVO:67985874 Keywords : genetic algorithm * optimisation * rainfall-runoff modeling * random generator Subject RIV: DA - Hydrology ; Limnology OBOR OECD: Hydrology Impact factor: 3.483, year: 2016 https://ac.els- cdn .com/S0022169417305516/1-s2.0-S0022169417305516-main.pdf?_tid=fa1bad8a-bd6a-11e7-8567-00000aab0f27& amp ;acdnat=1509365462_a1335d3d997e9eab19e23b1eee977705
Luitel, Beda; Villarini, Gabriele; Vecchi, Gabriel A.
The goal of this study is the evaluation of the skill of five state-of-the-art numerical weather prediction (NWP) systems [European Centre for Medium-Range Weather Forecasts (ECMWF), UK Met Office (UKMO), National Centers for Environmental Prediction (NCEP), China Meteorological Administration (CMA), and Canadian Meteorological Center (CMC)] in forecasting rainfall from North Atlantic tropical cyclones (TCs). Analyses focus on 15 North Atlantic TCs that made landfall along the U.S. coast over the 2007-2012 period. As reference data we use gridded rainfall provided by the Climate Prediction Center (CPC). We consider forecast lead-times up to five days. To benchmark the skill of these models, we consider rainfall estimates from one radar-based (Stage IV) and four satellite-based [Tropical Rainfall Measuring Mission - Multi-satellite Precipitation Analysis (TMPA, both real-time and research version); Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); the CPC MORPHing Technique (CMORPH)] rainfall products. Daily and storm total rainfall fields from each of these remote sensing products are compared to the reference data to obtain information about the range of errors we can expect from "observational data." The skill of the NWP models is quantified: (1) by visual examination of the distribution of the errors in storm total rainfall for the different lead-times, and numerical examination of the first three moments of the error distribution; (2) relative to climatology at the daily scale. Considering these skill metrics, we conclude that the NWP models can provide skillful forecasts of TC rainfall with lead-times up to 48 h, without a consistently best or worst NWP model.
Full Text Available Conventional outputs of physics-based landslide forecasting models are presented as deterministic warnings by calculating the safety factor (Fs of potentially dangerous slopes. However, these models are highly dependent on variables such as cohesion force and internal friction angle which are affected by a high degree of uncertainty especially at a regional scale, resulting in unacceptable uncertainties of Fs. Under such circumstances, the outputs of physical models are more suitable if presented in the form of landslide probability values. In order to develop such models, a method to link the uncertainty of soil parameter values with landslide probability is devised. This paper proposes the use of Monte Carlo methods to quantitatively express uncertainty by assigning random values to physical variables inside a defined interval. The inequality Fs < 1 is tested for each pixel in n simulations which are integrated in a unique parameter. This parameter links the landslide probability to the uncertainties of soil mechanical parameters and is used to create a physics-based probabilistic forecasting model for rainfall-induced shallow landslides. The prediction ability of this model was tested in a case study, in which simulated forecasting of landslide disasters associated with heavy rainfalls on 9 July 2013 in the Wenchuan earthquake region of Sichuan province, China, was performed. The proposed model successfully forecasted landslides in 159 of the 176 disaster points registered by the geo-environmental monitoring station of Sichuan province. Such testing results indicate that the new model can be operated in a highly efficient way and show more reliable results, attributable to its high prediction accuracy. Accordingly, the new model can be potentially packaged into a forecasting system for shallow landslides providing technological support for the mitigation of these disasters at regional scale.
Chen, Yu-Wen; Wang, Yetmen; Chang, Liang-Cheng
Groundwater resources play a vital role on regional supply. To avoid irreversible environmental impact such as land subsidence, the characteristic identification of groundwater system is crucial before sustainable management of groundwater resource. This study proposes a signal process approach to identify the character of groundwater systems based on long-time hydrologic observations include groundwater level and rainfall. The study process contains two steps. First, a linear signal model (LSM) is constructed and calibrated to simulate the variation of underground hydrology based on the time series of groundwater levels and rainfall. The mass balance equation of the proposed LSM contains three major terms contain net rate of horizontal exchange, rate of rainfall recharge and rate of pumpage and four parameters are required to calibrate. Because reliable records of pumpage is rare, the time-variant groundwater amplitudes of daily frequency (P ) calculated by STFT are assumed as linear indicators of puamage instead of pumpage records. Time series obtained from 39 observation wells and 50 rainfall stations in and around the study area, Pintung Plain, are paired for model construction. Second, the well-calibrated parameters of the linear signal model can be used to interpret the characteristic of groundwater system. For example, the rainfall recharge coefficient (γ) means the transform ratio between rainfall intention and groundwater level raise. The area around the observation well with higher γ means that the saturated zone here is easily affected by rainfall events and the material of unsaturated zone might be gravel or coarse sand with high infiltration ratio. Considering the spatial distribution of γ, the values of γ decrease from the upstream to the downstream of major rivers and also are correlated to the spatial distribution of grain size of surface soil. Via the time-series of groundwater levels and rainfall, the well-calibrated parameters of LSM have
Parker, Hannah R.; Lott, Fraser C.; Cornforth, Rosalind J.; Mitchell, Daniel M.; Sparrow, Sarah; Wallom, David
In 2012, heavy rainfall resulted in flooding and devastating impacts across West Africa. With many people highly vulnerable to such events in this region, this study investigates whether anthropogenic climate change has influenced such heavy precipitation events. We use a probabilistic event attribution approach to assess the contribution of anthropogenic greenhouse gas emissions, by comparing the probability of such an event occurring in climate model simulations with all known climate forcings to those where natural forcings only are simulated. An ensemble of simulations from 10 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) is compared to two much larger ensembles of atmosphere-only simulations, from the Met Office model HadGEM3-A and from weather@home with a regional version of HadAM3P. These are used to assess whether the choice of model ensemble influences the attribution statement that can be made. Results show that anthropogenic greenhouse gas emissions have decreased the probability of high precipitation across most of the model ensembles. However, the magnitude and confidence intervals of the decrease depend on the ensemble used, with more certainty in the magnitude in the atmosphere-only model ensembles due to larger ensemble sizes from single models with more constrained simulations. Certainty is greatly decreased when considering a CMIP5 ensemble that can represent the relevant teleconnections due to a decrease in ensemble members. An increase in probability of high precipitation in HadGEM3-A using the observed trend in sea surface temperatures (SSTs) for natural simulations highlights the need to ensure that estimates of natural SSTs are consistent with observed trends in order for results to be robust. Further work is needed to establish how anthropogenic forcings are affecting the rainfall processes in these simulations in order to better understand the differences in the overall effect.
H. J. Fowler
Full Text Available A stochastic model is developed for the synthesis of daily precipitation using conditioning by weather types. Daily precipitation statistics at multiple sites within the region of Yorkshire, UK, are linked to objective Lamb weather types (LWTs and used to split the region into three distinct precipitation sub-regions. Using a variance minimisation criterion, the 27 LWTs are clustered into three physically realistic groups or ‘states'. A semi-Markov chain model is used to synthesise long sequences of weather states, maintaining the observed persistence and transition probabilities. The Neyman-Scott Rectangular Pulses (NSRP model is then fitted for each weather state, using a defined summer and winter period. The combined model reproduces key aspects of the historic precipitation regime at temporal resolutions down to the hourly level. Long synthetic precipitation series are useful in the sensitivity analysis of water resource systems under current and changed climatic conditions. This methodology enables investigation of the impact of variations in weather type persistence or frequency. In addition, rainfall model statistics can be altered to simulate instances of increased intensity or proportion of dry days for example, for individual weather groups. The input of such data into a water resource model, simulating potential atmospheric circulation changes, will provide a valuable tool for future planning of water resource systems. The ability of the model to operate at an hourly level also allows its use in a wider range of hydrological impact studies, e.g. variations in river flows, flood risk estimation etc. Keywords: water resources; climate change; impacts; stochastic rainfall model; Lamb weather types
Dakhlaoui, H.; Ruelland, D.; Tramblay, Y.; Bargaoui, Z.
To evaluate the impact of climate change on water resources at the catchment scale, not only future projections of climate are necessary but also robust rainfall-runoff models that must be fairly reliable under changing climate conditions. The aim of this study was thus to assess the robustness of three conceptual rainfall-runoff models (GR4j, HBV and IHACRES) on five basins in northern Tunisia under long-term climate variability, in the light of available future climate scenarios for this region. The robustness of the models was evaluated using a differential split sample test based on a climate classification of the observation period that simultaneously accounted for precipitation and temperature conditions. The study catchments include the main hydrographical basins in northern Tunisia, which produce most of the surface water resources in the country. A 30-year period (1970-2000) was used to capture a wide range of hydro-climatic conditions. The calibration was based on the Kling-Gupta Efficiency (KGE) criterion, while model transferability was evaluated based on the Nash-Sutcliffe efficiency criterion and volume error. The three hydrological models were shown to behave similarly under climate variability. The models simulated the runoff pattern better when transferred to wetter and colder conditions than to drier and warmer ones. It was shown that their robustness became unacceptable when climate conditions involved a decrease of more than 25% in annual precipitation and an increase of more than +1.75 °C in annual mean temperatures. The reduction in model robustness may be partly due to the climate dependence of some parameters. When compared to precipitation and temperature projections in the region, the limits of transferability obtained in this study are generally respected for short and middle term. For long term projections under the most pessimistic emission gas scenarios, the limits of transferability are generally not respected, which may hamper the
Arnone, E.; Noto, L. V.; Lepore, C.; Bras, R. L.
Landslides are a serious threat to lives and property throughout the world. Over the last few years the need to provide consistent tools and support to decision-makers and land managers have led to significant progress in the analysis and understanding of the occurrence of landslides. The causes of landslides are varied. Multiple dynamic processes are involved in driving slope failures. One of these causes is prolonged rainfall, which affect slope stability in different ways. Water entering the ground beneath a slope always causes a rise of the piezometric surface, which in turn involves an increase of the pore-water pressure and a decrease of the soil shear resistance. For this reason, knowledge of spatio-temporal dynamics of soil water content, groundwater and infiltration processes is of considerable importance in the understanding and prediction of landslides dynamics. Many methods and techniques have been proposed to estimate when and where rainfall could trigger slope failure. In this paper a spatially distributed and physically based approach is presented, which integrates of a failure model with an hydrological one. The hydrological model used in the study is the tRIBS model (Triangulated Irregular Network (TIN-based) Real-Time Integrated Basin Simulator) that allows simulation of spatial and temporal hydrological dynamics influencing the landsliding, in particular infiltration, evapotranspiration, groundwater dynamics and soil moisture conditions. In order to evaluate the slope stability, the infinite slope model has been implemented in tRIBS, making up a new component of the model. For each computational element, the model is able to verify the stability condition as a function of the safety factor, splitting between the unconditionally stable and the conditionally stable computational cells. The amount of detached soil and its possible path are also estimated. The variations in elevation due to the landslides modify the basin morphology. The
Full Text Available Data scarcity and model over-parameterisation, leading to model equifinality and large prediction uncertainty, are common barriers to effective hydrological modelling. The problem can be alleviated by constraining the prior parameter space using parameter regionalisation. A common basis for regionalisation in the UK is the HOST database which provides estimates of hydrological indices for different soil classifications. In our study, Base Flow Index is estimated from the HOST database and the power of this index for constraining the parameter space is explored. The method is applied to a highly discretised distributed model of a 12.5 km2 upland catchment in Wales. To assess probabilistic predictions against flow observations, a probabilistic version of the Nash-Sutcliffe efficiency is derived. For six flow gauges with reliable data, this efficiency ranged between 0.70 and 0.81, and inspection of the results shows that the model explains the data well. Knowledge of how Base Flow Index and interception losses may change under future land use management interventions was then used to further condition the model. Two interventions are considered: afforestation of grazed areas, and soil degradation associated with increased grazing intensity. Afforestation leads to median reduction in modelled runoff volume of 24% over the simulated 3 month period; and a median peak flow reduction ranging from 12 to 15% over the six gauges for the largest simulated event. Uncertainty in all results is low compared to prior uncertainty and it is concluded that using Base Flow Index estimated from HOST is a simple and potentially powerful method of conditioning the parameter space under current and future land management.
Asquith, W.H.; Bumgarner, J.R.; Fahlquist, L.S.
A synthetic triangular hyetograph for a large data base of Texas rainfall and runoff is needed. A hyetograph represents the temporal distribution of rainfall intensity at a point or over a watershed during a storm. Synthetic hyetographs are estimates of the expected time distribution for a design storm and principally are used in small watershed hydraulic structure design. A data base of more than 1,600 observed cumulative hyetographs that produced runoff from 91 small watersheds (generally less than about 50 km2) was used to provide statistical parameters for a simple triangular shaped hyetograph model. The model provides an estimate of the average hyetograph in dimensionless form for storm durations of 0 to 24 hours and 24 to 72 hours. As a result of this study, the authors concluded that the expected dimensionless cumulative hyetographs of 0 to 12 hour and 12 to 24 hour durations were sufficiently similar to be combined with minimal information loss. The analysis also suggests that dimensionless cumulative hyetographs are independent of the frequency level or return period of total storm depth and thus are readily used for many design applications. The two triangular hyetographs presented are intended to enhance small watershed design practice in applicable parts of Texas.
A. E. Sikorska
Full Text Available Urbanization and the resulting land-use change strongly affect the water cycle and runoff-processes in watersheds. Unfortunately, small urban watersheds, which are most affected by urban sprawl, are mostly ungauged. This makes it intrinsically difficult to assess the consequences of urbanization. Most of all, it is unclear how to reliably assess the predictive uncertainty given the structural deficits of the applied models. In this study, we therefore investigate the uncertainty of flood predictions in ungauged urban basins from structurally uncertain rainfall-runoff models. To this end, we suggest a procedure to explicitly account for input uncertainty and model structure deficits using Bayesian statistics with a continuous-time autoregressive error model. In addition, we propose a concise procedure to derive prior parameter distributions from base data and successfully apply the methodology to an urban catchment in Warsaw, Poland. Based on our results, we are able to demonstrate that the autoregressive error model greatly helps to meet the statistical assumptions and to compute reliable prediction intervals. In our study, we found that predicted peak flows were up to 7 times higher than observations. This was reduced to 5 times with Bayesian updating, using only few discharge measurements. In addition, our analysis suggests that imprecise rainfall information and model structure deficits contribute mostly to the total prediction uncertainty. In the future, flood predictions in ungauged basins will become more important due to ongoing urbanization as well as anthropogenic and climatic changes. Thus, providing reliable measures of uncertainty is crucial to support decision making.
Cardozo, Nilceu P.; Sentelhas, Paulo C.; Panosso, Alan R.; Palhares, Antonio L.; Ide, Bernardo Y.
The effect of weather variables on sugarcane ripening is a process still not completely understood, despite its huge impact on the quality of raw material for the sugar energy industry. The aim of the present study was to evaluate the influence of weather variables on sugarcane ripening in southern Brazil, propose empirical models for estimating total recoverable sugar (TRS) content, and evaluate the performance of these models with experimental and commercial independent data from different regions. A field experiment was carried out in Piracicaba, in the state of São Paulo, Brazil, considering eight sugarcane cultivars planted monthly, from March to October 2002. In 2003, at the harvest, 12 months later, samples were collected to evaluate TRS (kg t-1). TRS and weather variables (air temperature, solar radiation, relative humidity, and rainfall) were analyzed using descriptive and multivariate statistical analysis to understand their interactions. From these correlations, variables were selected to generate empirical models for estimating TRS, according to the cultivar groups and their ripening characteristics (early, mid, and late). These models were evaluated by residual analysis and regression analysis with independent experimental data from two other locations in the same years and with independent commercial data from six different locations from 2005 to 2010. The best performances were found with exponential models which considered cumulative rainfall during the 120 days before harvest as an independent variable ( R 2 adj ranging from 0.92 to 0.95). Independent evaluations revealed that our models were capable of estimating TRS with reasonable to high precision ( R 2 adj ranging from 0.66 to 0.99) and accuracy ( D index ranging from 0.90 to 0.99), and with low mean absolute percentage errors (MAPE ≤ 5 %), even in regions with different climatic conditions.
Sagalgile, Archana P.; Chowdary, Jasti S.; Srinivas, G.; Gnanaseelan, C.; Parekh, Anant; Attada, Raju; Singh, Prem
This study investigates predictability of the sub-seasonal Indian summer monsoon (ISM) circulation and its relation with rainfall variations in the coupled model National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2). Hindcasts based on CFSv2 for the period of 1982-2009 are used for detailed analysis. Though the model is capable of predicting the seasonal ISM rainfall at long lead months, the predication skill of the model for sub-seasonal rainfall in general is poor for short and long lead except for September. Rainfall over the ISM region/Indian Subcontinent is highly correlated with the low-level jet (LLJ) or Somali jet both in the observations and the model. The model displays improved skill in predicting LLJ as compared to precipitation in seasonal mean and September, whereas the model skill is poor for June and August. Detailed analysis reveals that the model LLJ variations throughout the season are overdependent on the El Niño-Southern Oscillation (ENSO) unlike in the observations. This is mainly responsible for the model's low skill in predicting LLJ especially in July and August, which is the primary cause for the poor rainfall skill. Though LLJ is weak in September, the model skill is reasonably good because of its ENSO dependency both in model and the observations and which is contributed to the seasonal mean skill. Thus, to improve the skill of seasonal mean monsoon forecast, it is essential to improve the skill of individual months/sub-seasonal circulation and rainfall skill.
Mufeti, P.; Rientjes, T.H.M.; Mabande, P.; Maathuis, B.H.P.
Applications of distributed hydrological models are often constrained by poor data availability. Models rely on distributed inputs for meteorological forcing and land surface parameterization. In this pilot the rainfall runoff model LISFLOOD for large scale streamflow simulation is tested for the
Full Text Available Rainfall-runoff models are crucial tools for the statistical prediction of flash floods and real-time forecasting. This paper focuses on a karstic basin in the South of France and proposes a distributed parsimonious event-based rainfall-runoff model, coherent with the poor knowledge of both evaporative and underground fluxes. The model combines a SCS runoff model and a Lag and Route routing model for each cell of a regular grid mesh. The efficiency of the model is discussed not only to satisfactorily simulate floods but also to get powerful relationships between the initial condition of the model and various predictors of the initial wetness state of the basin, such as the base flow, the Hu2 index from the Meteo-France SIM model and the piezometric levels of the aquifer. The advantage of using meteorological radar rainfall in flood modelling is also assessed. Model calibration proved to be satisfactory by using an hourly time step with Nash criterion values, ranging between 0.66 and 0.94 for eighteen of the twenty-one selected events. The radar rainfall inputs significantly improved the simulations or the assessment of the initial condition of the model for 5 events at the beginning of autumn, mostly in September–October (mean improvement of Nash is 0.09; correction in the initial condition ranges from −205 to 124 mm, but were less efficient for the events at the end of autumn. In this period, the weak vertical extension of the precipitation system and the low altitude of the 0 °C isotherm could affect the efficiency of radar measurements due to the distance between the basin and the radar (~60 km. The model initial condition S is correlated with the three tested predictors (R2 > 0.6. The interpretation of the model suggests that groundwater does not affect the first peaks of the flood, but can strongly impact subsequent peaks in the case of a multi-storm event. Because this kind of model is based on a limited
Sagalgile, Archana P.
This study investigates predictability of the sub-seasonal Indian summer monsoon (ISM) circulation and its relation with rainfall variations in the coupled model National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2). Hindcasts based on CFSv2 for the period of 1982–2009 are used for detailed analysis. Though the model is capable of predicting the seasonal ISM rainfall at long lead months, the predication skill of the model for sub-seasonal rainfall in general is poor for short and long lead except for September. Rainfall over the ISM region/Indian Subcontinent is highly correlated with the low-level jet (LLJ) or Somali jet both in the observations and the model. The model displays improved skill in predicting LLJ as compared to precipitation in seasonal mean and September, whereas the model skill is poor for June and August. Detailed analysis reveals that the model LLJ variations throughout the season are overdependent on the El Niño-Southern Oscillation (ENSO) unlike in the observations. This is mainly responsible for the model’s low skill in predicting LLJ especially in July and August, which is the primary cause for the poor rainfall skill. Though LLJ is weak in September, the model skill is reasonably good because of its ENSO dependency both in model and the observations and which is contributed to the seasonal mean skill. Thus, to improve the skill of seasonal mean monsoon forecast, it is essential to improve the skill of individual months/sub-seasonal circulation and rainfall skill.
Gelati, E.; Dommar, C.; Lowe, R.; Polcher, J.; Rodó, X.
We present a lumped rainfall-runoff model aimed at providing useful information for the prediction of waterborne viral contamination in small rivers. Viral contamination of water bodies may occur because of the discharge of sewage effluents and of surface runoff over areas affected by animal waste loads. Surface runoff is caused by precipitation that cannot infiltrate due to its intensity and to antecedent soil water content. It may transport animal feces to adjacent water bodies and cause viral contamination. We model streamflow by separating it into two components: subsurface flow, which is produced by infiltrated precipitation; and surface runoff. The model estimates infiltrated and non-infiltrated precipitation and uses impulse-response functions to compute the corresponding fractions of streamflow. The developed methodologies are applied to the Glafkos river, whose catchment extends for 102 km2 and includes the city of Patra. Streamflow and precipitation observations are available at a daily time resolution. Waterborne virus concentration measurements were performed approximately every second week from the beginning of 2011 to mid 2012. Samples were taken at several locations: in river water upstream of Patras and in the urban area; in sea water at the river outlet and approximately 2 km south-west of Patras; in sewage effluents before and after treatment. The rainfall-runoff model was calibrated and validated using observed streamflow and precipitation data. The model contribution to waterborne viral contamination prediction was benchmarked by analyzing the virus concentration measurements together with the estimated surface runoff values. The presented methodology may be a first step towards the development of waterborne viral contamination alert systems. Predicting viral contamination of water bodies would benefit sectors such as water supply and tourism.
Egüen, M.; Aguilar, C.; Solari, S.; Losada, M. A.
In areas in which natural water resources are variable over time, tools that determine the probability distribution of hydrological variables are required to evaluate various management alternatives. In this article, a stochastic simulation framework of hydrological variables through atmospheric pressure modeling is proposed. This methodology employs the mean value of the atmospheric pressure in the winter to differentiate the wet, medium and dry years in terms of rainfall and flow at different temporal scales. Monthly mean and daily maximum rainfall and flow data series are stochastically replicated. To achieve this replication, a non-stationary parametric mixture distribution model that combines a Weibull and a Normal distribution is fitted to the univariate distribution of the atmospheric pressure. This model includes interannual variability through two covariables: extraterrestrial solar radiation and the NAO index. This model is applied to the Guadalete River Basin in southern Spain, in which the river flow regime is influenced by the highly seasonal precipitation regime typically found in the Mediterranean area. The non-stationary parametric mixture distribution model with the two covariables showed a good fit to the observed sea level pressure, displaying an important reduction on the BIC. A good correlation was obtained between the average sea level pressure in winter and the accumulated precipitation and flow (r = -0.8 for monthly values and -0.6 for maximum daily values). The statistical similarity indicated that the synthetic series of precipitation and flow preserved the distribution trends in the observed data. The identical methodology can be applied in other watersheds once the direct relationship between the mean atmospheric pressure and the hydrology of the area is known.
Yeung, Chiu W.
The U.S. Geological Survey's Precipitation-Runoff Modeling System (PRMS) and a generalized water-balance model were calibrated and verified for use in estimating future availability of water in the Fena Valley Reservoir in response to various combinations of water withdrawal rates and rainfall conditions. Application of PRMS provides a physically based method for estimating runoff from the Fena Valley Watershed during the annual dry season, which extends from January through May. Runoff estimates from the PRMS are used as input to the water-balance model to estimate change in water levels and storage in the reservoir. A previously published model was calibrated for the Maulap and Imong River watersheds using rainfall data collected outside of the watershed. That model was applied to the Almagosa River watershed by transferring calibrated parameters and coefficients because information on daily diversions at the Almagosa Springs upstream of the gaging station was not available at the time. Runoff from the ungaged land area was not modeled. For this study, the availability of Almagosa Springs diversion data allowed the calibration of PRMS for the Almagosa River watershed. Rainfall data collected at the Almagosa rain gage since 1992 also provided better estimates of rainfall distribution in the watershed. In addition, the discontinuation of pan-evaporation data collection in 1998 required a change in the evapotranspiration estimation method used in the PRMS model. These reasons prompted the update of the PRMS for the Fena Valley Watershed. Simulated runoff volume from the PRMS compared reasonably with measured values for gaging stations on Maulap, Almagosa, and Imong Rivers, tributaries to the Fena Valley Reservoir. On the basis of monthly runoff simulation for the dry seasons included in the entire simulation period (1992-2001), the total volume of runoff can be predicted within -3.66 percent at Maulap River, within 5.37 percent at Almagosa River, and within 10
Wang-Erlandsson, Lan; van der Ent, Ruud; Fetzer, Ingo; Keys, Patrick; Savenije, Hubert; Gordon, Line
Anthropogenic land-use change has profoundly changed the Earth's terrestrial water cycle. Studies of how land-use change induced modifications in terrestrial evaporation alters atmospheric moisture content and subsequent precipitation (i.e.., moisture recycling) have primarily focussed on the annual mean impacts. However, the functioning of agriculture and ecosystems are often dependent on the onset, length, and magnitude of the growing season rainfall. Hence, rainfall seasonality is of crucial importance. Here, we (1) analyse how humans have altered rainfall seasonality through land-use change induced modification of moisture recycling, (2) investigate the mechanisms for the rainfall seasonality changes, and (3) discuss how downwind regions may be affected by rainfall seasonality changes. We model human land-use change effects (including irrigation) on evaporation using the global hydrological model STEAM and trace precipitation changes using the atmospheric moisture tracking scheme WAM-2layers. We find that changes in rainfall seasonality is considerably stronger than changes to mean annual precipitation, and is accentuated in locations downwind to significant land-use changes. In particular, we associate sustained rainfall season downwind with land-use types that favour transpiration. This effect is explained by the long residence time of transpiration in both the unsaturated zone and the atmosphere, in contrast to interception and soil evaporation. Our results shed light on the human influence of hydrological systems both locally and at large distances, and which may have crucial implications for agricultural production and ecosystem functioning. These insights are important in a time of both rapid land-use and climate change.
Long, Andrew J.
The Rainfall-Response Aquifer and Watershed Flow Model (RRAWFLOW) is a lumped-parameter model that simulates streamflow, spring flow, groundwater level, or solute transport for a measurement point in response to a system input of precipitation, recharge, or solute injection. I introduce the first version of RRAWFLOW available for download and public use and describe additional options. The open-source code is written in the R language and is available at http://sd.water.usgs.gov/projects/RRAWFLOW/RRAWFLOW.html along with an example model of streamflow. RRAWFLOW includes a time-series process to estimate recharge from precipitation and simulates the response to recharge by convolution, i.e., the unit-hydrograph approach. Gamma functions are used for estimation of parametric impulse-response functions (IRFs); a combination of two gamma functions results in a double-peaked IRF. A spline fit to a set of control points is introduced as a new method for estimation of nonparametric IRFs. Several options are included to simulate time-variant systems. For many applications, lumped models simulate the system response with equal accuracy to that of distributed models, but moreover, the ease of model construction and calibration of lumped models makes them a good choice for many applications (e.g., estimating missing periods in a hydrologic record). RRAWFLOW provides professional hydrologists and students with an accessible and versatile tool for lumped-parameter modeling.
Doss-Gollin, J.; Munoz, A. G.; Pastén, M.
During the austral summer 2015-16 severe flooding displaced over 150,000 people on the Paraguay River system in Paraguay, Argentina, and Southern Brazil. This flooding was out of phase with the typical seasonal cycle of the Paraguay River, and was driven by repeated intense rainfall events in the Lower Paraguay River basin. Using a weather typing approach within a diagnostic framework, we show that enhanced moisture inflow from the low-level jet and local convergence associated with baroclinic systems favored the development of mesoscale convective activity and enhanced precipitation. The observed circulation patterns were made more likely by the cross-timescale interactions of multiple climate mechanisms including the strong, mature El Niño event and an active Madden-Julien Oscillation in phases four and five. We also perform a comparison of the rainfall predictability using seasonal forecasts from the Latin American Observatory of Climate Events (OLE2) and sub-seasonal forecasts produced by the ECMWF. We find that the model output precipitation field exhibited limited skill at lead times beyond the synoptic timescale, but that a Model Output Statistics (MOS) approach, in which the leading principal components of the observed rainfall field are regressed on the leading principal components of model-simulated rainfall fields, substantially improves spatial representation of rainfall forecasts. Possible implications for flood preparedness are briefly discussed.
Rakesh, V.; Kantharao, B.
Data assimilation is considered as one of the effective tools for improving forecast skill of mesoscale models. However, for optimum utilization and effective assimilation of observations, many factors need to be taken into account while designing data assimilation methodology. One of the critical components that determines the amount and propagation observation information into the analysis, is model background error statistics (BES). The objective of this study is to quantify how BES in data assimilation impacts on simulation of heavy rainfall events over a southern state in India, Karnataka. Simulations of 40 heavy rainfall events were carried out using Weather Research and Forecasting Model with and without data assimilation. The assimilation experiments were conducted using global and regional BES while the experiment with no assimilation was used as the baseline for assessing the impact of data assimilation. The simulated rainfall is verified against high-resolution rain-gage observations over Karnataka. Statistical evaluation using several accuracy and skill measures shows that data assimilation has improved the heavy rainfall simulation. Our results showed that the experiment using regional BES outperformed the one which used global BES. Critical thermo-dynamic variables conducive for heavy rainfall like convective available potential energy simulated using regional BES is more realistic compared to global BES. It is pointed out that these results have important practical implications in design of forecast platforms while decision-making during extreme weather events
Dieppois, Bastien; Pohl, Benjamin; Crétat, Julien; Keenlyside, Noel; New, Mark
This study examines for the first time the ability of 28 global climate models from the Coupled Model Intercomparison Project 5 (CMIP5) to reproduce southern African summer rainfall variability and their teleconnections with large-scale modes of climate variability across the dominant timescales. In observations, summer southern African rainfall exhibits three significant timescales of variability over the twentieth century: interdecadal (15-28 years), quasi-decadal (8-13 years), and interannual (2-8 years). Most of CMIP5 simulations underestimate southern African summer rainfall variability at these three timescales, and this bias is proportionally stronger from high- to low-frequency. The inter-model spread is as important as the spread between the ensemble members of a given model, which suggests a strong influence of internal climate variability, and/or large model uncertainties. The underestimated amplitude of rainfall variability for each timescale are linked to unrealistic spatial distributions of these fluctuations over the subcontinent in most CMIP5 models. This is, at least partially, due to a poor representation of the tropical/subtropical teleconnections, which are known to favour wet conditions over southern African rainfall in the observations. Most CMIP5 realisations (85%) fail at simulating sea-surface temperature (SST) anomalies related to a negative Pacific Decadal Oscillation during wetter conditions at the interdecadal timescale. At the quasi-decadal timescale, only one-third of simulations display a negative Interdecadal Pacific Oscillation during wetter conditions, but these SST anomalies are anomalously shifted westward and poleward when compared to observed anomalies. Similar biases in simulating La Niña SST anomalies are identified in more than 50% of CMIP5 simulations at the interannual timescale. These biases in Pacific SST anomalies result in important shifts in the Walker circulation. This impacts southern Africa rainfall variability
Li, Jingwan; Sharma, Ashish; Evans, Jason; Johnson, Fiona
Addressing systematic biases in regional climate model simulations of extreme rainfall is a necessary first step before assessing changes in future rainfall extremes. Commonly used bias correction methods are designed to match statistics of the overall simulated rainfall with observations. This assumes that change in the mix of different types of extreme rainfall events (i.e. convective and non-convective) in a warmer climate is of little relevance in the estimation of overall change, an assumption that is not supported by empirical or physical evidence. This study proposes an alternative approach to account for the potential change of alternate rainfall types, characterized here by synoptic weather patterns (SPs) using self-organizing maps classification. The objective of this study is to evaluate the added influence of SPs on the bias correction, which is achieved by comparing the corrected distribution of future extreme rainfall with that using conventional quantile mapping. A comprehensive synthetic experiment is first defined to investigate the conditions under which the additional information of SPs makes a significant difference to the bias correction. Using over 600,000 synthetic cases, statistically significant differences are found to be present in 46% cases. This is followed by a case study over the Sydney region using a high-resolution run of the Weather Research and Forecasting (WRF) regional climate model, which indicates a small change in the proportions of the SPs and a statistically significant change in the extreme rainfall over the region, although the differences between the changes obtained from the two bias correction methods are not statistically significant.
Full Text Available level fluctuations (WLF) on a monthly basis was proposed in the rainfall infiltration breakthrough (RIB) model for the purpose of groundwater recharge estimation. In this paper, the physical meaning of parameters in the CRD and previous RIB models...
Full Text Available Introduction: Rainfall is affected by changes in the global sea level change, especially changes in sea surface temperature SST Sea Surface Temperature and sea level pressure SLP Sea level Pressure. Climate anomalies being related to each other at large distance is called teleconnection. As physical relationships between rainfall and teleconnection patterns are not defined clearly, we used intelligent models for forecasting rainfall. The intelligent models used in this study included Fuzzy Inference Systems, neural network and Neuro-fuzzy. In this study, first the teleconnection indices that could affect rainfall in the study area were identified. Then intelligent models were trained for rainfall forecasting. Finally, the most capable model for forecasting rainfall was presented. The study area for this research is the Khorasan Razavi Province. In order to present a model for rainfall forecasting, rainfall data of seven synoptic stations including Mashhad, Golmakan, Nishapur, Sabzevar, Kashmar, Torbate and Sharks since 1991 to 2010 were used. Materials and Methods: Based on previous studies about Teleconnection Patterns in the study area, effective Teleconnection indexes were identified. After calculating the correlation between the identified teleconnection indices and rainfall in one, two and three months ahead for all stations, fourteen teleconnection indices were chosen as inputs for intelligent models. These indices include, SLP Adriatic , SLP northern Red Sea, SLP Mediterranean Sea, SLP Aral sea, SST Sea surface temperature Labrador sea, SST Oman Sea, SST Caspian Sea, SST Persian Gulf, North Pacific pattern, SST Tropical Pacific in NINO12 and NINO3 regions, North Pacific Oscillation, Trans-Nino Index, Multivariable Enso Index. Inputs of the intelligent models include fourteen teleconnection indices, latitude and altitude of each station and their outputs are the prediction of rainfall for one, two and three months ahead. For calibration of
F. S. Marzano
Full Text Available A large set of ground-based multi-frequency microwave radiometric simulations and measurements during different precipitation regimes are analysed. Simulations are performed for a set of frequencies from 22 to 60 GHz, representing the channels currently available on an operational ground-based radiometric system. Results are illustrated in terms of comparisons between measurements and model data in order to show that the observed radiometric signatures can be attributed to rainfall scattering and absorption. An inversion algorithm has been developed, basing on the simulated data, to retrieve rain rate from passive radiometric observations. As a validation of the approach, we have analyzed radiometric measurements during rain events occurred in Boulder, Colorado, and at the Atmospheric Radiation Measurement (ARM Program's Southern Great Plains (SGP site in Lamont, Oklahoma, USA, comparing rain rate estimates with available simultaneous rain gauge data.
Full Text Available The optimization of irrigation use in agriculture is a key challenge to increase farm profitability and reduce its ecological footprint. To this context, an understanding of more efficient irrigation systems includes the assessment of water redistribution at the microscale. This study aimed to investigate rainfall interception by maize canopy and to model the soil water dynamics at row scale as a result of rain and sprinkler irrigation with HYDRUS 2D/3D. On average, 78% of rainfall below the maize canopy was intercepted by the leaves and transferred along the stem (stemflow, while only 22% reached the ground directly (throughfall. In addition, redistribution of the water with respect to the amount (both rain and irrigation showed that the stemflow/throughfall ratio decreased logarithmically at increasing values of incident rainfall, suggesting the plant capacity to confine the water close to the roots and diminish water stress conditions. This was also underlined by higher soil moisture values observed in the row than in the inter-row at decreasing rainfall events. Modelled data highlighted different behavior in terms of soil water dynamics between simulated irrigation water distributions, although they did not show significant changes in terms of crop water use efficiency. These results were most likely affected by the soil type (silty-loam where the experiment was conducted, as it had unfavorable physical conditions for the rapid vertical water movement that would have increased infiltration and drainage.
Müller, H.; Haberlandt, U.
Rainfall time series of high temporal resolution and spatial density are crucial for urban hydrology. The multiplicative random cascade model can be used for temporal rainfall disaggregation of daily data to generate such time series. Here, the uniform splitting approach with a branching number of 3 in the first disaggregation step is applied. To achieve a final resolution of 5 min, subsequent steps after disaggregation are necessary. Three modifications at different disaggregation levels are tested in this investigation (uniform splitting at Δt = 15 min, linear interpolation at Δt = 7.5 min and Δt = 3.75 min). Results are compared both with observations and an often used approach, based on the assumption that a time steps with Δt = 5.625 min, as resulting if a branching number of 2 is applied throughout, can be replaced with Δt = 5 min (called the 1280 min approach). Spatial consistence is implemented in the disaggregated time series using a resampling algorithm. In total, 24 recording stations in Lower Saxony, Northern Germany with a 5 min resolution have been used for the validation of the disaggregation procedure. The urban-hydrological suitability is tested with an artificial combined sewer system of about 170 hectares. The results show that all three variations outperform the 1280 min approach regarding reproduction of wet spell duration, average intensity, fraction of dry intervals and lag-1 autocorrelation. Extreme values with durations of 5 min are also better represented. For durations of 1 h, all approaches show only slight deviations from the observed extremes. The applied resampling algorithm is capable to achieve sufficient spatial consistence. The effects on the urban hydrological simulations are significant. Without spatial consistence, flood volumes of manholes and combined sewer overflow are strongly underestimated. After resampling, results using disaggregated time series as input are in the range of those using observed time series. Best
Ramarohetra, J.; Sultan, B.
Agriculture is considered as the most climate dependant human activity. In West Africa and especially in the sudano-sahelian zone, rain-fed agriculture - that represents 93% of cultivated areas and is the means of support of 70% of the active population - is highly vulnerable to precipitation variability. To better understand and anticipate climate impacts on agriculture, crop models - that estimate crop yield from climate information (e.g rainfall, temperature, insolation, humidity) - have been developed. These crop models are useful (i) in ex ante analysis to quantify the impact of different strategies implementation - crop management (e.g. choice of varieties, sowing date), crop insurance or medium-range weather forecast - on yields, (ii) for early warning systems and to (iii) assess future food security. Yet, the successful application of these models depends on the accuracy of their climatic drivers. In the sudano-sahelian zone , the quality of precipitation estimations is then a key factor to understand and anticipate climate impacts on agriculture via crop modelling and yield estimations. Different kinds of precipitation estimations can be used. Ground measurements have long-time series but an insufficient network density, a large proportion of missing values, delay in reporting time, and they have limited availability. An answer to these shortcomings may lie in the field of remote sensing that provides satellite-based precipitation estimations. However, satellite-based rainfall estimates (SRFE) are not a direct measurement but rather an estimation of precipitation. Used as an input for crop models, it determines the performance of the simulated yield, hence SRFE require validation. The SARRAH crop model is used to model three different varieties of pearl millet (HKP, MTDO, Souna3) in a square degree centred on 13.5°N and 2.5°E, in Niger. Eight satellite-based rainfall daily products (PERSIANN, CMORPH, TRMM 3b42-RT, GSMAP MKV+, GPCP, TRMM 3b42v6, RFEv2 and
Full Text Available Several natural disasters occur because of torrential rainfalls. The change in global climate most likely increases the occurrences of such downpours. Hence, it is necessary to investigate the characteristics of the torrential rainfall events in order to introduce effective measures for mitigating disasters such as urban floods and landslides. However, one of the major problems is evaluating the number of torrential rainfall events from a statistical viewpoint. If the number of torrential rainfall occurrences during a month is considered as count data, their frequency distribution could be identified using a probability distribution. Generally, the number of torrential rainfall occurrences has been analyzed using the Poisson distribution (POI or the Generalized Poisson Distribution (GPD. However, it was reported that POI and GPD often overestimated or underestimated the observed count data when additional or fewer zeros were included. Hence, in this study, a zero-inflated model concept was applied to solve this problem existing in the conventional models. Zero-Inflated Poisson (ZIP model, Zero-Inflated Generalized Poisson (ZIGP model, and the Bayesian ZIGP model have often been applied to fit the count data having additional or fewer zeros. However, the applications of these models in water resource management have been very limited despite their efficiency and accuracy. The five models, namely, POI, GPD, ZIP, ZIGP, and Bayesian ZIGP, were applied to the torrential rainfall data having additional zeros obtained from two rain gauges in South Korea, and their applicability was examined in this study. In particular, the informative prior distributions evaluated via the empirical Bayes method using ten rain gauges were developed in the Bayesian ZIGP model. Finally, it was suggested to avoid using the POI and GPD models to fit the frequency of torrential rainfall data. In addition, it was concluded that the Bayesian ZIGP model used in this study
Silva, F. E. O. E.; Naghettini, M. D. C.; Fernandes, W.
This paper evaluated the uncertainties associated with the estimation of the parameters of a conceptual rainfall-runoff model, through the use of Bayesian inference techniques by Monte Carlo simulation. The Pará River sub-basin, located in the upper São Francisco river basin, in southeastern Brazil, was selected for developing the studies. In this paper, we used the Rio Grande conceptual hydrologic model (EHR/UFMG, 2001) and the Markov Chain Monte Carlo simulation method named DREAM (VRUGT, 2008a). Two probabilistic models for the residues were analyzed: (i) the classic [Normal likelihood - r ≈ N (0, σ²)]; and (ii) a generalized likelihood (SCHOUPS & VRUGT, 2010), in which it is assumed that the differences between observed and simulated flows are correlated, non-stationary, and distributed as a Skew Exponential Power density. The assumptions made for both models were checked to ensure that the estimation of uncertainties in the parameters was not biased. The results showed that the Bayesian approach proved to be adequate to the proposed objectives, enabling and reinforcing the importance of assessing the uncertainties associated with hydrological modeling.
Bastin, Julien; Calvin, Sarah; Montagne, Gilles
The authors proposed a model of the control of interceptive action over a ground plane (Chardenon, Montagne, Laurent, & Bootsma, 2004). This model is based on the cancellation of the rate of change of the angle between the current position of the target and the direction of displacement (i.e., the bearing angle). While several sources of visual…
monthly rainfall forecasting using monthly rainfall data for ... Note above, rj is the autocorrelation of the nor- malized variates belonging to period j to those immediately preceding them (period j-1). Index i is consecutively numbered from 1 to total no of data whilst ...... relevance vector machine, extreme learning machine and ...
Abiodun, B J; Pal, J S; Afiesimama, E A; Gutowski, W J; Adedoyin, A
The study found that deforestation causes more monsoon moisture to be retained in the mid-troposphere, thereby reducing the northward transport of moisture needed for rainfall over West Africa. Hence, deforestation has dynamical impacts on the West African monsoon and rainfall.
Versini, Pierre-Antoine; Tchiguirinskaia, Ioulia; Schertzer, Daniel
Green roofs are commonly considered as efficient tools to mitigate urban runoff as they can store precipitation, and consequently provide retention and detention performances. Designed as a compromise between water holding capacity, weight and hydraulic conductivity, their substrate is usually an artificial media differentiating significantly from a traditional soil. In order to assess green roofs hydrological performances, many models have been developed. Classified into two categories (conceptual and physically based), they are usually applied to reproduce the discharge of a particular monitored green roof considered as homogeneous. Although the resulted simulations could be satisfactory, the question of robustness and consistency of the calibrated parameters is often not addressed. Here, a modeling framework has been developed to assess the efficiency and the robustness of both modelling approaches (conceptual and physically based) in reproducing green roof hydrological behaviour. SWMM and VS2DT models have been used for this purpose. This work also benefits from an experimental setup where several green roofs differentiated by their substrate thickness and vegetation cover are monitored. Based on the data collected for several rainfall events, it has been studied how the calibrated parameters are effectively linked to their physical properties and how they can vary from one green roof configuration to another. Although both models reproduce correctly the observed discharges in most of the cases, their calibrated parameters exhibit a high inconsistency. For a same green roof configuration, these parameters can vary significantly from one rainfall event to another, even if they are supposed to be linked to the green roof characteristics (roughness, residual moisture content for instance). They can also be different from one green roof configuration to another although the implemented substrate is the same. Finally, it appears very difficult to find any
Löwe, Roland; Mikkelsen, Peter Steen; Madsen, Henrik
We present stochastic flow forecasts to be used in a real-time control setup for urban drainage systems. The forecasts are generated using greybox models with rain gauge and radar rainfall observations as input. Predictions are evaluated as intervals rather than just mean values. We obtain...
Full Text Available Spatial variability plays an important role in nonlinear hydrologic processes. Due to the limitation of computational efficiency and data resolution, subgrid variability is usually assumed to be uniform for most grid-based rainfall-runoff models, which leads to the scale-dependence of model performances. In this paper, the scale effect on the Grid-Xinanjiang model was examined. The bias of the estimation of precipitation, runoff, evapotranspiration and soil moisture at the different grid scales, along with the scale-dependence of the effective parameters, highlights the importance of well representing the subgrid variability. This paper presents a subgrid parameterization method to incorporate the subgrid variability of the soil storage capacity, which is a key variable that controls runoff generation and partitioning in the Grid-Xinanjiang model. In light of the similar spatial pattern and physical basis, the soil storage capacity is correlated with the topographic index, whose spatial distribution can more readily be measured. A beta distribution is introduced to represent the spatial distribution of the soil storage capacity within the grid. The results derived from the Yanduhe Basin show that the proposed subgrid parameterization method can effectively correct the watershed soil storage capacity curve. Compared to the original Grid-Xinanjiang model, the model performances are quite consistent at the different grid scales when the subgrid variability is incorporated. This subgrid parameterization method reduces the recalibration necessity when the Digital Elevation Model (DEM resolution is changed. Moreover, it improves the potential for the application of the distributed model in the ungauged basin.
Ghosh, Soumik; Bhatla, R.; Mall, R. K.; Srivastava, Prashant K.; Sahai, A. K.
Climate model faces considerable difficulties in simulating the rainfall characteristics of southwest summer monsoon. In this study, the dynamical downscaling of European Centre for Medium-Range Weather Forecast's (ECMWF's) ERA-Interim (EIN15) has been utilized for the simulation of Indian summer monsoon (ISM) through the Regional Climate Model version 4.3 (RegCM-4.3) over the South Asia Co-Ordinated Regional Climate Downscaling EXperiment (CORDEX) domain. The complexities of model simulation over a particular terrain are generally influenced by factors such as complex topography, coastal boundary, and lack of unbiased initial and lateral boundary conditions. In order to overcome some of these limitations, the RegCM-4.3 is employed for simulating the rainfall characteristics over the complex topographical conditions. For reliable rainfall simulation, implementations of numerous lower boundary conditions are forced in the RegCM-4.3 with specific horizontal grid resolution of 50 km over South Asia CORDEX domain. The analysis is considered for 30 years of climatological simulation of rainfall, outgoing longwave radiation (OLR), mean sea level pressure (MSLP), and wind with different vertical levels over the specified region. The dependency of model simulation with the forcing of EIN15 initial and lateral boundary conditions is used to understand the impact of simulated rainfall characteristics during different phases of summer monsoon. The results obtained from this study are used to evaluate the activity of initial conditions of zonal wind circulation speed, which causes an increase in the uncertainty of regional model output over the region under investigation. Further, the results showed that the EIN15 zonal wind circulation lacks sufficient speed over the specified region in a particular time, which was carried forward by the RegCM output and leads to a disrupted regional simulation in the climate model.
Singh, S.V.; Storch, H.V.
It is more or less accepted that the increasing anthropogenic gases will result in global warming through the greenhouse effect. The major influence of this will be felt in the form of ice melts and rising sea levels. The influence on regional climates like monsoons is not very clear. Since the monsoons arise due to surface heating, one would expect that global warming will lead to more vigorous monsoons. The expected change in a climate parameter can be studied by analyzing the historical data and then extrapolating in time. Alternatively, one can use the state-of-the-art coupled GCMs which are able to simulate the earth's climate with reasonable accuracy. Both methods have some limitations. The first method cannot adequately consider the nonlinearity, and the second method may not be efficient for regional scales. So that the projections can be trusted, the regional features should be well simulated. None of the current models are able to simulate the Indian monsoon satisfactorily. Therefore it is desirable to infer the expected change in monsoons from other large and near global scale features which are better simulated. This approach, which depends on the concurrent association between a large-scale modeled feature and a regional scale, is known as downscaling, after Storch et al., and is adopted here to project the Indian monsoon rainfall for the next 100 years from the ECHAM T21 coupled model
In statistics, extreme events are often defined as excesses above a given large threshold. This definition allows hydrologists and flood planners to apply Extreme-Value Theory (EVT) to their time series of interest. Even in the stationary univariate context, this approach has at least two main drawbacks. First, working with excesses implies that a lot of observations (those below the chosen threshold) are completely disregarded. The range of precipitation is artificially shopped down into two pieces, namely large intensities and the rest, which necessarily imposes different statistical models for each piece. Second, this strategy raises a nontrivial and very practical difficultly: how to choose the optimal threshold which correctly discriminates between low and heavy rainfall intensities. To address these issues, we propose a statistical model in which EVT results apply not only to heavy, but also to low precipitation amounts (zeros excluded). Our model is in compliance with EVT on both ends of the spectrum and allows a smooth transition between the two tails, while keeping a low number of parameters. In terms of inference, we have implemented and tested two classical methods of estimation: likelihood maximization and probability weighed moments. Last but not least, there is no need to choose a threshold to define low and high excesses. The performance and flexibility of this approach are illustrated on simulated and hourly precipitation recorded in Lyon, France.
Full Text Available In many hydrological models, such as those derived by analytical probabilistic methods, the precipitation stochastic process is represented by means of individual storm random variables which are supposed to be independent of each other. However, several proposals were advanced to develop joint probability distributions able to account for the observed statistical dependence. The traditional technique of the multivariate statistics is nevertheless affected by several drawbacks, whose most evident issue is the unavoidable subordination of the dependence structure assessment to the marginal distribution fitting. Conversely, the copula approach can overcome this limitation, by dividing the problem in two distinct parts. Furthermore, goodness-of-fit tests were recently made available and a significant improvement in the function selection reliability has been achieved. Herein the dependence structure of the rainfall event volume, the wet weather duration and the interevent time is assessed and verified by test statistics with respect to three long time series recorded in different Italian climates. Paired analyses revealed a non negligible dependence between volume and duration, while the interevent period proved to be substantially independent of the other variables. A unique copula model seems to be suitable for representing this dependence structure, despite the sensitivity demonstrated by its parameter towards the threshold utilized in the procedure for extracting the independent events. The joint probability function was finally developed by adopting a Weibull model for the marginal distributions.
Oriani, F.; Stisen, S.
Rainfall amount is one of the most sensitive inputs to distributed hydrological models. Its spatial representation is of primary importance to correctly study the uncertainty of basin recharge and its propagation to the surface and underground circulation. We consider here the 10-km-grid rainfall product provided by the Danish Meteorological Institute as input to the National Water Resources Model of Denmark. Due to a drastic reduction in the rain gauge network in recent years (from approximately 500 stations in the period 1996-2006, to 250 in the period 2007-2014), the grid rainfall product, based on the interpolation of these data, is much less reliable. Consequently, the related hydrological model shows a significantly lower prediction power. To give a better estimation of spatial rainfall at the grid points far from ground measurements, we use the direct sampling technique (DS) , belonging to the family of multiple-point geostatistics. DS, already applied to rainfall and spatial variable estimation [2, 3], simulates a grid value by sampling a training data set where a similar data neighborhood occurs. In this way, complex statistical relations are preserved by generating similar spatial patterns to the ones found in the training data set. Using the reliable grid product from the period 1996-2006 as training data set, we first test the technique by simulating part of this data set, then we apply the technique to the grid product of the period 2007-2014, and subsequently analyzing the uncertainty propagation to the hydrological model. We show that DS can improve the reliability of the rainfall product by generating more realistic rainfall patterns, with a significant repercussion on the hydrological model. The reduction of rain gauge networks is a global phenomenon which has huge implications for hydrological model performance and the uncertainty assessment of water resources. Therefore, the presented methodology can potentially be used in many regions where
Ramachandran, Vishnampettai G; Roy, Priyamvada; Das, Shukla; Mogha, Narendra Singh; Bansal, Ajay Kumar
Aedes mosquitoes are responsible for transmitting the dengue virus. The mosquito lifecycle is known to be influenced by temperature, rainfall, and relative humidity. This retrospective study was planned to investigate whether climatic factors could be used to predict the occurrence of dengue in East Delhi. The number of monthly dengue cases reported over 19 years was obtained from the laboratory records of our institution. Monthly data of rainfall, temperature, and humidity collected from a local weather station were correlated with the number of monthly reported dengue cases. One-way analysis of variance was used to analyse whether the climatic parameters differed significantly among seasons. Four models were developed using negative binomial generalized linear model analysis. Monthly rainfall, temperature, humidity, were used as independent variables, and the number of dengue cases reported monthly was used as the dependent variable. The first model considered data from the same month, while the other three models involved incorporating data with a lag phase of 1, 2, and 3 months, respectively. The greatest number of cases was reported during the post-monsoon period each year. Temperature, rainfall, and humidity varied significantly across the pre-monsoon, monsoon, and post-monsoon periods. The best correlation between these three climatic factors and dengue occurrence was at a time lag of 2 months. This study found that temperature, rainfall, and relative humidity significantly affected dengue occurrence in East Delhi. This weather-based dengue empirical model can forecast potential outbreaks 2-month in advance, providing an early warning system for intensifying dengue control measures.
Thorndahl, Søren; Nielsen, Jesper Ellerbæk; Jensen, David Getreuer
Flooding produced by high-intensive local rainfall and drainage system capacity exceedance can have severe impacts in cities. In order to prepare cities for these types of flood events - especially in the future climate - it is valuable to be able to simulate these events numerically, both historically and in real-time. There is a rather untested potential in real-time prediction of urban floods. In this paper, radar data observations with different spatial and temporal resolution, radar nowcasts of 0-2 h leadtime, and numerical weather models with leadtimes up to 24 h are used as inputs to an integrated flood and drainage systems model in order to investigate the relative difference between different inputs in predicting future floods. The system is tested on the small town of Lystrup in Denmark, which was flooded in 2012 and 2014. Results show it is possible to generate detailed flood maps in real-time with high resolution radar rainfall data, but rather limited forecast performance in predicting floods with leadtimes more than half an hour.
Full Text Available The Precipitation-Runoff Modeling System (PRMS has been applied to simulate rainfall runoff in Zamask-Yingluoxia subbasin of the Heihe River Basin in this study. By using observed data in the subbasin, the model has been calibrated by comparing model simulations of daily stream flow to observed data at Yinglouxia station for the period of summer in 2004. Then model verification was conducted by keeping the same model parameters for the simulation of the period from 1 January 2003 to 31 December 2006. Results from model verification indicate that the model is able to provide good accuracy of simulations of daily rainfall runoff and river flow at Yinglouxia station, with a Nash-Sutcliffe Efficiency coefficient of 0.90 and the root-mean-square error of 15.7 m3/s. The error of maximum peak flow is 6.9 m3/s (1.8% and the error of mean flow is 1.4 m3/s (2.5%. Comparing to previous studies, results indicate the improvement of model accuracy in simulations of daily rainfall runoff. The calibrated and verified hydrological model can be used to support flood hazard mitigations and water resource management in the Zamask-Yingluoxia subbasin.
Jakobi, Jannis; Bogena, Heye; Huisman, Johan Alexander; Diekkrüger, Bernd; Vereecken, Harry
Cosmic-ray neutron soil moisture probes are an emerging technology that relies on the negative correlation between near-surface fast neutron counts and soil moisture content. Hydrogen atoms in the soil, which are mainly present as water, moderate the secondary neutrons on the way back to the surface. Any application of this method needs to consider the sensitivity of the neutron counts to additional sources of hydrogen (e.g. above- and below-ground biomass, humidity of the lower atmosphere, lattice water of the soil minerals, organic matter and water in the litter layer, intercepted water in the canopy, and soil organic matter). In this study, we analyzed the effects of canopy-intercepted water on the cosmic-ray neutron counts. For this, an arable field cropped with sugar beet was instrumented with several cosmic-ray neutron probes and a wireless sensor network with more than 140 in-situ soil moisture sensors. Additionally rainfall interception was estimated using a new approach coupling throughfall measurements and leaf wetness sensors. The derived interception storage was used to correct for interception effects on cosmic ray neutrons to enhance soil water content prediction. Furthermore, the potential for a simultaneous prediction of above- and below-ground biomass, soil moisture and interception was tested.
Borga, M.; Creutin, J. D.
issues are examined: advantages and caveats of using radar rainfall estimates in operational flash flood forecasting, methodological problems as- sociated to the use of hydrological models for distributed flash flood forecasting with rainfall input estimated from radar.
Green, Daniel; Pattison, Ian; Yu, Dapeng
Surface water (pluvial) flooding occurs when excess rainfall from intense precipitation events is unable to infiltrate into the subsurface or drain via natural or artificial drainage channels. Surface water flood events pose a major hazard to urban regions across the world, with nearly two thirds of flood damages in the UK being caused by surface water flood events. The perceived risk of surface water flooding appears to have increased in recent years due to several factors, including (i) precipitation increases associated with climatic change and variability; (ii) population growth meaning more people are occupying flood risk areas, and; (iii) land-use changes. Because urban areas are often associated with a high proportion of impermeable land-uses (e.g. tarmacked or paved surfaces and buildings) and a reduced coverage of vegetated, permeable surfaces, urban surface water flood risk during high intensity precipitation events is often exacerbated. To investigate the influence of urbanisation and terrestrial factors on surface water flood outputs, rainfall intensity, catchment slope, permeability, building density/layout scenarios were designed within a novel, 9m2 physical modelling environment. The two-tiered physical model used consists of (i) a low-cost, nozzle-type rainfall simulator component which is able to simulate consistent, uniformly distributed rainfall events of varying duration and intensity, and; (ii) a reconfigurable, modular plot surface. All experiments within the physical modelling environment were subjected to a spatiotemporally uniform 45-minute simulated rainfall event, while terrestrial factors on the physical model plot surface were altered systematically to investigate their hydrological response on modelled outflow and depth profiles. Results from the closed, controlled physical modelling experiments suggest that meteorological factors, such as the duration and intensity of simulated rainfall, and terrestrial factors, such as model slope
Mathew, M.; Paul, S.; Devanand, A.; Ghosh, S.
Urban precipitation enhancement has been identified over many cities in India by previous studies conducted. Anthropogenic effects such as change in land cover from hilly forest areas to flat topography with solid concrete infrastructures has certain effect on the local weather, the same way the greenhouse gas has on climate change. Urbanization could alter the large scale forcings to such an extent that it may bring about temporal and spatial changes in the urban weather. The present study investigate the physical processes involved in urban forcings, such as the effect of sudden increase in wind velocity travelling through the channel space in between the dense array of buildings, which give rise to turbulence and air mass instability in urban boundary layer and in return alters the rainfall distribution as well as rainfall initiation. A numerical model study is conducted over Mumbai metropolitan city which lies on the west coast of India, to assess the effect of urban morphology on the increase in number of extreme rainfall events in specific locations. An attempt has been made to simulate twenty extreme rainfall events that occurred over the summer monsoon period of the year 2014 using high resolution WRF-ARW (Weather Research and Forecasting-Advanced Research WRF) model to assess the urban land cover mechanisms that influences precipitation variability over this spatially varying urbanized region. The result is tested against simulations with altered land use. The correlation of precipitation with spatial variability of land use is found using a detailed urban land use classification. The initial and boundary conditions for running the model were obtained from the global model ECMWF(European Centre for Medium Range Weather Forecast) reanalysis data having a horizontal resolution of 0.75 °x 0.75°. The high resolution simulations show significant spatial variability in the accumulated rainfall, within a few kilometers itself. Understanding the spatial
So, Byung-Jin; Kim, Jin-Young; Kwon, Hyun-Han; Lima, Carlos H. R.
A conditional copula function based downscaling model in a fully Bayesian framework is developed in this study to evaluate future changes in intensity-duration frequency (IDF) curves in South Korea. The model incorporates a quantile mapping approach for bias correction while integrated Bayesian inference allows accounting for parameter uncertainties. The proposed approach is used to temporally downscale expected changes in daily rainfall, inferred from multiple CORDEX-RCMs based on Representative Concentration Pathways (RCPs) 4.5 and 8.5 scenarios, into sub-daily temporal scales. Among the CORDEX-RCMs, a noticeable increase in rainfall intensity is observed in the HadGem3-RA (9%), RegCM (28%), and SNU_WRF (13%) on average, whereas no noticeable changes are observed in the GRIMs (-2%) for the period 2020-2050. More specifically, a 5-30% increase in rainfall intensity is expected in all of the CORDEX-RCMs for 50-year return values under the RCP 8.5 scenario. Uncertainty in simulated rainfall intensity gradually decreases toward the longer durations, which is largely associated with the enhanced strength of the relationship with the 24-h annual maximum rainfalls (AMRs). A primary advantage of the proposed model is that projected changes in future rainfall intensities are well preserved.
Mounkaila, Moussa S.; Abiodun, Babatunde J.; `Bayo Omotosho, J.
Reliable forecasts of rainfall-onset dates (RODs) are crucial for agricultural planning and food security in West Africa. This study evaluates the ability of nine CORDEX regional climate models (RCMs: ARPEGE, CRCM5, RACMO, RCA35, REMO, RegCM3, PRECIS, CCLM and WRF) in simulating RODs over the region. Four definitions are used to compute RODs, and two observation datasets (GPCP and TRMM) are used in the model evaluation. The evaluation considers how well the RCMs, driven by ERA-Interim reanalysis (ERAIN), simulate the observed mean, standard deviation and inter-annual variability of RODs over West Africa. It also investigates how well the models link RODs with the northward movement of the monsoon system over the region. The model performances are compared to that of the driving reanalysis—ERAIN. Observations show that the mean RODs in West Africa have a zonal distribution, and the dates increase from the Guinea coast northward. ERAIN fails to reproduce the spatial distribution of the RODs as observed. The performance of some RCMs in simulating the RODs depends on the ROD definition used. For instance, ARPEGE, RACMO, PRECIS and CCLM produce a better ROD distribution than that of ERAIN when three of the ROD definitions are used, but give a worse ROD distribution than that of ERAIN when the fourth definition is used. However, regardless of the definition used, CCRM5, RCA35, REMO, RegCM3 and WRF show a remarkable improvement over ERAIN. The study shows that the ability of the RCMs in simulating RODs over West Africa strongly depends on how well the models reproduce the northward movement of the monsoon system and the associated features. The results show that there are some differences in the RODs obtained between the two observation datasets and RCMs, and the differences are magnified by differences in the ROD definitions. However, the study shows that most CORDEX RCMs have remarkable skills in predicting the RODs in West Africa.
Bruni, G.; Reinoso Rondinel, R.R.; Van de Giesen, N.C.; Clemens, F.H.L.R.; Ten Veldhuis, J.A.E.
Cities are increasingly vulnerable to floods generated by intense rainfall, because of their high degree of imperviousness, implementation of infrastructures, and changes in precipitation patterns due to climate change. Accurate information of convective storm characteristics at high spatial and
Roberts, J. Brent; Robertson, Franklin R.; Funk, Chris
Providing advance warning of East African rainfall variations is a particular focus of several groups including those participating in the Famine Early Warming Systems Network. Both seasonal and long-term model projections of climate variability are being used to examine the societal impacts of hydrometeorological variability on seasonal to interannual and longer time scales. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of both seasonal and climate model projections to develop downscaled scenarios for using in impact modeling. The utility of these projections is reliant on the ability of current models to capture the embedded relationships between East African rainfall and evolving forcing within the coupled ocean-atmosphere-land climate system. Previous studies have posited relationships between variations in El Niño, the Walker circulation, Pacific decadal variability (PDV), and anthropogenic forcing. This study applies machine learning methods (e.g. clustering, probabilistic graphical model, nonlinear PCA) to observational datasets in an attempt to expose the importance of local and remote forcing mechanisms of East African rainfall variability. The ability of the NASA Goddard Earth Observing System (GEOS5) coupled model to capture the associated relationships will be evaluated using Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations.
Rabiei, Ehsan; Haberlandt, Uwe; Sester, Monika; Fitzner, Daniel; Wallner, Markus
The need for high temporal and spatial resolution precipitation data for hydrological analyses has been discussed in several studies. Although rain gauges provide valuable information, a very dense rain gauge network is costly. As a result, several new ideas have been emerged to help estimating areal rainfall with higher temporal and spatial resolution. Rabiei et al. (2013) observed that moving cars, called RainCars (RCs), can potentially be a new source of data for measuring rainfall amounts...
Ji, H. J.; Liu, J.
Evaluation TRMM Rainfall Data In Hydrological Modeling For An Ungaged In Lhasa River BasinHaijuan Ji1* Jintao Liu1,2 Shanshan Xu1___________________ 1College of Hydrology and Water Resources, Hohai University, Nanjing 210098, People's Republic of China 2State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, People's Republic of China ___________________ * Corresponding author. Tel.: +86-025-83786973; Fax: +86-025-83786606. E-mail address: Hhu201510@163.com (H.J. Ji). Abstract: The Tibetan Plateau plays an important role in regulating the regional hydrological processes due to its high elevations and being the headwaters of many major Asian river basins. If familiar with the distribution of hydrological characteristics, will help us improve the level of development and utilization the water resources. However, there exist glaciers and snow with few sites. It is significance for us to understand the glacier and snow hydrological process in order to recognize the evolution of water resources in the Tibetan. This manuscript takes Lhasa River as the study area, taking use of ground, remote sensing and assimilation data, taking advantage of high precision TRMM precipitation data and MODIS snow cover data, first, according to the data from ground station evaluation of TRMM data in the application of the accuracy of the Lhasa River, and based on MODIS data fusion of multi source microwave snow making cloudless snow products, which are used for discriminant and analysis glacier and snow regulation mechanism on day scale, add snow and glacier unit into xinanjing model, this model can simulate the study region's runoff evolution, parameter sensitivity even spatial variation of hydrological characteristics the next ten years on region grid scale. The results of hydrological model in Lhasa River can simulate the glacier and snow runoff variation in high cold region better, to enhance the predictive ability of the spring
Oliveira, R. A. J.; Vila, D. A.; Maggioni, V.; Morales, C. A.
This study aims to investigate, over the different regions of Brazil, the error characteristics and uncertainties (random and systematic errors components) in satellite-based precipitation estimates by comparing the Goddard Profiling Algorithm (GPROF), through different sensors from GPM database (such as GMI, TMI, SSMI/S, AMSR2, MHS, among others), and Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithms. The analyses are made with other ground (S- and X-band dual polarization weather radar) and space (e.g., TRMM-PR and GPM-DPR [at Ku-band] active radars) based rainfall estimates as references at instantaneous timescales and respecting their temporal limitations. The Precipitation Uncertainties for Satellite Hydrology (PUSH) framework is used for the analysis and uncertainties characterization and error modeling. Specially, this study are focused on specific regions of Brazil, where the campaigns of the CHUVA project occurred (CHUVA/GoAmazon [IOP1 and 2] in Amazon and over southern Brazil where the S-band dual polarization radars (e.g., the FCTH radar) are located.
Steinig, S; Harlaß, J; Park, W; Latif, M
The simulation of Sahel rainfall and its onset during the West African Monsoon (WAM) remains a challenge for current state-of-the-art climate models due to their persistent biases, especially in the tropical Atlantic region. Here we show that improved representation of Atlantic Cold Tongue (ACT) development is essential for a more realistic seasonal evolution of the WAM, which is due to a further inland migration of the precipitation maximum. The observed marked relationship between ACT development and Sahel rainfall onset only can be reproduced by a climate model, the Kiel Climate Model (KCM), when sufficiently high resolution in its atmospheric component is employed, enabling enhanced equatorial Atlantic interannual sea surface temperature variability in the ACT region relative to versions with coarser atmospheric resolution. The ACT/Sahel rainfall relationship in the model critically depends on the correct seasonal phase-locking of the interannual variability rather than on its magnitude. We compare the KCM results with those obtained from climate models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5).
Full Text Available This paper presents the development of a rainfall-triggered landslide module within an existing physically based spatially distributed ecohydrologic model. The model, tRIBS-VEGGIE (Triangulated Irregular Networks-based Real-time Integrated Basin Simulator and Vegetation Generator for Interactive Evolution, is capable of a sophisticated description of many hydrological processes; in particular, the soil moisture dynamics are resolved at a temporal and spatial resolution required to examine the triggering mechanisms of rainfall-induced landslides. The validity of the tRIBS-VEGGIE model to a tropical environment is shown with an evaluation of its performance against direct observations made within the study area of Luquillo Forest. The newly developed landslide module builds upon the previous version of the tRIBS landslide component. This new module utilizes a numerical solution to the Richards' equation (present in tRIBS-VEGGIE but not in tRIBS, which better represents the time evolution of soil moisture transport through the soil column. Moreover, the new landslide module utilizes an extended formulation of the factor of safety (FS to correctly quantify the role of matric suction in slope stability and to account for unsaturated conditions in the evaluation of FS. The new modeling framework couples the capabilities of the detailed hydrologic model to describe soil moisture dynamics with the infinite slope model, creating a powerful tool for the assessment of rainfall-triggered landslide risk.
Lepore, C.; Arnone, E.; Noto, L. V.; Sivandran, G.; Bras, R. L.
This paper presents the development of a rainfall-triggered landslide module within an existing physically based spatially distributed ecohydrologic model. The model, tRIBS-VEGGIE (Triangulated Irregular Networks-based Real-time Integrated Basin Simulator and Vegetation Generator for Interactive Evolution), is capable of a sophisticated description of many hydrological processes; in particular, the soil moisture dynamics are resolved at a temporal and spatial resolution required to examine the triggering mechanisms of rainfall-induced landslides. The validity of the tRIBS-VEGGIE model to a tropical environment is shown with an evaluation of its performance against direct observations made within the study area of Luquillo Forest. The newly developed landslide module builds upon the previous version of the tRIBS landslide component. This new module utilizes a numerical solution to the Richards' equation (present in tRIBS-VEGGIE but not in tRIBS), which better represents the time evolution of soil moisture transport through the soil column. Moreover, the new landslide module utilizes an extended formulation of the factor of safety (FS) to correctly quantify the role of matric suction in slope stability and to account for unsaturated conditions in the evaluation of FS. The new modeling framework couples the capabilities of the detailed hydrologic model to describe soil moisture dynamics with the infinite slope model, creating a powerful tool for the assessment of rainfall-triggered landslide risk.
Neal, J. C.; Wood, M.; Bermúdez, M.; Hostache, R.; Freer, J. E.; Bates, P. D.; Coxon, G.
Remote sensing of flood inundation extent has long been a potential source of data for constraining and correcting simulations of floodplain inundation. Hydrodynamic models and the computing resources to run them have developed to the extent that simulation of flood inundation in two-dimensional space is now feasible over large river basins in near real-time. However, despite substantial evidence that there is useful information content within inundation extent data, even from low resolution SAR such as that gathered by Envisat ASAR in wide swath mode, making use of the information in a data assimilation system has proved difficult. He we review recent applications of the Ensemble Kalman Filter (EnKF) and Particle Filter for assimilating SAR data, with a focus on the River Severn UK and compare these with complementary research that has looked at the internal error sources and boundary condition errors using detailed terrestrial data that is not available in most locations. Previous applications of the EnKF to this reach have focused on upstream boundary conditions as the source of flow error, however this description of errors was too simplistic for the simulation of summer flood events where localised intense rainfall can be substantial. Therefore, we evaluate the introduction of uncertain lateral inflows to the ensemble. A further limitation of the existing EnKF based methods is the need to convert flood extent to water surface elevations by intersecting the shoreline location with a high quality digital elevation model (e.g. LiDAR). To simplify this data processing step, we evaluate a method to directly assimilate inundation extent as a EnKF model state rather than assimilating water heights, potentially allowing the scheme to be used where high-quality terrain data are sparse.
Montgomery, D.R.; Schmidt, K.M.; Dietrich, W.E.; McKean, J.
The middle of a hillslope hollow in the Oregon Coast Range failed and mobilized as a debris flow during heavy rainfall in November 1996. Automated pressure transducers recorded high spatial variability of pore water pressure within the area that mobilized as a debris flow, which initiated where local upward flow from bedrock developed into overlying colluvium. Postfailure observations of the bedrock surface exposed in the debris flow scar reveal a strong spatial correspondence between elevated piezometric response and water discharging from bedrock fractures. Measurements of apparent root cohesion on the basal (Cb) and lateral (Cl) scarp demonstrate substantial local variability, with areally weighted values of Cb = 0.1 and Cl = 4.6 kPa. Using measured soil properties and basal root strength, the widely used infinite slope model, employed assuming slope parallel groundwater flow, provides a poor prediction of hydrologie conditions at failure. In contrast, a model including lateral root strength (but neglecting lateral frictional strength) gave a predicted critical value of relative soil saturation that fell within the range defined by the arithmetic and geometric mean values at the time of failure. The 3-D slope stability model CLARA-W, used with locally observed pore water pressure, predicted small areas with lower factors of safety within the overall slide mass at sites consistent with field observations of where the failure initiated. This highly variable and localized nature of small areas of high pore pressure that can trigger slope failure means, however, that substantial uncertainty appears inevitable for estimating hydrologie conditions within incipient debris flows under natural conditions. Copyright 2009 by the American Geophysical Union.
Liu, Jian; Wang, Hongli; Ti, Ruyuan [Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, State Key Laboratory of Lake Science and Environment, Nanjing (China); Wang, Bin [University of Hawaii at Manoa, Department of Meteorology and IPRC, Honolulu, HI (United States); Kuang, Xueyuan [Nanjing University, School of Atmospheric Sciences, Nanjing (China)
The centennial-millennial variation of the East Asian summer monsoon (EASM) precipitation over the past 1000 years was investigated through the analysis of a millennium simulation of the coupled ECHO-G model. The model results indicate that the centennial-millennial variation of the EASM is essentially a forced response to the external radiative forcing (insolation, volcanic aerosol, and green house gases). The strength of the response depends on latitude; and the spatial structure of the centennial-millennial variation differs from the interannual variability that arises primarily from the internal feedback processes within the climate system. On millennial time scale, the extratropical and subtropical precipitation was generally strong during Medieval Warm Period (MWP) and weak during Little Ice Age (LIA). The tropical rainfall is insensitive to the effective solar radiation forcing (insolation plus radiative effect of volcanic aerosols) but significantly responds to the modern anthropogenic radiative forcing. On centennial time scale, the variation of the extratropical and subtropical rainfall also tends to follow the effective solar radiation forcing closely. The forced response features in-phase rainfall variability between the extratropics and subtropics, which is in contrast to the anti-correlation on the interannual time scale. Further, the behavior of the interannual-decadal variation in the extratropics is effectively modulated by change of the mean states on the millennial time scale, suggesting that the structure of the internal mode may vary with significant changes in the external forcing. These findings imply that on the millennial time scale, (a) the proxy data in the extratropical EA may more sensitively reflect the EASM rainfall variations, and (b) the Meiyu and the northern China rainfall provide a consistent measure for the EASM strength. (orig.)
Kemala Sari Lubis
Full Text Available Abstract Sediment transport has relationship with hydrologic input primarily river discharge and rainfall intensity. Fluctuation of river discharge and rainfall intensity have great effect on suspended sediment concentration. Bayesian Dynamic Linear Model DLMs is used to study relation of input hydrology and basin response variables. Response variables were taken from suspended sediment concentration and river discharge from a year July 2012 to June 2013 at two outlets at Padang sub-watershed upstream and Padang Hilir sub-watershed downstream of Padang watershed North Sumatra. Datas were analyzed by regression analysis of Suspended Sediment Concentration SSC as a dependent variables while river discharge and rainfall intensity as independent variables. The results showed that river discharge value are the highest on July 2012 and October 2012 at upstream and downstream of Padang watershed respectively. The SSC value are the highest on July 2012 and April 2013 at upstream and downstream of Padang watershed respectively. There is a weak correlation r2 0.002 between SSC and rainfall intensity at source points of outlet at upstream of Padang watershed. There is decreasing of forest paddy and plantation areas but increasing of bush and farming areas from 2012 to 2015 at upstream of Padang watershed. Meanwhile at downstream of Padang watershed were increasing of plantation areas since 2012 to 2015
Full Text Available Increasing importance of watershed management during last decades highlighted the need for sufficient data and accurate estimation of rainfall and runoff within watersheds. Therefore, various conceptual models have been developed with parameters based on observed data. Since further investigations depend on these parameters, it is important to accurately estimate them. This study by utilizing various methods, tries to estimate Nash rainfall-runoff model parameters and then evaluate the reliability of parameter estimation methods; moment, least square error, maximum likelihood, maximum entropy and genetic algorithm. Results based on a case study on the data from Ammameh watershed in Central Iran, indicate that the genetic algorithm method, which has been developed based on artificial intelligence, more accurately estimates Nash’s model parameters.
Rainfall is one of the meteorological forcing terms in hydrologic modelling and therefore its spatial variability in coverage, frequency and intensity affects simulation results. Rainfall variability in particular under the effect of orography adjacent to a large water body is not fully explored.
J. R. Santillan
Full Text Available In this paper, we present a combined geospatial and two dimensional (2D flood modeling approach to assess the impacts of flooding due to extreme rainfall events. We developed and implemented this approach to the Tago River Basin in the province of Surigao del Sur in Mindanao, Philippines, an area which suffered great damage due to flooding caused by Tropical Storms Lingling and Jangmi in the year 2014. The geospatial component of the approach involves extraction of several layers of information such as detailed topography/terrain, man-made features (buildings, roads, bridges from 1-m spatial resolution LiDAR Digital Surface and Terrain Models (DTM/DSMs, and recent land-cover from Landsat 7 ETM+ and Landsat 8 OLI images. We then used these layers as inputs in developing a Hydrologic Engineering Center Hydrologic Modeling System (HEC HMS-based hydrologic model, and a hydraulic model based on the 2D module of the latest version of HEC River Analysis System (RAS to dynamically simulate and map the depth and extent of flooding due to extreme rainfall events. The extreme rainfall events used in the simulation represent 6 hypothetical rainfall events with return periods of 2, 5, 10, 25, 50, and 100 years. For each event, maximum flood depth maps were generated from the simulations, and these maps were further transformed into hazard maps by categorizing the flood depth into low, medium and high hazard levels. Using both the flood hazard maps and the layers of information extracted from remotely-sensed datasets in spatial overlay analysis, we were then able to estimate and assess the impacts of these flooding events to buildings, roads, bridges and landcover. Results of the assessments revealed increase in number of buildings, roads and bridges; and increase in areas of land-cover exposed to various flood hazards as rainfall events become more extreme. The wealth of information generated from the flood impact assessment using the approach can be very
Fei Teng; Wenrui Huang; Yi Cai; Chunmiao Zheng; Songbin Zou
The Precipitation-Runoff Modeling System (PRMS) has been applied to simulate rainfall runoff in Zamask-Yingluoxia subbasin of the Heihe River Basin in this study. By using observed data in the subbasin, the model has been calibrated by comparing model simulations of daily stream flow to observed data at Yinglouxia station for the period of summer in 2004. Then model verification was conducted by keeping the same model parameters for the simulation of the period from 1 January 2003 to 31 Decem...
Beljadid, A.; Cueto-Felgueroso, L.; Juanes, R.
Feedbacks between climate, soil moisture and vegetation are essential in the sustainability of arid and semiarid ecosystems. Models of the water balance in ecohydrology estimate deep drainage by coupling rainfall, water flow and evapotranspiration in the vadose zone. When rainfall water infiltrates into dry soil, a hydrodynamic instability leads to the onset of columnar preferential flow paths, or fingers (Hill and Parlange, 1972; Glass et al., 1989; Ritsema et al., 1998). The stability of infiltration fronts varies with the properties of the soil and the infiltrating flux; in general, fingering is the dominant mode of infiltration for medium- and coarse-textured dry soils, and low to intermediate infiltration rates (Glass et al., 1989). Here, we develop a multiphase flow model that is able to describe gravity fingering during infiltration in heterogeneous soils, based on the phase-field methodology (Cueto-Felgueroso and Juanes, 2008, 2009). We design the free energy of the system such that it leads to a nonlinear square saturation-gradient term, which leads to the formation of "compactons" and a quantitative agreement with laboratory experiments in heterogeneous media. We apply this model to numerically investigate the impact of stochastic rainfall, evapotranspiration and soil heterogeneity on unsaturated flow. From our analysis, we develop a quantitative understanding of the field soil conditions and climatic conditions under which gravity fingering is expected to promote deep drainage, potentially increasing the resilience of water-stressed ecosystems, moderating their response to climate variability. Keywords: Infiltration, gravity fingering, phase-field model, unsaturated soil, heterogeneity.
Murla, Damian; Thorndahl, Søren Liedtke
Intense rainfall can often cause severe floods, especially in urbanized areas, where population density or large impermeable areas are found. In this context, floods can generate a direct impact in a social-environmental-economic viewpoint. Traditionally, in design of Urban Drainage Systems (UDS......), correlation between return period (RP) of a given rainfall and RP of its consequent flood has been assumed to be linear (e.g.DS/EN752 (2008)). However, this is not always the case. Complex UDS, where diverse hydraulic infrastructures are often found, increase the heterogeneity of system response, which may...... cause an alteration of the mentioned correlation. Consequently, reliability on future urban planning, design and resilience against floods may be also affected by this misassumption. In this study, an assessment of surface flood RP across rainfall RP has been carried out at Lystrup, a urbanized...
Loh, Jui Le; Tangang, Fredolin; Juneng, Liew; Hein, David; Lee, Dong-In
This study investigates projected changes in rainfall and temperature over Malaysia by the end of the 21st century based on the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) A2, A1B and B2 emission scenarios using the Providing Regional Climates for Impacts Studies (PRECIS). The PRECIS regional climate model (HadRM3P) is configured in 0.22° × 0.22° horizontal grid resolution and is forced at the lateral boundaries by the UKMO-HadAM3P and UKMOHadCM3Q0 global models. The model performance in simulating the present-day climate was assessed by comparing the modelsimulated results to the Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) dataset. Generally, the HadAM3P/PRECIS and HadCM3Q0/PRECIS simulated the spatio-temporal variability structure of both temperature and rainfall reasonably well, albeit with the presence of cold biases. The cold biases appear to be associated with the systematic error in the HadRM3P. The future projection of temperature indicates widespread warming over the entire country by the end of the 21st century. The projected temperature increment ranges from 2.5 to 3.9°C, 2.7 to 4.2°C and 1.7 to 3.1°C for A2, A1B and B2 scenarios, respectively. However, the projection of rainfall at the end of the 21st century indicates substantial spatio-temporal variation with a tendency for drier condition in boreal winter and spring seasons while wetter condition in summer and fall seasons. During the months of December to May, ~20-40% decrease of rainfall is projected over Peninsular Malaysia and Borneo, particularly for the A2 and B2 emission scenarios. During the summer months, rainfall is projected to increase by ~20-40% across most regions in Malaysia, especially for A2 and A1B scenarios. The spatio-temporal variations in the projected rainfall can be related to the changes in the weakening monsoon circulations, which in turn alter the patterns of
Di Giuseppe, Edmondo; Lasinio, Giovanna Jona; Pasqui, Massimiliano; Esposito, Stanislao
We propose a new statistical protocol for the estimation of precipitation using lightning data. We first identify rainy events using a scan statistics, then we estimate Rainfall Lighting Ratio (RLR) to convert lightning number into rain volume given the storm intensity. Then we build a hierarchical Bayesian model aiming at the prediction of 15- and 30-minutes cumulated precipitation at unobserved locations and time using information on lightning in the same area. More specifically, we build a...
David Hui; Karen Shum; Ji Chen; Shyh-Chin Chen; Jack Ritchie; John Roads
Seasonal climate forecasts are one of the most promising tools for providing early warnings for natural hazards such as floods and droughts. Using two case studies, this paper documents the skill of a regional climate model in the seasonal forecasting of below normal rainfall in southern China during the rainy seasons of JulyâAugustâSeptember 2003 and Aprilâ...
Fotso-Nguemo, Thierry C.; Vondou, Derbetini A.; Tchawoua, Clément; Haensler, Andreas
This work investigates spatial and temporal changes in rainfall and temperature over Central Africa, using historical and representative concentration pathways (RCP2.6, RCP4.5 and RCP8.5) of the regional climate model REMO forced by two general climate models: the Europe-wide Consortium Earth System Model (EC-Earth) and the Max Planck Institute-Earth System Model (MPI-ESM). We found that in the present period (1980-2005), the spatial distribution of rainfall is simulated with an annual spatial pattern correlation coefficient (PCC) of 0.76 for REMO driven by EC-Earth and 0.74 for REMO driven by MPI-ESM respectively when compared to CRU data. In terms of temperature, the annual PCC is 0.93 for the two REMO outputs. According to the climatology of Central Africa, we subdivided the study area into five sub-regions, we also noticed that the annual and seasonal PCC depend on the considered sub-region. For the future period (2070-2095), temperature is projected to increase following all the three scenarios. The rainfall amount is projected to decrease by up to 5 mm/day towards the end of the twenty first century under RCP8.5 scenario, and by 1-2 mm/day under RCP4.5 and RCP2.6 scenarios over Equatorial Guinea, Gabon, Congo, north-western Democratic Republic of Congo (DRC) and the Lake Victoria. Significant decrease is predicted to occur mostly in the northern part of the domain under RCP8.5 scenario. However, future rainfall over High Lands of Cameroon, Adamawa Plateau, north-eastern DRC and Atlantic Ocean is projected to increase.
Capparelli, Giovanna; Damiano, Emilia; Olivares, Lucio; Spolverino, Gennaro; Versace, Pasquale
The prediction of natural processes, such as weather-induced landslide, an issue that is of great importance. Were held numerous research to understand the processes underlying the triggering of a landslide, and to improve the forecasting systems. A valid prediction model can allow the implementation of an equally valid announcement and warning system, thus reducing the risk caused by such phenomena. The hydraulic and hydrologic modeling of the process that takes place in an unstable slope subjected to rainfall, can be performed using two approaches: through mathematical models or physical models. Our research uses an integrated approach, making system data of experimental sites, with both the results and interpretations of physical models, both with simulations of mathematical models. The intent is to observe and interpret laboratory experiments to reproduce and simulate the phenomenon with mathematical models. The research aims to obtain interpretations of hydrological and hydraulic processes, which occur in the slopes as a result of rain, more and more accurate. For our research we use a scaled-down physical model and a mathematical model FEM. The physical model is a channel with transparent walls composed of two floors at a variable angle (ignition and propagation) 1 meter wide and 3 meters long each. The model is instrumented with sensors that control the hydraulic and geotechnical parameters within the slopes and devices that simulate natural events. The model is equipped with a monitoring system able to keep under observation the physical quantities of interest. In particular, the apparatus is equipped with tensiometers miniaturized, that can be installed in different positions and at different depths, for the measurement of suction within the slope, miniaturized pressure transducers on the bottom of the channel for the measurement of any pressure neutral positive , TDR system for the measurement of the volumetric water content, and displacement transducers
Milinski, S.; Bader, J.; Jungclaus, J. H.; Marotzke, J.
There is some consensus on mean state changes of rainfall under global warming; changes of the internal variability, on the other hand, are more difficult to analyse and have not been discussed as much despite their importance for understanding changes in extreme events, such as droughts or floodings. We analyse changes in the rainfall variability in the tropical Atlantic region. We use a 100-member ensemble of historical (1850-2005) model simulations with the Max Planck Institute for Meteorology Earth System Model (MPI-ESM1) to identify changes of internal rainfall variability. To investigate the effects of global warming on the internal variability, we employ an additional ensemble of model simulations with stronger external forcing (1% CO2-increase per year, same integration length as the historical simulations) with 68 ensemble members. The focus of our study is on the oceanic Atlantic ITCZ. We find that the internal variability of rainfall over the tropical Atlantic does change due to global warming and that these changes in variability are larger than changes in the mean state in some regions. From splitting the total variance into patterns of variability, we see that the variability on the southern flank of the ITCZ becomes more dominant, i.e. explaining a larger fraction of the total variance in a warmer climate. In agreement with previous studies, we find that changes in the mean state show an increase and narrowing of the ITCZ. The large ensembles allow us to do a statistically robust differentiation between the changes in variability that can be explained by internal variability and those that can be attributed to the external forcing. Furthermore, we argue that internal variability in a transient climate is only well defined in the ensemble domain and not in the temporal domain, which requires the use of a large ensemble.
Lima, de J.L.M.P.
This study concerns the theory and some practical aspects of overland flow under rainfall. Of the conditioning factors and processes which govern the generation of overland flow, the following were studied: depression storage, infiltration, morphology and wind. Special attention was paid to
Lovring, Maite Monica; Löwe, Roland; Courdent, Vianney Augustin Thomas
An early flood warning system has been developed for urban catchments and is currently running in online operation in Copenhagen. The system is highly dependent on the quality of rainfall forecast inputs. An investigation of precipitation inputs from Radar Nowcast (RN), Numerical Weather Prediction...... of the three forecast products is expected to yield the optimal input for flood warning....
Ayron M. Strauch; Richard A. MacKenzie; Christian P. Giardina; Gregory L. Bruland
Rising atmospheric CO2 and resulting warming are expected to impact freshwater resources in the tropics, but few studies have documented how natural stream flow regimes in tropical watersheds will respond to changing rainfall patterns. To address this data gap, we utilized a space-for-time substitution across a naturally occurring and highly...
Full Text Available Pa forecasts, and then downscaling them using CCA. Downscaling is performed onto the 0.5° × 0.5° resolution of the CRU rainfall data set south of 10° south over Africa. Forecast verification is performed using the relative operating characteristic (ROC...
Full Text Available . 2000; Goddard and Mason, 2002). Such a so-called two-tiered procedure to predict the outcome of the rainfall season has been employed in South Africa for a number of years already (e.g., Landman et al., 2001). The advent of fully coupled ocean...
Buchtele, Josef; Buchtelová, Marie; Cissé, Youssouf
Roč. 50, č. 3 (2002), s. 185-197 ISSN 0042-790X R&D Projects: GA AV ČR IAA3060002 Institutional research plan: CEZ:AV0Z2060917 Keywords : rainfall-runoff process * land use changes Subject RIV: DA - Hydrology ; Limnology
Bastin, Julien; Calvin, Sarah; Montagne, Gilles
International audience; The authors proposed a model of the control of interceptive action over a ground plane (Chardenon, Montagne, Laurent, & Bootsma, 2004). This model is based on the cancellation of the rate of change of the angle between the current position of the target and the direction of displacement (i.e., the bearing angle). While several sources of visual information specify this angle, the contribution of proprioceptive information has not been directly tested. In this study, th...
Valencia, Jose Luis; María Tarquis, Ana; Saá-Requejo, Antonio; Villeta, María; María Gascó, Jose
Water supplies in the Ebro River Basin present high seasonal fluctuations, with extreme rainfall events during autumn and spring, and demands are increasingly stressed during summer. At the same time, repeated anomalous annual fluctuations in recent decades have become a serious concern for regional hydrology, agriculture and several related industries in the region. In fact, it has had a devastating impact, both socially and economically. In addition it has resulted in debate over the changing seasonal patterns of rainfall and the increasing frequency of extreme rainfall events. The aim of this work is to evaluate these challenges on the Ebro River Basin.For this purpose, 132 complete and regular spatial rainfall daily datasets (from 1931 to 2009) were analyzed. Each dataset corresponds to a grid of 25 km x 25 km and belongs to the area studied. First, classical statistical tests were applied to the series at annual scale to check the randomness and trends. No trends where found. Then, we analyzed the change in the rainfall variability pattern in the Ebro River Basin. We have used universal multifractal (UM) analysis, which estimates the concentration of the data around the precipitation average (C1, codimension average), the degree of multiscaling behavior in time (α index) and the maximum probable singularity in the rainfall distribution (γs). Daily rainfall series were subdivided (1931-1975 and 1965-2009) to study the difference between the two periods in these three UM parameters, in an attempt to relate them to geographical coordinates and relative positions in the river basin. The variations observed in C1 and α in some areas of the Ebro River Basin indicate that a precipitation regime change has begun in the last few decades, and therefore, this change should be considered in terms of its potential effects on the social and economical development of the region. This confirms some postulates drawn by conservative scientists who reject a catastrophic
Baum, Rex L.; Godt, Jonathan W.; Savage, William Z.
Shallow rainfall-induced landslides commonly occur under conditions of transient infiltration into initially unsaturated soils. In an effort to predict the timing and location of such landslides, we developed a model of the infiltration process using a two-layer system that consists of an unsaturated zone above a saturated zone and implemented this model in a geographic information system (GIS) framework. The model links analytical solutions for transient, unsaturated, vertical infiltration above the water table to pressure-diffusion solutions for pressure changes below the water table. The solutions are coupled through a transient water table that rises as water accumulates at the base of the unsaturated zone. This scheme, though limited to simplified soil-water characteristics and moist initial conditions, greatly improves computational efficiency over numerical models in spatially distributed modeling applications. Pore pressures computed by these coupled models are subsequently used in one-dimensional slope-stability computations to estimate the timing and locations of slope failures. Applied over a digital landscape near Seattle, Washington, for an hourly rainfall history known to trigger shallow landslides, the model computes a factor of safety for each grid cell at any time during a rainstorm. The unsaturated layer attenuates and delays the rainfall-induced pore-pressure response of the model at depth, consistent with observations at an instrumented hillside near Edmonds, Washington. This attenuation results in realistic estimates of timing for the onset of slope instability (7 h earlier than observed landslides, on average). By considering the spatial distribution of physical properties, the model predicts the primary source areas of landslides.
Full Text Available Flash flood hazard maps provide a scientific support to mitigate flash flood risk. The present study develops a practical framework with the help of integrated hydrological and hydrodynamic modelling in order to estimate the potential flash floods. We selected a small pilot catchment which has already suffered from flash floods in the past. This catchment is located in the Nan River basin, northern Thailand. Reliable meteorological and hydrometric data are missing in the catchment. Consequently, the entire upper basin of the main river was modelled with the help of the hydrological modelling system PANTA RHEI. In this basin, three monitoring stations are located along the main river. PANTA RHEI was calibrated and validated with the extreme flood events in June 2011 and July 2008, respectively. The results show a good agreement with the observed discharge data. In order to create potential flash flood scenarios, synthetic rainfall series were derived from temporal rainfall patterns based on the radar-rainfall observation and different rainfall depths from regional rainfall frequency analysis. The temporal rainfall patterns were characterized by catchment-averaged rainfall series selected from 13 rainstorms in 2008 and 2011 within the region. For regional rainfall frequency analysis, the well-known L-moments approach and related criteria were used to examine extremely climatic homogeneity of the region. According to the L-moments approach, Generalized Pareto distribution was recognized as the regional frequency distribution. The synthetic rainfall series were fed into the PANTA RHEI model. The simulated results from PANTA RHEI were provided to a 2-D hydrodynamic model (MEADFLOW, and various simulations were performed. Results from the integrated modelling framework are used in the ongoing study to regionalize and map the spatial distribution of flash flood hazards with four levels of flood severities. As an overall outcome, the presented framework
Watson, Andrew; Miller, Jodie; Fleischer, Melanie; de Clercq, Willem
Wetlands are conservation priorities worldwide, due to their high biodiversity and productivity, but are under threat from agricultural and climate change stresses. To improve the water management practices and resource allocation in these complex systems, a modelling approach has been developed to estimate potential recharge for data poor catchments using rainfall data and basic assumptions regarding soil and aquifer properties. The Verlorenvlei estuarine lake (RAMSAR #525) on the west coast of South Africa is a data poor catchment where rainfall records have been supplemented with farmer's rainfall records. The catchment has multiple competing users. To determine the ecological reserve for the wetlands, the spatial and temporal distribution of recharge had to be well constrained using the J2000 rainfall/runoff model. The majority of rainfall occurs in the mountains (±650 mm/yr) and considerably less in the valley (±280 mm/yr). Percolation was modelled as ∼3.6% of rainfall in the driest parts of the catchment, ∼10% of rainfall in the moderately wet parts of the catchment and ∼8.4% but up to 28.9% of rainfall in the wettest parts of the catchment. The model results are representative of rainfall and water level measurements in the catchment, and compare well with water table fluctuation technique, although estimates are dissimilar to previous estimates within the catchment. This is most likely due to the daily timestep nature of the model, in comparison to other yearly average methods. These results go some way in understanding the fact that although most semi-arid catchments have very low yearly recharge estimates, they are still capable of sustaining high biodiversity levels. This demonstrates the importance of incorporating shorter term recharge event modeling for improving recharge estimates.
Sai Krishna, V. V.; Dikshit, Anil Kumar; Pandey, Kamal
Urban expansion, water bodies and climate change are inextricably linked with each other. The macro and micro level climate changes are leading to extreme precipitation events which have severe consequences on flooding in urban areas. Flood simulations shall be helpful in demarcation of flooded areas and effective flood planning and preparedness. The temporal availability of satellite rainfall data at varying spatial scale of 0.10 to 0.50 is helpful in near real time flood simulations. The present research aims at analysing stream flow and runoff to monitor flood condition using satellite rainfall data in a hydrologic model. The satellite rainfall data used in the research was NASA's Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG), which is available at 30 minutes temporal resolution. Landsat data was used for mapping the water bodies in the study area. Land use land cover (LULC) data was prepared using Landsat 8 data with maximum likelihood technique that was provided as an input to the HEC-HMS hydrological model. The research was applied to one of the urbanized cities of India, viz. Dehradun, which is the capital of Uttarakhand State. The research helped in identifying the flood vulnerability at the basin level on the basis of the runoff and various socio economic parameters using multi criteria analysis.
Full Text Available This study aims to assess the characteristics and uncertainty of Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM (IMERG Level 3 rainfall estimates and to improve those estimates using an error model over the central Amazon region. The S-band Amazon Protection National System (SIPAM radar is used as reference and the Precipitation Uncertainties for Satellite Hydrology (PUSH framework is adopted to characterize uncertainties associated with the satellite precipitation product. PUSH is calibrated and validated for the study region and takes into account factors like seasonality and surface type (i.e., land and river. Results demonstrated that the PUSH model is suitable for characterizing errors in the IMERG algorithm when compared with S-band SIPAM radar estimates. PUSH could efficiently predict the satellite rainfall error distribution in terms of spatial and intensity distribution. However, an underestimation (overestimation of light satellite rain rates was observed during the dry (wet period, mainly over rivers. Although the estimated error showed a lower standard deviation than the observed error, the correlation between satellite and radar rainfall was high and the systematic error was well captured along the Negro, Solimões, and Amazon rivers, especially during the wet season.
Baracchini, Theo; King, Aaron A.; Bouma, Menno J.; Rodó, Xavier; Bertuzzo, Enrico; Pascual, Mercedes
Seasonal patterns in cholera dynamics exhibit pronounced variability across geographical regions, showing single or multiple peaks at different times of the year. Although multiple hypotheses related to local climate variables have been proposed, an understanding of this seasonal variation remains incomplete. The historical Bengal region, which encompasses the full range of cholera's seasonality observed worldwide, provides a unique opportunity to gain insights on underlying environmental drivers. Here, we propose a mechanistic, rainfall-temperature driven, stochastic epidemiological model which explicitly accounts for the fluctuations of the aquatic reservoir, and analyze with this model the historical dataset of cholera mortality in the Bengal region. Parameters are inferred with a recently developed sequential Monte Carlo method for likelihood maximization in partially observed Markov processes. Results indicate that the hydrological regime is a major driver of the seasonal dynamics of cholera. Rainfall tends to buffer the propagation of the disease in wet regions due to the longer residence times of water in the environment and an associated dilution effect, whereas it enhances cholera resurgence in dry regions. Moreover, the dynamics of the environmental water reservoir determine whether the seasonality is unimodal or bimodal, as well as its phase relative to the monsoon. Thus, the full range of seasonal patterns can be explained based solely on the local variation of rainfall and temperature. Given the close connection between cholera seasonality and environmental conditions, a deeper understanding of the underlying mechanisms would allow the better management and planning of public health policies with respect to climate variability and climate change.
Capra, Lucia; Coviello, Velio; Borselli, Lorenzo; Márquez-Ramírez, Víctor-Hugo; Arámbula-Mendoza, Raul
The Volcán de Colima, one of the most active volcanoes in Mexico, is commonly affected by tropical rains related to hurricanes that form over the Pacific Ocean. In 2011, 2013 and 2015 hurricanes Jova, Manuel and Patricia, respectively, triggered tropical storms that deposited up to 400 mm of rain in 36 h, with maximum intensities of 50 mm h -1. The effects were devastating, with the formation of multiple lahars along La Lumbre and Montegrande ravines, which are the most active channels in sediment delivery on the south-southwest flank of the volcano. Deep erosion along the river channels and several marginal landslides were observed, and the arrival of block-rich flow fronts resulted in damages to bridges and paved roads in the distal reaches of the ravines. The temporal sequence of these flow events is reconstructed and analyzed using monitoring data (including video images, seismic records and rainfall data) with respect to the rainfall characteristics and the hydrologic response of the watersheds based on rainfall-runoff numerical simulation. For the studied events, lahars occurred 5-6 h after the onset of rainfall, lasted several hours and were characterized by several pulses with block-rich fronts and a maximum flow discharge of 900 m3 s -1. Rainfall-runoff simulations were performer using the SCS-curve number and the Green-Ampt infiltration models, providing a similar result in the detection of simulated maximum watershed peaks discharge. Results show different behavior for the arrival times of the first lahar pulses that correlate with the simulated catchment's peak discharge for La Lumbre ravine and with the peaks in rainfall intensity for Montegrande ravine. This different behavior is related to the area and shape of the two watersheds. Nevertheless, in all analyzed cases, the largest lahar pulse always corresponds with the last one and correlates with the simulated maximum peak discharge of these catchments. Data presented here show that flow pulses
Rahman, Mohammad Atiqur; Yunsheng, Lou; Sultana, Nahid
In this study, 60-year monthly rainfall data of Bangladesh were analysed to detect trends. Modified Mann-Kendall, Spearman's rho tests and Sen's slope estimators were applied to find the long-term annual, dry season and monthly trends. Sequential Mann-Kendall analysis was applied to detect the potential trend turning points. Spatial variations of the trends were examined using inverse distance weighting (IDW) interpolation. AutoRegressive integrated moving average (ARIMA) model was used for the country mean rainfall and for other two stations data which depicted the highest and the lowest trend in the Mann-Kendall and Spearman's rho tests. Results showed that there is no significant trend in annual rainfall pattern except increasing trends for Cox's Bazar, Khulna, Satkhira and decreasing trend for Srimagal areas. For the dry season, only Bogra area represented significant decreasing trend. Long-term monthly trends demonstrated a mixed pattern; both negative and positive changes were found from February to September. Comilla area showed a significant decreasing trend for consecutive 3 months while Rangpur and Khulna stations confirmed the significant rising trends for three different months in month-wise trends analysis. Rangpur station data gave a maximum increasing trend in April whereas a maximum decreasing trend was found in August for Comilla station. ARIMA models predict +3.26, +8.6 and -2.30 mm rainfall per year for the country, Cox's Bazar and Srimangal areas, respectively. However, all the test results and predictions revealed a good agreement among them in the study.
Full Text Available Throughfall volumes and incident rainfall were measured between 23 August and 2 December 2004 as well as from 6 January to 15 April 2005 for individual rain events of differing intensities and magnitudes in an open tropical rainforest in Rondônia, Brazil. Temporal patterns of throughfall spatial variability were examined. Estimated interception was compared to modeled interception obtained by applying the revised Gash model in order to identify sources of throughfall variability in open tropical rainforests. Gross precipitation of 97 events amounted to 1309 mm, 89±5.6% (S.E. of which reached the forest floor as throughfall. The redistribution of water within the canopy was highly variable in time, which we attribute to the high density of babassu palms (Orbignya phalerata, their seasonal leaf growth, and their conducive morphology. We identified a 10-min rainfall intensity threshold of 30 mmh-1 above which interception was highly variable. This variability is amplified by funneling and shading effects of palms. This interaction between a rainfall variable and vegetation characteristics is relevant for understanding the hydrology of all tropical rainforests with a high palm density.
Radi, Noor Fadhilah Ahmad; Zakaria, Roslinazairimah; Satari, Siti Zanariah; Azman, Muhammad Az-zuhri
This study aims to model the spatial extreme daily rainfall process using the max-stable model. The max-stable model is used to capture the dependence structure of spatial properties of extreme rainfall. Three models from max-stable are considered namely Smith, Schlather and Brown-Resnick models. The methods are applied on 12 selected rainfall stations in Kelantan, Malaysia. Most of the extreme rainfall data occur during wet season from October to December of 1971 to 2012. This period is chosen to assure the available data is enough to satisfy the assumption of stationarity. The dependence parameters including the range and smoothness, are estimated using composite likelihood approach. Then, the bootstrap approach is applied to generate synthetic extreme rainfall data for all models using the estimated dependence parameters. The goodness of fit between the observed extreme rainfall and the synthetic data is assessed using the composite likelihood information criterion (CLIC). Results show that Schlather model is the best followed by Brown-Resnick and Smith models based on the smallest CLIC's value. Thus, the max-stable model is suitable to be used to model extreme rainfall in Kelantan. The study on spatial dependence in extreme rainfall modelling is important to reduce the uncertainties of the point estimates for the tail index. If the spatial dependency is estimated individually, the uncertainties will be large. Furthermore, in the case of joint return level is of interest, taking into accounts the spatial dependence properties will improve the estimation process.
Jaiswal, Neeru; Kishtawal, C. M.; Bhomia, Swati
The southwest (SW) monsoon season (June, July, August and September) is the major period of rainfall over the Indian region. The present study focuses on the development of a new multi-model ensemble approach based on the similarity criterion (SMME) for the prediction of SW monsoon rainfall in the extended range. This approach is based on the assumption that training with the similar type of conditions may provide the better forecasts in spite of the sequential training which is being used in the conventional MME approaches. In this approach, the training dataset has been selected by matching the present day condition to the archived dataset and days with the most similar conditions were identified and used for training the model. The coefficients thus generated were used for the rainfall prediction. The precipitation forecasts from four general circulation models (GCMs), viz. European Centre for Medium-Range Weather Forecasts (ECMWF), United Kingdom Meteorological Office (UKMO), National Centre for Environment Prediction (NCEP) and China Meteorological Administration (CMA) have been used for developing the SMME forecasts. The forecasts of 1-5, 6-10 and 11-15 days were generated using the newly developed approach for each pentad of June-September during the years 2008-2013 and the skill of the model was analysed using verification scores, viz. equitable skill score (ETS), mean absolute error (MAE), Pearson's correlation coefficient and Nash-Sutcliffe model efficiency index. Statistical analysis of SMME forecasts shows superior forecast skill compared to the conventional MME and the individual models for all the pentads, viz. 1-5, 6-10 and 11-15 days.
Jaiswal, Neeru; Kishtawal, C. M.; Bhomia, Swati
The southwest (SW) monsoon season (June, July, August and September) is the major period of rainfall over the Indian region. The present study focuses on the development of a new multi-model ensemble approach based on the similarity criterion (SMME) for the prediction of SW monsoon rainfall in the extended range. This approach is based on the assumption that training with the similar type of conditions may provide the better forecasts in spite of the sequential training which is being used in the conventional MME approaches. In this approach, the training dataset has been selected by matching the present day condition to the archived dataset and days with the most similar conditions were identified and used for training the model. The coefficients thus generated were used for the rainfall prediction. The precipitation forecasts from four general circulation models (GCMs), viz. European Centre for Medium-Range Weather Forecasts (ECMWF), United Kingdom Meteorological Office (UKMO), National Centre for Environment Prediction (NCEP) and China Meteorological Administration (CMA) have been used for developing the SMME forecasts. The forecasts of 1-5, 6-10 and 11-15 days were generated using the newly developed approach for each pentad of June-September during the years 2008-2013 and the skill of the model was analysed using verification scores, viz. equitable skill score (ETS), mean absolute error (MAE), Pearson's correlation coefficient and Nash-Sutcliffe model efficiency index. Statistical analysis of SMME forecasts shows superior forecast skill compared to the conventional MME and the individual models for all the pentads, viz. 1-5, 6-10 and 11-15 days.
Joshi, Manish K.; Kucharski, Fred
The present study evaluates the fidelity of 32 models from the fifth Coupled Model Intercomparison Project (CMIP5) in simulating the observed teleconnection of Interdecadal Pacific Oscillation (IPO) with Indian summer monsoon rainfall (ISMR). Approximately two-thirds of the models show well-defined spatial pattern of IPO over the Pacific basin and most amongst these capture the IPO-ISMR teleconnection. In general, the models that fail to reproduce the IPO-ISMR teleconnection are the ones that are also showing a poor spatial pattern of IPO, irrespective of the extent to which they reproduce the precipitation climatology and seasonal cycle. The results reveal a strong relationship between the quality of reproducing the IPO pattern and the IPO-ISMR teleconnection in the models, in particular with respect to the tropical-extratropical as well as the equatorial Pacific-Indian Ocean sea surface temperature gradients during IPO phases. Furthermore, the CMIP5 models that are capable of reproducing the IPO-ISMR teleconnection also reasonably simulate the atmospheric circulation as well as the convergence/divergence patterns associated with the IPO. Thus, for the better understanding of decadal-to-multidecadal variability and to improve decadal prediction of rainfall over India it is therefore vital that models should simulate the IPO skillfully.
E. Rabiei; U. Haberlandt; M. Sester; D. Fitzner; M. Wallner
The need for high temporal and spatial resolution precipitation data for hydrological analyses has been discussed in several studies. Although rain gauges provide valuable information, a very dense rain gauge network is costly. As a result, several new ideas have emerged to help estimating areal rainfall with higher temporal and spatial resolution. Rabiei et al. (2013) observed that moving cars, called RainCars (RCs), can potentially be a new source of data for measuring rai...
Hazenberg, Pieter; Leijnse, Hidde; Uijlenhoet, Remko
Radars are known for their ability to obtain a wealth of information about the spatial stormfield characteristics. Unfortunately, rainfall estimates obtained by this instrument are known to be affected by multiple sources of error. Especially for stratiform precipitation systems, the quality of radar rainfall estimates starts to decrease at relatively close ranges. In the current study the hydrological potential of weather radar is analyzed during a winter half-year for the hilly region of the Belgian Ardennes. A correction algorithm is proposed taking into account attenuation, ground clutter, anomalous propagation, the vertical profile of reflectivity (VPR) and advection. No final bias correction with respect to rain gauge data were implemented, because that does not add to a better understanding of the quality of the radar. Largest quality improvements in the radar data are obtained by ground clutter removal. The influence of VPR correction and advection depends on the precipitation system observed. Overall, the radar shows an underestimation as compared to the rain gauges, which becomes smaller after averaging at the scale of the medium-sized Ourthe catchment. Remaining differences between both devices can mainly be attributed to an improper choice of the Z-R relationship. Conceptual rainfall-runoff simulations show similar results using either catchment average radar or rain gauge data, although the largest discharge peak observed, is seriously underestimated when applying radar data. Overall, for hydrological applications corrected weather radar information in a hilly environment can be used up to 70 km during a winter half-year.
Rankl, James G.
A physically based point-infiltration model was developed for computing infiltration of rainfall into soils and the resulting runoff from small basins in Wyoming. The user describes a 'design storm' in terms of average rainfall intensity and storm duration. Information required to compute runoff for the design storm by using the model include (1) soil type and description, and (2) two infiltration parameters and a surface-retention storage parameter. Parameter values are tabulated in the report. Rainfall and runoff data for three ephemeral-stream basins that contain only one type of soil were used to develop the model. Two assumptions were necessary: antecedent soil moisture is some long-term average, and storm rainfall is uniform in both time and space. The infiltration and surface-retention storage parameters were determined for the soil of each basin. Observed rainstorm and runoff data were used to develop a separation curve, or incipient-runoff curve, which distinguishes between runoff and nonrunoff rainfall data. The position of this curve defines the infiltration and surface-retention storage parameters. A procedure for applying the model to basins that contain more than one type of soil was developed using data from 7 of the 10 study basins. For these multiple-soil basins, the incipient-runoff curve defines the infiltration and retention-storage parameters for the soil having the highest runoff potential. Parameters were defined by ranking the soils according to their relative permeabilities and optimizing the position of the incipient-runoff curve by using measured runoff as a control for the fit. Analyses of runoff from multiple-soil basins indicate that the effective contributing area of runoff is less than the drainage area of the basin. In this study, the effective drainage area ranged from 41.6 to 71.1 percent of the total drainage area. Information on effective drainage area is useful in evaluating drainage area as an independent variable in
The Amazon is the largest tropical rainforest in the world, and thus plays a major role on global water, energy, and carbon cycles. However, it is still unknown how the Amazon forest will respond to the ongoing changes in climate, especially droughts, which are expected to become more frequent. To help answering this question, in this thesis I developed and improved the representation of biophysical processes and photosynthesis in the Ecosystem Demography model (ED-2.2), an individual-based land ecosystem model. I also evaluated the model biophysics against multiple data sets for multiple forest and savannah sites in tropical South America. Results of this comparison showed that ED-2.2 is able to represent the radiation and water cycles, but exaggerates heterotrophic respiration seasonality. Also, the model generally predicted correct distribution of biomass across different areas, although it overestimated biomass in subtropical savannahs. To evaluate the forest resilience to droughts, I used ED-2.2 to simulate the plant community dynamics at two sites in Eastern Amazonia, and developed scenarios by resampling observed annual rainfall but increasing the probability of selecting dry years. While the model predicted little response at French Guiana, results at the mid-Eastern Amazonia site indicated substantial biomass loss at modest rainfall reductions. Also, the response to drier climate varied within the plant community, with evergreen, early-successional, and larger trees being the most susceptible. The model also suggests that competition for water during prolonged periods of drought caused the largest impact on larger trees, when insufficient wet season rainfall did not recharge deeper soil layers. Finally, results suggested that a decrease in return period of long-lasting droughts could prevent ecosystem recovery. Using different rainfall datasets, I defined vulnerability based on the change in climate needed to reduce the return period of long droughts. The
van Emmerik, Tim; Eilander, Dirk; Piet, Marijn; Mulder, Gert
The Chamcar Bei catchment in southern Cambodia is a typical ungauged basin. Neither meteorological data or discharge measurements are available. In this catchment, local farmers are highly dependent on the irrigation system. However, due to the unreliability of the water supply, it was required to make a hydrological model, with which further improvements of the irrigation system could be planned. First, we used knowledge generated in the IAHS decade on Predictions in Ungauged Basins (PUB) to estimate the annual water balance of the Chamcar Bei catchment. Next, using remotely sensed precipitation, vegetation, elevation and transpiration data, a monthly rainfall-runoff model has been developed. The rainfall-runoff model was linked to the irrigation system reservoir, which allowed to validate the model based on soft data such as historical knowledge of the reservoir water level and groundwater levels visible in wells. This study shows that combining existing remote sensing data and soft ground data can lead to useful modeling results. The approach presented in this study can be applied in other ungauged basins, which can be extremely helpful in managing water resources in developing countries.
Danandeh Mehr, Ali; Nourani, Vahid; Hrnjica, Bahrudin; Molajou, Amir
The effectiveness of genetic programming (GP) for solving regression problems in hydrology has been recognized in recent studies. However, its capability to solve classification problems has not been sufficiently explored so far. This study develops and applies a novel classification-forecasting model, namely Binary GP (BGP), for teleconnection studies between sea surface temperature (SST) variations and maximum monthly rainfall (MMR) events. The BGP integrates certain types of data pre-processing and post-processing methods with conventional GP engine to enhance its ability to solve both regression and classification problems simultaneously. The model was trained and tested using SST series of Black Sea, Mediterranean Sea, and Red Sea as potential predictors as well as classified MMR events at two locations in Iran as predictand. Skill of the model was measured in regard to different rainfall thresholds and SST lags and compared to that of the hybrid decision tree-association rule (DTAR) model available in the literature. The results indicated that the proposed model can identify potential teleconnection signals of surrounding seas beneficial to long-term forecasting of the occurrence of the classified MMR events.
Chattopadhyay, Manojit; Chattopadhyay, Surajit
The present paper reports a study, where growing hierarchical self-organising map (GHSOM) has been applied to achieve a visual cluster analysis to the Indian rainfall dataset consisting of 142 years of Indian rainfall data so that the yearly rainfall can be segregated into small groups to visualise the pattern of clustering behaviour of yearly rainfall due to changes in monthly rainfall for each year. Also, through support vector machine (SVM), it has been observed that generation of clusters impacts positively on the prediction of the Indian summer monsoon rainfall. Results have been presented through statistical and graphical analyses.
Van Haren, J. L. M.; Sanchez-Canete, E. P.; Juarez, S.; Howard, E. L.; Dontsova, K.; Le Galliard, J. F.; Barron-Gafford, G.; Volkmann, T.; Troch, P. A.
Basalt is one of the most important rock types in controlling atmospheric carbon dioxide concentrations on a geologic scale. At the University of Arizona's Biosphere 2 facility, we have built the world's largest geological model system - the Landscape Evolution Observatory (LEO) - to determine the hydrological and biogeochemical changes before and after the addition of plants. LEO consists of three 30x11 m and 1-m deep hillslope landscapes of basaltic tephra ground to homogenous loamy sand inside an environmentally controlled facility. Each landscape contains a sensor network capable of capturing water, carbon, and energy cycling processes at 15-min resolution and sub-meter to whole-landscape scales. At LEO, we measured the soil carbon dynamics in bare soil, with only minimal biological activity, after multiple rainfall events. These measurements consistently showed that rainfall, soil moisture, and soil gas diffusion are strong drivers of carbon uptake in a porous basalt matrix. Our expectation is that the addition of plants will dramatically change the carbon dynamics following rainfall events and produce Birch-effect-like pulses of carbon dioxide following rainfall events. We tested this prediction in smaller-scale and shorter-term experiments done at the CEREEP-ECOTRON lab in Ile de France, France, where we experimented with three different plant species grown in the same LEO soil. Soil carbon responses were similar to the LEO slope irrespective of whether plants were grown in the soil: initial wetting leads to a strong drawdown of carbon dioxide in the soil. However, due to plant activity, the soil carbon dioxide concentration recovered faster in the basalt soil when plants were present. Only in small scale incubations with a mixture of LEO soil with an organic-rich (6.5% carbon) prairie soil did we see the expected pulse of carbon dioxide following the addition of water. The smaller-scale experiments suggest that the occurrence of carbon dioxide fluxes
Full Text Available Missile defense systems are often related to major military resources aimed at shielding a specific region from incoming attacks. They are intended to detect, track, intercept, and destruct incoming enemy missiles. These systems vary in cost, efficiency, dependability, and technology. In present times, the possession of these types of systems is associated with large capacity military countries. Demonstrated here are the mathematical techniques behind missile systems which calculate trajectories of incoming missiles and potential intercept positions after initial missile detection. This procedure involved the use of vector-valued functions, systems of equations, and knowledge of projectile motion concepts.
Singh, Ankita; Acharya, Nachiketa; Mohanty, Uma Charan; Mishra, Gopbandhu
The emerging advances in the field of dynamical prediction of monsoon using state-of-the-art General Circulation Models (GCMs) have led to the development of various multi model ensemble techniques (MMEs). In the present study, the concept of Canonical Correlation Analysis is used for making MME (referred as Multi Model Canonical Correlation Analysis or MMCCA) for the prediction of Indian summer monsoon rainfall (ISMR) during June-July-August-September (JJAS). This method has been employed on the rainfall outputs of six different GCMs for the period 1982 to 2008. The prediction skill of ISMR by MMCCA is compared with the simple composite method (SCM) (i.e. arithmetic mean of all GCMs), which is taken as a benchmark. After a rigorous analysis through different skill metrics such as correlation coefficient and index of agreement, the superiority of MMCCA over SCM is illustrated. Performance of both models is also evaluated during six typical monsoon years and the results indicate the potential of MMCCA over SCM in capturing the spatial pattern during extreme years.
Clemens, S. C.; Holbourn, A.; Kubota, Y.; Lee, K. E.; Liu, Z.; Chen, G.
Confidence in reconstruction of East Asian paleomonsoon rainfall using precipitation isotope proxies is a matter of considerable debate, largely due to the lack of correlation between precipitation amount and isotopic composition in the present climate. We present four new, very highly resolved records spanning the past 300,000 years ( 200 year sample spacing) from IODP Site U1429 in the East China Sea. We demonstrate that all the orbital- and millennial-scale variance in the onshore Yangtze River Valley speleothem δ18O record1 is also embedded in the offshore Site U1429 seawater δ18O record (derived from the planktonic foraminifer Globigerinoides ruber and sea surface temperature reconstructions). Signal replication in these two independent terrestrial and marine archives, both controlled by the same monsoon system, uniquely identifies δ18O of precipitation as the primary driver of the precession-band variance in both records. This proxy-proxy convergence also eliminates a wide array of other drivers that have been called upon as potential contaminants to the precipitation δ18O signal recorded by these proxies. We compare East Asian precipitation isotope proxy records to precipitation amount from a CCSM3 transient climate model simulation of the past 300,000 years using realistic insolation, ice volume, greenhouse gasses, and sea level boundary conditions. This model-proxy comparison suggests that both Yangtze River Valley precipitation isotope proxies (seawater and speleothem δ18O) track changes in summer-monsoon rainfall amount at orbital time scales, as do precipitation isotope records from the Pearl River Valley2 (leaf wax δ2H) and Borneo3 (speleothem δ18O). Notably, these proxy records all have significantly different spectral structure indicating strongly regional rainfall patterns that are also consistent with model results. Transient, isotope-enabled model simulations will be necessary to more thoroughly evaluate these promising results, and to
Pinker, R. T.; Zhao, Y.; Akoshile, C.; Janowiak, J.; Arkin, P.
Several climatic parameters are observed at the campus of the University of Ilorin, Ilorin, Nigeria in sub-Sahel Africa. This climatically important region is in the desert transition zone between the Sahara and the savanna of upper Nigeria and is influenced by the dusty Harmattan wind. It is characterized by persistent conditions with high aerosol loading as well as intense dust outbreaks that affect the local climate. The region is under the influence of the West African Monsoon (WAM) which has exhibited a dramatic change from wet conditions in the 50s and 60s to much drier conditions in the 70s, 80s and 90s. Observations at the Ilorin site were established to provide the Earth Observing System (EOS) Community with high quality climatic data, in particular, related to radiative fluxes and aerosols. The station is located in a region with extreme environmental conditions that are not common at other sites around the globe. For example, at Ilorin observed is the highest aerosol optical depth in the AErosol RObotic NETwork (AERONET) and the site is frequented by biomass burning during the dry season, thus adding new radiative effects to the dust aerosols (Pinker et al., 2001; Pandithurai et al., 2001; Holben et al., 2001, Smirnov et al., 2002). Biomass burning in savanna and forest ecosystems over Africa is believed to contribute about 20% to the global biomass burning. Dynamical models to predict WAM are not accurate in West Africa and tropical Atlantic regions, and are unable to simulate fundamental characteristics of rainfall (diurnal, seasonal and annual cycles). In this study we present an analysis of the diurnal and seasonal variability for several years of rainfall observations. Comparison is made with the output of a numerical model and satellite based estimates. It was found that two observed relative peaks in the annual rainfall distribution are seen in the satellite data but are not detected by the numerical model. It was also shown that the satellite
Full Text Available With the advance of GIS technology, hydrology model can simulated at catchment wide scale. The objective is to integrate National Resource Conservation Service (NRCS Curve Number (CN with kinematic wave and manning’s equation using GIS to develop a simple GIS-based distributed model to simulate rainfall runoff in Bentong catchment. Model was built using Spatial Distributed Direct Hydrograph (SDDH concept and applying the time area (TA approach in presenting the predicted discharge hydrograph. The effective precipitation estimation was first calculated using the NRCS CN method. Then, the core maps that consists of digital elevation model (DEM, soil and land use map in grid. DEM was used to derive slope, flow direction and flow accumulation while soil and land use map used to derive roughness coefficient and CN. The overland velocity and channel velocity estimation derived from combination of kinematic wave theory with Manning’s equation. To capture the time frame, the travel time map was divided into isochrones in order to generate the TA histogram and finally. The creation of SDDH using the TA histogram which will lead to the estimation of travel time for the catchment. Simulated hydrograph was plotted together with the observed discharge for comparison. Six storm events used for model performance evaluation using statistical measure such as Nash-Sutcliffe efficiency (NSE, percent bias (PBIAS and coefficient of determination (R2;. SDDH model performed quite well as NSE gave result ranging from 0.55 to 0.68 with mean of 0.6. PBIAS indicate that the model slightly over predicted compared to observed hydrograph with result ranges from -46.71 (the most over predicted to +4.83 (the most under predicted with average of -20.73%. R2; ranges between 0.55 to 0.82 with mean of 0.67. When comparing the time to peak, (tp, min, and peak discharge, (pd, m3/s, results gave NSEtp 0.82, PBIAStp 0.65, R2tp 0.32, NSEpd 0.95, PBIASpd 14.49 and R2pd 0
Gash, J.H.C.; Rosier, P.T.W.; Ragab, R.
A model developed for estimating the evaporation of rainfall intercepted by forest canopies is applied to estimate measurements of the average runoff from the roofs of six houses made in a previous study of hydrological processes in an urban environment. The model is applied using values of the mean
Breinholt, Anders; Møller, Jan Kloppenborg; Madsen, Henrik
evaluation of the modelled output, and we attach particular importance to inspecting the residuals of the model outputs and improving the model uncertainty description. We also introduce the probabilistic performance measures sharpness, reliability and interval skill score for model comparison...... and for checking the reliability of the confidence bounds. Using point rainfall and evaporation data as input and flow measurements from a sewer system for model conditioning, a state space model is formulated that accounts for three different flow contributions: wastewater from households, and fast rainfall......-runoff from paved areas and slow rainfall-dependent infiltration-inflow from unknown sources. We consider two different approaches to evaluate the model output uncertainty, the output error method that lumps all uncertainty into the observation noise term, and a method based on Stochastic Differential...
The results could also be used in the process of modelling energy use reduction, air pollution uptake, rainfall interception, carbon sequestration and microclimate modification of urban forests such as those found in the City of Tshwane. Keywords: allometry; regression; size relationships; tree growth; urban forests. Southern ...
Full Text Available This paper shows the combined use of remotely sensed data and hydraulic geometry methods as an alternative to rainfall-runoff models. Hydraulic geometric data and boolean images of water sheets obtained from satellite images after storm events were integrated in a Geographical Information System. Channel cross-sections were extracted from a high resolution Digital Terrain Model (DTM and superimposed on the image cover to estimate the peak flow using HEC-RAS. The proposed methodology has been tested in ephemeral channels (ramblas on the coastal zone in south-eastern Spain. These fluvial systems constitute an important natural hazard due to their high discharges and sediment loads. In particular, different areas affected by floods during the period 1997 to 2009 were delimited through HEC-GeoRAs from hydraulic geometry data and Landsat images of these floods (Landsat‑TM5 and Landsat-ETM+7. Such an approach has been validated against rainfall-surface runoff models (SCS Dimensionless Unit Hydrograph, SCSD, Témez gamma HU Tγ and the Modified Rational method, MRM comparing their results with flood hydrographs of the Automatic Hydrologic Information System (AHIS in several ephemeral channels in the Murcia Region. The results obtained from the method providing a better fit were used to calculate different hydraulic geometry parameters, especially in residual flood areas.
V. A. Bell
Full Text Available A simple two-dimensional rainfall model, based on advection and conservation of mass in a vertical cloud column, is investigated for use in short-term rainfall and flood forecasting at the catchment scale under UK conditions. The model is capable of assimilating weather radar, satellite infra-red and surface weather observations, together with forecasts from a mesoscale numerical weather prediction model, to obtain frequently updated forecasts of rainfall fields. Such data assimilation helps compensate for the simplified model dynamics and, taken together, provides a practical real-time forecasting scheme for catchment scale applications. Various ways are explored for using information from a numerical weather prediction model (16.8 km grid within the higher resolution model (5 km grid. A number of model variants is considered, ranging from simple persistence and advection methods used as a baseline, to different forms of the dynamic rainfall model. Model performance is assessed using data from the Wardon Hill radar in Dorset for two convective events, on 10 June 1993 and 16 July 1995, when thunderstorms occurred over southern Britain. The results show that (i a simple advection-type forecast may be improved upon by using multiscan radar data in place of data from the lowest scan, and (ii advected, steady-state predictions from the dynamic model, using 'inferred updraughts', provides the best performance overall. Updraught velocity is inferred at the forecast origin from the last two radar fields, using the mass-balance equation and associated data and is held constant over the forecast period. This inference model proves superior to the buoyancy parameterisation of updraught employed in the original formulation. A selection of the different rainfall forecasts is used as input to a catchment flow forecasting model, the IH PDM (Probability Distributed Moisture model, to assess their effect on flow forecast accuracy for the 135 km2 Brue catchment
Zhang Zhou; Ying Ouyang; Yide Li; Zhijun Qiu; Matt Moran
Climate change over the past several decades has resulted in shifting rainfall pattern and modifying rain-fall intensity, which has exacerbated hydrological processes and added the uncertainty and instability tothese processes. This study ascertained impacts of potential future rainfall change on hydrological pro-cesses at the Jianfengling (JFL) tropical mountain...
Han, Jing-Cheng; Huang, Guohe; Huang, Yuefei; Zhang, Hua; Li, Zhong; Chen, Qiuwen
Lack of hydrologic process representation at the short time-scale would lead to inadequate simulations in distributed hydrological modeling. Especially for complex mountainous watersheds, surface runoff simulations are significantly affected by the overland flow generation, which is closely related to the rainfall characteristics at a sub-time step. In this paper, the sub-daily variability of rainfall intensity was considered using a probability distribution, and a chance-constrained overland flow modeling approach was proposed to capture the generation of overland flow within conceptual distributed hydrologic simulations. The integrated modeling procedures were further demonstrated through a watershed of China Three Gorges Reservoir area, leading to an improved SLURP-TGR hydrologic model based on SLURP. Combined with rainfall thresholds determined to distinguish various magnitudes of daily rainfall totals, three levels of significance were simultaneously employed to examine the hydrologic-response simulation. Results showed that SLURP-TGR could enhance the model performance, and the deviation of runoff simulations was effectively controlled. However, rainfall thresholds were so crucial for reflecting the scaling effect of rainfall intensity that optimal levels of significance and rainfall threshold were 0.05 and 10 mm, respectively. As for the Xiangxi River watershed, the main runoff contribution came from interflow of the fast store. Although slight differences of overland flow simulations between SLURP and SLURP-TGR were derived, SLURP-TGR was found to help improve the simulation of peak flows, and would improve the overall modeling efficiency through adjusting runoff component simulations. Consequently, the developed modeling approach favors efficient representation of hydrological processes and would be expected to have a potential for wide applications. Copyright © 2015 Elsevier B.V. All rights reserved.
on the DPRK TPD-2 ballistic missile. A 3 degree-of-freedom ( 3DoF ) mathematical model was previously developed and used to simulate the trajectory...Characteristics(estimated) TPD-2 ICBM Data Input to Simulation(From ) Figure 3. Reach of TPD-2 Missile A 3DoF ballistic missile
performs the signal processing . Processor 30 performs a continuous sweep over the photodetector 38 output to isolate and amplify the optical signals ...December 2017 The below identified patent application is available for licensing. Requests for information should be addressed to...1 of 12 LOW PROBABILITY OF INTERCEPT LASER RANGE FINDER STATEMENT OF GOVERNMENT INTEREST  The invention described herein may be
Modelo de armadilha etanólica de interceptação de voo para captura de escolitíneos (Curculionidae: Scolytinae Ethanolic model of flight interception trap to capture scolytine (Curculionidae: Scolytinae
Augusto Bolson Murari
Full Text Available
Este estudo teve por objetivo desenvolver um modelo alternativo de armadilha etanólica de interceptação de insetos voadores, visando à redução dos custos relacionados aos levantamentos de insetos da subfamília Scolytinae (Curculionidae, realizados em ecossistemas florestais. O modelo de armadilha, denominado de PET-SM, foi confeccionado com materiais recicláveis: prato plástico, garrafa de polietileno (PET de dois litros, garrafa PET de 600 mL, e mangueira com álcool 96° GL empregado como atrativo. Em comparação a outros modelos utilizados para monitoramento de Scolytinae, o modelo PET-SM mostrou-se eficiente na captura, apresentando um maior número de espécies coletadas e oferecendo um menor custo de confecção.
This study aimed to develop an alternative model of trap for interception with ethanol for flying insects, in order to reduce the costs related to surveys of insects of the subfamily Scolytinae (Curculionidae, conducted in forest ecosystems. The model of trap, called PET-SM, was manufactured with recyclable materials: plastic plate, polyethylene (PET bottle of two liters, PET bottle of 600 mL, and a hose with alcohol 96 GL used as attractive. Compared to other models used to monitor Scolytinae, the PET-SM model proved to be effective for capture, presenting a greater number of species and offering a lower cost of manufacture.
Han, Jing-Cheng; Huang, Guohe; Huang, Yuefei; Zhang, Hua; Li, Zhong; Chen, Qiuwen
Lack of hydrologic process representation at the short time-scale would lead to inadequate simulations in distributed hydrological modeling. Especially for complex mountainous watersheds, surface runoff simulations are significantly affected by the overland flow generation, which is closely related to the rainfall characteristics at a sub-time step. In this paper, the sub-daily variability of rainfall intensity was considered using a probability distribution, and a chance-constrained overland flow modeling approach was proposed to capture the generation of overland flow within conceptual distributed hydrologic simulations. The integrated modeling procedures were further demonstrated through a watershed of China Three Gorges Reservoir area, leading to an improved SLURP-TGR hydrologic model based on SLURP. Combined with rainfall thresholds determined to distinguish various magnitudes of daily rainfall totals, three levels of significance were simultaneously employed to examine the hydrologic-response simulation. Results showed that SLURP-TGR could enhance the model performance, and the deviation of runoff simulations was effectively controlled. However, rainfall thresholds were so crucial for reflecting the scaling effect of rainfall intensity that optimal levels of significance and rainfall threshold were 0.05 and 10 mm, respectively. As for the Xiangxi River watershed, the main runoff contribution came from interflow of the fast store. Although slight differences of overland flow simulations between SLURP and SLURP-TGR were derived, SLURP-TGR was found to help improve the simulation of peak flows, and would improve the overall modeling efficiency through adjusting runoff component simulations. Consequently, the developed modeling approach favors efficient representation of hydrological processes and would be expected to have a potential for wide applications. - Highlights: • We develop an improved hydrologic model considering the scaling effect of rainfall. • A
Fathalli, Bilel; Pohl, Benjamin; Castel, Thierry; Safi, Mohamed Jomâa
Temporal and spatial variability of rainfall over Tunisia (at 12 km spatial resolution) is analyzed in a multi-year (1992-2011) ten-member ensemble simulation performed using the WRF model, and a sample of regional climate hindcast simulations from Euro-CORDEX. RCM errors and skills are evaluated against a dense network of local rain gauges. Uncertainties arising, on the one hand, from the different model configurations and, on the other hand, from internal variability are furthermore quantified and ranked at different timescales using simple spread metrics. Overall, the WRF simulation shows good skill for simulating spatial patterns of rainfall amounts over Tunisia, marked by strong altitudinal and latitudinal gradients, as well as the rainfall interannual variability, in spite of systematic errors. Mean rainfall biases are wet in both DJF and JJA seasons for the WRF ensemble, while they are dry in winter and wet in summer for most of the used Euro-CORDEX models. The sign of mean annual rainfall biases over Tunisia can also change from one member of the WRF ensemble to another. Skills in regionalizing precipitation over Tunisia are season dependent, with better correlations and weaker biases in winter. Larger inter-member spreads are observed in summer, likely because of (1) an attenuated large-scale control on Mediterranean and Tunisian climate, and (2) a larger contribution of local convective rainfall to the seasonal amounts. Inter-model uncertainties are globally stronger than those attributed to model's internal variability. However, inter-member spreads can be of the same magnitude in summer, emphasizing the important stochastic nature of the summertime rainfall variability over Tunisia.
Full Text Available Reliable real-time forecasts of the discharge can provide valuable information for the management of a river basin system. For the management of ecological releases even discharge forecasts with moderate accuracy can be beneficial. Sequential data assimilation using the Ensemble Kalman Filter provides a tool that is both efficient and robust for a real-time modelling framework. One key parameter in a hydrological system is the soil moisture, which recently can be characterized by satellite based measurements. A forecasting framework for the prediction of discharges is developed and applied to three different sub-basins of the Zambezi River Basin. The model is solely based on remote sensing data providing soil moisture and rainfall estimates. The soil moisture product used is based on the back-scattering intensity of a radar signal measured by a radar scatterometer. These soil moisture data correlate well with the measured discharge of the corresponding watershed if the data are shifted by a time lag which is dependent on the size and the dominant runoff process in the catchment. This time lag is the basis for the applicability of the soil moisture data for hydrological forecasts. The conceptual model developed is based on two storage compartments. The processes modeled include evaporation losses, infiltration and percolation. The application of this model in a real-time modelling framework yields good results in watersheds where soil storage is an important factor. The lead time of the forecast is dependent on the size and the retention capacity of the watershed. For the largest watershed a forecast over 40 days can be provided. However, the quality of the forecast increases significantly with decreasing prediction time. In a watershed with little soil storage and a quick response to rainfall events, the performance is relatively poor and the lead time is as short as 10 days only.
Nguyen, Truong-Huy; El Outayek, Sarah; Lim, Sun Hee; Nguyen, Van-Thanh-Van
Many probability distributions have been developed to model the annual maximum rainfall series (AMS). However, there is no general agreement as to which distribution should be used due to the lack of a suitable evaluation method. This paper presents hence a general procedure for assessing systematically the performance of ten commonly used probability distributions in rainfall frequency analyses based on their descriptive as well as predictive abilities. This assessment procedure relies on an extensive set of graphical and numerical performance criteria to identify the most suitable models that could provide the most accurate and most robust extreme rainfall estimates. The proposed systematic assessment approach has been shown to be more efficient and more robust than the traditional model selection method based on only limited goodness-of-fit criteria. To test the feasibility of the proposed procedure, an illustrative application was carried out using 5-min, 1-h, and 24-h annual maximum rainfall data from a network of 21 raingages located in the Ontario region in Canada. Results have indicated that the GEV, GNO, and PE3 models were the best models for describing the distribution of daily and sub-daily annual maximum rainfalls in this region. The GEV distribution, however, was preferred to the GNO and PE3 because it was based on a more solid theoretical basis for representing the distribution of extreme random variables.
Full Text Available This paper investigates the effect of sub-grid rainfall variability on the simulation of land surface hydrologic processes of three regions (Europe, Africa and Amazon with contrasting precipitation and vegetation characteristics. The sub-grid rainfall variability is defined in terms of the rainfall coverage fraction at the model's grid cells, and the statistical distribution of rain rates within the rain-covered areas. A statistical-dynamic approach is devised to incorporate the above variability properties into the canopy interception process of a land surface model. Our results reveal that incorporation of sub-grid rainfall variability significantly impacts the land-atmosphere water vapor exchanges. Specifically, it alters the partitioning between runoff and total evapotranspiration as well as the partitioning among the three components of evapotranspiration (canopy interception loss, ground evaporation and plant transpiration. This further influences the soil water, and to a lesser effect surface/vegetation temperatures and surface heat fluxes. It is shown that, overall, rainfall variability exerts less of an impact on the land-atmosphere flux exchanges over Europe compared to Africa and Amazon.
Panagos, Panos; Ballabio, Cristiano; Borrelli, Pasquale; Meusburger, Katrin; Klik, Andreas; Rousseva, Svetla; Tadić, Melita Perčec; Michaelides, Silas; Hrabalíková, Michaela; Olsen, Preben; Aalto, Juha; Lakatos, Mónika; Rymszewicz, Anna; Dumitrescu, Alexandru; Beguería, Santiago; Alewell, Christine
Rainfall is one the main drivers of soil erosion. The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most commonly expressed as the R-factor in the USLE model and its revised version, RUSLE. At national and continental levels, the scarce availability of data obliges soil erosion modellers to estimate this factor based on rainfall data with only low temporal resolution (daily, monthly, annual averages). The purpose of this study is to assess rainfall erosivity in Europe in the form of the RUSLE R-factor, based on the best available datasets. Data have been collected from 1541 precipitation stations in all European Union (EU) Member States and Switzerland, with temporal resolutions of 5 to 60 min. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 min using linear regression functions. Precipitation time series ranged from a minimum of 5 years to a maximum of 40 years. The average time series per precipitation station is around 17.1 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression (GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 1 km resolution. The covariates used for the R-factor interpolation were climatic data (total precipitation, seasonal precipitation, precipitation of driest/wettest months, average temperature), elevation and latitude/longitude. The mean R-factor for the EU plus Switzerland is 722 MJ mm ha(-1) h(-1) yr(-1), with the highest values (>1000 MJ mm ha(-1) h(-1) yr(-1)) in the Mediterranean and alpine regions and the lowest (<500 MJ mm ha(-1) h(-1) yr(-1)) in the Nordic countries. The erosivity density (erosivity normalised to annual precipitation amounts) was also the highest in Mediterranean regions which implies high risk for erosive events and floods
Yan, Eugene [Argonne National Lab. (ANL), Argonne, IL (United States); Pierce, Julia [Argonne National Lab. (ANL), Argonne, IL (United States); Mahat, Vinod [Argonne National Lab. (ANL), Argonne, IL (United States); Jared, Alissa [Argonne National Lab. (ANL), Argonne, IL (United States); Collis, Scott [Argonne National Lab. (ANL), Argonne, IL (United States); Verner, Duane [Argonne National Lab. (ANL), Argonne, IL (United States); Wall, Thomas [Argonne National Lab. (ANL), Argonne, IL (United States)
This project is a part of the Regional Resiliency Assessment Program, led by the Department of Homeland Security, to address flooding hazards of regional significance for Portland, Maine. The pilot study was performed by Argonne National Laboratory to identify differences in spatial rainfall distributions between the radar-derived and rain-gauge rainfall datasets and to evaluate their impacts on urban flooding. The flooding impact analysis utilized a high-resolution 2-dimensional (2-D) hydrodynamic model (15 ft by 15 ft) incorporating the buildings, streets, stream channels, hydraulic structures, an existing city storm drain system, and assuming a storm surge along the coast coincident with a heavy rainfall event. Two historical storm events from April 16, 2007, and September 29, 2015, were selected for evaluation. The radar-derived rainfall data at a 200-m resolution provide spatially-varied rainfall patterns with a wide range of intensities for each event. The resultant maximum flood depth using data from a single rain gauge within the study area could be off (either under- or over-estimated) by more than 10% in the 2007 storm and more than 60% in the 2015 storm compared to the radar-derived rainfall data. The model results also suggest that the inundation area with a flow depth at or greater than 0.5 ft could reach 11% (2007 storm) and 17% (2015 storm) of the total study area, respectively. The lowland areas within the neighborhoods of North Deering, East Deering, East and West Baysides and northeastern Parkside, appear to be more vulnerable to the flood hazard in both storm events. The high-resolution 2-D hydrodynamic model with high-resolution radar-derived rainfall data provides an excellent tool for detailed urban flood analysis and vulnerability assessment. The model developed in this study could be potentially used to evaluate any proposed mitigation measures and optimize their effects in the future for Portland, ME.
Rajesh, P. V.; Pattnaik, S.; Rai, D.; Osuri, K. K.; Mohanty, U. C.; Tripathy, S.
In 2013, Indian summer monsoon witnessed a very heavy rainfall event (>30 cm/day) over Uttarakhand in north India, claiming more than 5000 lives and property damage worth approximately 40 billion USD. This event was associated with the interaction of two synoptic systems, i.e., intensified subtropical westerly trough over north India and north-westward moving monsoon depression formed over the Bay of Bengal. The event had occurred over highly variable terrain and land surface characteristics. Although global models predicted the large scale event, they failed to predict realistic location, timing, amount, intensity and distribution of rainfall over the region. The goal of this study is to assess the impact of land state conditions in simulating this severe event using a high resolution mesoscale model. The land conditions such as multi-layer soil moisture and soil temperature fields were generated from High Resolution Land Data Assimilation (HRLDAS) modelling system. Two experiments were conducted namely, (1) CNTL (Control, without land data assimilation) and (2) LDAS, with land data assimilation (i.e., with HRLDAS-based soil moisture and temperature fields) using Weather Research and Forecasting (WRF) modelling system. Initial soil moisture correlation and root mean square error for LDAS is 0.73 and 0.05, whereas for CNTL it is 0.63 and 0.053 respectively, with a stronger heat low in LDAS. The differences in wind and moisture transport in LDAS favoured increased moisture transport from Arabian Sea through a convectively unstable region embedded within two low pressure centers over Arabian Sea and Bay of Bengal. The improvement in rainfall is significantly correlated to the persistent generation of potential vorticity (PV) in LDAS. Further, PV tendency analysis confirmed that the increased generation of PV is due to the enhanced horizontal PV advection component rather than the diabatic heating terms due to modified flow fields. These results suggest that, two
Caviedes-Voullième, Daniel; Domin, Andrea; Hinz, Christoph
The quantitative description and prediction of hydrological response of hillslopes or hillslope-scale catchments to rainfall events is becoming evermore relevant. At the hillslope scale, the onset of runoff and the overall rainfall-runoff transformation are controlled by multiple interacting small-scale processes, that, when acting together produce a response described in terms of hydrological variables well-defined at the catchment and hillslope scales. We hypothesize that small scale features such microtopography of the land surface will will govern large scale signatures of temporal runoff evolution. This can be tested directly by numerical modelling of well-defined surface geometries and adequate process description. It requires a modelling approach consistent with fundamental fluid mechanics, well-designed numerical methods, and computational efficiency. In this work, an idealized rectangular domain representing a hillslope with an idealized 2D sinusoidal microtopography is studied by simulating surface water redistribution by means of a 2D diffusive-wave (zero-inertia) shallow water model. By studying more than 500 surfaces and performing extensive sensitivity analysis forced by a single rainfall pulse, the dependency of characteristic hydrological responses to microtopographical properties was assessed. Despite of the simplicity of periodic surface and the rain event, results indicate complex surface flow dynamics during the onset of runoff observed at the macro and micro scales. Macro scale regimes were defined in terms of characteristics hydrograph shapes and those were related to surface geometry. The reference regime was defined for smooth topography and consisted of a simple hydrograph with smoothly rising and falling limbs with an intermediate steady state. In constrast, rough surface geometry yields stepwise rising limbs and shorter steady states. Furthermore, the increase in total infiltration over the whole domain relative to the smooth reference
Rainfall data for the Netherlands have been used in this study to investigate aspects of heterogeneity of rainfall, in particular local differences in rainfall levels, time trends in rainfall, and local differences in rainfall trend. The possible effect of urbanization and industrialization on the
Hazenberg, P.; Leijnse, H.; Torfs, P.; Uijlenhoet, R.; Weerts, A.; Reggiani, P.; Delobbe, L.
In the southern Ardennes region of Belgium near the border with Luxembourg, the Royal Meteorological Institute of Belgium (RMI) installed a C-band Doppler weather radar at an elevation of 600 m in the year 2001. This volumetric weather radar scans over multiple elevations at a temporal resolution of 5 minutes. The current study explores the possibility of using the volumetric information of the precipitation field to correct for the effects of the Vertical Profile of Reflectivity (VPR) over the period October 1, 2002 until March 31, 2003. During this winter half year storm events are mainly stratiform, giving rise to bright band effects which can decrease the performance of the radar. Previous studies have shown multiple drawbacks in applying a single estimated VPR profile to correct such reflectivity data. Therefore, the focus here is on the temporal variability of the VPR as measured by the radar and its variability over different spatial scales. This information is applied to generate a number of possible rainfall fields. These realizations are employed to try to quantify some of the discrepancies in precipitation intensities as estimated by the weather radar and those measured by a raingauge network. The final step then is to assess their potential within a distributed rainfall runoff model. The 1597 km2 Ourthe catchment lies within 60 km of the radar. Over this medium sized watershed ten raingauges measuring at an hourly interval are more or less equally distributed. Near the outlet discharge data are collected at the same time step. The distributed hydrological Representative Elementary Watershed (REW) model is applied to model the hydrological behavior of the Ourthe over the six month period. The benefits of the high spatial and temporal resolution of weather radar data compared to a conventional raingauge network plus the possibility of generating multiple realizations of the precipitation field are expected to yield more information about the hydrological
Representation of precipitation is one of the most difficult aspects of modeling post-fire runoff and erosion and also one of the most sensitive input parameters to rainfall-runoff models. The impact of post-fire convective rainstorms, especially in semi-arid watersheds, depends on the overlap betwe...
Ng, Gene-Hua Crystal; Bedford, David R.; Miller, David M.
The perennial shrub Larrea tridentata is widely successful in North American warm deserts but is also susceptible to climatic perturbations. Understanding its response to rainfall variability requires consideration of multiple timescales. We examine intra-annual to multi-year relationships using model simulations of soil moisture and vegetation growth over 50 years in the Mojave National Preserve in southeastern California (USA). Ecohydrological model parameters are conditioned on field and remote sensing data using an ensemble Kalman filter. Although no specific periodicities were detected in the rainfall record, simulated leaf-area-index exhibits multi-year dynamics that are driven by multi-year (∼3-years) rains, but with up to a 1-year delay in peak response. Within a multi-year period, Larrea tridentata is more sensitive to winter rains than summer. In the most active part of the root zone (above ∼80 cm), >1-year average soil moisture drives vegetation growth, but monthly average soil moisture is controlled by root uptake. Moisture inputs reach the lower part of the root zone (below ∼80 cm) infrequently, but once there they can persist over a year to help sustain plant growth. Parameter estimates highlight efficient plant physiological properties facilitating persistent growth and high soil hydraulic conductivity allowing deep soil moisture stores. We show that soil moisture as an ecological indicator is complicated by bidirectional interactions with vegetation that depend on timescale and depth. Under changing climate, Larrea tridentata will likely be relatively resilient to shorter-term moisture variability but will exhibit higher sensitivity to shifts in seasonal to multi-year moisture inputs.
Ng, Gene-Hua Crystal; Bedford, David R.; Miller, David M.
The perennial shrub Larrea tridentata is widely successful in North American warm deserts but is also susceptible to climatic perturbations. Understanding its response to rainfall variability requires consideration of multiple time scales. We examine intra-annual to multiyear relationships using model simulations of soil moisture and vegetation growth over 50 years in the Mojave National Preserve in southeastern California (USA). Ecohydrological model parameters are conditioned on field and remote sensing data using an ensemble Kalman filter. Although no specific periodicities were detected in the rainfall record, simulated leaf-area-index exhibits multiyear dynamics that are driven by multiyear (˜3 years) rains, but with up to a 1 year delay in peak response. Within a multiyear period, Larrea tridentata is more sensitive to winter rains than summer. In the most active part of the root zone (above ˜80 cm), >1 year average soil moisture drives vegetation growth, but monthly average soil moisture is controlled by root uptake. Moisture inputs reach the lower part of the root zone (below ˜80 cm) infrequently, but once there they can persist over a year to help sustain plant growth. Parameter estimates highlight efficient plant physiological properties facilitating persistent growth and high soil hydraulic conductivity allowing deep soil moisture stores. We show that soil moisture as an ecological indicator is complicated by bidirectional interactions with vegetation that depend on time scale and depth. Under changing climate, Larrea tridentata will likely be relatively resilient to shorter-term moisture variability but will exhibit higher sensitivity to shifts in seasonal to multiyear moisture inputs.
Jiang Zhe; Li Xiao-Fan; Zhou Yu-Shu; Gao Shou-Ting
The effects of sea surface temperature (SST), cloud radiative and microphysical processes, and diurnal variations on rainfall statistics are documented with grid data from the two-dimensional equilibrium cloud-resolving model simulations. For a rain rate of higher than 3 mm·h −1 , water vapor convergence prevails. The rainfall amount decreases with the decrease of SST from 29 °C to 27 °C, the inclusion of diurnal variation of SST, or the exclusion of microphysical effects of ice clouds and radiative effects of water clouds, which are primarily associated with the decreases in water vapor convergence. However, the amount of rainfall increases with the increase of SST from 29 °C to 31 °C, the exclusion of diurnal variation of solar zenith angle, and the exclusion of the radiative effects of ice clouds, which are primarily related to increases in water vapor convergence. For a rain rate of less than 3 mm·h −1 , water vapor divergence prevails. Unlike rainfall statistics for rain rates of higher than 3 mm·h −1 , the decrease of SST from 29 °C to 27 °C and the exclusion of radiative effects of water clouds in the presence of radiative effects of ice clouds increase the rainfall amount, which corresponds to the suppression in water vapor divergence. The exclusion of microphysical effects of ice clouds decreases the amount of rainfall, which corresponds to the enhancement in water vapor divergence. The amount of rainfall is less sensitive to the increase of SST from 29 °C to 31 °C and to the radiative effects of water clouds in the absence of the radiative effects of ice clouds. (electromagnetism, optics, acoustics, heat transfer, classical mechanics, and fluid dynamics)
El Kenawy, Ahmed M.
Many arid and semi-arid regions have sparse precipitation observing networks, which limits the capacity for detailed hydrological modelling, water resources management and flood forecasting efforts. The objective of this work is to evaluate the utility of relatively high-spatial resolution rainfall products to reproduce observed multi-decadal rainfall characteristics such as climatologies, anomalies and trends over Saudi Arabia. Our study compares the statistical characteristics of rainfall from 53 observatories over the reference period 1965-2005, with rainfall data from six widely used gauge-based products, including APHRODITE, GPCC, PRINCETON, UDEL, CRU and PREC/L. In addition, the performance of three global climate models (GCMs), including CCSM4, EC-EARTH and MRI-I-CGCM3, integrated as part of the Fifth Coupled Model Intercomparison Project (CMIP5), was also evaluated. Results indicate that the gauge-based products were generally skillful in reproducing rainfall characteristics in Saudi Arabia. In most cases, the gauge-based products were also able to capture the annual cycle, anomalies and climatologies of observed data, although significant inter-product variability was observed, depending on the assessment metric being used. In comparison, the GCM-based products generally exhibited poor performance, with larger biases and very weak correlations, particularly during the summertime. Importantly, all products generally failed to reproduce the observed long-term seasonal and annual trends in the region, particularly during the dry seasons (summer and autumn). Overall, this work suggests that selected gauge-based products with daily (APHRODITE and PRINCETON) and monthly (GPCC and CRU) resolutions show superior performance relative to other products, implying that they may be the most appropriate data source from which multi-decadal variations of rainfall can be investigated at the regional scale over Saudi Arabia. Discriminating these skillful products is
Full Text Available Evidence is presented that coupled ocean-atmosphere models can already outscore computationally less expensive atmospheric models. However, if the atmospheric models are forced with highly skillful SST predictions, they may still be a very strong...
Full Text Available Stormwater runoff poses serious environmental problems and public health issues in Rwanda, a tropical country that is increasingly suffering from severe floods, landslides, soil erosion and water pollution. Using the WetSpa Extension model, this study assessed the changes in rainfall runoff depth in Rwanda from 1990 to 2016 in response to precipitation and land use changes. Our results show that Rwanda has experienced a significant conversion of natural forest and grassland to cropland and built-up areas. During the period 1990–2016, 7090.02 km2 (64.5% and 1715.26 km2 (32.1% of forest and grassland covers were lost, respectively, while the cropland and built-up areas increased by 135.3% (8503.75 km2 and 304.3% (355.02 km2, respectively. According to our estimates, the land use change effect resulted in a national mean runoff depth increase of 2.33 mm/year (0.38%. Although precipitation change affected the inter-annual fluctuation of runoff, the long-term trend of runoff was dominated by land use change. The top five districts that experienced the annual runoff depth increase (all >3.8 mm/year are Rubavu, Nyabihu, Ngororero, Gakenke, and Musanze. Their annual runoff depths increased at a rate of >3.8 mm/year during the past 27 years, due to severe deforestation (ranging from 62% to 85% and cropland expansion (ranging from 123% to 293%. These areas require high priority in runoff control using terracing in croplands and rainwater harvesting systems such as dam/reservoirs, percolation tanks, storage tanks, etc. The wet season runoff was three times higher than the dry season runoff in Rwanda; appropriate rainwater management and reservation could provide valuable irrigation water for the dry season or drought years (late rainfall onsets or early rainfall cessations. It was estimated that a reservation of 30.5% (3.99 km3 of the runoff in the wet season could meet the cropland irrigation water gap during the dry season in 2016.
Zheng, Y.; Luo, X.; Lin, Z.
The urban environment has a variety of Persistent Toxic Substances (PTS), such as Polycyclic Aromatic Hydrocarbons (PAHs) and mercury. Soil in pervious lands and dust deposited on impervious surfaces are two major sinks of PTSs in urbanized areas, which could contribute significant nonpoint source loadings of PTSs to adjacent waterbodies during rainfall-runoff events and therefore jeopardize aquatic ecosystems. However, PTSs have been much less understood regarding their export mechanisms in urban land uses, and efforts to model nonpoint source pollution processes of PTSs have been rare. We designed and performed in-lab rainfall-runoff simulation experiments to investigate transport of PAHs and mercury by runoff from urban soils. Organic petrology analysis (OPA) techniques were introduced to analyze the soil and sediment compositions. Our study revealed the limitation of the classic enrichment theory which attributes enrichment of pollutants in eroded sediment solely to the sediment's particle size distribution and adopts simple relationships between enrichment ratio and sediment flux. We found that carbonaceous materials (CMs) in soil are the direct and major sorbents for PAHs and mercury, and highly different in content, mobility and adsorption capacity for the PTSs. Anthropogenic CMs like black carbon components largely control the transport of soil PAHs, while humic substances have a dominant influence on the transport of soil mercury. A model was further developed to estimate the enrichment ratio of PAHs, which innovatively applies the fugacity concept.We also conducted field studies on export of PAHs by runoff from urban roads. A variable time-step model was developed to simulate the continuous cycles of PAH buildup and washoff on urban roads. The dependence of the pollution level on antecedent weather conditions was investigated and embodied in the model. The applicability of this approach and its value to environmental management was demonstrated by a case
Zabret, Katarina; Rakovec, Jože; Šraj, Mojca
Rainfall partitioning is an important part of the ecohydrological cycle, influenced by numerous variables. Rainfall partitioning for pine (Pinus nigra Arnold) and birch (Betula pendula Roth.) trees was measured from January 2014 to June 2017 in an urban area of Ljubljana, Slovenia. 180 events from more than three years of observations were analyzed, focusing on 13 meteorological variables, including the number of raindrops, their diameter, and velocity. Regression tree and boosted regression tree analyses were performed to evaluate the influence of the variables on rainfall interception loss, throughfall, and stemflow in different phenoseasons. The amount of rainfall was recognized as the most influential variable, followed by rainfall intensity and the number of raindrops. Higher rainfall amount, intensity, and the number of drops decreased percentage of rainfall interception loss. Rainfall amount and intensity were the most influential on interception loss by birch and pine trees during the leafed and leafless periods, respectively. Lower wind speed was found to increase throughfall, whereas wind direction had no significant influence. Consideration of drop size spectrum properties proved to be important, since the number of drops, drop diameter, and median volume diameter were often recognized as important influential variables.
Thirel, Guillaume; Delaigue, Olivier; Coron, Laurent; Perrin, Charles; Andréassian, Vazken
Lumped hydrological models are useful and convenient tools for research, engineering and educational purposes. They propose catchment-scale representations of the precipitation-discharge relationship. Thanks to their limited data requirements, they can be easily implemented and run. With such models, it is possible to simulate a number of hydrological key processes over the catchment with limited structural and parametric complexity, typically evapotranspiration, runoff, underground losses, etc. The Hydrology Group at Irstea (Antony) has been developing a suite of rainfall-runoff models over the past 30 years with the main objectives of designing models as efficient as possible in terms of streamflow simulation, applicable to a wide range of catchments and having low data requirements. This resulted in a suite of models running at different time steps (from hourly to annual) applicable for various issues including water balance estimation, forecasting, simulation of impacts and scenario testing. Recently, Irstea has developed an easy-to-use R-package (R Core Team, 2016), called airGR (Coron et al., 2016, 2017), to make these models widely available. It includes: - the water balance annual GR1A model, - the monthly GR2M model, - three versions of the daily model, namely GR4J, GR5J and GR6J, - the hourly GR4H model, - a degree-day snow model CemaNeige. The airGR package has been designed to facilitate the use by non-expert users and allow the addition of evaluation criteria, models or calibration algorithm selected by the end-user. Each model core is coded in FORTRAN to ensure low computational time. The other package functions (i.e. mainly the calibration algorithm and the efficiency criteria) are coded in R. The package is also used for educational purposes. It allows for convenient implementation of model inter-comparisons and large sample hydrology experiments. The airGR package undergoes continuous developments for improving the efficiency, computational time
Chaudhuri, Sutapa; Goswami, Sayantika; Das, Debanjana; Middey, Anirban
Forecasting summer monsoon rainfall with precision becomes crucial for the farmers to plan for harvesting in a country like India where the national economy is mostly based on regional agriculture. The forecast of monsoon rainfall based on artificial neural network is a well-researched problem. In the present study, the meta-heuristic ant colony optimization (ACO) technique is implemented to forecast the amount of summer monsoon rainfall for the next day over Kolkata (22.6°N, 88.4°E), India. The ACO technique belongs to swarm intelligence and simulates the decision-making processes of ant colony similar to other adaptive learning techniques. ACO technique takes inspiration from the foraging behaviour of some ant species. The ants deposit pheromone on the ground in order to mark a favourable path that should be followed by other members of the colony. A range of rainfall amount replicating the pheromone concentration is evaluated during the summer monsoon season. The maximum amount of rainfall during summer monsoon season (June—September) is observed to be within the range of 7.5-35 mm during the period from 1998 to 2007, which is in the range 4 category set by the India Meteorological Department (IMD). The result reveals that the accuracy in forecasting the amount of rainfall for the next day during the summer monsoon season using ACO technique is 95 % where as the forecast accuracy is 83 % with Markov chain model (MCM). The forecast through ACO and MCM are compared with other existing models and validated with IMD observations from 2008 to 2012.
Nourani, Vahid; Fard, Ahmad Fakheri; Niazi, Faegheh; Gupta, Hoshin V.; Goodrich, David C.; Kamran, Khalil Valizadeh
This study investigates the effect of land use on the Geomorphological Cascade of Unequal linear Reservoirs (GCUR) model using the Normalized Difference Vegetation Index (NDVI) derived from remotely sensed data as a measure of land use. The proposed modeling has two important aspects: it considers the effects of both watershed geomorphology and land use/cover, and it requires only one parameter to be estimated through the use of observed rainfall-runoff data. Geographic Information System (GIS) tools are employed to determine the parameters associated with watershed geomorphology, and the Vegetation Index parameter is extracted from historical Landsat images. The modeling is applied via three formulations to a watershed located in Southeastern Arizona, which consists of two gaged sub-watersheds with different land uses. The results show that while all of the formulations generate forecasts of the basin outlet hydrographs with acceptable accuracy, only the two formulations that consider the effects of land cover (using NDVI) provide acceptable results at the outlets of the sub-watersheds.
McGuire, Luke; Rengers, Francis K.; Kean, Jason W.; Coe, Jeffrey A.; Mirus, Benjamin B.; Baum, Rex L.; Godt, Jonathan W.
More than 1100 debris flows were mobilized from shallow landslides during a rainstorm from 9 to 13 September 2013 in the Colorado Front Range, with the vast majority initiating on sparsely vegetated, south facing terrain. To investigate the physical processes responsible for the observed aspect control, we made measurements of soil properties on a densely forested north facing hillslope and a grassland-dominated south facing hillslope in the Colorado Front Range and performed numerical modeling of transient changes in soil pore water pressure throughout the rainstorm. Using the numerical model, we quantitatively assessed interactions among vegetation, rainfall interception, subsurface hydrology, and slope stability. Results suggest that apparent cohesion supplied by roots was responsible for the observed connection between debris flow initiation and slope aspect. Results suggest that future climate-driven modifications to forest structure could substantially influence landslide hazards throughout the Front Range and similar water-limited environments where vegetation communities may be more susceptible to small variations in climate.
Chardon, J.; Mathevet, T.; Le Lay, M.; Gailhard, J.
In the context of a national energy company (EDF : Electricité de France), hydro-meteorological forecasts are necessary to ensure safety and security of installations, meet environmental standards and improve water ressources management and decision making. Hydrological ensemble forecasts allow a better representation of meteorological and hydrological forecasts uncertainties and improve human expertise of hydrological forecasts, which is essential to synthesize available informations, coming from different meteorological and hydrological models and human experience. An operational hydrological ensemble forecasting chain has been developed at EDF since 2008 and is being used since 2010 on more than 30 watersheds in France. This ensemble forecasting chain is characterized ensemble pre-processing (rainfall and temperature) and post-processing (streamflow), where a large human expertise is solicited. The aim of this paper is to compare 2 hydrological ensemble post-processing methods developed at EDF in order improve ensemble forecasts reliability (similar to Monatanari &Brath, 2004; Schaefli et al., 2007). The aim of the post-processing methods is to dress hydrological ensemble forecasts with hydrological model uncertainties, based on perfect forecasts. The first method (called empirical approach) is based on a statistical modelisation of empirical error of perfect forecasts, by streamflow sub-samples of quantile class and lead-time. The second method (called dynamical approach) is based on streamflow sub-samples of quantile class and streamflow variation, and lead-time. On a set of 20 watersheds used for operational forecasts, results show that both approaches are necessary to ensure a good post-processing of hydrological ensemble, allowing a good improvement of reliability, skill and sharpness of ensemble forecasts. Th