The Coupled Routing and Excess STorage model (CREST, jointly developed by the University of Oklahoma and NASA SERVIR) is a distributed hydrological model developed to simulate the spatial and temporal variation of land surface, and subsurface water fluxes and storages by cell-to-cell simulation. CREST's distinguishing characteristics include: (1) distributed rainfall-runoff generation and cell-to-cell routing; (2) coupled runoff generation and routing via three feedback mechanisms; and (3) representation of sub-grid cell variability of soil moisture storage capacity and sub-grid cell routing (via linear reservoirs). The coupling between the runoff generation and routing mechanisms allows detailed and realistic treatment of hydrological variables such as soil moisture. Furthermore, the representation of soil moisture variability and routing processes at the sub-grid scale enables the CREST model to be readily scalable to multi-scale modelling research. This paper presents the model development and demonstrates its applicability for a case study in the Nzoia basin located in Lake Victoria, Africa.Key words distributed hydrological model; cell-to-cell routing; excess storage; water balance; CREST; Lake Victoria
Le modèle hydrologique distribué couplé routage et stockage des excédents (CREST)Résumé Le modèle couplé routage et stockage des excédents (CREST, développé conjointement par l'Université de l'Oklahoma et NASA SERVIR) est un modèle hydrologique distribué développé pour simuler les variations spatiales et temporelles des flux d'eau de surface et souterraine ainsi que les stockages, par simulation de cellule à cellule. Les caractéristiques distinctives de CREST sont les suivantes: (1) production pluie-débit distribuée et routage de cellule à cellule; (2) couplage de la production et du routage du ruissellement via trois mécanismes de rétroaction; et (3) représentation de la variabilité sub-cellulaire de la capacité de stockage en eau du sol et du routage infra-cellulaire (via des réservoirs linéaires). Le couplage entre la genèse du ruissellement et les mécan-ismes de routage permet un traitement détaillé et réaliste des variables hydrologiques telles que l'humidité du sol. En outre, la représentation de la variabilité de l'humidité du sol et des processus de routage à l'échelle subcellulaire permet au modèle CREST d'être facilement étendu à la recherche sur la modélisation multi-échelles. Cet article présente le développement du modèle et démontre son applicabilité pour une étude de cas dans le bassin de la Nzoia, Lac Victoria, Afrique.Mots clefs modèle hydrologique distribué; routage de cellule à cellule; stockage des excédents; bilan hydrique; CREST; Lac Victoria
Many researchers seek to take advantage of the recently available and virtually uninterrupted supply of satellite-based rainfall information as an alternative and supplement to the ground-based observations in order to implement a cost-effective flood prediction in many under-gauged regions around the world. Recently, NASA Applied Science Program has partnered with USAID and African-RCMRD to implement an operational water-hazard warning system, SERVIR-Africa. The ultimate goal of the project is to build up disaster management capacity in East Africa by providing local governmental officials and international aid organizations a practical decision-support tool in order to better assess emerging flood impacts and to quantify spatial extent of flood risk, as well as to respond to such flood emergencies more expediently. The objective of this article is to evaluate the applicability of integrating NASA's standard satellite precipitation product with a flood prediction model for disaster management in Nzoia, sub-basin of Lake Victoria, Africa. This research first evaluated the TMPA real-time rainfall data against gauged rainfall data from the year 2002 through 2006. Then, the gridded Xinanjiang Model was calibrated to Nzoia basin for period of 1985-2006. Benchmark streamflow simulations were produced with the calibrated hydrological model using the rain gauge and observed streamflow data. Afterward, continuous discharge predictions forced by TMPA 3B42RT real-time data from 2002 through 2006 were simulated, and acceptable results were obtained in comparison with the benchmark performance according to the designated statistic indices such as bias ratio (20%) and NSCE (0.67). Moreover, it is identified that the flood prediction results were improved with systematically bias-corrected TMPA rainfall data with less bias (3.6%) and higher NSCE (0.71). Although the results justify to suggest to us that TMPA real-time data can be acceptably used to drive hydrological models for flood prediction purpose in Nzoia basin, continuous progress in space-borne rainfall estimation technology toward higher accuracy and higher spatial resolution is highly appreciated. Finally, it is also highly recommended that to increase flood forecasting lead time, more reliable and more accurate short-or medium-range quantitative precipitation forecasts is a must.
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