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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

This study describes and demonstrates different techniques for surface fitting daily environmental hazards data of particulate matter with aerodynamic diameter less than or equal to 2.5 m (PM 2.5 ) for the purpose of integrating respiratory health and environmental data for the Centers for Disease Control and Prevention (CDC) pilot study of Health and Environment Linked for Information Exchange (HELIX)-Atlanta. It presents a methodology for estimating daily spatial surfaces of ground-level PM 2.5 concentrations using the B-Spline and inverse distance weighting (IDW) surface-fitting techniques, leveraging National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectrometer (MODIS) data to complement U.S. Environmental Protection Agency (EPA) ground observation data. The study used measurements of ambient PM 2.5 from the EPA database for the year 2003 as well as PM 2.5 estimates derived from NASA's satellite data. Hazard data have been processed to derive the surrogate PM 2.5 exposure estimates. This paper shows that merging MODIS remote sensing data with surface observations of PM 2.5 not only provides a more complete daily representation of PM 2.5 than either dataset alone would allow, but it also reduces the errors in the PM 2.5 -estimated surfaces. The results of this study also show that IMPLICATIONSThe described method of estimating concentrations of PM 2.5 by merging NASA MODIS remote sensing data with surface observations provides a more complete daily representation of PM 2.5 than either dataset alone, and it reduces the errors in the PM 2.5 -estimated surfaces with respect to observations. These new data products have the potential to serve as a tool for environmental public health surveillance to monitor trends and as an early warning system for prevention of human exposure to potential hazards. Such continuous spatial surfaces of environmental hazards as those developed in this study would enable linking environmental hazards to health outcomes on the grid-aggregated level as well as the individual level at the geographic locations of patients' residences.