Titlebook: Distributed Hydrologic Modeling Using GIS; Baxter E. Vieux Book 2016Latest edition Springer Science+Business Media Dordrecht 2016 Distribu

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Spatial Variability Measuring Information Content, for capturing the spatial variability of a parameter or input? The model computational elements, whether finite element or difference, require parameter values that are representative of the grid cell. The composite for the model should then be representative of the spatial variation found in the w

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Infiltration, areas poses a challenge to distributed watershed modeling. Accuracy may vary depending on how well the soil maps represent the soil and the hydrologic conditions controlling the process across scales from field to watershed. How the soil properties are represented in the distributed model, its conf

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Hydraulic Roughness,ortant since land use/cover categories must be interpreted to yield a hydraulic parameter that governs the relationship between runoff depth and velocity. Despite what the map classification indicates, a more important aspect is the hydraulic roughness that runoff experiences as it flows over the la

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Watersheds and Drainage Networks,ing the terrain and drainage network. Processing steps and DEM cell size affect the land surface slope, drainage network length and connectivity properties. Derived drainage networks and the hydraulic parameters used to represent the conveyance of runoff to the outlet of the river basin are dependen

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Distributed Precipitation Estimation,ts and therefore may not accurately reflect the spatial distribution of rainfall, especially from convective storms. Interest in using radar estimates of rainfall in distributed modeling comes from the desire to reduce errors due to imprecise knowledge of rainfall distribution in time and space. Tra

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Distributed Model Calibration,istributed model. As we have shown in previous chapters, the drainage length, slope, and other parameters extracted from DEMs and geospatial data are .. Thus, it is more likely than not, such parameters would require some adjustment. This chapter presents a method for calibrating a distributed model

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