Arcgis Python-Based Hybrid Hydrologic Model (Distributed-Clark) for Spatially Distributed Rainfall-Runoff Generation and Routing

An ArcGIS Python-based hybrid hydrologic model (conceptually lumped and distributed feature model), Distributed-Clark, was developed for spatially distributed rainfall-runoff generation and routing (flow simulation). In this model, the SCS curve number method estimated spatially distributed excess rainfall and a GIS-based set of separated unit hydrographs are utilized to calculate a direct runoff flow hydrograph. This simple approach, using few modeling parameters, reduces calibration complexity relative to physically-based distributed (PBD) models by only focusing on integrated flow estimation at watershed outlets.

Development of the Distributed-Clark model includes four main steps: watershed pre-processing; spatially distributed excess rainfall (runoff) estimation, which includes NEXRAD quantitative precipitation estimates (QPEs) data processing - map projection transformation (HRAP to Albers Equal Area Conic regular grid), modeling extent and NEXRAD grid sub-setting, and raster and time-series data generation; spatially distributed unit hydrograph derivation; and direct runoff hydrograph convolution. For this implementation, ArcGIS 10.1 was used as the GIS platform to build and execute the Python script tools (DistributedClark_10.1; Python Toolbox, storm event ver.) for each step.

A model case study with single storm event application (four cases) for a river basin was conducted. The Muscatatuck River near Deputy, IN using NEXRAD QPEs demonstrated relatively good fit (direct runoff E_NS 0.94, R² 0.96, and PBIAS -0.60%) against observed streamflow as well as a slightly better fit (direct runoff; E_NS of 2.0% and R² 1.0%) in comparison with the outputs of spatially averaged gauged rainfall data simulations.