Analytical probabilistic hydrologic models (APMs) are computationally efficient producing validated storm water outputs comparable to continuous simulation for storm water planning level analyses. To date, APMs have been run as spatially lumped or semi-distributed models relying upon calibrated and spatially averaged system state variable inputs/parameters limiting model system representation and ultimately impacting model uncertainty. Here, APMs are integrated within Geographic Information Systems (GIS) and remote sensing image analyses (RSIA) deriving a planning-level distributed model under refined model system representation. The hypothesis is refinements alone, foregoing model calibration, will produce trial average annual storm water runoff volume estimates comparable to former research estimates (employing calibration) demonstrating the benefits of improved APM system representation and detail. To test the hypothesis three key system state variables – sewershed area, runoff coefficients and depression storage – are digitally extracted in GIS and RSIA through: automated delineation upon a digitally inscribed digital elevation model; unsupervised classification of an orthophotograph; and a slope-based expression, respectively. The parameters are spatially-distributed as continuous raster data layers and integrated with an APM. Spatially-distributed trial runoff volumes are within a range of 4–29% of earlier lumped/semi-distributed research estimates validating the hypothesis that further detail and physically-explicit representations of model systems improve simulation results.
Keywords: analytical probabilistic models, distributed hydrologic modeling, geographic information systems, impervious surface analysis, remote sensing technology, storm water modeling