Integration of a stochastic space-time rainfall model and distributed hydrologic simulation with GIS

Abstract

This research presents an integration of a stochastic space-time rainfall model and distributed hydrologic simulation with GIS. The integrated simulation system consists of three subsystems: a stochastic space-time rainfall model, a geographical information system (GIS), and a distributed physically-based hydrologic model. The developed stochastic space-time rainfall model is capable of estimating the storm movement and simulating a random rainfall field over a study area, based on the measurement from three raingauges. An optimization-based lag-k correlation method was developed to estimate the storm movement, and a stochastic model was developed to simulate the rainfall field. A GIS tool, ARC/INFO, was integrated into this simulation system. GIS has been applied to automatically extract the spatially distributed parameters for hydrologic modeling. Digital elevation modeling techniques were used to process a high resolution digital map. A distributed physically-based hydrologic model, operated in HEC-1, simulated the stochastic, distributed, interrelated hydrological processes. The Green-Ampt equation is used for modeling the infiltration process, kinematic wave approximation for infiltration-excess overland flow, and the diffusion wave model for the unsteady channel flow. Two small nested experimental watersheds in southern Arizona were chosen as the study area where three raingauges are located. Using five recorded storm events, a series of simulations were performed under a variety of conditions. The simulation results show the model performs very well, by comparing the simulated runoff peak flow and runoff depth with the measured ones, and evaluated by the model efficiency. Both model structure and model parameter uncertainties were investigated in the sensitivity analysis. The statistical tests for the simulation results show that it is important to model stochastic rainfall with storm movement, which caused a significant change in runoff peak flow and runoff depth from that where the input is only one gage data. The sensitivity of runoff to roughness factor N and hydraulic conductivity Ks were intensively investigated. The research demonstrated this integrated system presents an improved simulation environment for the distributed hydrology.