Delineating hydrological response units in a mountainous catchment and its evaluation on water mass balance and model performance
Date Issued
January 1, 2014
DOI
10.1117/12.2068592
Abstract
Semi-distributed physically-based models are well established and widely used for hydrological modeling due to their ability to capture the spatial variability of the watershed among land use, soil types and topographic characteristics; and to characterize distributed inputs in different areas within the watershed. They offer a more realistic watershed representation, allowing for better predictions of the behavior of a hydrologic system, based on novel climatic inputs. Watershed subdivision and the question of an optimum discretization level is an important issue in distributed hydrological modeling as it affects the setup of hydrologic models and has the potential to affect model output. Soil and Water Assessment Tool (SWAT), a semi-distributed physically-based hydrologic model, divides the watershed into smaller subwatersheds which are further subdivided into HRUs consisting of homogeneous land use, soil, slope and management characteristics. The number and size of HRUs is calculated based on user-specified land use, soil and slope thresholds. This study investigates the impact of the slope threshold in the HRU definition on flow predictions and hydrologic mass balance, applied on three subwatersheds of the Evrotas River Basin (1348km2), a mountainous catchment in Peloponnesus, Greece. The catchment is delineated using a 90m DEM and then divided into 150 subwatersheds. The model was calibrated, and simulations were performed on three subwatersheds using a range of 5%- 30% slope thresholds for the HRU definition while land use and soil thresholds remained the same. Results showed that the coarser delineation (13 HRUs) produced a very accurate hydrologic mass balance and satisfactory flow predictions (RSR, PBIAS, NSE) while, finer delineations (21 HRUs) produces inaccurate hydrologic mass balance (54.49% lower surface runoff) but more accurate flow predictions (RSR, PBIAS, NSE).