Abstract
Estimating surface runoff for ungauged watershed is an important issue. The Soil Conservation Service Curve Number (SCS-CN) method developed from long-term experimental data is widely used to estimate surface runoff from gaged or ungauged watersheds. Many modelers have used the documented SCS-CN parameters without calibration, sometimes resulting in significant errors in estimating surface runoff. Several methods for regionalization of SCS-CN parameters were evaluated. The regionalization methods include: (1) average; (2) land use area weighted average; (3) hydrologic soil group area weighted average; (4) area combined land use and hydrologic soil group weighted average; (5) spatial nearest neighbor; (6) inverse distance weighted average; and (7) global calibration method, and model performance for each method was evaluated with application to 14 watersheds located in Indiana. Eight watersheds were used for calibration and six watersheds for validation. For the validation results, the spatial nearest neighbor method provided the highest average Nash-Sutcliffe (NS) value at 0.58 for six watersheds but it included the lowest NS value and variance of NS values of this method was the highest. The global calibration method provided the second highest average NS value at 0.56 with low variation of NS values. Although the spatial nearest neighbor method provided the highest average NS value, this method was not statistically different than other methods. However, the global calibration method was significantly different than other methods except the spatial nearest neighbor method. Therefore, we conclude that the global calibration method is appropriate to regionalize SCS-CN parameters for ungauged watersheds.
Highlights
Watershed modeling is one of the rational, economical, and useful approaches for water quality and quantity management and is widely used in planning, design, management and developing watershed management plans, including those for total maximum daily loads (TMDLs)
A number of studies have reported that the Shuffled Complex Evolution Algorithm (SCE-UA) algorithm provided better results compared to Genetic Algorithms (GA) approaches for calibrating watershed models [16,17].The SCE-UA method is based on a synthesis of four concepts including: (i) combination of deterministic and probabilistic approaches; (ii) systematic evolution of a “complex”
The negative average error (AE) and relative error (RE) values for all watersheds indicate that the runoff results for default Soil Conservation Service Curve Number (SCS-CN) values were underestimated compared with observed data
Summary
Watershed modeling is one of the rational, economical, and useful approaches for water quality and quantity management and is widely used in planning, design, management and developing watershed management plans, including those for total maximum daily loads (TMDLs). A number of studies have reported that the SCE-UA algorithm provided better results compared to GA approaches for calibrating watershed models [16,17].The SCE-UA method is based on a synthesis of four concepts including:. Of points spanning the parameter space in the direction of global improvement; (iii) competitive evolution; and (iv) complex shuffling. These four concepts improve its efficiency, flexibility, and effectiveness [18]. The descriptions of each SCE-UA step and more detailed explanations are provided by Duan et al [19,20] including: (i) generating samples; (ii) ranking points; (iii) partitioning into complexes; (iv) evolving each complex; (v) shuffle complexes; (vi) checking convergence; and (vii) checking the reduction in the number of complexes. Convergence and the reduction in the number of complexes are checked
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