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The identification of runoff contributing areas would provide the necessary focus points for water quality monitoring and management. The objective of this study was to use a field scale approach to delineate critical runoff source areas and runoff mechanisms in a pasture watershed of the Ozark Highlands of USA. Three adjacent hillslope plots located at the Savoy Experimental Watershed, northwest Arkansas, were bermed to isolate runoff. Each plot was equipped with paired subsurface saturation and surface runoff sensors, shallow groundwater wells, H-flumes and rain gauges to quantify rainfall characteristics and runoff mechanisms at continuous 5-minute intervals. The spatial extent of runoff source areas was determined by incorporating sensor data into a geographic information based system (ArcGIS) and performing geostatistical computations (Inverse Distance Weighting method). Results indicate that both infiltration excess runoff and saturation excess runoff mechanisms occur at varying extents on each plot. Two rainfall events that occurred on January 4, 2005 and January 5, 2005 are used to illustrate the spatial and temporal dynamics of the critical runoff source areas. The methodology presented can serve as a framework upon which critical runoff source areas can be identified and managed for water quality protection in a watershed.

A physics based hydrologic model (either lumped or distributed) is generally characterized by a multitude of parameters. The calibration of such models is always a complex task. Though, manual calibration is by far the most widely used approach for parameter estimation of such models, it is highly time consuming, and uncertainty of obtaining the best fit exists. Attempts to develop automatic calibration procedures that use optimization algorithms for distributed watershed models have resulted in long computational time and effort. This was mainly because the model has to perform simulations a number of times before it can evaluate the objective function of the algorithm during the parameter search. This study presents a novel framework for calibration of watershed models under simulation-optimization environment. The framework identifies the parameters of the model through an optimization algorithm that uses the objective function from a pseudo simulator, which reduces the computational time significantly. The pseudo simulator will map the objective function in terms of the parameters thus avoiding complex simulation every time. The methodology is demonstrated by calibrating SWAT model for Illinois watershed in Arkansas, USA. The calibrated model is found to simulate the flows reasonably good. The model performance indices when computed on a daily basis, the R2 value between the simulated and measured flows ranged from 0.36 to 0.60 during calibration period and are 0.77 for the validation period. The model resulted in an efficiency of 0.75 during the validation period. Overall, the study reports an auto-calibration procedure for watershed models that can be used for calibration of models irrespective of their complexity.