METHODOLOGIES FOR CALIBRATION AND PREDICTIVE ANALYSIS OF A WATERSHED MODEL1

ABSTRACT: The use of a fitted parameter watershed model to address water quantity and quality management issues requires that it be calibrated under a wide range of hydrologic conditions. However, rarely does model calibration result in a unique parameter set. Parameter nonuniqueness can lead to predictive nonuniqueness. The extent of model predictive uncertainty should be investigated if management decisions are to be based on model projections. Using models built for four neighboring watersheds in the Neuse River Basin of North Carolina, the application of the automated parameter optimization software PEST in conjunction with the Hydrologic Simulation Program Fortran (HSPF) is demonstrated. Parameter nonuniqueness is illustrated, and a method is presented for calculating many different sets of parameters, all of which acceptably calibrate a watershed model. A regularization methodology is discussed in which models for similar watersheds can be calibrated simultaneously. Using this method, parameter differences between watershed models can be minimized while maintaining fit between model outputs and field observations. In recognition of the fact that parameter nonuniqueness and predictive uncertainty are inherent to the modeling process, PEST's nonlinear predictive analysis functionality is then used to explore the extent of model predictive uncertainty.     

Testing of the Storm Water Management Model Low Impact Development Modules

Stormwater infrastructure designers and operators rely heavily on the United States Environmental Protection Agency’s Storm Water Management Model (SWMM) to simulate stormwater and wastewater infrastructure performance. Since its inception in the late 1970s, improvements and extensions have been tested and evaluated rigorously to verify the accuracy of the model. As a continuation of this progress, the main objective of this study was to quantify how accurately SWMM simulates the hydrologic activity of low impact development (LID) storm control measures. Model performance was evaluated by quantitatively comparing empirical data to model results using a multievent, multiobjective calibration method. The calibration methodology utilized the PEST software, a Parameter ESTimation tool, to determine unmeasured hydrologic parameters for SWMM’s LID modules. The calibrated LID modules’ Nash–Sutcliffe efficiencies averaged 0.81; average percent bias (PBIAS) ?9%; average ratio of root mean square error to standard deviation of measured values 0.485; average index of agreement 0.94; and the average volume error, simulated vs. observed, was +9%. SWMM accurately predicted the timing of peak flows, but usually underestimated their magnitudes by 10%. The average volume reduction, measured outflow volume divided by inflow volume, was 48%. We had more difficulty in calibrating one study, an infiltration trench, which identified a significant limitation of the current version of the SWMM LID module; it cannot simulate lateral exfiltration of water out of the storage layers of a LID storm control measure. This limitation is especially severe for a deep LIDs, such as infiltration trenches. Nevertheless, SWMM satisfactorily simulated the hydrologic performance of eight of the nine LID practices.     

Simulation of Surface Water for Un‐Gauged Areas with Storage‐Attenuation Wetlands1

Abstract: A nontraditional application of the Hydrological Simulation Program – FORTRAN (HSPF) model to simulate freshwater discharge to upper Charlotte Harbor along Florida’s west coast was performed. This application was different from traditional HSPF applications in three ways. First, the domain of the model was defined based on the hydraulic characteristics of the landforms using small distributed parameter discretization. Second, broad wetland land forms, representing more than 20% of this area, were simulated as reaches with storage‐attenuation characteristics and not as pervious land segments (PERLNDs). Finally, the reach flow‐tables (F‐Tables) were configured in a unique way to be calibrated representing the uncertainty of the storage‐attenuation process. Characterizing wetlands as hydrography elements allows flow from the wetlands to be treated as a stage‐dependent flux. The study was conducted for the un‐gauged portion of the Peace and Myakka rivers in west‐central Florida. Due to low gradient tidal influences, a large portion of the basin is un‐gauged. The objective of this study was to simulate stream flow discharges and to estimate freshwater inflow from these un‐gauged areas to upper Charlotte Harbor. Two local gauging stations were located within the model domain and were used for calibration. Another gauge with a shorter period of record was used for verification. A set of global hydrologic parameters were selected and tested using the parameter optimization software (PEST) during the calibration. Model results were evaluated using PEST and well‐known statistical indices. The correlation coefficients were very high (0.899 and 0.825) for the two calibration stations. Further testing of this approach appears warranted for watersheds with significant wetlands coverage.