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.

Delineation of the Pahute Mesa–Oasis Valley groundwater basin, Nevada

“…Tikhonov regularization limited differences between pilot points by penalizing sharp differences, and thereby ensuring relatively continuous transmissivity and recharge distributions (Doherty and Johnston, 2003). Contrasts across hydrogeologic changes were not penalized.…”

Integrated Modeling of Groundwater and Surface Water Interactions in a Manmade Wetland

“…The model-independent parameter estimation and uncertainty analysis package PEST (Doherty and Johnston 2003) was applied in historical matching. This is performed by trying to minimize the absolute difference between observed and simulated surface and groundwater water levels.…”

AnalyzeHOLE - An Integrated Wellbore Flow Analysis Tool

“…Each hydraulicconductivity interval also can be estimated independently where lithology constrains parameter estimates through Tikhonov regularization. The variability of hydraulic conductivity estimates in each lithology is minimized when Tikhonov regularization is applied (Doherty and Johnston, 2003).…”

“…Lithology is incorporated as an observation, instead of a parameter, so hydraulic conductivities in a lithology can differ where dictated by measurements. This approach is a form of Tikhonov regularization that minimizes differences between parameters and affect the objective function little (Doherty and Johnston, 2003). Regularization also extrapolates estimates to cased sections by minimizing the variability of hydraulic conductivity in a lithology and preserving the transmissivity of the aquifer system.…”