Abstract
Abstract. Computer models of hydrologic systems are frequently used to investigate the hydrologic response of land-cover change. If the modeling results are used to inform resource-management decisions, then providing robust estimates of uncertainty in the simulated response is an important consideration. Here we examine the importance of parameterization, a necessarily subjective process, on uncertainty estimates of the simulated hydrologic response of land-cover change. Specifically, we applied the soil water assessment tool (SWAT) model to a 1.4 km2 watershed in southern Texas to investigate the simulated hydrologic response of brush management (the mechanical removal of woody plants), a discrete land-cover change. The watershed was instrumented before and after brush-management activities were undertaken, and estimates of precipitation, streamflow, and evapotranspiration (ET) are available; these data were used to condition and verify the model. The role of parameterization in brush-management simulation was evaluated by constructing two models, one with 12 adjustable parameters (reduced parameterization) and one with 1305 adjustable parameters (full parameterization). Both models were subjected to global sensitivity analysis as well as Monte Carlo and generalized likelihood uncertainty estimation (GLUE) conditioning to identify important model inputs and to estimate uncertainty in several quantities of interest related to brush management. Many realizations from both parameterizations were identified as behavioral in that they reproduce daily mean streamflow acceptably well according to Nash–Sutcliffe model efficiency coefficient, percent bias, and coefficient of determination. However, the total volumetric ET difference resulting from simulated brush management remains highly uncertain after conditioning to daily mean streamflow, indicating that streamflow data alone are not sufficient to inform the model inputs that influence the simulated outcomes of brush management the most. Additionally, the reduced-parameterization model grossly underestimates uncertainty in the total volumetric ET difference compared to the full-parameterization model; total volumetric ET difference is a primary metric for evaluating the outcomes of brush management. The failure of the reduced-parameterization model to provide robust uncertainty estimates demonstrates the importance of parameterization when attempting to quantify uncertainty in land-cover change simulations.
Highlights
An important use for computer models of hydrologic systems is simulation of the hydrologic response of land-cover change (Fohrer et al, 2001; DeFries and Eshleman, 2004); many modeling analyses have been undertaken in attempts to better understand how changes in land cover may change the timing and quantity of runoff, recharge, and evapotranspiration (e.g., Schilling et al, 2014; Ahn and Merwade, 2017; Chu et al, 2010)
The reduced-parameterization model grossly underestimates uncertainty in the total volumetric ET difference compared to the full-parameterization model; total volumetric ET difference is a primary metric for evaluating the outcomes of brush management
Of the 1305 model inputs treated as parameters, the method of Morris analysis indicates that only 194 parameters are noninfluential to the three conditioning measures and five brush-management quantities of interest (QOIs)
Summary
An important use for computer models of hydrologic systems is simulation of the hydrologic response of land-cover change (Fohrer et al, 2001; DeFries and Eshleman, 2004); many modeling analyses have been undertaken in attempts to better understand how changes in land cover may change the timing and quantity of runoff, recharge, and evapotranspiration (e.g., Schilling et al, 2014; Ahn and Merwade, 2017; Chu et al, 2010). Given the uncertainties that exist in nearly every hydrologic model input dataset, the potential exists for the simulated outcomes to be highly uncertain, even after conditioning to streamflow data. Given this potential uncertainty in model outcomes, quantifying uncertainty in the simulated results of land-cover change is an important consideration, especially if simulation results are to be used in resource-management decision making. J. White et al.: Parameterization of land-cover change simulation refers to the subjective and necessary process of selecting uncertain model inputs to treat as adjustable in the conditioning process. We investigate how parameterization may affect the uncertainty quantification process when simulating a discrete, vegetative land-cover change, the mechanical removal of woody plants
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