The importance of parameterization when simulating the hydrologic response of vegetative land-use change
Other literature type
(issn: 1607-7938, eissn: 1607-7938)
Computer models of hydrologic systems are frequently used to investigate the hydrologic response of land-use 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-use change. Specifically, we apply the soil water assessment tool (SWAT) model to a 1.4 km<sup>2</sup> watershed in south Texas to investigate the simulated hydrologic response of brush management (the mechanical removal of woody plants), a discrete land-use change. The watershed was previously 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 1,305 adjustable parameters (full parameterization). Both models were subjected to global sensitivity analysis, 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 <q>behavorial</q> in that they reproduce daily streamflow acceptably well according to Nash-Sutcliffe, percent bias and coefficient of determination. However, the total volumetric ET difference resulting from simulated brush management remains highly uncertain after conditioning to daily streamflow, indicating that streamflow data alone are not sufficient to inform the model inputs that most influence the simulated outcomes of brush management. 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-use change simulations.