Understanding parameter sensitivity and its management implications in watershed-scale water quality modeling

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Abstract

[1] Because of uncertainty and variability in input parameter values, watershed-scale water quality modeling can result in significant output uncertainty. Quantifying this uncertainty is very important for policy makers and stakeholders who rely on the output of these models for watershed management. Given the large number of parameters in these complex models, a preliminary sensitivity analysis is needed before the uncertainty analysis. A few sensitivity studies have been conducted for watershed models, but efficiently selecting critical parameters for the uncertainty analysis remains an issue. This study aimed to (1) develop a framework for systematically conducting a preliminary sensitivity analysis for complex watershed models using generalized sensitivity analysis (GSA) as a global technique and (2) evaluate the relevance of incorporating management concerns in the preliminary sensitivity analysis and its impact on parameter selection. Although the proposed approach is valid for any complex watershed model, for this study the Watershed Analysis Risk Management Framework (WARMF) model was implemented using data from the Santa Clara River in southern California. To simulate hydrology, sediment, and pesticide transport, 121 parameters are needed for a single catchment/reach combination; an efficient selection method is paramount for an uncertainty analysis. The results show that GSA can be implemented efficiently, yielding insights into model and parameter behavior. The sensitivity analysis must consider management concerns early on in the process to identify parameters and parameter values that can influence management decisions. The number of parameters that must be considered in a subsequent uncertainty analysis was significantly reduced. This study also provides guidance for future research on parameter sensitivity and uncertainty in complex watershed models.

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