Watershed Risk Analysis Model for TVA’s Holston River Basin

2007

[1] Watershed-scale water quality models involve substantial uncertainty in model output because of sparse water quality observations and other sources of uncertainty. Assessing the uncertainty is very important for those who use the models to support management decision making. Systematic uncertainty analysis for these models has rarely been done and remains a major challenge. This study aimed (1) to develop a framework to characterize all important sources of uncertainty and their interactions in managementoriented watershed modeling, (2) to apply the generalized likelihood uncertainty estimation (GLUE) approach for quantifying simulation uncertainty for complex watershed models, and (3) to investigate the influence of subjective choices (especially the likelihood measure) in a GLUE analysis, as well as the availability of observational data, on the outcome of the uncertainty analysis. A two-stage framework was first established as the basis for uncertainty assessment and probabilistic decision-making. A watershed model (watershed analysis risk management framework (WARMF)) was implemented using data from the Santa Clara River Watershed in southern California. A typical catchment was constructed on which a series of experiments was conducted. The results show that GLUE can be implemented with affordable computational cost, yielding insights into the model behavior. However, in complex watershed water quality modeling, the uncertainty results highly depend on the subjective choices made by the modeler as well as the availability of observational data. The importance of considering management concerns in the uncertainty estimation was also demonstrated. Overall, this study establishes guidance for uncertainty assessment in management-oriented watershed modeling. The study results have suggested future efforts we could make in a GLUE-based uncertainty analysis, which has led to the development of a new method, as will be introduced in a companion paper. Eventually, the study should assist in the development of a new generation of watershed water quality models. Citation: Zheng, Y., and A. A. Keller (2007), Uncertainty assessment in watershed-scale water quality modeling and management: 1. Framework and application of generalized likelihood uncertainty estimation (GLUE) approach, Water Resour. Res., 43, W08407,

Section: Introductionmentioning

confidence: 99%

Uncertainty assessment in watershed‐scale water quality modeling and management: 1. Framework and application of generalized likelihood uncertainty estimation (GLUE) approach

2007

“…The WARMF model simulates a variety of watershed processes, including catchment and stream hydrology, temperature fluctuations, sediment movement, plant growth, nutrient cycles, pesticide fate and transport, oxygen demand, etc. [ Chen et al , 1996]. More detailed information about this model, including a detailed description of the underlying equations, can be found at USEPA's website (http://www.epa.gov/athens/wwqtsc/html/warmf.html).…”

Section: Methodsmentioning

confidence: 99%

“…In this context, the objective is that the water quality needed for a particular beneficial use is met at an acceptable frequency, that exceedances not go beyond a particular threshold value (e.g., an acute toxicity end point), and/or that exceedances do not occur during particular critical periods of time. A number of watershed water quality models, such as SWAT [e.g., Neitsch et al, 2001], HSPF [e.g., Bicknell et al, 2001] and watershed analysis risk management framework (WARMF) [Chen et al, 1996[Chen et al, , 1999[Chen et al, , 2004Keller et al, 2004] are available to make predictions about future water quality under particular scenarios (e.g., reducing the pollutant load to a lower level), but it is highly desirable to know how good the predictions really are, before making costly management decisions. To our knowledge, there have only been a few studies systematically addressing the uncertainty analysis of watershed-scale water quality modeling.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty assessment in watershed‐scale water quality modeling and management: 2. Management objectives constrained analysis of uncertainty (MOCAU)

2007

[1] Watershed-scale water quality models are increasingly used to support management decision making. However, significant uncertainty in model output remains an unaddressed issue. In our first study, a framework for assessing the uncertainty in watershed modeling and management was developed, and the application of the generalized likelihood uncertainty estimation (GLUE) approach was examined. The influence of subjective choices (especially the likelihood measure) in a GLUE analysis, as well as of availability of observational data, was investigated. On the basis of GLUE, we developed a new Bayesian approach of uncertainty analysis, specifically for management-oriented watershed water quality modeling, as introduced in this paper. The approach, named management objectives constrained analysis of uncertainty (MOCAU), inherits GLUE's equifinality ideology while explicitly considering management objectives and observational uncertainty. It has many unique features that have not been covered (or have not been explored in great detail) by previous GLUE studies. A series of experiments was conducted to investigate the performance of MOCAU. The results show that MOCAU can be applied efficiently, generating accurate uncertainty estimates for management applications. Subjective assumptions in the uncertainty analysis are explicit and realistic, on the basis of management objectives such as nonattainment frequency of water quality objectives. MOCAU also yields insights into watershed model structure improvement and strategic data collection to reduce uncertainty. Besides water quality modeling, MOCAU can also be applied to other complicated modeling problems where errors are significant, observational data is limited, and management objectives are involved. Citation: Zheng, Y., and A. A. Keller (2007), Uncertainty assessment in watershed-scale water quality modeling and management: 2. Management objectives constrained analysis of uncertainty (MOCAU), Water Resour. Res., 43, W08408,

“…[2] In recent years, watershed-scale water quality models, such as HSPF [e.g., Bicknell et al, 2001], SWAT [e.g., Neitsch et al, 2001] and WARMF [Chen et al, 1996[Chen et al, , 1999[Chen et al, , 2004Systech Engineering, 2001, Keller et al, 2004, have gained increased use for watershed-scale water quality analysis. Although these physically based models have considerable spatial and temporal complexity, preparing any one of these models for simulation involves significant parameter uncertainty resulting from natural variability and/or imperfect knowledge Zheng, 2006a, 2006b].…”

Section: Introductionmentioning

confidence: 99%

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

2006

[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.Citation: Zheng, Y., and A. A. Keller (2006), Understanding parameter sensitivity and its management implications in watershedscale water quality modeling, Water Resour. Res., 42, W05402,