Uncertainty Analysis and Calibration of SWMM Using a Formal Bayesian Methodology Read More: http://ascelibrary.org/doi/abs/10.1061/9780784412312.060

Importance of uncertainty analysis (UA) to estimate the degree of reliability associated with model predictions is being understood. Consequently, literature that describes various Bayesian methods for the assessment of parameter and model predictive uncertainty has been steadily rising. Applications dealing with urban stormwater management are, however, very limited. This study demonstrates successful application of a formal Bayesian methodology for UA of the U.S. EPA Stormwater Management Model (SWMM), a widely used urban stormwater management model, and illustrates the methodology using a highly urbanized watershed in southern California. DREAM(ZS), a recently developed effective and efficient sampling algorithm, and a generalized, formal likelihood function that addresses the assumptions commonly made regarding error structure including independence, normality and homoscedasticity are used for the UA. Results will include comparison of the simulated error structure with the assumptions made by the likelihood function, histogram of the parameters posteriors, bounds of the 95 percent confidence interval, and the maximum likelihood (ML) predictions. A conventional calibration attempted to compare the ML results derived from the UA with the optimal solutions identified by the single objective calibration will also be presented. Besides illustrating state-of the-art in UA, the study will highlight application of the methodology to developing a watershed management model to mitigate stormwater quantity and quality problems associated with urbanization

Comparison of Model Evaluation Methods to Develop a Comprehensive Watershed Simulation Model

Comprehensive environmental models such as the Soil and Water Assessment Tool (SWAT) are becoming an integral part of decision making processes for effective planning and management of natural resources. Before their use as decision making aid, however, models must be properly evaluated to improve their prediction accuracy and reduce the likelihood of making decisions that could lead to undesirable policy outcomes. Model evaluation refers to practices such as quality analysis of input data, sensitivity analysis, calibration and verification, and uncertainty analysis. Many methodologies have been developed for model evaluations over the years. One of the major limitations of the existing model evaluation methods, in particular model calibration methods, is their computational inefficiency, especially when used to calibrate comprehensive watershed simulation models. It may take weeks to months of CPU time, depending on the problem size, to successfully calibrate a comprehensive watershed simulation model on a standard PC. In this study, two sensitivity analysis methods and four calibration methods are used to evaluate SWAT to improve its streamflow prediction accuracy for the Morro Bay watershed located on the central coast of California. Parameter sensitivity analysis was performed using step-wise-regression analysis and the one-factor-at-a time screening method. Calibration was performed using PEST, Genetic Algorithms, the Shuffled Complex Evolution, and the Dynamically Dimensioned Search using observed data from multiple sites in the watershed. The model evaluation methods are compared in terms of their computational efficiency as well as effectiveness to determine “accurate” results. The developed SWAT model can be used to evaluate effectiveness of the Best Management Practices installed in the Morro Bay watershed, and to also prioritize sites where BMPs may be implemented in the future to further improve ecological integrity of the Morro Bay Estuary, which is one of the most important wetlands in California as it supports wide variety of habitats including numerous sensitive and endangered plant and animal species

Modeling Erosion and Sedimentation Processes in the Chorro Creek Subwatershed to Evaluate and Develop Effective Watershed Management Approaches

The Morro Bay Watershed, located in San Luis Obispo County, California, covers more than 48,000 acres of land and discharges into Morro Bay through the Morro Bay National Estuary (MBNE). The Chorro Creek Subwatershed consists of approximately 30,000 acres of the overall watershed. The MBNE provides an ecosystem that supports a variety of wildlife, from the common sea gull to the endangered sea otter. The estuary is also home to over 200 species of birds. The operational waterfront of the Morro Bay Harbor was and continues to be a strong supporter to the local economy of the City of Morro Bay. Numerous studies were conducted since the 1990s throughout the watershed to study the sedimentation of the estuary and bay and identified accelerated erosion and subsequent sedimentation as a major threat to sustainability of the bay. As a result, various Best Management Practices (BMPs) have been implemented in the watershed to reduce sediment loading and transport to the bay. Localized evaluations of various BMPs have been performed to investigate effectiveness of individual BMPs. This paper consolidates this information and develops a comprehensive spatially distributed watershed simulation model (1) for detailed understanding of the erosion and sedimentation processes in the watershed; (2) to evaluate a watershed scale effectiveness of the conservation practices that have been installed in the watershed; (3) to identify optimal BMP types and sites that may be used in the future to further reduce sedimentation of the bay at minimal cost; (4) to organize and document the various sources of data and studies that have been performed to date in the Chorro Creek subwatershed. Soil and Water Assessment Tool (SWAT) was used to develop the model and to evaluate the pre and post BMP implementation characteristics in the subwatershed. Combining the data and efforts of past BMP evaluations, land use, soil type, climate data, and streamflow data, statistical evaluations, and model sensitivity analysis will help build and calibrate a robust SWAT model that can be used to track BMP evaluation efforts, as well as other watershed management tasks. Through the evaluation of BMPs in the watershed, efforts can be made to implement the more successful BMPs in the watershed or in other similar watersheds. Sensitivity analysis was performed using a global sensitivity analysis method and streamflow and sediment yield was calibrated using the Shuffled Complex Evolution-University of Arizona

Evaluating Alternative Hydraulic Solutions to Limit Nutrient Contamination of an Aquifer in Southern California Read More: http://ascelibrary.org/doi/abs/10.1061/9780784412312.009

Many small communities depend on groundwater sources for drinking water and they often use septic tanks for wastewater treatment and disposal. Nitrate and other pollutants leaking from poorly designed septic tank systems can percolate to the aquifers and alter quality of the groundwater. This study describes a groundwater model developed using Visual MODFLOW for an aquifer that is used as a water supply source for the communities of Beaumont and Cherry Valley, CA. The aquifer has been contaminated by nitrates leaking from septic tank systems. The model will assist in clarifying the extent of interactions between nitrate pollutants, percolation from a recently established series of artificial recharge ponds, natural groundwater recharge, and production wells. The primary objective of the study is to evaluate alternative hydraulic solutions that would limit the movement of contaminants and minimize the risk of polluting production wells. The study will identify artificial recharge scenarios that would limit movement of the nitrates so that polluted waters may be remediated in the future, rather than allowed to encroach on critical production wells or forced away from production wells to become a problem for future generations or neighboring areas. The data needed to build the model including geological logs, aquifer properties, hydrologic data, well locations, pumping schedules, water levels and septic tank density have been collected from various sources. The groundwater model is calibrated to accurately simulate observed groundwater levels and the extent of pollution corresponding to historical pumping rates, recharge rates and climate. The calibrated model is used to evaluate alternative hydraulic solutions that would localize the nitrate pollutions thus limiting impact on public welfare

Using Genetic Algorithms and SWAT to Minimize Sediment Yield From an Agriculturally Dominated Watershed

Non-point source pollution is well recognized as one of the most critical environmental hazards of modern times. In Illinois, non-point source pollution is the major cause of water quality problems, and soil erosion from agricultural lands is the major source of such pollution. Accelerated by anthropogenic activities, soil erosion reduces crop productivity and leads to subsequent problems from deposition on farmlands and in water bodies. Watershed management, however, promotes protection and restoration of these natural resources while allowing for sustainable economic growth and development. In this study a discrete time optimal control methodology and computational model are developed for determining land use and management alternatives that minimize sediment yield from agriculturally dominated watersheds. The methodology is based on an interface between a genetic algorithm and a U.S. Department of Agriculture watershed model known as Soil and Water Assessment Tool (SWAT). The original structure of the SWAT model is preserved and modifications are embedded for computational efficiency. The analysis is based on a farm field level to capture the perspectives of different stakeholders. The model thus supports Illinois EPA’s plan of developing a program based on enabling and empowering local stakeholders to take charge of the fate of their watershed. Management alternatives available for all land uses modeled by SWAT are developed considering rotation patterns of three years. The decision support tool is applied to Big Creek sub-watershed in the Cache River watershed, located in Southern Illinois. Big Creek subwatershed has been sighted by the Illinois EPA for excessive sediment and nutrient loadings and has been targeted by the Illinois Pilot Watershed Program. This research is part of an ongoing effort to develop a comprehensive decision support tool that uses multi-criteria evaluation to address social, economic and hydrologic issues for integrative watershed management

An Innovative Geocentric Decision Support Solution to Comprehensive Planning, Design, Operation, and Management of Urban Drainage Systems

Geographic Information System (GIS) is quickly becoming a critical component to develop and sustain asset management for today’s wastewater utilities as most of their data is geographically referenced. This technology offers sophisticated data management and spatial analysis capabilities that can greatly improve and facilitate urban drainage infrastructure modeling and analysis applications. This paper presents a comprehensive GIS-based decision support system that integrates several technologies for use in the effective management of urban stormwater collection systems. It explicitly integrates ESRI ArcGIS geospatial model with advanced hydrologic, hydraulic, and water quality simulation algorithms, nature-based global optimization techniques including genetic algorithms for design and calibration of stormwater management models, automated dry weather flow generation and allocation, and automated subcatchment delineation and parameter extraction tools to address every facet of urban drainage infrastructure management. The geocentric interface allows seamless communication among the various modules. The resulting decision support system effortlessly reads GIS datasets, extracts necessary modeling information, and automatically constructs, loads, designs, calibrates, analyzes and optimizes a representative urban drainage model considering hydrologic and hydraulic performance requirements. It also makes it easy to run, simulate and compare various modeling scenarios, identify system deficiencies, and determine cost-effective physical and operational improvements to achieve optimum performance and regulatory compliance. These combined capabilities provide favorable geospatial environment to assist wastewater utilities in planning, designing, and operating reliable systems and in optimizing their capital improvement programs

A Formal Bayesian Approach for Uncertainty Analysis of a Watershed Model

Uncertainty analysis (UA) has received substantial attention in water resources during the last decade. Bayesian approaches are often preferred for UA. This study describes a formal Bayesian approach for the assessment of parameter uncertainty and predictive uncertainty using a spatially distributed hydrologic model and will demonstrate its application using data from a well monitored experimental watershed. A Markov-Chain Monte Carlo (MCMC) scheme has been used to sample posterior parameter distributions. A formal, flexible likelihood function that explicitly accounts for heteroscedasticity, temporal correlation and non-normality of simulation residuals has been used to describe closeness of the simulated and observed streamflow. Performance of the formal likelihood function will be compared to that of simple least squares with regard to generating accurate predictive uncertainty estimates at multiple streamflow gaging stations available in the experimental watershed. Limitation of the SLS assumptions with regard to the structure of model residuals will be illustrated and capability of the formal likelihood function to address these assumptions will be scrutinized. Finally, the maximum likelihood solutions identified by the uncertainty analysis method will be compared to the optimal solutions determined using a single objective optimization exercise to test effectiveness of the uncertainty analysis method to also identify the optimal solutions sought during model calibration

Evaluating Effectiveness of Best Management Practices to Control Accelerated Sedimentation of the Morro Bay Estuary

The Morro Bay estuary, located on the central Coast of California approximately half way between Los Angeles and San Francisco, is one of the most important wetlands on the west Coast as it supports wide variety of habitats including numerous sensitive and endangered plant and animal species. Various studies have identified accelerated erosion and subsequent sedimentation as a major threat for sustainability of the bay. Watershed disturbances caused by agricultural activities are believed to be one of the major causes of the accelerated erosion and sedimentation. More than 200 conservation practices have been installed in the watershed since the mid-1990 to reduce erosion and sedimentation. This paper will review the implemented BMPs and will evaluate effectiveness of the BMPs using observations and modeling exercise. Streamflow and sediment concentration, measured mainly during the rainy seasons, are available for multiple locations in the watershed. However, the observations are not sufficient in terms of spatial density and data length to evaluate effectiveness of the mitigation measures at various locations in the watershed. It would be daunting in terms of cost to develop an intensive network of monitoring sites that would be needed for reliable management of NPS pollutants. As a result, comprehensive watershed simulation models that integrate watershed and climate characteristics and can estimate pollutant quantity at various locations, and that can also identify source of the contaminants, is emerging as a key component of watershed management. In this regard, a comprehensive watershed simulation model for the Morro Bay watershed has been developed using Soil and Water Assessment Tool (SWAT) to simulate both streamflow and sediment concentration. The observed data was used to improve prediction accuracy of the SWAT model through parameter sensitivity analysis and calibration steps. Parameter sensitivity analysis was performed using step-wise-regression analysis and Morris’s one-at-a time (OAT) method. Calibration was performed using four different optimization methods: PEST, Genetic Algorithms, the Shuffled Complex Evolution Algorithm, and Dynamically Dimensioned Search. Relative performance of the sensitivity analysis methods and the calibration algorithms will be discussed in terms of effectiveness and computational efficiency. The developed model was used to evaluate effectiveness of the BMPs implemented in the Morro Bay watershed, and can also be used to prioritize sites where BMPs may be implemented in the future to further improve ecological integrity of the estuary

Uncertainty Analysis and Calibration of SWMM Using a Formal, Bayesian Methodology

Importance of uncertainty analysis (UA) to estimate the degree of reliability associated with model predictions is being understood. Consequently, literature that describes various Bayesian methods for the assessment of parameter and model predictive uncertainty has been steadily rising. Applications dealing with urban stormwater management are, however, very limited. This study demonstrates successful application of a formal Bayesian methodology for UA of the U.S. EPA Stormwater Management Model (SWMM), a widely used urban stormwater management model, and illustrates the methodology using a highly urbanized watershed in southern California. DREAM(ZS), a recently developed effective and efficient sampling algorithm, and a generalized, formal likelihood function that addresses the assumptions commonly made regarding error structure including independence, normality and homoscedasticity are used for the UA. Results will include comparison of the simulated error structure with the assumptions made by the likelihood function, histogram of the parameters posteriors, bounds of the 95 percent confidence interval, and the maximum likelihood (ML) predictions. A conventional calibration attempted to compare the ML results derived from the UA with the optimal solutions identified by the single objective calibration will also be presented. Besides illustrating state-of-the- art in UA, the study will highlight application of the methodology to developing a watershed management model to mitigate stormwater quantity and quality problems associated with urbanization

Improving Model Performance Using Dynamic Evaluation and Proper Objective Function

Models have become important decision making aids. Model evaluation (i.e., global sensitivity analysis, calibration and uncertainty analysis), is crucial to improve their prediction accuracy and reduce the likelihood of making decisions that could lead to undesirable policy outcomes. The conventional approach assumes that model parameters are insensitive to season irrespective of the temporal variability of input forcings such as rainfall. This assumption could significantly compromise model performance for low flow seasons and/or high flow seasons depending on the calibration method pursued. This study will demonstrate the advantage of dynamic (seasonal) model evaluation in improving performance compared to the traditional approach. In addition, the impact of the goodnessof- fit criteria (e.g., mean of sum of square of residuals, Nash-Sutcliffe efficiency criteria, volume based efficiency criteria, etc) used as an objective function during automatic calibration on model performance has been examined. Objective functions that would improve the accuracy of simulating high flows as well as low flows were identified. The added values of using multiobjective calibration, over the more widely used single objective calibration, has also been explored. The Little River Experimental Watershed, one of the U.S. Department of Agriculture’s experimental watersheds, has been used to illustrate the approaches tested in the study. Soil and Water Assessment Tool is the watershed simulation model used for the work. Results show that the season based model calibration approach significantly improved model performance, and calibration is sensitive to the efficiency measure used as object function. As such, multiple efficiency criteria should be used to report model performance as no single efficiency measure performed consistently well in describing goodness of model results. Another important finding is that parameter values that are significantly divergent from their “true” values may lead to model performance that may be considered near perfect even when judged using multiple efficiency measures underlining the challenge of parameter identifiability