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

Decision Support for Watershed Management Using Evolutionary Algorithms

An integrative computational methodology is developed for the management of nonpoint source pollution from watersheds. The associated decision support system is based on an interface between evolutionary algorithms (EAs) and a comprehensive watershed simulation model, and is capable of identifying optimal or near-optimal land use patterns to satisfy objectives. Specifically, a genetic algorithm (GA) is linked with the U.S. Department of Agriculture’s Soil and Water Assessment Tool (SWAT) for single objective evaluations, and a Strength Pareto Evolutionary Algorithm has been integrated with SWAT for multiobjective optimization. The model can be operated at a small spatial scale, such as a farm field, or on a larger watershed scale. A secondary model that also uses a GA is developed for calibration of the simulation model. Sensitivity analysis and parameterization are carried out in a preliminary step to identify model parameters that need to be calibrated. Application to a demonstration watershed located in Southern Illinois reveals the capability of the model in achieving its intended goals. However, the model is found to be computationally demanding as a direct consequence of repeated SWAT simulations during the search for favorable solutions. An artificial neural network (ANN) has been developed to mimic SWAT outputs and ultimately replace it during the search process. Replacement of SWAT by the ANN results in an 84% reduction in computational time required to identify final land use patterns. The ANN model is trained using a hybrid of evolutionary programming (EP) and the back propagation (BP) algorithms. The hybrid algorithm was found to be more effective and efficient than either EP or BP alone. Overall, this study demonstrates the powerful and multifaceted role that EAs and artificial intelligence techniques could play in solving the complex and realistic problems of environmental and water resources systems

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

Dominance and G×E interaction effects improvegenomic prediction and genetic gain inintermediate wheatgrass (Thinopyrumintermedium)

Genomic selection (GS) based recurrent selection methods were developed to accelerate the domestication of intermediate wheatgrass [IWG, Thinopyrum intermedium (Host) Barkworth & D.R. Dewey]. A subset of the breeding population phenotyped at multiple environments is used to train GS models and then predict trait values of the breeding population. In this study, we implemented several GS models that investigated the use of additive and dominance effects and G×E interaction effects to understand how they affected trait predictions in intermediate wheatgrass. We evaluated 451 genotypes from the University of Minnesota IWG breeding program for nine agronomic and domestication traits at two Minnesota locations during 2017–2018. Genet-mean based heritabilities for these traits ranged from 0.34 to 0.77. Using fourfold cross validation, we observed the highest predictive abilities (correlation of 0.67) in models that considered G×E effects. When G×E effects were fitted in GS models, trait predictions improved by 18%, 15%, 20%, and 23% for yield, spike weight, spike length, and free threshing, respectively. Genomic selection models with dominance effects showed only modest increases of up to 3% and were trait-dependent. Crossenvironment predictions were better for high heritability traits such as spike length, shatter resistance, free threshing, grain weight, and seed length than traits with low heritability and large environmental variance such as spike weight, grain yield, and seed width. Our results confirm that GS can accelerate IWG domestication by increasing genetic gain per breeding cycle and assist in selection of genotypes with promise of better performance in diverse environments

Predictors and outcome of surgical repair of obstetric fistula at a regional referral hospital, Mbarara, western Uganda

<p>Abstract</p> <p>Background</p> <p>Obstetric fistula although virtually eliminated in high income countries, still remains a prevalent and debilitating condition in many parts of the developing world. It occurs in areas where access to care at childbirth is limited, or of poor quality and where few hospitals offer the necessary corrective surgery.</p> <p>Methods</p> <p>This was a prospective observational study where all women who attended Mbarara Regional Referral Hospital in western Uganda with obstetric fistula during the study period were assessed pre-operatively for social demographics, fistula characteristics, classification and outcomes after surgery. Assessment for fistula closure and stress incontinence after surgery was done using a dye test before discharge</p> <p>Results</p> <p>Of the 77 women who were recruited in this study, 60 (77.9%) had successful closure of their fistulae. Unsuccessful fistula closure was significantly associated with large fistula size (Odds Ratio 6 95% Confidential interval 1.46-24.63), circumferential fistulae (Odds ratio 9.33 95% Confidential interval 2.23-39.12) and moderate to severe vaginal scarring (Odds ratio 12.24 95% Confidential interval 1.52-98.30). Vaginal scarring was the only factor independently associated with unsuccessful fistula repair (Odds ratio 10 95% confidential interval 1.12-100.57). Residual stress incontinence after successful fistula closure was associated with type IIb fistulae (Odds ratio 5.56 95% Confidential interval 1.34-23.02), circumferential fistulae (Odds ratio 10.5 95% Confidential interval 1.39-79.13) and previous unsuccessful fistula repair (Odds ratio 4.8 95% Confidential interval 1.27-18.11). Independent predictors for residual stress incontinence after successful fistula closure were urethral involvement (Odds Ratio 4.024 95% Confidential interval 2.77-5.83) and previous unsuccessful fistula repair (Odds ratio 38.69 95% Confidential interval 2.13-703.88).</p> <p>Conclusions</p> <p>This study demonstrated that large fistula size, circumferential fistulae and marked vaginal scarring are predictors for unsuccessful fistula repair while predictors for residual stress incontinence after successful fistula closure were urethral involvement, circumferential fistulae and previous unsuccessful fistula repair.</p

A sorghum practical haplotype graph facilitates genome‐wide imputation and cost‐effective genomic prediction

Successful management and utilization of increasingly large genomic datasets is essential for breeding programs to accelerate cultivar development. To help with this, we developed a Sorghum bicolor Practical Haplotype Graph (PHG) pangenome database that stores haplotypes and variant information. We developed two PHGs in sorghum that were used to identify genome-wide variants for 24 founders of the Chibas sorghum breeding program from 0.01x sequence coverage. The PHG called single nucleotide polymorphisms (SNPs) with 5.9% error at 0.01x coverage—only 3% higher than PHG error when calling SNPs from 8x coverage sequence. Additionally, 207 progenies from the Chibas genomic selection (GS) training population were sequenced and processed through the PHG. Missing genotypes were imputed from PHG parental haplotypes and used for genomic prediction. Mean prediction accuracies with PHG SNP calls range from .57–.73 and are similar to prediction accuracies obtained with genotyping-by-sequencing or targeted amplicon sequencing (rhAmpSeq) markers. This study demonstrates the use of a sorghum PHG to impute SNPs from low-coverage sequence data and shows that the PHG can unify genotype calls across multiple sequencing platforms. By reducing input sequence requirements, the PHG can decrease the cost of genotyping, make GS more feasible, and facilitate larger breeding populations. Our results demonstrate that the PHG is a useful research and breeding tool that maintains variant information from a diverse group of taxa, stores sequence data in a condensed but readily accessible format, unifies genotypes across genotyping platforms, and provides a cost-effective option for genomic selection