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The Best Management Practices Statistical Estimator (BMPSE) was developed by the U.S. Geological Survey (USGS), in cooperation with the Federal Highway Administration (FHWA) Office of Project Delivery and Environmental Review to provide planning-level information about the performance of structural best management practices for decision makers, planners, and highway engineers to assess and mitigate possible adverse effects of highway and urban runoff on the Nation's receiving waters (Granato 2013, 2014; Granato and others, 2021a,b). The BMPSE was used to calculate statistics and create input files for fitting the trapezoidal distribution to data from studies documenting the performance of individual structural stormwater...
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The Stochastic Empirical Loading and Dilution Model (SELDM) was developed by the U.S. Geological Survey (USGS), in cooperation with the Federal Highway Administration (FHWA) Office of Project Delivery and Environmental Review to transform complex scientific data into meaningful information about the risk of adverse effects of runoff on receiving waters, the potential need for mitigation measures, and the potential effectiveness of such management measures for reducing these risks (Granato 2013; Granato and Jones, 2014). SELDM is a stochastic model because it uses Monte Carlo methods to produce the random combinations of input variable values needed to generate the stochastic population of values for each component...
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Topock Marsh is a large wetland adjacent to the Colorado River and main feature of Havasu National Wildlife Refuge (Havasu NWR) in southern Arizona. In 2010, U.S. Fish and Wildlife Service (FWS) and Bureau of Reclamation began a project to improve water management capabilities at Topock Marsh and protect habitats and species. Initial construction required a drawdown, which caused below-average inflows and water depths in 2010-2011. Co-applicants Daniels and Haegele of FORT monitored Topock Marsh during the drawdown and immediately after, thus obtained information on immediate effects. However, stress from the drawdown may have a delayed effect on aquatic resources; additionally, significant changes to the infrastructure...
Categories: Data, Software; Types: ArcGIS REST Map Service, ArcGIS Service Definition, Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: 2013, AZ-04, Applications and Tools, Arizona, CA-08, All tags...
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We have developed pyEMU, a python framework for Environmental Modeling Uncertainty analyses, open-source tool that is non-intrusive, easy-to-use, computationally efficient, and scalable to highly-parameterized inverse problems. The framework implements several types of linear (first-order, second-moment (FOSM)) and non-linear uncertainty analyses. The FOSM-based analyses can also be completed prior to parameter estimation to help inform important modeling decisions, such as parameterization and objective function formulation. Complete workflows for several types of FOSM-based and non-linear analyses are documented in example notebooks implemented using Jupyter that are available in the online pyEMU repository. Example...


    map background search result map search result map pyEMU: a python framework for environmental model uncertainty analysis Decision Support Tool for Water and Resource Management of Topock Marsh Best Management Practices Statistical Estimator (BMPSE) Version 1.2.0 Stochastic Empirical Loading and Dilution Model (SELDM) software archive Decision Support Tool for Water and Resource Management of Topock Marsh Stochastic Empirical Loading and Dilution Model (SELDM) software archive Best Management Practices Statistical Estimator (BMPSE) Version 1.2.0 pyEMU: a python framework for environmental model uncertainty analysis