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  • A Review of Sensor-Based Approaches for Monitoring Rapid Response Treatments of cyanoHABs

    Abstract: Water quality sensors are dynamic and vary greatly both in terms of utility and data acquisition. Data collection can range from single-parameter and one-dimensional to highly complex multiparameter spatiotemporal. Likewise, the analytical and statistical approaches range from relatively simple (e.g., linear regression) to more complex (e.g., artificial neural networks). Therefore, the decision to implement a particular water quality monitoring strategy is dependent upon many factors and varies widely. The purpose of this review was to document the current scientific literature to identify and compile approaches for water quality monitoring as well as statistical methodologies required to analyze and visualize highly diverse spatiotemporal water quality data. The literature review identified two broad categories: (1) sensor-based approaches for monitoring rapid response treatments of cyanobacterial harmful algal blooms (cyanoHABs), and (2) analytical tools and techniques to analyze complex high resolution spatial and temporal water quality data. The ultimate goal of this review is to provide the current state of the science as an array of scalable approaches, spanning from simple and practical to complex and comprehensive, and thus, equipping the US Army Corps of Engineers (USACE) water quality managers with options for technology-analysis combinations that best fit their needs.
  • Stormwater Management Practices, Monitoring, and Maintenance Plan for US Army Garrison at West Point, NY

    Abstract: Structural stormwater management practices (SMPs) are designed and installed with the goal of reducing runoff and improving water quality through a variety of built (e.g., underground chamber and filter systems), nature-based and natural features (e.g., rain gardens, swales). In compliance with Section 402 of the US Clean Water Act (CWA), US Army Garrisons at West Point MS4 operators are required to obtain a National Pollutant Discharge Elimination System permit or a New York State Pollutant Discharge Elimination System (SPDES). These permits require development of stormwater management plans to reduce pollutants to meet the appropriate water quality standards. Over 62 structural SMPs have been installed at the US Army Garrison (USAG) to meet permit requirements. Monitoring and maintenance are essential to maintain and understand the effectiveness of these structures, track their maintenance needs, and improve their function. This document provides guidance for conducting stormwater management practice, inspection, and maintenance at the United States Army Garrison at West Point. The objectives are to inform installation managers on general SMP functions and designs, highlight key maintenance triggers affecting SMP functionality, and provide guidance on when and how to conduct inspections and maintenance actions specific to USAG SMPs and in accordance to NYS DEC.
  • waterquality for ArcGIS Pro Toolbox: User’s Guide

    Abstract: Monitoring water quality of small inland lakes and reservoirs is a critical component of the US Army Corps of Engineers (USACE) water quality management plans. However, limited resources for traditional field-based monitoring of numerous lakes and reservoirs covering vast geographic areas often leads to reactional responses to harmful algal bloom (HAB) outbreaks. Satellite remote sensing methodologies using HAB indicators is a good low-cost option to traditional methods and has been proven to maximize and complement current field-based approaches while providing a synoptic view of water quality (Beck et al. 2016; Beck et al. 2017; Beck et al. 2019; Johansen et al. 2019; Mishra et al. 2019; Stumpf and Tomlinson 2007; Wang et al. 2020; Xu et al. 2019; Reif 2011). To assist USACE water quality management, we developed an Environmental Systems Research Institute (ESRI) ArcGIS Pro desktop software toolbox (waterquality for ArcGIS Pro) founded on the design and research established in the waterquality R software package (Johansen et al. 2019; Johansen 2020). The toolbox enables the detection, monitoring, and quantification of HAB indicators (chlorophyll-a, phycocyanin, and turbidity) using Sentinel-2 satellite imagery. Four tools are available: (1) automating the download of Sentinel-2 Level-2A imagery, (2) creating stacked image with options for cloud and non-water features masks, (3) applying water quality algorithms to generate relative estimations of one to three water quality parameters (chlorophyll-a, phycocyanin, and turbidity), and (4) creating linear regression graphs and statistics comparing in situ data (from field-based water sampling) to relative estimation data. This document serves as a user’s guide for the waterquality for ArcGIS Pro toolbox and includes instructions on toolbox installation and descriptions of each tool’s inputs, outputs, and troubleshooting guidance.
  • A Review of Empirical Algorithms for the Detection and Quantification of Harmful Algal Blooms Using Satellite-Borne Remote Sensing

    Abstract: Harmful Algal Blooms (HABs) continue to be a global concern, especially since predicting bloom events including the intensity, extent, and geographic location, remain difficult. However, remote sensing platforms are useful tools for monitoring HABs across space and time. The main objective of this review was to explore the scientific literature to develop a near-comprehensive list of spectrally derived empirical algorithms for satellite imagers commonly utilized for the detection and quantification HABs and water quality indicators. This review identified the 29 WorldView-2 MSI algorithms, 25 Sentinel-2 MSI algorithms, 32 Landsat-8 OLI algorithms, 9 MODIS algorithms, and 64 MERIS/Sentinel-3 OLCI algorithms. This review also revealed most empirical-based algorithms fell into one of the following general formulas: two-band difference algorithm (2BDA), three-band difference algorithm (3BDA), normalized-difference chlorophyll index (NDCI), or the cyanobacterial index (CI). New empirical algorithm development appears to be constrained, at least in part, due to the limited number of HAB-associated spectral features detectable in currently operational imagers. However, these algorithms provide a foundation for future algorithm development as new sensors, technologies, and platforms emerge.
  • Optimizing the Harmful Algal Bloom Interception, Treatment, and Transformation System (HABITATS)

    Abstract: Harmful algal blooms (HABs) continue to affect lakes and waterways across the nation, often resulting in environmental and economic damage at regional scales. The US Army Engineer Research and Development Center (ERDC) and collaborators have continued research on the Harmful Algal Bloom Interception, Treatment, and Transformation System (HABITATS) project to develop a rapidly deployable and scalable system for mitigating large HABs. The second year of the project focused on optimization research, including (1) development of a new organic flocculant formulation for neutralization and flotation of algal cells; (2) testing and initial optimization of a new, high-throughput biomass dewatering system with low power requirements; (3) development, design, assembly, and initial testing of the first shipboard HABITATS prototype; (4) execution of two field pilot studies of interception and treatment systems in coordination with the Florida Department of Environmental Protection and New York State Department of Environmental Conservation; (5) conversion of algal biomass into biocrude fuel at pilot scale with a 33% increase in yield compared to the previous bench scale continuous-flow reactor studies; and (6) refinement of a scalability analysis and optimization model to guide the future development of full-scale prototypes.
  • Stormwater Management and Optimization Toolbox

    Abstract: As stormwater regulations for hydrologic and water quality control become increasingly stringent, Department of Defense (DoD) facilities are faced with the daunting task of complying with multiple laws and regulations. This often requires facilities to plan, design, and implement structural best management practices (BMPs) to capture, filter, and/or infiltrate runoff—requirements that can be complicated, contradictory, and difficult to plan. This project demonstrated the Stormwater Management Optimization Toolbox (SMOT), a spreadsheet-based tool that effectively analyzes and plans for compliance to the Energy Independence and Security Act (EISA) of 2007 pre-hydrologic conditions through BMP implementation, resulting in potential cost savings by reducing BMP sizes while simultaneously achieving compliance with multiple objectives. SMOT identifies the most cost-effective modeling method based on an installation’s local conditions (soils, rainfall patterns, drainage network, and regulatory requirements). The work first demonstrated that the Model Selection Tool (MST) recommendation accurately results in the minimum BMP cost for 45 facilities of widely varying climatic and regional conditions, and then demonstrated SMOT at two facilities.