22 Apr 2020

PUBLICATION NOTICE: A Practical Two-Phase Approach to Improve the Reliability and Efficiency of Markov Chain Monte Carlo Directed Hydrologic Model Calibration

ABSTRACT: Markov chain Monte Carlo (MCMC) methods are widely used in hydrology and other fields for posterior inference in a Bayesian framework. A properly constructed MCMC sampler is guaranteed to converge to the correct limiting distribution, but convergence can be very slow. While most research is focused on improving the proposal distribution used to generate trial moves in the Markov chain, this work instead focuses on efficiently finding an initial population for population-based MCMC samplers that will expedite convergence. Four case studies, including two hydrological models, are used to demonstrate that using multi-level single linkage implicit filtering stochastic global optimization to initialize the population both reduces the overall computational cost and significantly increases the chance of finding the correct limiting distribution within the constraint of a fixed computational budget.

20 Apr 2020

PUBLICATION NOTICE: Application of Chirp Acoustic Sub-Bottom Data in Riverine Environments: Identification of Underlying Rocky Hazards at Cape Girardeau, Missouri, and Thebes, Illinois

ABSTRACT: Shallow acoustic reflection (chirp) data have been utilized to map the elevation of underlying stratigraphy in a wide range of aqueous environments. Of particular concern in riverine regions is the elevation of near-surface underlying rock that, if exposed during normal migration of sedimentary bedforms, can cause grounding and damage to vessels transiting the region during periods of low water. Given the ephemeral nature of the rock’s exposure, traditional surveying methods are insufficient to map rock when it is covered by a thin veneer of sediment, increasing the potential hazard. Accordingly, the US Army Corps of Engineers, St. Louis District, (MVS) explored the use of chirp sub-bottom surveys to identify buried rock within the Mississippi River in the vicinity of Cape Girardeau, MO, and Thebes, IL. Hazard maps showing the distribution of buried rock were generated, and the base of the mobile sediment layer was identified where possible. These data will allow MVS to accurately identify potentially hazardous regions during periods of low water. Although the study did not result in the complete mapping of all near-surface geologic hazards, regions that warrant further study are identified, and modifications to the original survey plan are provided to improve the accuracy of future data collection efforts.

20 Apr 2020

PUBLICATION NOTICE: Bed-Load Transport Measurements on the Chippewa River Using the ISSDOTv2 Method

PURPOSE: This Regional Sediment Management (RSM) Technical Note (TN) provides information on bed-load measurements obtained on the Chippewa River, Wisconsin, in the spring of 2018. The ISSDOTv2 method was developed by the U.S. Army Corps of Engineers (USACE), Engineering Research and Development Center (ERDC), Coastal and Hydraulics Laboratory (CHL), River and Estuarine Engineering Branch. The method uses time-sequenced bathymetric data to determine a bed-load transport rate. When transport rates are obtained with concurrent flow-rate data, it is possible to develop bed-load rating curves. Such rating curves are extremely valuable in forecasting or hindcasting bed-load sediment delivery for the location at which the data were obtained. This is very important for river managers in developing sediment budgets and in the planning of dredging operations.  In the present study, the USACE Mississippi Valley Division (MVD), St. Paul District (MVP), had contracted with the U.S. Geological Survey (USGS) for real-time monitoring of suspended-sediment concentrations (suspended sand load and bed-load sediment) on the lower Chippewa River, a major source and contributor of sand-sized sediment to the Upper Mississippi River (UMR). The bed-load values obtained using ISSDOTv2 are presented in this RSM TN.

20 Apr 2020

PUBLICATION NOTIFICATION: Local Spatial Dispersion for Multiscale Modeling of Geospatial Data: Exploring Dispersion Measures to Determine Optimal Raster Data Sample Sizes

ABSTRACT: Scale, or spatial resolution, plays a key role in interpreting the spatial structure of remote sensing imagery or other geospatially dependent data. These data are provided at various spatial scales. Determination of an optimal sample or pixel size can benefit geospatial models and environmental algorithms for information extraction that require multiple datasets at different resolutions. To address this, an analysis was conducted of multiple scale factors of spatial resolution to determine an optimal sample size for a geospatial dataset. Under the NET-CMO project at ERDC-GRL, a new approach was developed and implemented for determining optimal pixel sizes for images with disparate and heterogeneous spatial structure. The application of local spatial dispersion was investigated as a three-dimensional function to be optimized in a resampled image space. Images were resampled to progressively coarser spatial resolutions and stacked to create an image space within which pixel-level maxima of dispersion was mapped. A weighted mean of dispersion and sample sizes associated with the set of local maxima was calculated to determine a single optimal sample size for an image or dataset. This size best represents the spatial structure present in the data and is optimal for further geospatial modeling.

20 Apr 2020

PUBLICATION NOTICE: Design considerations for beneficial use sites along the Channel to Victoria, Calhoun County, Texas

Purpose: This U.S. Army Corps of Engineers (USACE) Regional Sediment Management (RSM) investigation considered implementation of new or historically underutilized beneficial use (BU) sites for the Channel to Victoria (CTV) in Calhoun County, Texas. The utilization of alternative  placement areas is justified on two main grounds: (1) there is cost savings associated with the shorter pump distance compared to the existing upland confined placement areas and (2) shoaling reduction relative to a without project condition. Additional benefits realized by utilizing the proposed sites include (1) increased safety for vessels navigating CTV due to the reduction/elimination of open fetch and currents, (2) additional placement options available in times of emergency dredging, and (3) increased bird habitat, particularly for the endangered whooping crane. These sites have received National Environmental Policy Act (NEPA) clearance in previous project documents, and it is anticipated minimal or no additional NEPA coordination will be required to construct/restore these sites.

20 Apr 2020

PUBLICATION NOTICE: New and Enhanced Tools for Civil Military Operations (NET-CMO)

Abstract: Civil Military Operations (CMO) associated geospatial modeling is intended to enable increased knowledge of regional stability, assist in Foreign Humanitarian Assistance (FHA), and provide support to Force Health Protection (FHP) operational planning tasks. However, current geoenabled methodologies and technologies are lacking in their overall capacity to support complex mission analysis efforts focused on understanding these important stability factors and mitigating threats to Army soldiers and civilian populations. CMO analysts, planners, and decision-makers do not have a robust capability to both spatially and quantitatively identify Regions of Interest (ROI), which may experience a proliferation in health risks such as vector-borne diseases in areas of future conflict. Additionally, due to this general absence of geoenabled health assessment models and derived end-products, CMO stakeholders are adversely impacted in their Military Decision Making Process (MDMP) capabilities to develop comprehensive area studies and plans such as Course of Action (COA). The NET-CMO project is focused on fostering emerging geoenabling capabilities and technologies to improve military situational awareness for assessment and planning of potential health threat-risk vulnerabilities.

14 Apr 2020

PUBLICATION NOTICE: Spatial Downscaling Disease Risk Using Random Forests Machine Learning

 Link: http://dx.doi.org/10.21079/11681/35618Report Number: ERDC/GRL TN-20-1Title: Spatial Downscaling Disease Risk Using Random Forests Machine LearningBy Sean P. GriffinApproved for Public Release; Distribution is Unlimited February 2020Purpose: Mosquito-borne illnesses are a significant public health concern, both to the Department of Defense

14 Apr 2020

PUBLICATION NOTICE: Update to: Use of Engineering With Nature® Concepts on the Savannah Harbor Navigation Project, Dredged Material Containment Areas, Savanna, GA

 NOTE: A new PDF for this report was uploaded on 2/20/2020 to correct an error that was in the previous version. The link to the report on Knowledge Core will still remain the same. If you have downloaded a version of the report prior to now please replace it with the new version now available.Link: http://dx.doi.org/10.21079/11681/35353  Report

14 Apr 2020

PUBLICATION NOTICE: Species Distribution Modeling of Ixodes scapularis and Associated Pathogens in States East of the Mississippi River

 Link: http://dx.doi.org/10.21079/11681/35615Report Number: ERDC/GRL TR-20-2Title: Species Distribution Modeling of Ixodes scapularis and Associated Pathogens in States East of the Mississippi RiverBy Kathleen V. Payne, Sean P. Griffin, Susan L. Lyon, Robin E. Lopez, and Nicole M. WayantApproved for Public Release; Distribution is Unlimited

14 Apr 2020

PUBLICATION NOTIFICATION: Coincidence Processing of Photon-Sensitive Mapping Lidar Data

 Link: http://dx.doi.org/10.21079/11681/35599 Report Number: ERDC/GRL TR-20-1Title: Coincidence Processing of Photon-Sensitive Mapping Lidar DataBy Christian Marchant, Ryan Kirkpatrick, and David OberApproved for Public Release; Distribution is Unlimited February 2020Abstract: Photon-sensitive mapping lidar systems are able to image at greater