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The depth grids show the depth of flooding on the Clear Fork Mohican River near Bellville, Ohio on local map backgrounds, based on stages of 9.0 ft to 17.0 ft at the USGS streamgage, Clear Fork Mohican River at Bellville, Ohio, 03131982.
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Digital flood-inundation maps for an approximate 2.5-mile (mi) reach of the Clear Fork Mohican River that extends approximately from State Route 97 to the downstream corporate boundary for Bellville, Ohio, were created by the U.S. Geological Survey (USGS) in cooperation with the Muskingum Watershed Conservancy District. The flood-inundation maps show estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the Clear Fork Mohican River at Bellville (station number 03131982). The maps can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/. Near-real-time stages at this streamgage...
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This data release is comprised of a set of eight time travel map shapefiles (two tsunami inundation zones and four travel times) for use in GIS software applications and two population exposure by travel time tables (residents and nonresidences) for use in GIS software applications and other standalone spreadsheet applications. The travel time map was generated using the Pedestrian Evacuation Analyst model (version 1.0.1 for ArcGIS 10.5) from the USGS (https://geography.wr.usgs.gov/science/vulnerability/tools.html). The travel time analysis uses ESRI's Path Distance tool to find the shortest distance across a cost surface from any point in the hazard zone to a safe zone. This cost analysis considers the direction...
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Ecological models facilitate evaluation and assessment of alternative plans for restoring the Greater Everglades ecosystem. Modeling outputs were used in evaluations of alternative water control plans to be performed by the Combined Operational Plan (COP). The models used were: (1) Cape Sable Seaside Sparrow Marl Prairie Indicator in conjunction with (2) Cape Sable Seaside Sparrow Helper, (3) Florida apple snail (native) population model (EverSnail), (4) Wader Distribution Evaluation Modeling (WADEM), (5) Small-sized freshwater fish density, and (6) Alligator production probability (i.e., habitat suitability index (HSI)). These ecological models are used to examine potential impacts on the above listed flora and...
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Ecological models facilitate evaluation and assessment of alternative plans for restoring the Greater Everglades ecosystem. Modeling outputs were used in evaluations of alternative water control plans to be performed by the Combined Operational Plan (COP). The models used were: (1) Cape Sable Seaside Sparrow Marl Prairie Indicator in conjunction with (2) Cape Sable Seaside Sparrow Helper, (3) Florida apple snail (native) population model (EverSnail), (4) Wader Distribution Evaluation Modeling (WADEM), (5) Small-sized freshwater fish density, and (6) American alligator production probability (i.e., breeding potential). These ecological models are used to examine potential impacts on the above listed flora and fauna...
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These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data. Observed water temperatures from 1980-2019 were compiled for 2,332 lakes in the US. These data were used as training, test, and error-estimation data for process-guided deep learning models and the evaluation of process-based models. The data are formatted as a single csv (comma separated values) file with attributes corresponding...
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Digital flood-inundation maps for selected streams in Stark County were created by the U.S. Geological Survey (USGS) in cooperation with the Muskingum Watershed Conservancy District and the Stark County Commissioners as part of a Federal Emergency Management Agency (FEMA) Flood Insurance Study (FIS). The flood-inundation maps show estimates of the areal extent corresponding to the 1% and 0.2% annual-exceedance probability floods. Flood profiles were computed for the stream reach by means of the one-dimensional step-backwater model.
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Multiple modeling frameworks were used to predict daily temperatures at 0.5m depth intervals for a set of diverse lakes in the U.S. states of Minnesota and Wisconsin. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added...
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This dataset includes model inputs that describe local weather conditions for Sparkling Lake, WI. Weather data comes from two sources: locally measured (2009-2017) and gridded estimates (all other time periods). There are two comma-delimited files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of...
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This dataset includes model inputs that describe weather conditions for the 68 lakes included in this study. Weather data comes from gridded estimates (Mitchell et al. 2004). There are two comma-separated files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of lake temperature model inputs and outputs...
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This dataset provides model specifications used to estimate water temperature from a process-based model (Hipsey et al. 2019). The format is a single JSON file indexed for each lake based on the "site_id". This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).
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The travel time map was generated using the Pedestrian Evacuation Analyst model (version 1.0.1 for ArcGIS 10.5) from the USGS (https://geography.wr.usgs.gov/science/vulnerability/tools.html). The travel time analysis uses ESRI's Path Distance tool to find the shortest distance across a cost surface from any point in the hazard zone to a safe zone. This cost analysis considers the direction of movement and assigns a higher cost to steeper slopes, based on a table contained within the model. The analysis also adds in the energy costs of crossing different types of land cover, assuming that less energy is expended walking along a road than walking across a sandy beach. To produce the time map, the evacuation surface...
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The travel time map was generated using the Pedestrian Evacuation Analyst model (version 1.0.1 for ArcGIS 10.5) from the USGS (https://geography.wr.usgs.gov/science/vulnerability/tools.html). The travel time analysis uses ESRI's Path Distance tool to find the shortest distance across a cost surface from any point in the hazard zone to a safe zone. This cost analysis considers the direction of movement and assigns a higher cost to steeper slopes, based on a table contained within the model. The analysis also adds in the energy costs of crossing different types of land cover, assuming that less energy is expended walking along a road than walking across a sandy beach. To produce the time map, the evacuation surface...
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A simple water budget includes precipitation, streamflow, change in storage, evapotranspiration, and residuals: P=Q + ET + ΔS + e. It is essential to include the managed component (i.e., the “human” component) to close the water budget and reduce the magnitude of the residuals from “natural” water budgets. Some of the largest components of managed water withdraws are public supply, irrigation, and thermoelectric. The modified water budget is: P=Q + ET + ΔS + (PS + Irr + TE) + e, where PS is public supply, Irr is irrigation, and TE is thermoelectric water use. This data release contains both the natural and managed components of the water budget for a region within the Apalachicola-Chattahoochee-Flint (ACF) River...
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This dataset includes model inputs (specifically, weather and flags for predicted ice-cover) and is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).
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The travel time map was generated using the Pedestrian Evacuation Analyst model from the USGS (https://geography.wr.usgs.gov/science/vulnerability/tools.html). The travel time analysis uses ESRI's Path Distance tool to find the shortest distance across a cost surface from any point in the hazard zone to a safe zone. This cost analysis considers the direction of movement and assigns a higher cost to steeper slopes, based on a table contained within the model. The analysis also adds in the energy costs of crossing different types of land cover, assuming that less energy is expended walking along a road than walking across a sandy beach. To produce the time map, the evacuation surface output from the model is grouped...
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This dataset contains O'ahu resident count estimates as a function of travel time out of the standard and extreme tsunami-evacuation zones for three different travel speeds (impaired, slow, and fast walk). The data are organized in a manner which permits summarizing or visualizing the data by tsunami-evacuation zone and/or travel time, with communities listed across the top as columns and individual rows representing the number of residents present in the specific evacuation zone/travel time combination. Due to the nature of the methodology used to distribute residential population to structures, resident numbers are not integers. This dataset is intended for use in the U.S. Geological Survey's O'ahu, HI tsunami...
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The travel time map was generated using the Pedestrian Evacuation Analyst model (version 1.0.1 for ArcGIS 10.5) from the USGS (https://geography.wr.usgs.gov/science/vulnerability/tools.html). The travel time analysis uses ESRI's Path Distance tool to find the shortest distance across a cost surface from any point in the hazard zone to a safe zone. This cost analysis considers the direction of movement and assigns a higher cost to steeper slopes, based on a table contained within the model. The analysis also adds in the energy costs of crossing different types of land cover, assuming that less energy is expended walking along a road than walking across a sandy beach. To produce the time map, the evacuation surface...
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This dataset contains American Samoa resident count estimates as a function of travel time out of the 2009 and probable maximum tsunami (PMT) inundation zones for four different travel speeds (slow walk, fast walk, slow run, and fast run). The data are organized in a manner which permits summarizing or visualizing the data by village, tsunami-evacuation zone, and/or travel time, with individual rows representing the number of residents present in the specific village/evacuation zone/travel time combination. Due to the nature of the methodology used to distribute residential population to structures, resident numbers are not integers. These data, in tabular format, are intended for use in GIS software applications...
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These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data. This dataset provides shapefile outlines of the 2,332 lakes that had temperature modeled as part of this study. The format is a shapefile for all lakes combined (.shp, .shx, .dbf, and .prj files). A csv file of lake metadata is included, which includes lake metadata and all features that were considered for the meta transfer...


map background search result map search result map Tsunami evacuation time map for the island of O'ahu, Hawai'i, extreme tsunami evacuation zone and fast walk speed Pedestrian evacuation times for residents on the island of O'ahu, Hawai'i, for standard and extreme tsunami evacuation zones by community, modeled at three travel speeds (impaired, slow, and fast walk) Pedestrian tsunami evacuation results for two tsunami-inundation zones (2009 and probable maximum tsunami (PMT)) and four travel speeds (slow walk, fast walk, slow run, and fast run) for American Samoa Tsunami evacuation time map for American Samoa 2009 tsunami inundation zone and fast walk speed Tsunami evacuation time map for American Samoa 2009 tsunami inundation zone and slow run speed Tsunami evacuation time map for American Samoa 2009 tsunami inundation zone and fast run speed Pedestrian evacuation times for residents on the islands of American Samoa, for 2009 and predicted maximum tsunami (PMT) inundation zones by village, modeled at four travel speeds (slow walk, fast walk, slow run, and fast run) Natural and managed components of the water-budget from 2008–2012 for 43 HUC10s in the Apalachicola-Chattahoochee-Flint River Basin, Georgia, U.S. Depth grids for flood-inundation maps in and near Bellville, Ohio Floodplain boundaries for flood-inundation maps in and near Bellville, Ohio Input and output shapefiles used in the hydraulic modeling for selected streams in Stark County, Ohio. Process-guided deep learning water temperature predictions: 3 Model inputs (meteorological inputs and ice flags) Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values) Process-guided deep learning water temperature predictions: 5c All lakes historical prediction data Process-guided deep learning water temperature predictions: 3c All lakes historical inputs Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs Ecological modeling output for the Combined Operational Plan, Round 3 in the Greater Everglades, 2018-2019 Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning: 1 Lake information for 2,332 lakes (Provisional Data Release) Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning: 2 Water temperature observations (Provisional Data Release) Ecological modeling output for the Combined Operational Plan Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs Floodplain boundaries for flood-inundation maps in and near Bellville, Ohio Depth grids for flood-inundation maps in and near Bellville, Ohio Pedestrian evacuation times for residents on the island of O'ahu, Hawai'i, for standard and extreme tsunami evacuation zones by community, modeled at three travel speeds (impaired, slow, and fast walk) Tsunami evacuation time map for the island of O'ahu, Hawai'i, extreme tsunami evacuation zone and fast walk speed Ecological modeling output for the Combined Operational Plan, Round 3 in the Greater Everglades, 2018-2019 Ecological modeling output for the Combined Operational Plan Natural and managed components of the water-budget from 2008–2012 for 43 HUC10s in the Apalachicola-Chattahoochee-Flint River Basin, Georgia, U.S. Process-guided deep learning water temperature predictions: 3 Model inputs (meteorological inputs and ice flags) Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values) Process-guided deep learning water temperature predictions: 5c All lakes historical prediction data Process-guided deep learning water temperature predictions: 3c All lakes historical inputs Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning: 2 Water temperature observations (Provisional Data Release) Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning: 1 Lake information for 2,332 lakes (Provisional Data Release)