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This dataset contains csv files in support of the conclusions published in "Water use demand in Mediterranean California under multiple scenarios of developed and agricultural land use " in the journal PLOS One. We used the USGS's LUCAS model to examine a broad suite of spatially explicit future land use scenarios and their associated county-level water use demand, including the historical (1992-2011) and projected periods (2012-2062) across 40 Monte Carlo simulations.We examined a range of potential water demand futures sampled from a 20-year record of historical (1992-2012) data to develop a suite of potential future land change scenarios from 2012-2062. These scenario simulations include a 1) business-as-usual...
This dataset provides shapefile outlines of the 7,150 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 also included. This dataset is part of a larger data release of lake temperature model inputs and outputs for 7,150 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9CA6XP8).
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This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. This section contains observations related to the amount and quality of water in the Delaware River Basin. Data from a subset of reservoirs in the basin include observed daily depth-resolved water temperature, water levels, diversions, and releases. Data from streams in the basin include daily flow and temperature observations. Observations were compiled from a variety of sources, including the National Water Inventory System, Water Quality Portal, EcoSHEDS stream...
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This section provides code for reproducing the figures in Rahmani et al. (2023b). The full model archive is organized into these four child items: 1. Model code - Python files and README for reproducing model training and evaluation 2. Inputs - Basin attributes and shapefiles, forcing data, and stream temperature observations 3. Simulations - Simulation descriptions, configurations, and outputs [THIS ITEM] 4. Figure code - Jupyter notebook to recreate the figures in Rahmani et al. (2023b) The publication associated with this model archive is: Rahmani, F., Appling, A.P., Feng, D., Lawson, K., and Shen, C. 2023b. Identifying structural priors in a hybrid differentiable model for stream water temperature modeling....
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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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This dataset contains .csv and .tif image files in support of the conclusions published in "Mediterranean California’s water use future under multiple scenarios of developed and agricultural land use change" in the journal PLOS One. We used the USGS's LUCAS model to examine a broad suite of spatially explicit future land use scenarios and their associated county-level water use demand, including the historical (1992-2011) and projected periods (2012-2062) across 40 Monte Carlo simulations.We examined a range of potential water demand futures sampled from a 20-year record of historical (1992-2012) data to develop a suite of potential future land change scenarios from 2012-2062. These scenario simulations include...
This dataset summarized a collection of annual thermal metrics to characterize lake temperature impacts on fish habitat for 7,150 lakes from uncalibrated models (PB0) and 449 from calibrated models (PBALL). The dataset includes over 172 annual thermal metrics.
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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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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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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 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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The U.S. Geological Survey is developing national water-use models to support water resources management in the United States. Model benefits include a nationally consistent estimation approach, greater temporal and spatial resolution of estimates, efficient and automated updates of results, and capabilities to forecast water use into the future and assess model uncertainty. This data release contains data used in a machine learning model to estimate monthly water use for communities that are supplied by public-supply water systems in the conterminous United States for 2000-2020. This data release also contains associated scripts used to produce input features as well as model output values by 12-digit hydrologic...
Categories: Data; Tags: Alabama, Arizona, Arkansas, California, Colorado, All tags...
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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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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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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 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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This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that makes predictions at 70 river reaches in the upper Delaware River Basin. This section contains forcing data for water temperature forecasting models reported in Zwart et al. (2023), including a process-based pre-trainer, gridded weather and forecasted weather data, and flow and temperature for reservoir inlets and outlets.
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This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. This section provides spatial data files that describe the rivers, reservoirs, and observational data in the Delaware River Basin included in this release. One shapefile of polylines describes the 459 river reaches that define the modeling network, and another shapefile of polygons includes the three reservoirs (Pepacton, Cannonsville, and Neversink) for which data are included in this release. Additionally, a point shapefile contains locations of monitoring sites...


map background search result map search result map Mediterranean California’s water use future under multiple scenarios of developed and agricultural land use change Mediterranean California’s water use future based on scenarios of land use change 1992-2062 - Tabular Data 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) 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) 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 Process-based water temperature predictions in the Midwest US: 1 Spatial data (GIS polygons for 7,150 lakes) Process-based water temperature predictions in the Midwest US: 6 Habitat metrics Data to support water quality modeling efforts in the Delaware River Basin: 1) Spatial data for rivers, reservoirs, and monitoring locations Data to support water quality modeling efforts in the Delaware River Basin: 2) River and Reservoir Observations Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin: 2) model driver data Public supply water use reanalysis for the 2000-2020 period by HUC12, month, and year for the conterminous United States 4. Figure code for model archive: Identifying structural priors in a hybrid differentiable model for stream water temperature modeling Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs 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 Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin: 2) model driver data Data to support water quality modeling efforts in the Delaware River Basin: 2) River and Reservoir Observations Data to support water quality modeling efforts in the Delaware River Basin: 1) Spatial data for rivers, reservoirs, and monitoring locations 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-based water temperature predictions in the Midwest US: 6 Habitat metrics Mediterranean California’s water use future under multiple scenarios of developed and agricultural land use change Mediterranean California’s water use future based on scenarios of land use change 1992-2062 - Tabular Data Process-based water temperature predictions in the Midwest US: 1 Spatial data (GIS polygons for 7,150 lakes) 4. Figure code for model archive: Identifying structural priors in a hybrid differentiable model for stream water temperature modeling Public supply water use reanalysis for the 2000-2020 period by HUC12, month, and year for the conterminous United States