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This reports summarizes work and key findings to date from the Upper RIO Grande Basin SNOwfall Measurement and streamFLOW (RIO-SNO-FLOW) Forecasting Improvement Project conducted from Jan. 1, 2014 through Dec. 31, 2015. The project area was centered over the upper mainstem Rio Grande and Conejos River basins in southern Colorado. This report is organized into 7 chapters that detail the major elements of the project including; a Project Description, NOAA Gap-filling Radar, NASA Airborne Snow Observatory, In-Situ Ground Observations, Distributed Hydrologic Modeling, and Community Engagement. While several follow-on activities are still in progress, a number of conclusions and recommendations have emerged from the...
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This metadata record describes model outputs and supporting model code for the Data-Driven Drought Prediction project of the Water Resources Mission Area Drought Program. The data listed here include outputs of multiple machine learning model types for predicting hydrological drought at select locations within the conterminous United States. The child items referenced below correspond to different models and spatial extents (Colorado River Basin region or conterminous United States). See the list below or metadata files in each sub-folder for more details. Daily streamflow percentile predictions for the Colorado River Basin region — Outputs from long short-term memory (LSTM) deep learning models corresponding to...
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This project aims to improve seasonal water supply forecasts on the Upper Rio Grande River basin and, in doing so, help to minimize the substantial costs associated with erroneous forecasts and related sub-optimal allocations of water for surface irrigation, groundwater recharge and endangered specifies management. Erroneous seasonal water supply forecasts in the Upper Rio Grande River basin have a profound impact on water management, agricultural production and economic vitality.The specific goals of this project are to: Develop state-of the art precipitation and snowpack monitoring products through the use of experimental radar, surface observations and land data assimilation systems Improve the spatial and...
Categories: Data, Project; Types: ArcGIS REST Map Service, ArcGIS Service Definition, Downloadable, GeoTIFF, Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service, Raster; Tags: CO-03, Colorado, Colorado, Data Acquisition and Development, Federal resource managers, All tags...
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This metadata record describes outputs from 12 configurations of long short-term memory (LSTM) models which were used to predict streamflow drought occurrence at 384 stream gage locations in the Colorado River Basin region. The models were trained on data from 01-Oct-1981 to 31-Mar-2005 and validated over the period of record spanning 01-Apr-2005 to 31-Mar- 2014. The models use explanatory variable inputs described in Wieczorek (2023) (doi.org/10.5066/P98IG8LO) to predict daily streamflow and streamflow percentiles as described in Simeone (2022) (doi.org/10.5066/P92FAASD). Separate models were trained to predict daily streamflow and streamflow percentiles. Two types of percentiles were modeled: (1) fixed-threshold...


    map background search result map search result map Improving Seasonal Water Supply Predictions and Water Management in the Upper Rio Grande River Basin through use of Enhanced Observations of Snowfall, Snowpack and Physics-Based Modeling Systems Data-Driven Drought Prediction Project Model Outputs for Select Spatial Units within the Conterminous United States Data-Driven Drought Prediction Project Model Outputs: Daily Streamflow and Streamflow Percentile Predictions for the Colorado River Basin Region Improving Seasonal Water Supply Predictions and Water Management in the Upper Rio Grande River Basin through use of Enhanced Observations of Snowfall, Snowpack and Physics-Based Modeling Systems Data-Driven Drought Prediction Project Model Outputs: Daily Streamflow and Streamflow Percentile Predictions for the Colorado River Basin Region Data-Driven Drought Prediction Project Model Outputs for Select Spatial Units within the Conterminous United States