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The creation of this tool began with continuous streamflow observations below the outlets of spawning ponds in each of our watersheds. The weather generator for this region built into the Soil and Water Assessment Tool (SWAT) was used to generate 10,000 years of synthetic weather data. SWAT was used to generate hydrologic models of streamflow calibrated to observed streamflow at our monitoring sites. The 10,000 years of synthetic weather data was input into calibrated SWAT hydrologic models for each site, giving us an output of 10,000 years of synthetic streamflow. The Random Forest model is the machine learning algorithm that we trained using reservoir depth and previous 30-days’ precipitation to predict when streamflow will fall below a migration threshold, indicating the loss of juvenile river-herring out-migration potential. |
| summary:
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The creation of this tool began with continuous streamflow observations below the outlets of spawning ponds in each of our watersheds. The weather generator for this region built into the Soil and Water Assessment Tool (SWAT) was used to generate 10,000 years of synthetic weather data. SWAT was used to generate hydrologic models of streamflow calibrated to observed streamflow at our monitoring sites. The 10,000 years of synthetic weather data was input into calibrated SWAT hydrologic models for each site, giving us an output of 10,000 years of synthetic streamflow. The Random Forest model is the machine learning algorithm that we trained using reservoir depth and previous 30-days’ precipitation to predict when streamflow will fall below a migration threshold, indicating the loss of juvenile river-herring out-migration potential. |
| accessInformation:
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| thumbnail:
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0 |
| typeKeywords:
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["Tool","Service","Geoprocessing Service","Web Tool","ArcGIS Server"] |
| description:
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| licenseInfo:
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| catalogPath:
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| title:
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Migration_Loss_Prediction_v3 |
| type:
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Geoprocessing Service |
| url:
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https://clear-gis.uconn.edu:6443/arcgis/admin |
| tags:
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["LISS_Fish"] |
| culture:
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en-US |
| name:
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Migration_Loss_Prediction_v3 |
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0 |
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