Item talk:Q227112

{

 "@context": "http://schema.org/",
 "@type": "WebPage",
 "additionalType": "Activity",
 "url": "https://www.usgs.gov/mission-areas/water-resources/science/drought-prediction-science",
 "headline": "Drought Prediction Science",
 "datePublished": "March 23, 2023",
 "author": [
   {
     "@type": "Person",
     "name": "Erik Smith, Ph.D.",
     "url": "https://www.usgs.gov/staff-profiles/erik-smith",
     "identifier": {
       "@type": "PropertyValue",
       "propertyID": "orcid",
       "value": "0000-0001-8434-0798"
     }
   },
   {
     "@type": "Person",
     "name": "Stacey A Archfield",
     "url": "https://www.usgs.gov/staff-profiles/stacey-a-archfield",
     "identifier": {
       "@type": "PropertyValue",
       "propertyID": "orcid",
       "value": "0000-0002-9011-3871"
     }
   },
   {
     "@type": "Person",
     "name": "John C. Hammond, PhD",
     "url": "https://www.usgs.gov/staff-profiles/john-c-hammond",
     "identifier": {
       "@type": "PropertyValue",
       "propertyID": "orcid",
       "value": "0000-0002-4935-0736"
     }
   }
 ],
 "description": [
   {
     "@type": "TextObject",
     "text": "Hamshaw, S.D., Sando, R.R., Goodling, P.J., McShane, R.R., Watkins, W., and White, E. (2023) Data-Driven Drought Prediction Project Model Outputs for Select Spatial Units within the Conterminous United States: U.S. Geological Survey data release, https://doi.org/10.5066/P97NIH7Y"
   },
   {
     "@type": "TextObject",
     "text": "Wieczorek, M.E., Hafen, K.C., and Staub, L.E. (2023) Data-Driven Drought Prediction Project Model Inputs for Upper and Lower Colorado Portions of the National Hydrologic Geo-Spatial Fabric version 1.1 and Select U.S. Geological Survey Streamgage Basins: U.S. Geological Survey data release, https://doi.org/10.5066/P98IG8LO"
   },
   {
     "@type": "TextObject",
     "text": "Recent publications:"
   },
   {
     "@type": "TextObject",
     "text": "Drought is a prolonged and widespread deficit in available water supplies that creates multiple stressors across ecosystems and communities. The U.S. Geological Survey Water Mission Area conducts drought research and modeling to improve drought prediction capabilities. The research focus is on understanding the hydrological, ecological, and economic ramifications of drought. The modeling focus builds on the research to provide predictive capacity for resilience planning at local, regional, and national levels. Prediction of the onset, severity, and duration of hydrologic drought conditions is critical for the USGS to identify and evaluate multi-sector responses to alterations in water availability."
   },
   {
     "@type": "TextObject",
     "text": "The U.S. Geological Survey drought prediction science capacity includes monitoring, research, and modeling. Long-term water monitoring has allowed for identification of drought periods in the context of climatic variability and change, and for statistical trend analysis of continuous data to support earlier and more accurate detection of low-flow and drought conditions. Recent drought science research has focused on modeling and analysis of drivers of hydrologic drought."
   },
   {
     "@type": "TextObject",
     "text": "Ongoing scientific inquiries and activities to support drought characterization and prediction include:"
   },
   {
     "@type": "TextObject",
     "text": "Hammond, J. C., Simeone, C., Hecht, J. S., Hodgkins, G. A., Lombard, M., McCabe, G., et al. (2022) Going beyond low flows: Streamflow drought deficit and duration illuminate distinct spatiotemporal drought patterns and trends in the U.S. during the last century. Water Resources Research, 58, e2022WR031930. https://doi.org/10.1029/2022WR031930"
   },
   {
     "@type": "TextObject",
     "text": "In addition, the U.S. Geological Survey is exploring the usage of new data-driven methods (artificial intelligence and machine learning) to predict and deliver early warning of hydrological drought conditions at higher spatial and temporal resolution than currently available. These data-driven drought prediction models will provide useful information to stakeholders on drought onset, duration, and severity metrics (and their uncertainty) for surface water and groundwater."
   },
   {
     "@type": "TextObject",
     "text": "Hamshaw, S., Goodling, P., Hafen, K., Hammond, J., McShane, R., Sando, R., Shastry, A., Simeone, C., Watkins, D., White, E., and Wieczorek, M. (2023) Regional Streamflow Drought Forecasting in the Colorado River Basin using Deep Neural Network Models: SEDHYD 2023 Conference, St. Louis, MO, May 8-12, 2023, https://www.sedhyd.org/2023Program/1/181.pdf"
   }
 ],
 "funder": {
   "@type": "Organization",
   "name": "Water Resources Mission Area",
   "url": "https://www.usgs.gov/mission-areas/water-resources"
 },
 "about": [
   {
     "@type": "Thing",
     "name": "Science Technology"
   },
   {
     "@type": "Thing",
     "name": "Modeling and Prediction"
   },
   {
     "@type": "Thing",
     "name": "Information Systems"
   },
   {
     "@type": "Thing",
     "name": "Geology"
   },
   {
     "@type": "Thing",
     "name": "Water Availability"
   },
   {
     "@type": "Thing",
     "name": "Environmental Health"
   },
   {
     "@type": "Thing",
     "name": "Forecasts: Drought/Floods/Water Supply"
   },
   {
     "@type": "Thing",
     "name": "Drought Impacts"
   },
   {
     "@type": "Thing",
     "name": "Water"
   },
   {
     "@type": "Thing",
     "name": "National Water Census"
   },
   {
     "@type": "Thing",
     "name": "Methods and Analysis"
   },
   {
     "@type": "Thing",
     "name": "Water Availability and Use"
   },
   {
     "@type": "Thing",
     "name": "Prediction and Modeling"
   },
   {
     "@type": "Thing",
     "name": "Drought Resilience"
   },
   {
     "@type": "Thing",
     "name": "Energy"
   },
   {
     "@type": "Thing",
     "name": "Drought Prediction"
   }
 ]

}