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OUR PLANET SCIENCE MISSION DIRECTORATE A probabilistic flood stage simulation from a model that uses machine learning applied to U.S. Geological Survey streamflow gauge data to improve representation of the rainfall -runoff process. Grey Nearing, University of Alabama; Hoshin Gupta, University of Arizona Grey Nearing, University of Alabama Craig Pelissier, NASA Goddard Space Flight Center Modern hydrology models like the NASA Land Information System (LIS) integrate satellite and ground-based observational data products with advanced modeling techniques to support a variety of water resources applications, including drought monitoring, river forecasting, food security, and weather prediction. Machine learning offers new opportunities to reduce uncertainties in hydrology modeling, extract more information from Earth-observing satellite data, and improve our ability to predict the global water cycle. An illustration of the terrestrial water cycle as simulated by the NASA Land Information System (LIS). Marit Jentoft-Nilsen, Deborah McLean, NASA/Goddard Information-theory dynamic process networks illustrating the connectivity in an ecosystem at different timescales. Allison Goodwell, University of Colorado, Denver; Praveen Kumar, University of Illinois Learning and Simulating the Earth’s Water Cycle
Transcript of Learning and Simulating the Earth’s Water Cycle › upload › files › sc18posters › 33...OUR...
O U R P L A N E TSCIENCE MISSION DIRECTORATE
A probabilistic flood stage simulation from a model that uses machine learning applied to U.S. Geological Survey streamflow gauge data to improve representation of the rainfall-runoff process. Grey Nearing, University of
Alabama; Hoshin Gupta, University of Arizona
Grey Nearing, University of AlabamaCraig Pelissier, NASA Goddard Space Flight Center
Modern hydrology models like the NASA Land Information System (LIS) integrate satellite and ground-based observational data products with advanced modeling techniques to support a variety of water resources applications, including drought monitoring, river forecasting, food security, and weather prediction. Machine learning offers new opportunities to reduce uncertainties in hydrology modeling, extract more information from Earth-observing satellite data, and improve our ability to predict the global water cycle.
An illustration of the terrestrial water cycle as simulated by the NASA Land Information System (LIS). Marit Jentoft-Nilsen, Deborah McLean, NASA/Goddard
Information-theory dynamic process networks illustrating the connectivity in an ecosystem at different timescales.Allison Goodwell, University of Colorado,
Denver; Praveen Kumar, University of Illinois
Learning and Simulating the Earth’s Water Cycle
