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Title: Science-integrated Artificial-intelligence for Flooding and precipitation Extremes (SAFE)

Abstract

A grand challenge in hydrologic science is to understand why signals of climate change and variability, which are often visible in precipitation extremes at aggregate scales, are not consistently observed in the case of extreme flooding. However, a solution to this challenge may prove elusive unless the water cycle is viewed in an integrative manner. Thus, for riverine flooding, while Hortonian (infiltration excess) runoff may have stronger correlation with precipitation extremes and hence perhaps to warming trends or climate oscillators, Dunne (saturation excess) runoff may have a more complex relationships with time series of precipitation and with evaporation and transpiration, but rain-on-snow and snowmelt events may depend on land-surface and atmospheric temperatures. Atmospheric rivers and tropical cyclones lead to precipitation or flooding and are impacted by climate. Flooding assessments need to consider long-term baselines, evolving risk factors, coupled natural-human systems, and novel adaptation such as nature-inspired design.

Authors:
 [1];  [2];  [3];  [4];  [5];  [6];  [3];  [3];  [1];  [7];  [8];  [9]
  1. Northeastern Univ., Boston, MA (United States)
  2. National Center for Atmospheric Research (NCAR), Boulder, CO (United States)
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  4. Univ. of Minnesota, Minneapolis, MN (United States)
  5. Columbia University; (United States)
  6. Univ. of Colorado, Boulder, CO (United States)
  7. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)
  8. Tufts Univ., Medford, MA (United States)
  9. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Northeastern Univ., Boston, MA (United States); National Center for Atmospheric Research (NCAR), Boulder, CO (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Minnesota, Minneapolis, MN (United States); Columbia Univ., New York, NY (United States); (United States); Univ. of Colorado, Boulder, CO (United States); Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Tufts Univ., Medford, MA (United States); Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1769776
Report Number(s):
AI4ESP1047
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Ganguly, Auroop R., Haupt, Sue, Hoffman, Forrest, Kumar, Vipin, Lall, Upmanu, Monteleoni, Claire, Kumar, Jitendra, Singh, Nagendra, Hopkins, Julia, Karpatne, Anuj, Islam, Shafiqul, and Chatterjee, Samrat. Science-integrated Artificial-intelligence for Flooding and precipitation Extremes (SAFE). United States: N. p., 2021. Web. doi:10.2172/1769776.
Ganguly, Auroop R., Haupt, Sue, Hoffman, Forrest, Kumar, Vipin, Lall, Upmanu, Monteleoni, Claire, Kumar, Jitendra, Singh, Nagendra, Hopkins, Julia, Karpatne, Anuj, Islam, Shafiqul, & Chatterjee, Samrat. Science-integrated Artificial-intelligence for Flooding and precipitation Extremes (SAFE). United States. https://doi.org/10.2172/1769776
Ganguly, Auroop R., Haupt, Sue, Hoffman, Forrest, Kumar, Vipin, Lall, Upmanu, Monteleoni, Claire, Kumar, Jitendra, Singh, Nagendra, Hopkins, Julia, Karpatne, Anuj, Islam, Shafiqul, and Chatterjee, Samrat. 2021. "Science-integrated Artificial-intelligence for Flooding and precipitation Extremes (SAFE)". United States. https://doi.org/10.2172/1769776. https://www.osti.gov/servlets/purl/1769776.
@article{osti_1769776,
title = {Science-integrated Artificial-intelligence for Flooding and precipitation Extremes (SAFE)},
author = {Ganguly, Auroop R. and Haupt, Sue and Hoffman, Forrest and Kumar, Vipin and Lall, Upmanu and Monteleoni, Claire and Kumar, Jitendra and Singh, Nagendra and Hopkins, Julia and Karpatne, Anuj and Islam, Shafiqul and Chatterjee, Samrat},
abstractNote = {A grand challenge in hydrologic science is to understand why signals of climate change and variability, which are often visible in precipitation extremes at aggregate scales, are not consistently observed in the case of extreme flooding. However, a solution to this challenge may prove elusive unless the water cycle is viewed in an integrative manner. Thus, for riverine flooding, while Hortonian (infiltration excess) runoff may have stronger correlation with precipitation extremes and hence perhaps to warming trends or climate oscillators, Dunne (saturation excess) runoff may have a more complex relationships with time series of precipitation and with evaporation and transpiration, but rain-on-snow and snowmelt events may depend on land-surface and atmospheric temperatures. Atmospheric rivers and tropical cyclones lead to precipitation or flooding and are impacted by climate. Flooding assessments need to consider long-term baselines, evolving risk factors, coupled natural-human systems, and novel adaptation such as nature-inspired design.},
doi = {10.2172/1769776},
url = {https://www.osti.gov/biblio/1769776}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2021},
month = {4}
}