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Title: HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

Journal Article · · Hydrology and Earth System Sciences (Online)
 [1];  [2]; ORCiD logo [3];  [4]; ORCiD logo [5];  [6];  [7];  [8];  [1]; ORCiD logo [9];  [1];  [9];  [10];  [1]
  1. Pennsylvania State Univ., University Park, PA (United States)
  2. Belgian Nuclear Research Centre, Mol (Belgium)
  3. Univ. of Saskatchewan, Saskatoon, SK (Canada)
  4. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  5. Consortium of Univ. for the Advancement of Hydrologic Science, Inc. (CUAHSI), Cambridge, MA (United States)
  6. National Taiwan Univ., Taipei (Taiwan)
  7. NASA Ames Research Center (ARC), Moffett Field, Mountain View, CA (United States)
  8. Univ. of California, Irvine, CA (United States)
  9. Univ. of Texas, Arlington, TX (United States)
  10. Sichuan Univ., Sichuan (China)
+ Show Author Affiliations

Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in hydrology has so far been gradual, but the field is now ripe for breakthroughs. This paper suggests that DL-based methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data. Interrogative methods are then invoked to interpret DL models for scientists to further evaluate. However, hydrology presents many challenges for DL methods, such as data limitations, heterogeneity and co-evolution, and the general inexperience of the hydrologic field with DL. The roadmap toward DL-powered scientific advances will require the coordinated effort from a large community involving scientists and citizens.Integrating process-based models with DL models will help alleviate data limitations. The sharing of data and baseline models will improve the efficiency of the community as a whole. Open competitions could serve as the organizing events to greatly propel growth and nurture data science education in hydrology, which demands a grassroots collaboration. The area of hydrologic DL presents numerous research opportunities that could, in turn,stimulate advances in machine learning as well.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC); National Science Foundation (NSF)
Grant/Contract Number:
AC02-05CH11231; SC0016605
OSTI ID:
1542338
Journal Information:
Hydrology and Earth System Sciences (Online), Journal Name: Hydrology and Earth System Sciences (Online) Journal Issue: 11 Vol. 22; ISSN 1607-7938
Publisher:
European Geosciences Union (EGU)Copyright Statement
Country of Publication:
United States
Language:
English

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