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

Research output: Contribution to journalArticlepeer-review


  • Chaopeng Shen
  • Eric Laloy
  • Amin Elshorbagy
  • Adrian Albert
  • Jerad Bales
  • Fi-John Chang
  • Sangram Ganguly
  • Kuo-Lin Hsu
  • Daniel Kifer
  • Zheng Fang
  • Kuai Fang
  • Dongfeng Li
  • Xiaodong Li
  • Wen-Ping Tsai

Institutes & Expert groups

  • Pennsylvania State University - Civil and Environmental Engineering
  • Sichuan University - State Key Laboratory of Hydraulics and Mountain River Engineering
  • University of Texas at Arlington - Civil Engineering
  • Pennsylvania State University - Computer Science and Engineering
  • University of Irvine, California - Civil and Environmental Engineering
  • NASA - Ames Research Center
  • National Taiwan University - Department of Bioenvironmental Systems Engineering
  • CUAHSI - Consortium of Universities for the Advancement of Hydrologic Science, Inc. (), Cambridge, MA
  • LBNL - Lawrence Berkeley National Laboratory
  • USASK - University of Saskatchewan - Civil, Geological, and Environmental Engineering

Documents & links


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 DLbased 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.


Original languageEnglish
Pages (from-to)5639–5656
Number of pages18
JournalHydrology and Earth System Sciences
Publication statusPublished - 1 Nov 2018


  • deep learning, hydrology

ID: 4928773