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Accelerating advances in continental domain hydrologic modeling
Author(s) -
Archfield Stacey A.,
Clark Martyn,
Arheimer Berit,
Hay Lauren E.,
McMillan Hilary,
Kiang Julie E.,
Seibert Jan,
Hakala Kirsti,
Bock Andrew,
Wagener Thorsten,
Farmer William H.,
Andréassian Vazken,
Attinger Sabine,
Viglione Alberto,
Knight Rodney,
Markstrom Steven,
Over Thomas
Publication year - 2015
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2015wr017498
Subject(s) - hydrological modelling , water cycle , computer science , water resources , environmental science , water balance , water security , process (computing) , hydrology (agriculture) , environmental resource management , geology , climatology , ecology , geotechnical engineering , biology , operating system
In the past, hydrologic modeling of surface water resources has mainly focused on simulating the hydrologic cycle at local to regional catchment modeling domains. There now exists a level of maturity among the catchment, global water security, and land surface modeling communities such that these communities are converging toward continental domain hydrologic models. This commentary, written from a catchment hydrology community perspective, provides a review of progress in each community toward this achievement, identifies common challenges the communities face, and details immediate and specific areas in which these communities can mutually benefit one another from the convergence of their research perspectives. Those include: (1) creating new incentives and infrastructure to report and share model inputs, outputs, and parameters in data services and open access, machine‐independent formats for model replication or reanalysis; (2) ensuring that hydrologic models have: sufficient complexity to represent the dominant physical processes and adequate representation of anthropogenic impacts on the terrestrial water cycle, a process‐based approach to model parameter estimation, and appropriate parameterizations to represent large‐scale fluxes and scaling behavior; (3) maintaining a balance between model complexity and data availability as well as uncertainties; and (4) quantifying and communicating significant advancements toward these modeling goals.