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Advancing Process Representation in Hydrological Models: Integrating New Concepts, Knowledge, and Data
Author(s) -
Guse Björn,
Fatichi Simone,
Gharari Shervan,
Melsen Lieke A.
Publication year - 2021
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 - 10.1029/2021wr030661
Subject(s) - representation (politics) , computer science , streamflow , process (computing) , scientific modelling , inference , hydrological modelling , data science , fidelity , management science , artificial intelligence , geography , geology , cartography , engineering , drainage basin , telecommunications , philosophy , epistemology , climatology , politics , political science , law , operating system
Model fidelity and accuracy in process representations have been the crux of scientific hydrological modeling, creating a pressing need for a better linkage between the development of hydrological models and the growing number of data sources and measurement techniques. Improved representation of process dynamics in hydrological models can provide new insights into complex hydrological systems and point out less understood natural phenomena that need further investigation. This special issue includes contributions that offer potential solutions and strategies to improve and test the representation of hydrological processes. We have organized the special issue contributions into four topical categories: (a) Beyond streamflow, which looks into the power of complementary data sources in addition to traditionally used streamflow for process inference. (b) Challenge of subsurface hydrology, that reflects on lesser understood processes under the surface and their impact on the model structure. (c) Evaporation in hydrological modeling, linking ecological aspects to the hydrological functioning of the natural system. Finally, (d) top down vs. bottom up modeling approaches, relied upon for process representation analysis. The special issue and our reflection on the contributions present a snapshot of ongoing efforts for integrating new concepts, knowledge, and data in process representation in hydrological models.