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Improving the Representation of Long‐Term Storage Variations With Conceptual Hydrological Models in Data‐Scarce Regions
Author(s) -
Hulsman Petra,
Hrachowitz Markus,
Savenije Hubert H. G.
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 - 10.1029/2020wr028837
Subject(s) - forcing (mathematics) , environmental science , groundwater , water storage , term (time) , conceptual model , surface runoff , climate change , precipitation , representation (politics) , hydrology (agriculture) , climatology , meteorology , computer science , geography , geology , ecology , oceanography , politics , political science , law , physics , geotechnical engineering , geomorphology , quantum mechanics , database , inlet , biology
Abstract In the Luangwa basin in Zambia, long‐term total water storage variations were observed with Gravity Recovery and Climate Experiment, but not reproduced by a standard conceptual hydrological model that encapsulates our current understanding of the dominant regional hydrological processes. The objective of this study was to identify potential processes underlying these low‐frequency variations through combined data analysis and model hypothesis testing. First, we analyzed the effect of data uncertainty by contrasting observed storage variations with multi‐annual estimates of precipitation and evaporation from multiple data sources. Second, we analyzed four different combinations of model forcing and evaluated their skill to reproduce the observed long‐term storage variations. Third, we formulated alternative model hypotheses for groundwater export to potentially explain low‐frequency storage variations. Overall, the results suggest that the initial model's inability to reproduce the observed low‐frequency storage variations was partly due to the forcing data used and partly due to the missing representation of regional groundwater export. More specifically, the choice of data source affected the model's ability to reproduce annual maximum storage fluctuations, whereas the annual minima improved by adapting the model structure to allow for groundwater export from a deeper groundwater layer. This suggests that, in contrast to previous research, conceptual models can reproduce long‐term storage fluctuations if a suitable model structure is used. Overall, the results highlight the value of alternative data sources and iterative testing of model structural hypotheses to improve runoff predictions in a poorly gauged basin leading to enhanced understanding of its hydrological processes.