Articles | Volume 27, issue 7
https://doi.org/10.5194/hess-27-1531-2023
https://doi.org/10.5194/hess-27-1531-2023
Research article
 | 
14 Apr 2023
Research article |  | 14 Apr 2023

Diagnosing modeling errors in global terrestrial water storage interannual variability

Hoontaek Lee, Martin Jung, Nuno Carvalhais, Tina Trautmann, Basil Kraft, Markus Reichstein, Matthias Forkel, and Sujan Koirala

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Cited articles

Ahlstrom, A., Raupach, M. R., Schurgers, G., Smith, B., Arneth, A., Jung, M., Reichstein, M., Canadell, J. G., Friedlingstein, P., Jain, A. K., Kato, E., Poulter, B., Sitch, S., Stocker, B. D., Viovy, N., Wang, Y. P., Wiltshire, A., Zaehle, S., and Zeng, N.: The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink, Science, 348, 895–899, https://doi.org/10.1126/science.aaa1668, 2015. a
Awange, J. L., Anyah, R., Agola, N., Forootan, E., and Omondi, P.: Potential impacts of climate and environmental change on the stored water of Lake Victoria Basin and economic implications, Water Resour. Res., 49, 8160–8173, https://doi.org/10.1002/2013WR014350, 2013. a
Awange, J. L., Saleem, A., Sukhadiya, R. M., Ouma, Y. O., and Kexiang, H.: Physical dynamics of Lake Victoria over the past 34 years (1984–2018): Is the lake dying?, Sci. Total Environ., 658, 199–218, https://doi.org/10.1016/j.scitotenv.2018.12.051, 2019. a, b
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., v. Dijk, A. I. J. M., McVicar, T. R., and Adler, R. F.: MSWEP V2 Global 3-Hourly 0.1 Precipitation: Methodology and Quantitative Assessment, B. Am. Meteorol. Soc., 100, 473–500, https://doi.org/10.1175/BAMS-D-17-0138.1, 2019. a, b
Bolaños, S., Salazar, J. F., Betancur, T., and Werner, M.: GRACE reveals depletion of water storage in northwestern South America between ENSO extremes, J. Hydrol., 596, 125687, https://doi.org/10.1016/j.jhydrol.2020.125687, 2021. a
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Short summary
We spatially attribute the variance in global terrestrial water storage (TWS) interannual variability (IAV) and its modeling error with two data-driven hydrological models. We find error hotspot regions that show a disproportionately large significance in the global mismatch and the association of the error regions with a smaller-scale lateral convergence of water. Our findings imply that TWS IAV modeling can be efficiently improved by focusing on model representations for the error hotspots.
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