05 Aug 2022
05 Aug 2022
Status: this preprint is currently under review for the journal HESS.

Diagnosing modeling errors of global terrestrial water storage interannual variability

Hoontaek Lee1,2, Martin Jung1, Nuno Carvalhais1,3,4, Tina Trautmann1, Basil Kraft1, Markus Reichstein1,4, Matthias Forkel2, and Sujan Koirala1 Hoontaek Lee et al.
  • 1Max Planck Institute for Biogeochemistry, Germany
  • 2Technische Universität Dresden, Institute of Photogrammetry and Remote Sensing, Germany
  • 3Departamento de Ciências e Engenharia do Ambiente, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal
  • 4ELLIS Unit Jena at Michael-Stifel-Center Jena for Data-driven and Simulation Science

Abstract. Terrestrial water storage (TWS) is an integrative hydrological state that is key for our understanding of the global water cycle. The TWS observation from GRACE missions has, therefore, been instrumental in calibration and validation of hydrological models and understanding the variations of the hydrological storages. The models, however, still show significant uncertainties in reproducing observed TWS variations, especially for the interannual variability (IAV) at the global scale. Here, we diagnose the regions dominating the variance of globally integrated TWS IAV, and sources of the errors in two data-driven hydrological models that were calibrated against global TWS, snow water equivalent, evapotranspiration, and runoff data: 1) a parsimonious process-based hydrological model, the Strategies to INtegrate Data and BiogeochemicAl moDels (SINDBAD) framework, and 2) a machine learning-physically based hybrid hydrological model (H2M) that combines a dynamic neural network with a water balance concept.

While both models agree with GRACE that global TWS IAV is largely driven by the semi-arid regions of southern Africa, Indian subcontinent and northern Australia, and the humid regions of northern South America and Mekong River Basin, the models still show errors such as overestimation of the observed magnitude of TWS IAV at the global scale. Our analysis identifies modeling error hotspots of the global TWS IAV mostly in the tropical regions including Amazon, sub–Saharan regions, and Southeast Asia, indicating that the regions that dominate global TWS IAV are not necessarily the same as those that dominate the error in global TWS IAV. Excluding those error hotspot regions in the global integration yields large improvements of simulated global TWS IAV, which implies that model improvements can focus on improving processes in these hotspot regions. Further analysis indicates that error hotspot regions are associated with lateral flow dynamics, including both sub-pixel moisture convergence and across pixel lateral river flow, or with interactions between surface processes and groundwater. The association of model deficiencies with land processes that delay the TWS variation could, in part, explain why the models cannot represent the observed lagged response of TWS IAV to precipitation IAV in hotspot regions that manifest to errors in global TWS IAV. Our approach presents a general avenue to better diagnose model simulation errors for global data streams to guide efficient and focused model development for regions and processes that matter the most.

Hoontaek Lee et al.

Status: open (until 30 Sep 2022)

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Hoontaek Lee et al.

Hoontaek Lee et al.


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Short summary
We spatially attribute the variance of global terrestrial water storage (TWS) interannual variability (IAV) and its modeling error by 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 smaller-scales lateral convergence of water. Our findings imply that the TWS IAV modeling can be efficiently improved by focusing on model representations for the error hotspots.