Diagnosing modeling errors of global terrestrial water storage interannual variability
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.
Hoontaek Lee et al.
Hoontaek Lee et al.
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This study quantified the contribution of each pixel to the global TWS IAV of GRACE observations and two selected predominantly data-driven models, SINDBAD and H2M, as well as its modeling errors. The results show that the global TWS IAV is mainly driven by humid tropical and semi-arid region. The hotspots of modeling errors of the global TWS IAV are mainly located in tropical regions that span across climatic regions. The study provides an improved understanding of the global TWS IAV and its modeling error. Generally, the topic is important, and the study is well written and easy to follow. My comments are as follows.
1. In the high latitudes of the northern hemisphere, glacier changes contribute to TWS, whether the SINDBAD model and the H2M model have a glacier module.
2. It needs to be further pointed out that the model is inconsistent with GRACE in typical irrigation areas, such as the western United States, northern India, etc.
3. Figure 2(a) shows that the two models are in good agreement, and they both have some differences from GRACE. Does the input of precipitation significantly affect the simulation results of the model? If other precipitation products are used as input, will the results be different?
4. The abscissa and ordinate of the scatter plot in Figure 3 have no text description
5. How much different precipitation inputs affect the modeling error of global terrestrial water storage interannual variability? Does the precipitation input or the different model structure affect the simulation error more?