Rivers and Floodplains as Key Components of Global Terrestrial Water Storage Variability

Abstract

This study quantifies the contribution of rivers and floodplains to terrestrial water storage (TWS) variability. We use state‐of‐the‐art models to simulate land surface processes and river dynamics and to separate TWS into its main components. Based on a proposed impact index, we show that surface water storage (SWS) contributes 8% of TWS variability globally, but that contribution differs widely among climate zones. Changes in SWS are a principal component of TWS variability in the tropics, where major rivers flow over arid regions and at high latitudes. SWS accounts for ~22–27% of TWS variability in both the Amazon and Nile Basins. Changes in SWS are negligible in the Western U.S., Northern Africa, Middle East, and central Asia. Based on comparisons with Gravity Recovery and Climate Experiment‐based TWS, we conclude that accounting for SWS improves simulated TWS in most of South America, Africa, and Southern Asia, confirming that SWS is a key component of TWS variability.

Tracking Seasonal Fluctuations in Land Water Storage Using Global Models and GRACE Satellites

“…It is not clear which storage compartment should be varied in tropical basins to match GRACE data (e.g., SMS, SWS, or overbank storage from flood inundation). General underestimation of seasonal amplitudes in the remaining three LSMs (NOAH‐3.3, VIC‐4.0.4, and CLSM‐F2.5) relative to GRACE may be related to lack of SWS or low SMS capacity, consistent with findings from Getirana et al, ; Tables S1 and S2). The equivocal importance of a separate GWS compartment for matching GRACE seasonal amplitudes is seen in the similarity in TWSA seasonal amplitudes between PCR‐GLOBWB (with GWS) and CLM‐5.0 (without GWS), because the latter can approximate GWS by using thick soil profiles (≤8–10 m) in tropical basins, such as the Amazon.…”

“…Uncertainties in simulating human water use include water source (SW/GW) and fate of return flows (Veldkamp et al, ). Discrepancies between LSMs and GRACE TWSA have been attributed to lack of human intervention in these models (Getirana et al, ). Sensitivity analysis using the WGHM model suggests that human water use has a limited effect on water storages globally, ranking it fifth after calibration and climate factors because of the small number of grid cells impacted (Müller‐Schmied et al, ).…”

Toward continental hydrologic–hydrodynamic modeling in South America

“…There is a good The good agreement found in the Amazon basin can be attributed to the explicit representation of surface water reservoir (channels and floodplains), which has been demonstrated to play an important role on both magnitude and timing of TWS 10 (Alkama et al, 2010;Paiva et al, 2013;Getirana et al, 2017a). Other authors have pointed out that the contribution of surface storage to TWS is also potentially high in the Orinoco (~45 %) (e.g., Frappart et al, 2014), suggesting a large underestimation of the soil storage (in the eastern part of the basin) because anomalies of water level were reasonably well simulated.…”

“…Indeed, surface storage has been understood as a major component of TWS variability over tropical regions of South America, and may also be relevant for large rivers crossing semiarid areas such as the Sao Francisco (Getirana et al, 15 2017a). In the case of La Plata, the TWS amplitude is likely to be amplified if surface water is anticipated in time (Getirana et al, 2017a), which is probably occurring due to the low correlation of water levels previously simulated for the Paraguay River. In addition, the absence of a well-defined river system due to very flat terrains (e.g., Chaco Region, in the west part of La Plata) potentially favors the dominance of the groundwater dynamics over TWS, as already reported by Kuppel et al, (2015) in the Western Pampas more in the south.…”

Evaluation of Simulated Snow and Snowmelt Timing in the Community Land Model Using Satellite‐Based Products and Streamflow Observations

“…At each time step, the inflow water is redistributed between the river channel and floodplain reservoirs following stage-volume relationships derived from the topography of each grid cell. HyMAP has been extensively evaluated on a regional scale over the Amazon basin (Getirana et al, 2012(Getirana et al, , 2014Getirana, Kumar, et al, 2017), East Africa region , and the Middle East ; on a continental scale over North America (Kumar et al, 2015); and on a global scale (Getirana, Kumar, et al, 2017). In this study, HyMAP is utilized with a local inertia formulation, which enables a more stable and computationally efficient representation of backwater effects.…”