[1] While continental water storage plays a key role in the Earth's water, energy, and biogeochemical cycles, its temporal and spatial variations are poorly known, in particular, for large areas. This study analyzes water storage simulated with the Watergap Global Hydrology Model. The model represents four major storage compartments: surface water, snow, soil, and groundwater. Water storage variations are analyzed for the period 1961-1995 with 0.5°resolution, for the major global climate zones, and for the 30 largest river basins worldwide. Seasonal variations are the dominant storage change signal with maximum values in the marginal tropics and in snow-dominated high-latitude areas. Interannual variations are associated with large-scale oscillations such as El Niño Southern Oscillation. The contribution of individual water storage compartments to total storage change varies with the climate region and the timescale under consideration. In most regions, a prominent role of storage variations in surface water bodies is found. Surface water reduces markedly the spatial correlation lengths of water storage fields. The simulation results are evaluated against storage variations of combined atmospheric-terrestrial water balance studies and other global models. This study contributes to an improved understanding of continental water storage for which the consistent integration of model results and new observations such as from time-variable gravity data of the Gravity Recovery and Climate Experiment (GRACE) satellite mission is required.
The accelerated rate of decline in groundwater levels across California's Central Valley results from overdrafting and low rates of natural recharge and is exacerbated by droughts. The lack of observations with an adequate spatiotemporal resolution to constrain the evolution of groundwater resources poses severe challenges to water management efforts. Here we present SAR interferometric measurements of high‐resolution vertical land motion across the valley, revealing multiscale patterns of aquifer hydrogeological properties and groundwater storage change. Investigating the depletion and degradation of the aquifer‐system during 2007–2010, when the entire valley experienced a severe drought, we find that ~2% of total aquifer‐system storage was permanently lost, owing to irreversible compaction of the system. Over this period, the seasonal groundwater storage change amplitude of 10.11 ± 2.5 km3 modulates a long‐term groundwater storage decline of 21.32 ± 7.2 km3. Estimates for subbasins show more complex patterns, most likely associated with local hydrogeology, recharge, demand, and underground flow. Presented measurements of aquifer‐system compaction provide a more complete understanding of groundwater dynamics and can potentially be used to improve water security.
International audienceThis study presents monthly estimates of groundwater anomalies in a large river basin dominated by extensive floodplains, the Negro River Basin, based on the synergistic analysis using multisatellite observations and hydrological models. For the period 2003-2004, changes in water stored in the aquifer is isolated from the total water storage measured by GRACE by removing contributions of both the surface reservoir, derived from satellite imagery and radar altimetry, and the root zone reservoir simulated by WGHM and LaD hydrological models. The groundwater anomalies show a realistic spatial pattern compared with the hydrogeological map of the basin, and similar temporal variations to local in situ groundwater observations and altimetry-derived level height measurements. Results highlight the potential of combining multiple satellite techniques with hydrological modeling to estimate the evolution of groundwater storage
Abstract. The aim of this study is to provide an improved global simulation of continental water storage variations by calibrating the WaterGAP Global Hydrology Model (WGHM) for 28 of the largest river basins worldwide. Five years (January 2003–December 2007) of satellite-based estimates of the total water storage changes from the GRACE mission were combined with river discharge data in a multi-objective calibration framework that uses the most sensitive WGHM model parameters. The uncertainty and significance of the calibration results were analysed with respect to errors in the observation data. An independent simulation period (January 2008–December 2008) was used for validation. The contribution of single storage compartments to the total water budget before and after calibration was analysed in detail. A multi-objective improvement of the model states was obtained for most of the river basins, with mean error reductions of up to 110 km3/month for discharge and up to 24 mm of a water mass equivalent column for total water storage changes, such as for the Amazon basin. Errors in the phase and signal variability of seasonal water mass changes were reduced. The calibration is shown to primarily affect soil water storage in most river basins. The variability of groundwater storage variations was reduced on a global scale after calibration. Structural model errors were identified from a small contribution of surface water storage including wetlands in river basins with large inundation areas, such as the Amazon or the Mississippi. Our results demonstrate the value of both the GRACE data and the multi-objective calibration approach for improving large-scale hydrological simulations, and they provide a starting-point for improving model structures. The integration of complimentary observation data to further constrain the simulation of single storage compartments is encouraged.
S U M M A R YApproximately seven years of time-variable gravity data from the satellite mission Gravity Recovery and Climate Experiment (GRACE) are available to quantify present-day mass variations on and near the Earth's surface. Mass variations caused by the continental water cycle are the dominant signal component after subtracting contributions from atmosphere and oceans. This makes hydrology a primary area of application of GRACE data. To derive water storage variations at the scale of large river basins, appropriate filter techniques have to be applied to GRACE gravity fields given in a global spherical harmonic representation. A desirable filter technique minimises both GRACE data error and signal leakage across the border of the region of interest. This study evaluates the performance of six widely used filter methods (isotropic filters, anisotropic filters and decorrelation methods) and their parameter values to derive regionally averaged water mass variations from GRACE data. To this end, filtered time series from GRACE for 22 of the world's largest river basins were compared to continental water mass variations from a multimodel mean of three global hydrological models (WGHM, GLDAS and LaD). Filter-induced biases for seasonal amplitudes and phases of water storage variations, as well as satellite and leakage error budgets, were quantified for each river basin and explained in terms of storage variations in and around the basin. The optimum filter types and filter parameters were identified for each basin. The best results were provided by a decorrelation method that uses GRACE orbits for the filter design. Our ranking between all filter types and parameters depended on the geographical location, shape and signal characteristics of the specific river basin. Based on a multicriterial evaluation of satellite and leakage error, as well as an error assessment of the hydrological data, the filter selection and parameter optimisation results were shown to be reliable for 17 river basins. The results serve as a guideline for the optimal filtering of GRACE global spherical harmonic coefficients for hydrological applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.