Abstract. Total water storage change (TWSC) reflects the balance of all water fluxes in a hydrological system. The Gravity Recovery and Climate Experiment/Follow-On (GRACE/GRACE-FO) monthly gravity field models, distributed as spherical harmonic (SH) coefficients, are the only means of observing this state variable. The well-known correlated noise in these observations requires filtering, which scatters the actual mass changes from their true locations. This effect is known as leakage. This study explores the traditional basin and grid scaling approaches, and develops a novel frequency-dependent scaling for leakage correction of GRACE TWSC in a unique, basin-specific assessment for the Indus Basin. We harness the characteristics of significant heterogeneity in the Indus Basin due to climate and human-induced changes to study the physical nature of these scaling schemes. The most recent WaterGAP (Water Global Assessment and Prognosis) hydrology model (WGHM v2.2d) with its two variants, standard (without glacier mass changes) and Integrated (with glacier mass changes), is used to derive scaling factors. For the first time, we explicitly show the effect of inclusion or exclusion of glacier mass changes in the model on the gridded scaling factors. The inferences were validated in a detailed simulation environment designed using WGHM fields corrupted with GRACE-like errors using full monthly error covariance matrices. We find that frequency-dependent scaling outperforms both basin and grid scaling for the Indus Basin, where mass changes of different frequencies are localized. Grid scaling can resolve trends from glacier mass loss and groundwater loss but fails to recover the small seasonal signals in trunk Indus. Frequency-dependent scaling can provide a robust estimate of the seasonal cycle of TWSC for practical applications such as regional-scale water availability assessments. Apart from these novel developments and insights into the traditional scaling approach, our study encourages the regional scale users to conduct specific assessments for their basin of interest.

<p>Time variable satellite gravimetry, realized with the missions GRACE and GRACE-FO, allows for the only global observation of total water storage (TWS) changes. These observations are inherently smoothed due to the upward continuation of the gravity field at the satellite orbits. Additionally, the correlated errors seen as north-south stripes in global maps require further filtering to separate signal from noise. This causes the signal at any region to be biased by signal at neighboring regions, better known as leakage effect. Various methods have been proposed to mitigate leakage and to spatially assign TWS changes at smaller spatial scales than the satellite data is available by using auxiliary information. Unfortunately, there is a large spatio-temporally variable degree of discrepancy in the agreement or the disagreement within these methods, leaving the non-geodetic users of GRACE TWS changes with the complex question of choosing an appropriate method. The scaling factor approach and the Data-Driven Correction (DDC) approach are the most widely used methods. The scaling factor approach uses a numerical model output of TWS changes, whereas the DDC approach uses only GRACE observations to account for leakage.<br />Tripathi et al., 2022 (10.5194/hess-26-4515-2022) found for the Indus basin, that a newly proposed variant of the scaling factor method, called Frequency-Dependent scaling, using the WaterGAP (Water Global Assessment and Prognosis) hydrology model (WGHM v2.2d), produced results with a striking agreement against the results from the DDC approach. Therefore, this contribution extends the comparison of Frequency-Dependent scaling using WGHM v2.2d against the DDC method for 189 global hydrological basins. We achieved an agreement between the results from both methods well within the uncertainties of GRACE TWS observations for almost 85-90% of the global hydrological basins. Such an agreement can bring a much-needed consolidation in the treatment of leakage effect across the user community. The disagreement in the rest of the basins varies across time scales, such as long-term trends and periodic signals, and is being further analysed.</p>

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