GRACE-REC: a reconstruction of climate-driven water storage changes over the last century

Abstract

Abstract. The amount of water stored on continents is an important constraint for\nwater mass and energy exchanges in the Earth system and exhibits large\ninter-annual variability at both local and continental scales. From 2002 to\n2017, the satellites of the Gravity Recovery and Climate Experiment\n(GRACE) mission have observed changes in terrestrial water storage (TWS) with an\nunprecedented level of accuracy. In this paper, we use a statistical model\ntrained with GRACE observations to reconstruct past climate-driven changes\nin TWS from historical and near-real-time meteorological datasets at daily\nand monthly scales. Unlike most hydrological models which represent water\nreservoirs individually (e.g., snow, soil moisture) and usually provide\na single model run, the presented approach directly reconstructs total TWS\nchanges and includes hundreds of ensemble members which can be used to\nquantify predictive uncertainty. We compare these data-driven TWS estimates\nwith other independent evaluation datasets such as the sea level budget,\nlarge-scale water balance from atmospheric reanalysis, and in situ streamflow\nmeasurements. We find that the presented approach performs overall as well\nor better than a set of state-of-the-art global hydrological models (Water\nResources Reanalysis version\xa02). We provide reconstructed TWS anomalies at a\nspatial resolution of 0.5 ∘ , at both daily and monthly scales over\nthe period 1901 to present, based on two different GRACE products and three\ndifferent meteorological forcing datasets, resulting in six reconstructed TWS\ndatasets of 100 ensemble members each. Possible user groups and applications\ninclude hydrological modeling and model benchmarking, sea level budget\nstudies, assessments of long-term changes in the frequency of droughts, the\nanalysis of climate signals in geodetic time series, and the interpretation\nof the data gap between the GRACE and GRACE Follow-On missions. The\npresented dataset is published at https://doi.org/10.6084/m9.figshare.7670849 (Humphrey and Gudmundsson, 2019) and updates will be\npublished regularly.