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 water mass and energy exchanges in the Earth system and exhibits large inter-annual variability at both local and continental scales. From 2002 to 2017, the satellites of the Gravity Recovery and Climate Experiment (GRACE) mission have observed changes in terrestrial water storage (TWS) with an unprecedented level of accuracy. In this paper, we use a statistical model trained with GRACE observations to reconstruct past climate-driven changes in TWS from historical and near-real-time meteorological datasets at daily and monthly scales. Unlike most hydrological models which represent water reservoirs individually (e.g., snow, soil moisture) and usually provide a single model run, the presented approach directly reconstructs total TWS changes and includes hundreds of ensemble members which can be used to quantify predictive uncertainty. We compare these data-driven TWS estimates with other independent evaluation datasets such as the sea level budget, large-scale water balance from atmospheric reanalysis, and in situ streamflow measurements. We find that the presented approach performs overall as well or better than a set of state-of-the-art global hydrological models (Water Resources Reanalysis version 2). We provide reconstructed TWS anomalies at a spatial resolution of 0.5∘, at both daily and monthly scales over the period 1901 to present, based on two different GRACE products and three different meteorological forcing datasets, resulting in six reconstructed TWS datasets of 100 ensemble members each. Possible user groups and applications include hydrological modeling and model benchmarking, sea level budget studies, assessments of long-term changes in the frequency of droughts, the analysis of climate signals in geodetic time series, and the interpretation of the data gap between the GRACE and GRACE Follow-On missions. The presented dataset is published at https://doi.org/10.6084/m9.figshare.7670849 (Humphrey and Gudmundsson, 2019) and updates will be published regularly.

Detection and Characterization of Active Slope Deformations with Sentinel-1 InSAR Analyses in the Southwest Area of Shanxi, China

“…Rainfall is an important factor affecting the loess slope stability [2,12]. Seasonal rainwater recharge will cause the water storage variation [37] and change the pore pressure, shear strength. As a result, seasonal accelerations will occur on slopes [38].…”

“…In general, the displacement signals are correlated with rainfall in the temporal dimension [39]. Here, we attempt to employ a climate-driven displacement model [37] to reconstruct the relationship between rainfall and time series loess slope displacement. We assume the nonlinear displacement is directly proportional to the former displacement and the residence time of rainfall.…”

“…Meanwhile, the nonlinear displacement vector can also be called the daily displacement rate vector. The values of rainwater residence time τ(t) mean the rainwater leaves the region quickly (small value) or slowly (large value) through runoff or evapotranspiration [37]. It can be expressed as a function of daily air temperature:…”

Human-Induced and Climate-Driven Contributions to Water Storage Variations in the Haihe River Basin, China

“…These datasets were validated with gauge observations by generalized cross-validation, Root Mean Square Error (RMSE), absolute error and relative error [55]. Humphrey and Gudmundsson [56] highlighted that the quality of the reconstruction is heavily dependent upon the quality of the input precipitation forcing. Two global precipitation forcing datasets applied by Humphrey and Gudmundsson [56] are also used to compare with the CMA precipitation dataset, including the European Centre for Medium-Range Weather Forecast (ECMWF) re-analysis (ERA-Interim) dataset and the multi-source weighted-ensemble precipitation dataset (MSWEP).…”

Final response to referees' comments and suggestions

“…The presented dataset is publicly available (https://doi.org/10.6084/m9.figshare.7670849, Humphrey and Gudmundsson, 2019) and updates of the two reconstructions driven by ERA5 will be published when needed. We note that because including additional GRACE months only barely improves the quality of the model fit, no systematic recalibration of the models is planned at this stage.…”