DOI: 10.5061/dryad.z612jm6bt
title: Data from: Long-term (1979-present) total water storage anomalies over the global land derived by reconstructing GRACE data

description: This research data is associated with the manuscript entitled
 “Long-term (1979-present) Total Water Storage Anomalies Over the
 Global Land Derived by Reconstructing GRACE data
 (https://doi.org/10.1029/2021GL093492)”. The study focused on the
 reconstruction of long-term GRACE-like gridded total water
 storage anomalies over the global land surface. Since 2002, the Gravity
 Recovery and Climate Experiment (GRACE) mission has monitored global total
 water storage anomalies (TWSA) with an unprecedented accuracy. Yet, many
 applications require a longer record, i.e. extending prior to the
 GRACE period. Besides, the Global Climate Observing System (GCOS) Steering
 Committee has made great efforts towards establishing TWSA as a new
 Essential Climate Variable (ECV). Here, we produced a new global
 (excluding Antarctica) total water storage anomaly data set by
 reconstructing the RL06 CSR
 mascons using precipitation, land temperature, sea
 surface temperature, soil moisture, evaporation, surface runoff,
 subsurface runoff, and several climate indices as inputs. The
 data set is provided with equivalent water height [unit: cm]. The
 grid resolution of this data set is 0.5° and the
 monthly time series covers the full period from
 July 1979 through June 2020. We compared our
 dataset to previously published products using the Satellite
 Laser Ranging (SLR) solution and the observed global mean sea level change
 as validations. The comparison suggests that we provided a more accurate
 dataset at the global scale than ever before. This dataset will
 contribute to the filling of the GRACE data gap and can
 contribute to the potential studies
 on testing climate model simulation, constructing the
 sea level budget, or understanding drought/flood events prior to the GRACE
 period.
description: GRID_CSR_GRACE_REC is derived based on the synthetic methodology
 framework developed by Li et al. (2020). The synthetic methodology
 framework contains seven data-driven methods, which could be classified
 into three groups of techniques – i.e, 1) two statistical decomposition
 techniques Principal Component Analysis (PCA) and Independent Component
 Analysis (ICA), 2) two time series decomposition techniques Least Square
 (LS) and Seasonal-Trend decomposition based on Loess (STL), and 3) three
 machine learning techniques Artificial Neural Network(ANN), AutoRegressive
 model with eXogenous variables (ARX), and Multiple Linear Regression
 (MLR).
description: One could read this global total water storage reconstruction
 data set by using any version of the software Matlab with a simple command
 'load'.<br> ====================<br>  Variable
 descriptions <br>  <br> lat : Double type latitude.<br>
 long : Double type longitude.<br> time : Double type time.<br>
 str_month : String type month.<br> str_year : String type
 year.<br> grace_rec_full : Double type gridded global TWSA
 reconstructions.<br> grace_rec_detrended : Double type gridded
 global TWSA reconstructions, linear trends removed.<br>
 grace_rec_deseasonalised : Double type gridded global TWSA
 reconstructions, linear trend and seasonal signals removed.  
 ==================== When using the
 GRID_CSR_GRACE_REC data set, please cite Li, F.,
 Kusche, J., Rietbroek, R., Wang, Z., Forootan, E., Schulze, K., &
 Lück, C. (2020). Comparison of Data‐driven Techniques to Reconstruct
 (1992‐2002) and Predict (2017‐2018) GRACE‐like Gridded Total Water Storage
 Changes using Climate Inputs[J]. Water Resources Research, 56(5),
 e2019WR026551. https://doi.org/10.1029/2019wr026551.          <br>  
                                           Li, F.,
 Kusche, J., Chao, N., Wang, Z., & Löcher, A. (2021). Long-term
 (1979-present) total water storage anomalies over the global land derived
 by reconstructing GRACE data[J]. Geophysical Research Letters, 48,
 e2021GL093492. https://doi.org/10.1029/2021GL093492.