scholarly journals Continental-Scale Basin Water Storage Variation from Global and Dynamically Downscaled Atmospheric Water Budgets in Comparison with GRACE-Derived Observations

2012 ◽  
Vol 13 (5) ◽  
pp. 1589-1603 ◽  
Author(s):  
Benjamin Fersch ◽  
Harald Kunstmann ◽  
András Bárdossy ◽  
Balaji Devaraju ◽  
Nico Sneeuw
Keyword(s):  
Large Scale ◽  
Amazon Basin ◽  
Climate Model ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Atmospheric Water ◽  
Flux Divergence ◽  
Water Budgets ◽  
Continental Scale ◽  
Terrestrial Water

Abstract Since 2002, the Gravity Recovery and Climate Experiment (GRACE) has provided gravity-derived observations of variations in the terrestrial water storage. Because of the lack of suitable direct observations of large-scale water storage changes, a validation of the GRACE observations remains difficult. An approach that allows the evaluation of terrestrial water storage variations from GRACE by a comparison with those derived from aerologic water budgets using the atmospheric moisture flux divergence is presented. In addition to reanalysis products from the European Centre for Medium-Range Weather Forecasts and the National Centers for Environmental Prediction, high-resolution regional atmospheric simulations were produced with the Weather Research and Forecast modeling system (WRF) and validated against globally gridded observational data of precipitation and 2-m temperature. The study encompasses six different climatic and hydrographic regions: the Amazon basin, the catchments of Lena and Yenisei, central Australia, the Sahara, the Chad depression, and the Niger. Atmospheric-related uncertainty bounds based on the range of the ensemble of estimated terrestrial water storage variations were computed using different configurations of the regional climate model WRF and different global reanalyses. Atmospheric-related uncertainty ranges with those originating from the GRACE products of GeoForschungsZentrum Potsdam, the Center for Space Research, and the Jet Propulsion Laboratory were also compared. It is shown that dynamically downscaled atmospheric fields are able to add value to global reanalyses, depending on the geographical location of the considered catchments. Global and downscaled atmospheric water budgets are in reasonable agreement (r ≈ 0.7 − 0.9) with GRACE-derived terrestrial mass variations. However, atmospheric- and satellite-based approaches show shortcomings for regions with small storage change rates (<20–25 mm month−1).

scholarly journals Past terrestrial water storage (1980–2008) in the Amazon Basin reconstructed from GRACE and in situ river gauging data

2011 ◽  
Vol 15 (2) ◽  
pp. 533-546 ◽  
Author(s):  
M. Becker ◽  
B. Meyssignac ◽  
L. Xavier ◽  
A. Cazenave ◽  
R. Alkama ◽  
...  
Keyword(s):  
Large Scale ◽  
Amazon Basin ◽  
Hydrological Modeling ◽  
Water Cycle ◽  
Southern Oscillation ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Direct Estimate ◽  
Terrestrial Water

Abstract. Terrestrial water storage (TWS) composed of surface waters, soil moisture, groundwater and snow where appropriate, is a key element of global and continental water cycle. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) space gravimetry mission provides a new tool to measure large-scale TWS variations. However, for the past few decades, direct estimate of TWS variability is accessible from hydrological modeling only. Here we propose a novel approach that combines GRACE-based TWS spatial patterns with multi-decadal-long in situ river level records, to reconstruct past 2-D TWS over a river basin. Results are presented for the Amazon Basin for the period 1980–2008, focusing on the interannual time scale. Results are compared with past TWS estimated by the global hydrological model ISBA-TRIP. Correlations between reconstructed past interannual TWS variability and known climate forcing modes over the region (e.g., El Niño-Southern Oscillation and Pacific Decadal Oscillation) are also estimated. This method offers new perspective for improving our knowledge of past interannual TWS in world river basins where natural climate variability (as opposed to direct anthropogenic forcing) drives TWS variations.

scholarly journals Past terrestrial water storage (1980–2008) in the Amazon Basin reconstructed from GRACE and in situ river gauging data

2010 ◽  
Vol 7 (5) ◽  
pp. 8125-8155
Author(s):  
M. Becker ◽  
B. Meyssignac ◽  
L. Xavier ◽  
R. Alkama ◽  
B. Decharme
Keyword(s):  
Large Scale ◽  
Amazon Basin ◽  
Hydrological Modeling ◽  
Water Cycle ◽  
Southern Oscillation ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Direct Estimate ◽  
Terrestrial Water

Abstract. Terrestrial water storage (TWS) composed of surface waters, soil moisture, groundwater and snow where appropriate, is a key element of global and continental water cycle. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) space gravimetry mission provides a new tool to measure large-scale TWS variations. However, for the past few decades, direct estimate of TWS variability is accessible from hydrological modeling only. Here we propose a novel approach that combines GRACE-based TWS spatial patterns with multi-decadal-long in situ river level records, to reconstruct past 2-dimensional TWS over a river basin. Results are presented for the Amazon Basin for the period 1980–2008, focusing on the interannual time scale. Results are compared with past TWS estimated by the global hydrological model ISBA-TRIP. Correlations between reconstructed past interannual TWS variability and known climate forcing modes over the region (e.g., El Niño-Southern Oscillation and Pacific Decadal Oscillation) are also estimated. This method offers new perspective for improving our knowledge of past interannual TWS in world river basins where natural climate variability (as opposed to direct anthropogenic forcing) drives TWS variations.

Links between global terrestrial water storage and large-scale modes of climatic variability

2021 ◽  
pp. 126419
Author(s):  
Lanlan Guo ◽  
TieWei Li ◽  
Deliang Chen ◽  
Junguo Liu ◽  
Bin He ◽  
...  
Keyword(s):  
Large Scale ◽  
Climatic Variability ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Terrestrial Water

scholarly journals Changes in terrestrial water storage versus rainfall and discharges in the Amazon basin

2013 ◽  
Vol 33 (14) ◽  
pp. 3029-3046 ◽  
Author(s):  
Frédéric Frappart ◽  
Guillaume Ramillien ◽  
Josyane Ronchail
Keyword(s):  
Amazon Basin ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Terrestrial Water

scholarly journals The importance of vegetation to understand terrestrial water storage variations

2021 ◽  
Author(s):  
Tina Trautmann ◽  
Sujan Koirala ◽  
Nuno Carvalhais ◽  
Andreas Güntner ◽  
Martin Jung
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Water Storage ◽  
Model Parameters ◽  
Intermediate Water ◽  
Model Structure ◽  
Hydrological Models ◽  
Terrestrial Water Storage ◽  
Terrestrial Water

Abstract. So far, various studies aimed at decomposing the integrated terrestrial water storage variations observed by satellite gravimetry (GRACE, GRACE-FO) with the help of large-scale hydrological models. While the results of the storage decomposition depend on model structure, little attention has been given to the impact of the way how vegetation is represented in these models. Although vegetation structure and activity represent the crucial link between water, carbon and energy cycles, their representation in large-scale hydrological models remains a major source of uncertainty. At the same time, the increasing availability and quality of Earth observation-based vegetation data provide valuable information with good prospects for improving model simulations and gaining better insights into the role of vegetation within the global water cycle. In this study, we use observation-based vegetation information such as vegetation indices and rooting depths for spatializing the parameters of a simple global hydrological model to define infiltration, root water uptake and transpiration processes. The parameters are further constrained by considering observations of terrestrial water storage anomalies (TWS), soil moisture, evapotranspiration (ET) and gridded runoff (Q) estimates in a multi-criteria calibration approach. We assess the implications of including vegetation on the simulation results, with a particular focus on the partitioning between water storage components. To isolate the effect of vegetation, we compare a model experiment with vegetation parameters varying in space and time to a baseline experiment in which all parameters are calibrated as static, globally uniform values. Both experiments show good overall performance, but including vegetation data led to even better performance and more physically plausible parameter values. Largest improvements regarding TWS and ET were seen in supply-limited (semi-arid) regions and in the tropics, whereas Q simulations improve mainly in northern latitudes. While the total fluxes and storages are similar, accounting for vegetation substantially changes the contributions of snow and different soil water storage components to the TWS variations, with the dominance of an intermediate water pool that interacts with the fast plant accessible soil moisture and the delayed water storage. The findings indicate the important role of deeper moisture storages as well as groundwater-soil moisture-vegetation interactions as a key to understanding TWS variations. We highlight the need for further observations to identify the adequate model structure rather than only model parameters for a reasonable representation and interpretation of vegetation-water interactions.

scholarly journals Suitability of 17 gridded rainfall and temperature datasets for large-scale hydrological modelling in West Africa

2020 ◽  
Vol 24 (11) ◽  
pp. 5379-5406
Author(s):  
Moctar Dembélé ◽  
Bettina Schaefli ◽  
Nick van de Giesen ◽  
Grégoire Mariéthoz
Keyword(s):  
Soil Moisture ◽  
Spatial Patterns ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Water Storage ◽  
Hydrological Processes ◽  
Terrestrial Water Storage ◽  
Meteorological Forcing ◽  
Actual Evaporation ◽  
Terrestrial Water

Abstract. This study evaluates the ability of different gridded rainfall datasets to plausibly represent the spatio-temporal patterns of multiple hydrological processes (i.e. streamflow, actual evaporation, soil moisture and terrestrial water storage) for large-scale hydrological modelling in the predominantly semi-arid Volta River basin (VRB) in West Africa. Seventeen precipitation products based essentially on gauge-corrected satellite data (TAMSAT, CHIRPS, ARC, RFE, MSWEP, GSMaP, PERSIANN-CDR, CMORPH-CRT, TRMM 3B42 and TRMM 3B42RT) and on reanalysis (ERA5, PGF, EWEMBI, WFDEI-GPCC, WFDEI-CRU, MERRA-2 and JRA-55) are compared as input for the fully distributed mesoscale Hydrologic Model (mHM). To assess the model sensitivity to meteorological forcing during rainfall partitioning into evaporation and runoff, six different temperature reanalysis datasets are used in combination with the precipitation datasets, which results in evaluating 102 combinations of rainfall–temperature input data. The model is recalibrated for each of the 102 input combinations, and the model responses are evaluated by using in situ streamflow data and satellite remote-sensing datasets from GLEAM evaporation, ESA CCI soil moisture and GRACE terrestrial water storage. A bias-insensitive metric is used to assess the impact of meteorological forcing on the simulation of the spatial patterns of hydrological processes. The results of the process-based evaluation show that the rainfall datasets have contrasting performances across the four climatic zones present in the VRB. The top three best-performing rainfall datasets are TAMSAT, CHIRPS and PERSIANN-CDR for streamflow; ARC, RFE and CMORPH-CRT for terrestrial water storage; MERRA-2, EWEMBI/WFDEI-GPCC and PGF for the temporal dynamics of soil moisture; MSWEP, TAMSAT and ARC for the spatial patterns of soil moisture; ARC, RFE and GSMaP-std for the temporal dynamics of actual evaporation; and MSWEP, TAMSAT and MERRA-2 for the spatial patterns of actual evaporation. No single rainfall or temperature dataset consistently ranks first in reproducing the spatio-temporal variability of all hydrological processes. A dataset that is best in reproducing the temporal dynamics is not necessarily the best for the spatial patterns. In addition, the results suggest that there is more uncertainty in representing the spatial patterns of hydrological processes than their temporal dynamics. Finally, some region-tailored datasets outperform the global datasets, thereby stressing the necessity and importance of regional evaluation studies for satellite and reanalysis meteorological datasets, which are increasingly becoming an alternative to in situ measurements in data-scarce regions.

Data-driven and physically informed modelling of the Terrestrial Water Storage dynamics 

2021 ◽  
Author(s):  
Karim Douch ◽  
Peyman Saemian ◽  
Nico Sneeuw
Keyword(s):  
Transition Matrix ◽  
Amazon Basin ◽  
Stochastic Matrix ◽  
Water Storage ◽  
Water Volume ◽  
Terrestrial Water Storage ◽  
Total Water ◽  
Monthly Basis ◽  
Terrestrial Water ◽  
Volume Anomaly

<p>The Gravity Recovery and Climate Experiment (GRACE) mission, and its successor GRACE Follow-On, have enabled to map on a monthly basis the Terrestrial Water Storage Anomaly (TWSA) since 2002. This unprecedented capability has provided hydrologists with new observations of the spatiotemporal evolution of TWSA, which have been used, among others, to better constrain numerical runoff models, to characterize empirically the relations between runoff and TWSA, or to simply monitor and quantify groundwater depletions. In this study, we explore the possibility to infer from GRACE observations a physically informed and linear dynamical system that models the intrinsic dynamics of TWSA at sub-basin scales.</p><p>First, we apply a hexagonal binning over the study area and aggregate the total water volume anomaly derived from GRACE data for each bin. Assuming that each bin exchanges water with the others in proportion to its water content, we then reformulate the mass balance equation of the whole basin as a first order matrix differential equation. All the proportionality coefficients encoding the bin exchanges are gathered in an unknown transition matrix to be determined.  Such a transition matrix must satisfy different algebraic properties to be physically consistent and interpretable. In particular, we show that this matrix is necessarily a left stochastic matrix. Finally, we used the time series of total water volume anomaly to estimate this transition matrix by solving an optimization problem on the manifold defined by the aforementioned matrix constraint. This method is applied to the Amazon basin and to mainland Australia respectively, and the predictive performances of the derived dynamical systems are quantified and discussed.</p>

Extracting terrestrial water storage signals from GRACE solutions in the Amazon Basin using an iterative filtering approach

2021 ◽  
Vol 13 (1) ◽  
pp. 14-23
Author(s):  
Lun Pu ◽  
Dongming Fan ◽  
Wei You ◽  
Xinchun Yang ◽  
Zemede M. Nigatu ◽  
...  
Keyword(s):  
Amazon Basin ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Terrestrial Water ◽  
Iterative Filtering ◽  
Filtering Approach

Evaluation of land water storage prediction skill in CMIP5 decadal hindcasts by means of a GRACE-based data set

2020 ◽  
Author(s):  
Laura Jensen ◽  
Annette Eicker ◽  
Tobias Stacke ◽  
Henryk Dobslaw
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Water Storage ◽  
Cmip5 Models ◽  
Climate Projections ◽  
Terrestrial Water Storage ◽  
Data Set ◽  
Model Experiments ◽  
Terrestrial Water ◽  
Decadal Predictions

<p>Reliable predictions of terrestrial water storage (TWS) changes for the next couple of years would be extremely valuable for, e.g., agriculture and water management. In contrast to long-term projections of future climate conditions, so-called decadal predictions do not depend on prescribed CO<sub>2 </sub>scenarios but provide unconditional forecasts similar to numerical weather models. Therefore, opposed to climate projections, decadal predictions (or hindcasts, if run for the past) can directly be compared to observations. Here, we evaluate decadal hindcasts of TWS related variables from an ensemble of 5 coupled CMIP5 climate models against a TWS data set based on GRACE satellite observations.</p> <p>Since data from the CMIP5 models and GRACE is jointly available in only 9 years, we access a GRACE-like reconstruction of TWS derived from precipitation and temperature data sets (Humphrey and Gudmundsson, 2019), which expands the analysis time-frame to 41 years. The skill of the decadal hindcasts is assessed by means of anomaly correlations and root-mean-square deviations (RMSD) for the yearly global average and aggregated over different climate zones. Furthermore, we compute global maps of correlation and RMSD.</p> <p>We find that at least for the first two prediction years the decadal model experiments clearly outperform the classical climate projections, regionally even for the third year. We can thereby demonstrate that the observation type “terrestrial water storage” as available from the GRACE and GRACE-FO missions is suitable as additional data set in the validation and/or calibration of climate model experiments.</p>

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