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

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.

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

10.5194/egusphere-egu21-15253 ◽

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. &#160;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>

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

Journal of Hydrometeorology ◽

10.1175/jhm-d-11-0143.1 ◽

2012 ◽

Vol 13 (5) ◽

pp. 1589-1603 ◽

Cited By ~ 13

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).

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Radar Altimetry as a Proxy for Determining Terrestrial Water Storage Variability in Tropical Basins

Remote Sensing ◽

10.3390/rs11212487 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2487 ◽

Cited By ~ 1

Author(s):

Melo ◽

Getirana

Keyword(s):

Time Series ◽

Amazon Basin ◽

Anthropogenic Impacts ◽

Water Storage ◽

Principal Component ◽

Water Levels ◽

Terrestrial Water Storage ◽

Validation Experiment ◽

Physically Based ◽

Terrestrial Water

The Gravity Recovery and Climate Experiment (GRACE) mission has provided us with unforeseen information on terrestrial water-storage (TWS) variability, contributing to our understanding of global hydrological processes, including hydrological extreme events and anthropogenic impacts on water storage. Attempts to decompose GRACE-based TWS signals into its different water storage layers, i.e., surface water storage (SWS), soil moisture, groundwater and snow, have shown that SWS is a principal component, particularly in the tropics, where major rivers flow over arid regions at high latitudes. Here, we demonstrate that water levels, measured with radar altimeters at a limited number of locations, can be used to reconstruct gridded GRACE-based TWS signals in the Amazon basin, at spatial resolutions ranging from 0.5 to 3, with mean absolute errors (MAE) as low as 2.5 cm and correlations as high as 0.98. We show that, at 3 spatial resolution, spatially-distributed TWS time series can be precisely reconstructed with as few as 41 water-level time series located within the basin. The proposed approach is competitive when compared to existing TWS estimates derived from physically based and computationally expensive methods. Also, a validation experiment indicates that TWS estimates can be extrapolated to periods beyond that of the model regression with low errors. The approach is robust, based on regression models and interpolation techniques, and offers a new possibility to reproduce spatially and temporally distributed TWS that could be used to fill inter-mission gaps and to extend GRACE-based TWS time series beyond its timespan.

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Terrestrial Water Storage (TWS) Patterns Monitoring in the Amazon Basin Using Grace Observed: its Trends and Characteristics

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8519454 ◽

2018 ◽

Author(s):

Stephen Dankwa ◽

Wenfeng Zheng ◽

Bin Gao ◽

Xiaolu Li

Keyword(s):

Amazon Basin ◽

Water Storage ◽

Terrestrial Water Storage ◽

Terrestrial Water

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

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-8125-2010 ◽

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.

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Prospects for Imaging Terrestrial Water Storage in South America Using Daily GPS Observations

Remote Sensing ◽

10.3390/rs11060679 ◽

2019 ◽

Vol 11 (6) ◽

pp. 679 ◽

Cited By ~ 7

Author(s):

Vagner Ferreira ◽

Christopher Ndehedehe ◽

Henry Montecino ◽

Bin Yong ◽

Peng Yuan ◽

...

Keyword(s):

South America ◽

Amazon Basin ◽

Mean Squared Error ◽

Water Storage ◽

Terrestrial Water Storage ◽

Terrestrial Water ◽

Resolution Analysis ◽

Land Data Assimilation ◽

Land Data Assimilation System ◽

Better Than

Few studies have used crustal displacements sensed by the Global Positioning System (GPS) to assess the terrestrial water storage (TWS), which causes loadings. Furthermore, no study has investigated the feasibility of using GPS to image TWS over South America (SA), which contains the world’s driest (Atacama Desert) and wettest (Amazon Basin) regions. This work presents a resolution analysis of an inversion of GPS data over SA. Firstly, synthetic experiments were used to verify the spatial resolutions of GPS-imaged TWS and examine the resolving accuracies of the inversion based on checkerboard tests and closed-loop simulations using “TWS” from the Noah-driven Global Land Data Assimilation System (GLDAS-Noah). Secondly, observed radial displacements were used to image daily TWS. The inverted results of TWS at a resolution of 300 km present negligible errors, as shown by synthetic experiments involving 397 GPS stations across SA. However, as a result of missing daily observations, the actual daily number of available stations varied from 60–353, and only 6% of the daily GPS-imaged TWS agree with GLDAS-Noah TWS, which indicates a root-mean-squared error (RMSE) of less than 100 kg/m 2 . Nevertheless, the inversion shows agreement that is better than 0.50 and 61.58 kg/m 2 in terms of the correlation coefficient (Pearson) and RMSE, respectively, albeit at each GPS site.

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Statistical prediction of terrestrial water storage changes in the Amazon Basin using tropical Pacific and North Atlantic sea surface temperature anomalies

Hydrology and Earth System Sciences ◽

10.5194/hess-18-2089-2014 ◽

2014 ◽

Vol 18 (6) ◽

pp. 2089-2102 ◽

Cited By ~ 16

Author(s):

C. de Linage ◽

J. S. Famiglietti ◽

J. T. Randerson

Keyword(s):

North Atlantic ◽

Lead Time ◽

Amazon Basin ◽

Water Storage ◽

Sea Surface ◽

Climate Indices ◽

Western Region ◽

Terrestrial Water Storage ◽

Combined Model ◽

Terrestrial Water

Abstract. Floods and droughts frequently affect the Amazon River basin, impacting transportation, agriculture, and ecosystem processes within several South American countries. Here we examine how sea surface temperature (SST) anomalies influence interannual variability of terrestrial water storage anomalies (TWSAs) in different regions within the Amazon Basin and propose a statistical modeling framework for TWSA prediction on seasonal timescales. Three simple semi-empirical models forced by a linear combination of lagged spatial averages of central Pacific and tropical North Atlantic climate indices (Niño 4 and TNAI) were calibrated against a decade-long record of 3°, monthly TWSAs observed by the Gravity Recovery And Climate Experiment (GRACE) satellite mission. Niño 4 was the primary external forcing in the northeastern region of the Amazon Basin, whereas TNAI was dominant in central and western regions. A combined model using the two indices improved the fit significantly (p < 0.05) for at least 64% of the grid cells within the basin, compared to models forced solely with Niño 4 or TNAI. The combined model explained 66% of the observed variance in the northeastern region, 39% in the central and western region, and 43% for the Amazon Basin as a whole, with a 3-month lead time between the climate indices and the predicted TWSAs. Model performance varied seasonally: it was higher than average during the wet season in the northeastern Amazon and during the dry season in the central and western region. The predictive capability of the combined model was degraded with increasing lead times. Degradation rates were lower in the northeastern Amazon (where 49% of the variance was explained using an 8-month lead time versus 69% for a 1-month lead time) compared to the central and western Amazon (where 22% of the variance was explained at 8 months versus 43% at 1 month). These relationships may contribute to an improved understanding of the climate processes regulating the spatial patterns of flood and drought risk in South America.

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Forecasting terrestrial water storage changes in the Amazon Basin using Atlantic and Pacific sea surface temperatures

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-12453-2013 ◽

2013 ◽

Vol 10 (10) ◽

pp. 12453-12483 ◽

Cited By ~ 3

Author(s):

C. de Linage ◽

J. S. Famiglietti ◽

J. T. Randerson

Keyword(s):

Interannual Variability ◽

Lead Time ◽

Amazon Basin ◽

Water Storage ◽

Sea Surface ◽

Sea Surface Temperatures ◽

Terrestrial Water Storage ◽

Combined Model ◽

Northeastern Region ◽

Terrestrial Water

Abstract. Floods and droughts frequently affect the Amazon River basin, impacting transportation, river navigation, agriculture, and ecosystem processes within several South American countries. Here we examined how sea surface temperatures (SSTs) influence interannual variability of terrestrial water storage anomalies (TWSAs) in different regions within the Amazon basin and propose a modeling framework for inter-seasonal flood and drought forecasting. Three simple statistical models forced by a linear combination of lagged spatial averages of central Pacific (Niño 4 index) and tropical North Atlantic (TNAI index) SSTs were calibrated against a decade-long record of 3°, monthly TWSAs observed by the Gravity Recovery And Climate Experiment (GRACE) satellite mission. Niño 4 was the primary external forcing in the northeastern region of the Amazon basin whereas TNAI was dominant in central and western regions. A combined model using the two indices improved the fit significantly (p < 0.05) for at least 64% of the grid cells within the basin, compared to models forced solely with Niño 4 or TNAI. The combined model explained 66% of the observed variance in the northeastern region, 39% in the central and western regions, and 43% for the Amazon basin as a whole with a 3 month lead time between the SST indices and TWSAs. Model performance varied seasonally: it was higher than average during the rainfall wet season in the northeastern Amazon and during the dry season in the central and western regions. The predictive capability of the combined model was degraded with increasing lead times. Degradation was smaller in the northeastern Amazon (where 49% of the variance was explained using an 8 month lead time vs. 69% for a 1 month lead time) compared to the central and western Amazon (where 22% of the variance was explained at 8 months vs. 43% at 1 month). These relationships may enable the development of an early warning system for flood and drought risk. This work also strengthens our understanding of the mechanisms regulating interannual variability in Amazon fires, as water storage deficits may subsequently lead to decreases in transpiration and atmospheric water vapor that cause more severe fire weather.

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