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

2013 ◽  
Vol 10 (10) ◽  
pp. 12453-12483 ◽  
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.

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

2014 ◽  
Vol 18 (6) ◽  
pp. 2089-2102 ◽  
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.

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 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 Comparison and Assessment of Three Advanced Land Surface Models in Simulating Terrestrial Water Storage Components over the United States

2017 ◽  
Vol 18 (3) ◽  
pp. 625-649 ◽  
Author(s):  
Youlong Xia ◽  
David Mocko ◽  
Maoyi Huang ◽  
Bailing Li ◽  
Matthew Rodell ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Land Surface ◽  
Water Storage ◽  
The United States ◽  
Major Contributor ◽  
Terrestrial Water Storage ◽  
Land Surface Models ◽  
Community Land Model ◽  
Surface Models ◽  
Terrestrial Water

Abstract To prepare for the next-generation North American Land Data Assimilation System (NLDAS), three advanced land surface models [LSMs; i.e., Community Land Model, version 4.0 (CLM4.0); Noah LSM with multiphysics options (Noah-MP); and Catchment LSM-Fortuna 2.5 (CLSM-F2.5)] were run for the 1979–2014 period within the NLDAS-based framework. Unlike the LSMs currently executing in the operational NLDAS, these three advanced LSMs each include a groundwater component. In this study, the model simulations of monthly terrestrial water storage anomaly (TWSA) and its individual water storage components are evaluated against satellite-based and in situ observations, as well as against reference reanalysis products, at basinwide and statewide scales. The quality of these TWSA simulations will contribute to determining the suitability of these models for the next phase of the NLDAS. Overall, it is found that all three models are able to reasonably capture the monthly and interannual variability and magnitudes of TWSA. However, the relative contributions of the individual water storage components to TWSA are very dependent on the model and basin. A major contributor to the TWSA is the anomaly of total column soil moisture content for CLM4.0 and Noah-MP, while the groundwater storage anomaly is the major contributor for CLSM-F2.5. Other water storage components such as the anomaly of snow water equivalent also play a role in all three models. For each individual water storage component, the models are able to capture broad features such as monthly and interannual variability. However, there are large intermodel differences and quantitative uncertainties, which are motivating follow-on investigations in the NLDAS Science Testbed developed by the NASA and NCEP NLDAS teams.

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

&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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. &amp;#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.&lt;/p&gt;

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

PCA study of the interannual variability of GPS heights and environmental parameters

2020 ◽  
Author(s):  
Letizia Elia ◽  
Susanna Zerbini ◽  
Fabio Raicich
Keyword(s):  
Time Series ◽  
Interannual Variability ◽  
Water Storage ◽  
Environmental Parameters ◽  
Space Time ◽  
Terrestrial Water Storage ◽  
Coupled Modes ◽  
Entire Study ◽  
Terrestrial Water ◽  
The Mediterranean

&lt;p&gt;Time series of GPS coordinates longer than two decades are now available at many stations around the world. The objective of our study is to investigate large networks of GPS stations to identify and analyze spatially coherent signals present in the coordinate time series and, at the same locations, to identify and analyze common patterns in the series of environmental parameters and climate indexes. The study is confined to Europe and the Mediterranean area, where 107 GPS stations were selected from the Nevada Geodetic Laboratory (NGL) archive on the basis of the completeness and length of the data series. The parameters of interest for this study are the stations height (H), the atmospheric surface pressure (AP), the terrestrial water storage (TWS) and the various climate indexes, such as NAO (North Atlantic Oscillation), AO (Artic Oscillation), SCAND (Scandinavian Index) and MEI (Multivariate ENSO Index). The Empirical Orthogonal Function (EOF) is the methodology adopted to extract the main patterns of space/time variability of these parameters. We also focus on the coupled modes of space/time interannual variability between pairs of variables using the singular value decomposition (SVD) methodology. The coupled variability between all the afore mentioned parameters is investigated. It shall be pointed out that EOF and SVD are mathematical tools providing common modes on the one hand, and statistical correlations between pairs of parameters on the other. Therefore, these methodologies do not allow to directly infer the physical mechanisms responsible for the observed behaviors which should be explained through appropriate modelling. Our study has identified, over Europe and the Mediterranean, main modes of variability in the time series of GPS heights, atmospheric pressure and terrestrial water storage. For example, regarding the station heights, the EOF1 explains about 30% of the variance and the spatial pattern is coherent over the entire study area. The SVD analysis of coupled parameters, namely H-AP, TWS-AP and H-TWS, showed that most of the common variability is explained by the first 3 modes. In particular, 70% for the H-AP, 67% for the TWS-AP and 49% for the H-TWS pair. Moreover, we correlated the stations heights with the NAO, AO, SCAND and MEI indexes to investigate the possible influence of climate variability on the height behavior. To do so, the stations heights were represented using the first three EOFs to reduce the potential effect of local anomalies. More than 30 stations, over the total of 107, show significant correlations up to about 0.3 with the AO and SCAND indexes. The correlation coefficients with MEI turn out to be significant and up to 0.5 for about half of the stations.&lt;/p&gt;

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 (&lt;20–25 mm month−1).

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

2019 ◽  
Vol 11 (21) ◽  
pp. 2487 ◽  
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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