Deep learning-based recovery of high-resolution terrestrial water storage from space-borne gravimetry and altimetry

2020 ◽  
Author(s):  
Christopher Irrgang ◽  
Jan Saynisch-Wagner ◽  
Robert Dill ◽  
Eva Boergens ◽  
Maik Thomas
Keyword(s):  
Neural Network ◽  
Satellite Altimetry ◽  
Water Cycle ◽  
Water Storage ◽  
Digital Object Identifier ◽  
Terrestrial Water Storage ◽  
Network Training ◽  
The Neural Network ◽  
Terrestrial Water ◽  
Groundwater Basins

<p>Space-borne observations of terrestrial water storage (TWS) are an essential ingredient for understanding the Earth's global water cycle, its susceptibility to climate change, and for risk assessments of ecosystems, agriculture, and water management. However, the complex distribution of water masses in rivers, lakes, or groundwater basins remains elusive in coarse-resolution gravimetry observations. We combine machine learning, numerical modeling, and satellite altimetry to build and train a downscaling neural network that recovers simulated TWS from synthetic space-borne gravity observations. The neural network is designed to adapt and validate its training progress by considering independent satellite altimetry records. We show that the neural network can accurately derive TWS anomalies in 2019 after being trained over the years 2003 to 2018. Specifically for validated regions in the Amazonas, we highlight that the neural network can outperform the numerical hydrology model used in the network training.</p> <p><a class="epub-doi" href="https://doi.org/10.1029/2020GL089258" aria-label="Digital Object Identifier">https://doi.org/10.1029/2020GL089258</a></p> <p> </p>

Self-validating deep learning of continental hydrology through satellite gravimetry and altimetry

2021 ◽  
Author(s):  
Christopher Irrgang ◽  
Jan Saynisch-Wagner ◽  
Robert Dill ◽  
Eva Boergens ◽  
Maik Thomas
Keyword(s):  
Neural Network ◽  
Satellite Altimetry ◽  
Water Cycle ◽  
Global Water Cycle ◽  
Network Training ◽  
The Neural Network ◽  
Continental Hydrology ◽  
Terrestrial Water ◽  
Combine Machine ◽  
Groundwater Basins

<p>Space-borne observations of terrestrial water storage (TWS) are an essential ingredient for understanding the Earth's global water cycle, its susceptibility to climate change, and for risk assessments of ecosystems, agriculture, and water management. However, the complex distribution of water masses in rivers, lakes, or groundwater basins remains elusive in coarse-resolution gravimetry observations. We combine machine learning, numerical modeling, and satellite altimetry to build and train a downscaling neural network that recovers simulated TWS from synthetic space-borne gravity observations. The neural network is designed to adapt and validate its training progress by considering independent satellite altimetry records. We show that the neural network can accurately derive TWS anomalies in 2019 after being trained over the years 2003 to 2018. Specifically for validated regions in the Amazonas, we highlight that the neural network can outperform the numerical hydrology model used in the network training.</p><p>https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL089258</p>

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.

The AmazonWater Cycle: Perspectives fromWater Budget Closure and Ocean Salinity

2020 ◽  
pp. 1-44
Author(s):  
J. E. Jack Reeves Eyre ◽  
Xubin Zeng
Keyword(s):  
Water Vapor ◽  
Land Surface ◽  
Water Cycle ◽  
Water Storage ◽  
River Mouth ◽  
Small Subset ◽  
Terrestrial Water Storage ◽  
Crucial Component ◽  
Ocean Salinity ◽  
Terrestrial Water

AbstractGlobal and regional water cycle includes precipitation, water vapor divergence, and change of column water vapor in the atmosphere, and land surface evapotranspiration, terrestrial water storage change, and river discharge which is linked to ocean salinity near the river mouth. The water cycle is a crucial component of the Earth system, and numerous studies have addressed its individual components (e.g., precipitation). Here we assess, for the first time, if remote sensing and reanalysis datasets can accurately and self consistently portray the Amazon water cycle. This is further assisted with satellite ocean salinity measurements near the mouth of the Amazon River. The widely-used practice of taking the mean of an ensemble of datasets to represent water cycle components (e.g., precipitation) can produce large biases in water cycle closure. Closure is achieved with only a small subset of data combinations (e.g., ERA5 reanalysis precipitation and evapotranspiration plus GRACE satellite terrestrial water storage), which rules out the lower precipitation and higher evapotranspiration estimates, providing valuable constraints on assessments of precipitation, evapotranspiration and their ratio. The common approach of using the Óbidos stream gauge (located hundreds of kilometres from the river mouth) multiplied by a constant (1.25) to represent the entire Amazon discharge is found to misrepresent the seasonal cycle, and this can affect the apparent influence of Amazon discharge on tropical Atlantic salinity.

scholarly journals Validation of terrestrial water storage variations as simulated by different global numerical models with GRACE satellite observations

2017 ◽  
Vol 21 (2) ◽  
pp. 821-837 ◽  
Author(s):  
Liangjing Zhang ◽  
Henryk Dobslaw ◽  
Tobias Stacke ◽  
Andreas Güntner ◽  
Robert Dill ◽  
...  
Keyword(s):  
Large Scale ◽  
Water Cycle ◽  
Numerical Models ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Satellite Mission ◽  
Validation Data ◽  
Data Set ◽  
Terrestrial Water ◽  
Grace Satellite

Abstract. Estimates of terrestrial water storage (TWS) variations from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are used to assess the accuracy of four global numerical model realizations that simulate the continental branch of the global water cycle. Based on four different validation metrics, we demonstrate that for the 31 largest discharge basins worldwide all model runs agree with the observations to a very limited degree only, together with large spreads among the models themselves. Since we apply a common atmospheric forcing data set to all hydrological models considered, we conclude that those discrepancies are not entirely related to uncertainties in meteorologic input, but instead to the model structure and parametrization, and in particular to the representation of individual storage components with different spatial characteristics in each of the models. TWS as monitored by the GRACE mission is therefore a valuable validation data set for global numerical simulations of the terrestrial water storage since it is sensitive to very different model physics in individual basins, which offers helpful insight to modellers for the future improvement of large-scale numerical models of the global terrestrial water cycle.

Terrestrial Water Storage from GRACE and Satellite Altimetry in the Okavango Delta (Botswana)

2010 ◽  
pp. 521-526 ◽  
Author(s):  
O. B. Andersen ◽  
P. E. Krogh ◽  
P. Bauer-Gottwein ◽  
S. Leiriao ◽  
R. Smith ◽  
...  
Keyword(s):  
Satellite Altimetry ◽  
Water Storage ◽  
Okavango Delta ◽  
Terrestrial Water Storage ◽  
Terrestrial Water

The role of groundwater in the Amazon water cycle: 3. Influence on terrestrial water storage computations and comparison with GRACE

2013 ◽  
Vol 118 (8) ◽  
pp. 3233-3244 ◽  
Author(s):  
Yadu N. Pokhrel ◽  
Ying Fan ◽  
Gonzalo Miguez-Macho ◽  
Pat J.-F. Yeh ◽  
Shin-Chan Han
Keyword(s):  
Water Cycle ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Terrestrial Water

Dynamics of Terrestrial Water Storage Change from Satellite and Surface Observations and Modeling

2010 ◽  
Vol 11 (1) ◽  
pp. 156-170 ◽  
Author(s):  
Qiuhong Tang ◽  
Huilin Gao ◽  
Pat Yeh ◽  
Taikan Oki ◽  
Fengge Su ◽  
...  
Keyword(s):  
Water Cycle ◽  
Regional Scale ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Surface Observation ◽  
Grace Data ◽  
Terrestrial Water ◽  
Gravity Recovery ◽  
Storage Change ◽  
Surface Observations

Abstract Terrestrial water storage (TWS) is a fundamental component of the water cycle. On a regional scale, measurements of terrestrial water storage change (TWSC) are extremely scarce at any time scale. This study investigates the feasibility of estimating monthly-to-seasonal variations of regional TWSC from modeling and a combination of satellite and in situ surface observations based on water balance computations that use ground-based precipitation observations in both cases. The study area is the Klamath and Sacramento River drainage basins in the western United States (total area of about 110 000 km2). The TWSC from the satellite/surface observation–based estimates is compared with model results and land water storage from the Gravity Recovery and Climate Experiment (GRACE) data. The results show that long-term evapotranspiration estimates and runoff measurements generally balance with observed precipitation, suggesting that the evapotranspiration estimates have relatively small bias for long averaging times. Observations show that storage change in water management reservoirs is about 12% of the seasonal amplitude of the TWSC cycle, but it can be up to 30% at the subbasin scale. Comparing with predevelopment conditions, the satellite/surface observation–based estimates show larger evapotranspiration and smaller runoff than do modeling estimates, suggesting extensive anthropogenic alteration of TWSC in the study area. Comparison of satellite/surface observation–based and GRACE TWSC shows that the seasonal cycle of terrestrial water storage is substantially underestimated by GRACE.

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.

scholarly journals HydroSat: a repository of global water cycle products from spaceborne geodetic sensors

2021 ◽  
Author(s):  
Mohammad J. Tourian ◽  
Omid Elmi ◽  
Yasin Shafaghi ◽  
Sajedeh Behnia ◽  
Peyman Saemian ◽  
...  
Keyword(s):  
Satellite Altimetry ◽  
Water Cycle ◽  
Water Storage ◽  
Reservoir Water ◽  
Global Database ◽  
In Situ Data ◽  
Global Water Cycle ◽  
Terrestrial Water ◽  
Global Water

Abstract. Against the backdrop of global change, both in terms of climate and demography, there is a pressing need for monitoring the global water cycle. The publicly available global database is very limited in its spatial and temporal coverage worldwide. Moreover, the acquisition of in situ data and their delivery to the database are in decline since the late 1970s, be it for economical or political reasons. Given the insufficient monitoring from in situ gauge networks, and with no outlook for improvement, spaceborne approaches have been under investigation for some years now. Satellite-based Earth observation with its global coverage and homogeneous accuracy has been demonstrated to be a potential alternative to in situ measurements. This paper presents HydroSat as a repository of global water cycle products from spaceborne geodetic sensors. HydroSat provides time series and their uncertainty of: water level from satellite altimetry, surface water extent from satellite imagery, terrestrial water storage anomaly from satellite gravimetry, lake and reservoir water storage anomaly from a combination of satellite altimetry and imagery, and river discharge from either satellite altimetry or imagery. These products can contribute to understanding the global water cycle within the Earth system in several ways. They can act as inputs to hydrological models, they can play a complementary role to current and future spaceborne observations, and they can define indicators of the past and future state of the global freshwater system. The repository is publicly available through http://hydrosat.gis.uni-stuttgart.de.

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