Data assimilation of GRACE terrestrial water storage to improve sea level rise estimates and isolate anthropogenic influences

2021 ◽  
Author(s):  
Ann Scheliga ◽  
Manuela Girotto
Keyword(s):  
Data Assimilation ◽  
Sea Level Rise ◽  
Sea Level ◽  
Land Surface ◽  
Water Budget ◽  
Water Storage ◽  
Surface Model ◽  
Terrestrial Water Storage ◽  
Terrestrial Water ◽  
Land Hydrology

<p>Sea level rise (SLR) projections rely on the accurate and precise closure of Earth’s water budget. The Gravity Recovery and Climate Experiment (GRACE) mission has provided global-coverage observations of terrestrial water storage (TWS) anomalies that improve accounting of ice and land hydrology changes and how these changes contribute to sea level rise. The contribution of land hydrology TWS changes to sea level rise is much smaller and less certain than contributions from glacial melt and thermal expansion. Although land hydrology TWS plays a smaller role, it is still important to investigate to improve the precision of the overall global water budget. This study analyzes how data assimilation techniques improve estimates of the land hydrology contribution to sea level rise. To achieve this, three global TWS datasets were analyzed: (1) GRACE TWS observations alone, (2) TWS estimates from the model-only simulation using Catchment Land Surface Model, and (3) TWS estimates from a data assimilation product of (1) and (2). We compared the data assimilation product with the GRACE observations alone and the model-only simulation to isolate the contribution to sea level rise from anthropogenic activities. We assumed a balanced water budget between land hydrology and the ocean, thus changes in global TWS are considered equal and opposite to sea level rise contribution.  Over the period of 2003-2016, we found sea level rise contributions from each dataset of +0.35 mm SLR eq/yr for GRACE, -0.34 mm SLR eq/yr for model-only, and a +0.09 mm SLR eq/yr for DA (reported as the mean linear trend). Our results indicate that the model-only simulation is not capturing important hydrologic processes. These are likely anthropogenic driven, indicating direct anthropogenic and climate-driven TWS changes play a substantial role in TWS contribution to SLR.</p>

Data assimilation of satellite-based terrestrial water storage changes into a hydrology land-surface model

2020 ◽  
pp. 125744
Author(s):  
Ala Bahrami ◽  
Kalifa Goïta ◽  
Ramata Magagi ◽  
Bruce Davison ◽  
Saman Razavi ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Terrestrial Water Storage ◽  
Terrestrial Water

Terrestrial Water-Storage Contributions to Sea-Level Rise and Variability

2010 ◽  
pp. 226-255 ◽  
Author(s):  
P. C. D. Chris Milly ◽  
Anny Cazenave ◽  
James S. Famiglietti ◽  
Vivien Gornitz ◽  
Katia Laval ◽  
...  
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Terrestrial Water

scholarly journals Development and evaluation of 0.05° terrestrial water storage estimates using CABLE land surface model and GRACE data assimilation

2021 ◽  
Author(s):  
Natthachet Tangdamrongsub ◽  
Michael F. Jasinski ◽  
Peter Shellito
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Parameters ◽  
Terrestrial Water Storage ◽  
Grace Data ◽  
Terrestrial Water

Abstract. Accurate estimation of terrestrial water storage (TWS) at a meaningful spatiotemporal resolution is important for reliable assessments of regional water resources and climate variability. Individual components of TWS include soil moisture, snow, groundwater, and canopy storage and can be estimated from the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model. The spatial resolution of CABLE is currently limited to 0.5° by the resolution of soil and vegetation datasets that underlie model parameterizations, posing a challenge to using CABLE for hydrological applications at a local scale. This study aims to improve the spatial detail (from 0.5° to 0.05°) and timespan (1981–2012) of CABLE TWS estimates using rederived model parameters and high-resolution meteorological forcing. In addition, TWS observations derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are assimilated into CABLE to improve TWS accuracy. The success of the approach is demonstrated in Australia, where multiple ground observation networks are available for validation. The evaluation process is conducted using four different case studies that employ different model spatial resolutions and include or omit GRACE data assimilation (DA). We find that the CABLE 0.05° developed here improves TWS estimates in terms of accuracy, spatial resolution, and long-term water resource assessment reliability. The inclusion of GRACE DA increases the accuracy of groundwater storage (GWS) estimates and has little impact on surface soil moisture or evapotranspiration. The use of improved model parameters and improved state estimations (via GRACE DA) together is recommended to achieve the best GWS accuracy. The workflow elaborated in this paper relies only on publicly accessible global datasets, allowing reproduction of the 0.05° TWS estimates in any study region.

Closing the water budget at the global scale using observations, remote sensing, and reanalyses

2021 ◽  
Author(s):  
Fanny Lehmann ◽  
Brahma Dutt Vishwakarma ◽  
Jonathan Bamber
Keyword(s):  
Land Surface ◽  
Water Budget ◽  
Water Storage ◽  
Global Scale ◽  
Terrestrial Water Storage ◽  
Land Surface Models ◽  
Cold Regions ◽  
Climate Zones ◽  
Surface Models ◽  
Terrestrial Water

<p>Despite the accuracy of GRACE terrestrial water storage estimates and the variety of global hydrological datasets providing precipitations, evapotranspiration, and runoff data, it remains challenging to find datasets satisfying the water budget equation at the global scale.</p><p>We select commonly used and widely-assessed datasets. We use several precipitations (CPC, CRU, GPCC, GPCP, GPM, MSWEP, TRMM, ERA5 Land, MERRA2), evapotranspiration (land surface models CLSM, Noah, VIC from GLDAS 2.0, 2.1, and 2.2; GLEAM, MOD16, SSEBop, ERA5 Land, MERRA2), and runoff (land surface models CLSM, Noah, VIC from GLDAS 2.0, 2.1, and 2.2; GRUN, ERA5 Land, MERRA2) datasets to assess the water storage change over more than 150 hydrological basins. Both mascons and spherical harmonics coefficients are used as the reference terrestrial water storage from different centres processing GRACE data. The analysis covers a wide range of climate zones over the globe and is conducted over 2003-2014.</p><p>The water budget closure is evaluated with Root Mean Square Deviation (RMSD), Nash-Sutcliffe Efficiency (NSE), and seasonal decomposition. Each dataset is assessed individually across all basins and dataset combinations are also ranked according to their performances. We obtain a total of 1080 combinations, among which several are suitable to close the water budget. Although none of the combinations performs consistently well over all basins, GPCP precipitations provide generally good results, together with GPCC and GPM. A better water budget closure is generally obtained when using evapotranspiration from Catchment Land Surface Models (GLDAS CLSM), while reanalyses ERA5 Land and MERRA2 are especially suitable in cold regions. Concerning runoff, the machine learning GRUN dataset performs remarkably well across climate zones, followed by ERA5 Land and MERRA2 in cold regions. We also highlight highly unrealistic values in evapotranspiration computed with version 2.2 of GLDAS (using data assimilation from GRACE) in most of the cold basins. Our results are robust as changing the GRACE product from one centre to the other does not affect our conclusions.</p>

scholarly journals Assimilation of gridded terrestrial water storage observations from GRACE into a land surface model

2016 ◽  
Vol 52 (5) ◽  
pp. 4164-4183 ◽  
Author(s):  
Manuela Girotto ◽  
Gabriëlle J. M. De Lannoy ◽  
Rolf H. Reichle ◽  
Matthew Rodell
Keyword(s):  
Land Surface ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Terrestrial Water Storage ◽  
Terrestrial Water

scholarly journals Assimilation of GRACE Terrestrial Water Storage Observations into a Land Surface Model for the Assessment of Regional Flood Potential

2015 ◽  
Vol 7 (11) ◽  
pp. 14663-14679 ◽  
Author(s):  
John Reager ◽  
Alys Thomas ◽  
Eric Sproles ◽  
Matthew Rodell ◽  
Hiroko Beaudoing ◽  
...  
Keyword(s):  
Land Surface ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Terrestrial Water Storage ◽  
Terrestrial Water

scholarly journals Widespread Decline in Terrestrial Water Storage and Its Link to Teleconnections across Asia and Eastern Europe

2019 ◽  
Author(s):  
Xianfeng Liu ◽  
Xiaoming Feng ◽  
Philippe Ciais ◽  
Bojie Fu
Keyword(s):  
Land Surface ◽  
Southern Oscillation ◽  
Water Storage ◽  
Surface Model ◽  
Global Changes ◽  
Eastern European ◽  
Terrestrial Water Storage ◽  
European Regions ◽  
Multiple Sources ◽  
Terrestrial Water

Abstract. Recent global changes in terrestrial water storage (TWS) and associated freshwater availability raise major concerns over the sustainability of global water resources. However, our knowledge regarding the long-term trend in TWS and its components is still not well documented. In this work, we characterize the spatiotemporal variations in TWS and its components over the Asian and Eastern European regions during the period of April 2002 to June 2017 using multiple sources of data, including Gravity Recovery and Climate Experiment (GRACE) satellite observations, land surface model simulations and precipitation observations. The connections of TWS and global major teleconnections (TCs) are also discussed. The results indicate a widespread decline in TWS during 2002–2017, and five hotspots of TWS negative trends were identified with trends between −8.94 mm yr−1 and −21.79 mm yr−1. TWS partitioning suggests that these negative trends are primarily attributed to the intensive overextraction of groundwater and warm-induced surface water loss, but the contributions of each hydrological component vary among hotspots. The results also indicate that the El Niño-Southern Oscillation, Arctic Oscillation and North Atlantic Oscillation are the three largest, dominant factors controlling the variations in TWS through the covariability effect on climate variables. However, seasonal results suggest a divergent response of hydrological components to TCs among seasons and hotspots. Our findings provide insights into changes in TWS and its components over the Asian and Eastern European regions, where there is a growing demand for food grains and water supplies.

scholarly journals Development and evaluation of 0.05° terrestrial water storage estimates using Community Atmosphere Biosphere Land Exchange (CABLE) land surface model and assimilation of GRACE data

2021 ◽  
Vol 25 (7) ◽  
pp. 4185-4208
Author(s):  
Natthachet Tangdamrongsub ◽  
Michael F. Jasinski ◽  
Peter J. Shellito
Keyword(s):  
Land Surface ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Parameters ◽  
Data Sets ◽  
Terrestrial Water Storage ◽  
Grace Data ◽  
Terrestrial Water ◽  
Land Exchange

Abstract. Accurate estimation of terrestrial water storage (TWS) at a high spatiotemporal resolution is important for reliable assessments of regional water resources and climate variability. Individual components of TWS include soil moisture, snow, groundwater, and canopy storage and can be estimated from the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model. The spatial resolution of CABLE is currently limited to 0.5∘ by the resolution of soil and vegetation data sets that underlie model parameterizations, posing a challenge to using CABLE for hydrological applications at a local scale. This study aims to improve the spatial detail (from 0.5 to 0.05∘) and time span (1981–2012) of CABLE TWS estimates using rederived model parameters and high-resolution meteorological forcing. In addition, TWS observations derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are assimilated into CABLE to improve TWS accuracy. The success of the approach is demonstrated in Australia, where multiple ground observation networks are available for validation. The evaluation process is conducted using four different case studies that employ different model spatial resolutions and include or omit GRACE data assimilation (DA). We find that the CABLE 0.05∘ developed here improves TWS estimates in terms of accuracy, spatial resolution, and long-term water resource assessment reliability. The inclusion of GRACE DA increases the accuracy of groundwater storage (GWS) estimates and has little impact on surface soil moisture or evapotranspiration. Using improved model parameters and improved state estimations (via GRACE DA) together is recommended to achieve the best GWS accuracy. The workflow elaborated on in this paper relies only on publicly accessible global data sets, allowing the reproduction of the 0.05∘ TWS estimates in any study region.

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