scholarly journals Assimilation of GRACE Terrestrial Water Storage Data into a Land Surface Model: Results for the Mississippi River Basin

2008 ◽  
Vol 9 (3) ◽  
pp. 535-548 ◽  
Author(s):  
Benjamin F. Zaitchik ◽  
Matthew Rodell ◽  
Rolf H. Reichle
Keyword(s):  
River Basin ◽  
Mississippi River ◽  
Land Surface ◽  
River Flow ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Mississippi River Basin ◽  
Terrestrial Water Storage ◽  
Terrestrial Water

Abstract Assimilation of data from the Gravity Recovery and Climate Experiment (GRACE) system of satellites yielded improved simulation of water storage and fluxes in the Mississippi River basin, as evaluated against independent measurements. The authors assimilated GRACE-derived monthly terrestrial water storage (TWS) anomalies for each of the four major subbasins of the Mississippi into the Catchment Land Surface Model (CLSM) using an ensemble Kalman smoother from January 2003 to May 2006. Compared with the open-loop CLSM simulation, assimilation estimates of groundwater variability exhibited enhanced skill with respect to measured groundwater in all four subbasins. Assimilation also significantly increased the correlation between simulated TWS and gauged river flow for all four subbasins and for the Mississippi River itself. In addition, model performance was evaluated for eight smaller watersheds within the Mississippi basin, all of which are smaller than the scale of GRACE observations. In seven of eight cases, GRACE assimilation led to increased correlation between TWS estimates and gauged river flow, indicating that data assimilation has considerable potential to downscale GRACE data for hydrological applications.

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.

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

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

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.

scholarly journals Retrieving snow mass from GRACE terrestrial water storage change with a land surface model

2007 ◽  
Vol 34 (15) ◽  
Author(s):  
Guo-Yue Niu ◽  
Ki-Weon Seo ◽  
Zong-Liang Yang ◽  
Clark Wilson ◽  
Hua Su ◽  
...  
Keyword(s):  
Land Surface ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Terrestrial Water Storage ◽  
Terrestrial Water ◽  
Storage Change ◽  
Snow Mass

scholarly journals Widespread decline in terrestrial water storage and its link to teleconnections across Asia and eastern Europe

2020 ◽  
Vol 24 (7) ◽  
pp. 3663-3676
Author(s):  
Xianfeng Liu ◽  
Xiaoming Feng ◽  
Philippe Ciais ◽  
Bojie Fu
Keyword(s):  
Land Surface ◽  
Southern Oscillation ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Global Changes ◽  
Eastern European ◽  
Terrestrial Water Storage ◽  
European Regions ◽  
Terrestrial Water

Abstract. Recent global changes in terrestrial water storage (TWS) and associated freshwater availability raise major concerns about the sustainability of global water resources. However, our knowledge regarding the long-term trends in TWS and its components is still not well documented. In this study, we characterize the spatiotemporal variations in TWS and its components over the Asian and eastern European regions from April 2002 to June 2017 based on 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 and −21.79 mm yr−1. TWS partitioning suggests that these negative trends are primarily attributed to the intensive over-extraction of groundwater and warmth-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.

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