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 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 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

Variability of Basin-Scale Terrestrial Water Storage from a PER Water Budget Method: The Amazon and the Mississippi

2008 ◽  
Vol 21 (2) ◽  
pp. 248-265 ◽  
Author(s):  
Ning Zeng ◽  
Jin-Ho Yoon ◽  
Annarita Mariotti ◽  
Sean Swenson
Keyword(s):  
Time Scales ◽  
Land Surface ◽  
Seasonal Cycle ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Terrestrial Water Storage ◽  
Basin Scale ◽  
Terrestrial Water

Abstract In an approach termed the PER method, where the key input variables are observed precipitation P and runoff R and estimated evaporation, the authors apply the basin water budget equation to diagnose the long-term variability of the total terrestrial water storage (TWS). Unlike the typical offline land surface model estimate where only atmospheric variables are used as input, the direct use of observed runoff in the PER method imposes an important constraint on the diagnosed TWS. Although there is a lack of basin-scale observations of evaporation, the tendency of E to have significantly less variability than the difference between precipitation and runoff (P − R) minimizes the uncertainties originating from estimated evaporation. Compared to the more traditional method using atmospheric moisture convergence (MC) minus R (MCR method), the use of observed precipitation in the PER method is expected to lead to general improvement, especially in regions where atmospheric radiosonde data are too sparse to constrain the atmospheric model analyzed MC, such as in the remote tropics. TWS was diagnosed using the PER method for the Amazon (1970–2006) and the Mississippi basin (1928–2006) and compared with the MCR method, land surface model and reanalyses, and NASA’s Gravity Recovery and Climate Experiment (GRACE) satellite gravity data. The seasonal cycle of diagnosed TWS over the Amazon is about 300 mm. The interannual TWS variability in these two basins is 100–200 mm, but multidecadal changes can be as large as 600–800 mm. Major droughts, such as the Dust Bowl period, had large impacts, with water storage depleted by 500 mm over a decade. Within the short period 2003–06 when GRACE data were available, PER and GRACE show good agreement both for seasonal cycle and interannual variability, providing potential to cross validate each other. In contrast, land surface model results are significantly smaller than PER and GRACE, especially toward longer time scales. While the authors currently lack independent means to verify these long-term changes, simple error analysis using three precipitation datasets and three evaporation estimates suggest that the multidecadal amplitude can be uncertain up to a factor of 2, while the agreement is high on interannual time scales. The large TWS variability implies the remarkable capacity of land surface in storing and taking up water that may be underrepresented in models. The results also suggest the existence of water storage memories on multiyear time scales, significantly longer than typically assumed seasonal time scales associated with surface soil moisture.

Sign in / Sign up

Export Citation Format

Share Document