scholarly journals Multisensor snow data assimilation at the continental scale: The value of Gravity Recovery and Climate Experiment terrestrial water storage information

2010 ◽  
Vol 115 (D10) ◽  
Author(s):  
Hua Su ◽  
Zong-Liang Yang ◽  
Robert E. Dickinson ◽  
Clark R. Wilson ◽  
Guo-Yue Niu
Keyword(s):  
Data Assimilation ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Continental Scale ◽  
Terrestrial Water ◽  
Gravity Recovery

scholarly journals Data Assimilation of Terrestrial Water Storage Observations to Estimate Precipitation Fluxes: A Synthetic Experiment

2021 ◽  
Vol 13 (6) ◽  
pp. 1223
Author(s):  
Manuela Girotto ◽  
Rolf Reichle ◽  
Matthew Rodell ◽  
Viviana Maggioni
Keyword(s):  
Data Assimilation ◽  
Snow Water Equivalent ◽  
Water Storage ◽  
Synthetic Data ◽  
Terrestrial Water Storage ◽  
Grace Data ◽  
Precipitation Estimates ◽  
Terrestrial Water ◽  
Snow Water ◽  
Gravity Recovery

The Gravity Recovery and Climate Experiment (GRACE) mission and its Follow-On (GRACE-FO) mission provide unprecedented observations of terrestrial water storage (TWS) dynamics at basin to continental scales. Established GRACE data assimilation techniques directly adjust the simulated water storage components to improve the estimation of groundwater, streamflow, and snow water equivalent. Such techniques artificially add/subtract water to/from prognostic variables, thus upsetting the simulated water balance. To overcome this limitation, we propose and test an alternative assimilation scheme in which precipitation fluxes are adjusted to achieve the desired changes in simulated TWS. Using a synthetic data assimilation experiment, we show that the scheme improves performance skill in precipitation estimates in general, but that it is more robust for snowfall than for rainfall, and it fails in certain regions with strong horizontal gradients in precipitation. The results demonstrate that assimilation of TWS observations can help correct (adjust) the model’s precipitation forcing and, in turn, enhance model estimates of TWS, snow mass, soil moisture, runoff, and evaporation. A key limitation of the approach is the assumption that all errors in TWS originate from errors in precipitation. Nevertheless, the proposed approach produces more consistent improvements in simulated runoff than the established GRACE data assimilation techniques.

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>

The uncertainty of storage-based drought indices from GRACE

2020 ◽  
Author(s):  
Peyman Saemian ◽  
Mohammad Javad Tourian ◽  
Nico Sneeuw
Keyword(s):  
Land Surface ◽  
Water Storage ◽  
Extended Period ◽  
Drought Indices ◽  
Terrestrial Water Storage ◽  
Earth’S Gravity Field ◽  
Grace Data ◽  
Temporal And Spatial Variability ◽  
Terrestrial Water ◽  
Gravity Recovery

<p>Climate change and the growing demand for freshwater have raised the frequency and intensity of extreme events like drought. Satellite observations have improved our understanding of the temporal and spatial variability of droughts. Since March 2002, the Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE Follow-On (GRACE-FO) have been observing variations in Earth's gravity field yielding valuable information about changes in terrestrial water storage anomaly (TWSA). The terrestrial water storage vertically integrates all forms of water on and beneath land surface including snow, surface water, soil moisture, and groundwater storage.</p><p>Drought indices help to monitor drought by characterizing it in terms of their severity, location, duration and timing. Several drought indices have been developed based on GRACE water storage anomaly from a GRACE-based climatology, most of which suffer from the short record of GRACE, about 15 years, for their climatology. The limited duration of the GRACE observations necessitates the use of external datasets of TWSA with a more extended period for climatology. Drought characterization comes with its own uncertainties due to the inherent uncertainty in the GRACE data, the various post-processing approaches of GRACE data, and different options for external datasets on the other hand.</p><p>This study offers a method to quantify uncertainties for the storage-based drought index. Moreover, we assess the sensitivity of major global river basins to the duration of the observations. The outcome of the study is invaluable in the sense that it allows for a more informative storage based drought, including uncertainty, thus enabling a more realistic risk assessment.</p>

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 (<20–25 mm month−1).

scholarly journals Monitoring and Analysis of Drought Using Gravity Recovery and Climate Experiment (GRACE)

Hydrology ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. 75 ◽  
Author(s):  
Ahmad Nemati ◽  
Seyed Hossein Ghoreishi Najafabadi ◽  
Gholamreza Joodaki ◽  
S. Saeid Mousavi Nadoushani
Keyword(s):  
Water Resource Management ◽  
Arid Zone ◽  
Water Storage ◽  
Drought Indices ◽  
Arid Zones ◽  
Terrestrial Water Storage ◽  
Study Results ◽  
Terrestrial Water ◽  
Gravity Recovery ◽  
Semi Arid

Drought monitoring needs comprehensive and integrated meteorological and hydrologic data. However, such data are generally not available in extensive catchments. The present study aimed to analyze drought in the central plateau catchment of Iran using the terrestrial water storage deficit index (TSDI). In this arid catchment, the meteorological and hydrologic observed data are scarce. First, the time series of terrestrial water storage changes (TWSC) obtained from the gravity recovery and climate experiment (GRACE) was calculated and validated by the water budget output. Then, the studied area was divided into semi-arid, arid, and hyper-arid zones and the common drought indices of SPI and RDIe within a timescale of 3, 6, and 12 months were calculated to compare the results obtained from the TSDI by using the meteorological data of 105 synoptic stations. Based on the results, the study area experienced a drought with extreme severity and expansion during 2007–2008. The drought spatial distribution map obtained from three indices indicated good conformity. Based on the maps, the severity, duration, and frequency of drought in the semi-arid zone were greater than that in other zones, while no significant drought occurred in the hyper-arid zone. Furthermore, the temporal distribution of drought in all three zones indicated that the TSDI could detect all short- and long-term droughts. The study results showed that the TSDI is a reliable, integrated, and comprehensive index. Using this index in arid areas with little field data led to some valuable results for planning and water resource management.

scholarly journals Characterizing Drought in India Using GRACE Observations of Terrestrial Water Storage Deficit

2017 ◽  
Vol 18 (2) ◽  
pp. 381-396 ◽  
Author(s):  
Debanjan Sinha ◽  
Tajdarul H. Syed ◽  
James S. Famiglietti ◽  
John T. Reager ◽  
Reis C. Thomas
Keyword(s):  
Spatial Scales ◽  
Water Storage ◽  
Central India ◽  
Terrestrial Water Storage ◽  
Drought Intensity ◽  
West Central ◽  
Terrestrial Water ◽  
Almost All ◽  
Gravity Recovery

Abstract Frequent recurrences of drought in India have had major societal, economical, and environmental impacts. While region-specific assessments are abundant, exhaustive appraisal over large spatial scales has been insubstantial. Here a new drought index called Water Storage Deficit Index (WSDI) is devised and analyzed for holistic representation of drought. The crux of the method is the employment of terrestrial water storage (TWS) variations from Gravity Recovery and Climate Experiment (GRACE) for quantification of drought intensity and severity. Drought events in recent times are well identified and quantified using the approach over four homogenous rainfall regions of India over the period from April 2002 to April 2015. Among the four regions, the highest peak deficit of −158.00 mm is observed in January 2015 over central India. While the drought of 2002–04 is prominent in peninsular and west-central India, the drought of 2009–10 and 2012–13 is conspicuous in almost all four regions of India. The longest deficit period of 23 months (from February 2009 to December 2010) and the highest severity value of −26.31 are observed in central and northwestern India, respectively. WSDI values show an increasing trend in west-central India (0.07 yr−1), indicating recovery from previously existing drought conditions. On the contrary, a decreasing trend in WSDI is observed in northwestern (−0.07 yr−1) and central (−0.18 yr−1) India. Results demonstrate considerable confidence in the potential of WSDI for robust characterization of drought over large spatial scales.

Uncertainties of Terrestrial Water Storage Anomalies for Global Basins – A Comparison Between Modelled and Formal Covariances

2020 ◽  
Author(s):  
Eva Boergens ◽  
Andreas Kvas ◽  
Henryk Dobslaw ◽  
Annette Eicker ◽  
Christoph Dahle ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Spherical Harmonic ◽  
Water Storage ◽  
Covariance Matrices ◽  
Model Parameters ◽  
Data Sets ◽  
Terrestrial Water Storage ◽  
Covariance Model ◽  
Terrestrial Water ◽  
Level 3

<p class="western">The application of GRACE and GRACE-FO observed gridded terrestrial water storage data (TWS) often requires realistic assumptions of the data variances and covariances. Such covariances are, e.g., needed for data assimilation in various models or combinations with other data sets. The formal variance-covariance matrices now provided with the Stokes coefficients can yield such spatial variances and covariances after variance propagating them through the various post-processing steps, including the filtering, and spherical harmonic synthesis. However, a rigorous variance propagation to the TWS grids is beyond the capabilities of most non-geodetic users.</p> <p class="western">That is why we developed a new spatial covariance model for global TWS grids. This covariance model is non-stationary (time-depending), non-homogeneous (location-depending), and anisotropic (direction-depending). Additionally, it allows latitudinal wave-like correlations caused by residual striping errors. The model is tested for both GFZ RL06 Level-3 TWS data as provided via the GravIS portal (gravis.gfz-potsdam.de) and ITSG-Grace2018 GravIS-like processed Level-3 TWS data. The model parameters are fitted to empirical correlations derived from both TWS fields. Both data sets yield the same model parameters within the uncertainty of the parameter estimation.</p> <p class="western">Now, the covariance model derived thereof can be used to estimate uncertainties of mean TWS time series of arbitrary regions such as river basins. Here, we use a global basin segmentation covering all continents. At the same time, such regional uncertainties can be derived from formal variance-covariance matrices as well. To this end, the formal ITSG-Grace2018 variance-covariance matrices of the spherical harmonic coefficients are used. Thus, the modelled and formal basin uncertainties can be compared against each other globally, both spatially and temporally. Further, external validation investigates the usefulness of the basin uncertainties for applications such as data assimilation into hydrological models. Our results show a high agreement between the modelled and the formal basin uncertainties proving our approach of modelled covariance to be a suitable surrogate for the formal variance-covariance matrices.</p>

Sign in / Sign up

Export Citation Format

Share Document