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.

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 CONVERSE TRENDS OF THE TERRESTRIAL AND GROUND WATER STORAGE CHANGES IN CANADA AND THE UNITED STATES

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1793-1796
Author(s):  
H. Wang ◽  
L. Xiang ◽  
H. Steffen ◽  
P. Wu ◽  
L. Jiang ◽  
...  
Keyword(s):  
United States ◽  
Snow Water Equivalent ◽  
Water Storage ◽  
High Water ◽  
The United States ◽  
Water Usage ◽  
Grace Data ◽  
Terrestrial Water ◽  
Snow Water ◽  
Satellite Altimetry Data

<p><strong>Abstract.</strong> We study the terrestrial water storage (TWS) and groundwater storage (GWS) changes in Canada and United States. We employ the separation approach from Wang et al. (2013) together with the improved GRACE data of Release 6 for a longer time span until December, 2016. The TWS signals from lake levels are derived from satellite altimetry data over the lakes while TWS signals due to soil moisture (SM) and snow water equivalent (SWE) changes from hydrology models. There are four significant trend anomalies in North America for both TWS and GWS changes. Two positive anomalies are found in Canada with their centers in the provinces of Saskatchewan and Quebec, respectively, due to increased precipitation and/or increased runoff in their surroundings. Two negative anomalies are shown in the United States with their centers in California and the northwest of Texas, respectively, which are due to decreased precipitation and, especially for California, high water usage for agriculture.</p>

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 Benefits and pitfalls of GRACE data assimilation: A case study of terrestrial water storage depletion in India

2017 ◽  
Vol 44 (9) ◽  
pp. 4107-4115 ◽  
Author(s):  
Manuela Girotto ◽  
Gabriëlle J. M. De Lannoy ◽  
Rolf H. Reichle ◽  
Matthew Rodell ◽  
Clara Draper ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Grace Data ◽  
Terrestrial Water

scholarly journals A time series assessment of terrestrial water storage and its relationship with hydro-meteorological factors in Gilgit-Baltistan region using GRACE observation and GLDAS-Noah model

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Dostdar Hussain ◽  
Aftab Ahmed Khan ◽  
Syed Najam Ul Hassan ◽  
Syed Ali Asad Naqvi ◽  
Akhtar Jamil
Keyword(s):  
Snow Water Equivalent ◽  
Gravity Data ◽  
Water Storage ◽  
Indus Basin ◽  
Flood Hazards ◽  
Terrestrial Water Storage ◽  
Satellite Gravity ◽  
Terrestrial Water ◽  
Gilgit Baltistan

AbstractMountains regions like Gilgit-Baltistan (GB) province of Pakistan are solely dependent on seasonal snow and glacier melt. In Indus basin which forms in GB, there is a need to manage water in a sustainable way for the livelihood and economic activities of the downstream population. It is important to monitor water resources that include glaciers, snow-covered area, lakes, etc., besides traditional hydrological (point-based measurements by using the gauging station) and remote sensing-based studies (traditional satellite-based observations provide terrestrial water storage (TWS) change within few centimeters from the earth’s surface); the TWS anomalies (TWSA) for the GB region are not investigated. In this study, the TWSA in GB region is considered for the period of 13 years (from January 2003 to December 2016). Gravity Recovery and Climate Experiment (GRACE) level 2 monthly data from three processing centers, namely Centre for Space Research (CSR), German Research Center for Geosciences (GFZ), and Jet Propulsion Laboratory (JPL), System Global Land Data Assimilation System (GLDAS)-driven Noah model, and in situ precipitation data from weather stations, were used for the study investigation. GRACE can help to forecast the possible trends of increasing or decreasing TWS with high accuracy as compared to the past studies, which do not use satellite gravity data. Our results indicate that TWS shows a decreasing trend estimated by GRACE (CSR, GFZ, and JPL) and GLDAS-Noah model, but the trend is not significant statistically. The annual amplitude of GLDAS-Noah is greater than GRACE signal. Mean monthly analysis of TWSA indicates that TWS reaches its maximum in April, while it reaches its minimum in October. Furthermore, Spearman’s rank correlation is determined between GRACE estimated TWS with precipitation, soil moisture (SM) and snow water equivalent (SWE). We also assess the factors, SM and SWE which are the most efficient parameters producing GRACE TWS signal in the study area. In future, our results with the support of more in situ data can be helpful for conservation of natural resources and to manage flood hazards, droughts, and water distribution for the mountain regions.

scholarly journals Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions

2016 ◽  
Author(s):  
Jean M. Bergeron ◽  
Mélanie Trudel ◽  
Robert Leconte
Keyword(s):  
Data Assimilation ◽  
State Vector ◽  
Snow Water Equivalent ◽  
Synthetic Data ◽  
Hydrologic Model ◽  
Open Loop ◽  
The State ◽  
Wide Range ◽  
Snow Water ◽  
The Impact

Abstract. The potential of data assimilation for hydrologic predictions has been demonstrated in many research studies. Watersheds over which multiple observation types are available can potentially further benefit from data assimilation by having multiple updated states from which hydrologic predictions can be generated. However, the magnitude and time span of the impact of the assimilation of an observation varies according not only to its type, but also to the variables included in the state vector. This study examines the impact of multivariate synthetic data assimilation using the Ensemble Kalman Filter (EnKF) into the spatially distributed hydrologic model CEQUEAU for the mountainous Nechako River located in British-Columbia, Canada. Synthetic data includes daily snow cover area (SCA), daily measurements of snow water equivalent (SWE) at three different locations and daily streamflow data at the watershed outlet. Results show a large variability of the continuous rank probability skill score over a wide range of prediction horizons (days to weeks) depending on the state vector configuration and the type of observations assimilated. Overall, the variables most closely linearly linked to the observations are the ones worth considering adding to the state vector. The performance of the assimilation of basin-wide SCA, which does not have a decent proxy among potential state variables, does not surpass the open loop for any of the simulated variables. However, the assimilation of streamflow offers major improvements steadily throughout the year, but mainly over the short-term (up to 5 days) forecast horizons, while the impact of the assimilation of SWE gains more importance during the snowmelt period over the mid-term (up to 50 days) forecast horizon compared with open loop. The combined assimilation of streamflow and SWE performs better than its individual counterparts, offering improvements over all forecast horizons considered and throughout the whole year, including the critical period of snowmelt. This highlights the potential benefit of using multivariate data assimilation for streamflow predictions in snow-dominated regions.

scholarly journals Data assimilation of GRACE terrestrial water storage estimates into a regional hydrological model of the Rhine River basin

2015 ◽  
Vol 19 (4) ◽  
pp. 2079-2100 ◽  
Author(s):  
N. Tangdamrongsub ◽  
S. C. Steele-Dunne ◽  
B. C. Gunter ◽  
P. G. Ditmar ◽  
A. H. Weerts
Keyword(s):  
Correlation Coefficient ◽  
River Basin ◽  
Hydrological Cycle ◽  
Water Storage ◽  
Model Parameters ◽  
Hydrological Models ◽  
Rhine River ◽  
Terrestrial Water Storage ◽  
Grace Data ◽  
Terrestrial Water

Abstract. The ability to estimate terrestrial water storage (TWS) realistically is essential for understanding past hydrological events and predicting future changes in the hydrological cycle. Inadequacies in model physics, uncertainty in model land parameters, and uncertainties in meteorological data commonly limit the accuracy of hydrological models in simulating TWS. In an effort to improve model performance, this study investigated the benefits of assimilating TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) data into the OpenStreams wflow_hbv model using an ensemble Kalman filter (EnKF) approach. The study area chosen was the Rhine River basin, which has both well-calibrated model parameters and high-quality forcing data that were used for experimentation and comparison. Four different case studies were examined which were designed to evaluate different levels of forcing data quality and resolution including those typical of other less well-monitored river basins. The results were validated using in situ groundwater (GW) and stream gauge data. The analysis showed a noticeable improvement in GW estimates when GRACE data were assimilated, with a best-case improvement of correlation coefficient from 0.31 to 0.53 and root mean square error (RMSE) from 8.4 to 5.4 cm compared to the reference (ensemble open-loop) case. For the data-sparse case, the best-case GW estimates increased the correlation coefficient from 0.46 to 0.61 and decreased the RMSE by 35%. For the average improvement of GW estimates (for all four cases), the correlation coefficient increases from 0.6 to 0.7 and the RMSE was reduced by 15%. Only a slight overall improvement was observed in streamflow estimates when GRACE data were assimilated. Further analysis suggested that this is likely due to sporadic short-term, but sizeable, errors in the forcing data and the lack of sufficient constraints on the soil moisture component. Overall, the results highlight the benefit of assimilating GRACE data into hydrological models, particularly in data-sparse regions, while also providing insight on future refinements of the methodology.

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