scholarly journals A Spatial Downscaling Methodology for GRACE Total Water Storage Anomalies Using GPM IMERG Precipitation Estimates

2021 ◽  
Vol 13 (24) ◽  
pp. 5149
Author(s):  
Alexandra Gemitzi ◽  
Nikos Koutsias ◽  
Venkataraman Lakshmi
Keyword(s):  
High Performance ◽  
Hydrological Modeling ◽  
Water Storage ◽  
Model Performance ◽  
Total Water ◽  
Coarse Resolution ◽  
Grace Data ◽  
Precipitation Estimates ◽  
Residual Correction ◽  
Global Precipitation

A downscaling framework for coarse resolution Gravity Recovery and Climate Experiment (GRACE) Total Water Storage Anomaly (TWSA) data is described, exploiting the observations of precipitation from the Global Precipitation Measurement (GPM) mission, using the Integrated Multi-satellite Retrievals for GPM (IMERG). Considering that the major driving force for changes in TWS is precipitation, we tested our hypothesis that coarse resolution, i.e., 1°, GRACE TWSA can be effectively downscaled to 0.1° using GPM IMERG data. The algorithm for the downscaling process comprises the development of a regression equation at the coarse resolution between the GRACE and GPM IMERG data, which is then applied at the finer resolution with a subsequent residual correction procedure. An ensemble of GRACE data from three processing centers, i.e., GFZ, JPL and CSR, was used for the time period from June 2018 until March 2021. To verify our downscaling methodology, we applied it with GRACE data from 2005 to 2015, and we compared it against modeled TWSA from two independent datasets in the Thrace and Thessaly regions in Greece for the same period and found a high performance in all examined metrics. Our research indicates that the downscaled GRACE observations are comparable to the TWSA estimated with hydrological modeling, thus highlighting the potential of GRACE data to contribute to the improvement of hydrological model performance, especially in ungauged basins.

scholarly journals An Efficient Downscaling Scheme for High-Resolution Precipitation Estimates over a High Mountainous Watershed

2021 ◽  
Vol 13 (2) ◽  
pp. 234
Author(s):  
Na Zhao
Keyword(s):  
High Resolution ◽  
Model Performance ◽  
Absolute Error ◽  
Rain Gauge ◽  
Monthly Precipitation ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Residual Correction ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Satellites are capable of observing precipitation over large areas and are particularly suitable for estimating precipitation in high mountains and poorly gauged regions. However, the coarse resolution and relatively low accuracy of satellites limit their applications. In this study, a downscaling scheme was developed to obtain precipitation estimates with high resolution and high accuracy in the Heihe watershed. Shannon’s entropy, together with a semi-variogram, was applied to establish the optimal precipitation station network. A combination of the random forest (RF) method and the residual correction approach with the established rain gauge network was applied to downscale monthly precipitation products from Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). The results indicated that the RF model showed little improvement in the accuracy of IMERG-based precipitation downscaling. Including residual modification could improve the results of the RF model. The mean absolute error (MAE) and root mean square error (RMSE) values decreased by 19% and 21%, respectively, after residual corrections were added to the RF approach. Moreover, we found that enough rain gauge records are necessary for and remain an important component of tuning model performance. The application of more rain gauges improves the performance of the combined RF and residual modification methods, with the MAE and RMSE values reduced by 8% and 9%, respectively. Residual correction, together with enough precipitation stations, can effectively enhance the quality of the precipitation patterns and magnitudes obtained in the RF downscaling process. The proposed downscaling scheme is an effective tool for increasing the accuracy and spatial resolution of precipitation fields in the Heihe watershed.

scholarly journals State-of-the-art potential of the GRACE satellite mission for solving modern hydrological problems

2021 ◽  
Vol 66 (1) ◽  
Author(s):  
Natalia L. Frolova ◽  
◽  
Vadim Yu. Grigorev ◽  
Inna N. Krylenko ◽  
Elena A. Zakharova ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Hydrological Modeling ◽  
Water Storage ◽  
Winter Period ◽  
Total Water ◽  
Satellite Mission ◽  
The North ◽  
Grace Data ◽  
North Part ◽  
Grace Mission

The paper presents main results of GRACE mission using in such fields of study as estimations of components of basins water storage and water balance, hydrological modeling. It is shown that error of GRACE data is of the order 11 mm for watersheds with area about 100 000 km2 and decreasing with increasing of basin area. This accuracy made it possible to identify long-term and seasonal water storage. It is shown, that decreasing of total water storage in the Don basin for 2002–2019 is approximately equally caused by both soil moisture and groundwater changes. At the same time, minimum of groundwater was already reached in 2010, and soil moisture in 2015. Since 2016, Don basin groundwater changes a little during the winter period that is due, probably, with increase number of thaws and thinning of the freezing layer during this period. By the data of meteorological stations for the precipitation of cold period for the European Russia the value of their systematic error was estimated, it is about 20-25%. The comparison of the values of total water storage for the river basins of the north part of European part of Russia, according GRACE data and ECOMAG runoff modeling results has shown their good coincidence (NSE =0.78 0.89). In comparison with GRACE, ECOMAG shows a smaller increase in water storage during the winter and a faster decline during spring flood period. Currently, progress in the use of GRACE in hydrology is limited by low spatial-temporal resolution of data, which, within the framework of the GRACE mission itself, will not be improved in the coming years. At the same time, the principle of GRACE operation can be applied in future to various satellite constellations.

Long‐Term (1979‐Present) Total Water Storage Anomalies Over the Global Land Derived by Reconstructing GRACE Data

2021 ◽  
Vol 48 (8) ◽  
Author(s):  
Fupeng Li ◽  
Jürgen Kusche ◽  
Nengfang Chao ◽  
Zhengtao Wang ◽  
Anno Löcher
Keyword(s):  
Water Storage ◽  
Total Water ◽  
Grace Data ◽  
Global Land

scholarly journals Calibration and evaluation of a semi-distributed watershed model of Sub-Saharan Africa using GRACE data

2012 ◽  
Vol 16 (9) ◽  
pp. 3083-3099 ◽  
Author(s):  
H. Xie ◽  
L. Longuevergne ◽  
C. Ringler ◽  
B. R. Scanlon
Keyword(s):  
Regional Scale ◽  
Water Storage ◽  
Hydrologic Model ◽  
Sub Saharan Africa ◽  
Model Parameters ◽  
Total Water ◽  
Discharge Data ◽  
Model Based ◽  
Grace Data ◽  
Sub Saharan

Abstract. Irrigation development is rapidly expanding in mostly rainfed Sub-Saharan Africa. This expansion underscores the need for a more comprehensive understanding of water resources beyond surface water. Gravity Recovery and Climate Experiment (GRACE) satellites provide valuable information on spatio-temporal variability in water storage. The objective of this study was to calibrate and evaluate a semi-distributed regional-scale hydrologic model based on the Soil and Water Assessment Tool (SWAT) code for basins in Sub-Saharan Africa using seven-year (July 2002–April 2009) 10-day GRACE data and multi-site river discharge data. The analysis was conducted in a multi-criteria framework. In spite of the uncertainty arising from the tradeoff in optimising model parameters with respect to two non-commensurable criteria defined for two fluxes, SWAT was found to perform well in simulating total water storage variability in most areas of Sub-Saharan Africa, which have semi-arid and sub-humid climates, and that among various water storages represented in SWAT, water storage variations in soil, vadose zone and groundwater are dominant. The study also showed that the simulated total water storage variations tend to have less agreement with GRACE data in arid and equatorial humid regions, and model-based partitioning of total water storage variations into different water storage compartments may be highly uncertain. Thus, future work will be needed for model enhancement in these areas with inferior model fit and for uncertainty reduction in component-wise estimation of water storage variations.

scholarly journals Analysis of Groundwater and Total Water Storage Changes in Poland Using GRACE Observations, In-situ Data, and Various Assimilation and Climate Models

2019 ◽  
Vol 11 (24) ◽  
pp. 2949 ◽  
Author(s):  
Justyna Śliwińska ◽  
Monika Birylo ◽  
Zofia Rzepecka ◽  
Jolanta Nastula
Keyword(s):  
Climate Models ◽  
Water Storage ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Climate Data ◽  
Total Water ◽  
In Situ Data ◽  
Grace Data ◽  
Land Data Assimilation System

The Gravity Recovery and Climate Experiment (GRACE) observations have provided global observations of total water storage (TWS) changes at monthly intervals for over 15 years, which can be useful for estimating changes in GWS after extracting other water storage components. In this study, we analyzed the TWS and groundwater storage (GWS) variations of the main Polish basins, the Vistula and the Odra, using GRACE observations, in-situ data, GLDAS (Global Land Data Assimilation System) hydrological models, and CMIP5 (the World Climate Research Programme’s Coupled Model Intercomparison Project Phase 5) climate data. The research was conducted for the period between September 2006 and October 2015. The TWS data were taken directly from GRACE measurements and also computed from four GLDAS (VIC, CLM, MOSAIC, and NOAH) and six CMIP5 (FGOALS-g2, GFDL-ESM2G, GISS-E2-H, inmcm4, MIROC5, and MPI-ESM-LR) models. The GWS data were obtained by subtracting the model TWS from the GRACE TWS. The resulting GWS values were compared with in-situ well measurements calibrated using porosity coefficients. For each time series, the trends, spectra, amplitudes, and seasonal components were computed and analyzed. The results suggest that in Poland there has been generally no major TWS or GWS depletion. Our results indicate that when comparing TWS values, better compliance with GRACE data was obtained for GLDAS than for CMIP5 models. However, the GWS analysis showed better consistency of climate models with the well results. The results can contribute toward selection of an appropriate model that, in combination with global GRACE observations, would provide information on groundwater changes in regions with limited or inaccurate ground measurements.

Assessing Impacts of Climate Extremes and Human Water Use on GRACE Total Water Storage Trends in Major U.S. Aquifers

2020 ◽  
Author(s):  
Bridget Scanlon ◽  
Ashraf Rateb ◽  
Alexander Sun ◽  
Himanshu Save
Keyword(s):  
Surface Water ◽  
Water Use ◽  
Groundwater Level ◽  
Water Storage ◽  
Climate Extremes ◽  
High Plains ◽  
Total Water ◽  
Groundwater Storage ◽  
Grace Data ◽  
The U.S

<p>There is considerable concern about water depletion caused by climate extremes (e.g., drought) and human water use in the U.S. and globally. Major U.S. aquifers provide an ideal laboratory to assess water storage changes from GRACE satellites because the aquifers are intensively monitored and modeled. The objective of this study was to assess the relative importance of climate extremes and human water use on GRACE Total Water Storage Anomalies in 14 major U.S. aquifers and to evaluate the reliability of the GRACE data by comparing with groundwater level monitoring (~-23,000 wells) and regional and global models. We quantified total water and groundwater storage anomalies over 2002 – 2017 from GRACE satellites and compared GRACE data with groundwater level monitoring and regional and global modeling results.  </p> <p>The results show that water storage changes were controlled primarily by climate extremes and amplified or dampened by human water use, primarily irrigation. The results were somewhat surprising, with stable or rising long-term trends in the majority of aquifers with large scale depletion limited to agricultural areas in the semi-arid southwest and southcentral U.S. GRACE total water storage in the California Central Valley and Central/Southern High Plains aquifers was depleted by drought and amplified by groundwater irrigation, totaling ~70 km<sup>3</sup> (2002–2017), about 2× the capacity of Lake Mead, the largest surface reservoir in the U.S. In the Pacific Northwest and Northern High Plains aquifers, lower drought intensities were partially dampened by conjunctive use of surface water and groundwater for irrigation and managed aquifer recharge, increasing water storage by up to 22 km<sup>3</sup> in the Northern High Plains over the 15 yr period. GRACE-derived total water storage changes in the remaining aquifers were stable or slightly rising throughout the rest of the U.S.</p> <p>GRACE data compared favorably with composite groundwater level hydrographs for most aquifers except for those with very low signals, indicating that GRACE tracks groundwater storage dynamics. Comparison with regional models was restricted to the limited overlap periods but showed good correspondence for modeled aquifers with the exception of the Mississippi Embayment, where the modeled trend is 4x the GRACE trend. The discrepancy is attributed to uncertainties in model storage parameters and groundwater/surface water interactions. Global hydrologic models (WGHM-2d and PCR-GLOBWB-5.0 overestimated trends in groundwater storage in heavily exploited aquifers in the southwestern and southcentral U.S. Land surface models (CLSM-F2.5 and NOAH-MP) seem to track GRACE TWSAs better than global hydrologic models but underestimated TWS trends in aquifers dominated by irrigation.</p> <p>Intercomparing GRACE, traditional hydrologic monitoring, and modeling data underscore the importance of considering all data sources to constrain water storage changes.  GRACE satellite data have critical implications for many nationally important aquifers, highlighting the importance of conjunctively using surface-water and groundwater and managed aquifer recharge to enhance sustainable development.</p>

Assessing Detectability of Global Flood Occurrences using Daily and Monthly GRACE/GRACE-FO

2020 ◽  
Author(s):  
Ashraf Rateb ◽  
Alexander Sun ◽  
Bridget Scanlon ◽  
Himanshu Save
Keyword(s):  
Water Storage ◽  
Rate Of Change ◽  
Economic Losses ◽  
Lead Times ◽  
Monthly Precipitation ◽  
Coincidence Rate ◽  
Flood Events ◽  
Total Water ◽  
Climate Signal ◽  
Grace Data

<p> </p> <p>Floods pose a threat to the lives of millions of people globally each year, with economic losses exceeding those of any other natural hazard. Improving flood forecasting with longer lead times can support enhanced risk management strategies and reduce associated socioeconomic losses. The objective of this study was to assess the detectability of floods using newly developed GRACE daily and regular monthly total water storage data. </p> <p>We compared total water storage (TWS) maxima from GRACE and GRACE-FO with flood occurrences from 2002 to 2020. GRACE daily TWS maxima were based on three daily GRACE solutions (UTCSR-RSWM, GFZ-RBF, and ITSG-2018) derived using statistical learning and geophysical models for the GRACE period (2002-2017). Monthly GRACE and GRACE-FO data were based on mascons solutions from UT-CSR and NASA-JPL for 2002-2020. A flood susceptibility index was developed based on the climate signal portion in the TWSA and compared with other flood indices (e.g., standardized precipitation index and streamflow). We evaluated the spatiotemporal coincidence rate of change of the 90th percentile of the daily and monthly precipitation based on the GPM-Imerg and GPCP rainfall data and the corresponding 90th percentile of the daily and monthly TWSA. The coincidence rate between GRACE TWSA maxima and precipitation were also compared relative to actual flood data (~3000 events) from the Dartmouth flood Observatory (DFO) catalog. </p> <p>Preliminary results using precipitation data from GPCP reveal that monthly GRACE/GRACE-FO data have a high predication rate for the monthly maxima precipitation > 90th percentile with a lead time of ~ two months across the tropical rain belt. Assessment against the real flood events shows that the three daily GRACE data perform well for flood events resulting from heavy and monsoonal rain and slightly differ for the events triggered by snowmelt and storm surges. The duration of flood events from GRACE data is generally shorter than the periods reported by DFO. An empirical relationship was derived between floods' duration based on the cause and the expected precursor coincidence rate from daily GRACE data. Further analysis is necessary to evaluate the GRACE precursor rate using different lead times and tolerance windows, quantify the change in rate relative to climate, topography, and soil types, and interpret the different performance GRACE products. This preliminary analysis suggests the high potential for GRACE/GRACE-FO data to extend flood forecast lead times and potentially improve the mitigation strategies</p>

scholarly journals Informing a hydrological model of the Ogooué with multi-mission remote sensing data

2018 ◽  
Vol 22 (2) ◽  
pp. 1453-1472 ◽  
Author(s):  
Cecile M. M. Kittel ◽  
Karina Nielsen ◽  
Christian Tøttrup ◽  
Peter Bauer-Gottwein
Keyword(s):  
Remote Sensing ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Water Storage ◽  
Total Water ◽  
Support Tool ◽  
River Stage ◽  
Satellite Missions ◽  
Storage Change

Abstract. Remote sensing provides a unique opportunity to inform and constrain a hydrological model and to increase its value as a decision-support tool. In this study, we applied a multi-mission approach to force, calibrate and validate a hydrological model of the ungauged Ogooué river basin in Africa with publicly available and free remote sensing observations. We used a rainfall–runoff model based on the Budyko framework coupled with a Muskingum routing approach. We parametrized the model using the Shuttle Radar Topography Mission digital elevation model (SRTM DEM) and forced it using precipitation from two satellite-based rainfall estimates, FEWS-RFE (Famine Early Warning System rainfall estimate) and the Tropical Rainfall Measuring Mission (TRMM) 3B42 v.7, and temperature from ECMWF ERA-Interim. We combined three different datasets to calibrate the model using an aggregated objective function with contributions from (1) historical in situ discharge observations from the period 1953–1984 at six locations in the basin, (2) radar altimetry measurements of river stages by Envisat and Jason-2 at 12 locations in the basin and (3) GRACE (Gravity Recovery and Climate Experiment) total water storage change (TWSC). Additionally, we extracted CryoSat-2 observations throughout the basin using a Sentinel-1 SAR (synthetic aperture radar) imagery water mask and used the observations for validation of the model. The use of new satellite missions, including Sentinel-1 and CryoSat-2, increased the spatial characterization of river stage. Throughout the basin, we achieved good agreement between observed and simulated discharge and the river stage, with an RMSD between simulated and observed water amplitudes at virtual stations of 0.74 m for the TRMM-forced model and 0.87 m for the FEWS-RFE-forced model. The hydrological model also captures overall total water storage change patterns, although the amplitude of storage change is generally underestimated. By combining hydrological modeling with multi-mission remote sensing from 10 different satellite missions, we obtain new information on an otherwise unstudied basin. The proposed model is the best current baseline characterization of hydrological conditions in the Ogooué in light of the available observations.

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.

scholarly journals Downscaling GRACE total water storage change using partial least squares regression

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Bramha Dutt Vishwakarma ◽  
Jinwei Zhang ◽  
Nico Sneeuw
Keyword(s):  
Water Mass ◽  
Spatial Scales ◽  
Water Storage ◽  
Temporal Variations ◽  
Least Squares Regression ◽  
Total Water ◽  
Satellite Mission ◽  
Grace Data ◽  
Gravity Recovery ◽  
Storage Change

AbstractThe Gravity Recovery And Climate Experiment (GRACE) satellite mission recorded temporal variations in the Earth’s gravity field, which are then converted to Total Water Storage Change (TWSC) fields representing an anomaly in the water mass stored in all three physical states, on and below the surface of the Earth. GRACE provided a first global observational record of water mass redistribution at spatial scales greater than 63000 km2. This limits their usability in regional hydrological applications. In this study, we implement a statistical downscaling approach that assimilates 0.5° × 0.5° water storage fields from the WaterGAP hydrology model (WGHM), precipitation fields from 3 models, evapotranspiration and runoff from 2 models, with GRACE data to obtain TWSC at a 0.5° × 0.5° grid. The downscaled product exploits dominant common statistical modes between all the hydrological datasets to improve the spatial resolution of GRACE. We also provide open access to scripts that researchers can use to produce downscaled TWSC fields with input observations and models of their own choice.

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