Estimation of hydrological drought recovery based on GRACE water storage deficit

Abstract. Drought is a natural climate extreme phenomenon that presents great challenges in forecasting and monitoring for water management purposes. Previous studies have examined the use of Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage anomalies to measure the amount of water missing from a drought-affected region, and other studies have attempted statistical approaches to drought recovery forecasting based on joint probabilities of precipitation and soil moisture. The goal of this study is to combine GRACE data with historical precipitation observations to quantify the amount of precipitation required to achieve normal storage conditions in order to estimate a likely drought recovery time. First, linear relationships between terrestrial water storage anomaly (TWSA) and cumulative precipitation anomaly are established across a range of conditions. Then, historical precipitation data are statistically modeled to develop simplistic precipitation forecast skill. Three different precipitation scenarios are simulated by using a standard deviation in climatology. Precipitation scenarios are convolved with precipitation deficit estimates to calculate best-estimate of a drought recovery period. The results show that in the regions of strong seasonal amplitude (like monsoon belt) drought continues even with the above-normal precipitation until its wet season. Historical GRACE-observed drought recovery period is used to validate the approach. Estimated drought for an example month demonstrated 80% similar recovery period as observed by the GRACE.

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Estimation of hydrological drought recovery based on precipitation and Gravity Recovery and Climate Experiment (GRACE) water storage deficit

Hydrology and Earth System Sciences ◽

10.5194/hess-25-511-2021 ◽

2021 ◽

Vol 25 (2) ◽

pp. 511-526

Author(s):

Alka Singh ◽

John Thomas Reager ◽

Ali Behrangi

Keyword(s):

Recovery Period ◽

Water Storage ◽

Precipitation Forecast ◽

Terrestrial Water Storage ◽

Term Trend ◽

Drought Recovery ◽

Terrestrial Water ◽

Gravity Recovery ◽

Long Term Trend

Abstract. Drought is a natural extreme climate phenomenon that presents great challenges in forecasting and monitoring for water management purposes. Previous studies have examined the use of Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage anomalies to measure the amount of water missing from a drought-affected region, and other studies have attempted statistical approaches to drought recovery forecasting based on joint probabilities of precipitation and soil moisture. The goal of this study is to combine GRACE data and historical precipitation observations to quantify the amount of precipitation required to achieve normal storage conditions in order to estimate a likely drought recovery time. First, linear relationships between terrestrial water storage anomaly (TWSA) and cumulative precipitation anomaly are established across a range of conditions. Then, historical precipitation data are statistically modeled to develop simplistic precipitation forecast skill based on climatology and long-term trend. Two additional precipitation scenarios are simulated to predict the recovery period by using a standard deviation in climatology and long-term trend. Precipitation scenarios are convolved with water deficit estimates (from GRACE) to calculate the best estimate of a drought recovery period. The results show that, in the regions of strong seasonal amplitude (like a monsoon belt), drought continues even with above-normal precipitation until its wet season. The historical GRACE-observed drought recovery period is used to validate the approach. Estimated drought for an example month demonstrated an 80 % recovery period, as observed by the GRACE.

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The efficacy of seasonal terrestrial water storage forecasts for predicting vegetation activity over Africa

Journal of Hydrometeorology ◽

10.1175/jhm-d-21-0046.1 ◽

2021 ◽

Author(s):

Benjamin I Cook ◽

Kimberly Slinski ◽

Christa Peters-Lidard ◽

Amy McNally ◽

Kristi Arsenault ◽

...

Keyword(s):

Regression Model ◽

Southern Africa ◽

Water Storage ◽

Dry Season ◽

Forecast Skill ◽

Terrestrial Water Storage ◽

Wet Season ◽

Area Index ◽

Vegetation Activity ◽

Terrestrial Water

AbstractTerrestrial water storage (TWS) provides important information on terrestrial hydroclimate and may have value for seasonal forecasting because of its strong persistence. We use the NASA Hydrological Forecast and Analysis System (NHyFAS) to investigate TWS forecast skill over Africa and assess its value for predicting vegetation activity from satellite estimates of leaf area index (LAI). Forecast skill is high over East and Southern Africa, extending up to 3–6 months in some cases, with more modest skill over West Africa. Highest skill generally occurs during the dry season or beginning of the wet season when TWS anomalies from the previous wet season are most likely to carry forward in time. In East Africa, this occurs prior to and during the transition into the spring “Long Rains” from January–March, while in Southern Africa this period of highest skill starts at the beginning of the dry season in April and extends through to the start of the wet season in October. TWS is highly and positively correlated with LAI, and a logistic regression model shows high cross-validation skill in predicting above or below normal LAI using TWS. Combining the LAI regression model with the NHyFAS forecasts, 1-month lead LAI predictions have high accuracy over East and Southern Africa, with reduced but significant skill at 3-month leads over smaller sub-regions. This highlights the potential value of TWS as an additional source of information for seasonal forecasts over Africa, with direct applications to some of the most vulnerable agricultural regions on the continent.

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Benchmark, predictability, and forecast skill of terrestrial water storage based on CMIP6 decadal hindcasts and land surface ensemble simulations

10.5194/egusphere-egu2020-6304 ◽

2020 ◽

Author(s):

Enda Zhu ◽

Xing Yuan

Keyword(s):

Initial Conditions ◽

Water Storage ◽

Forecast Skill ◽

Precipitation Forecast ◽

Climate Forecast ◽

Terrestrial Water Storage ◽

Ensemble Simulations ◽

Climate Forecasts ◽

Terrestrial Water ◽

Decadal Climate

<p><span>Terrestrial water storage (TWS), including surface water storage, soil water storage, and groundwater storage, is critical for the global hydrological cycle and freshwater resources. A reliable decadal prediction of TWS can provide valuable information for sustainable managements of water resources and infrastructures in the face of climate change. Generally, the hydrological predictability mainly comes from two sources, i.e., initial conditions and boundary conditions. To date, the dependence of TWS forecast skill on the accuracy of initial hydrological conditions and decadal climate forecasts is not clear, and the benchmark skill remains unknown. In this work, we use decadal climate hindcasts from CMIP and perform hydrological ensemble simulations to estimate a baseline decadal forecast skill containing the two predictability sources information for TWS over global major river basins with an elasticity framework that considers varying skill of initial conditions and climate forecasts. With the incorporation of decadal climate forecast, our benchmark skill for TWS incorporated is significantly higher than initial conditions-based forecast skill over 25% and 31% basins for the leads of 1&#8211;4 and 3&#8211;6 years, especially over mid- and high-latitudes. Although the decadal precipitation forecast skill based on individual model is limited, the ensemble forecasts from multiple climate models are better than individuals. In addition, the standardized precipitation index (SPI) predictability and forecast skill from the latest CMIP6 decadal hindcast data are being investigated. Preliminary results suggest that predictability and forecast skill of SPI are positively correlated in general, and the predictability is higher than forecast skill, indicating the room for improving hydro-climate forecast. Our findings provide a new benchmark for verifying the success of decadal TWS forecasts and imply the possibility of improving decadal hydrological forecasts by using dynamical climate prediction information which still has room for improvement.</span></p>

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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

SN Applied Sciences ◽

10.1007/s42452-021-04525-4 ◽

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.

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Opportunities for Gravity and GNSS Surveying to Monitor Changes in the Terrestrial Water Storage in the Congo River Basin

Remote Sensing in Earth Systems Sciences ◽

10.1007/s41976-021-00049-y ◽

2021 ◽

Author(s):

Donghwan Kim

Keyword(s):

River Basin ◽

Water Storage ◽

Terrestrial Water Storage ◽

Congo River ◽

Congo River Basin ◽

Terrestrial Water

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Evaluation of hydrological and cryospheric angular momentum estimates based on GRACE, GRACE-FO and SLR data for their contributions to polar motion excitation

Earth Planets and Space ◽

10.1186/s40623-021-01393-5 ◽

2021 ◽

Vol 73 (1) ◽

Author(s):

Justyna Śliwińska ◽

Jolanta Nastula ◽

Małgorzata Wińska

Keyword(s):

Angular Momentum ◽

Spherical Harmonic ◽

Polar Motion ◽

Spectral Band ◽

Water Storage ◽

Terrestrial Water Storage ◽

Terrestrial Water ◽

High Consistency ◽

Polar Motion Excitation

AbstractIn geodesy, a key application of data from the Gravity Recovery and Climate Experiment (GRACE), GRACE Follow-On (GRACE-FO), and Satellite Laser Ranging (SLR) is an interpretation of changes in polar motion excitation due to variations in the Earth’s surficial fluids, especially in the continental water, snow, and ice. Such impacts are usually examined by computing hydrological and cryospheric polar motion excitation (hydrological and cryospheric angular momentum, HAM/CAM). Three types of GRACE and GRACE-FO data can be used to determine HAM/CAM, namely degree-2 order-1 spherical harmonic coefficients of geopotential, gridded terrestrial water storage anomalies computed from spherical harmonic coefficients, and terrestrial water storage anomalies obtained from mascon solutions. This study compares HAM/CAM computed from these three kinds of gravimetric data. A comparison of GRACE-based excitation series with HAM/CAM obtained from SLR is also provided. A validation of different HAM/CAM estimates is conducted here using the so-called geodetic residual time series (GAO), which describes the hydrological and cryospheric signal in the observed polar motion excitation. Our analysis of GRACE mission data indicates that the use of mascon solutions provides higher consistency between HAM/CAM and GAO than the use of other datasets, especially in the seasonal spectral band. These conclusions are confirmed by the results obtained for data from first 2 years of GRACE-FO. Overall, after 2 years from the start of GRACE-FO, the high consistency between HAM/CAM and GAO that was achieved during the best GRACE period has not yet been repeated. However, it should be remembered that with the systematic appearance of subsequent GRACE-FO observations, this quality can be expected to increase. SLR data can be used for determination of HAM/CAM to fill the one-year-long data gap between the end of GRACE and the start of the GRACE-FO mission. In addition, SLR series could be particularly useful in determination of HAM/CAM in the non-seasonal spectral band. Despite its low seasonal amplitudes, SLR-based HAM/CAM provides high phase consistency with GAO for annual and semiannual oscillation.

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