A Novel Standardized Drought and Flood Potential Index Based on Reconstructed Daily GRACE Data

Abstract

Multiple indicators derived from the Gravity Recovery and Climate Experiment (GRACE) satellite have been used in monitoring floods and droughts. However, these measures are constrained by the relatively short time span (∼20 years) and coarse temporal resolution (1 month) of the GRACE and GRACE Follow-On missions, and the inherent decay mechanism of the land surface system has not been considered. Here we reconstructed the daily GRACE-like terrestrial water storage anomaly (TWSA) in the Yangtze River basin (YRB) during 1961–2015 based on the Institute of Geodesy at Graz University of Technology (ITSG)-Grace2018 solution using the random forest (RF) model. A novel antecedent metric, namely, standardized drought and flood potential index (SDFPI), was developed using reconstructed TWSA, observed precipitation, and modeled evapotranspiration. The potential of SDFPI was evaluated against in situ discharge, VIC simulations, and several widely used indices such as total storage deficit index (TSDI), self-calibrated Palmer drought severity index (sc-PDSI), and multiscale standardized precipitation evapotranspiration index (SPEI). Daily SDFPI was utilized to monitor and characterize short-term severe floods and droughts. The results illustrate a reasonably good accuracy of ITSG-Grace2018 solution when compared with the hydrological model output and regional water balance estimates. The RF model presents satisfactory performances for the TWSA reconstruction, with a correlation coefficient of 0.88 and Nash–Sutcliffe efficiency of 0.76 during the test period 2011–15. Spatiotemporal propagation of the developed SDFPI corresponds well with multiple indices when examined for two typical short-term events, including the 2003 flood and 2013 drought. A total of 22 submonthly exceptional floods and droughts were successfully detected and featured using SDFPI, highlighting its outperformance and capabilities in providing inferences for decision-makers and stakeholders to monitor and mitigate the short-term floods and droughts.

Converting Seasonal Measurements to Monthly Groundwater Levels through GRACE Data Fusion

“…A statistic or dynamic downscaling algorithm produced high spatial resolution GWS changes from the GRACE data [38,39]. A high spatial resolution GWS or a groundwater level map was produced by utilizing the relationship between TWS and hydro-climatic variables using machine learning, e.g., a regression tree [17,33]. To increase the model performance, we will consider machine learning or deep learning approaches as data fusion models in the future.…”

“…Limited groundwater level in the long-term and dense measurements serve as quantitative indicators of local groundwater change, so that a high spatiotemporal resolution groundwater level cannot be acquired easily [4]. Groundwater level data can be reconstructed using machine learning methods based on simulation models and GRACE data [17]. Groundwater data are temporally sparse in developing countries.…”

A global analysis of water storage variations from remotely sensed soil moisture and daily satellite gravimetry

“…Recent developments in GRACE data processing (Kvas et al, 2019) have enabled the computation of daily gravity fields with increased accuracy. Such daily gravity data were successfully used to study highfrequency, wind-driven sea level changes (Bonin and Chambers, 2011), short-term transport variations in the Antarctic Circumpolar Current (Bergmann and Dobslaw, 2012), the characteristics of major flood events (Gouweleeuw et al, 2018), high-frequency atmospheric fluxes (Eicker et al, 2020), and to analyze the development and propagation of water extremes by using a standardized drought and flood potential index (SDFPI; Xiong et al, 2022). Furthermore, the daily gravity-based TWS data appear particularly promising for capturing SM variations at short timescales but have not been used for this purpose yet.…”

Applying Reconstructed Daily Water Storage and Modified Wetness Index to Flood Monitoring: A Case Study in the Yangtze River Basin

“…Han et al [16] estimated the daily time series of water storage change in Bangladesh by inverting line-of-sight gravity difference (LGD) between two GRACE-FO spacecraft, which indicated that the daily LGD data can capture rapid water mass change at a sub-monthly time scale. A standardized flood potential index based on ITSG−Grace2018 daily solution was proposed by Xiong et al [17] and successfully detected 22 sub-monthly exceptional floods and droughts in the YRB between 1961 and 2015. As for the 2020 catastrophic flood in the YRB, Wang et al [18] combined GRACE data and the high-frequency ground gravity observations of gPhone to characterize 2020 flood events in YRB.…”