Tracking seasonal and monthly drought with GRACE-based terrestrial water storage assessments over major river basins in South India

Global terrestrial drought and its projected socioeconomic implications under different warming targets

Droughts are increasingly frequent as the Earth warms, presenting adaptation challenges for ecosystems and human communities worldwide. A strategic environmental assessment (SEA) and the integration of adaptation strategies into policies, plans, and programs (PPP) are two important approaches for enhancing climate resilience and fostering sustainable development. This study developed an innovative approach to strengthen the SEA of droughts by quantifying the impacts of future temperature increases. A novel method for projecting drought events was integrated into the SEA process by leveraging multiple data sources, including atmospheric reanalysis, reconstructions, satellite-based observations, and model simulations. We identified drought conditions using terrestrial water storage (TWS) anomalies and applied a random forest (RF) model for disentangling the drivers behind drought events. We then set two global warming targets (2.0 °C and 2.5 °C) and analyzed drought changes under three shared socioeconomic pathways (SSP126, SSP370, SSP585). In a 2.0 °C warming world, over 50 % of the global surface will face increased drought risk. With an additional 0.5 °C increase, >60 % of the land will be prone to further drought escalation. We utilized copulas to build the joint distribution for drought duration and severity, estimating the joint return periods (JRP) for bivariate drought hazard. In tropical and subtropical regions, JRP reductions exceeding half are projected for >33 % of the regional land surface under 2.0 °C warming and for >50 % under 2.5 °C warming. Finally, we projected the impacts of drought events on population and gross domestic product (GDP). Among the three SSPs, under SSP370, population exposure is highest and GDP exposure is minimal under 2.0 °C warming. Global GDP and population risks from drought are projected to increase by 37 % and 24 %, respectively, as warming continues. This study enhances the accuracy of SEA in addressing drought risks and vulnerabilities, supporting climate-resilient planning and adaptive strategies.

Evaluating flood potential in the Mahanadi River Basin, India, using Gravity Recovery and Climate Experiment (GRACE) data and topographic flood susceptibility index under non-stationary framework

Extreme flood events have been recorded recently in the Mahanadi River basin in India with a high destructive potential that causes large social and economic damages. Because fewer hydrometeorological stations can record the flood magnitude in the basin, exploring new datasets like Gravity Recovery and Climate Experiment (GRACE) becomes important to overcome the barriers of assessing the hydrological extremes. The study estimates the flood potential using the GRACE-based terrestrial water storage (TWS) and analytical hierarchy process (AHP)-based topographic flood susceptibility to model the non-stationary flood frequency. During extreme flood events, the magnitude of the combined flood potential index (CFPI) is high (CFPI > 0.6), which correlates with higher river discharge. The CFPI value for the 2012 flood event with a discharge of 11,000 m3/sec (corresponds to a 35-year return period) is recorded at 0.67. Likewise, the CFPI for the flood event in 2011, which corresponds to a return period of 17 years, also stands at 0.63. The overall correlation between the discharge values of various flood events and CFPI values is above 0.8 for all locations, indicating GRACE-based CFPI's applicability for identifying the flood risk for larger basins like Mahanadi. Furthermore, on integrating CFPI as a covariate in non-stationary flood frequency modeling, the study found its superior performance when compared to both stationary models and non-stationary models with time or other climate indices as covariates, thus, helping in accurate estimation of flood return levels that are very useful in the hydrological design of water resources projects.

Drought characterization over Indian sub-continent using GRACE-based indices

Drought is a natural disaster affects water resources, agriculture, and social and economic development due to its long-term and frequent occurrence. It is crucial to characterize and monitor drought and its propagation to minimize the impact. However, spatiotemporal assessment of drought characteristics over India at the sub-basin scale based on terrestrial water storage is unexplored. In this study, the Terrestrial water storage anomalies (TWSA) obtained from a Gravity Recovery and Climate Experiment and precipitation data are used to characterize the propagation of drought. Combined Climatological Deviation Index (CCDI) and GRACE-Drought Severity Index (GRACE-DSI) were computed as CCDI utilizes both precipitation and TWSA data while GRACE-DSI uses only TWSA data. Our results showed that GRACE-DSI exhibits significant negative trends over most of the Indian sub-basins compared to CCDI, indicating that most of the drought events are due to depletion of TWS. While other sub-basins show changing trends for GRACE-DSI and CCDI. The number of sub-basins showing significant negative trends for GRACE-DSI is more than that for CCDI. Hence TWS is depleting for most of the subbasins in India. Our results show that Indo-Gangetic plains face many drought events during 2002–2004, 2009–2014 & 2015–2017. Maximum drought duration and drought severity obtained for the area of North Ladakh (not draining into Indus basins) by GRACE-DSI are 26 months (2002–2004) and − 44.2835, respectively. The maximum drought duration and drought severity obtained for the Shyok sub-basin by CCDI is 17 months (2013–2015) and − 13.4392, respectively. Monthly trend analysis revealed that 39 & 23 no. of sub-basins show significant negative GRACE-DSI trends for October and CCDI for November, respectively. At the same time, the seasonal trend shows that total 34 and 14 sub-basins exhibited a significant negative trend at post-monsoon Kharif season for both the GRACE-DSI & CCDI, respectively.

Hydrological drought characterization based on GNSS imaging of vertical crustal deformation across the contiguous United States

Continuous Global Navigation Satellite System (GNSS) measurements allow us to track subtle elastic crustal deformation in the response to hydrological mass variations and provide an additional tool to independently characterize hydrological extremes (e.g., droughts and floods). In this study, we develop a time-varying GNSS imaging strategy that depends on the principal component analysis of GNSS-sensed vertical crustal displacement (VCD) in 2006-2020 and the monthly images of hydrology-induced deformation are generated for drought characterization across the contiguous United States. The first 12 principal components are selected in our time-varying imaging system, which account for 85% of the data variance. Considering that surface water loads are inversely correlated with the induced elastic vertical motions, we reverse the signs of the GNSS-imaged time series in all grids in subsequent studies (referred to as negative VCD (NVCD)). The GNSS-NVCD data generally correlate well with the water estimates from the Gravity Recovery and Climate Experiment (GRACE) and North American Land Data Assimilation System (NLDAS). Using the GNSS-imaged gridded NVCD products, we produce a GNSS-based drought severity index (GNSS-DSI) based on the climatological methodology, which is implemented by standardizing the GNSS NVCD anomalies that deviate from climatological normal. In most regions, strong linear correlations are accessible for GNSS-DSI relative to GRACE-DSI and the self-calibrating Palmer Drought Severity Index (scPDSI). The new drought monitoring tool, which is based solely on GNSS-measured vertical positions, is used for hydrological drought characterization (onset, end, duration, magnitude, intensity, and recovery); it succeeds in identifying well-documented historical droughts from the US drought monitor (USDM). Our study presents a new drought characterization framework using solely GNSS-measured hydrological loading displacements from a dense GNSS network, which has great potential to strengthen operational drought monitoring and assessment.

Bridging the gap between GRACE and GRACE-FO using a hydrological model

Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO), two successive satellite-based missions starting in 2002, have provided an unprecedented way of measuring global terrestrial water storage anomalies (TWSA). However, a temporal gap exists between GRACE and GRACE-FO products from July 2017 to May 2018, which introduces bias and uncertainties in TWSA calculations and modeling. Previous studies have incorporated hydroclimatic factors as predictors for filling the gap, but most of them utilized artificial intelligence or pure statistical models that generally de-trended TWSA and had no physical foundation. Thus, a physically-based reconstruction is required for increasing robustness. In this study, we bridge the temporal gap by developing an empirical hydrological model. The "abcd" model, a T-based snow component, and linear correction are utilized to represent runoff generation, snow dynamics, and long-term trends. The testing results indicate that our hydrological model can successfully reconstruct TWSA in tropical, temperature, and continental climates, although further improvement is needed for arid climates. Our reconstruction for the gap achieves high accuracy and robustness as shown by the evaluations against sea-level budget and GLDAS-derived TWSA. Compared to previous studies using artificial intelligence or statistical techniques, our hydrological model performs similarly in the gap filling but does not involve de-trended or de-seasonalized transformations, which will facilitate the combination of GRACE and GRACE-FO products and improve the physical understanding of global TWSA.

An improved daily standardized precipitation index dataset for mainland China from 1961 to 2018

The standardized precipitation index (SPI), one of the most commonly used drought indicators, is widely used in the research areas of drought analysis and drought prediction in different fields such as meteorology, agriculture, and hydrology. However, its main disadvantage is the relatively coarse time resolution of one month. To improve the time resolution of SPI to identify flash droughts, we have refined the traditional SPI calculation method and developed a new multi-scale daily SPI dataset based on data from 484 meteorological stations in mainland China from 1961 to 2018. SPI data from three different sites (located in Henan, Yunnan, and Fujian Provinces) at the three-month timescale were analyzed by comparing with historically recorded drought events. We found that the new multi-scale daily SPI can effectively capture drought events in different periods and locations and identify the specific start and end times of drought events. In short, our SPI dataset appears reasonable and capable of facilitating drought research in different fields.

Multidecadal Land Water and Groundwater Drought Evaluation in Peninsular India

To efficiently mitigate the multitude of direct and indirect impacts, basin-scale drought characterization is imperative, particularly for India, where water scarcity is continually increasing. We jointly used the GRACE gravity data, PCR-GLOBWB model outputs, and in situ data to quantify the deficits based on land water storage (LWS) and groundwater storage (GWS) in Peninsular India for 35 years from January 1980 to December 2014. The results showed that the study basins experienced high interannual variations despite the minimal linear LWS trends (0.26–0.56 mm yr−1). GWS showed a slow but persistent response (longest deficit spanning ~6 years) to the seasonal variations in the hydrological fluxes and remained the major contributor to LWS. We demonstrated (1) the potential of the PCR-GLOBWB model to analyze LWS and its segregated components beyond GRACE data records, (2) the LWS-based index as a better drought indicator than traditional drought indices, and (3) GRACE-LWS as a proxy indicator of real-time groundwater monitoring without relying upon the intermittent in situ observations. This study underscores the need to revise the water allocation strategies and irrigation systems to maintain the sustainability of groundwater systems and may serve as a holistic framework for remotely monitoring the water beneath our feet, especially in the data-limited regions globally.

Spatial Variations and Influencing Factors of River Networks in River Basins of China

Analysis of the spatial variations in river networks and the related influencing factors is crucial for the management and protection of basins. To gain insight into the spatial variations and influencing factors of river networks between large basins, in this study, three river basins from north to south in China (Songhua River Basin, Yellow River Basin and Pearl River Basin) were selected for investigation. First, based on a digital elevation model, different river networks with six drainage accumulation thresholds of three basins were extracted using ArcGIS. The optimal networks were determined through fitting the relationship between the accumulation threshold and related drainage density. Then, we used two indicators, drainage density and water surface ratio, to characterize the spatial variations of three basins. Finally, Pearson’s correlation coefficients were calculated between those two indicators and natural/human influencing factors. The results showed that drainage density and water surface ratio decreased from north to south in China and were negatively correlated with natural/human influencing factors. Drainage density was more influenced by natural factors than by human factors, while the opposite was true for water surface ratio. These findings may provide some basis for the management and protection of the river network.

Monthly and Seasonal Drought Characterization Using GRACE-Based Groundwater Drought Index and Its Link to Teleconnections across South Indian River Basins

Traditional drought monitoring is based on observed data from both meteorological and hydrological stations. Due to the scarcity of station observation data, it is difficult to obtain accurate drought distribution characteristics, and also tedious to replicate the large-scale information of drought. Thus, Gravity Recovery and Climate Experiment (GRACE) data are utilized in monitoring and characterizing regional droughts where ground station data is limited. In this study, we analyzed and assessed the drought characteristics utilizing the GRACE Groundwater Drought Index (GGDI) over four major river basins in India during the period of 2003–2016. The spatial distribution, temporal evolution of drought, and trend characteristics were analyzed using GGDI. Then, the relationship between GGDI and climate factors were evaluated by the method of wavelet coherence. The results indicate the following points: GRACE’s quantitative results were consistent and robust for drought assessment; out of the four basins, severe drought was noticed in the Cauvery river basin between 2012 and 2015, with severity of −27 and duration of 42 months; other than Godavari river basin, the remaining three basins displayed significant negative trends at monthly and seasonal scales; the wavelet coherence method revealed that climate factors had a substantial effect on GGDI, and the impact of Southern Oscillation Index (SOI) on drought was significantly high, followed by Sea Surface Temperature (SST) Index (namely, NINO3.4) and Multivariate El Niño–Southern Oscillation Index (MEI) in all the basins. This study provides reliable and robust quantitative result of GRACE water storage variations that shares new insights for further drought investigation.