Groundwater Drought Research Articles

Temporal interpolation of groundwater level hydrographs for regional drought analysis using mixed models

Large-scale studies of the spatial and temporal variation of groundwater drought status require complete inventories of groundwater levels on regular time steps from many sites so that a standardised drought index can be calculated for each site. However, groundwater levels are often measured sporadically, and inventories include missing or erroneous data. A flexible and efficient modelling framework is developed to fill gaps and regularise data in such inventories. It uses linear mixed models to account for seasonal variation, long-term trends and responses to precipitation and temperature over different temporal scales. The only data required to estimate the models are the groundwater level measurements and freely available gridded weather products. The contribution of each of the four types of trends at a site can be determined and thus the causes of temporal variation of groundwater levels can be interpreted. Validation reveals that the models explain a substantial proportion of groundwater level variation and that the uncertainty of the predictions is accurately quantified. The computation for each site takes less than 130 s and requires little supervision. Hence, the approach is suitable to be upscaled to represent the variation of groundwater levels in large datasets consisting of thousands of boreholes.

A Multivariate Drought Index for Seasonal Agriculture Drought Classification in Semiarid Regions

Drought assessment in any region primarily hinges on precipitation deficiency, which is subsequently propagated to various components and sectors, leading to different drought types. In countries such as India, an intricate relationship between various governing factors, drought types, and their quantification methodologies make it elusive to timely initiate government relief measures. This also prevents comprehensive inclusion of the integrated effect of the principal drivers of drought, resulting in ambiguous categorization of severity, where groundwater storage variability is often neglected despite its significant role in irrigation. Here, we developed a multivariate Joint Drought Index (JDI) combining satellite and model-based standardized indices of precipitation and evapotranspiration (SPEI), soil moisture (SSI), groundwater (SGI), and surface runoff (SRI) with different temporal scales by employing two robust methods, principal component analysis (PCA) and Gaussian copula, and applied the index to highly drought-prone Marathwada region from central India. Our novel approach of using different scale combinations of integrated indices for two primary seasons (Kharif and Rabi) provides more realistic drought intensities than multiple univariate indices, by incorporating the response from each index, representing the seasonal drought conditions corroborating with the seasonal crop yields. JDI, with both methods, successfully identified two major drought events in 2015 and 2018, while effectively capturing the groundwater drought. Moreover, despite the high correlation between JDI using PCA and copula, we observed a significant difference in the intensities reported by these methods, where copula detected exceptional drought conditions more frequently than PCA. JDI effectively detected the onset, duration, and termination of drought, where the improved accuracy of drought detection can play a critical role in policy formation and socioeconomic security of the related stakeholders. Seasonal agriculture drought categorization for holistic quantification of drought conditions as presented in this study should provide broad methodological implications on drought monitoring and mitigation measures, especially for agriculture-dominated regions in semiarid climates.

Exploring groundwater drought responsiveness in lowland post-glacial environments

Groundwater drought response to meteorological forcing depends on initial hydrological conditions. This makes it difficult to characterise groundwater droughts and identify the drought vulnerability of aquifers. The objective is to increase the understanding of groundwater memory and response to meteorological forcing in lowland post-glacial environments. Eighty-one groundwater hydrographs are analysed, using the standardised groundwater level index (SGI) and the precipitation index. Memory and response times are assessed using auto- and cross-correlation functions. Response time is estimated by comparing two approaches: (1) the traditional use of the maximum cross-correlation, and (2) the alternative use of the cross-correlation slope. Results are interpreted for different hydrogeological settings. The analysis showed that sand aquifers have the longest memory and response times, particularly in confined settings where the memory could be over 4 years. Silts and tills have relatively short memories and response times, at less than 1 year, though median values for silt are higher than for unconfined sand aquifers. In this study, estimating response time using the correlation slope is superior at capturing the initial response time of groundwater to precipitation. However, the results showed that groundwater anomalies in lowland post-glacial environments are sometimes more influenced by climate teleconnections than concurrent forcing. This emphasises the need for a holistic approach for the characterisation and projection of groundwater drought, as it develops in simultaneous response to meteorological forcing at different timescales.

Constructing high-resolution groundwater drought at spatio-temporal scale using GRACE satellite data based on machine learning in the Indus Basin

The complicated phenomenon induced by inadequate precipitation is a drought that impacts water resources and human life. Traditional methods to assess groundwater drought events are hindered due to sparse groundwater observations on a spatio-temporal scale. These groundwater drought events are not well studied in the study area of the Indus Basin Irrigation System (IBIS) holistically. This study applied four machine learning models to the training datasets of Gravity Recovery and Climate Experiment (GRACE) Terrestrial Water Storage (TWS) and Groundwater Storage (GWS) data to improve resolution to 0.25° from 1°. The Extreme Gradient Boosting (XGBoost) model outperformed the four models and results showed Pearson correlation (R) (0.99), Nash Sutcliff Efficiency (NSE) (0.99), Root Mean Square Error (RMSE) (5.22 mm), and Mean Absolute Error (MAE) (2.75 mm). The GRACE Groundwater Drought Index (GGDI) was calculated by normalizing XGBoost-downscaled GWS. The trend characteristics, the temporal evolution, and spatial distribution of GGDI were analyzed across the IBIS from 2003 to 2016. The wavelet coherence approach was used to evaluate the relationship between teleconnection factors and GGDI. The XGBoost downscaling model can accurately reproduce local groundwater behavior, with the acceptable correlation of coefficient values for validation (ranging from 0.02 to 0.84). The accumulated Standardized Precipitation Evapotranspiration Index (SPEI) with the time of 1, 3, and 6 months, and self-calibrated Palmer Drought Severity Index (sc-PDSI) were used to validate GGDI. The findings have demonstrated that GGDI has comparable drought patterns to SPEI-3 and SPEI-6 and sc-PDSI. The teleconnection factors have a significant impact on the GGDI shown by the wavelet coherence technique. The impact of the sea surface temperature index (namely, NINO3.4) on GGDI was observed significantly high among other teleconnection factors in the IBIS. The proposed framework can serve as a useful tool for drought monitoring and a better understanding of extreme hydroclimatic conditions in the IBIS and other similar climatic regions.

Mapping the groundwater memory across Ireland: A step towards a groundwater drought susceptibility assessment

The occurrence of groundwater drought is closely linked to the meteorological input but also to the surface and subsurface properties, which function as lagging filter for the input signal.Knowledge about the potential occurrence of groundwater droughts is relevant for existing and future groundwater users, particularly in regions where climate change is expected to extend or increase the number of dry periods. This study proposes a method to quantify the groundwater memory as basis for characterising the intrinsic susceptibility of groundwater to drought.The memory of groundwater was estimated using a sliding window autocorrelation function applied on 114 groundwater level time series. A Random Forest regressor then modelled the groundwater memory across Ireland, using national digital maps as input files.The key variables explaining groundwater memory are: the relative and absolute surface topography and a thick overburden (>10 m). Accordingly, the lowest memory appears in elevated areas with overburden thicknesses of <10 m, and vice versa. Areas of low, moderate and high groundwater memory relate to high, moderate and low groundwater drought susceptibility. The uncertainty of the results is lowest in areas of low memory and highest in areas of high memory, presumably related to the distribution of the observations.The results are considered relevant in the context of water resources planning across sectors (agriculture, industry, domestic), particularly in the context of climate change adaptation.

Integrated assessment of drought vulnerability for water resources management of Bina basin in Central India

Drought is an extreme event and its frequency is expected to increase in future under the imminent threats of climate change. The areas vulnerable to drought are increasing due to increase in the spatial extent and severity of droughts. This necessitates the need for development of an integrated framework for assessment of drought vulnerability, which will be vital for water resources management policies focused towards such vulnerable areas. An integrated drought vulnerability assessment framework has been developed considering the physical indicators that vary spatially, social indicators that vary spatially but their temporal variation may be at longer time-frames, and spatio-temporal drought indicators that vary spatially and temporally during various months during drought years. This framework has been tested for Bina basin located in the drought prone Bundelkhand region of Madhya Pradesh. The drought indicators used in the study include (i) Standardized Precipitation Index (SPI) for evaluating meteorological drought characteristics, (ii) Surface water Drought Index (SDI) for evaluating streamflow drought characteristics, and (iii) Groundwater Drought Index (GDI) for evaluating groundwater drought characteristics. Groundwater levels arebeing observed at quarterly (3 monthly) time steponly. So the relationships between GDI and 3-m SPI, 6-m SPI, and 12-m SPI have been investigated. Based on the best correlation, the 12-m SPI can be used to represent the groundwater drought in Bina basin and has therefore been used to assess the monthly variability in the groundwater drought characteristics. The spatially varying physical indicators including basin reach (elevation band), land use pattern and soil type; the spatio-temporal drought indicators including soil moisture drought, surface water drought and groundwater drought, rainfall departure and number of consecutive dry days; and the spatially varying social indicators including infants and young children, illiterate population, marginal workers and rural population have been used for the development of a Drought Vulnerability Index (DVI). The integrated drought vulnerability assessment framework has been conceptualized on the basis of DVI. Four vulnerability classes have been defined and the study area falls in mild to moderate vulnerable class, based on the analysis carried out for the various drought years in the basin. Appropriate drought management plans and mitigation strategies need to be developed to target these vulnerable areas in Bina basin.

Groundwater invertebrates and droughts: resistance in stygobiont isopods and planarians

In surface freshwater habitats water level can be strongly variable and benthic invertebrates are usually adapted to cope with hydrological variability. Groundwater habitats are usually more stable, even if in sites at the interface with the vadose zone, in epikarst streams and dripping pools hydroperiod may vary. Adaptations to survive droughts are thus likely to have been developed also by groundwater-dwelling animals. However very few studies have been performed to assess stygobiont resistance to dryness. They involve mainly the amphipods of the genus Niphargus and Stygobromus; one case is reported for a triclad of the genus Atrioplanaria. Here we describe cases of resistance to drying in stygobiont planarians of the genus Dendrocoelum and the in the isopod Monolistra pavani. Since 2016 we performed multiple surveys in 53 caves in Italy and Switzerland, sampling different stable and unstable groundwater environments. Stygobionts were searched visually and by distressing the substrate. When we detected stygobionts in dry sectors of a cave, we observed their features and behaviours, we pictured them, and we tried to rinse them. We rinsed M. pavani individuals in small plastic tanks and recorded the time of reactivation. During the surveys we recorded a Dendrocoelum planarian encapsulated in a thick mucus layer on the substrate of a dry subterranean stream. When placed in water the planarian started gliding slowly and reached a length of 18 mm. During the drought of January-April 2022, we detected individuals of M. pavani in dry areas of two different caves. 72% of the individuals were able to reactivate. Time to reactivate ranged between 0 s to 30 s. We recorded living M. pavani individuals that were able to reactivate themselves even after 39 days of drought. Our observations provide new insights into the natural history of groundwater-dwelling invertebrate taxa which global climatic changes increase the risk of drought occurrence in subterranean environments.

Evaluating dynamics of GRACE groundwater and its drought potential in Taihang Mountain Region, China

The Taihang Mountain Region (TMR) of China is an important headwater area that provides substantial water resources supporting the social and ecological stability of the North China Plain. Currently, groundwater resources in the TMR are rapidly declining, but the degree of groundwater storage (GWS) depletion and the driving mechanism of groundwater drought remain poorly understood. This study combined the Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) datasets to thoroughly analyze GWS changes in the TMR and the dominant driving factors during 2003–2016. In addition, a new index—the groundwater drought potential index (GDPI)—was established to quantify the potential of groundwater drought. We found that GRACE-derived terrestrial water storage in the TMR experienced a significant reduction (-21.82 mm/yr, p < 0.01) during 2003–2016, with a similar reduction in GWS (-21.22 mm/yr, p < 0.01). Human-induced impacts exerted a decisive role in GWS decreases in this region, with a relative contribution rate of 96% compared to the climatic contribution (4%). Climatic impacts on GWS were primarily observed in the southeastern TMR, where climate change appeared to be unbeneficial to groundwater recovery and exacerbated local groundwater depletion. Specifically, the groundwater drought potential was entirely increasing, indicating a worsened climate condition that precipitation increasingly failed to fill the groundwater deficit. Actions should be taken urgently to address the local groundwater crisis and to maintain water security. Our proposed GDPI not only reflected the degree of groundwater depletion but also revealed the relationship between groundwater drought and precipitation, making the index capable of being applied in drought forecasting. This study delivers a new perspective to better understand the mechanisms of GWS change and groundwater drought, and the results are expected to provide a valuable reference for groundwater management and water sustainability goals.

WITHDRAWN: Mapping the groundwater memory across Ireland: a step towards a groundwater drought susceptibility assessment

• National assessment of the memory of groundwater across the Republic of Ireland. • The machine learning model is able to simulate the observed memory. • The study provides a first known high resolution map of the groundwater memory. • Groundwater memory informs on the susceptibility of groundwater to drought. • Lowest drought susceptibility appears in low-elevated areas with thick overburden, and vice versa. The occurrence of groundwater drought is closely linked to the meteorological input but also to the surface and subsurface properties, which function as lagging filter for the input signal. Knowledge about the potential occurrence of groundwater droughts is relevant for existing and future groundwater users, particularly in regions where climate change is expected to extend or increase the number of dry periods. This study proposes a method to quantify the groundwater memory as basis for characterising the intrinsic susceptibility of groundwater to drought. The memory of groundwater was estimated using a sliding window autocorrelation function applied on 114 groundwater level time series. A Random Forest regressor then modelled the groundwater memory across Ireland, using national digital maps as input files. The key variables explaining groundwater memory are: the relative and absolute surface topography and a thick overburden (>10 metres). Accordingly, the lowest memory appears in elevated areas with overburden thicknesses of less than 10 m, and vice versa. Areas of low, moderate and high groundwater memory relate to high, moderate and low groundwater drought susceptibility. The uncertainty of the results is lowest in areas of low memory and highest in areas of high memory, presumably related to the distribution of the observations. The results are considered relevant in the context of water resources planning across sectors (agriculture, industry, domestic), particularly in the context of climate change adaptation.

Groundwater levels hierarchical clustering and regional groundwater drought assessment in heavily drafted aquifers

Abstract Groundwater overexploitation along with decreasing precipitation exacerbates groundwater level decline and causes groundwater drought. Efficient assessment of the drought is critical to water management, especially in drought-prone agriculture regions. It remains challenging to characterize groundwater drought quantitatively due to the difficulty in obtaining groundwater observation data and the complexity of groundwater flow systems. To this end, agglomerative hierarchical cluster analysis was performed on long-term groundwater levels to classify wells in the San Joaquin River Basin, California. A Modified Mann–Kendall (MMK) test was undertaken to detect seasonal groundwater level trends from 1980 to 2019, and the magnitude was calculated using Sen's slope estimator. A nonparametric Standardized Groundwater level Index (SGI) was used to quantify the characteristics of groundwater drought. Results show that long-term (40-year) temporal patterns in groundwater levels varied significantly over the San Joaquin River Basin. Significantly decreasing trends were observed among more than 34.6% of wells, with an average decline of 0.69 m/year. Wells suffered frequent and severe groundwater droughts in the last decade, which were mainly driven by heavy groundwater exploitation. Findings provide useful information about the long-term behavior of regional groundwater levels, which in turn help stakeholders monitor droughts and adapt groundwater management strategies.

Influence of low-frequency variability on high and low groundwater levels: example of aquifers in the Paris Basin

Abstract. Groundwater levels (GWLs) very often fluctuate over a wide range of timescales (intra-annual, annual, multi-annual, and decadal). In many instances, aquifers act as low-pass filters, dampening the high-frequency variability and amplifying low-frequency variations (from multi-annual to decadal timescales) which basically originate from large-scale climate variability. Within the aim of better understanding and ultimately anticipating groundwater droughts and floods, it appears crucial to evaluate whether (and how much) the very high or very low GWLs are resulting from such low-frequency variability (LFV), which was the main objective of the study presented here. As an example, we focused on exceedance and non-exceedance of the 80 % and 20 % GWL percentiles respectively, in the Paris Basin aquifers over the 1976–2019 period. GWL time series were extracted from a database consisting of relatively undisturbed GWL time series regarding anthropogenic influence (water abstraction by either continuous or periodic pumping) over metropolitan France. Based on this dataset, our approach consisted in exploring the effect of GWL low-frequency components on threshold exceedance and non-exceedance by successively filtering out low-frequency components of GWL signals using maximum overlap discrete wavelet transform (MODWT). Multi-annual (∼7-year) and decadal (∼17-year) variabilities were found to be the predominant LFVs in GWL signals, in accordance with previous studies in the northern France area. By filtering out these components (either independently or jointly), it is possible to (i) examine the proportion of high-level (HL) and low-level (LL) occurrences generated by these variabilities and (ii) estimate the contribution of each of these variabilities in explaining the occurrence of major historical events associated with well-recognized societal impacts. A typology of GWL variations in Paris Basin aquifers was first determined by quantifying the variance distribution across timescales. Four GWL variation types could be found according to the predominance of annual, multi-annual, or/and decadal variabilities in these signals: decadal-dominant (type iD), multi-annual- and decadal-dominant (type iMD), annual-dominant (type cA), and annual- and multi-annual-dominant (type cAM). We observed a clear dependence of high and low GWL on LFV for aquifers exhibiting these four GWL variation types. In addition, the respective contribution of multi-annual and decadal variabilities in the threshold exceedance varied according to the event. In numerous aquifers, it also appeared that the sensitivity to LFV was higher for LLs than HLs. A similar analysis was conducted on the only available long-term GWL time series which covered a hundred years. This allowed us to highlight the potential influence of multidecadal variability on HLs and LLs too. This study underlined the key role of LFV in the occurrence of HLs and LLs. Since LFV originates from large-scale stochastic climate variability as demonstrated in many previous studies in the Paris Basin or nearby regions, our results point out that (i) poor representation of LFV in general circulation model (GCM) outputs used afterwards for developing hydrological projections can result in strong uncertainty in the assessment of future groundwater extremes (GWEs), and (ii) potential changes in the amplitude of LFV, be they natural or induced by global climate change, may lead to substantial changes in the occurrence and severity of GWEs for the next decades. Finally, this study also stresses the fact that due to the stochastic nature of LFV, no deterministic prediction of future GWEs for the mid- or long-term horizons can be achieved, even though LFV may look periodic.

Intermittency as an indicator of drought in streamflow and groundwater

AbstractUnder normal precipitation conditions, increasing water demand causes groundwater level to deplete and streamflow to decrease. This results in more frequent intermittency. The so‐called intermittency cycle hypothesis is examined in this study. It is a vicious circle in hydrology making both surface and groundwater resources less efficient in supplying ever‐increasing water demand. To test the hypothesis, a data‐based analysis is applied to Gediz, Kucuk Menderes, and Ergene river basins in western Turkey. Results at basin‐ and station‐scale show no change in precipitation while streamflow fluctuates with a tendency to decrease and groundwater steeply declines. Groundwater is used excessively to meet the drastically increased water demand. With the overuse of groundwater, water table descends, streamflow decreases and intermittency increases. Intermittency ratio of a river is found related to the decrease in streamflow and the drop in groundwater level. It can trigger drought in streamflow and groundwater. It is concluded that intermittency can be taken as a quantitative indicator of drought in streamflow and groundwater.

Investigating meteorological/groundwater droughts by copula to study anthropogenic impacts

A critical understanding of the water crisis of Lake Urmia is the driver in this paper for a basin-wide investigation of its Meteorological (Met) droughts and Groundwater (GW) droughts. The challenge is to formulate a data-driven modelling strategy capable of discerning anthropogenic impacts and resilience patterns through using 21-years of monthly data records. The strategy includes: (i) transforming recorded timeseries into Met/GW indices; (ii) extracting their drought duration and severity; and (iii) deriving return periods of the maximum drought event through the copula method. The novelty of our strategy emerges from deriving return periods for Met and GW droughts and discerning anthropogenic impacts on GW droughts. The results comprise return periods for Met/GW droughts and their basin-wide spatial distributions, which are delineated into four zones. The information content of the results is statistically significant; and our interpretations hint at the basin resilience is already undermined, as evidenced by (i) subsidence problems and (ii) altering aquifers' interconnectivity with watercourses. These underpin the need for a planning system yet to emerge for mitigating impacts and rectifying their undue damages. The results discern that aquifer depletions stem from mismanagement but not from Met droughts. Already, migration from the basin area is detectable.

Hydrological Drought Assessment in a Small Lowland Catchment in Croatia

Hydrological drought is critical from both water management and ecological perspectives. Depending on its hydrological and physical features, the resilience level of a catchment to groundwater drought can differ from that of meteorological drought. This study presents a comparison of hydrological and meteorological drought indices based on groundwater levels from 1987 to 2018. A small catchment area in Croatia, consisting of two sub-catchments with a continental climate and minimum land-use changes during the observed period, was studied. The first analysis was made on a comparison of standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI). The results showed their very high correlation. The correlation between the standardized precipitation index (SPI) and standardized groundwater index (SGI) of different time scales (1, 3, 6, 12, 24 and 48 months) showed different values, but had the highest value in the longest time scale, 48 months, for all observation wells. Nevertheless, the behavior of the SPI and groundwater levels (GW) correlation showed results more related to physical catchment characteristics. The results showed that groundwater drought indices, such as SGI, should be applied judiciously because of their sensitivity to geographical, geomorphological, and topographical catchment characteristics, even in small catchment areas.

Spatial-temporal evolution of meteorological and groundwater droughts and their relationship in the North China Plain

Understanding the propagation dynamics from meteorological droughts to groundwater droughts and their spatial–temporal evolution is essential for monitoring and assessing groundwater drought risk. In this study, the Standardized Precipitation and Evapotranspiration Index (SPEI) and Gravity Recovery and Climate Experiment (GRACE) Groundwater Drought Index (GDI) were used for assessing meteorological and groundwater droughts, respectively, in the North China Plain (NCP). The Directed Information Transfer Index (DITI) was used to identify the propagation from meteorological droughts to groundwater droughts. The verification results indicated good reliability of GRACE data for assessing drought events in the NCP. From the view of temporal variation, the SPEI-1 was first decreased and then increased, and the minimum value was in 2011. The GDI was first increased and then decreased, and the maximum was in 2008. In terms of spatial distribution, the SPEI-1 significantly decreased in the central part of the NCP before 2011. The 2011–2020 period revealed an increasing trend in SPEI-1 in many parts of the NCP. According to the GDI results, an increasing trend was observed in the central and western parts of the NCP before 2008, and many parts of the study region revealed a decreasing trend in GDI during the 2008–2020 period. The propagation time of SPEI-1 to GDI was long in spring and winter and short in summer. The maximum DITI values ranged from 0.4 to 0.9 and the most spread time was about 18 to 24 months in the northern and southern parts of Hebei and the northern part of Henan province, covering 36.40% of the total surface area of the NCP. Regarding the drought sensitivity and propagation rate, the dominated distribution was in a pattern of high in the northern and low in southern parts of the NCP, with the rates of 38.44% and 34.17%, respectively.

Hydrometeorological consequences on the water balance in the Ganga river system under changing climatic conditions using land surface model

The United Nations Sustainable Development Goal (SDG) ensures adequately accessible water and management for all. Due to the rapid increase in population and industries along the Ganga river, it is necessary to estimate the water budget to fulfill the demand for water in the future. The Mann-Kendall (MK) test conducted on the Noah-Land Surface Model data for 72 years results in a maximum declining trend of water budget in the Yamuna Lower (Q = −3.82 BCM/year), and a minimum in the Damodar sub-basin (Q = −0.10 BCM/year). All the sub-basins show an increase in groundwater level (mbgl) except the Kali Sindh, which shows a decreasing trend (Q = −0.07 m/year). The percentage change was also estimated in all the sub-basins, including various parameters such as precipitation, evapotranspiration, runoff, water budget, and temperature. The extremely severe groundwater drought was estimated using the Standard Groundwater Level Index (SGWLI), from which the values for the Ram Ganga Confluence (SGWLI = 2.44; 2005), Upper stream of Gomti (SGWLI = 2.06; 2014), Ghaghra (SGWLI = 2.22; 2005), Ram Ganga (SGWLI = 2.28; 2005), Yamuna Lower (SGWLI = 2.13; 2007), Kali Sindh (SGWLI = 2.30,2.67; 2002, 2003), Chambal Upper (SGWLI = 2.30,2.20; 2001, 2003), Son (SGWLI = 2.02; 2010), Gandak (SGWLI = 2.37; 2010), Kosi (SGWLI = 2.08; 2012), Damodar (SGWLI = 2.72; 2010), and Bhagirathi (SGWLI = 2.06; 2014) were obtained for a total of 62,050 observed well data.The obtained in-situ point data is converted into the surface raster using a geostatistical technique. Our results show a declining trend in the water budget of all the 19 sub-basins of the Ganga basin and also the groundwater drought in several parts.

Groundwater drought characteristics and its influencing factors with corresponding quantitative contribution over the two largest catchments in China

Investigating the characteristics and influencing factors of groundwater drought, and identifying their difference in the regions with different climate conditions can provide an essential reference for the sustainable management of groundwater resources. As a case study in the two largest and typical catchments in China, the temporal evolution, spatial distribution and trend characteristics of groundwater drought are identified by GRACE-based groundwater drought index in the Yangtze River catchments (YZRC) with a humid climate condition, and Yellow River catchments (YRC) with an arid/semi-arid climate condition. Their influencing factors, which include natural net recharge (P-ET), teleconnection factors (ENSO, PDO and AO), and anthropogenic factors (land use and land cover, LULC), are investigated and quantitatively compared based on Pearson correlation analysis, cross wavelet transform and Random Forest. The results indicate that: (1) The groundwater droughts in the YZRC mainly occur during 2002–2009, which are mostly in the middle and lower reaches of the catchments, and they show a decreasing trend in most regions of the catchments. In the YRC, they mainly occur during 2015–2019, which influence almost the whole catchments, and they show an increasing trend in most regions of the catchments. (2) The P-ET has a significant positive impact on the mitigation of groundwater drought in the YZRC, while it fails to reverse the groundwater drought aggravation in the YRC. The teleconnection factors have obvious influences on groundwater drought evolution, among which Arctic Oscillation (AO) and Pacific Decadal Oscillation (PDO) are the important factors affecting groundwater drought in the YZRC and YRC, respectively. For LULC, the decrease of cropland and grassland could alleviate groundwater drought in the YZRC, and the increase of grassland, forest and urban area in the YRC could aggravate groundwater drought in the catchments. (3) When quantitatively comparing the contributions, the LULC and P-ET are the crucial factors affecting groundwater drought in the two catchments, while in the YRC, the impact of teleconnection factors cannot be ignored, although it is relatively weak. Moreover, groundwater exploitation plays an important role on groundwater drought in the YZRC and YRC. Its contribution in the YZRC and YRC is slightly lower than that of P-ET, but obviously higher than that of teleconnection factors.

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.

The use of predefined drought indices for the assessment of groundwater drought episodes in the Baltic States over the period 1989–2018

Study regionThe Baltic region (Estonia, Latvia, Lithuania) located at the Eastern Shore of the Baltic Sea, Northern Europe. Study focusThe spatial and temporal distribution of groundwater drought events over the period 1989–2018 was examined. Standardized Groundwater level Index (SGI) was calculated for six monitoring wells installed into shallow unconfined or semiconfined aquifers evenly distributed across the region. A set of meteorological and hydrological drought indices were used to characterize drought events using data from EAR-5 reanalysis. New hydrological insightsFour major drought events affecting all six monitoring wells were identified: 1992–1994, 1996–1997, 2002–2004 and 2005–2007 with another episode apparently starting in 2018. Meteorological drought indices - Standardize Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Reconnaissance Drought Index (RDI) - showed the highest correlation with groundwater drought events. The ERA-5 parametrization of surface and subsurface runoff used to calculate the hydrological indices - Standardized Runoff Index (SRI) and Standardized Sub-surface Runoff Index (SSRI) - most likely did not adequately represent the hill-slope scale processes affecting the unconfined groundwater dynamics, thus indices SRI and SSRI showed less clear relationship with groundwater drought events. Different patterns of SGI fluctuations and correlation with other drought indices highlight the importance of localized meteorological, land use, geomorphological or geological factors in controlling the groundwater response to the atmospheric forcing.

Steady declining trend of groundwater table and severe water crisis in unconfined hard rock aquifers in extended part of Chota Nagpur Plateau, India

Scarcity of groundwater is a severe problem in this region due to over exploitation of groundwater from unconfined hard rock aquifers. The main objectives of this study are to analyse the spatiotemporal variability and fluctuation of groundwater table and to predict the location of groundwater depression pockets. Total 21 consecutive years (1996–2017) groundwater monitoring well data (pre- and post-monsoon) have been collected from CGWB, Government of India. The nonparametric Mann–Kendall trend analysis and standardized precipitation index (SPI) have been applied to detect the trend of groundwater level and rainfall variability, respectively. Exponential smoothing has also been fitted for future prediction. The pre- and post-monsoon results (1996–2017) showed that around 77% (22 stations) and 78% (23) monitoring stations were indicating declining trend of groundwater table at the rate of −0.006 to −0.205 m/year and −0.005 to −0.192 m/year, respectively. Similarly, future (2040) groundwater depression result predicted that around 75% (21) stations, the groundwater table will be depleted above 5 m during pre-monsoon while about 53% (16 stations) monitoring wells, the groundwater table will be fallen above 5 m during post-monsoon. Consequently, around 52% (15) and 50% (14) stations are being faced groundwater drought in the recurrent interval of above 2 years during pre-monsoon and post-monsoon, respectively. Driving factors of water table depletion are huge withdrawal of groundwater for dry farming and reduction of recharge areas due to rapid land use modification. The uniqueness of this study exhibits the nature of declining trend of groundwater table and identification of depression pockets.