Groundwater Drought Research Articles

Past Groundwater Drought in the North American Cordillera

AbstractGroundwater level records in North America are relatively short (<60 years), preventing long‐term analysis of historical changes in groundwater levels associated with drought. In this study, tree ring widths are used to reconstruct groundwater levels in three regions in the North American Cordillera: Central British Columbia (BC), Canada, the Southern Interior Region of BC, and the San Luis Valley in Colorado, USA. Periods with severe drought conditions, identified using regime shift and threshold analyses were: 1890–1900 and 1950–1970 in Colorado, around 1920–1940 in the BC Interior, and 1935–1945 in Central BC. The groundwater level reconstructions are correlated with several climate indices and have similar regime shifts as identified in streamflow and drought records. The groundwater level reconstructions are strongly related to winter snowpack, suggesting that the observed trend of declining snowpack in recent years may lead to declining groundwater availability in these regions.

Characterisation of Groundwater Drought Using Distributed Modelling, Standardised Indices, and Principal Component Analysis

Characterisation of Groundwater Drought Using Distributed Modelling, Standardised Indices, and Principal Component Analysis

The Spatiotemporal Variations in and Propagation of Meteorological, Agricultural, and Groundwater Droughts in Henan Province, China

As the global climate changes and droughts become more frequent, understanding the characteristics and propagation dynamics of drought is critical for monitoring and early warning. This study utilized the Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), and Groundwater Drought Index (GDI) to identify meteorological drought (MD), agricultural drought (AD), and groundwater drought (GD), respectively. Sen’s slope method and Mann–Kendall trend analysis were used to examine drought trends. The Pearson correlation coefficient (PCC) and theory of run were utilized to identify the propagation times between different types of droughts. Cross-wavelet transform (XWT) and wavelet coherence (WTC) were applied to investigate the linkages among the three types of droughts. The results showed that, from 2004 to 2022, the average durations of MD, AD, and GD in Henan Province were 4.55, 8.70, and 29.03 months, respectively. MD and AD were gradually alleviated, while GD was exacerbated. The average propagation times for the different types of droughts were as follows: 6.1 months (MD-AD), 4.4 months (MD-GD), and 16.3 months (AD-GD). Drought propagation exhibited significant seasonality, being shorter in summer and autumn than in winter and spring, and there were close relationships among MD, AD, and GD. This study revealed the characteristics and propagation dynamics of different types of droughts in Henan Province, providing scientific references for alleviating regional droughts and promoting the sustainable development of agriculture and food production.

Forecasting the groundwater levels in the Baltic through standardized index analysis

In regions where groundwater forms the primary source of drinking water, comprehending the prospective availability of subsurface water resources due to climate change is of paramount importance.This study evaluates the impact of climate change on groundwater levels in the Baltic States until the end of this century. It employs link between surface and subsurface standardized indices. For forecast it uses various Representative Concentration Pathways (RCP) alongside different Regional Climate Models (RCM).By linking historical groundwater drought episodes with calculated surface drought indices and accumulation periods observed during defined climate Normals, we project groundwater levels for the short, medium, and long-term future. The study incorporates 13 EURO-CORDEX RCMs under three RCP scenarios.Our analysis reveals that, compared to the recent climate Normals, an overall increase in groundwater levels is expected at most study sites. However, lower groundwater levels are estimated in the near future. The projected impacts show no significant seasonal bias or spatial conformity. Although these findings are specific to the Baltic region, the methodologies described can be readily adapted for global application.

Quantifying Water Storage Changes and Groundwater Drought in the Huaihe River Basin of China Based on GRACE Data

Quantifying Water Storage Changes and Groundwater Drought in the Huaihe River Basin of China Based on GRACE Data

Analysis of factors influencing groundwater drought in the Loess zone of China

Analysis of factors influencing groundwater drought in the Loess zone of China

A framework for drought monitoring and assessment from a drought propagation perspective under non-stationary environments

According to the coupled influence of climate variation and anthropogenic activities, hydro-meteorological variables are hard to keep stationary in a changing environment. Consequently, the efficacy of traditional standardized drought indices, predicated upon the assumption of stationarity, has been called into question. In China, the challenge of drought monitoring and declaration is exacerbated by the need for multiple drought indices covering meteorological, agricultural, hydrological, and groundwater aspects, often lacking real-time availability. To address these challenges, we developed a framework for drought monitoring and assessment from a drought propagation perspective. Central to this is the Nonstationary Integrated Drought Index (NIDI), which integrates responses from meteorological, agricultural, hydrological, and groundwater droughts, accounting for climate change and anthropogenic influences. First, we analyse the process of drought propagation to select the suitable time scale standardized drought index. Subsequently, significant large-scale climatic indices are selected through linear and nonlinear correlation analyses to identify climate anomalies. Anthropogenic influences are assessed using indicators such as the Normalized Difference Vegetation Index (NDVI), Impervious Surface Ratio (ISR), and population density (POP). Nonstationary probability models are then developed for precipitation, soil moisture, runoff, and groundwater series, incorporating climatic and human-induced factors. Finally, the NIDI is calculated using a D-vine copula model, with parameter estimation and updating facilitated by a genetic algorithm, representing the temporal dependence structure among the variables. A case study in the Hulu River Basin of western China validated the NIDI. Results showed that the NIDI effectively accounts for nonstationary hydro-meteorological variables due to climate change and human activities, accurately reproducing their time-dependent structure. Compared to conventional indices like SPI, SSI, SRI, and SGI, the NIDI identifies more extreme drought events. In conclusion, the presented NIDI offers a more comprehensive approach to drought identification, providing valuable insights for accurate drought detection and effective drought-related policy-making.

GRACE based groundwater drought evaluation of Ganga Basin and analysis of drought propagation using wavelet based quantitative approach

The Ganga River Basin which is the home of almost half a billion people have plunged into groundwater drought due to anthropogenic activities. Hence groundwater drought assessment and its propagation from the meteorological drought is highly required for the Ganga River Basin. This paper focuses on the evaluation of historical groundwater drought using Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) dataset, further to obtain the drought propagation times. Traditionally the drought propagation time is obtained from the correlation between groundwater drought time series and various scales of meteorological time series. However, the GRACE-derived groundwater drought index (GGDI) showed lesser correlation with the Standardized Precipitation Index (SPI) / Standardized Precipitation Evapotranspiration Index (SPEI) due to the presence of consistent trend in the GGDI series. Hence, a novel quantitative approach using Cross Wavelet Transform (XWT) is introduced to determine the drought propagation time which can devoid of the contribution of anthropogenic activities. Extracting the significant power area of XWT of GGDI with SPI/SPEI of different scales led to the determination of groundwater drought propagation time. The results showed Groundwater Storage Anomaly (GWSA) has a steep downtrend for Upper Gangetic Basin (UGB) (−26.2 mm/year) and Yamuna Chambal Basin (YCB) (−21.8 mm/year). It was observed that UGB and YCB faced groundwater drought from 2017 to 2022. The wavelet analysis showed that the drought propagation time of YCB is 14 months, UGB is 17 months, and the Lower Gangetic Basin (LGB) is 21 months. The frequency domain analysis of the drought signals suggested YCB had a faster response to the meteorological forcing, and LGB had the slowest response.

Increasing vulnerability of vegetation to Meteorological and Groundwater drought: A Case study in Argentina

Study RegionContinental Argentina, South America. Study FocusThis study integrates multiple data sources to analyze the response of vegetation to meteorological and groundwater drought events in Argentina. It utilizes GRACE satellite data to monitor water storage variations, the GLDAS hydrological model to track soil moisture levels, and NDVI to assess vegetation health, categorized by different vegetation types. Meteorological data facilitated the computation of drought severity using the Standardized Precipitation-Evapotranspiration Index (SPEI). Anomalies in groundwater storage, derived from these data, informed the calculation of the Grace Groundwater Drought Index (GGDI). A comprehensive statistical analysis was conducted to examine the impact of meteorological (SPEI) and groundwater (GGDI) droughts on various vegetation types. New Hydrological Insights for the RegionThe study reveals that from 2002 to 2022, GRACE-TWSA and GLDAS-TWSA displayed consistent trends in Argentina. Meteorological droughts, characterized by higher frequency and intensity, predominantly affect croplands and grasslands. Compared to lagged effects, the cumulative impact of droughts was found to be more significant. Shrublands demonstrated the greatest resistance to drought, whereas vegetation in humid regions showed lower drought tolerance. Notably, the duration associated with the lag effects of meteorological droughts decreased from 2.16 months in 2002–1.52 months in 2020, suggesting an increasing sensitivity of vegetation to these droughts. This underscores the urgency of developing targeted drought resilience and water resource management strategies.

Long-range hydrological drought forecasting using multi-year cycles in the North Atlantic Oscillation

With global temperatures, populations and ecological stressors expected to rise, hydrological droughts are projected to have progressively severe economic and environmental impacts. As a result, hydrological drought forecasting systems have become increasingly important water resource management tools for mitigating these impacts. However, high frequency behaviours in meteorological or atmospheric conditions often limit the lead times of hydrological drought forecasts to seasonal timescales, either through poorer performance of multi-year meteorological forecasts or the lack of multi-year lags in atmosphere-hydrology systems. By contrast, low frequency behaviours in regionally important teleconnection systems (such as the North Atlantic Oscillation, NAO) offer a novel way to forecast hydrological drought at longer lead times. This paper shows that, by using a data-driven modelling approach, long-term behaviours within the NAO can be skilful predictors of hydrological drought conditions at a four-year forecasting horizon. Multi-year semi-periodic patterns in the NAO were used to forecast regional groundwater drought coverage in the UK (proportion of groundwater boreholes in drought), with the greatest forecast performance achieved for longer duration droughts, and for hydrogeological regions with longer response times. Model errors vary from 14 % (proportion of boreholes, (MAE)) in flashy hydrological regions or short droughts (<3 months), to 2 % for longer duration droughts (>8 months). Model fits of r2 up to 0.8 were produced between simulated and recorded regional drought coverage. As such our results show that teleconnection indices can be a skilful predictor of hydrological drought dynamics at multi-year timescales, opening new opportunities for long-lead groundwater drought forecasts to be integrated within existing drought management strategies in Europe and beyond.

Characteristics of groundwater drought and its propagation dynamics with meteorological drought in the Sanjiang Plain, China: Irrigated versus nonirrigated areas

Study regionSanjiang Plain, China Study focusThe characterization of groundwater drought is inadequate, particularly in terms of the differences in the drought propagation dynamics between cropland and other regions. In this study, we identified groundwater drought events and characteristics in the Sanjiang Plain, using the standardized groundwater drought index (SGDI); we also quantified the mechanism of dynamic factors influencing drought propagation characteristics based on the geodetector method and wavelet coherence analysis and discussed the correlation between the NDVI and SGDI.New Hydrological Insights for the Region: The drought propagation time was shorter during summer and longer during spring and autumn in irrigated areas than in nonirrigated areas. The groundwater system exhibited greater resistance to drought during summer, and spatially nonirrigated area was more vulnerable to meteorological drought. The factor that best explained the variation in propagation time and threshold differed across seasons. A significant correlation time-frequency domain existed between all the factors and SGDI in nonirrigated area, with the SGDI exhibiting the fastest response to soil moisture. The relationship between the SGDI and factors was characterized by a discontinuous response of less than 8 months and a continuous response of more than 30 months. The correlations between the SGDI and NDVI in irrigated and nonirrigated area exhibit different changes due to groundwater exploitation and differences in plant physiology between artificial crops and natural vegetation.

Development of a Multi-Scale Groundwater Drought Prediction Model Using Deep Learning and Hydrometeorological Data

Development of a Multi-Scale Groundwater Drought Prediction Model Using Deep Learning and Hydrometeorological Data

Explaining the Mechanism of Multiscale Groundwater Drought Events: A New Perspective From Interpretable Deep Learning Model

AbstractThis study presents a new approach to understand the causes of groundwater drought events with interpretable deep learning (DL) models. As prerequisites, accurate long short‐term memory (LSTM) models for simulating groundwater are built for 16 regions representing three types of spatial scales in the southeastern United States, and standardized groundwater index is applied to identify 233 groundwater drought events. Two interpretation methods, expected gradients (EG) and additive decomposition (AD), are adopted to decipher the DL‐captured patterns and inner workings of LSTM networks. The EG results show that: (a) temperature‐related features were the primary drivers of large‐scale groundwater droughts, with their importance increasing from 56.1% to 63.1% as the drought events approached from 6 months to 15 days. Conversely, precipitation‐related features were found to be the dominant factors in the formation of groundwater drought in small‐scale catchments, with the overall importance ranging from 59.8% to 53.3%; (b) Seasonal variations in the importance of temperature‐related factors are inversely related between large and small spatial scales, being more significant in summer for larger regions and in winter for catchments; and (c) temperature‐related factors exhibited an overall “trigger effect” on causing groundwater drought events in the studying areas. The AD method unveiled how the LSTM network behaved differently in retaining and discarding information when emulating different groundwater droughts. In summary, this study provides a new perspective for the causes of groundwater drought events and highlights the potential and prospect of interpretable DL in enhancing our understanding of hydrological processes.

Groundwater drought risk assessment in the semi-arid Kansai river basin, West Bengal, India using SWAT and machine learning models

Increasing concerns over groundwater drought risks, which threaten water availability and adversely impact ecosystems, agriculture, and human activities, underscore the necessity of comprehensive evaluation methods. This research introduces a meticulous approach to evaluating groundwater drought risk (GWDR) in the semi-arid expanse of the Kansai River Basin, West Bengal, India. It intricately amalgamates the Soil and Water Assessment Tool (SWAT) model with three distinct machine learning algorithms namely, Support Vector Machine (SVM), Random Forest (RF), and Neural Networks (NN). The assessment relies on a diverse array of 26 thematic datasets encompassing hydrological, meteorological drought risk, and socioeconomic conditioning variables. The SWAT model has been used to derive hydrological parameters including groundwater recharge, lateral flow, base flow, surface runoff, evapotranspiration, return flow, and soil water content. Simultaneously, a pre-monsoonal water level dataset from 503 well locations is adhered to an impartial sampling strategy, maintaining a 70:30 ratio for training and testing datasets. The ensuing GWDR maps, derived through SVM, RF, and NN models, reveal four discerning risk classes across the study area. High-risk zones conspicuously predominate in upper catchment areas, while low-risk zones find their strategic position in the lower catchment regions. The area under the receiver operating characteristic curve (AUC-ROC) for the RF model, showcases an impressive 91% success rate, surpassing its counterparts SVM and NN models, which attained success rates of 88.4% and 80.7%, respectively. The Mann–Kendall test with Sen's slope analysis confirms a noticeable decline in groundwater levels within high to moderately high GWDR zones, supporting the study's findings. These findings significantly impact water resource management in semi-arid regions, emphasising the need for proactive measures to address evolving groundwater drought risks.

Analysis of trends and abrupt changes in groundwater and meteorological droughts in the United Kingdom

In recent years, the escalating impact of climate change, coupled with growing concerns regarding groundwater drought, has underscored the critical importance of analyzing hydrological trends. This is a foundational step in advancing effective water resource planning and management. This study presents a regional investigation of groundwater and meteorological droughts in the UK, utilizing the Standardized Groundwater Index (SGI) and the Standardized Precipitation Index (SPI). The analysis encompasses drought duration and an evaluation of the correlation between the SGI and SPI. Subsequently, the seasonal Mann-Kendall (MK) test and the Bayesian Changepoint Detection and Time Series Decomposition (BEAST) algorithm were employed to assess overall trends for both SGI and SPI. Additionally, these methods were utilized to pinpoint trend and seasonal abrupt changes within the time series of both indices.The seasonal MK test revealed statistically significant trends in many SGI stations across the UK, spanning from Northern Scotland down to the South of England, encompassing both increasing and decreasing trends. Additionally, the MK test showed slightly positive trends at the low SPI time scale, though these were not statistically significant. In contrast, more consistent and significant increasing trends were observed throughout the entire UK, except for Northern Scotland, which exhibited negative trends. Despite indications of a progressively more humid UK, the BEAST analysis enabled the detection of decreasing abrupt changes in trends. These changes result in sudden transitions from wet to dry conditions, which can be challenging to identify using the MK test alone. However, BEAST revealed both positive and negative abrupt changes across the entire UK, as well as positive or negative variations in seasonality. This suggests a high degree of variability in seasonal patterns. In summary, this study provides a detailed assessment of groundwater and meteorological drought trends and abrupt changes, considering the impact of climate change on various regions of the UK.

Estimation of Groundwater Drought Propagation in the Selected Districts of Palar River Basin, Southern Peninsular India

Estimation of Groundwater Drought Propagation in the Selected Districts of Palar River Basin, Southern Peninsular India

Assessment of future trends and spatial orientation of groundwater resources as an essential climate variable in the Ganga basin

The dynamic climatic conditions, and anthropogenic activities have a significant impact on the Ganga basin, which is crucial for sustaining the eco-hydrological processes within the agricultural landscape of northern India. Additionally, the Ganga basin represents one of the largest and most densely inhabited aquifer systems globally. Inadequate management practices of groundwater in the current context are likely to contribute to a shortage of food insecurity in the near future. The multi-point statistics using non-parametric test based on the well data point for the period of 1996–2016, were analysed and geoprocessed for the seasonal (pre-monsoon, monsoon, post-monsoon) trend of groundwater depth, fluctuation, and drought in the Ganga basin. The zonal statistical mean of groundwater depletion/loss were maximum observed in post-monsoon season (10 ± 21 cm/year), followed by pre-monsoon(9 ± 22 cm/year), and monsoon (9 ± 21 cm/year). The groundwater depth fluctuation trend magnitude in the Ganga basin ranges from −0.57 m/year to 0.85 m/year, having mean of −0.01 ± 0.07 m/year. The areal percentage of the Ganga basin showing the groundwater drought propagation regions for the pre-monsoon, monsoon, and post-monsoon are found as 74.86%, 74.17%, and 78.52%, respectively. The severity of groundwater drought increased 22 times during pre-monsoon, 9 times in monsoon, and 7 times in the post-monsoon season from 1996 to 2016. The negative groundwater depth fluctuation leads to the groundwater mining at an alarming rate, and formation of dark zone. The decadal trend migration of the mean center shows high spatial variability based on the overexploitation of groundwater. The spatial mapping of declining depth, and increasing drought magnitude of groundwater leads to the groundwater scarcity in most of the regions, which could affect to achieve UN-SDG, Sendai Framework for Disaster Risk Reduction (SFDRR) (2015–2030).

Modelling impacts of climate change on snow drought, groundwater drought, and their feedback mechanism in a snow-dominated watershed in western Canada

Snow accumulation and its melt are key hydrological processes in cold watersheds, which can affect groundwater (GW). With climate change projected to alter snow processes in these regions, understanding their impacts on the development of droughts is vital. A deficit in snow precipitation or accelerated snowmelt due to warming can trigger snow drought, potentially leading to GW drought. To investigate this relationship at a watershed scale, we coupled the Soil and Water Assessment Tool (SWAT) and MODFLOW to simulate various surface and groundwater processes under historical (1980–2013) and future (2040–2073) warming scenarios in western Canada. We calibrated, validated, and verified our models using streamflow, GW heads, and snow depth data from multiple hydrometric stations, observation wells, and a grid product. Using simulated data, we analyzed snow and GW drought characteristics, their dominant physical processes, and GW response time across eco-hydro(geo)logical regions like Mountains, Foothills, and Plains. Historical data show that Mountains are experiencing more snow droughts while Plains are facing greater GW droughts. However, future scenarios suggest increased snow droughts in all regions and a shift towards more severe GW droughts in Plains. Historical response time of GW to changes in snow processes spans 4–6 months from Mountains to Plains, with projected reductions in Mountains and Foothills, and a slight increase in Plains in the future. The dominant physical processes controlling GW response across all regions are soil moisture and percolation, with curve number displaying more significance in Mountains, and water yield exerts more control in Foothills and Plains. During cold seasons, SWE and snowmelt had minimal impact on GW response in Plains, while they presented a major role in Mountains. This study lays the basis for further research on snow-groundwater interactions in cold watersheds, aiding water resource management in mid-to-high latitude regions and providing a unified framework for analyzing snow and GW drought relationships.

Assessment of meteorological, hydrological and groundwater drought in the Konya closed basin, Türkiye

Water scarcity, and drought in particular, is a major challenge worldwide, causing direct and indirect negative effects on ecological systems and water resources, as well as social and economic aspects of life. Climate change and increasing human pressure are contributing to increasing the likelihood of droughts, impacting regions which were not used to be dry. To address this challenge properly, studies should be performed at a multi-scale level, addressing hydrological and hydrogeological drought. Focusing on the Konya Closed Basin in Türkiye, data derived from nineteen stations were used to analyze drought conditions, looking at multiple meteorological—Standardized Precipitation Index (SPI), Z Score Index (ZSI), China Z Index (CZI), Modified China Z Index (MCZI)—hydrological—Streamflow Drought Index (SDI), Surface Water Supply Index (SWSI)—and hydrogeological—Standardized Groundwater Level Index (SGI)—assessment indices for different time scales (1, 3, 6,12, 24, 36 and 48 months). The results show that extreme drought (ED) conditions computed by SPI at 1 month (1.9–2.5%) were higher than that of all indices for all stations. Moderate drought occurred at least according to the ZSI-1. It was determined that the percentage of SPI and CZI had greater Extremely, Severely and Moderately Dry events (10.7–13.4% for CZI; 10.7–14.2% for SPI) than those of ZSI and MCZI. On the other part, MCZI has shown fewer total drought events (6.2–10%). Since 2008, extremely dry conditions in the Konya Closed Basin are generally caused by groundwater drought, which is higher than meteorological and hydrological droughts. The results reported in this work might help in better planning drought-resilient strategies in the basin, which will be paramount in light of climate change.

Assessing groundwater drought vulnerability through baseflow separation and index-based analysis under climate change projections

Groundwater is an essential resource, providing drinking water to millions of people globally and supporting agriculture, industry, and ecosystems. It acts as a drought buffer and provides a stable source of water during dry periods. However, groundwater is often underestimated and overexploited, leading to aquifer depletion and other negative impacts. Groundwater is invisible and difficult to measure, which makes it challenging for sustainable management. This study employed the Soil & Water Assessment Tool (SWAT) hydrological model to simulate streamflow in an eastern Afghan watershed using satellite-based and reanalysis data. Variables from five Coupled model intercomparison project phase 6 (CMIP6) general circulation models (GCMs), including minimum and maximum temperature and precipitation, were bias-corrected (downscaled) using Quantile Mapping (QM). The results from these five GCMs were then compared based on statistical criteria to determine the most reliable model, and the selected model was used to derive future data (i.e., 2021 to 2050) under different climate scenarios after applying QM. Using the future climate data and the developed SWAT model, streamflow values were projected for future periods. Considering that there are various methods to estimate groundwater recharge and the most commonly used technique involves baseflow separation procedures, this study used seven baseflow separation methods to estimate groundwater recharge values based on streamflow time series from the baseline period and different climate scenarios. To evaluate the groundwater drought characteristics, a recently proposed index called the Groundwater Recharge Drought Index (GRDI) and the run theory method were used. Results indicated that the SWAT model performed well in simulating and predicting streamflow. The sixth version of the Model for Interdisciplinary Research on Climate (MIROC6) provided the highest accuracy compared to other climate models. Moreover, it was observed that the baseflow separation methods significantly affected the estimation of groundwater recharge, thus the obtained results from all developed methods were considered. The log-normal function was found to provide the best correlation for estimating GRDI. The findings revealed a gradual decrease in drought duration in the coming years. However, the magnitude and severity of droughts are projected to increase, especially under pessimistic climate scenarios.