Global analysis of the correlation and propagation among meteorological, agricultural, surface water, and groundwater droughts

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.

Propagation pathways from meteorological to agricultural drought in different climatic basins in iran

The propagation of meteorological drought (MD) to agricultural drought (AD) is influenced by various factors, particularly the climate type. This research examined the characteristics of drought propagation, encompassing propagation rates, lag time, and response time, from MD to AD within the context of Iran's diverse climate conditions. This was accomplished using three crucial meteorological and agricultural drought indices, namely the Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI), and Standardized Soil Moisture Index. The research data included in-situ and ERA5 datasets from 30 basins (catchments) across Iran in the 1979-2021 period. Based on reports from the Global Drought Observatory, the three most prominent MD events were identified in 1999-2002, 2008-2009, and 2017-2019. The correlation coefficients between MD and AD indices across various timescales, in climates ranging from hyper-arid to humid, exhibited a decline from 0.75 to 0.44. The response time, varying between 2.42 to 6.63 months, was determined by the strong correlation between SPI (or SPEI) and SSI1 (or SSI2) within the studied basins. Furthermore, the lag time, which was affected by the onset of MD and AD events, fluctuated between 2 to 6 months in hyper-arid and arid basins, revealing a 1-3 month variation compared to humid basins. The findings on propagation rates highlighted heightened sensitivity or response from meteorological to agricultural drought in humid climates, as opposed to hyper-arid climates. In light of these outcomes, comprehending the transition from MD to AD holds substantial significance for predicting, issuing early warnings, and fortifying preparedness in managing drought risks.

Soil moisture inversion based on multiple drought indices and RBFNN: A case study of northern Hebei Province

Drought has a significant impact on crop growth and productivity, highlighting the critical need for precise and timely soil moisture estimation to mitigate agricultural losses. This study focuses on soil moisture retrieval in northern Hebei Province during July 2012, utilizing eight widely employed remote sensing drought indices derived from MODIS satellite data. These indices were cross-referenced with measured soil moisture levels for analysis. Based on their correlation coefficients, a composite remote sensing drought index set comprising six indices was identified. Furthermore, a radial basis function neural network (RBFNN) was employed to estimate soil relative humidity. The accuracy evaluation of the soil moisture estimation model, which integrates multiple remote sensing drought indices and the RBFNN, demonstrated clear superiority over models relying on single drought indices. The model achieved an average estimation accuracy of 87.54 % for soil relative humidity at a depth of 10 cm (SM10) and 87.36 % for a 20 cm depth (SM20). The root mean square errors (RMSE) for the test sets were 0.093 and 0.092, respectively. Validation results for July 2013 indicated that the inversion accurately reflected the actual soil moisture conditions, effectively capturing dynamic moisture changes. These results fully verify the reliability and practicability of the model. These findings introduce a novel approach to local agricultural soil moisture estimation, with significant implications for enhancing agricultural water resource management and decision-making processes.

Multifractal characterization of meteorological to agricultural drought propagation over India

Agricultural drought affects the regional food security and thus understanding how meteorological drought propagates to agricultural drought is crucial. This study examines the temporal scaling trends of meteorological and agricultural drought data over 34 Indian meteorological sub-divisions from 1981 to 2020. A maximum Pearson's correlation coefficient (MPCC) derived between multiscale Standardised Precipitation Index (SPI) and monthly Standardised Soil Moisture Index (SSMI) time series was used to assess the seasonal as well as annual drought propagation time (DPT). The multifractal characteristics of the SPI time series at a time scale chosen from propagation analysis as well as the SSMI-1 time series were further examined using Multifractal Detrended Fluctuation Analysis (MF-DFA). Results reveal longer average annual DPT in arid and semi-arid regions like Saurashtra and Kutch (~ 6 months), Madhya Maharashtra (~ 5 months), and Western Rajasthan (~ 6 months), whereas, humid regions like Arunachal Pradesh, Assam and Meghalaya, and Kerala exhibit shorter DPT (~ 2 months). The Hurst Index values greater/less than 0.5 indicates the existence of long/short-term persistence (LTP/STP) in the SPI and SSMI time series. The results of our study highlights the inherent connection among drought propagation time, multifractality, and regional climate variations, and offers insights to enhance drought prediction systems in India.

Differences in drought characteristics, progression, and recession across ecosystem types in the pantropical region of the Lancang-Mekong River Basin

Exploring the development and impacts of drought across different ecosystems can offer new insights for mitigating the adverse effects of drought events. Using the pantropical Lancang-Mekong River Basin as the study region, we investigated the agricultural, ecological, and hydrological drought characteristics and explored their drought progression and recession rates across four vegetation ecosystem types: tropical forests, subtropical forests, shrubs, and crops. We utilized newly developed drought indices based on the ERA5-Land reanalysis dataset, GOSIF chlorophyll fluorescence data, and modified Moderate Resolution Imaging Spectroradiometer (MODIS) land cover data. The results showed that agricultural and hydrological droughts exhibited increasing trends from 2001 to 2021, whereas ecological drought displayed a decreasing trend over the same period. The cropland region experienced the fewest drought events, shortest drought durations, slowest progression rates, and lowest recession rates. By contrast, the two evergreen, broadleaf forest ecosystems (subtropical and tropical forests) experienced the highest number of drought events and fastest progression and recession rates. The findings suggest a trade-off relationship between vegetation resistance and recovery, where faster drought onset is associated with faster drought recession for ecological drought. Given the more severe challenges posed by agricultural and hydrological droughts, the riparian countries in the Lancang-Mekong River Basin should adopt proactive financial and management measures to mitigate the adverse impacts of these drought types. The insights gained from this study can inform the development of targeted strategies for drought monitoring, preparedness, and response across diverse ecosystems.

How Human Activities Affect Groundwater Storage

Despite the recognized influence of natural factors on groundwater, the impact of human activities remains less explored because of the challenges in measuring such effects. To address this gap, our study proposes an approach that considers carbon emissions as an indicator of human activity intensity and quantifies their impact on groundwater storage. The combination of carbon emission data and groundwater storage data for 17,152 grid cells over 16 years in 4 typical basins shows that they were generally negatively correlated, whereas both agriculture and aviation had positive impacts on groundwater storage. The longest impact from aviation and agriculture can even persist for 7 years. Furthermore, an increase of 1 Yg CO2/km2 per second in emissions from petroleum processing demonstrates the most pronounced loss of groundwater storage in the Yangtze River Basin (approximately 4.1 mm). Moreover, regions characterized by high-quality economic development tend to have favorable conditions for groundwater storage. Overall, our findings revealed the substantial role of human activities in influencing groundwater dynamics from both temporal and spatial aspects. This study fills a crucial gap by exploring the relationship between human activities and groundwater storage through the introduction of a quantitative modeling framework based on carbon emissions. It also provides insights for facilitating empirical groundwater management planning and achieving optimal emission reduction levels.

Response of tree growth to drought variability in arid areas: Local hydroclimate and large-scale precipitation

The impact of drought on terrestrial ecosystems is increasing, and the spatiotemporal heterogeneity of drought changes exacerbates the difficulty of determining ecosystem responses, especially in arid regions far from oceans. Tree rings have been widely used to understand how forest ecosystems respond to drought. However, the link between local hydroclimate variations related to tree rings and large-scale climate changes is not clear in the Qilian Mountains. Here, we used the tree ring width index to analyze the trend of Picea crassifolia growth and its relationship with climate in the middle Qilian Mountains. The results showed that the radial growth trend of Picea crassifolia is synchronized in the middle Qilian Mountains by calculating the Gleichläufigkeit index (GLK). Our analyses indicated that tree radial growth is positively correlated with drought during the growing season. Tree growth responds stably to drought (scPDSI and SPEI) and precipitation but unstably to temperature during 1950-2019. We further traced the meteorological factors that cause regional drought changes associated with radial growth. An increased total precipitation and decreased evaporation contribute to drought alleviation, favoring an increased tree radial growth. The increased total precipitation is mainly due to increased large-scale precipitation, which is related to water vapor transport changes. This study attempts to explore the influence of large-scale meteorology on regional drought change and its related tree radial growth response, which helps us to better understand the changes in forest ecosystems under climate change.

Assessing the role of drought in dust storm formation in the Tigris and Euphrates basin

Drought is a common meteorological phenomenon and one of the world's most costly natural hazards. A large part of the Tigris and Euphrates basin (TEB) is located in the arid and semi-arid regions of western Asia and suffers from drought. Drought has many destructive effects on the environment and human societies, among which the formation of dust storms, is a major global challenge. This study aims to figure out the role of different types of drought on dust storm formation in the TEB. Standardized precipitation index (SPI), Tasseled Cap greenness index, and surface water area changes based on time series of satellite remote sensing data were considered as proxies to investigate meteorological, agricultural, and hydrological droughts, respectively. Our results show that the continuation of the 5-month and 27-month meteorological droughts are followed by agricultural and hydrological droughts, respectively. In recent decades, the TEB has experienced two prominent drought periods in 2008-2012 and 2021-2022, resulting in a 214 % and 200 % increase in dust events, respectively, compared to the 23-year (2000-2022) average. Overall, 84 %, 10 %, and 6 % of the TEB dust events can be attributed to meteorological, agricultural, and hydrological droughts, respectively.

Understanding drought propagation through coupling spatiotemporal features using vine copulas: A compound drought perspective

Accurately evaluating drought impact on agriculture poses a challenge to regional food security, particularly in compound drought (i.e., meteorological and agricultural drought co-occurring) scenarios. This study presents a novel approach utilizing Vine copula for coupling spatiotemporal features to evaluate drought propagation. Three-dimensional clustering method was employed to identify meteorological and agricultural drought events, which excelled in capturing dynamic evolution characteristics (duration, area, severity, etc.) as well as integrating them into comprehensive meteorological drought intensity (IMD) and agricultural drought intensity (IAD). Through spatiotemporal matching, compound drought events were extracted from the meteorological-agricultural drought event pairs. From compound drought perspective, compound duration (CD) and compound area (CA) were devised to characterize drought propagation potential across time and space. Finally, the Vine copula method was employed to model the interdependence between four key coupling features, namely IMD, IAD, CD, and CA, and evaluate the probability of triggering agricultural drought with different intensity levels. Results showed that CD and CA can respectively characterize the temporal and spatial accumulation scale of drought propagation. At a certain IMD level, CD significantly influences the propagation probability (i.e., "stratification" phenomenon), while CA increases the probability proportionally. Probability evaluation lacking spatiotemporal information may underestimate the likelihood of drought propagation characterized by "low-IMD" but "long-CD" or "large-CA". The four-dimensional Vine copula structure can effectively couple dependence relationships of compound drought characteristics, and exhibits reliable robustness. This research provides stakeholders accurate probabilistic evaluation under compound drought scenarios, offering new insight into drought propagation.

Spatial and temporal variations of ecosystem water use efficiency and its response to soil moisture drought in a water-limited watershed of northern China

Drought synchronously affects the water cycle and interferes with the carbon cycle in terrestrial ecosystems. Ecosystem water use efficiency (WUE), serving as a vital metric for assessing the interplay between water and carbon cycles, has found extensively use in exploring how ecosystems responses to drought. However, the effects of soil moisture drought on WUE are still poorly recognized. Taking Ziya River Basin as an example, the spatial-temporal variations of WUE from 2001 to 2020 were estimated by the Penman-Monteith-Leuning Version 2 (PML-V2) data. Based on the Standardized Soil Moisture Index (SSI) calculated from Soil Moisture of China by in situ data, version 1.0 (SMCI1.0) data, the sensitivity and thresholds of different vegetation WUE to drought magnitudes were investigated, and the influences of both lagged and cumulative effects of drought on WUE were further analyzed. Results showed that the annual mean WUE was 2.160 ± 0.975 g C kg-1 H2O-1 in the Ziya River Basin, with a significant increasing trend of 0.037 g C kg-1 H2O-1 yr-1 (p < 0.05). For all the vegetation types, the WUE reached the maximum value at a certain drought threshold (SSI = -1.5 ± 0.1). The dominant factor controlling WUE sensitivity to drought changed from evapotranspiration (ET) to gross primary production (GPP) when severe drought transformed into extreme drought. Significant lagged and cumulative effects were found in the response of WUE to drought in nearly 58.64 % (72.94 %) of the study area, with an average time scale of 6.65 and 2.11 months (p < 0.05) respectively. Drought resistance in descending order was: forest > shrub > grassland > cropland. Our findings enrich the understanding of the coupled carbon and water cycle processes in terrestrial ecosystems and their response to soil moisture drought in the context of global climate change.

A comprehensive drought index based on spatial principal component analysis and its application in northern China

In the background of the greenhouse effect, drought events occurred more frequently. How to monitor drought events scientifically and efficiently is very urgent at present. In this study, we employed the Vegetation Water Supply Index (VSWI), Temperature Vegetation Drought Index (TVDI), and Crop Water Stress Index (CWSI) as individual variables to construct a composite drought index (CDI) using spatial principal component analysis (SPCA). The validity of CDI was assessed using gross primary productivity (GPP), soil moisture (SM), Standardized Precipitation Evapotranspiration Index (SPEI), and Vegetation Condition Index (VCI). CDI was subsequently used for drought monitoring in northern China from 2011 to 2020. The results showed that (1) at a 99% confidence level, the Pearson correlation coefficients between CDI and GPP was 0.72, while the value between CDI and SM was 0.69, which indicated the relationship between SM, GPP, and CDI was significant. (2) We compared CDI with other variables such as Standardized Precipitation Evapotranspiration Index (SPEI) and Crop Drought Index (CDI) and found that the monitoring result of CDI was more sensitive, which indicated that the proposed CDI had a better effect in local drought monitoring. (3) The results of CDI showed that the drought status in the northern region during 2011-2020 lasted from March to October, and the high severe drought period generally occurs in March-May and September-October, with low severe drought in June-August.

Dryness stress weakens the sustainability of global vegetation cooling

Dryness stress can limit vegetation growth, and the cooling potential of vegetation will also be strongly influenced. However, it is still unclear how dryness stress feedback weakens the sustainability of vegetation-based cooling. Based on the long-time series of multi-source remote sensing product data for the period 2001-2020, the relative contribution rate, and the method of decoupling and boxing, we determined that greening will likely mitigate global warming by 0.065 ± 0.009 °C/a, but nearly 47 % of the area is unsustainable. This phenomenon is strongly related to dryness stress. The restricted area of soil moisture (SM: 68.35 %) to vegetation is larger than that of the atmospheric vapor pressure deficit (VPD: 34.19 %). With the decrease in SM, vegetation will decrease by an average of 14.9 %, and with the increase in VPD, vegetation will decrease by 3.8 %. With the continuous increase in the dryness stress area, the sustainability of the vegetation cooling effect will be threatened in an area of about 21.03 million km2, which is equivalent to the area of North America. Specifically, we found that with the decrease in SM and the increase in VPD, the contribution of vegetation to the cooling effect has been weakened by 10.8 %. This conclusion confirms that dryness stress will threaten the sustainability of vegetation-based climate cooling and provides further insight into the effect of dryness stress on vegetation cooling.

GRACE-based groundwater drought in the Indochina Peninsula during 1979-2020: Changing properties and possible teleconnection mechanisms

Groundwater is very important for human productivity and daily life, hydrological cycle regulation, and ecosystem stability. However, due to the complex mechanisms of groundwater drought, the spatial and temporal variations of groundwater drought and its driving mechanisms are still not fully understood, especially in Indochina Peninsula. In this work, we used a reconstructed long-term terrestrial water storage dataset from the Gravity Recovery and Climate Experiment (GRACE) emission and a GRACE-based groundwater drought index to investigate the spatial and temporal characteristics of groundwater drought during 1979-2020 in the Indochina Peninsula. The possible teleconnection mechanisms between groundwater drought and the Indian Ocean Dipole (IOD), El Niño-Southern Oscillation (ENSO), and El Niño Modoki (ENSO_M) were also investigated using cross wavelet transform method. The results show that groundwater drought worsens significantly during 1979-2020, and becomes much more frequent and intensified after 2000 in the southern Indochina Peninsula. Both univariate and bivariate (logic 'or' and 'and') return periods for duration, severity, and peak of groundwater drought are short in the southern Indochina Peninsula, and thus the risk of groundwater drought is high. The IOD, ENSO, and ENSO_M can reduce the intensity of groundwater drought to a certain extent during the warm phases, but only ENSO_M tends to significantly exacerbate the intensity of groundwater drought during the cold phases in the southern Indochina Peninsula. The variations in groundwater drought are dominated by ENSO_M, and are also coupled influenced by the IOD and ENSO in the southern Indochina Peninsula. The results provide valuable information for the sustainable ecological environment and socioeconomic development, especially development of groundwater drought early warning and prediction models in the Indochina Peninsula.

The negative-positive feedback transition thresholds of meteorological drought in response to agricultural drought and their dynamics

There are complex bidirectional feedback relationships among different types of droughts (e.g., meteorological and agricultural droughts). As agricultural drought intensifies, meteorological drought response to agricultural drought may be changed from negative to positive feedback. Nevertheless, the negative-positive feedback transition thresholds of meteorological drought in response to agricultural drought and their dynamics have remained unsolved. Herein, we proposed a new quantitative method to characterize the mutual feedback between meteorological drought and agricultural drought based on the vine copula function for the first time in this study. The negative-positive feedback transition threshold and the sensitivity of the feedback were quantified under certain drought conditions. In order to investigate the feedback relationship dynamics under a changing environment, the total study period was evenly divided into two stages: stage 1 (1982-1999) and stage 2 (2000-2018). Finally, the random forest method was used to explore the dominant factors on the transition threshold. Results indicate that: (1) the negative-positive feedback transition thresholds in August is generally lower than June and July in mainland China, the basin with large threshold is the Southwest River Basin; (2) the sensitivity of meteorological drought in response to agricultural drought was higher in positive feedback than in negative feedback; (3) the transition thresholds of stage 2 was mostly reduced, while the feedback sensitivity of positive feedback was mostly increased; and (4) compared with the single factor, the land-meteorological coupling strength (the correlation between precipitation and soil moisture) dominants the negative-positive feedback transition threshold. This study sheds new insights into droughts feedback.

Monitoring agricultural drought in Peshawar Valley, Pakistan using long -term satellite and meteorological data

Monitoring agricultural drought across a large area is challenging, especially in regions with limited data availability, like the Peshawar Valley, which holds great agricultural significance in Pakistan. Although remote sensing provides biophysical variables such as precipitation (P), land surface temperature (LST), normalized difference vegetation index (NDVI), and relative soil moisture (RSM) to assess drought conditions at various spatiotemporal scales, these variables have limited capacity to capture the complex nature of agricultural drought and associated crop responses. Here, we developed a composite drought index named "Temperature Vegetation ET Dryness Index" (TVEDI) by modifying the Temperature Vegetation Precipitation Dryness Index (TVPDI) and integrating NDVI, LST, and remotely sensed evapotranspiration (ET) using 3D space and Euclidean distance. Several statistical techniques were employed to examine TVPDI and TVEDI trends and relationships with other commonly used drought indices such as the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), and standardized soil moisture index (SSI), as well as crop yield, to better understand how these indices captured the spatial and temporal distribution of agricultural drought in the Peshawar valley between 1986 and 2018. Results indicated that while the temporal patterns of the 3-month SPI, SPEI, and SSI generally align with those of TVEDI and TVPDI, TVEDI was more strongly correlated with these indices (e.g., correlation coefficient, r = 0.78-0.84 from TVEDI and r = 0.73-0.79 from TVPDI). Moreover, the crop yield, a measure of crop response to agricultural drought, demonstrated a significant positive correlation with TVEDI (r = 0.60-0.80), much higher than its correlation with TVPDI (r = 0.30-0.48). These outcomes indicate that the inclusion of ET in TVEDI effectively captured changes in soil moisture, crop water status, and their impact on crop yield. Overall, TVEDI exhibited enhanced capability to identify drought impacts compared to TVPDI, showing its potential for characterizing agricultural drought in regions with limited data availability.