Extent Of Drought Research Articles

The novel triangular spectral indices for characterizing winter wheat drought

Agricultural drought threatens food security and agricultural sustainable development. There have been numerous spectral indices from remote sensing images developed for monitoring crop drought. However, most present spectral indices are focusing on crop growth and Land Surface Temperature (LST), and the crop canopy water content are in less consideration simultaneously. Additionally, the Normalized Difference Vegetation Index (NDVI) is used for characterizing crop growth in almost all spectral drought indices, with the spectral saturation problem of NDVI for closed crop canopy. When vegetation cover is high, NDVI values tend to saturate, which makes them insensitive to further changes in crop health. Therefore, the NDVI saturation phenomenon may lead to an underestimation of the extent of crop drought, as it is not effective in identifying subtle changes in crops under high-density vegetation conditions. Hence, we propose three novel triangular spectral indices for characterizing winter wheat drought using three features including LAI, Land Surface Water Index (LSWI) and LST. For validating the proposed spectral indices, we compared the agreement between these indices with measured Relative Soil Moisture (RSM) and Volumetric Water Content (VWC) of soil in agricultural meteorological station and present popular indices including Crop Water Stress Index (CWSI), Temperature-Vegetation Drought Index (TVDI), and Vegetation Health Index (VHI). The results revealed that our proposed indices including Euclidean distance Crop Health Index (ECHI), Difference Crop Health Index (DCHI) and Perpendicular Water Stress Index (PWSI) outperformed the popular CWSI, TVDI and VHI, with stronger correlations with measured RSM and VWC in agricultural meteorological station. Secondly, there are spatial consistencies for characterizing winter wheat drought between proposed ECHI, DCHI and PWSI with popular CWSI, TVDI and VHI. In addition, our proposed ECHI, DCHI and PWSI have achieved good performance of drought monitoring both in irrigated and rainfed croplands. All these results suggest that our proposed indices have great potential in crop drought monitoring.

The effects of drought on biodiversity in UK river ecosystems: Drying rivers in a wet country

AbstractClimate change is interacting with water resource pressures to alter the frequency, severity and spatial extent of drought, which can thus no longer be considered a purely natural hazard. Although particularly severe ecological impacts of drought have occurred in drylands, its effects on temperate ecosystems, including rivers, are also considerable. Extensive research spanning a diverse range of UK rivers offers an opportunity to place the effects of past drought in the context of intensifying climate change and to examine the likely effects of future drought in a typically cool, wet country. Here, drought manifests instream as deficits in surface water, modified flow velocities, and—increasingly—partial or complete drying of previously perennial and naturally non‐perennial reaches. As a result, drought causes declines in the taxonomic and functional biodiversity of freshwater communities including microorganisms, algae, plants, invertebrates and fish, altering ecological processes and associated benefits to people. Although freshwater communities have typically recovered quickly after previous UK droughts, an increase in drought extremity may compromise recovery following future events. The risk of droughts that push ecosystems beyond thresholds to persistent, species‐poor, functionally simplified states is increasing. Research and monitoring are needed to enable timely identification of rivers approaching such thresholds and thus to inform interventions that pull these ecosystems back from the brink. Management actions that support natural flow regimes and promote natural processes that diversify instream habitats, including drought refuges, are also crucial to support biodiversity within functional river ecosystems as they adapt to a changing world.This article is categorized under: Water and Life > Nature of Freshwater Ecosystems Water and Life > Stresses and Pressures on Ecosystems Water and Life > Conservation, Management, and Awareness

Assessment the impact of drought on vegetation carbon source/sink at a spatial scale and identify the dominant factors in the Middle Reaches of the Yellow River Basin, China

Drought poses a severe threat to terrestrial ecosystems’ carbon sequestration function, which in turn affects regional sustainable development. Understanding its impact on carbon source/sink dynamics is crucial for addressing extreme climate change challenges. This study, based on the Carnegie-Ames-Stanford approach (CASA) model and soil heterotrophic respiration equation, the Net Ecosystem Productivity (NEP) of the middle reaches of the Yellow River Basin (MRYRB) from 2001 to 2019 were estimated and utilized to characterize the vegetation carbon source/sink. Additionally, four factors, namely, average surface temperature (AST), root-zone soil moisture (RM), drought frequency (DF), and drought intensity (DI), were selected and a multiscale geographically weighted regression (MGWR) model was applied to assess the dominant factors influencing vegetation carbon source/sink dynamics at the spatial scale. The results indicated that: (1) MRYRB vegetation acted as a carbon sink, showing an increasing trend. Carbon sink was primarily located in Shaanxi’s central region, while carbon source was primarily located in Inner Mongolia and Shaanxi’s northern part. (2) Both the extent and severity of drought in the MRYRB showed a mitigating trend, presenting a spatial distribution of high-intensity low-frequency and low-intensity high-frequency. (3) AST, RM, DF, and DI had larger effects on carbon source than carbon sink. AST dominated mainly in northern Shanxi and Shaanxi, RM in central Shaanxi and Gansu, while DI and DF were concentrated in central Shaanxi and northern Shanxi.

Impact assessment of agricultural droughts on water use efficiency in different climatic regions of Punjab Province Pakistan using MODIS time series imagery

AbstractDrought is the most destructive phenomenon that distresses the terrestrial carbon cycle balance and crop production. The variation in evapotranspiration (ET) and gross primary productivity (GPP) is a significant cause of agricultural drought effects on water use efficiency. This study aims to evaluate the impact of agricultural drought on WUE and it's anomalies in different climate regions. The standard vegetation index was used to measure the extent of agricultural drought. WUE was calculated using the ratio of ET, GPP, and climate classification using the De Martonne method. This study was conducted over the last 22 years, from 2001 to 2022. Meanwhile, 2001, 2002, 2014, and 2018 were considered high drought years based on a 22‐year analysis. According to remote sensing analysis of ET and GPP, the WUE increased throughout drought in all climate regions and more strongly in the arid zone than in humid regions. Humid areas were more vital due to drought than arid ones. WUE anomalies badge with increased drought severity across all climates except the very humid zone. Humid climates saw faster recovery times than arid ones, and areas experienced severe droughts. The findings of this research are essential for understanding the carbon and water cycles for agriculture management. The analysis of drought severity and WUE across arid climate regions helped analyse the varying impact of drought on WUE due to climate change. The significance of this study includes informing agricultural, water resource, and drought management planning in the Punjab Province, an agricultural region vulnerable to climate change. This study holds important learnings for agricultural regions worldwide. It has practical and scientific importance regarding agricultural systems' specific stresses and responses to droughts.

Harnessing SMAP satellite soil moisture product to optimize soil properties to improve water resource management for agriculture

Estimation of accurate soil physical and hydraulic properties are of prime importance for the management of water resources in agriculture-dominant regions. This study introduces a simplified framework for estimating soil physical and hydraulic properties crucial for managing agricultural water resources. The developed framework optimizes soil properties for the Regional Hydrological Extremes Assessment System (RHEAS) to enhance the performance of its core hydrological model, Variable Infiltration Capacity (VIC). These soil properties were optimized using six years (2015–2021) of satellite soil moisture observations from NASA’s Soil Moisture Active Passive (SMAP) mission with a modified Shuffled Complex Evolution (SCE-UA) optimization algorithm. A total of three most sensitive soil properties that control model soil moisture simulations, such as Ksat (Saturated hydraulic conductivity), expt (exponent parameter in Campbell’s equation for hydraulic conductivity), and bd (Bulk density) were optimized for the Lower Mekong River (LMR) basin. To better assess the impact of optimized soil properties, streamflow simulation as well as agricultural drought severity assessment, were estimated using the RHEAS framework’s VIC Routing module and Soil Moisture Deficit Index (SMDI) module, respectively. The streamflow simulation involved four approaches: an initial open-loop setup, one optimized with SMAP soil moisture data (SMAP), another optimized with actual streamflow data (Runoff), and a final one combining the previous two datasets (SMAP_Runoff). Switching from the initial setup to the SMAP-optimized model increased the Nash-Sutcliffe Efficiency (NSE) by 56.4 % and upgrading from the streamflow-optimized to the combined data model raised the NSE by 21.9 %. This showcases the benefits of optimizing soil properties for more accurate simulations. Furthermore, the optimized model accurately represented the severity and extent of historical agricultural droughts, aligning with regional drought reports of LMR basin. This framework offers a valuable tool for hydrological modeling and drought management, particularly in data-scarce and agriculture-intensive regions, informing agricultural water resource management, irrigation decision-making, and food security initiatives within the LMR basin and beyond.

Drought Spatial Extent and Dependence Increase During Drought Propagation From the Atmosphere to the Hydrosphere

AbstractAs droughts propagate both in time and space, their impacts increase because of changes in drought properties. Because temporal and spatial drought propagation are mostly studied separately, it is yet unknown how drought spatial extent and connectedness change as droughts propagate though the hydrological cycle from precipitation to streamflow and groundwater. Here, we use a large‐sample dataset of 70 catchments in Central Europe to study the propagation of local and spatial drought characteristics. We show that drought propagation leads to longer, later, and fewer droughts with larger spatial extents. 75% of the precipitation droughts propagate to P‐ET, among these 20% propagate further to streamflow and 10% to groundwater. Of the streamflow droughts, 40% propagate to groundwater. Drought extent and dependence increase during drought propagation along the drought propagation pathway from precipitation to streamflow thanks to synchronizing effects of the land‐surface but decreases again for groundwater because of sub‐surface heterogeneity.

The first global multi-timescale daily SPEI dataset from 1982 to 2021

Global warming accelerates water cycle, causing more droughts globally that challenge monitoring and forecasting. The Standardized Precipitation Evapotranspiration Index (SPEI) is used to assess drought characteristics and response time of natural and economic systems at various timescales. However, existing SPEI datasets have coarse spatial or temporal resolution or limited spatial extent, restricting their ability to accurately identify the start or end dates or the extent of drought at the global scale. To narrow these gaps, we developed a global daily SPEI dataset (SPEI-GD), with a 0.25° spatial resolution from 1982 to 2021 at multiple timescales (5, 30, 90, 180 and 360 days), based on the precipitation from European Center for Medium Weather Forecasting Reanalysis V5 (ERA5) dataset and the potential evapotranspiration from Singer’s dataset. Compared to widely used SPEIbase dataset, the SPEI-GD can improve the spatial-temporal resolution and the accuracy of SPEI in areas where meteorological sites are lacking. The SPEI-GD significantly correlates with site-based SPEI and soil moisture. Our dataset solidly supports sub-seasonal and daily-scale global and regional drought research.

Analysis of Meteorological Drought Changes in Afghanistan Using SPI Index

Meteorological drought, as a natural and significant phenomenon in the field of hydrometeorology, refers to the scarcity of precipitation and moisture in a specific region. This research was conducted with the objectives of understanding the intensity and duration of weather droughts in Afghanistan, categorizing the extent of weather droughts in Afghanistan, and detecting the trend of weather droughts across the country. To investigate the droughts in Afghanistan, daily precipitation data from the Afrodite database version V1101 was utilized, which covered the statistical precipitation records during the period of 1950-2015. Using the Standardized Precipitation Index (SPI) as a measure of drought severity, duration, and frequency, the status of droughts was examined. Based on the analysis, significant trends of droughts were observed in the northern, northeastern, southwestern, and northwestern regions of Afghanistan. Furthermore, the results indicated the occurrence of three periods: wet, normal, and drought, during the study period. The climatic conditions classification during our study period demonstrated that mild moist conditions occurred repeatedly in certain regions of Afghanistan, especially in provinces such as Khost, Paktia, Logar, Kabul, eastern Ghazni, Parwan, Panjshir, Bamyan, Kunduz, southern Takhar, Balkh, Jawzjan, Faryab, Badghis, and northern Herat. On the contrary, severe droughts were more prevalent in the eastern, northeastern, central, and southern regions of Afghanistan. Additionally, mild and moderate droughts were observed in many areas.

Climate change‐related shocks, assets and welfare outcomes in South Africa

AbstractClimate change and its consequences pose significant economic and social challenges around the world. Droughts have frequently afflicted South Africa, with the most recent severe drought occurring in 2015/2016. However, there has been little empirical research estimating the impact of climate‐related shocks on individual well‐being in South Africa. In this paper, we investigate the impact of the 2015/2016 drought on individual welfare. We also examine whether access to assets helps to mitigate the negative effects of climate change‐related shocks on individual welfare. For estimating the impact of droughts on individual welfare outcomes, weather data are combined with individual panel data from the National Income Dynamics Study dataset. We use weather data from meteorological satellites to measure the extent of droughts across the country, and we measure individual welfare using real per capita consumption expenditure. Our estimation results show that the 2015/2016 drought had no significant effect on real per capita consumption expenditure in South Africa. We hypothesise that this is due in part to the structure of the labour market, with few people relying on subsistence farming, combined with social grants and remittances being the primary source of income for people at the bottom of the consumption distribution. Using anthropometric measurements as an alternative welfare indicator, we find that children living in drought‐affected areas had lower weight‐for‐height measurements than those living in areas not affected by the drought. The findings imply that when food prices increased as a result of the drought, households may have chosen welfare‐costly coping techniques such as reducing the quantity and quality of food consumed while keeping overall expenditure the same.

Climate change determines the sign of productivity trends in US forests

Forests are integral to the global land carbon sink, which has sequestered ~30% of anthropogenic carbon emissions over recent decades. The persistence of this sink depends on the balance of positive drivers that increase ecosystem carbon storage-e.g., CO2 fertilization-and negative drivers that decrease it-e.g., intensifying disturbances. The net response of forest productivity to these drivers is uncertain due to the challenge of separating their effects from background disturbance-regrowth dynamics. We fit non-linear models to US forest inventory data (113,806 plot remeasurements in non-plantation forests from ~1999 to 2020) to quantify productivity trends while accounting for stand age, tree mortality, and harvest. Productivity trends were generally positive in the eastern United States, where climate change has been mild, and negative in the western United States, where climate change has been more severe. Productivity declines in the western United States cannot be explained by increased mortality or harvest; these declines likely reflect adverse climate-change impacts on tree growth. In the eastern United States, where data were available to partition biomass change into age-dependent and age-independent components, forest maturation and increasing productivity (likely due, at least in part, to CO2 fertilization) contributed roughly equally to biomass carbon sinks. Thus, adverse effects of climate change appear to overwhelm any positive drivers in the water-limited forests of the western United States, whereas forest maturation and positive responses to age-independent drivers contribute to eastern US carbon sinks. The future land carbon balance of forests will likely depend on the geographic extent of drought and heat stress.

Evapotranspiration and surface energy fluxes across Europe, Africa and Eastern South America throughout the operational life of the Meteosat second generation satellite

AbstractThe exchange of energy and water fluxes between the Earth's surface and the atmosphere is crucial to a series of processes that impact human life. Noteworthy examples are agriculture yields, water availability, intensity and extent of droughts and the ability of ecosystems to provide services to society. The relevance of these processes has motivated the Satellite Application Facility on Land Surface Analysis (LSA SAF) programme to set up an operational framework to estimate—among other variables—evapotranspiration (ET) and surface energy fluxes (SEF) on the basis of observations by the Meteosat Second Generation (MSG) satellite. The LSA SAF programme has recently launched the reprocessing of the ET and SEF datasets on the basis of the most recent version of the algorithm and homogenous forcing datasets. This article features the resulting ET/SEF dataset, a Data Record that encompasses the period from the start of the operational life of the MSG satellite (2004) till 2020 and covers the field of view of the MSG satellite (i.e. Europe, Africa and Eastern South America). Details on the algorithm and the datasets driving the ET/SEF estimates are also provided as well as a quality assessment.

Trout and invertebrate assemblages in stream pools through wildfire and drought

Abstract Climate change is increasing the frequency, severity, and extent of wildfires and drought in many parts of the world, with numerous repercussions for the physical, chemical, and biological characteristics of streams. However, information on how these perturbations affect top predators and their impacts on lower trophic levels in streams is limited. The top aquatic predator in southern California streams is native Oncorhynchus mykiss, the endangered southern California steelhead trout (trout). To examine relationships among the distribution of trout, environmental factors, and stream invertebrate resources and assemblages, we sampled pools in 25 stream reaches that differed in the presence (nine reaches) or absence (16 reaches) of trout over 12 years, including eight reaches where trout were extirpated during the study period by drought or post‐fire flood disturbances. Trout were present in deep pools with high water and habitat quality. Invertebrate communities in trout pools were dominated by a variety of medium‐sized collector–gatherer and shredder invertebrate taxa with non‐seasonal life cycles, whereas tadpoles and large, predatory invertebrates (Odonata, Coleoptera, Hemiptera [OCH]), often with atmospheric breather traits, were more abundant in troutless than trout pools. Structural equation modelling of the algal‐based food web indicated a trophic cascade from trout to predatory invertebrates to collector–gatherer taxa and weaker direct negative trout effects on grazers; however, both grazers and collector–gatherers also were positively related to macroalgal biomass. Structural equation modelling also suggested that bottom‐up interactions and abiotic factors drove the detritus‐based food web, with shredder abundance being positively related to leaf litter (coarse particulate organic matter) levels, which, in turn, were positively related to canopy cover and negatively related to flow. These results emphasise the context dependency of trout effects on prey communities and of the relative importance of top‐down versus bottom‐up interactions on food webs, contingent on environmental conditions (flow, light, nutrients, disturbances) and the abundances and traits of component taxa. Invertebrate assemblage structure changed from a trout to a troutless configuration within a year or two after trout were lost owing to post‐fire scouring flows or drought. Increases in OCH abundance after trout were lost were much more variable after drought than after fire. The reappearance of trout in one stream resulted in quick, severe reductions in OCH abundance. These results indicate that climate‐change induced disturbances can result in the extirpation of a top predator, with cascading repercussions for stream communities and food webs. This study also emphasises the importance of preserving or restoring refuge habitats, such as deep, shaded, perennial, cool stream pools with high habitat and water quality, to prevent the extirpation of sensitive species and preserve native biodiversity during a time of climate change.

Assessing CYGNSS Satellite Soil Moisture Data for Drought Monitoring with Multiple Datasets and Indicators

Drought monitoring is crucial for various sectors, and soil moisture data play a pivotal role, especially in agricultural contexts. This study focuses on the recent CYGNSS Level 3 soil moisture data derived from the NASA Cyclone Global Navigation Satellite System (CYGNSS), notable for its wide coverage and rapid revisit times, yet underexplored in drought research. Spanning from 1 January 2018 to 31 December 2022, this research analyzed daily CYGNSS soil moisture data, comparing them with the ERA5, SMAP, and GLDAS-NOAH datasets. It was found that the average and standard deviation (std) of CYGNSS soil moisture exhibited spatial patterns largely similar to other datasets, although some regions showed discrepancies (std differences reached up to 0.05 in some regions). The correlation coefficients and RMSE values between CYGNSS and other datasets depended on climate and land cover types. Four drought indicators from different soil moisture datasets were compared with the improved monthly Standardized Precipitation Evapotranspiration Index (SPEI). The drought indicators based on CYGNSS data demonstrate the capacity to describe drought extent and intensity. The correlation coefficients between certain drought indicators obtained from CYGNSS and SPEI reached 0.27 for drought percentage and 0.16 for drought intensity. Further investigations with selected extreme drought cases revealed that the indicator from CYGNSS data is relatively weak, influenced by the selected regions, times, and drought indicators. The results of this study provide insights into the potential application of CYGNSS soil moisture data in drought monitoring, offering a foundation for future research and practical implementation with current and future improved products.

Global high-resolution drought indices for 1981–2022

Abstract. Droughts are among the most complex and devastating natural hazards globally. High-resolution datasets of drought metrics are essential for monitoring and quantifying the severity, duration, frequency, and spatial extent of droughts at regional and particularly local scales. However, current global drought indices are available only at a coarser spatial resolution (>50 km). To fill this gap, we developed four high-resolution (5 km) gridded drought records based on the standardized precipitation evaporation index (SPEI) covering the period 1981–2022. These multi-scale (1–48 months) SPEI indices are computed based on monthly precipitation (P) from the Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS, version 2) and Multi-Source Weighted-Ensemble Precipitation (MSWEP, version 2.8), and potential evapotranspiration (PET) from the Global Land Evaporation Amsterdam Model (GLEAM, version 3.7a) and hourly Potential Evapotranspiration (hPET). We generated four SPEI records based on all possible combinations of P and PET datasets: CHIRPS_GLEAM, CHIRPS_hPET, MSWEP_GLEAM, and MSWEP_hPET. These drought records were evaluated globally and exhibited excellent agreement with observation-based estimates of SPEI, root zone soil moisture, and vegetation health indices. The newly developed high-resolution datasets provide more detailed local information and can be used to assess drought severity for particular periods and regions and to determine global, regional, and local trends, thereby supporting the development of site-specific adaptation measures. These datasets are publicly available at the Centre for Environmental Data Analysis (CEDA; https://doi.org/10.5285/ac43da11867243a1bb414e1637802dec) (Gebrechorkos et al., 2023).

Vegetation Stress Monitor—Assessment of Drought and Temperature-Related Effects on Vegetation in Germany Analyzing MODIS Time Series over 23 Years

Over the past two decades, and particularly since 2018, Central Europe has experienced several droughts with strong impacts on ecosystems and food production. It is expected that under accelerating climate change, droughts and resulting vegetation and ecosystem stress will further increase. Against this background, there is a need for techniques and datasets that allow for monitoring of the timing, extent and effects of droughts. Vegetation indices (VIs) based on satellite Earth observation (EO) can be used to directly assess vegetation stress over large areas. Here, we use a MODIS Enhanced Vegetation Index (EVI) time series to analyze and characterize the vegetation stress on Germany’s croplands and grasslands that has occurred since 2000. A special focus is put on the years from 2018 to 2022, an extraordinary 5-year period characterized by a high frequency of droughts and heat waves. The study reveals strong variations in agricultural drought patterns during the past major drought years in Germany (such as 2003 or 2018), as well as large regional differences in climate-related vegetation stress. The northern parts of Germany showed a higher tendency to be affected by drought effects, particularly after 2018. Further, correlation analyses showed a strong relationship between annual yields of maize, potatoes and winter wheat and previous vegetation stress, where the timing of strongest relationships could be related to crop-specific development stages. Our results support the potential of VI time series for robustly monitoring and predicting effects of climate-related vegetation development and agricultural yields.

The limits of stress-tolerance for zooplankton resting stages in freshwater ponds.

In seasonal environments, many organisms evolve strategies such as diapause to survive stressful periods. Understanding the link between habitat stability and diapause strategy can help predict a population's survival in a changing world. Indeed, resting stages may be an important way freshwater organisms can survive periods of drought or freezing, and as the frequency and extent of drought or freezing vary strongly among habitats and are predicted to change with climate change, it raises questions about how organisms cope with, and survive, environmental stress. Using Daphnia magna as a model system, we tested the ability of resting stages from different populations to cope with stress during diapause. The combination of elevated temperatures and wet conditions during diapause shows to prevent hatching altogether. In contrast, hatching is relatively higher after a dry and warm diapause, but declines with rising temperatures, while time to hatch increases. Resting stages produced by populations from summer-dry habitats perform slightly, but consistently, better at higher temperatures and dryness, supporting the local adaptation hypothesis. A higher trehalose content in resting eggs from summer-dry habitat might explain such pattern. Considering that temperatures and summer droughts are projected to increase in upcoming years, it is fundamental to know how resting stages resist stressful conditions so as to predict and protect the ecological functioning of freshwater ecosystems.

The Association between Meteorological Drought and the State of the Groundwater Level in Bursa, Turkey

This study addressed the intricate interplay between meteorological droughts and groundwater level fluctuations in the vicinity of Mount Uludag in Bursa, Turkey. To achieve this, an exhaustive analysis encompassing monthly precipitation records and groundwater level data sourced from three meteorological stations and eight groundwater observation points spanning the period from 2007 to 2018 was performed. Subsequently, this study employed the Standard Precipitation Index (SPI) and Standard Groundwater Level (SGL) metrics, meticulously calculating the temporal extents of drought events for each respective time series. Following this, a judicious application of both the Thiessen and Support Vector Machine (SVM) methodologies was undertaken to ascertain the optimal groundwater observation wells and their corresponding SGL durations, aligning them with SPI durations tied to the selected meteorological stations. The SVM technique, in particular, excelled in the identification of the most pertinent observation wells. Additionally, the Elman Neural Network (ENN) and its optimized version through the Firefly Algorithm (ENN-FA), demonstrated their prowess in accurately predicting SPI durations based on SGL durations. The results were favorable, as evidenced by the commendable performance metrics of the Normalized Root Mean Square Error (NRMSE), the Nash–Sutcliffe Efficiency (NSE), the product of the coefficient of determination and the slope of the regression line (bR2), and the Kling–Gupta Efficiency (KGE). Consequently, the favorable simulation results were construed as evidence supporting the presence of a discernible association between SGL and the duration of the SPI. As we substantiate the concordance between the temporal extent of meteorological droughts and the perturbations in groundwater levels, this unmistakably underscores the fact that the historical fluctuations in groundwater levels within the region were predominantly attributable to climatic influences, rather than being instigated by anthropogenic activities. Nevertheless, it is imperative to underscore that this revelation should not be misconstrued as an endorsement of future heedless exploitation of groundwater resources.

Drought analysis in southern region of Tamil Nadu using meteorological and remote sensing indices

Drought is a natural phenomenon caused due to inadequate rainfall over a region as compared to the expected amount, which when sustained over an extended period of time, eventually leads to shortage of water to sustain various human activities. One-month SPI showed that the southern zone is highly prone to moderate drought conditions. The seasonal analysis of SPI showed that the region faced more drought instances during the South West Monsoon compared with North East Monsoon season. Thoothukudi, Dindigul, Pudukkottai and Virudhunagar showed the high occurrences of drought at seasonal and annual scale. The weekly MAI calculated indicated a risk in the rainfed cropping season. Tirunelveli and Tenkasi showed highly vulnerable to moderate drought. NDVI during the NEM 2016, 2017 and 2018 showed that more than 80 per cent of the total area in the southern districts was under drought stress. NDVI analysis showed that Thoothukudi, Ramanathapuram, Pudukkottai, Sivagangai and Virudhunagar districts are highly vulnerable to drought. NDWI analysis during the NEM 2016, 2017 and 2018 showed high drought stresses with more than 90 per cent of the area showing drought stress during these three years. NDVI and NDWI analysis showed that the Southern Zone of Tamil Nadu was most vulnerable to Moderate and Severe droughts. The comparison of NDVI and NDWI and 3-, 6-, 9- and 12-month SPI showed that the three indices are fairly accurate with each other and hence are useful in the analysis of drought. However, just a single drought index cannot clearly define accurately the spatial and temporal extent of drought. Thus, a combination of meteorological and remote sensing indices gave a detailed idea about the spatio-temporal extent of drought.

Assessing the Spatial–Temporal Pattern of Spring Maize Drought in Northeast China Using an Optimised Remote Sensing Index

Northeast China plays a pivotal role in producing commodity grains. The precipitation and temperature distribution during the growth season is impacted by geographical and climate factors, rendering the region vulnerable to drought. However, relying on a single index does not reflect the severity and extent of drought in different regions. This research utilised the random forest (RF) model for screening remote sensing indices. Relative soil moisture (RSM) was employed to compare seven commonly used indices: the temperature vegetation dryness index (TVDI), vegetation supply water index (VSWI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), multi-band drought index (MBDI), and normalised difference drought index (NDDI). The effectiveness of these indices for monitoring drought during different developmental stages of spring maize was evaluated. Trend rates were employed to investigate the temporal changes in drought patterns of spring maize from 2003 to 2020, and the Sen + Mann–Kendall test was used to analyse spatial variations. The results showed the following: (1) The seven remote sensing indices could accurately track drought during critical growth stages with the TVDI demonstrating higher applicability than the other six indices. (2) The application periods of two TVDIs with different parameters differed for the drought monitoring of spring maize in different developmental periods. The consistency and accuracy of the normalised difference vegetation index (NDVI)-based TVDI (TVDIN) were 5.77% and 34.62% higher than those of the enhanced vegetation index (EVI)-based TVDI (TVDIE), respectively, in the early stage. In contrast, the TVDIE exhibited 13.46% higher consistency than the TVDIN in the middle stage, and the accuracy was the same. During the later stage, the TVDIE showed significantly higher consistency and accuracy than the TVDIN with consistency increases of 9.61% and 38.64%, respectively. (3) The drought trend in northeast China increased from 2003 to 2020, exhibiting severe spring drought and a weakening of the drought in summer. The southern, southwestern, and northwestern parts of northeast China showed an upward drought trend; the drought-affected areas accounted for 37.91% of the study area. This paper identified the most suitable remote sensing indices for monitoring drought in different developmental stages of spring maize. The results provide a comprehensive understanding of the spatial–temporal patterns of drought during the past 18 years. These findings can be used to develop a dynamic agricultural drought monitoring model to ensure food security.

Flash drought evaluation using evaporative stress and evaporative demand drought indices: a case study from Awash River Basin (ARB), Ethiopia

Drought is one of the most devastating phenomena that affect the livelihood of most communities in Ethiopia as they have low adaptive capacity. Recent advancements in remote sensing and drought investigations have made it possible to identify a new type of flash drought that has rapid intensification with a short duration (i.e., less than 1 month unlike conventional droughts). This study intends to identify flash drought in the Awash River Basin (ARB) based on Moderate Resolution Imaging Spectroradiometer (MODIS) data of actual evapotranspiration and potential evapotranspiration using Evaporative Demand Drought Index (EDDI) and Evaporative Stress Index (ESI) indices. The flash drought result exhibited that agricultural lands, grasslands, vegetation areas, and irrigational croplands along the river were vulnerable to flash drought in the ARB. Using ESI, the area of ARB that experienced flash drought in 2002, 2008, 2009, 2012, and 2015 were 23%, 40%, 20%, 40%, and 24%, respectively. These intense flash drought areas can be used as drought monitoring sites. The flash drought extent of EDDI is more compared to ESI because of ESI’s dependency on vegetation coverages and soil moisture. The lowland downstream part of the ARB is highly prone to flash drought, particularly in the major rainy season (MRS) and the last two months of the minor rainy season (mRS). EDDI can discern the onset of flash drought better compared to ESI, but both can be used as a drought early warning mechanisms to minimize agricultural losses and drought-associated risks in the basin.