Drought Index Research Articles

Drought Quantification in Africa Using Remote Sensing, Gaussian Kernel, and Machine Learning

Effective drought management requires precise measurement, but this is challenging due to the variety of drought indices and indicators, each with unique methods and specific uses, and limited ground data availability. This study utilizes remote sensing data from 2001 to 2020 to compute drought indices categorized as meteorological, agricultural, and hydrological. A Gaussian kernel convolves these indices into a denoised, multi-band composite image. Further refinement with a Gaussian kernel enhances a single drought index from each category: Reconnaissance Drought Index (RDI), Soil Moisture Agricultural Drought Index (SMADI), and Streamflow Drought Index (SDI). The enhanced index, encompassing all bands, serves as a predictor for classification and regression tree (CART), support vector machine (SVM), and random forest (RF) machine learning models, further improving the three indices. CART demonstrated the highest accuracy and error minimization across all drought categories, with root mean square error (RMSE) and mean absolute error (MAE) values between 0 and 0.4. RF ranked second, while SVM, though less reliable, achieved values below 0.7. The results show persistent drought in the Sahel, North Africa, and southwestern Africa, with meteorological drought affecting 30% of Africa, agricultural drought affecting 22%, and hydrological drought affecting 21%.

Total Polyphenol Content and Antioxidant Activity of Leaves and Fine Roots as Indicators of Drought Resistance in the Native Quercus robur and Alien Quercus rubra

Research Highlights: Environmental abiotic stressors generate secondary stresses in plants, such as osmotic and oxidative stresses, which negatively influence their normal growth, development, and metabolism. Research about other non-enzymatic components with antioxidant capacity has recently focused on polyphenols. However, their role as indicators of drought and shade tolerance in woody species leaves and roots has been poorly explored or was limited to leaves only. Background and Objectives: Under a scenario of increasing drought, understanding the seedling responses in terms of total polyphenols and their antioxidant activity, in particular at the fine root system level, may help to elucidate the native–alien species interaction. Materials and Methods: At the beginning of July, 5-month-old native Quercus robur and alien Quercus rubra seedlings were transferred indoors to the growth chamber and subjected to progressive soil drying for 21 days. Results: The decrease in soil water content was more pronounced for Q. robur (9%) than for Q. rubra (34% of field capacity). Leaf water potential significantly decreased over time in Q. robur but did not differ from the control in Q. rubra. The total polyphenol concentration in Q. robur was markedly lower in the leaves and significantly higher in the fine roots than in Q. rubra. For the leaves, both species showed markedly higher values if well-watered, and the values significantly decreased in response to drought only in Q. rubra. In contrast, the fine root values for both species were markedly higher if droughted and decreased significantly in time only in Q. robur. Differently from the polyphenol concentration, the antioxidant capacity of Q. rubra was always higher in both the leaves and fine roots. Conclusions: The higher antioxidant activity of the alien species Q. rubra revealed by this work, combined with its isohydric behaviour, could further shed some light on our understanding of its competitive performance at the seedling stage against the native Q. robur.

Intensifying droughts render more Sahel drylands unsuitable for cultivation

Two-fifth of the world's population will be confronted by dire land and water shortage for food production by 2050. Here we provide nuanced insights into the Sahel dryland dynamics and rationale behind its underperforming croplands amid climate extremes. We develop a gridded multi-criteria drought index for the growing season (June-October) and analyse its spatial and temporal degree of uniformity to designate the drought, climate and cultivable zones. Evidence is drawn from Sahelian Sudan, representing 1.03 million km2 of the African Sahel, during 1940–2020. Results show that cultivation of marginal lands has persisted apace. The peak areas of these marginal lands explain ∼50 % of the variations in crop yield, considering the two staple crops, sorghum and millet. Furthermore, the low yields mismatch the steadily growing planted areas of these crops. Compared to wet conditions, droughts expand (shrink) the median size of hyper-arid (arid) area by 466 % (46 %), limiting farming opportunities for 3.5–35.8 % of the croplands. The northernmost borderline of the arid zone determines the rainfed suitability, but potentially cultivable arid areas require contingency risk-reduction plans. Conversely, semi-arid and dry sub-humid zones reveal areas endowed with uniform climate. Skillful climate forecasting should thus guide policymaking towards sustainable agriculture therein. The paper suggests paths towards more effective agricultural policy interventions. Agricultural production entails the Sahel drought being defined in terms of agricultural impacts instead of meteorological conditions. Land use planners and inhabitants must relieve the plight of misconceiving and overlooking the fact of intrinsic interannual rainfall variability. Determining what a dangerous drought is for the Sahel agriculture sector or system is crucial. Sahel farming systems should opt for highly flexible agricultural practices based on the above-identified cultivable areas.

Observation-constrained projections reveal longer-than-expected dry spells

Climate models indicate that dry extremes will be exacerbated in many regions of the world1,2. However, confidence in the magnitude and timing of these projected changes remains low3,4, leaving societies largely unprepared5,6. Here we show that constraining model projections with observations using a newly proposed emergent constraint (EC) reduces the uncertainty in predictions of a core drought indicator, the longest annual dry spell (LAD), by 10–26% globally. Our EC-corrected projections reveal that the increase in LAD will be 42–44% greater, on average, than ‘mid-range’ or ‘high-end’ future forcing scenarios currently indicate. These results imply that by the end of this century, the global mean land-only LAD could be 10 days longer than currently expected. Using two generations of climate models, we further uncover global regions for which historical LAD biases affect the magnitude of projected LAD increases, and we explore the role of land–atmosphere feedbacks therein. Our findings reveal regions with potentially higher- and earlier-than-expected drought risks for societies and ecosystems, and they point to possible mechanisms underlying the biases in the current generation of climate models.

Re-evaluating drought indicators: Learning from small-scale farmers in South Africa

Re-evaluating drought indicators: Learning from small-scale farmers in South Africa

Agricultural Drought Monitoring: A Comparative Review of Conventional and Satellite-Based Indices

Drought is a natural hazard that causes significant economic and human losses by creating a persistent lack of precipitation that impacts agriculture and hydrology. It has various characteristics, such as delayed effects and variability across dimensions like severity, spatial extent, and duration, making it difficult to characterize. The agricultural sector is especially susceptible to drought, which is a primary cause of crop failures and poses a significant threat to global food security. To address these risks, it is crucial to develop effective methods for identifying, classifying, and monitoring agricultural drought, thereby aiding in planning and mitigation efforts. Researchers have developed various tools, including agricultural drought indices, to quantify severity levels and determine the onset and evolution of droughts. These tools help in early-stage forecasting and ongoing monitoring of drought conditions. The field has been significantly advanced by remote sensing technology, which now offers high-resolution spatial and temporal data, improving our capacity to monitor and assess agricultural drought. Despite these technological advancements, the unpredictable nature of environmental conditions continues to pose challenges in drought assessment. It remains essential to provide an overview of agricultural drought indices, incorporating both conventional methods and modern remote sensing-based indices used in drought monitoring and assessment.

Wildfire Burnt Area and Associated Greenhouse Gas Emissions under Future Climate Change Scenarios in the Mediterranean: Developing a Robust Estimation Approach

Wildfires burn annually over 400,000 ha in Mediterranean countries. By the end of the 21st century, wildfire Burnt Area (BA) and associated Green House Gas (GHG) emissions may double to triple due to climate change. Regional projections of future BA are urgently required to update wildfire policies. We present a robust methodology for estimating regional wildfire BA and GHG emissions under future climate change scenarios in the Mediterranean. The Fire Weather Index, selected drought indices, and meteorological variables were correlated against BA/GHG emissions data to create area-specific statistical projection models. State-of-the-art regional climate models (horizontal resolution: 12 km), developed within the EURO-CORDEX initiative, simulated data under three climate change scenarios (RCP2.6, RCP4.5, and RCP8.5) up to 2070. These data drove the statistical models to estimate future wildfire BA and GHG emissions in three pilot areas in Greece, Montenegro, and France. Wildfire BA is projected to increase by 20% to 130% up to 2070, depending on the study area and climate scenario. The future expansion of fire-prone areas into the north Mediterranean and mountain environments is particularly alarming, given the large biomass present here. Fire-smart landscape management may, however, greatly reduce the projected future wildfire BA and GHG increases.

Exploring Relationships Between Drought Indices and Ecological Drought Impacts Using Machine Learning and Explainable AI

Rangeland ecosystems in the United States hold great ecological, economic, and cultural value. However, the increasing frequency and severity of droughts pose potential threats to these ecosystems. This study used remotely sensed data, machine learning, and explainable artificial intelligence (XAI) to explore relationships between drought indices and vegetation health in the Cheyenne River Basin, USA. The study employed XGBoost Regressor and Extra Trees Regressor models in conjunction with SHapley Additive exPlanations (SHAP) to identify the most influential drought indices and environmental variables for predicting Normalized Difference Vegetation Index (NDVI), thereby uncovering indicators of vegetation stress in the basin. The XGBoost regressor was moderately successful at predicting NDVI, making the model suitable for subsequent XAI analysis using SHAP. SHAP results revealed that the Palmer Drought Severity Index (PDSI), the 90-day Standardized Precipitation Index (SPI), and snow water equivalent (SWE) were the most influential predictors of NDVI, indicating their strong association with changes in vegetation health in the Cheyenne River Basin. This study demonstrates the feasibility and value of applying XAI methods to investigate both the strength and directionality of ecological drought indicators—an approach that has been underutilized in drought research. These insights can inform future drought research, improve monitoring efforts, and help anticipate ecological drought impacts in the region.

Drought index time series forecasting via three-in-one machine learning concept for the Euphrates basin

Drought index time series forecasting via three-in-one machine learning concept for the Euphrates basin

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.

Assessing the Impact of Agricultural Practices and Urban Expansion on Drought Dynamics Using a Multi-Drought Index Application Implemented in Google Earth Engine: A Case Study of the Oum Er-Rbia Watershed, Morocco

Drought monitoring is a critical environmental challenge, particularly in regions where irrigated agricultural intensification and urban expansion pressure water resources. This study assesses the impact of these activities on drought dynamics in Morocco’s Oum Er-Rbia (OER) watershed from 2002 to 2022, using the newly developed Watershed Integrated Multi-Drought Index (WIMDI), through Google Earth Engine (GEE). WIMDI integrates several drought indices, including SMCI, ESI, VCI, TVDI, SWI, PCI, and SVI, via a localized weighted averaging model (LOWA). Statistical validation against various drought-type indices including SPI, SDI, SEDI, and SMCI showed WIMDI’s strong correlations (r-values up to 0.805) and lower RMSE, indicating superior accuracy. Spatiotemporal validation against aggregated drought indices such as VHI, VDSI, and SDCI, along with time-series analysis, confirmed WIMDI’s robustness in capturing drought variability across the OER watershed. These results highlight WIMDI’s potential as a reliable tool for effective drought monitoring and management across diverse ecosystems and climates.

A novel ecological evaluation index based on geospatial principles and remote sensing techniques

ABSTRACT The evaluation of regional ecological status has far-reaching significance for understanding regional ecological conditions and promoting sustainable development. Herein, a geospatial ecological index (GEI) was developed on the basis of Landsat data and the principles of soil lines and spatial geometry. Specifically, the GEI integrates four remote sensing indicators: Perpendicular Vegetation Index (PVI) representing greenness, Modified Perpendicular Drought Index (MPDI) representing drought, Normalized Difference Built-up and Soil Index (NDSI) representing the dryness of land surface, and Land Surface Temperature (LST) representing the hotness of land surface. Two typical regions, Fuzhou City and Zijin mining area, in Fujian Province, China, were selected to evaluate regional ecological quality via the proposed GEI. The results show an improvement in the overall ecological quality of Fuzhou City, with an increase in the average GEI value from 0.49 in 2001 to 0.53 in 2020. In the case of the Zijin mining area, regions with poor ecological status are concentrated in the main mining areas. However, the average GEI value rose from 0.51 in 1992 to 0.57 in 2020, illustrating an improvement in its ecological conditions. The study demonstrates the robustness and effectiveness of GEI, objectively revealing the spatial distribution and ecological status.

Multi-locus genome-wide association study for grain yield and drought tolerance indices in sorghum accessions.

Drought is a significant factor that causes yield loss in essential cereal crops such as sorghum [Sorghum bicolor (L.) Moench], necessitating the development of drought-tolerant varieties adaptable to various water conditions. This study aimed to pinpoint drought-tolerant sorghum lines and genomic regions for tolerance by utilizing 216 sorghum accessions in stressed and non-stressed environments at two locations. Genetic diversity was evident among accessions in terms of grain yield under different watering regimes. Drought stress indices such as the stress tolerance index, mean productivity, geometric mean productivity, harmonic mean productivity, yield stability index, and yield index were identified as effective measures for selecting drought-tolerant sorghum. Cluster analysis classified genotypes into four groups based on their association with grain yield, highlighting Acc. #28546 and Acc. #216739 as highly drought tolerant across environments. This study identified 32 and 22 quantitative trait nucleotides (QTNs) for drought indices and grain yield under stress and non-stress conditions, respectively, at two locations, with five common QTNs linked to multiple drought indices. Colocation analysis revealed that these QTNs were associated with known stay-green-related quantitative trait loci (QTLs), and 47 putative genes near these QTNs potentially influenced drought tolerance traits. It is suggested that accession selection considers multiple indices for robust evaluation. Understanding the identified genes and their functions provides insights into the genetic mechanisms governing plant responses to drought stress, offering prospects for developing improved drought-resistant sorghum varieties through further genetic research.

Applicability of Remote Sensing Techniques in Drought Predicting: A Case Study in Ben Tre Province, Vietnam

Background: Drought poses significant threats to ecosystems, agriculture and the environment, including reduced crop yields, habitat degradation and increased risk of forest fires and soil erosion. In addressing this issue, remote sensing technology (RST) has emerged as a valuable tool for studying and monitoring drought phenomena, offering advantages over traditional research methods. Methods: This study focuses on the application of RST, specifically utilizing Landsat satellite images, to calculate drought indices and develop drought maps for the Ben Tre province in Vietnam. The research involved the analysis of Landsat satellite images collected between 2000 and 2020, using various drought indices such as TCI, VCI, SAVI and WSVI. Result: By employing these indices, the study was able to classify the levels of drought and generate a drought map for the study area. The findings revealed that the coastal regions encompassing Thanh Phu, Ba Tri and Binh Dai districts face a high risk of drought during the dry season.

Development of soil water content retrieving method for irrigation agriculture areas using the red-edge band of Gaofen-6 satellite

Soil water content (SWC) is an essential index that reflects water conditions in the soil surface during drought, and changes in SWC are critical in guiding agricultural irrigation management. Remote sensing is a crucial means of monitoring SWC, and the red and near-infrared bands of optical remote sensing data are often used to monitor SWC through remote sensing retrieval. With the development of remote sensing technology, red-edge band has also been used to retrieve SWC. It exhibits higher sensitivity to changes in vegetation and SWC, relatively more precise differentiation between vegetation and soil, and more accurate SWC detection. However, there has been relatively little research regarding the use of the red-edge band for retrieving SWC. In this study, we developed a novel method, based on the principle of spectral space and the calculation of modified perpendicular drought index, to calculate the soil water index using the red-edge band of the Gaofen-6 (GF-6) satellite by comparing the retrieval accuracy of GF-6 using different combinations of red, near-infrared, and red-edge bands. Based on the measured SWC in the Shijin Irrigation District of Hebei Province, an SWC retrieval model was established using linear regression. Moreover, the new method was used to retrieve SWC from GF-6 and Sentinel-2 with the same central wavelength, and the reliability of the new method was compared and analyzed. The results revealed that the combination of red band and red-edge band 2 with a central wavelength of 0.74 μm substantial. improved the retrieval accuracy of SWC. Our study provides a new method for retrieving SWC using optical remote sensing, which can effectively improve retrieval accuracy and provide data support for agricultural irrigation monitoring and water management.

A Stepwise Multifactor Regression Analysis of the Interactive Effects of Multiple Climate Factors on the Response of Vegetation Recovery to Drought

In this study, a stepwise multifactor vegetation regression analysis (SMVRA) approach was proposed to investigate the interaction of multiple climate factors on vegetative growth in the study area from 2000 to 2020. It was developed by integrating the stepwise linear regression method, Standardized Precipitation Evapotranspiration Index (SPEI), Normalized Difference Vegetation Index (NDVI), and Pearson correlation coefficient. SMVRA can be used to intuitively understand the interactive effects of multiple correlated factors (e.g., temperature, precipitation, potential evapotranspiration, and the drought index) upon vegetation. The results show that the resilience of vegetation in the BLR basin is influenced by the severity of drought. Annual changes in SPEI over the BLR basin show an increasing trend, with rates of 3.12 × 10−2. Precipitation and NDVI had a strong positive correlation (p < 0.05), found for 34.93% of the total pixels in the study area. In the BLR basin, vegetation growth is inhibited in the 4 years following a drought event. The area near 800 m is most sensitive to drought events. It provides a theoretical basis for future drought response and effective vegetation restoration in the region.

Spatio-temporal assessment of multi-scalar meteorological and hydrological droughts over Bundelkhand, India

Drought, a recurrent natural hazard, significantly impacts agricultural productivity, water resources, and socio-economic development, especially in semi-arid regions like Bundelkhand in India. Here, frequent droughts challenge the livelihoods of its predominantly agrarian population. These challenges, compounded by poverty, land degradation, and resource conflicts, have profound implications for local communities and the attainment of Sustainable Development Goals (SDGs). Understanding the spatiotemporal conditions of drought is crucial for effective planning and management. This study evaluates meteorological and hydrological drought attributes at a high resolution within the region, using the Standardized Precipitation Index (SPI) and the Streamflow Drought Index (SDI) across a range of temporal scales. Findings indicate moderate droughts are more probable than severe or extreme droughts, with SPI-12 showing up to 20% probability over some grids, implying drought occurrence once in five years. Trend analysis reveals decreasing SPI trends in certain northern districts during monsoon months, suggesting more frequent droughts in vulnerable areas. The study also finds higher drought probabilities with longer SPI durations, highlighting significant temporal and spatial variability. Additionally, the applicability of SDI using GloFAS discharge data is evaluated, offering an alternative in the absence of gauge data. The combined SPI and SDI analysis underscores higher and more extensive drought probabilities for longer periods, emphasizing the need for multi-temporal scale assessment for effective drought management. This study enhances understanding of drought characteristics in the region, aiding policymaking for drought management and supporting evidence-based strategies to achieve SDGs, particularly those related to water security, climate resilience, and sustainable development.

Dependence of Drought Characteristics: Parametric and Non-parametric Copula Approach

Drought, which has harmful impacts both environmentally and economically, is one of the most devastating natural phenomena. In order to better understand and monitor the effects of drought, various methods have been developed in recent decades to quantify drought characteristics, with a primary focus on univariate drought indices. Mainly, drought characteristics are crucial to examine the impacts of drought in-depth on any specific area. This study endeavours to investigate univariate and bivariate drought indices using both parametric and non-parametric copula techniques. For that purpose, drought characteristics, such as duration, severity, mean intensity and peak intensity are analysed relying on different drought indices. The dependence among the main characteristics is evaluated and corresponding bivariate return period calculations are investigated. The data set used in this study is retrieved from monthly meteorological observations collected at five different Stations in Konya, located in the Central Anatolia Region of Turkey. Main numerical findings indicate the importance of using multiple drought indices for different geographical reasons for extreme dry periods.

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.

A unified streamflow drought index for both perennial and intermittent rivers at global scale

ABSTRACT The modelling of streamflow drought is usually performed with different approaches for perennial and intermittent rivers, as no-flow periods require a dedicated statistical treatment. Here, a unified drought index is proposed by extending the threshold method to all flow regimes. The method generalizes the approach commonly adopted for low-flow droughts by accounting for the probability of occurrence in a yearly reference period. Application to a global dataset for 1991–2020, derived from ERA5-forced LISFLOOD model simulations, demonstrates how consistent outcomes can be obtained in all major perennial and intermittent streams. A simplified version of the method is also discussed, detailing how reliable drought detection can be achieved in many parts of the world (e.g. central Europe, the eastern US for low-flow, and northern Russia and the Middle East for no-flow) even when the frequency distribution of the spells in time is replaced by the simpler average number of events.