Drought Assessment Research Articles

Unlocking the mysteries of drought: integrating snowmelt dynamics into drought analysis at the Narayani River Basin, Nepal

AbstractDrought is among the most impactful natural hazards, undermining water security, agriculture, and livelihoods worldwide. Analysing droughts in large catchments presents several unique challenges, primarily related to the complexity of land surface characteristics and data availability limitations. Conducting drought analysis in the Narayani River Basin, which encompasses a vast area within the Himalayan region of Nepal, is extremely challenging but crucial for maintaining the river basin's social, economic, and environmental balance. In response, this study develops a new combined drought index (CDI), integrating satellite-based reanalysis parameters [i.e., Land Surface Temperature (LST), Snow Cover (SC), and Normalised Difference Vegetation Index (NDVI)] with a meteorological parameter [i.e., Standardised Precipitation (std_prec)]. The novel CDI was applied at the Narayani Basin to assess the droughts over the 2004–2013 period, and the results were independently evaluated using streamflow observations to validate the accuracy of the novel drought index. The principal component analysis (PCA) technique was used to determine the contribution of input parameters to the multivariate drought index. The PCA results show a strong positive correlation (0.78) between the CDI and standardised streamflow, indicating the effectiveness of the novel index in monitoring drought conditions. Accordingly, it can be concluded that surface water availability is interdependent on landscape characteristics, such as LST, SC, and NDVI, in addition to the effects of precipitation. Also, the novel CDI can identify the specific drought-affected areas in the Narayani River Basin, offering insights into its drought characteristics beyond traditional drought assessment techniques.

Socio-economic assessment of drought impacts in Lesotho: implications for early action

Socio-economic assessment of drought impacts in Lesotho: implications for early action

Prediction and assessment of meteorological drought in southwest China using long short-term memory model

Abstract Drought prediction is crucial for mitigating risks and designing measures to alleviate its impact. Machine learning models have been widely applied in the field of drought prediction in recent years. This study concentrated on predicting meteorological droughts in southwest China, a region prone to frequent and severe droughts, particularly in areas with sparse meteorological station coverage. The long short-term memory (LSTM) predictive model, which is a deep learning model, was constructed by calculating standardized precipitation evapotranspiration index (SPEI) values based on 144 weather station observations from 1980 to 2020. The 5-fold cross-validation method was used for the hyperparameter optimization of the model. The LSTM model underwent comprehensive assessment and validation through multiple methods. This included the use of several accuracy assessment indicators and a comparison of results. The comparison covered different drought characteristics among the LSTM predictive model, the benchmark random forest (RF) predictive model, the historical drought situations, and the calculated SPEI values based on observations from 144 weather stations. The results showed that the training results of the LSTM predictive model basically agreed with the SPEI values calculated from weather station observations. The model-predicted variation trend of SPEI values for 2020 was similar to the variation in SPEI values calculated based on weather station observations. On the test set, the coefficient of determination (R 2), the root mean square error, the explained variance score, the Nash–Sutcliffe efficiency, and the Kling–Gupta efficiency were 0.757, 0.210, 0.802, 0.761, and 0.212, respectively. The total consistency rate of the drought grade was 59.26%. The spatial correlation distribution of SPEI values between LSTM model prediction and calculation from meteorological stations in 2020 was more than 0.5 for most regions. The correlation coefficients exceeded 0.6 in western Tibet and Chengdu Plains. Compared to the RF model, the LSTM model excelled in all five performance evaluation metrics and demonstrated a higher overall consistency rate for drought categories. The Kruskal–Wallis test for both the LSTM and RF models all indicated no significant difference in the distributions between the predicted and observed data. Scatter plots revealed that the prediction accuracy for both models in 2020 was suboptimal, with the SPEI showing a comparatively narrow range of values. Nonetheless, the LSTM model significantly outperformed the RF model in terms of prediction accuracy. In summary, the LSTM model demonstrated good overall performance, accuracy, and applicability. It has the potential to enhance dynamic drought prediction in regions with complex terrain, diverse climatic factors, and sparse weather station networks.

Future drought overestimations due to no constraints of CO2 physiological effect and land-atmosphere coupling on potential evapotranspiration

Abstract Various offline drought indices have been widely used to project dryness/wetness and drought changes. However, the results derived from these indices often differ from or even contradict observations and direct projections made by coupled climate models. Therefore, it is crucial to investigate this scientific debate thoroughly and identify the potential causes. This study adopts a water demand-side perspective, focusing on potential evapotranspiration (PET), to address such controversy. Employing the Standardized Precipitation-Evapotranspiration Index (SPEI), three PET models including the Food and Agriculture Organization of the United Nations’ report 56 (FAO-56) Penman-Monteith (PM) model, a corrected FAO-56 PM model incorporating CO2 physiological effect (PMCO2), and a land-atmosphere coupled PET model (PET-LAC) are further compared. Despite projected increases in PET across most land areas, the PM shows the most pronounced increases among these PET models. Compared to PMCO2 and PET-LAC, the PM model predicts the most significant drying, with the 3-month SPEI decreasing by 0.50±0.23 /yr. Additionally, it projects the most substantial drought intensification, with increases in areas, intensity, and duration of 28±6.9%, 0.70±0.20/yr, and 2.90±0.83 month/yr, respectively. Meanwhile, these projections correspond to the most extensive area percentages, with 78.5±10% for drying, 94.8±7.2% for drought intensity, and 93.6±7.9% for drought duration. These findings imply that the commonly used PM model overestimates future drought conditions. Differences and contradictions between the drought projections from PM-based offline indices and direct climate model outputs can be partly attributed to the omission of CO2 physiological effect and land-atmosphere coupling constraints in the PM model. This study highlights the importance of improving PET models by incorporating these constraints, thereby providing valuable insights for enhancing the accuracy of future drought assessments.

Wavelet-Entropy Enhanced Clustering: A Comprehensive Analysis of Drought Patterns in the Southern Plains, USA

Abstract Droughts may exhibit spatiotemporal heterogeneity at regional scale. Effective drought assessment and management necessitates identifying homogeneous areas. However, previous studies often simplified clustering analysis by focusing only on a single variable. In this study, we present a novel drought risk map for the Southern Plains (SP) region of the United States by integrating the wavelet-entropy approach with k-means clustering algorithm to capture spatio-temporal patterns of drought-related variables across various resolutions while eliminating redundant information. We considered multiple drought indicators and indices including gridded precipitation (P), potential evapotranspiration (PET), normalized difference vegetation index (NDVI), and Standardized Precipitation Evapotranspiration Index (SPEI) as well as geographical coordinates and topography map. Through evaluating five different combinations of input datasets, we selected the one demonstrating optimal results based on Davies-Bouldin and Calinski-Harabasz criteria. In addition to P, PET, and NDVI, including the coordinates and elevation as secondary variables significantly enhanced the clustering performance. Using these variables, the region was subdivided into 21 clusters. The Pearson’s correlation coefficients for the SPEI between centroid members and corresponding cells within clusters averaged between 0.84 to 0.94. Comparison with an existing cluster map (DRA) for the region revealed that our proposed cluster map showed higher variability between clusters for P, PET, and NDVI, confirming the robustness of the clustering results for drought conditions in the SP. The new clustering framework is expected to provide valuable insights for understanding and addressing drought dynamics in the SP region.

Integrating remote sensing modeling for drought assessment and vegetation monitoring in El Tarf, Algeria: a 40-year analysis

The escalating frequency and intensity of drought events, exacerbated by anthropogenic climate change, present critical challenges to hydrological resources, ecosystem resilience, and agricultural productivity. In North Africa, and specifically Algeria, comprehending the spatiotemporal patterns of drought is essential for sustainable water resource management and ecological conservation. This investigation focuses on the El Tarf region, located in northeastern Algeria, recognized for its ecological diversity and agricultural relevance. Employing a synergistic methodology, this study integrates the Standardized Precipitation Evapotranspiration Index (SPEI) for long-term drought severity evaluation with the Normalized Difference Vegetation Index (NDVI) to monitor vegetation phenology and health over time. Utilizing the computational power of Google Earth Engine, we process extensive temporal datasets of SPEI spanning from 1980 to 2023, complemented by high-resolution satellite imagery for NDVI computation. This comprehensive approach facilitates an in-depth analysis of drought dynamics, their severity, and impacts on vegetative cover in El Tarf over the past four decades. Our findings indicate significant long-term drought trends, with notable dry spells occurring between 1994 and 2003. Severe drought events were particularly pronounced in December 2022 (SPI = -2.50) and May 2002 (SPI = -2.2), correlating with substantial precipitation deficits. Although areas experiencing extreme drought have diminished, there has been an increase in moderate drought occurrences, suggesting an uptick in drought frequency, which poses risks to hydrological resources and ecosystem health. These observed patterns align with regional climate change projections, highlighting an urgent necessity for adaptive management strategies in agriculture and water resources.

Drought-Induced Agricultural and Food Security Challenges in the Baribo Basin, Cambodia

Rice production within the Tonle Sap basin is a critical driver of economic and social development in Cambodia. This region has been subject to various natural disasters, with increasing attention directed towards drought. This study aims to evaluate the impacts of drought on agriculture and food security through an in-depth case study of the Baribo basin, a sub-basin of the Tonle Sap. The analysis spans the period from 1985 to 2008, a timeframe characterized by relatively high-quality data. Drought assessment was conducted using ground observations and satellite-based products, with the Standardized Precipitation Index (SPI) and Standard Vegetation Index (SVI) employed to assess meteorological and agricultural droughts, respectively. Findings from both the SPI and SVI indicate that drought constitutes a significant natural hazard contributing to food insecurity in the study area. The highest drought intensity (DI) and drought severity (DS) were recorded during the 1993–1994 period, while the most prolonged drought duration (DD) was observed from 2002 to 2006. The year 2004 witnessed the most severe impact on rice production, with approximately 46% of the total cultivated area affected. The analysis further reveals a strong correlation between the drought duration and the extent of rice cultivation affected, as well as the overall food security in the Tonle Sap basin.

Evaluating Ecological Drought Vulnerability from Ecosystem Service Value Perspectives in North China

Existing studies on the vulnerability assessment of ecological drought often focus on analyzing vegetation phenotypic characteristics, overlooking the impact of drought on ecosystem services. This study proposes an ecosystem vulnerability assessment method under ecological drought stress from the perspective of ecosystem service value (ESV), considering the characteristics and interactions of hazard-causing factors and hazard-bearing bodies. The spatiotemporal evolution of ecological drought, the spatial characteristics of ecosystem vulnerability, and the vulnerability characteristics of different ecosystem types in the North China region from 1991 to 2021 were evaluated. The results showed that: (1) ecological drought exhibited a trend of intensification followed by alleviation, with the most severe droughts occurring between 2002 and 2011, affecting up to 64.3% of the region; (2) ESV was mainly influenced by vegetation cover and precipitation gradients, displaying a spatial pattern of high values in the southeast and low values in the northwest, with total ESV averaging CNY 18.23 trillion; (3) grasslands exhibited higher sensitivity to drought compared to forests, and the sensitivity was higher in summer and autumn than in winter and spring. This method assessed the vulnerability of ecological drought from the perspective of ecosystem services, providing a new approach for a more comprehensive understanding of the impact of drought on ecosystem service functions.

Monitoring and assessment of drought in Shaqlawa district using (VHI – TCI) indicators

Monitoring and assessment of drought in Shaqlawa district using (VHI – TCI) indicators

Drought Assessment Using Remote Sensing Techniques in the Great Rann of Kachchh and Adjoining Areas of Thar Desert

Drought Assessment Using Remote Sensing Techniques in the Great Rann of Kachchh and Adjoining Areas of Thar Desert

Applicability analysis of comprehensive drought index based on GRACE data in ten major river basins in China

As hydroclimatic extremes, drought triggered by climate change and human activities are recurrent over China. Selecting an appropriate comprehensive drought index is an important measure to comprehensively monitor drought characteristics. Precipitation and Terrestrial Water Storage Anomalies (TWSA) are important variables for assessing drought triggered by water deficiency in the atmosphere and terrestrial systems. The study aims to compare and discuss the applicability of drought index based solely on TWSA (DSI) and based on the combined effects of precipitation and TWSA (CCDI) in monitoring comprehensive drought in China. The results reveal that precipitation anomalies do not align with TWSA in arid basins, while they are synchronized in humid basins. CCDI reveals more favorable spatiotemporal agreement with the traditional drought indices, whereas DSI shows more consistency with common drought indices in humid basins. CCDI and DSI are suitable for assessing comprehensive drought in arid and humid basins, respectively. Moreover, almost both DSI and CCDI successfully monitored major drought periods in humid and arid basins during 2002–2006 and 2013–2017, respectively. Overall, this study highlights the applicability of CCDI and DSI in comprehensive drought assessment in arid and humid basins, respectively, providing valuable support for optimizing water resources utilization, management, and drought resistance decision-making.

Machine‐Learning Based Multi‐Layer Soil Moisture Forecasts—An Application Case Study of the Montana 2017 Flash Drought

AbstractSoil moisture (SM) is an essential climate variable, governing land‐atmosphere interactions, runoff generation, and vegetation growth and productivity. Timely forecasts of SM spatial distribution and vertical profiles are needed for early detection and prediction of potential droughts. However, previous studies have primarily concentrated on historical or near real‐time soil moisture mapping, with less effort devoted to the development and integration of soil moisture forecast components within drought assessment systems. A satellite‐driven machine‐learning approach was developed in this study to build complex relationships between diversified predictor data sets and in situ multi‐layer SM measurements from the Montana Mesonet, a regionally dense environmental station network in the US upper Missouri and Columbia basins. The resulting 30‐m daily SM predictions showed strong performance against in situ SM measurements from 4‐, 8‐ and 20‐inch soil layers, and with 1‐ to 2‐week forecast lead times (R > 0.91; RMSE ≤ 0.047 cm3/cm3). The machine‐learning model was subsequently applied to the entire Montana region, and the SM deficit forecasts with both 1‐ and 2‐week lead times successfully depicted onset, progression, and termination phases of the 2017 Montana flash drought, which was not effectively identified from prevailing operational systems. The resulting system is capable of delineating local scale SM heterogeneity, and could be extended to predict other critical water cycle variables, potentially enhancing future drought forecasts through multivariate assessments and benefiting water resource management, agricultural practices, and the provision of ecosystem services.

Assessing Drought Impacts on Gross Primary Productivity of Rubber Plantations Using Flux Observations and Remote Sensing in China and Thailand

Rubber (Hevea brasiliensis Muell.) plantations are vital agricultural ecosystems in tropical regions. These plantations provide key industrial raw materials and sequester large amounts of carbon dioxide, playing a vital role in the global carbon cycle. Climate change has intensified droughts in Southeast Asia, negatively affecting rubber plantation growth. Limited in situ observations and short monitoring periods hinder accurate assessment of drought impacts on the gross primary productivity (GPP) of rubber plantations. This study used GPP data from flux observations at four rubber plantation sites in China and Thailand, along with solar-induced chlorophyll fluorescence (SIF), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRv), and photosynthetically active radiation (PAR) indices, to develop a robust GPP estimation model. The model reconstructed eight-day interval GPP data from 2001 to 2020 for the four sites. Finally, the study analyzed the seasonal drought impacts on GPP in these four regions. The results indicate that the GPP prediction model developed using SIF, EVI, NDVI, NIRv, and PAR has high accuracy and robustness. The model’s predictions have a relative root mean square error (rRMSE) of 0.22 compared to flux-observed GPP, with smaller errors in annual GPP predictions than the MOD17A3HGF model, thereby better reflecting the interannual variability in the GPP of rubber plantations. Drought significantly affects rubber plantation GPP, with impacts varying by region and season. In China and northern Thailand (NR site), short-term (3 months) and long-term (12 months) droughts during cool and warm dry seasons cause GPP declines of 4% to 29%. Other influencing factors may alleviate or offset GPP reductions caused by drought. During the rainy season across all four regions and the cool dry season with adequate rainfall in southern Thailand (SR site), mild droughts have negligible effects on GPP and may even slightly increase GPP values due to enhanced PAR. Overall, the study shows that drought significantly impacts rubber the GPP of rubber plantations, with effects varying by region and season. When assessing drought’s impact on rubber plantation GPP or carbon sequestration, it is essential to consider differences in drought thresholds within the climatic context.

A comprehensive analysis and comparison of SPI and SPEI for spatiotemporal drought evaluation.

Assessing drought is crucial for effective water resources management and the development of mitigation strategies. Drought indices serve as indispensable tools in this evaluation process, and choosing an appropriate index is vital for accurate drought assessment. The characteristics and classification of drought depend entirely on the chosen index. Based on the existing literature, the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) are the most commonly and widely used indices, and there is a significant need for a comprehensive comparison between SPI and SPEI to understand their differences and implications for drought assessment. This research aims to compare SPI and SPEI based on drought indices, characteristics, and classifications using the innovative drought classification matrix (IDCM) for spatiotemporal drought evaluation, and the comparison process is done for events and monthly scales. Also, it aims to investigate the comparison between SPEI obtained from in situ meteorological stations and from the SPEI database. The application and the comparison are presented for Istanbul city between 1951 and 2020. The results show similar variations and high correlation (more than 0.65) between SPI and SPEI. For drought characteristics, there is no consistent relationship between SPI and SPEI at the drought event scale. The outcomes revealed that approximately 60% of the months exhibit consistent drought classifications between the two indices. Finally, it shows a significant difference between SPEI based on in situ meteorological stations and satellite data. The comparison between SPI and SPEI based on different aspects is necessary and essential for drought studies and water resources management.

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.

A novel statistical framework of drought projection by improving ensemble future climate model simulations under various climate change scenarios.

Unlike other natural disasters, drought is one of the most severe threats to all living beings globally. Due to global climate change, the frequency and duration of droughts have increased in many parts of the world. Therefore, accurate prediction and forecasting of droughts are essential for effective mitigation policies and sustainable research. In recent research, the use of ensemble global climate models (GCMs) for simulating precipitation data is common. The objective of this research is to enhance the multi-model ensemble (MME) for improving future drought characterizations. In this research, we propose the use of relative importance metric (RIM) to address collinearity effects and point-wise discrepancy weights (PWDW) in GCMs. Consequently, this paper introduces a new statistical framework for weighted ensembles called the discrepancy-enhanced beta weighting ensemble (DEBWE). DEBWE enhances the weighted ensemble data of precipitation simulated by multiple GCMs. In DEBWE, we addressed uncertainties in GCMs arising from collinearity and outliers. To evaluate the effectiveness of the proposed weighting framework, we compared its performance with the simple average multi-model ensemble (SAMME), Taylor skill score ensemble (TSSE), and mutual information ensemble (MIE). Based on the Kling-Gupta efficiency (KGE) metric, DEBWE outperforms all competitors across all evaluation criteria. These inferences are based on the analysis of historical simulated data from 22 GCMs in the CMIP6 project. The quantitative performance indicators strongly support the superiority of DEBWE. The median and mean KGE values for DEBWE are 0.2650 and 0.2429, compared to SAMME (0.1000, 0.0991), TSSE (0.2600, 0.2397), and MIE (0.1550, 0.1511). For drought assessment, we computed the adaptive standardized precipitation index (SPI) for three future scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. The steady-state probabilities suggest that normal drought (ND) is the most frequent condition, with extreme events (dry or wet) being less probable.

Integrating standardized indices and performance indicators for better drought assessment in semi-arid coastal aquifers

Aquifers in arid and semi-arid coastal regions, such as along the Mediterranean rim, are severely affected by droughts. The natural decrease in water levels is often exacerbated by excessive abstraction, resulting in both degradation of water quality and the risk of seawater intrusion. In these regions it is crucial to conduct thorough monitoring of wells, employing a wide range of indicators to forecast and mitigate the consequences of decreased precipitation and intensified pumping. This study proposes an analysis and monitoring methodology involving the calculation of performance indicators based on the Standardized Groundwater level Index (SGI), supplemented with information on the optimal accumulation time of the Standardized Precipitation Evapotranspiration Index (SPEI). Atmospheric reanalysis datasets and in-situ groundwater level observations are used together to derive the groundwater system memory and find consistent optimal SPEI accumulation times at each individual location. The inverse of memory derived from the autocorrelation of the SGI is used to estimate each well's ability to recover from drought conditions. This value provides the most reliable indication of resilience and sustainability. In the Algarve, the average regional variability of groundwater level is well captured by the SPEI-12 index. However, groundwater memories and optimal SPEI accumulation times are spatially very heterogeneous varying between SPEI-5 and SPEI-48. Wells with shorter memories (<6 months) demonstrate greater sustainability, whereas those with longer memories (>16 months), whether situated inland or along the coast, exhibit lower resilience and lower sustainability. Coastal wells with groundwater levels close to sea level, exhibiting minimal resilience, are of particular concern and require intensified monitoring efforts to adapt management strategies.

Response of remote sensing-based climatic indicators to drought conditions during El Niño events and implications for water–food–energy nexus

ABSTRACT Hydrometeorological extremes, such as droughts, are a major threat to society and can have extensive damaging effects. In this study, daily rainfall estimates from the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) quasi-global rainfall dataset were used to calculate the Standardised Precipitation Index (SPI) for the assessment of meteorological drought in Southern Province, Zambia. Normalised Difference Vegetation Index (NDVI) imagery (250 m resolution) from MODIS-Terra, for the period 2000–2021, were used to derive the Standardised Vegetation Index (SVI) in order to assess agricultural drought. The Mann–Kendall trend test and Sen's slope were used to determine the spatial-temporal trends and their magnitudes. This study demonstrated that the droughts of the Southern Province of Zambia can be classified into two categories: regressive and aggressive droughts. Regressive droughts are associated with moderate to strong El Niño events. Although El Niño events undermine water security, regressive droughts tend to result in resilient vegetation owing to residue soil moisture. In contrast, aggressive droughts are characterised by an increase in drought intensity as the season progresses. Water security prospects in the region should focus on climate-smart approaches, such as managed aquifer recharge, to ensure water availability even under extreme drought conditions.

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.

A modification of normalized difference drought index to enhance drought assessment using remotely sensed imagery.

Drought is one of the common natural disasters with a wide range of occurrences in terms of space and time, and with varying levels of severity, that may result in economic damage and health issues to humans. This study focuses on assessing drought severity in the Central Highlands of Vietnam based on ground meteorological stations and multispectral remote sensing data. A Modification of the Normalized Difference Drought Index (MNDDI) was developed to enhance the effectiveness of remote sensing indices in the drought assessment. Results indicate that MNDDI outperforms Normalized Difference Drought Index and other investigated indicators, such as Normalized Difference Vegetation Index, Normalized Difference Latent Heat Index, and Normalized Difference Water Index, in representing the Earth's surface response to drought events. Correlations ranging from 0.85 to 0.63 were identified between MNDDI and various time scales of the commonly used meteorological drought indicator, namely the Standardized Precipitation Index, during the drought year of 2015. This work also reveals the superiority of MNDDI in portraying the response of land cover types to drought situations. The finding of a severe drought phenomenon in critical agricultural zones is highly consistent with the report from the Ministry of Agriculture and Rural Development of Vietnam. This study contributes valuable insights to the preliminary assessment of drought through remote sensing data, offering a foundation for precise drought outlooks and effective risk management strategies.