Drought Maps Research Articles

Remote sensing-based agricultural drought mapping in Northern Jordan using Landsat and MODIS data

Monitoring agricultural drought in a semi-arid environment is critical, especially during the growing season, as it negatively impacts vegetation health and crop yield. This study aimed to monitor the spatiotemporal variation of agricultural drought in Northern Jordan using Landsat-8 and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. The Spatio-Temporal Image Fusion Model (STI-FM) was used to produce synthetic Landsat images with a high spatiotemporal resolution (30 m / 8 days) by utilizing a pair of successive MODIS images at two points in time (time-1 and time-2) and one Landsat-8 image at time-1. Agricultural drought was mapped and monitored using spectral indices namely, Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI). Additionally, the Standard Precipitation Index (SPI) -based meteorological rainfall data was used to validate the accuracy of the drought maps. The results revealed significant spatiotemporal variations in drought conditions, with April 2020 showing the least dry conditions, while 2019 was identified as the driest year. Validation through SPI indicated high accuracy, with kappa values ranging from 0.70 to 0.85 and overall accuracy ranging from 80 % to 92 %. Furthermore, the STI-FM data fusion algorithm effectively generated high-resolution Landsat-8 images, demonstrating a strong correlation between original and synthetic images for the red and NIR spectral bands (0.93 and 0.84, respectively). These findings highlight the effectiveness of integrating STI-FM with spectral indices and SPI for accurate and high-resolution agricultural drought monitoring, which can support improved water resource management and agricultural planning in semi-arid regions.

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.

Comprehensive drought risk assessment and mapping in Taiwan: An ANP-ANN ensemble approach

This study aims to comprehensively evaluate and map the risk of drought in Taiwan by employing a combination of two powerful models, the Analytic Network Process (ANP) and Artificial Neural Network (ANN). This innovative approach utilizes an ensemble learning method, where ANP constructs a logical network and assigns weights to various indicators. Subsequently, ANN leverages these weights to train the model effectively. A total of twenty indicators were incorporated into the analysis to create a holistic drought risk map for Taiwan. These indicators are thoughtfully categorized into three essential components: hazard, exposure, and vulnerability, providing a well-defined representation of drought risk. The trained ANN model showcases remarkable accuracy and performance, boasting values of 0.940 for accuracy, 0.946 for precision, 0.938 for recall, 0.942 for the F1 score, and 0.923 for the Kappa Index. These results unequivocally affirm the model's effectiveness in predicting drought risk. Furthermore, the final drought risk map underwent rigorous validation through fieldwork and statistical data. The validation process yielded high accuracies, ranging from 0.717 to 0.851, for assessing damage to crops, converted damaged areas, and estimated value product loss. This validation, conducted against multiple reference data sources, underscores the map's reliability and its alignment with various goodness-of-fit criteria. In summary, this study underscores the potency of the ANP-ANN ensemble approach, with the trained ANN model proving its robustness in swiftly predicting drought risk across diverse ecological and socioeconomic scenarios.

An Approach for Future Droughts in Northwest Türkiye: SPI and LSTM Methods

Predetermining the risk of possible future droughts enables proactive measures to be taken in key areas such as agriculture, water management, and food security. Through these predictions, governments, non-governmental organizations, and farmers can develop water-saving strategies, encourage more efficient use of water, and minimize economic losses that may occur due to drought. Thus, future drought forecasts stand out as a strategic planning tool for the protection of natural resources. To achieve this aim, forecasted drought conditions for the next decade (2024–2034) at nine meteorological stations in the Sakarya basin, located northwest of Türkiye, are examined, using historical monthly precipitation data from 1991 to 2023. This study uses the Standardized Precipitation Index (SPI) and Long Short-Term Memory (LSTM) deep learning methods to investigate future meteorological droughts. The research confirms the compatibility and reliability of the LSTM method for forecasting meteorological droughts by comparing historical and forecasted SPI values’ correlograms and trends. In addition, drought maps are created to visually represent the spatial distribution of the most severe droughts expected in the coming years, and areas at risk of drought in the Sakarya Basin are determined. The study contributes to the limited literature on forward-looking drought forecasts in the Sakarya Basin and provides valuable information for long-term water resource planning and drought management in the region.

Meteorological Drought Analysis and Regional Frequency Analysis in the Kızılırmak Basin: Creating a Framework for Sustainable Water Resources Management

Drought research is needed to understand the complex nature of drought phenomena and to develop effective management and mitigation strategies accordingly. This study presents a comprehensive regional frequency analysis (RFA) of 12-month meteorological droughts in the Kızılırmak Basin of Turkey using the L-moments approach. For this purpose, monthly precipitation data from 1960 to 2020 obtained from 22 meteorological stations in the basin are used. In the drought analysis, the Standard Precipitation Index (SPI), Z-Score Index (ZSI), China-Z Index (CZI) and Modified China-Z Index (MCZI), which are widely used precipitation-based indices in the literature, are employed. Here, the main objectives of this study are (i) to determine homogeneous regions based on drought, (ii) to identify the best-fit regional frequency distributions, (iii) to estimate the maximum drought intensities for return periods ranging from 5 to 1000 years, and (iv) to obtain drought maps for the selected return periods. The homogeneity test results show that the basin consists of a single homogeneous region according to the drought indices considered here. The best-fit regional frequency distributions for the selected drought indices are identified using L-moment ratio diagrams and ZDIST goodness-of-fit tests. According to the results, the best-fit regional distributions are the Pearson-Type 3 (PE3) for the SPI and ZSI, generalized extreme value (GEV) for the CZI, and generalized logistic distribution (GLO) for the MCZI. The drought maps obtained here can be utilized as a useful tool for estimating the probability of drought at any location across the basin, even without enough data for hydrological research.

Spatiotemporal Mapping of Agricultural and Meteorological Drought in Wasit Province based on GIS and Remote Sensing Data

Spatiotemporal Mapping of Agricultural and Meteorological Drought in Wasit Province based on GIS and Remote Sensing Data

A Geospatial Analysis of Environmental Impacts of Climate Change on Osogbo and Environs

Climate change is one of the alarming global environmental changes likely to have deleterious effects on natural, social, cultural, and human systems. The risks associated with it call for a broad spectrum of policy responses and strategies at local, regional, national, and global levels. This research aims to carry out a geospatial analysis of the environmental impact of climate change on Osogbo and its environs with a view to providing information towards mitigating the negative impact of climate change in the study area. This study uses Landsat 4, Landsat 7, and Landsat 8 satellite data from 1982, 2002, and 2022, respectively, to evaluate land cover and temperature variation as a climatic change in the study area over four decades. The land survey method was carried out to collect ground truth data for validation of the satellite imageries. The drought map was produced based on the Vegetation Condition Index (VCI) map computed from Normalized Difference Vegetation Index (NDVI) datasets and was further classified into three categories: moderate, mild, and no drought. Analysis of the Land Use Land Cover (LULC) of the study area shows that the vegetation decreased from 84% in 1982 to 59% in 2002 and 56% in 2022. The built-up/exposed surface increased from 15.28% in 1982 to 38% in 2002 and 41.28% in 2022. The water bodies also increased from 0.17% in 1982 to 15.31% in 2022. LST analysis reveals that the mean temperature of the study area increased by 10.73 °C during the past four decades (from 1982 to 2022), with the most accelerated warming (11.41 °C) occurring during the last two decades (from 2002 to 2022). The Vegetation Condition Index (VCI) based drought map shows that there was no extreme or severe drought in the study area. However, moderate and mild droughts were observed mostly in the developed part of the study area, which confirmed the presence of drought. It is hereby recommended to introduce sustainable water management strategies to mitigate water shortage in the study area.

Improving remote sensing based agricultural drought characterization in Saurashtra, Gujarat : A region-specific threshold approach

Remote sensing technology has demonstrated its significant utility in the monitoring and mapping of agricultural drought on a global scale. This study focused on the assessment of agricultural drought in the Saurashtra region of Gujarat, India, utilizing a comprehensive dataset spanning 33 years from Landsat and Sentinel satellites. It employed various vegetation indices, including NDVI (Normalized Difference Vegetation Index), Anomaly Index (NAI), Vegetation Condition Index (VCI) and NDWI Anomaly index (NDWIA), to gauge drought conditions. The performance of these indices was evaluated through the generation of drought severity maps and their correlation analysis with major Kharif crops in the region, specifically cotton and groundnut. The analysis pinpointed major agricultural drought years, such as 1986, 1987, 1991, 2000, 2002 and 2012, which corresponded to substantial crop yield losses ranging from 37% to 76% for cotton and 66% to 95% for groundnut, varying by district. Despite VCI demonstrating equivalent or superior correlations with crop yields (ranging from 0.32 to 0.73 for cotton and 0.33 to 0.75 for groundnut) compared to NAI in various districts, it tended to underestimate drought severities, designating only 2 to 9 drought years for different districts. Consequently, this study recommends revised VCI drought severity thresholds, which enhance the categorization of agricultural drought in terms of severity levels and corresponding yield losses for cotton and groundnut in the Saurashtra region of Gujarat. Furthermore, it underscores the need to establish region-specific drought severity thresholds by identifying the most suitable vegetation index for effective quantification of agricultural drought, thereby facilitating informed drought mitigation measures.

Assessment of agricultural drought in Iraq employing Landsat and MODIS imagery

Abstract Climate change poses an urgent global challenge in water resource management, with drought emerging as a pervasive threat worldwide. Over the past two decades, Iraq has faced severe droughts, categorized into meteorological, agricultural, hydrological, and socioeconomic types. Agricultural drought, marked by prolonged soil moisture deficits due to insufficient rainfall, often leads to crop failures. This comprehensive study delves into the impact of drought on Iraq’s vegetation cover from 2000 to 2022, utilizing advanced tools like remote sensing (RS) and geographic information systems. The normalized difference vegetation index (NDVI) facilitated the creation of multitemporal drought maps. Employing Landsat satellite imagery and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, the study revealed 2000, 2008, 2009, 2010, 2012, and 2022 as the most drought-prone years. In specific years such as 2000, 2008, 2010, and 2009, Landsat imagery showcased the lowest vegetation cover percentages (3.356, 4.984, 5.112, and 5.409%, respectively), while MODIS data indicated the lowest percentages in 2008, 2000, 2009, 2012, and 2022 (11.122, 11.260, 12.580, 13.026, and 14.445%, respectively). This study underscores the pivotal role of RS, particularly NDVI, as a valuable tool for agricultural drought early warning systems. The findings highlight the effectiveness of NDVI as a simple and cost-efficient index for monitoring changes in vegetation conditions and assessing the impact of droughts on agriculture.

The Vulnerability Model of Water Resources Management in Forest Edge Communities in Sumedang Regency

Purpose: This study aims to explain the vulnerability of institutional management practices of water resources, agent practices, and system reproduction that can potentially cause social vulnerability in society. Theoretical reference: Structuration theory was chosen because this theory can explain structural or institutional vulnerability seen from practices in water resources management. Method: A qualitative approach using the participatory rural appraisal (PRA) method was employed to map the social vulnerability of management structures. NVivo mind map analysis and geographic information system analysis were used to strengthen the relationship between social practices and vulnerability information through drought maps. Results and Conclusion: The results of the study show that resource management is vulnerable to maintaining old patterns or habits because agents are unable to break through institutional systems and structures do not have authority, and have no bargaining position. Reproduction of social systems takes place in accordance with ongoing social structures that traverse space and time. The forestry organization KPH Sumedang has the authority to manage water resources around the forestry area. Vulnerability becomes a phenomenon of water user communities on the forest's edge. Discursive and recursive practices of actors/agents perpetuate the social structure of water users, such as the potential vulnerability of the social structure of water user communities and practices that encourage social vulnerability. Implications of research: This study has shed light on the community vulnerability related to the management of water resources by the forest edge communities. In addition, this study has given insight into the vulnerability of the social structure that can be seen from the practices of water users in village communities on the edge of the forest. Originality/value: The originality lies in how this research scrutinizes the social vulnerability model in managing water resources in communities in villages on the edge of the forest. So far, vulnerability has been studied more often after a disaster occurs.

Satellite-based drought assessment in the endorheic basin of Lake Balkhash

Introduction: This study investigates into the drought-prone region of the Lake Balkhash basin, conducting a thorough analysis spanning 2 decades, emphasizing its significant impact on agriculture and water challenges in Central Asia. Beyond the specific focus on this region, the research aims to contribute valuable insights that extend our understanding of the broader implications of drought in the area.Methods: Utilizing MODIS satellite imagery, the study employs the Vegetation Health Index a comprehensive indicator combining the Vegetation Condition Index and Temperature Condition Index ranging from 0 (extreme aridity) to 100 (optimal moisture). The assessment of drought conditions from 2000 to 2020 includes probabilistic mapping, trend analysis, and Pearson’s correlation coefficients. Connections with hydrological factors, such as river water levels and the Balkhash Lake area, are explored, along with in-depth analyses of land use data and the GRACE dataset on water equivalent thickness, enhancing the study’s depth and reliability.Results: Drought affected approximately 44% of the Balkhash Lake Basin during the study period, impacting both Kazakhstan and China. Severe drought episodes occurred in 2000, 2008, 2014, and 2015, highlighting the region’s vulnerability. Analysis of drought trends revealed diverse patterns: 23% exhibited an increase, 17% showed a decrease, and 60% remained stable. Correlations between drought and hydrological parameters varied among stations, with positive correlations at Kapshagay and Shelek Stations, a weak correlation at Ayagoz Station, and a significant positive correlation at Lepsy despite the elevation.Discussion: This research underscores the intricate link between drought and hydrological factors in the Balkhash Lake Basin, emphasizing the need for precise water resource management and climate adaptation. Crucial strategies include proactive monitoring, tailored interventions, and the application of probabilistic drought mapping to enhance water supply management, contributing actionable insights for sustainable practices in the region.Conclusion: This study significantly advances our understanding of drought dynamics in the Balkhash Lake Basin, recommending adaptive strategies, site-specific interventions, and sustainable water management. The findings provide a crucial foundation for informed water resource decisions in Central Asia, emphasizing the importance of region-specific approaches to address diverse challenges posed by drought.

Development and allocation of improved eucalyptus for biotic an abiotic tolerance

Since the 1960s, Altri Florestal is running a programme for selecting trees based on growth, wood density and cellulose content. Currently Eucalyptus globulus breeding programme is in third generation. Capturing the genetic gain is conducted through vegetative propagation to produce clones, as well as through seed production from a mass-controlled pollination programme for full-sib families. Ongoing climate change raises the importance of considering tolerance to abiotic stress in the Eucalyptus globulus breeding programme. Pursuing this objective has been done by carrying out a Best Linear Unbiased Prediction (BLUP) analysis considering macro climate regions with different exposure to cold and drought. Cold and drought maps were combined in one resulting in four regions of exposure: drought region (R1); cold region (R2); cold and drought region (R3); region with low exposure to cold and drought (R4). BLUP analysis included data from eighty-four field breeding trials with the following climatic distribution: 30 (R1); 19 (R2); 4 (R3), and 31 (R4). Data used comprises 160k genotypes and 210k measurements of height, diameter at breast height, and survival. It was possible to select around four genotypes with an overall superior performance in terms of growth and survival for the four regions considered. Furthermore, within each macro climate region groups of genotypes were selected with an increase of 50% volume per hectare above the original eucalyptus in the breeding programme. Altri is also involved in the development of new eucalyptus hybrids aiming to develop a new generation of eucalyptus hybrids more resilient to drought and cold, as well as pests such as Gonipterus platensis. This ongoing work is carried out via interspecific hybridization between E. globulus and other eucalyptus species. Eucalyptus hybrids are in several stages of development, namely: tested in several field trials, early field selection, being raised in the nursery, and being done through controlled crosses. Selection criteria will be based on performance from growth, survival, wood characteristics in field trials and in trials in controlled conditions. Finally, it is not enough to develop a portfolio of improved eucalyptus to ensure that realised genetic gain will be captured into operations. For this to happen it is necessary to ensure that allocation of improved eucalyptus is carefully planned. A prototype of decision tree for deploying improved eucalyptus has been already developed.

Investigating spatial and temporal trend of groundwater quality in relation to water balance in 2007–2017: a case study of Chaharmahal va Bakhtiari Province, Iran

The extensive exploitation of water resources in Chaharmahal va Bakhtiari province has led to a destructive impact on the water balance and quality of the region. In order to evaluate water quality of the study area, water quality parameters from 132 wells were analyzed to prepare spatial distribution maps of the IRWQI index. To analyze spatial and temporal rainfall anomalies, the SPI index was spatially interpolated using the ordinary Kriging method. Principal component analysis was used to investigate the relationship between water quality parameters. The suitability of data for PCA was evaluated by the Kaiser–Meyer–Olkin and Bartlett tests. Additionally, water balance components of the study area, including surface runoff and ground water, were simulated using the WetSpass-M model. According to the results of the IRWQI index, 54 wells, mostly located in built-up and agricultural lands, had poor quality․ Investigation of the average groundwater quality during the years 2007 to 2017 shows that the trend of groundwater quality decreased. Comparison of drought and water quality maps showed similar patterns so that in areas with extreme drought, water quality was bad. The highest and lowest recorded concentrations for nitrates were related to built-up and rangeland lands with concentrations of 35 and 21 mg/l, respectively. Comparison among land use classes showed that in the rangelands, groundwater quality was better and nitrate level was lower compared to agricultural and built-up areas. Overall, the results of this study show that water quality can be affected by land use types and water balance components.

Ensemble learning based on remote sensing data for monitoring agricultural drought in major winter wheat-producing areas of China

Drought is mainly triggered by the lack of precipitation, which can lead to insufficient water supply for crops thus affecting their growth and development. Reliable drought monitoring is crucial to understanding drought risk and avoiding drought-induced crop yield losses. Based on the Stacking regression method and multiple remotely-sensed drought factors from 2001 to 2017, this study developed an ensemble learning framework for monitoring agricultural drought in major winter wheat-producing areas in China. Stacking used five machine learning algorithms, namely, extreme gradient boosting, support vector regression, extra trees, and multi-layer perceptron, as the base learners to model the relationship between remote sensing drought factors and 1-, 3-, and 6-month standardized precipitation evapotranspiration index (SPEI). In this study, county-level winter wheat yield records and drought maps provided by the Global SPEI database (SPEIbase) were adopted to assess the suitability of Stacking-predicted SPEI drought maps in agricultural drought monitoring. The results show that Stacking outperformed other machine learning algorithms in terms of estimation accuracy, with the highest R2 value of 0.77 and the lowest root mean square error (RMSE) of 0.47. The longer the time scale of model-predicted SPEI, the higher its correlation with detrended winter wheat yields. The comparison with the drought maps of SPEIbase shows that the Stacking-predicted drought maps successfully captured the spatial pattern and intensity change of drought events. The approach presented in the study has good applicability for agricultural drought monitoring and could be extended to the rest of the areas.

Spatial analysis of flash flood and Drought impact from Climate Change in Phongsaly District, Phogsaly Province, by using Geo-Informatics Technology and Modelling

A method for predicting the water resource in the region in the future to be used as a basis for mitigating the consequences is to study how climate change affects hydrology. The purpose of this study is to i). choose a global climate model that is suitable for the area, ii). rainfall run-off modelling, iii). drought and flood hazard index map. The SSP-126, SSP-245, and SSP-585 scenarios were chosen as the most appropriate global climate model among the four institutes, with efficiency criteria using the coefficient of Nash-Sutcliffe and Kling-Gupta and then calibrate the data with the Bias Correction Linear Scaling method which divides the analysis period into 2 periods for Near-Future and Far-Future from analyzing Rainfall Run-off Modeling from Rainfall Concentration 1-hours, 3-hours and 6-hours. It was found that the SSP-585 scenario in the Rainfall Concentration 1-hours model has the most dangerous area for very high risk until the end of the 21st century. For the analysis of drought indices SPI_1, SPI_3 and SPI_6 in Near-Future, it was found that the frequency of droughts is increasing according to the worst scenario, the scenario with the most drought is SSP-585 and in the Far-Future, the frequency of drought is decreasing according to the worst scenario, the scenario with the most drought is SSP-126.

Predicting agricultural drought indicators: ML approaches across wide-ranging climate and land use conditions

Agricultural drought can severely reduce crop yields, lead to large economic losses and health impacts. Combined climate and land use variations determine key indicators of agricultural drought, including soil moisture and the Palmer drought severity index (PDSI). This study investigated the use of machine learning (ML) methods for predicting these indicators over Sweden, spanning steep climate and land use gradients. Three data arrangement methods (multi-features, temporal, and spatial) were used and compared in combination with seven ML/deep learning (DL) models (random forest (RF), decision tree, multivariate linear regression, support vector regression, autoregressive integrated moving average (AMIRA), artificial neural network, and convolutional neural network). Seven investigated features, obtained from Google Earth Engine, were used in the ML/DL modeling (soil moisture, PDSI, precipitation, evapotranspiration, elevation, slope and soil texture). The temporal ARIMA model (found most suitable for local scale prediction) and the multi-features RF model (more suitable for national-scale prediction) emerged as best performing for soil moisture prediction (with MAE of 9.1 and 11.95, and R2 of 0.79 and 0.59, respectively). All models generally performed better in predicting the soil moisture than the PDSI indicator of drought. For drought indicator prediction and mapping, previous-year average monthly soil moisture emerged as the most important feature, combined with the four additional corresponding features of PDSI, precipitation, evapotranspiration and elevation.

Application of CHIRPS dataset in the selection of rain-based indices for drought assessments in Johor River Basin, Malaysia

This paper aims to select the most appropriate rain-based meteorological drought index for detecting drought characteristics and identifying tropical drought events in the Johor River Basin (JRB). Based on a multi-step approach, the study evaluated seven drought indices, including the Rainfall Anomaly Index (RAI), Standardized Precipitation Index (SPI), China-Z Index (CZI), Modified China-Z Index (MCZI), Percent of Normal (PN), Deciles Index (DI), and Z-Score Index (ZSI), based on the CHIRPS rainfall gridded-based datasets from 1981 to 2020. Results showed that CZI, MCZI, SPI, and ZSI outperformed the other indices based on their correlation and linearity (R2 = 0.96–0.99) along with their ranking based on the Compromise Programming Index (CPI). The historical drought evaluation revealed that MCZI, SPI, and ZSI performed similarly in detecting drought events, but SPI was more effective in detecting spatial coverage and the occurrence of ‘very dry’ and ‘extremely dry’ drought events. Based on SPI, the study found that the downstream area, north-easternmost area, and eastern boundary of the basin were more prone to higher frequency and longer duration droughts. Furthermore, the study found that prolonged droughts are characterized by episodic drought events, which occur with one to three months of ‘relief period’ before another drought event occurs. The study revealed that most drought events that coincide with El Niño, positive Indian Ocean Dipole (IOD), and negative Madden-Julian Oscillation (MJO) events, or a combination of these events, may worsen drought conditions. The application of CHIRPS datasets enables better spatiotemporal mapping and prediction of drought for JRB, and the output is pertinent for improving water management strategies and adaptation measures. Understanding spatiotemporal drought conditions is crucial to ensuring sustainable development and policies through better regulation of human activities. The framework of this research can be applied to other river basins in Malaysia and other parts of Southeast Asia.

Satellite-based drought monitoring using optimal indices for diverse climates and land types

Drought is considered one of the most destructive natural disasters, and many areas are experiencing water scarcity. Expanding knowledge of this phenomenon is a prerequisite for developing drought monitoring and forecasting tools. To this end, various indices are available for studying drought in different environments using field and remote sensing data. This study applies satellite-based indices for monitoring drought in different land cover, landforms, and climate classes. The in-situ standardized precipitation index (SPI) with a three-month time scale was applied to evaluate the performance of 13 remote sensing indices and parameters. The results indicated that the indices based on actual evapotranspiration, precipitation, and soil moisture, respectively, performed best in different parts of the basin. After additional analysis, the evapotranspiration condition index (ETCI), derived from actual evapotranspiration data, was deemed the optimal metric. The accuracy assessment results indicated that the correlation between the ETCI and the three-month SPI was 0.655, which was slightly higher than the actual evapotranspiration (0.637), and that the root-mean-squared error (RMSE) decreased from 0.71 to 0.65, indicating the best performance among the indices evaluated in the study area. Moreover, the drought map of the region was developed using the optimal indices, including the ETCI, the precipitation condition index (PCI), and the random forest (RF) algorithm. According to the results of the accuracy evaluation, the correlation between the estimated model and the observed three-month SPI values in 2017 was 0.72, with an RMSE of 0.60.

Spatiotemporal Drought Monitoring Using Remote Sensing Technique in Babel-Iraq

Drought is one of the most natural hazards that may harm human life and property under different weather and environmental conditions. This study used remote sensing data to monitor agricultural and meteorological drought in Babel Governorate. Drought maps were drawn using Landsat 8 images based on Normalized Difference Vegetation Index (NDVI) for 2015, 2018, and 2021. The meteorological drought was assessed using a standardized precipitation index (SPI 12) for the same years. The results showed that the SPI-12 indicated near-normal drought types in 2015 and 2018, whereas SPI values showed a lower value in 2021. Two drought categories were identified which were moderate drought and severe drought. The NDVI results showed that the vegetation area decreased in 2021 by 9 % in comparison to 2015. This result could help the government planners to develop and manage the drought impacts.

An Investigation of Spatial and Temporal Analysis of Drought Forecasting

Following the impact of droughts witnessed during the last decade there is an urgent need to develop a drought management strategy, policy framework, and action plan for Satara District. This study aims to provide a historical baseline using the standardized precipitation index (SPI) and meteorological drought maps, and to investigate the spatial and temporal trends using long-term historical precipitation records. Drought is a natural hazard that is difficult to quantify in terms of severity, duration, area and impact. The present study has been designed to evaluate the metrological drought for Man and Khatav (Maharashtra) using the Standard Precipitation index (SPI). The data and information obtained in this area to monitor and forecast drought can also be useful in future to control and reduce the impact of drought on drinking water and agriculture in this region. The SPI is calculated at Mann and Khatav in many series over different periods of time to show drought and drought-free years. The lowest rainfall in Khatav station was less than 200mm, while in Mann; it was about -2.5mm, indicating extremely dry seasons.