Drought Maps Research Articles

Automated Analysis of the US Drought Monitor Maps With Machine Learning and Multiple Drought Indicators.

The US Drought Monitor (USDM) is a hallmark in real time drought monitoring and assessment as it was developed by multiple agencies to provide an accurate and timely assessment of drought conditions in the US on a weekly basis. The map is built based on multiple physical indicators as well as reported observations from local contributors before human analysts combine the information and produce the drought map using their best judgement. Since human subjectivity is included in the production of the USDM maps, it is not an entirely clear quantitative procedure for other entities to reproduce the maps. In this study, we developed a framework to automatically generate the maps through a machine learning approach by predicting the drought categories across the domain of study. A persistence model served as the baseline model for comparison in the framework. Three machine learning algorithms, logistic regression, random forests, and support vector machines, with four different groups of input data, which formed an overall of 12 different configurations, were used for the prediction of drought categories. Finally, all the configurations were evaluated against the baseline model to select the best performing option. The results showed that our proposed framework could reproduce the drought maps to a near-perfect level with the support vector machines algorithm and the group 4 data. The rest of the findings of this study can be highlighted as: 1) employing the past week drought data as a predictor in the models played an important role in achieving high prediction scores, 2) the nonlinear models, random forest, and support vector machines had a better overall performance compared to the logistic regression models, and 3) with borrowing the neighboring grid cells information, we could compensate the lack of training data in the grid cells with insufficient historical USDM data particularly for extreme and exceptional drought conditions.

Integrating optical and microwave satellite observations for high res-olution soil moisture estimate and applications in CONUS drought analyses

In this study, optical and microwave satellite observations are integrated to estimate soil moisture at the same spatial resolution as the optical sensors (5km here) and applied for drought analysis in the continental United States. A new refined model is proposed to include auxiliary data like soil texture, topography, surface types, accumulated precipitation, in addition to Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) used in the traditional universal triangle method. It is found the new proposed soil moisture model using accumulated precipitation demonstrated close agreements with the U.S. Drought Monitor (USDM) spatial patterns. Currently, the USDM is providing a weekly map. Recently, “flash” drought concept appears. To obtain drought map on daily basis, LST is derived from microwave observations and downscaled to the same resolution as the thermal infrared LST product and used to fill the gaps due to clouds in optical LST data. With the integrated daily LST available under nearly all weather conditions, daily soil moisture can be estimated at relatively higher spatial resolution than those traditionally derived from passive microwave sensors, thus drought maps based on soil moisture anomalies can be obtained on daily basis and made the flash drought analysis and monitoring become possible.

Global drought monitoring with drought severity index (DSI) using Google Earth Engine

Unlike most disasters, drought does not appear abruptly. It slowly builds over time due to the changes in different environmental and climatological factors. It is one of the deadly disasters that has plagued almost every region of the globe since early civilization. Droughts are scientifically being studied with the help of either simple or composite indices. At 500-m spatial resolution, this study presents global scale drought severity index (DSI), a composite index using Moderate Resolution Imaging Spectroradiometer (MODIS), 8-day temporal resolution evapotranspiration (ET), potential evapotranspiration (PET), and normalized difference vegetation index (NDVI). This index is mainly used to identify meteorological droughts and also has proven reliable for studying agriculture droughts. In this study, Google Earth Engine (GEE), a cloud-based geospatial data computational platform, is used for drought mapping and monitoring from 2001 to 2019. For annual DSI spatial maps, the statistical median is computed ranging from − 1 to + 1, which means drought struck or dry regions have values closer to negative, and wet zones have values near to positive. For the validity of DSI results, the findings are compared with available records of droughts struck in previous years. This study declares that continent-wise, Australia, Africa, and Asia have the most extreme and frequent drought events while South America and North America come a close second. Europe is the least affected by this particular weather event when compared to other continents.

Drought Index Mapping in Java Island Using Sentinel-3 SLSTR

As the most populous island globally, Java Island has various vulnerabilities to disasters ranging from geological to hydro-meteorological. One of the most common hydro-meteorological disasters is the drought that occurs every year in the dry season. This disaster causes crop failure, land and forest fires, and clean water shortages. In this study, the Sea and Land Surface Temperature Radiometer (SLSTR) instrument onboard the Sentinel-3 platform was used to map drought using the Vegetation Temperature Condition Index (VTCI) algorithm based on the scattering space technique of Land Surface Temperature (LST) and Normalized Different Vegetation Index (NDVI). During 2018, the highest and lowest LST occurred on September 28 (309 oK), and February 1 (278 oK); the highest and lowest NDVI occurred on July 1 (0.466) and November 13 (0.221). In comparison, the driest conditions indicated the lowest VTCI (0.162) on July 17 and the wettest on August 28 (0.508). The driest and wettest situation co-occurred: at the end of the dry session and the rainy session, it shows that the rain greatly contributes to high VTCI. A regular drought mapping needs to be performed as an effort to disaster risk reduction. Drought maps are then used as a spatial recommendation in reforestation intervention to reduce drought in the future.

Determination of Meteorological and Hydrological Drought Maps with Various Interpolation Methods in the Euphrates Basin

Kuraklık hidroelektrik enerji üretimi, sağlık, sanayi, turizm, ekonomi, tarım ve hayvancılık gibi çeşitli sektörleri olumsuz etkileyen bir doğal afettir. Bu nedenle izlenmesi, zamansal ve mekânsal dağılımının belirlenmesi ve önlemler alınarak kuraklık risklerinin yönetilmesi büyük öneme sahiptir. Bu çalışmada, Fırat Havzası’ndaki çeşitli meteorolojik ve hidrolojik kuraklıkların Kriging, Radyal Tabanlı Fonksiyon (RTF) ve Ters Mesafe Ağırlıklandırma (TMA) enterpolasyon yöntemleri ile zamansal ve mekânsal değişimleri kuraklık sınıflarına göre renklendirilerek haritalandırması ve havzadaki kuraklık risk durumunun değerlendirilmesi amaçlanmıştır. Bunun için Fırat Havzası ve çevresinde bulunan 16 adet Meteoroloji Gözlem İstasyonu ve 18 adet Akım gözlem istasyonuna ait Standartlaştırılmış Yağış İndeksi, İstatistiksel Z-Skoru İndeksi, Yağış Anomalisi İndeksi, Standardize Yağış Evapotranspirasyon İndeksi, Keşif Kuraklık İndeksi ve Standart Akım İndeksi değerleri kullanılmıştır. Analizler sonucunda havzada meteorolojik kuraklıkların tahmininde Kriging metodu, hidrolojik kuraklıkların tahmininde ise RTF en etkili yöntemler olduğu tespit edilmiştir. Havzanın kuraklık risk düzeyi yüksek tespit edildiği için çalışma alanında kuraklık yönetim planının yapılması, olumsuz kuraklık etkilerinin azaltılması, erken uyarı ve kuraklık önlemlerinin alınması önerilmektedir.

Application of Remote Sensing Imagery and Algorithms in Google Earth Engine platform for Drought Assessment

In Vietnam, drought is one of the natural disasters caused by high temperatures and lack of precipitation, especially with El Nino and the global warming phenomenon. It affects directly environmental, economical, social issueproblems, and the lives of humans. Many methods have been used to assess drought, andin which remote sensing indices are considered the most commonly used tool today. They are used to analyze spatio-temporal distribution of drought conditions and identify drought severity. Especially with the launch of Google Earth Engine (GEE) - a cloud-based platform for geospatial analysis, it is easy to access high-performance computing resources for processing multi-temporal satellite data online. With the GEE platform, we focus on writing and running scripts with the indicators suitable for evaluating drought phenomenon, instead of calculating on software and downloading remote sensing imagery with large size. In this study, we collected 26 Landsat 8 images in the dry season in 2019 (from April to July) in Tay Hoa district, Phu Yen – a region in the South Central Coast of Vietnam where agricultural drought occurs frequently. wWe assessed the distribution of drought conditions in the dry season in 2019 in Tay Hoa district, Phu Yen – a region in the South Central Coast of Vietnam where agricultural drought occurs frequently by using a drought index (VHI index – Vegetation Health Index) produced from Landsat satellite data in the GEE platform. The study results indicated that the drought (from mild to severe) concentrated in the North of the region, corresponding to high surface temperature and NDVI low or NDVI moderate values. VHI maps were visually compared with the drought map of the South Central Coast and the Central Highlands. In general, the results also reflect the the method’s reliability and can be used to support the managers to plan policies, making long-term plans to cope with climate change in the future at Tay Hoa in particular and other regions in general.

Temporal analysis of drought coverage in a watershed area using remote sensing spectral indexes

The development of several time series analysis programs using satellite images has provided many applications based on resources from geostatistics field. Currently, the use of statistical tests applied to vegetation indexes has enabled the analysis of different natural phenomena, such as drought events in watershed areas. The objective of this article is to provide a comparative analysis between NDVI and EVI vegetation index data made available by MOD13Q1 project of MODIS sensor for drought mapping using vegetation condition index (VCI) in the Serra Azul stream sub-basin, MG. The methodology adopted the Cox-Stuart statistical test for seasonality analysis and Pearson's linear correlation to verify the influence of different indexes on delimitation of drought in a watershed. The results indicated the NDVI vegetation index as more efficient than EVI in spatial characterization of studied watershed region, mainly in identification of seasonality. The VCI proved to be highly feasible for monitoring drought in study period between 2013 and 2018, allowing the effective delimitation of drought conditions in the Serra Azul stream sub-basin. In addition, the effectiveness of MODIS sensor data in characterizing drought events that affected the study area was proven.

Mapping of Agricultural Drought in Bangladesh using Geographic Information System (GIS)

Mapping of Agricultural Drought in Bangladesh using Geographic Information System (GIS)

A Nationwide Analysis of Water Scarcity and Cloud Seeding Demand Levels From Analyzing Water Utilization Data, Agricultural Drought Maps, and Local Conditions in China Mainland

AbstractChina has lots of funds, materials, and human resources in cloud seeding operations to help with water shortage problems across the country. But there is little guide about when and where the operations would obtain better benefits. To fill this gap, the modified Water Utilization Level (WUL) map of China mainland was plotted with the acquirable water utilization data on sub‐watershed scales in this study. Then the agricultural drought levels map of different counties were integrated with the modified WUL Map under preset rules to generate an integrated water scarcity map. Lastly, the cloud seeding demand maps of different seasons were drawn by integrating the agricultural drought seasons map of different counties with the integrated water scarcity map and the modified WUL map. Results showed that the middle north of China should have the highest priorities for cloud seeding operations during spring and summer, and some regions at the south during winter and spring, a few scattered regions at the northwest during spring and summer and a few regions at the middle of southeast China during summer and autumn should also have high priorities of cloud seeding operations. This research can help rain enhancement operations to obtain better effects on easing water scarcity problems and deepen the cooperation of water resources department and weather modification department in China. The designed intersecting rules of different maps in this paper also might inspire other relevant researches.

Assessment of agricultural drought using MODIS derived FAO's agriculture stress index system (ASIS) over the Iran croplands

In Iran, the drought is one of the costliest natural disasters, which has devastating consequences for the food security of agricultural households. Drought monitoring characteristics are important for better understanding of the drought phases in mitigation planning. Various satellite-based drought indices and systems have been developed and applied at both the regional and global scales. Recently the remotely sensed agricultural stress index system (ASIS) based on imagery from the Advanced Very High Resolution Radiometer (AVHRR-NOAA) and Meteorological Operational Satellite (METOP) by the Global Information and Early Warning System (GIEWS) of FAO developed. It shows considerable potential for drought monitoring at the global scale. Vegetation Health Index (VHI), start and end of the crop season (SOS-EOS) are the main inputs of ASI S. While the GIEWS models use the METOP-AVHRR images (1984 at present), in this study, an attempt has been made to retrieve key ASI inputs from the Moderate Resolution Imaging Spectroradiometer (MODIS) to evaluate the ability of the MODIS ASIS for characterizing agricultural drought severity and explore the impacts of drought on crop production during growing season. Comparing national and sub-national wheat and barley yields with the ASIS drought maps, demonstrated that the proposed approach could identify major historical droughts over the observed period (2002-2015) in Iran. We detected that the extreme severe drought occurred during the year 2007-2008 crop season, affecting approximately 64% of crop land.

Assessment of Meteorological Drought with Application of Standardized Precipitation Evapotranspiration Index (SPEI) for Tripura, Northeast India

Drought is one of the major water-related natural hazards. Understanding the spatial and temporal variation of rainfall is of great importance in water resources planning and management as it is related with food security and management of scarce water resource, which becomes critical in case of drought events. The advent of GIS to produce spatially interpolated drought map helps the water managers to undertake appropriate measures in drought relief and prioritization of drought mitigation works. Limitation of literature on Tripura suggests that study of drought over Tripura could help in strengthening of mitigation planes and rationalization of disaster management policies. Hence, the present study is focused to investigate the drought persistence and severity in the Tripura state of India during the period 1980-2013, using Standardized Precipitation Evapotranspiration Index (SPEI). Three time scale i.e., 3, 6 and 12 month time scales were opted for the study. Gridded monthly precipitation data distributed over the four districts of Tripura was used for drought analysis. Significant drought events were detected over the study area during the selected period. Annual analysis of SPI time series showed that the study area received the intense drought during the year 1985. Geospatial technique was used to generate the SPEI drought map for the year 1985.

Comparison of various drought indices for assessing drought status of the Northern Mekerra watershed, Northwest of Algeria

Drought is a slow-onset, creeping natural hazard and a recurrent phenomenon in the arid and semi-arid region of the Mekerra watershed. Numerous drought indices have been used in different countries to implement drought mapping and monitoring of water resources management. This investigation proves meteorological drought index combined with remote sensing drought index, but had unsteady spatial and temporal variation. The results highlight that droughts occur in semi-arid climatic region of Sidi Bel Abbes at the Northern part of the watershed. The various drought indices differ in their detection and classification of this extreme climatic event in the study area. There is high correlation (r > 0.68) among the eight indices, with PD (Precipitation deciles), DI (Deciles Index), and PNI (Percent of Normal Index) showing the highest relationship to SPI (r = 0.95 and r = 0.99 respectively). The differences observed among the drought indices in detecting drought events in the study area can be reduced by using multiple drought indices in addition to the use of SPI (Standardized Precipitation Index) in drought assessments especially at the area of Sidi Bel Abbes since technological advancement in drought monitoring software’s has made it easier. In addition, it is worthwhile to investigate on why drought detection varies with indices. Furthermore, considering the advantages and disadvantages of the mentioned drought predictors in this study area will enhance the understanding of vegetation responses to drought threatens.

A drought monitoring tool for South Asia

Drought is one of the most extreme climatic events in South Asia (SA) and has affected 1.44 billion people in last 68 years. The agriculture in many areas of this region is highly dependent on rainfall, which increases the vulnerability to drought. To mitigate the impact of drought on agriculture and food security, this study aims to develop a state-of-the-art system for monitoring agricultural drought over SA at a high spatial resolution (0.25∘) in near real-time. This study currently focuses on the rain-fed area, and the impact of irrigation is not incorporated. This open and interactive tool can assist in monitoring the near-present soil moisture conditions, as well as assessing the historical drought conditions for better management. The South Asia Drought Monitor (SADM) runs the mesoscale hydrologic model to simulate the soil moisture using observation-based meteorological forcing (at near real-time), morphological variables, and land cover data. The soil moisture index (SMI) has been calculated by estimating the percentile of the simulated soil moisture. The drought monitor displays the SMI in five classes based on severity: abnormally dry, moderate drought, severe drought, extreme drought and exceptional drought. The main functions of this open interactive system include the provisioning of up-to-date and historical drought maps, displaying long-term drought conditions and downloading soil moisture data. Comparison of the SMI with the standardized precipitation evapotranspiration index (SPEI) shows that the SMI and SPEI depict similar temporal distribution patterns. However, the SPEI (for 4, 6, 9 and 12 months) differs in the representation of the dry conditions in 1992, 2009, and 2015 and the wet condition in 1983, 1988, and 1990. We evaluated the implications of using different precipitation forcings in a hydrological simulation. A comparison of major drought characteristics such as areal extent, duration, and intensity, using different precipitation datasets show that uncertainty in precipitation forcings can significantly influence model output and drought characteristics. For example, the areal extent of one of the most severe droughts from 1986 to 1988 differs by 9% between ERA5 and CHIRPSv2.

Geospatial approach to the risk assessment of climate-induced disasters (drought and erosion) and impacts on out-migration in Nepal

Out-migration is one of the most recognized adaptation practices when dealing with scarce resources and disasters. With the general objective of exploring migration as an impact of climate-induced disasters, our study was conducted in Khaniyapani, in the Sunapati rural municipality of Ramechhap district, Nepal. Disaster prioritization was conducted using the pair-wise ranking method, with results suggesting that drought and soil erosion are the most severe disasters in the study area. The severity maps were prepared using remotely sensed data. A Normalized Difference Drought Index and the Revised Universal Soil Loss Equation were used to produce the drought and erosion severity maps, respectively. Approximately 46.2% of the total study area was found to experience severe droughts, and almost 10% of the area had high soil erosion rates. In total, 100 out of 794 households were interviewed for a semi-structured questionnaire. Drought severity was found to directly impact livelihoods due to a decline in agricultural productivity, a decline in livestock, and drying of water sources. Out of 100 families, 64 practiced seasonal migration. A decline in agricultural productivity and livestock, and water scarcity were identified as the most influencing push factors. Excessive seasonal migration has reduced the resilience of these families. Drought-resistant land, water, and crop management techniques and practices, and alternative income-generating activities should be promoted to curb the seasonal migration.

Determining and forecasting drought susceptibility in southwestern Iran using multi-criteria decision-making (MCDM) coupled with CA-Markov model

Forecasting drought and determining relevant data to predict drought are an important topic for decision-makers and planners. It is critical to predicting drought in the south of Fars province, an important agricultural center in Iran located in arid and semi-arid climates. The purpose of this study was to generate a drought map in 2019 using 12 parameters: altitude, aridity index, erosion, groundwater depth, land use, PET (Potential evapotranspiration), precipitation days, precipitation, slope, soil texture, soil salinity, and distance to river, and predict drought maps in 2030 and 2040 using the cellular automata (CA)-Markov model spatially. The fuzzy method was first used to homogenize the data. Next, by evaluating each parameter, the weight of each parameter was calculated using the analytic hierarchy process (AHP), and a map of drought-prone areas was generated. The results of the fuzzy-AHP method showed that the eastern and southeastern regions of the study area were prone to drought. The four most predictive parameters in causing drought, i.e., aridity index, PET, precipitation, and soil texture, were selected using the Best search method and were then chosen as the input to determine drought mapping using the fuzzy and AHP methods. Finally, the CA-Markov model was used to predict future drought maps, and the results showed that in 2030 and 2040 the drought situation in the east and south of the study area would intensify.

Feet in the water and hands on the keyboard: A critical retrospective of crop modelling at AfricaRice

• Rice crop modelling since 1990 at AfricaRice addressed thermal and hydric stresses. • Applied foci are breeding support, thermal stress risks, crop calendars and yield gaps. • Leaf and panicle transpirational cooling sustain photosynthesis and spikelet fertility under heat. • Phenology and yield prediction require simulating floodwater and crop temperature. • Scarce soil and hydrology data in diverse systems hamper water-limited yield gap modelling. Rice is cultivated throughout Africa in a vast array of environments. Crop growth modelling at AfricaRice seeks to develop an understanding of genotype, management and environment interactions to inform research and development. This paper reviews progress made over thirty years of modelling, as well as the knowledge gaps remaining. Major advances were made in modelling phenology and heat- and cold-induced sterility. This crucially took into account the crop-generated microclimate via transpirational cooling in irrigated rice. On this basis, the RIDEV model and its successors provided effective support to applied breeding, genetics, agronomy and cropping systems research. As a major learning, rice very effectively avoids heat stress if it can transpire water abundantly. For water-limited systems, ORYZA2000 based yield gap, climate-change impact and drought mapping projects gave direction to AfricaRice’s applied research agenda. But large gaps remain in modelling capabilities and underlying knowledge, particularly regarding biotic stresses, inland valley hydrology, and rice-based cropping sequences, e.g. including vegetable crops. In terms of understanding the physiology, more research is needed to accurately model spikelet number, thermal acclimation, photosynthesis response to extreme temperatures, and variation in rooting depth. This will require enhanced collaboration between AfricaRice and advanced research centers to resolve the scientific and technical bottlenecks in crop modelling.

Mitigation Strategic of Drought in Central Java Indonesia during Covid-19 Pandemic

In 2019, most of the areas in Central Java Indonesia experienced drought disaster. There are 3 districts that conduct emergency response due to drought disasters. If there is another drought in this year during Covid-19 pandemic, it will be very fatal due to the need for more water than usual. Besides being used for daily needs, water is also needed for washing hands and bathing during the Covid-19 pandemic. This study aims to determine the level of drought in Central Java province and provide solutions on how to reduce the drought strategy so as not to aggravate the spread of Covid-19. The method used to determine rainfall prediction is the Canonical Correlation Analysis (CCA) method with input data of average monthly rainfall for approximately 30 years in Central Java. Then to determine the drought map using the Standardized Precipitation Index (SPI) method with input data of rainfall prediction for the last 3 months from the CCA method. The results of data processing show that in August to October 2020 there was a drought with normal levels in almost all regions in central Java, except in the Muria peninsula which is still very wet (August-September) and wet (October). In November and December 2020 there was a drought in all regions in Central Java with normal levels. The level of dryness in this period is normal, meaning it is neither too high nor too dry. Meanwhile, based on several references that at the end of the year there will be a La Nina phenomenon, so that at the end of the year there will be no drought but there will be lots of rain. Several recommendations to overcome this situation will be presented in the final section of this paper.

Developing spatial agricultural drought risk index with controllable geo-spatial indicators: A case study for South Korea and Kazakhstan

Constant environmental degradation and increased frequency and severity of natural disasters have been evident over the past few decades worldwide. As such, scientific tools to predict and assess risks keep being developed. Assessing disaster risk is an important task in supporting the transition to a sustainable society. However, as disasters and systems become more complex, disaster models combining diverse aspects including climatic, social, economic, and environmental factors are necessary. For this study, we set a model using the concept of risk by identifying hazards, exposure, and vulnerability. Here, the vulnerability was classified into two domains, sensitivity and adaptive capacity, and two spheres, natural/built environment and human environment. Also, we stressed that controllable geo-spatial indicators should be included in risk assessments to effectively reduce risk and implement adequate spatio-temporal actions. The approach of this study was applied to Kazakhstan and South Korea as a pilot study to develop Agricultural Drought Risk Index (ADRI) and maps. As a result, the agricultural drought risk could be analyzed for South Korea and Kazakhstan. In addition, we performed additional spatial analyses at a reasonable scale for practical use. It was concluded that prioritizing risk areas at administrative and site level could contribute in decision and policy-making for risk reduction. Furthermore, spatial data availability and quality were found to be significant in assessing disaster risk.

Drought vulnerability assessment and mapping using Multi-Criteria decision making (MCDM) and application of Analytic Hierarchy process (AHP) for Namakkal District, Tamilnadu, India

Abstract Droughts are increasingly impacting our societies due to its slow and steady development over a period of several years. Droughts affect agriculture and lead to devastating results. Droughts are highly dependent on a wide variety of parameters ranging from climatic conditions such as rainfall, temperature, to economic conditions such as population density, irrigated land condition per person, etc. Hence, remote sensing-based studies have the fullest potential to monitor and map drought in comparison to conventional methods. Namakkal district in Tamilnadu, primarily an agrarian region, is one of the several drought-prone regions due to scarcity in rainfall and high temperatures. This study presents the importance of Geographical Information System (GIS) in assessing drought vulnerability in combination with the Analytic Hierarchy Process (AHP). In this study, GIS is used for the spatial analysis of drought for Namakkal district, Tamilnadu, India. Eight parameters such as annual rainfall, monthly rainfall, Landuse/Landcover (LULC), slope, soil type, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and population were chosen and considered for the study. AHP is used to calculate weightage factors of each criterion based on the pairwise comparison matrices. The thematic maps of all the parameters were analyzed and Drought Vulnerability Assessment (DVA) map was generated using GIS. The results of the combined multi-criteria decision making and GIS revealed that 62% of the total area of Namakkal district came under mild drought conditions. The output DVA map will provide ample information on drought severity in the region and vulnerability related to agricultural importance. This study therefore, proposes to map the drought regions using a combination of AHP and GIS. The use of nanotechnology to crop helps in the alleviation of drought-related stress and enhances the growth pattern and therefore, increases crop production and yield.

Assessment of drought with a real-time web-based application for drought management in humid tropical Kerala, India.

Geospatial techniques are useful for near real-time monitoring of drought and towards devising local-level effectivedrought management plan. Based on thehistoric and current remote sensing data, one can identify the influence of drought on the vegetation status by analyzing the anomaly/drought condition of a particular area of interest (AOI) through different digital image processing techniques. In this study, an attempt has been made to develop a web-based application for generating drought maps and district-wise drought information at real time in the web server using Hypertext Preprocessor (PHP) and Python scripts. A web-based application was developed and drought conditions existing in the study area were understood both spatially and temporally.The results of the application showed distinct variation of drought prevalence within the administrative boundaries. This web-based application was validated with drought analysis carried out using different drought indices, viz., standard precipitation index and reconnaissance drought index. The results established that this validated approach could be used for developing disaster management plan well in advance to combat the consequences of drought across the globe and to evolve strategic decisions which will have implications in the various sectors of the economy.