Content area
Assessing and mitigating drought vulnerability is crucial in the context of global climate change. This research aims to improve the precision of drought vulnerability assessments in future. The study identifies key parameters that influence drought vulnerability and integrates environmental, social, and economic factors to develop a comprehensive framework for accurate drought vulnerability mapping. This enhanced accuracy is vital for resource management and mitigation planning in drought-prone regions. The study focuses on the Ranchi district in Jharkhand, India, to analyse various types of droughts with suitable drought vulnerability parameters. An expert survey was conducted to consider the significance of these parameters that implement multi-criteria decision-making (MCDM) entropy approaches. The study weighted parameters according to different drought types, including meteorological, hydrological, and agricultural, for detailed analysis. The findings highlight the potential of MCDM methods in generating high-resolution and accurate drought vulnerability assessments, significantly contributing to sustainable water resource management and resilience-building efforts. The synthesis of spatial data from remote sensing and GIS with socio-economic, environmental, and hydrological criteria creates a comprehensive drought vulnerability model. The final drought vulnerability map for the district revealed that 15% of the area is under severe drought conditions, while 71% is moderately affected. Hydrological drought was identified as the primary cause of vulnerability, indicating a critical need to improve the district's hydrological reservoirs. Geo-AI techniques, such as machine learning and deep learning algorithms, will analyse complex spatial and temporal patterns in drought-affected regions. This research highlights the transformative role of AI-driven decision-making frameworks in addressing complex environmental challenges to provide valuable insights for policymakers and resource managers in implementing targeted drought mitigation strategies.
Introduction
Droughts create a significant threat to environmental sustainability, agriculture, and socio-economic stability, and these scenarios require advanced methods for vulnerability assessment. Drought is a complex phenomenon which is affected by both climatic and geographical factors. The effect of drought is determined on various parameters. Its conditions are initiated by less than normal rainfall and further aggravated by other climatic factors like extreme temperature and concurrent heatwaves [1, 2]. The nature, severity and impact of drought can be diverse. Drought can be categorised as meteorological, hydrological, agricultural and socio-economic [3, 4].
The concept of vulnerability is complicated; it encompasses exposure and susceptibility to disturbances or external pressures, along with the capacity for adjustment [5]. Zhou et al. define vulnerability as a pre-disaster estimation of the probability of damage that a hazard can create [6]. Since drought has a long-term effect, it would be a smart approach to study the vulnerability to determine how prepared a region is for drought. Vulnerability analysis has also been done in previous literature. Asrari and Masoudi [7]; Khubaib Abuzar et al. [8]; Pandey et al. [9]; Su et al. [10] have also studied the drought risk and vulnerability [7, 8, 9–10].
The Indian population is highly vulnerable to drought as a major part of it is dependent on agriculture. Southwest monsoon brings 80% of the rainfall during June to September, and its annual variability is associated with sea surface temperature in the Pacific and Indian Ocean [11]. Guhathakurta & Rajeevan, in their study, have focused on the negative trend of rainfall during monsoon in the states of Jharkhand, Chhattisgarh and Kerala [12]. Impacts are associated with inadequate water availability for fulfilling the requirements of both human beings and the natural environment. These include seasonal moisture deficits, encompassing soil moisture and significant evaporation, leading to diminished vegetation greenness and health, plant fatalities, dwindling water reservoir levels, and other detrimental ecological and/or socioeconomic circumstances [1]. Furthermore, the agriculture drought parameter term is used in context with three categories of drought (meteorological, hydrological and agricultural). The parameters under the category are geophysical parameters, directly affecting the conditions of agricultural drought.
Although rainfall is a major parameter of drought, parameters like streamflow, soil moisture, ground water level and reservoir storage also reflect the impact of drought. Soil moisture exhibits a response to deviations in precipitation within a brief time frame, whereas groundwater, streamflow, and reservoir storage are indicative of longer-term precipitation anomalies. Agricultural and vegetative drought can be considered because of meteorological and hydrological droughts [13].
This study develops an inclusive method towards vulnerability analysis, considering various factors under each category of drought and finding their separate weightage with expert opinion. The Ranchi district has limited studies on drought vulnerability, although studies related to precipitation trend and historical drought frequencies can be found for the region [14, 15].
Literature review
A detailed literature survey was done using the PRISMA methodology of literature review for identifying the parameters contributing to the development of drought. Scopus-indexed literatures published in the past 10 years were considered for the study. The keywords used were parameters AND drought AND vulnerability. 133 literatures were found out of which 22 literatures were found most relevant for parameter selection. A detailed list of this literature is mentioned in Table 1.
Table 1. Significant literature for the identification of drought vulnerability parameters
S. No. | Research paper | Drought vulnerability parameters used | Key findings |
|---|---|---|---|
1. | Neto and Santos [12] | Precipitation | Introduced and validated the NIFT index’s potential as a crucial instrument for meteorological drought monitoring |
2. | Mahato et al. [16] | Physical attributes (rainfall, temperature, evapotranspiration and wet day frequency), water demand and use, agriculture, land use, ground water and population and development | The study considered 6 main parameters of drought vulnerability and further divided them into 22 subparameters. It generated a comprehensive drought susceptibility map for the northwestern region of Odisha. The map classified the region into five levels of drought vulnerability |
3. | Jia et al. [17] | NDVI, TVDI and EVI | Five regional drought vulnerability curves were constructed. Research highlighted significant regional differences in drought vulnerability, with the farming pastoral ecotone at high risk |
4. | Kumar Goyal et al. [18] | Precipitation (SPI), Runoff and Soil moisture (SRI and SSI) and NDVI (VCI) | The study assessed urban drought risk and provided valuable insights for policymakers for water resource management |
5. | Nyayapathi et al. [19] | LULC, LST, NDVI, elevation, slope, aspect, TWI, drainage density, rainfall, soil texture, lithology, groundwater and distance from water bodies | The study developed a comprehensive drought vulnerability map using geospatial techniques and found that Kurnool has a drought vulnerability index of 42.5 |
6. | Saha et al. [20] | VCI, VHI, NDVI, VF, forest fraction (FF), soil depth, LULC, geology, soil texture, rainfall, temperature, evapotranspiration, drainage density, slope, elevation, socioeconomic parameters | The study has prepared a drought susceptibility map for spatial drought vulnerability |
7. | Alharbi et al. [22] | Elevation, slope, aspect, LULC, NDVI, NDWI, LST, population density, NDDI, VCI and SMI | The study found the Drought risk caused by low vegetation cover, dense population, steep slopes, and moderate elevation |
8. | Soľáková et al. [23] | SPI and SSI | The study found precipitation as a primary cause for drought in basins; the long-term effect of it causes a deficit in the levels of water bodies |
9. | Farid Nabizada et al. [24] | NDVI, LST, precipitation and soil moisture, LULC and elevation | The study found the effect of soil moisture and LST on vegetation cover, while the relation between vegetation cover and meteorological drought was insignificant |
10. | Halder et al. [25] | NDVI, VCI, LST, TCI, VHI, NDWI, SAVI, SPI, SMI | An escalation in the adverse SPI values has been observed in recent years, serving as a key signal of drought. The primary drivers influencing the algorithm for forecasting drought-prone areas include VHI (0.169), SPI (0.144), and TCI (0.142). The intricate relationship among these three factors accounts for areas with limited water resources and inadequate vegetation cover |
11. | Elusma et al. [26] | Soil depth, soil moisture, soil texture, LULC, slope, elevation, population density, rainfall, temperature, evaporation, relative humidity, distance to river, river density, distance to road, available water capacity | The study focused on adaptive capacity and agricultural drought risk in the area. 68% of the study area had a moderate to very high risk of drought |
12. | Saha et al. [27] | Rainfall, temperature, evaporation, socio-economic factors | Spatial mapping of drought vulnerability offers a geographical framework for pinpointing susceptible areas at a sub-state level. The creation of such a vulnerability map holds significant value, especially within the context of India, where a considerable segment of the populace relies heavily on agricultural activities |
13. | Hoque et al. [28] | LULC, elevation, slope, surface water bodies, PAWC, soil depth, soil moisture, stream density, precipitation, evaporation | A drought vulnerability map for the overall assessment of drought vulnerability of the area was prepared. Fuzzy Analytical Hierarchy Process was used in the study along with geospatial techniques |
14. | Sivakumar et al. [29] | Rainfall, LULC, soil, slope, NDVI, NDWI, population | The study demonstrates that evaluating the spatial distribution of drought and mapping a specific area is achievable through the utilisation of the datasets and methodologies proposed in the research. This approach aids in mitigating the impact of drought by considering multiple factors and addressing drought management strategies |
15. | Oikonomou et al. [30] | Precipitation, evapotranspiration, landcover and NDVI | The study discussed the Standardised Drought Vulnerability Index (SDVI) for the assessment of drought vulnerability by integrating satellite and in-situ data |
16. | Hariyanto et al. [31] | Rainfall, LULC, LST, wetness index, brightness index and crown density with EVI | The study discussed the role of remote sensing and GIS in drought vulnerability mapping using the vulnerability parameters, by weighing and overlaying them |
17. | Das et al. [32] | NDVI, Rainfall, Landcover | In this study, the trend and vulnerability of meteorological drought were found to be decreasing across India. NDVI was found positive across most parts of the country, and positive trends of crop growing season were found in most parts of the country except the peninsular region |
18. | Panisset et al. [33] | Precipitation, LST and Solar radiation | The study analysed extreme episodes of drought dominated by different circulation regimes. They differed in terms of spatial pattern and temporal intensities |
19. | Sung et al. [34] | SPEI | The study used the Reliability-Resiliency-Vulnerability approach to analyse the future drought characteristics |
20. | Karamouz et al. [35] | Precipitation, temperature, solar radiation, slope, land use, soil type, evapotranspiration, ground water level, surface water storage | The study focused on drought vulnerability and resiliency. The resiliency was found to be a better means for finding vulnerability and its variation. The study is apt for arid and semi-arid regions |
21. | Palchaudhuri and Biswas [36] | Rainfall, evapotranspiration, temperature, humidity, LULC, ground water, slope, soil texture, population, cultivator, agricultural labourers | The study has evaluated the spatial extent and drought characteristics using AHP in combination with GIS, finding it a good solution in drought risk modelling |
22. | Ekrami et al. [37] | Slope, aspect, precipitation, geological formation, qanat discharge, evaporation, soil texture | The study has prepared agricultural drought vulnerability map of the study area |
Based on the above literature, 12 parameters were selected for the drought vulnerability study of Ranchi. These parameters were selected as per the availability of data, ease of access and their suitability in the study region. The socio-economic parameters, like agriculture, population density, have been excluded from this study. The indices using various criteria were also not considered for further study. The selected parameters have been formulated across three distinct categories of drought types (meteorological, hydrological, and agricultural). Multi-criteria decision-making (MCDM) techniques have been widely used by researchers for estimating the weightage of the parameters.
Geospatial Artificial Intelligence (Geo-AI) has emerged as a useful approach to provide an innovative structure for analysing complex spatial datasets in environmental monitoring and risk evaluation. Geo-AI enhances the accuracy and reliability of drought vulnerability estimation after mapping and integration with Multi-Criteria Decision-Making (MCDM) techniques. These connect numerous factors like climate, land use, and soil moisture for informed decision-making to offer better resource management and disaster preparedness. This study attempts to explore the synergistic use of GeoAI after the MCDM for effective assessment and mapping of drought-prone areas. The Multi-Criteria Decision-Making (MCDM) techniques utilise their ability to enhance drought vulnerability assessments. Application of Geospatial Artificial Intelligence (Geo-AI) after the MCDM approach improves the accuracy of mapping by considering diverse environmental factors such as climate, soil, and land use. It enables policymakers to make data-driven decisions, prioritise high-risk areas, and allocate resources efficiently. This integration supports proactive drought management, strengthens resilience, and fosters sustainable environmental planning in the face of climate change. Figure 1 provides the schematic for the selection of drought vulnerability parameters used for the analysis after a comprehensive literature review. The study with multi-criteria decision-making (MCDM) techniques for drought vulnerability estimation and mapping utilises the typical tools, like:
GIS Software (e.g., ArcGIS, QGIS)—For spatial data analysis and visualisation.
Remote Sensing Tools (e.g., Google Earth Engine)—To gather climate, soil, and land-use data.
Machine Learning Libraries (e.g., TensorFlow, Scikit-learn)—For Geospatial Artificial Intelligence (Geo-AI) based predictive modelling.
MCDM Tools (e.g., AHP, TOPSIS)—For evaluating and ranking multiple decision criteria.
Data Analytics Platforms (e.g., Python, R)—For processing large datasets and running algorithms.
[See PDF for image]
Fig. 1
Selection of parameters for Drought vulnerability
Geospatial Artificial Intelligence (Geo-AI) is applied to estimate and map drought vulnerability with the integration of advanced AI techniques with geospatial data analysis, with the following steps:
Data Collection: It collects extensive spatial data from sources like satellite imagery, remote sensing, weather stations, and ground surveys. This includes variables such as precipitation, temperature, soil moisture, vegetation indices, and land use patterns.
Data Preprocessing: This cleans and preprocesses the collected data to ensure accuracy. It involves error correction, filling in missing values in the datasets like satellite images, and standardising data formats.
Feature Extraction: Uses supervised or unsupervised algorithms to extract relevant features from the data. The satellite data is used to identify patterns in vegetation health using the Normalised Difference Vegetation Index (NDVI).
Integration with MCDM: Integration of Multi-Criteria Decision-Making methods to evaluate and prioritise the various factors of the drought vulnerability. Some other approaches, like the Analytic Hierarchy Process (AHP), also assist in assigning weights to different criteria based on their relative importance.
Model Development: The Geo-AI-based predictive models, like neural networks, random forests, or support vector machines, are used to analyse the relationships between the factors to predict areas of high drought vulnerability.
Spatial Analysis: The use of Geo-AI in the GIS-based system performs spatial analysis on the model outputs to visualise the spatial distribution of drought risk across the area.
Mapping and Visualisation: A detailed map is created to display varying levels of drought vulnerability to serve as a valuable tools for policymakers and stakeholders to identify high-risk zones.
Validation: The developed models and maps are validated with historical data and ground truthing to ensure their reliability and accuracy.
Decision Support: The obtained results provide insights to support decision-making processes related to drought preparedness, resource allocation, and mitigation strategies.
The proper use of predictive capabilities of Geo-AI after MCDM techniques offers an inclusive methodology to understand and manage drought vulnerability. This integration allows for more accurate predictions and effective interventions to mitigate the impacts of drought. The present study identifies the parameters that specifically contribute to the study area towards drought vulnerability, with an analysis of the parameters and categorising them into different drought types. Calculating the weightage of the parameters using a multi-criteria decision-making method. Preparing a vulnerability map for each type to find what type of drought affects the district most, and finally aggregating them to get a final drought vulnerability map of the district. The drought vulnerability map can help in policy making regarding land use and cropping patterns for preventing the effects of drought in the district.
Materials and methods
Study area
Ranchi is located at 22° 52′–23° 45′ North latitude and 84°45′–85°50′ East longitude in the state of Jharkhand, India, as shown in Fig. 2. It is a part of the Chottanagpur plateau with its altitude at 500–700 m above the mean sea level.
[See PDF for image]
Fig. 2
Location of study area
In the eastern direction of the district flows the Subarnarekha River, which descends at the Hundru fall. In the northwestern part of the district, originates Sankh River and the South Koel rises in Mandar and flows towards the north in the district. The district has a subtropical, semi-arid type of climate. It receives annual rainfall of 1057.12 mm, with the months of July–August receiving maximum rainfall and November receiving minimum rain. The maximum mean temperature of the district rises to 40 °C in summer, i.e. March to May and falls to a minimum of 7 °C during the winter months of November to February. The district is classified as a hot semi-arid eco-region of agroecological classification. Agriculture and forestry are the main land use/land cover of the district. The district is mostly dependent on agriculture and animal husbandry. Mostly cereals, vegetables and fruit crops are grown here. It has a diverse crop culture; rabi, kharif, and summer are major crops. Afghani crops are also sown in the monsoon, which is harvested in January- February [38].
Data and software
The data has been collected from various secondary sources and satellite imagery. The list of sources of data has been given in Table 2. The slope and elevation map has been derived from SRTM DEM data, and the same was used for calculating the hydrology of the area using the hydrology tool available in the spatial analyst toolbox of ArcMap 10.8. The sinks in the DEM were first filled using the fill tool, and then flow accumulation and direction were mapped, and later stream order and linked were generated. The raster layer was then converted to a feature layer using the stream to feature tool. The buffers were generated to calculate stream density and distance from water bodies [39]. The soil texture map was extracted from the soil map of the world prepared on a scale of 1:5,000,000.
Table 2. Sources of data layers used in the study
S. No. | Data layers | Data Sources |
|---|---|---|
1. | Slope, elevation, drainage density and distance from stream | SRTM DEM Data https://earthexplorer.usgs.gov/ |
2. | Soil Texture | FAO UNESCO Soil map of the world (DSMW) @ 1:5,000,000 scale.'Source: Land and Water Development Division, FAO, Rome' https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/ |
3. | LULC | Landsat8 15 m resolution LC08_L1 TP_140044_20200216_20200823_02_T1 LC08_L1 TP_141044_20200326_20200822_02_T1 https://earthexplorer.usgs.gov/ |
4. | Ground water | CGWB Data https://indiawris.gov.in/ |
5. | Precipitation | IMD Gridded Data https://imdpune.gov.in/lrfindex.php |
6. | Temperature | IMD Gridded Data https://imdpune.gov.in/lrfindex.php |
7. | PAWC | Global_climatic_plant_available_soil_water_1 km_1 m_v1.0.tif [40] https://zenodo.org/records/6777126 |
8. | Surface Water | Global Surface water [41] https://global-surface-water.appspot.com/download |
The classification is based on the dominant soil unit. The Land Use and Land Cover (LULC) has been calculated using a supervised calculation method for the year 2020. The data used was Landsat 8 imagery (OLI_TIRS) from Collection 2 level 1, extracted from the United States Geological Survey (USGS) Earth Explorer (http://earthexplorer.usgs.gov/). The images were pre-processed; hence, no atmospheric correction was done. The groundwater data was collected from the CGWB official website. A thematic map was prepared using block-wise ground availability data from the year 2020. The precipitation and temperature data have been collected from the climate data supply portal of the India Meteorological Department. The rainfall data has a high spatial resolution of 0.25 X 0.25 degrees, and temperature has a resolution of 1 × 1 degrees. These are daily gridded data with the unit of millimetres (mm) for rainfall and Celsius for temperature [42, 43]. The data was further converted into mean annual data, and an average of 30 (1990–2020) years was taken. Evapotranspiration was calculated using the Hargreaves method. The method utilises the precipitation, maximum and minimum temperature to calculate evapotranspiration. The details of the calculation can be found in [44]. Plant Available Water Capacity (PAWC) is the amount of water available for plant use. It can be defined as the difference between the water content at field capacity and the wilting point in the root zone. Climatic PAWC has been considered in this study. Surface water occurrence has been extracted from the global surface water dataset. The data set is prepared using long-term water history and presents different facets of surface water dynamics. Landsat satellite images over the decades have been used to generate these maps [41]. All the thematic maps are projected under the Universal Transverse Mercator (UTM) projection of World Geodetic System (WGS) 1984 zone. All the thematic maps have been prepared using ArcMap 10.8. The software is used for creating maps and spatial analysis. The analysis of data is done using MS Excel.
Methodology
In the current investigation, twelve essential drought indicators have been precisely selected with a thorough examination of literature, local research, data availability, data reliability, and their relevance to the drought vulnerability. The thematic maps have been prepared with reliable sources of data. The maps have been classified into five (5) categories of drought severity (normal, mild, moderate, severe and extreme) with a natural break method of statistics. The maps were reclassified to make raster layers. A survey was conducted to rate the parameters. The population of the survey was professors’ students and experts working on the relevant topic. Figure 3 indicates a detailed flowchart of the methodology used in the study. 25 responses were collected and evaluated using the entropy method of multi-criteria decision making (MCDM), for generating the weightage of each parameter. The weightage was determined by applying the entropy weighing method to the survey data.
[See PDF for image]
Fig. 3
Summary of research scheme
The raster layers were then overlaid through the weighted overlay method in ArcMap 10.8 for generating the overall drought vulnerability map of the area.
Entropy weight method
The parameters in this study have been weighed based on the entropy method of multi-criteria decision making (MCDM). The method has the advantage of avoiding human factor interference on the parameter weight, thus, the method enhances the objectivity of the evaluation [45]. It determines the variables which have the maximum impact on the occurrence of an event [46]. The method was first introduced by Shannon (1948, also known as Shannon Entropy. The method has been recently used by Arora et al., 2021; Haghizadeh et al., 2017; Mahmoodi et al., 2023[47, 48–49] for vulnerability and susceptibility analysis as GIS-based multi-criteria decision analysis (MCDA). The rating and pair-wise comparison method used in criteria weighing is are subjective process, but the entropy method is an objective process which determines the weightage mathematically. The ranks determined by experts were used to generate the weights of the criteria [50]. The steps for entropy calculation are as follows [51]:
Normalisation of decision matrix m:where, i = number of alternatives and j = number of parameters.
Calculation of entropy:where, i = 1, 2, 3, …., m
Derive weights based on entropy:where, is coefficient of variation and j = 1, 2, …., n
Weighted overlay method
Weighted overlay analysis is a suitability modelling tool in ArcGIS that allows for assigning weights to the criteria and overlaying the raster layers. It multiplies each raster cell’s value by the layer weight and sums up the values to derive a suitability value. The method has been used by many researchers for vulnerability assessment [52, 53].
where, are the weights which maximum sum up to 100.
are drought vulnerability scales.
Result and discussion
Meteorological drought parameters
Meteorological parameters selected for vulnerability analysis are mean annual rainfall, mean annual temperature and evapotranspiration.
The distribution of these parameters is shown in Fig. 4. A deficit of rainfall is one of the primary causes of drought. Higher vulnerability scales were allotted to areas with lesser rainfall, and a lower vulnerability class was assigned to areas with higher rainfall. Similarly, high-temperature areas are more vulnerable to drought than areas with low temperatures [20]. Evaporation has a direct relation to drought conditions. The area with high evapotranspiration rates has more risk of drought, as due to increased evaporation, soil moisture decreases.
[See PDF for image]
Fig. 4
Thematic maps of meteorological drought parameters (i.e. Average rainfall, average temperature and evapotranspiration)
This directly impacts agricultural practices and food production in the area [36, 54]. Therefore, a lower value of evaporation is given a lower vulnerability class, and a higher value is given a higher vulnerability class. Details of weightage, value and vulnerability class of parameters are described in Table 3. Vulnerability class 1 is the least vulnerable, and class 5 is the most vulnerable. Singh et.al [14] have revealed that on an annual basis potential evapotranspiration of the district exceeds precipitation, and the total annual water deficit during the year, on average, comes to 893.6 mm.
Table 3. List of values, weightage and vulnerability class of meteorological drought parameters
Meteorological parameter | Values | Weightage | Vulnerability class |
|---|---|---|---|
Temperature (°C) | 25.00–25.10 | 42 | 1 |
25.10–25.20 | 2 | ||
25.20–25.30 | 3 | ||
25.30–25.41 | 4 | ||
25.41–25.57 | 5 | ||
Precipitation (mm) | 973.13–1044.36 | 27 | 5 |
1044.36–1108.99 | 4 | ||
1108.99–1174.93 | 3 | ||
1174.93–1240.88 | 2 | ||
1240.88–1309.46 | 1 | ||
Evapotranspiration | 138.40–139.45 | 31 | 1 |
139.45–140.17 | 2 | ||
140.17–141.10 | 3 | ||
141.10–142 | 4 | ||
142–143.06 | 5 |
Geo-AI plays a crucial role in our research by providing an advanced framework for analysing complex spatial datasets. Integrating Geo-AI with Multi-Criteria Decision-Making (MCDM) techniques enhances the precision and reliability of our environmental monitoring and risk assessment. Geo-AI’s predictive capabilities, such as machine learning and neural networks, allow us to model and manage drought vulnerability effectively. By combining MCDM's structured evaluation process with Geo-AI's data-driven insights, we develop a comprehensive methodology for better understanding and mitigating environmental risks, leading to more informed decision-making and resource management.
Hydrological drought parameters
The parameters selected for hydrological drought vulnerability are elevation, drainage density, groundwater availability and surface water occurrence. Hydrological drought is the stage when there is a shortage of water due to a lack of rainfall. The hilly areas in the district occur at an altitude range of 680–580 m. The areas with higher elevation are more prone to drought [55]. The water always flows from higher to lower altitudes [56].
Drainage density is the total length of streams per unit area. Its unit is kilometres per square kilometre. A lesser value of drainage density shows that fewer channels have formed for channelisation of surface runoff. More surface water flow makes the area more prone to drought. Hence, the higher drainage density has a higher value of vulnerability class.
Groundwater is also an important parameter of drought. A decrease in groundwater will increase the vulnerability to drought. Sufficient availability of groundwater supports the water supply when rainfall is not sufficient. Southern and Eastern parts of the district have low groundwater availability compared to the central part of the district. Surface water availability controls the hydrological drought vulnerability [57]. They regulate soil moisture during the drought period in the nearby agricultural land. The area has three rivers flowing in the north, east and north-western part of the district. The distribution of the parameters is shown in Fig. 5. The details of the values, weights and vulnerability class of the parameters are given in Table 4.
[See PDF for image]
Fig. 5
Thematic maps of hydrological drought parameters (elevation, drainage density, groundwater availability, surface water availability)
Table 4. List of values, weightage and vulnerability class of meteorological drought parameters
Hydrological Parameters | Value | Weightage | Vulnerability class |
|---|---|---|---|
Elevation | 184–345 | 13 | 1 |
345–475 | 2 | ||
475–565 | 3 | ||
565–650 | 4 | ||
650–1053 | 5 | ||
Drainage Density | 0.40–0.61 | 22 | 1 |
0.61–0.82 | 2 | ||
0.82–1.03 | 3 | ||
1.03–1.24 | 4 | ||
1.24–1.45 | 5 | ||
Groundwater fluctuation | 945–1700 | 30 | 5 |
1700–2100 | 4 | ||
2100–2500 | 3 | ||
2500–3200 | 2 | ||
3200–4815 | 1 | ||
Surface water | 0–8 | 35 | 5 |
0–29 | 4 | ||
29–51 | 3 | ||
51–81 | 2 | ||
81–99 | 1 |
Agricultural drought parameters
The drought parameters selected for agricultural drought vulnerability are slope, soil texture, LULC, PAWC and Distance from water bodies. The distribution of these parameters is shown in Fig. 6. The ratio between “rainfall to runoff” and “rainfall to recharge” is determined by slope. Steep slopes will have less chance of infiltration [58]. The slope of the district is divided into five categories, and with the increase in slope, water infiltration decreases. It affects the water recharge and land use of the area. 79% area has a gentle to moderate slope, i.e. plateau region, and 21% area has a strong to steep slope, i.e. valley region. A high vulnerability class is assigned for a high slope.
[See PDF for image]
Fig. 6
Thematic maps showing agricultural drought parameters (slope, distance from water bodies, soil texture, PAWC, LULC)
The sand content in the soil determines the drought vulnerability of the agricultural land [59]. Among the four types of soil classified in the district, nitisols are the major soil type. It has 39% sand content. The second major soil texture that appears is lithosols, and it has 59% sand content. The higher the content of sand in the soil, the greater the vulnerability to drought. Surface near to stream will have more soil moisture and will be less affected by the soil compared to the distant surface.
So, the smaller distance to the stream was given a lesser vulnerability class. Agricultural land use will be more vulnerable to drought compared to the built-up area. Forest and crop land can be classified as dry areas and hence more drought vulnerable [6, 60]. Less water available for the soil will cause more vulnerability to agricultural drought. The value of parameters, their weightage and vulnerability class are given in Table 5.
Table 5. List of values, weightage and vulnerability class of agricultural drought parameters
Parameters | Value | Weightage | Vulnerability class |
|---|---|---|---|
Slope (%) | 0–5 | 10 | 1 |
5–13 | 2 | ||
13–26 | 3 | ||
26–43 | 4 | ||
43–206 | 5 | ||
Soil texture | Lithosols | 26 | 3 |
Ferric luvisols | 5 | ||
Distric Nitosols | 1 | ||
Euric nitosols | 4 | ||
LULC | Water body | 22 | 1 |
Agriculture | 5 | ||
Vegetation | 4 | ||
Barren | 2 | ||
Built up | 3 | ||
Distance from water bodies | 400 | 18 | 1 |
800 | 2 | ||
1200 | 3 | ||
1600 | 4 | ||
> 2000 | 5 | ||
PAWC | 90–115 | 25 | 5 |
115–125 | 4 | ||
125–135 | 3 | ||
135–145 | 2 | ||
145–160 | 1 |
Meteorological, hydrological and agricultural drought vulnerability
The parameters of the drought vulnerability were overlaid using a weighted overlay method to calculate the vulnerability of each of the drought types. The weightage of each parameter under different drought types is given in Table 6. The drought vulnerability map obtained after overlaying the thematic layer map of each drought category is given in Fig. 7.
Table 6. Weightage of each drought category (meteorological, hydrological and agricultural)
Meteorology | Hydrology | Agriculture | |||
|---|---|---|---|---|---|
Parameters | Weightage (%) | Parameters | Weightage (%) | Parameters | Weightage (%) |
Temperature | 42 | Elevation | 13 | Slope | 10 |
Precipitation | 27 | Drainage Density | 22 | Soil texture | 26 |
Evapotranspiration | 31 | Groundwater fluctuation | 30 | Land Use and Land Cover | 22 |
Surface water availability | 35 | Distance from water bodies | 18 | ||
PAWC | 25 | ||||
[See PDF for image]
Fig. 7
Weighted overlaid maps of meteorological, hydrological and agricultural drought
Drought vulnerability has been divided into five classes: normal, mild, moderate, severe and extreme. Similar classification was done by [52]. It was found that very less extreme drought condition prevails in the district. Although severe drought conditions do exist. 51% area was under mild meteorological drought, 41% was under moderate meteorological drought, and 8% area was under severe meteorological drought. Jeet et al., Singh et al., and Sinha et al. have also focused on the negative trend of monsoon rainfall in the district, and various indices indicate the presence of moderate to severe drought in the district [14, 15, 61]. Most of the area (43%) is under severe hydrological drought.
This was also evident from groundwater data, as most areas had low groundwater levels, especially the southern part of the district. Extreme hydrological drought exists in the district in some places, this area is very small, less than 1%. There is 49% moderate and 7% mild hydrological drought. Figure 8 shows the area of districts under different drought vulnerability categories, viz, meteorological, hydrological and agricultural. Most of the area, including 43%, is under severe and 49% under moderate agricultural drought vulnerability. 7% area is under mild drought, and less than 1% area is under extreme drought. Table 7 shows the area of the district with different drought vulnerability classes. Figure 8 exhibits the area of the district under different drought vulnerabilities.
[See PDF for image]
Fig. 8
Area of the district under different drought vulnerability
Table 7. Area of the district with different drought vulnerability classes
Meteorology | Hydrology | Agriculture | |||
|---|---|---|---|---|---|
Vulnerability class | Area% | Vulnerability class | Area% | Vulnerability class | Area% |
Normal | 0 | Normal | 0 | Normal | 0 |
Mild | 51 | Mild | 7 | Mild | 7 |
Moderate | 41 | Moderate | 49 | Moderate | 49 |
Severe | 8 | Severe | 43 | Severe | 43 |
Extreme | 0 | Extreme | 0 | Extreme | 0 |
Total drought vulnerability
The meteorological, hydrological and agricultural drought vulnerability maps were again overlaid with their respective weightage as calculated by the entropy method and resulting in an aggregated drought vulnerability map of the district. The hydrological drought had a weightage of 40%, agricultural drought had a weightage of 36%, and meteorological drought had 26% weightage, while the final overlay of the map for overall drought vulnerability. This shows that the district is facing more hydrological drought and hence struggling with water supply issues. The final drought vulnerability map is shown in Fig. 8.
71% of the area is under moderate, 15% under severe and 14% area is under mild drought vulnerability. Extreme drought is very little in the district, less than 1% area. Similar results were discussed by Pandey et al. [60] in Palamu district, which has similar climatic conditions to those of Ranchi district. Tigga and Malini [62] have also discussed similar results in the Ranchi district. They focused on that there is a threefold surge in impervious surface in the district, which prevents the ground water recharge, and an increase in population and urbanisation has posed a threat to the water supply. Table 8 shows the area of the district under the vulnerability class of total drought. Figure 9 shows the total drought vulnerability map of the Ranchi district.
Table 8. Area of the district under the vulnerability class of total drought
Vulnerability class | Area% |
|---|---|
Normal | 0 |
Mild | 14 |
Moderate | 71 |
Severe | 15 |
Extreme | 0 |
[See PDF for image]
Fig. 9
Total drought vulnerability map of Ranchi district
Discussion
Three meteorological drought parameters, precipitation, temperature and evapotranspiration, are considered. The evapotranspiration has the highest weightage. The drought begins with meteorological conditions, specifically due to precipitation deficit, but rising temperature and evapotranspiration can cause more water deficit, affecting the biophysical processes. The increased evapotranspiration can make the drought condition more severe by decreasing the soil moisture, crop failure and wilting of plants. Julia. S. Stoyanova has studied the effect of evapotranspiration as a water stress precursor [63]. Wang et al. has also investigated into the effect of PET in Yellow River basin, China which has faced many droughts and evapotranspiration has added to the severity of drought condition [64]. A similar result was also discussed by Singh et al. [14].
Geospatial Artificial Intelligence (Geo-AI) has emerged as a useful approach to provide an innovative structure for analysing complex spatial datasets in environmental monitoring and risk evaluation. Geo-AI enhances the accuracy and reliability of drought vulnerability estimation after mapping and integration with Multi-Criteria Decision-Making (MCDM) techniques.
43% of the district area was found under severe hydrological drought. Out of four parameters, surface water availability and ground water availability had the highest criteria weightage. In semi-arid regions, the groundwater and surface water are major sources of water for the population for various activities like domestic and agricultural. The district is a part of the Chota Nagpur plateau region. The hilly topography of the region leads to more runoff and less groundwater recharge [10]. Also, the irrigation in this region is done through bore wells or wells, which utilise the groundwater. Hence, the groundwater is overexploited. Apart from this, the prevalence of meteorological drought over a long period leads to hydrological drought. All these situations add up to the hydrological drought severity of the region. The district needs a better irrigation system and a rainwater harvesting system. Due to excess runoff, the drought vulnerability of the region becomes severe. Also, the cropping pattern of the district should include more drought-resistant crops like millets to ensure food security in the region.
Conclusion
Most area of the district 71%, falls under moderate drought vulnerability. 15% area is under severe drought vulnerability northwest, northeast, and southern parts of the district have severe drought vulnerability. Hydrological drought is one of the major categories of drought that prevails in the district. Although meteorological and agricultural drought also share a good percentage of vulnerability, i.e. 24% and 36% respectively. In hydrological drought, groundwater and surface water availability have a major share in weightage, i.e. 30% and 35% respectively. Groundwater is a major concern throughout the district, as a major part of the district has a low groundwater table. Conservation of lakes and rivers in the district and a proper groundwater recharge system can help in combating the drought situation in the district.
The integration of diverse environmental, social, and economic factors along with MCDM provides a comprehensive approach to assessing and mapping vulnerability for more accurate and targeted mitigation strategies. The study highlights the importance of a comprehensive, multi-dimensional approach to drought vulnerability, paving the way for more resilient communities and ecosystems. Future research should focus on refining these techniques and exploring their application in different geographic and climatic contexts to further strengthen drought preparedness and response. This combined methodology not only improves resource allocation and disaster preparedness but also supports sustainable development and resilience in the face of climate change. The results provide valuable insights for policymakers to implement effective drought mitigation strategies.
Author contributions
Ms Shalwee (SH), Dr. Deepak Kumar (DK), and Dr Anil Kumar Gupta (AG) conceived and designed the framework of the research, and Ms Shalwee (SH), performed the research, Dr Maya Kumari, Dr. Renu Dhupper (RD) along with Dr Deepak Kumar (DK) analyzed the data with contributions to the editorial input. Conceptualization, methodology and formal analysis: SH, DK, MK, AG; investigation: DK, AG, MK, RD; visualization: SH, MK, DK; writing—original draft: SH, MK, DK; writing—review and editing: SH, MK, DK; All authors read and approved the final manuscript.
Funding
“No funding was obtained for this study”.
Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Clinical trial number
Not applicable
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
1. Akinyemi FO. Vegetation trends, drought severity and land use-land cover change during the growing season in semi-arid contexts. Remote Sens (Basel). 2021;13(5). https://doi.org/10.3390/rs13050836.
2. Mangal, S; Kumar, D; Dhupper, R; Kumari, M; Gupta, AK. Identifying influential climatic factors for urban risk studies in rapidly urbanizing Region. Computat Urban Sci; 2024; 4,
3. Dai A. Drought under global warming: a review. Wiley-Blackwell; 2011. https://doi.org/10.1002/wcc.81.
4. Wilhite DA. Chapter 1 drought as a natural hazard: concepts and definitions; 2000. [Online]. Available http://digitalcommons.unl.edu/droughtfacpub
5. Gallopín, GC. Linkages between vulnerability, resilience, and adaptive capacity. Glob Environ Chang; 2006; 16,
6. Zhou, H; Wang, J; Wan, J; Jia, H. Resilience to natural hazards: a geographic perspective. Nat Hazards; 2010; 53,
7. Asrari, E; Masoudi, M. A new methodology for drought vulnerability assessment using SPI (standardized precipitation index). Int J Sci Res Knowl; 2014; 2,
8. Khubaib Abuzar M et al. Drought risk assessment using GIS and remote sensing: a case study of District Khushab, Pakistan; 2017.
9. Su Z, He Y, Dong X, Wang L. Drought monitoring and assessment using remote sensing; 2017. p. 151–72. https://doi.org/10.1007/978-3-319-43744-6_8.
10. Pandey, S; Pandey, AC; Nathawat, MS; Kumar, M; Mahanti, NC. Drought hazard assessment using geoinformatics over parts of Chotanagpur plateau region, Jharkhand, India. Nat Hazards; 2012; 63,
11. Mishra V. Long-term (1870–2018) drought reconstruction in context of surface water security in India. J Hydrol (Amst). 2020;580. https://doi.org/10.1016/j.jhydrol.2019.124228.
12. Brasil Neto RM, Santos CAG. The NIFT index: a new approach to assessing meteorological drought exposure. J Hydrol (Amst). 2024;632:130857. https://doi.org/10.1016/j.jhydrol.2024.130857.
13. Bhuiyan, C; Singh, RP; Kogan, FN. Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data. Int J Appl Earth Obs Geoinf; 2006; 8,
14. Singh PK et al. Frequencies of drought at Ranchi regions, Jharkhand; 2009.
15. Sinha, A; Kumari, A; Mahapatra, S; Singh, HP; Bharti, B. Temporal rainfall variability and its correlation with temperature over Ranchi, Jharkhand. Int J Eng Adv Technol; 2019; 9,
16. Mahato, S; Mandal, G; Kundu, B; Kundu, S; Joshi, PK; Kumar, P. Comprehensive drought vulnerability assessment in Northwestern Odisha: a fuzzy logic and analytical hierarchy process integration approach. Water (Basel); 2023; 15,
17. Jia, H; Chen, F; Du, E; Wang, L. Drought vulnerability curves based on remote sensing and historical disaster dataset. Remote Sens (Basel); 2023; 15,
18. Kumar Goyal M, Poonia V, Jain V. Three decadal urban drought variability risk assessment for Indian smart cities. J Hydrol (Amst). 2023;625:130056. https://doi.org/10.1016/j.jhydrol.2023.130056.
19. Nyayapathi, P; Penki, R; Basina, SS. Drought vulnerability assessment by employing the geographical information system and analytical hierarchy process for the Kurnool district of Andhra Pradesh, India. Ecocycles; 2023; 9,
20. Saha, A; Pal, SC; Chowdhuri, I; Roy, P; Chakrabortty, R; Shit, M. Vulnerability assessment of drought in India: Insights from meteorological, hydrological, agricultural and socio-economic perspectives. Gondwana Res; 2023; 123, pp. 68-88. [DOI: https://dx.doi.org/10.1016/j.gr.2022.11.006]
21. Alharbi RS, et al. Assessment of Drought vulnerability through an integrated approach using AHP and geoinformatics in the Kangsabati River Basin. J King Saud Univ Sci. 2022;34(8). https://doi.org/10.1016/j.jksus.2022.102332.
22. Alharbi, RS et al. Assessment of Drought vulnerability through an integrated approach using AHP and geoinformatics in the Kangsabati River Basin. J King Saud Univ Sci; 2022; 34,
23. Soľáková, T; Zeleňáková, M; Mikita, V; Hlavatá, H; Simonová, D; Elhamid, HA. Assessment of meteorological and hydrological drought using drought indices: SPI and SSI in eastern Slovakia. Acta Hydrologica Slovaca; 2022; 23,
24. Farid Nabizada A, et al. A remotely sensed study of the impact of meteorological parameters on vegetation for the eastern basins of Afghanistan; 2022. https://doi.org/10.21203/rs.3.rs-2267890/v1.
25. Halder, S; Roy, MB; Roy, PK. Modelling drought vulnerability tracts under changed climate scenario using fuzzy DEMATEL and GIS techniques. Theor Appl Climatol; 2022; 150,
26. Elusma M, Tung C, Lee C-C, Jou S. Agricultural Drought risk assessment in the Caribbean region: the case of Haiti. SSRN Electron J. 2022. https://doi.org/10.2139/ssrn.4102926.
27. Saha, S et al. Spatial assessment of drought vulnerability using fuzzy-analytical hierarchical process: a case study at the Indian state of Odisha. Geomat Nat Haz Risk; 2021; 12,
28. Hoque M, Pradhan B, Ahmed N, Alamri A. Drought vulnerability assessment using geospatial techniques in Southern Queensland, Australia. Sensors. 2021;21(20). https://doi.org/10.3390/s21206896.
29. Sivakumar VL, Radha Krishnappa R, Nallanathel M. Drought vulnerability assessment and mapping using multi-criteria decision making (MCDM) and application of Analytic Hierarchy process (AHP) for Namakkal District, Tamilnadu, India. In: Materials today: proceedings. Elsevier Ltd.; 2020. p. 1592–9. https://doi.org/10.1016/j.matpr.2020.09.657.
30. Oikonomou, PD; Tsesmelis, DE; Waskom, RM; Grigg, NS; Karavitis, CA. Enhancing the standardized drought vulnerability index by integrating spatiotemporal information from satellite and in situ data. J Hydrol (Amst); 2019; 569, pp. 265-277. [DOI: https://dx.doi.org/10.1016/j.jhydrol.2018.11.058]
31. Hariyanto T, Pribadi CB, Purwitasari A, Kurniawan A. Identification of potential drought in Lamongan Regency. In: IOP conference series: earth and environmental science. Institute of Physics Publishing; 2019.https://doi.org/10.1088/1755-1315/389/1/012009.
32. Das R, Das PK, Bandyopadhyay S, Raj U. Trends and vulnerability assessment of meteorological and agricultural drought conditions over Indian region using time-series (1982-2015) satellite data. In: International archives of the photogrammetry, remote sensing and spatial information sciences. ISPRS Archives, International Society for Photogrammetry and Remote Sensing, Jul 2019, pp. 453–459. https://doi.org/10.5194/isprs-archives-XLII-3-W6-453-2019.
33. Panisset, JS et al. Contrasting patterns of the extreme drought episodes of 2005, 2010 and 2015 in the Amazon Basin. Int J Climatol; 2018; 38,
34. Sung, J; Chung, E-S; Shahid, S. Reliability–resiliency–vulnerability approach for drought analysis in South Korea Using 28 GCMs. Sustainability; 2018; 10,
35. Karamouz M, Zeynolabedin A, Olyaei MA. Regional drought resiliency and vulnerability. J Hydrol Eng. 2016;21(11). https://doi.org/10.1061/(asce)he.1943-5584.0001423.
36. Palchaudhuri, M; Biswas, S. Application of AHP with GIS in drought risk assessment for Puruliya district, India. Nat Hazards; 2016; 84,
37. Ekrami M, Marj AF, Barkhordari J, Dashtakian K. Drought vulnerability mapping using AHP method in arid and semiarid areas: a case study for Taft Township, Yazd Province, Iran. Environ Earth Sci. 2016;75(12). https://doi.org/10.1007/s12665-016-5822-z.
38. jsac.jharkhand.gov.in, “Soil Resources of Ranchi District, Jharkhand REMOTE SENSING INSTRUMENTS,” 2009. Accessed: Jul. 09, 2023. [Online]. Available https://jsac.jharkhand.gov.in/Report_PDF/New_Soil_Report/Ranchi_Report_180510.pdf
39. Vujović, F; Ćulafić, G; Valjarević, A; Brđanin, E; Durlević, U. Comparative geomorphometric analysis of drainage basin using aw3d30 model in ARCGIS and QGIS environment: case study of the Ibar river drainage basin, Montenegro. Agriculture and Forestry; 2024; 70,
40. Gupta S, Lehmann P, Bickel S, Bonetti S, Or D. Global mapping of potential and climatic plant‐available soil water. J Adv Model Earth Syst. 2023;15(11). https://doi.org/10.1029/2022MS003277.
41. Pekel, JF; Cottam, A; Gorelick, N; Belward, AS. High-resolution mapping of global surface water and its long-term changes. Nature; 2016; 540,
42. Pai DS, Sridhar L, Rajeevan M, Sreejith OP, Satbhai NS, Mukhopadyay B. Development of a new high spatial resolution (0.25° × 0.25°) Long Period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region; 2014.
43. Srivastava, AK; Rajeevan, M; Kshirsagar, SR. Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region. Atmospheric Sci Lett; 2009; 10,
44. Hargreaves, GH; Asce, F; Allen, RG. History and evaluation of hargreaves evapotranspiration equation. J Irrig Drain Eng; 2003; 129,
45. Zhu Y, Tian D, Yan F. Effectiveness of entropy weight method in decision-making. Math Probl Eng. 2020;2020. https://doi.org/10.1155/2020/3564835.
46. Dam ND, et al. Evaluation of Shannon entropy and weights of evidence models in landslide susceptibility mapping for the Pithoragarh District of Uttarakhand State, India. Adv Civ Eng. 2022;2022. https://doi.org/10.1155/2022/6645007.
47. Arora, A; Pandey, M; Siddiqui, MA; Hong, H; Mishra, VN. Spatial flood susceptibility prediction in Middle Ganga Plain: comparison of frequency ratio and Shannon’s entropy models. Geocarto Int; 2021; 36,
48. Haghizadeh, A; Siahkamari, S; Haghiabi, AH; Rahmati, O. Forecasting flood-prone areas using Shannon’s entropy model. J Earth Syst Sci; 2017; 126,
49. Mahmoodi E, Azari M, Dastorani MT. Comparison of different objective weighting methods in a multi‐criteria model for watershed prioritization for flood risk assessment using morphometric analysis. J Flood Risk Manag. 2023;16(2). https://doi.org/10.1111/jfr3.12894.
50. Nyimbili PH, Erden T. A hybrid approach integrating entropy-AHP and GIS for suitability assessment of urban emergency facilities. ISPRS Int J Geoinf. 2020;9(7). https://doi.org/10.3390/ijgi9070419.
51. Abou S, Hussain I, Mandal UK, Uttam D, Mandal K. Entropy based MCDM approach for selection of material; 2016. [Online]. Available https://www.researchgate.net/publication/315668202
52. Saini, D; Singh, O; Sharma, T; Bhardwaj, P. Geoinformatics and analytic hierarchy process based drought vulnerability assessment over a dryland ecosystem of north-western India. Nat Hazards; 2022; 114,
53. Chaudhari V. 1; Second B Dhanesh Lal 2 , Indian Institute of Remote Sensing-ISRO 2; Third C Sayantan Dutta 3 , Kumaun University-SSJ Campus 3; Fourth D Dr. Bhavna Umrikar 4 , University of Pune 4. Int J Remote Sens Geosci (IJRSG). 2018;5 [Online]. Available https://www.researchgate.net/publication/322757481
54. Dabanli, I. Drought hazard, vulnerability, and risk assessment in Turkey. Arab J Geosci; 2018; 11,
55. Nasrollahi, M; Khosravi, H; Moghaddamnia, A; Malekian, A; Shahid, S. Assessment of drought risk index using drought hazard and vulnerability indices. Arab J Geosci; 2018; 11,
56. Zagade, ND; Umrikar, BN. Drought severity modeling of upper Bhima river basin, western India, using GIS–AHP tools for effective mitigation and resource management. Nat Hazards; 2021; 105,
57. Thomas, T; Jaiswal, RK; Galkate, R; Nayak, PC; Ghosh, NC. Drought indicators-based integrated assessment of drought vulnerability: a case study of Bundelkhand droughts in central India. Nat Hazards; 2016; 81,
58. Penki, R; Basina, SS; Tanniru, SR. Application of geographical information system-based analytical hierarchy process modeling for flood susceptibility mapping of Krishna District in Andhra Pradesh. Environ Sci Pollut Res; 2022; 30,
59. Yu M, Zhang J, Wei L, Wang G, Dong W, Liu X. Impact of soil textures on agricultural drought evolution and field capacity estimation in humid regions. J Hydrol (Amst). 2023;626:130257. https://doi.org/10.1016/j.jhydrol.2023.130257.
60. Sudaryatno. Drought vulnerability mapping with geomorphological approach in Yogyakarta Special Region (DIY) and Central Java. In: IOP conference series: earth and environmental science. Institute of Physics Publishing; 2016. https://doi.org/10.1088/1755-1315/47/1/012023.
61. Jeet, P; Singh, K; Kumar, RR; Gurang, B; Singh, A; Upadhyaya, A. Modeling and trend analysis of climatic variables of Ranchi District, Jharkhand. Journal of AgriSearch; 2021; 8,
62. Tigga A, Malini H. Impact assessment of environmental changes on droughts over Ranchi city, Jharkhand, India; 2014.
63. Stoyanova JS. Drought monitoring in terms of evapotranspiration based on satellite data from Meteosat in areas of strong land–atmosphere coupling. Land (Basel). 2023.
64. Wang Y, Wang S, Zhao W, Liu Y. The increasing contribution of potential evapotranspiration to severe droughts in the Yellow River basin. J Hydrol (Amst). 2022;605:127310. https://doi.org/10.1016/j.jhydrol.2021.127310.
© The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.