Comprehensive Drought Vulnerability Assessment in Northwestern Odisha: A Fuzzy Logic and Analytical Hierarchy Process Integration Approach
Abstract
:1. Introduction
2. Materials and Methods
3. Materials and Methods
3.1. Data Used and Their Sources
3.2. Drought Conditioning Factors
3.2.1. Parameters Used in Physical Drought Vulnerability
3.2.2. Parameters Used in Water Demand and Use-Induced Vulnerability
3.2.3. Parameters Used in Agricultural Component-Induced Vulnerability
3.2.4. Parameters Used in Land Use-Induced Vulnerability
3.2.5. Parameters Used in Ground Water Status-Induced Vulnerability
3.2.6. Parameters Used in Population- and Development-Induced Vulnerability
Factors | Causes of Selecting the Parameters | References |
---|---|---|
Annual rainfall | With higher rainfall the vulnerability of drought will decrease. | [47] |
Average temperature | Higher average temperature will enhance the drought condition. | [48] |
Evapotranspiration | Aspects have a severe impact on the landslide owing to the intense heat of the sun. | [49] |
Wet day frequency | With the rising frequency of wet day, the drought will reduce. | [50] |
Total water demand | The drought condition will increase by more water demand. | [51] |
Water use for irrigation | The demand of water for irrigation will increase the drought. | [52] |
Water for all use | For the more demand of water, the dryness is increased. | [53] |
Gross cropped area | Higher gross cropped area means more vulnerable to drought. | [54] |
Cropping intensity | High intensity increases the severity of drought | [45] |
Irrigation intensity | More water demand for irrigation means increasing amount of drought. | [55] |
Gross irrigated area | Larger amount of gross cropped area will accelerate the condition of drought. | [54] |
Net irrigated area | With the increasing amount of net irrigated area, the drought will enhance. | [56] |
Area under forest | Drought will accelerate by decreasing amount of area under forest cover. | [56] |
Total ground water | Sufficient amount of ground water reduces the drought vulnerability. | [57] |
Stage of ground water development | With the increasing percentage of this parameter drought will increase. | [58] |
Ground water for future irrigation | Less ground water for future irrigation means a greater number of droughts. | [57] |
Population density | Increasing number of people will increase drought. | [46] |
Health | Good health condition reduced drought situation. | [59] |
Education | High level of education can prevent a region from drought condition. | [60] |
Income index | Higher income level can decrease the number of droughts of a region. | [8] |
3.3. Application of Fuzzy AHP
3.4. Fuzzification of the Parameters
3.5. Computing the Weight of the Parameters by AHP
3.6. Validation Methods
3.6.1. MAE
3.6.2. RMSE
4. Results
4.1. Drought Vulnerability Mapping Based on Physical Aspect
4.2. Drought Vulnerability Mapping Based on Water Demand and Used
4.3. Drought Vulnerability Mapping Based on Agriculture
4.4. Drought Vulnerability Mapping Based on Land Use
4.5. Drought Vulnerability Mapping Based on Ground Water
4.6. Drought Vulnerability Mapping Based on Population and Development
4.7. Integrated Drought Vulnerability Mapping
4.8. Validation of Drought Vulnerability Model
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Samans, R.; Nelson, J. The New Business Operating Context of the 2020s. In Sustainable Enterprise Value Creation: Implementing Stakeholder Capitalism through Full ESG Integration; Springer International Publishing: Cham, Switzerland, 2022; pp. 35–62. [Google Scholar]
- Barbour, E.J.; Adnan, M.S.G.; Borgomeo, E.; Paprocki, K.; Khan, M.S.A.; Salehin, M.; Hall, J.W. The unequal distribution of water risks and adaptation benefits in coastal Bangladesh. Nat. Sustain. 2022, 5, 294–302. [Google Scholar] [CrossRef]
- Coly, S.M.; Zorom, M.; Leye, B.; Karambiri, H.; Guiro, A. Learning from history of natural disasters in the Sahel: A comprehensive analysis and lessons for future resilience. Environ. Sci. Pollut. Res. 2023, 49, 1–13. [Google Scholar] [CrossRef] [PubMed]
- Bogale, G.A.; Erena, Z.B. Drought vulnerability and impacts of climate change on livestock production and productivity in different agro-Ecological zones of Ethiopia. J. Appl. Anim. Res. 2022, 50, 471–489. [Google Scholar] [CrossRef]
- Dijoo, Z.K.; Khurshid, R. Environmental Degradation as a Multifaceted Consequence of Human Development. In Environmental Biotechnology: Sustainable Remediation of Contamination in Different Environs; CRC Press: New York, NY, USA, 2022. [Google Scholar]
- Price, J.; Warren, R.; Forstenhäusler, N.; Wallace, C.; Jenkins, R.; Osborn, T.J.; Van Vuuren, D.P. Quantification of meteorological drought risks between 1.5 °C and 4 °C of global warming in six countries. Clim. Chang. 2022, 174, 12. [Google Scholar] [CrossRef]
- Rusca, M.; Savelli, E.; Di Baldassarre, G.; Biza, A.; Messori, G. Unprecedented droughts are expected to exacerbate urban inequalities in Southern Africa. Nat. Clim. Chang. 2023, 13, 98–105. [Google Scholar] [CrossRef]
- Wang, P.; Asare, E.; Pitzer, V.E.; Dubrow, R.; Chen, K. Associations between long-term drought and diarrhea among children under five in low-and middle-income countries. Nat. Commun. 2022, 13, 3661. [Google Scholar] [CrossRef] [PubMed]
- Asif, Z.; Chen, Z.; Sadiq, R.; Zhu, Y. Climate Change Impacts on Water Resources and Sustainable Water Management Strategies in North America. Water Resour. Manag. 2023, 37, 2771–2786. [Google Scholar] [CrossRef]
- Koko, A.F.; Han, Z.; Wu, Y.; Abubakar, G.A.; Bello, M. Spatiotemporal Land Use/Land Cover Mapping and Prediction Based on Hybrid Modeling Approach: A Case Study of Kano Metropolis, Nigeria (2020–2050). Remote Sens. 2022, 14, 6083. [Google Scholar] [CrossRef]
- Minhas, P.S.; Saha, J.K.; Dotaniya, M.L.; Sarkar, A.; Saha, M. Wastewater irrigation in India: Current status, impacts and response options. Sci. Total Environ. 2022, 808, 152001. [Google Scholar] [CrossRef]
- Mwadzingeni, L.; Mugandani, R.; Mafongoya, P.; Mwadzingeni, L.; Mugandani, R.; Mafongoya, P. Risks of climate change on future water supply in smallholder irrigation schemes in Zimbabwe. Water 2022, 14, 1682. [Google Scholar] [CrossRef]
- Anandhi, A.; Douglas-Mankin, K.R.; Srivastava, P.; Aiken, R.M.; Senay, G.; Leung, L.R.; Chaubey, I. DPSIR-ESA vulnerability assessment (DEVA) framework: Synthesis, foundational overview, and expert case studies. Trans. ASABE 2020, 63, 741–752. [Google Scholar] [CrossRef]
- Roy, P.S.; Ramachandran, R.M.; Paul, O.; Thakur, P.K.; Ravan, S.; Behera, M.D.; Sarangi, C.; Kanawade, V.P. Anthropogenic land use and land cover changes—A review on its environmental consequences and climate change. J. Indian Soc. Remote Sens. 2022, 50, 1615–1640. [Google Scholar] [CrossRef]
- Belaïd, F. Implications of poorly designed climate policy on energy poverty: Global reflections on the current surge in energy prices. Energy Res. Soc. Sci. 2022, 92, 102790. [Google Scholar] [CrossRef]
- Hagenlocher, M.; Meza, I.; Anderson, C.C.; Min, A.; Renaud, F.G.; Walz, Y.; Siebert, S.; Sebesvari, Z. Drought vulnerability and risk assessments: State of the art, persistent gaps, and research agenda. Environ. Res. Lett. 2019, 14, 083002. [Google Scholar] [CrossRef]
- Batool, M.; El-Badri, A.M.; Hassan, M.U.; Haiyun, Y.; Chunyun, W.; Zhenkun, Y.; Jie, K.; Wang, B.; Zhou, G. Drought stress in Brassica napus: Effects, tolerance mechanisms, and management strategies. J. Plant. Growth Regul. 2022, 42, 21–45. [Google Scholar] [CrossRef]
- Cole, H.D.; Cole, M.J.; Simpson, K.J.; Simpson, N.P.; Ziervogel, G.; New, M.G. Managing city-scale slow-onset disasters: Learning from Cape Town’s 2015–2018 drought disaster planning. Int. J. Disaster Risk Reduct. 2021, 63, 102459. [Google Scholar] [CrossRef]
- Jafari, S.M.; Nikoo, M.R.; Sadegh, M.; Chen, M.; Gandomi, A.H. Non-parametric severity-duration-frequency analysis of drought based on satellite-based product and model fusion techniques. Environ. Sci. Pollut. Res. 2023, 30, 42087–42107. [Google Scholar] [CrossRef] [PubMed]
- Warter, M.M.; Singer, M.B.; Cuthbert, M.O.; Roberts, D.; Caylor, K.K.; Sabathier, R.; Stella, J. Drought onset and propagation into soil moisture and grassland vegetation responses during the 2012–2019 major drought in Southern California. Hydrol. Earth Syst. Sci. 2021, 25, 3713–3729. [Google Scholar] [CrossRef]
- Yao, Y.; Liu, Y.; Zhou, S.; Song, J.; Fu, B. Soil moisture determines the recovery time of ecosystems from drought. Glob. Chang. Biol. 2023, 29, 3562–3574. [Google Scholar] [CrossRef]
- Satoh, Y.; Yoshimura, K.; Pokhrel, Y.; Kim, H.; Shiogama, H.; Yokohata, T.; Hanasaki, N.; Wada, Y.; Burek, P.; Byers, E.; et al. The timing of unprecedented hydrological drought under climate change. Nat. Commun. 2022, 13, 3287. [Google Scholar] [CrossRef]
- Kumar, P.; Debele, S.E.; Sahani, J.; Rawat, N.; Marti-Cardona, B.; Alfieri, S.M.; Basu, B.; Basu, A.S.; Bowyer, P.; Charizopoulos, N.; et al. An overview of monitoring methods for assessing the performance of nature-based solutions against natural hazards. Earth-Sci. Rev. 2021, 217, 103603. [Google Scholar] [CrossRef]
- Wu, B.; Ma, Z.; Yan, N. Agricultural drought mitigating indices derived from the changes in drought characteristics. Remote Sens. Environ. 2020, 244, 111813. [Google Scholar] [CrossRef]
- Kassahun, Z.; Renninger, H.J. Effects of drought on water use of seven tree species from four genera growing in a bottomland hardwood forest. Agric. For. Meteorol. 2021, 301, 108353. [Google Scholar] [CrossRef]
- Konapala, G.; Mishra, A. Quantifying climate and catchment control on hydrological drought in the continental United States. Water Resour. Res. 2020, 56, e2018WR024620. [Google Scholar] [CrossRef]
- Mukhawana, M.B.; Kanyerere, T.; Kahler, D. Review of in-Situ and remote sensing-based indices and their Applicability for integrated drought monitoring in South Africa. Water 2023, 15, 240. [Google Scholar] [CrossRef]
- Mujere, N. Assessing Risks and Resilience to Hydro-Meteorological Disasters. In Disaster Risk Reduction for Resilience: Climate Change and Disaster Risk Adaptation; Springer International Publishing: Cham, Switzerland, 2023; pp. 143–159. [Google Scholar]
- Saldariaga, J.; Yevjevich, V. Application of Run-Lengths to Hydrologic Series Hydrol Paper; Colorado State University Publication, Colorado State University: Fort Collins, CO, USA, 1970. [Google Scholar]
- Saha, S.; Kundu, B.; Paul, G.C.; Mukherjee, K.; Pradhan, B.; Dikshit, A.; Abdul Maulud, K.N.; Alamri, A.M. Spatial assessment of drought vulnerability using fuzzy-analytical hierarchical process: A case study at the Indian state of Odisha. Geomat. Nat. Hazards Risk 2021, 12, 123–153. [Google Scholar] [CrossRef]
- Akturk, G.; Zeybekoglu, U.; Yildiz, O. Assessment of meteorological drought analysis in the Kizilirmak River Basin, Turkey. Arab. J. Geosci. 2022, 15, 850. [Google Scholar] [CrossRef]
- Şen, Z. Probabilistic modelling of crossing in small samples and application of runs to hydrology. J. Hydrol. 1991, 124, 345–362. [Google Scholar] [CrossRef]
- Cavus, Y.; Aksoy, H. Critical drought severity/intensity-duration-frequency curves based on precipitation deficit. J. Hydrol. 2020, 584, 124312. [Google Scholar] [CrossRef]
- Singh, U.; Sharma, P.K. Drought forecasting using the stochastic model in the Betwa river basin, India. Model. Earth Syst. Environ. 2021, 8, 1771–1786. [Google Scholar] [CrossRef]
- Vroege, W.; Bucheli, J.; Dalhaus, T.; Hirschi, M.; Finger, R. Insuring crops from space: The potential of satellite-retrieved soil moisture to reduce farmers’ drought risk exposure. Eur. Rev. Agric. Econ. 2021, 48, 266–314. [Google Scholar] [CrossRef]
- Kalubarme, M.; Acharya, M.; Shukla, S.H. Monitoring drought and its impact on agriculture using drought indices and geo-informatics technology in Patan District, Gujarat. Int. J. Environ. Geoinform. 2019, 6, 153–162. [Google Scholar] [CrossRef]
- Liu, C.; Yang, C.; Yang, Q.; Wang, J. Spatiotemporal drought analysis by the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) in Sichuan Province, China. Sci. Rep. 2021, 11, 1280. [Google Scholar] [CrossRef] [PubMed]
- Hu, X.; Ren, H.; Tansey, K.; Zheng, Y.; Ghent, D.; Liu, X.; Yan, L. Agricultural drought monitoring using European Space Agency Sentinel 3A land surface temperature and normalized difference vegetation index imageries. Agric. For. Meteorol. 2019, 279, 107707. [Google Scholar] [CrossRef]
- Kumar, P.V.; Osman, M.; Mishra, P.K. Development and application of a new drought severity index for categorizing drought-prone areas: A case study of undivided Andhra Pradesh state, India. Nat. Hazards 2019, 97, 793–812. [Google Scholar] [CrossRef]
- Barnard, D.M.; Germino, M.J.; Bradford, J.B.; O’Connor, R.C.; Andrews, C.M.; Shriver, R.K. Are drought indices and climate data good indicators of ecologically relevant soil moisture dynamics in drylands? Ecol. Indic. 2021, 133, 108379. [Google Scholar] [CrossRef]
- Shah, D.; Mishra, V. Integrated Drought Index (IDI) for drought monitoring and assessment in India. Water Resour. Res. 2020, 56, e2019WR026284. [Google Scholar] [CrossRef]
- Dikshit, A.; Pradhan, B.; Santosh, M. Artificial neural networks in drought prediction in the 21st century—A scientometric analysis. Appl. Soft Comput. 2022, 114, 108080. [Google Scholar] [CrossRef]
- Sokhi, R.S.; Moussiopoulos, N.; Baklanov, A.; Bartzis, J.; Coll, I.; Finardi, S.; Friedrich, R.; Geels, C.; Grönholm, T.; Halenka, T.; et al. Advances in air quality research—Current and emerging challenges. Atmos. Chem. Phys. 2022, 22, 4615–4703. [Google Scholar] [CrossRef]
- Haile, G.G.; Tang, Q.; Li, W.; Liu, X.; Zhang, X. Drought: Progress in broadening its understanding. Wiley Interdiscip. Rev. Water 2020, 7, e1407. [Google Scholar] [CrossRef]
- Sivakumar, V.L.; Krishnappa, R.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. Mater. Today Proc. 2021, 43, 1592–1599. [Google Scholar] [CrossRef]
- Alharbi, R.S.; Nath, S.; Faizan, O.M.; Hasan, M.S.U.; Alam, S.; Khan, M.A.; Bakshi, S.; Sahana, M.; Saif, M.M. Assessment of Drought vulnerability through an integrated approach using AHP and Geoinformatics in the Kangsabati River Basin. J. King Saud Univ.-Sci. 2022, 34, 102332. [Google Scholar] [CrossRef]
- Alkhalidi, A.; Assaf, M.N.; Alkaylani, H.; Halaweh, G.; Salcedo, F.P. Integrated innovative technique to assess and priorities risks associated with drought: Impacts, measures/strategies, and actions, global study. Int. J. Disaster Risk Reduct. 2023, 94, 103800. [Google Scholar] [CrossRef]
- Wijitkosum, S.; Sriburi, T. Fuzzy AHP integrated with GIS analyses for drought risk assessment: A case study from upper Phetchaburi River basin, Thailand. Water 2019, 11, 939. [Google Scholar] [CrossRef]
- Nicholson, C.C.; Egan, P.A. Natural hazard threats to pollinators and pollination. Glob. Chang. Biol. 2020, 26, 380–391. [Google Scholar] [CrossRef] [PubMed]
- Roy, P.; Pal, S.C.; Chakrabortty, R.; Chowdhuri, I.; Saha, A.; Shit, M. Climate change and groundwater overdraft impacts on agricultural drought in India: Vulnerability assessment, food security measures and policy recommendation. Sci. Total Environ. 2022, 849, 157850. [Google Scholar] [CrossRef] [PubMed]
- Pal, S.C.; Chowdhuri, I.; Das, B.; Chakrabortty, R.; Roy, P.; Saha, A.; Shit, M. Threats of climate change and land use patterns enhance the susceptibility of future floods in India. J. Environ. Manag. 2022, 305, 114317. [Google Scholar] [CrossRef] [PubMed]
- Ma, S.; Qiu, H.; Yang, D.; Wang, J.; Zhu, Y.; Tang, B.; Sun, K.; Cao, M. Surface multi-hazard effect of underground coal mining. Landslides 2023, 20, 39–52. [Google Scholar] [CrossRef]
- Yang, D.; Qiu, H.; Ye, B.; Liu, Y.; Zhang, J.; Zhu, Y. Distribution and Recurrence of Warming-induced Retrogressive Thaw Slumps on the Central Qinghai-Tibet Plateau. J. Geophys. Res. Earth Surf. 2023, 18, e2022JF007047. [Google Scholar] [CrossRef]
- Yuan, C.; Li, Q.; Nie, W.; Ye, C. A depth information-based method to enhance rainfall-induced landslide deformation area identification. Measurement 2023, 219, 113288. [Google Scholar] [CrossRef]
- Zhang, Y.; Luo, J.; Zhang, Y.; Huang, Y.; Cai, X.; Yang, J.; Mao, D.; Li, J.; Tuo, X.; Zhang, Y. Resolution Enhancement for Large-Scale Real Beam Mapping Based on Adaptive Low-Rank Approximation. IEEE Trans. Geosci. Remote Sens. 2022, 60, 1–21. [Google Scholar] [CrossRef]
- Yan, Y.; Jarvie, S.; Liu, Q.; Zhang, Q. Effects of fragmentation on grassland plant diversity depend on the habitat specialization of species. Biol. Conserv. 2022, 275, 109773. [Google Scholar] [CrossRef]
- Zhou, J.; Wang, L.; Zhong, X.; Yao, T.; Qi, J.; Wang, Y.; Xue, Y. Quantifying the major drivers for the expanding lakes in the interior Tibetan Plateau. Sci. Bull. 2022, 67, 474–478. [Google Scholar] [CrossRef] [PubMed]
- Li, J.; Wang, Z.; Wu, X.; Xu, C.; Guo, S.; Chen, X. Toward Monitoring Short-Term Droughts Using a Novel Daily Scale, Standardized Antecedent Precipitation Evapotranspiration Index. J. Hydrometeorol. 2020, 21, 891–908. [Google Scholar] [CrossRef]
- Li, J.; Wang, Z.; Wu, X.; Zscheischler, J.; Guo, S.; Chen, X. A standardized index for assessing sub-monthly compound dry and hot conditions with application in China. Hydrol. Earth Syst. Sci. 2021, 25, 1587–1601. [Google Scholar] [CrossRef]
- Zhu, G.; Liu, Y.; Wang, L.; Sang, L.; Zhao, K.; Zhang, Z.; Lin, X.; Qiu, D. The isotopes of precipitation have climate change signal in arid Central Asia. Glob. Planet. Chang. 2023, 225, 104103. [Google Scholar] [CrossRef]
- Sang, L.; Zhu, G.; Xu, Y.; Sun, Z.; Zhang, Z.; Tong, H. Effects of Agricultural Large-And Medium-Sized Reservoirs on Hydrologic Processes in the Arid Shiyang River Basin, Northwest China. Water Resour. Res. 2023, 59, e2022WR033519. [Google Scholar] [CrossRef]
- Wu, B.; Quan, Q.; Yang, S.; Dong, Y. A social-ecological coupling model for evaluating the human-water relationship in basins within the Budyko framework. J. Hydrol. 2023, 619, 129361. [Google Scholar] [CrossRef]
- Li, W.; Shi, Y.; Zhu, D.; Wang, W.; Liu, H.; Li, J.; Shi, N.; Ma, L.; Fu, S. Fine root biomass and morphology in a temperate forest are influenced more by the nitrogen treatment approach than the rate. Ecol. Indic. 2021, 130, 108031. [Google Scholar] [CrossRef]
- Fang, Y.; Wang, H.; Fang, P.; Liang, B.; Zheng, K.; Sun, Q.; Li, X.Q.; Zeng, R.; Wang, A. Life cycle assessment of integrated bioelectrochemical-constructed wetland system: Environmental sustainability and economic feasibility evaluation. Resour. Conserv. Recycl. 2023, 189, 106740. [Google Scholar] [CrossRef]
- Yuan, L.; Wu, X.; He, W.; Degefu, D.M.; Kong, Y.; Yang, Y.; Xu, S.; Ramsey, T.S. Utilizing the strategic concession behavior in a bargaining game for optimal allocation of water in a transboundary river basin during water bankruptcy. Environ. Impact Assess. Rev. 2023, 102, 107162. [Google Scholar] [CrossRef]
- Wang, X.; Wang, T.; Xu, J.; Shen, Z.; Yang, Y.; Chen, A.; Wang, S.; Liang, E.; Piao, S. Enhanced habitat loss of the Himalayan endemic flora driven by warming-forced upslope tree expansion. Nat. Ecol. Evol. 2022, 6, 890–899. [Google Scholar] [CrossRef] [PubMed]
- Tian, H.F.; Huang, N.; Niu, Z.; Qin, Y.C.; Pei, J.; Wang, J. Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm. Remote Sens. 2019, 11, 820. [Google Scholar] [CrossRef]
- Tian, H.; Pei, J.; Huang, J.; Li, X.; Wang, J.; Zhou, B.; Qin, Y.; Wang, L. Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China. Remote. Sens. 2020, 12, 3539. [Google Scholar] [CrossRef]
- Gao, C.; Zhang, B.; Shao, S.; Hao, M.; Zhang, Y.; Xu, Y.; Kuang, Y.; Dong, L.; Wang, Z. Risk assessment and zoning of flood disaster in Wuchengxiyu Region, China. Urban. Clim. 2023, 49, 101562. [Google Scholar] [CrossRef]
- Yin, Z.; Liu, Z.; Liu, X.; Zheng, W.; Yin, L. Urban heat islands and their effects on thermal comfort in the US: New York and New Jersey. Ecol. Indic. 2023, 154, 110765. [Google Scholar] [CrossRef]
- Chen, J.; Liu, Z.; Yin, Z.; Liu, X.; Li, X.; Yin, L.; Zheng, W. Predict the effect of meteorological factors on haze using BP neural network. Urban. Clim. 2023, 51, 101630. [Google Scholar] [CrossRef]
- Yin, L.; Wang, L.; Zheng, W.; Ge, L.; Tian, J.; Liu, Y.; Yang, B.; Liu, S. Evaluation of Empirical Atmospheric Models Using Swarm-C Satellite Data. Atmosphere 2022, 13, 294. [Google Scholar] [CrossRef]
- Yin, L.; Wang, L.; Huang, W.; Tian, J.; Liu, S.; Yang, B.; Zheng, W. Haze Grading Using the Convolutional Neural Networks. Atmosphere 2022, 13, 522. [Google Scholar] [CrossRef]
- Ren, B.; Zhang, Q.; Ren, J.; Ye, S.; Yan, F. A Novel Hybrid Approach for Water Resources Carrying Capacity Assessment by Integrating Fuzzy Comprehensive Evaluation and Analytical Hierarchy Process Methods with the Cloud Model. Water 2020, 12, 3241. [Google Scholar] [CrossRef]
- Kalisa, W.; Zhang, J.; Igbawua, T.; Ujoh, F.; Ebohon, O.J.; Namugize, J.N.; Yao, F. Spatio-temporal analysis of drought and return periods over the East African region using Standardized Precipitation Index from 1920 to 2016. Agric. Water Manag. 2020, 237, 106195. [Google Scholar] [CrossRef]
- Sur, C.; Kang, D.H.; Lim, K.J.; Yang, J.E.; Shin, Y.; Jung, Y. Soil Moisture–Vegetation–Carbon Flux Relationship under Agricultural Drought Condition using Optical Multispectral Sensor. Remote Sens. 2020, 12, 1359. [Google Scholar] [CrossRef]
- Miah, M.G.; Abdullah, H.M.; Jeong, C. Exploring standardized precipitation evapotranspiration index for drought assessment in Bangladesh. Environ. Monit. Assess. 2017, 189, 547. [Google Scholar] [CrossRef] [PubMed]
- Hao, Z.; Hao, F.; Singh, V.P.; Ouyang, W.; Cheng, H. An integrated package for drought monitoring, prediction and analysis to aid drought modeling and assessment. Environ. Model. Softw. 2017, 91, 199–209. [Google Scholar] [CrossRef]
- Jiang, W.; Wang, L.; Feng, L.; Zhang, M.; Yao, R. Drought characteristics and its impact on changes in surface vegetation from 1981 to 2015 in the Yangtze River Basin, China. Int. J. Climatol. 2020, 40, 3380–3397. [Google Scholar] [CrossRef]
- Liu, S.; Shi, H.; Niu, J.; Chen, J.; Kuang, X. Assessing future socioeconomic drought events under a changing climate over the Pearl River basin in South China. J. Hydrol. Reg. Stud. 2020, 30, 100700. [Google Scholar] [CrossRef]
- Katipoğlu, O.M.; Acar, R.; Şengül, S. Comparison of meteorological indices for drought monitoring and evaluating: A case study from Euphrates basin, Turkey. J. Water Clim. Chang. 2020, 11, 29–43. [Google Scholar] [CrossRef]
Parameters | Data Sources |
---|---|
Annual rainfall | Indian Meteorological Department |
Average temperature | Indian Meteorological Department |
Evapotranspiration | Indian Meteorological Department |
Wet day frequency | Indian Meteorological Department |
Total water demand | Ground water booklet 2016 |
Water use for irrigation | Ground water booklet 2016 |
Water for all use | Ground water booklet 2016 |
Gross cropped area | District irrigation plan 2016 |
Cropping intensity | District irrigation plan 2016 |
Irrigation intensity | A study on Irrigation and Agricultural productivity in Odisha 2018 |
Gross irrigated area | District irrigation plan 2016 |
Net irrigated area | District irrigation plan 2016 |
Area under forest | District irrigation plan 2016 |
Total ground water | Ground water booklet 2016 |
Stage of ground water development | Ground water booklet 2016 |
Ground water for future irrigation | Ground water booklet 2016 |
Population density | District irrigation plan 2016 |
Health | Odisha economic journal 2019 |
Education | Odisha economic journal 2019 |
Income index | Odisha economic journal 2019 |
Saaty Scale | Definition | Fuzzy Triangular Scale |
---|---|---|
1 | Equally important (Eq. Imp.) | (1, 1, 1) |
3 | Weakly important (W. Imp.) | (2, 3, 4) |
5 | Fairly important (F. Imp.) | (4, 5, 6) |
7 | Strongly important (S. Imp.) | (6, 7, 8) |
9 | Absolutely important (A. Imp.) | (9, 9, 9) |
2 | The intermittent values between two adjacent scales | (1, 2, 3) |
4 | (3, 4, 5) | |
6 | (5, 6, 7) | |
8 | (7, 8, 9) |
Sl No. | Parameters | Relationship of Parameters with Drought |
---|---|---|
1 | Annual rainfall | Negative |
2 | Average temperature | Positive |
3 | Evapotranspiration | Positive |
4 | Wet day frequency | Negative |
5 | Total water demand | Positive |
6 | Water for all use | Positive |
7 | Water use for irrigation | Positive |
8 | Gross cropped area | Positive |
9 | Net sown area | Positive |
10 | Cropping intensity | Positive |
11 | Irrigation intensity | Positive |
12 | Gross irrigated area | Positive |
13 | Net irrigated area | Positive |
14 | Area under forest | Negative |
15 | Total ground water | Negative |
16 | Stage of ground water development | Positive |
17 | Ground water for future irrigation | Negative |
18 | Population density | Positive |
19 | Income index | Negative |
20 | Education index | Negative |
21 | Health index | Negative |
Parameters | Rank | Weight |
---|---|---|
Physical | ||
Annual rainfall | 1 | 0.466 |
Average temperature | 2 | 0.277 |
Evapotranspiration | 3 | 0.161 |
Wet day frequency | 4 | 0.096 |
Water demand and use | ||
Total water demand | 1 | 0.539 |
Water for all use | 2 | 0.297 |
Water use for irrigation | 3 | 0.164 |
Agricultural component | ||
Gross cropped area | 1 | 0.466 |
Net sown area | 2 | 0.277 |
Cropping intensity | 3 | 0.161 |
Irrigation intensity | 4 | 0.096 |
Land use | ||
Gross irrigated area | 1 | 0.539 |
Net irrigated area | 2 | 0.297 |
Area under forest | 3 | 0.164 |
Ground water status | ||
Total ground water | 1 | 0.539 |
State of ground water development | 2 | 0.297 |
Ground water for future irrigation | 3 | 0.164 |
Population and development | ||
Population density | 1 | 0.466 |
Income index | 2 | 0.277 |
Education index | 3 | 0.161 |
Health index | 4 | 0.096 |
Drought Vulnerability Based on Physical Aspect | Vulnerability Class | Number of Pixels | Area (%) | Area in (km2) |
Very low | 4719 | 25.94 | 4.25 | |
Low | 2319 | 12.75 | 2.09 | |
Moderate | 3486 | 19.16 | 3.14 | |
High | 944 | 5.19 | 0.85 | |
Very high | 6725 | 36.96 | 6.05 | |
Drought Vulnerability Based on Water Demand and Use | Vulnerability class | Number of pixels | Area (%) | Area in (km2) |
Very low | 4143 | 20.76 | 3.73 | |
Low | 944 | 4.73 | 0.85 | |
Moderate | 3486 | 17.47 | 3.14 | |
High | 2319 | 11.62 | 2.09 | |
Very high | 9066 | 45.43 | 8.16 | |
Drought vulnerability based on agriculture | Vulnerability class | Number of pixels | Area (%) | Area in (km2) |
Very low | 4143 | 20.76 | 3.73 | |
Low | 944 | 4.73 | 0.85 | |
Moderate | 3486 | 17.47 | 3.14 | |
High | 2319 | 11.62 | 2.09 | |
Very high | 9066 | 45.43 | 8.16 | |
Drought vulnerability based on land use | Vulnerability class | Number of pixels | Area (%) | Area in (km2) |
Very low | 2895 | 14.51 | 2.61 | |
Low | 2192 | 10.98 | 1.97 | |
Moderate | 3486 | 17.47 | 3.14 | |
High | 4023 | 20.16 | 3.62 | |
Very high | 7362 | 36.89 | 6.63 | |
Drought vulnerability based on ground water | Vulnerability class | Number of pixels | Area (%) | Area in (km2) |
Very low | 4649 | 25.68 | 4.18 | |
Low | 2327 | 12.85 | 2.09 | |
Moderate | 3519 | 19.43 | 3.17 | |
High | 956 | 5.28 | 0.86 | |
Very high | 6656 | 36.76 | 5.99 | |
Drought vulnerability based on population and development | Vulnerability class | Number of pixels | Area (%) | Area in (km2) |
Very low | 4719 | 25.94 | 4.25 | |
Low | 2319 | 12.75 | 2.09 | |
Moderate | 3486 | 19.16 | 3.14 | |
High | 944 | 5.19 | 0.85 | |
Very high | 6725 | 36.96 | 6.05 |
Vulnerability Class | Number of Pixels | Area (%) | Area in (km2) |
---|---|---|---|
Very low | 6679 | 29.84 | 6.01 |
Low | 940 | 4.20 | 0.85 |
Moderate | 5760 | 25.74 | 5.18 |
High | 1693 | 7.56 | 1.52 |
Very high | 7309 | 32.66 | 6.58 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Mahato, S.; Mandal, G.; Kundu, B.; Kundu, S.; Joshi, P.K.; Kumar, P. Comprehensive Drought Vulnerability Assessment in Northwestern Odisha: A Fuzzy Logic and Analytical Hierarchy Process Integration Approach. Water 2023, 15, 3210. https://doi.org/10.3390/w15183210
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. 2023; 15(18):3210. https://doi.org/10.3390/w15183210
Chicago/Turabian StyleMahato, Susanta, Gita Mandal, Barnali Kundu, Sonali Kundu, P. K. Joshi, and Pankaj Kumar. 2023. "Comprehensive Drought Vulnerability Assessment in Northwestern Odisha: A Fuzzy Logic and Analytical Hierarchy Process Integration Approach" Water 15, no. 18: 3210. https://doi.org/10.3390/w15183210