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
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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 |
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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
APA StyleMahato, S., Mandal, G., Kundu, B., Kundu, S., Joshi, P. K., & Kumar, P. (2023). Comprehensive Drought Vulnerability Assessment in Northwestern Odisha: A Fuzzy Logic and Analytical Hierarchy Process Integration Approach. Water, 15(18), 3210. https://doi.org/10.3390/w15183210