Data used
To perform work, different types of data (e.g., remote sensing, metrological, morphological, soil, drainage, groundwater, and socio-economic status) have been used in calculation of the aforesaid three composite indexes. The detail description of data source and their specifications for Agricultural Drought Vulnerability Index (ADVI) are given below in Table 1.
Table 1 Details of Data sources used for thematic layers preparation
Data
|
Source
|
Exposure index
|
LANDSAT 8 (spatial resolution 30 m)(LC08_L1TP_139044_20181220_20181227_01_T1)
|
USGS earth explorer (https://earthexplorer.usgs.gov/)
|
Rainfall data (Daily basis) from 2003 to 2013
|
Climate forecast System Reanalysis (https://globalweather.tamu.edu/)
|
Sensitivity index
|
LANDSAT/LC08/C01/T1_8DAY_NDVI (30 meters spatial resolution) in 2016
|
Google earth engine (https://code.earthengine.google.com/)
|
LANDSAT/LC08/C01/T1_8DAY_NDWI (30 meters spatial resolution) in 2016
|
Google earth engine (https://code.earthengine.google.com/)
|
LANDSAT/LC08/C01/T1_8DAY_EVI(30 meters spatial resolution) in 2016
|
Google earth engine (https://code.earthengine.google.com/)
|
LST (MODIS/006/MOD11A1) 1000 meters spatial resolution in 2016
|
Google earth engine (https://code.earthengine.google.com/)
|
Adaptive capacity index
|
Ground water data
|
Central Ground Water Board (CGWB), India.(http://cgwb.gov.in/)
|
SRTM DEM(n23_e086_1arc_v3,n23_e087_1arc_v3)spatial resolution 30 meters
|
USGS earth explorer (https://earthexplorer.usgs.gov/)
|
Rainfall data
|
Indian Meteorological Department (IMD),Pune
(http://dsp.imdpune.gov.in/)
|
Soil depth, texture, and Drainage (Scale – 1: 50,000)
|
National Bureau of Soil Survey and Land Use planning (NBSS&LUP)
|
Aquifer systems of India (Scale – 1: 50,000)
|
Central Ground water Board, Ministry of water Resources, Government of India.
|
Population, no of agricultural labour and farmer, Literacy relates
|
Census of India, 2011
|
Health data, Old-aged Persons, Road(KM), drinking water facility, fertilizer depots, Seed stores, production, livestock, Pisiculture
|
District Statistical handbook, Bankura and Puruliya, in 2014
|
Irrigated area
|
Irrigation and Waterways Directorate, Govt of W.B,
|
Methodology
Three principal composite indexes these are Sensitivity Index (SI), Exposure Index (EI), and Adaptive Capacity Index (ACI) have been taken for vulnerability assessment. Each composite index has been generated using AHP technique which has the ability to explain complex problems and make the decision where multiple factors are employed. Factors of each individual composite index (e.g., SI, EI and ACI), have been shown in Table 2. Them, multi-dimensional index-based integrated ADVI has also been measured using this formula [ADVI= (EI + SI) – ACI]. The details methodology of this research is shown using flow chart (Pic. 2).
Table 2 Selected parameters for agricultural drought vulnerability assessment in the upper Dwarakeshwar river basin.
Vulnerability component
|
Thematic layers
|
Sensitivity
|
(1) Vegetation Health Index (VHI), (2) Normalized Difference Water Index (NDWI), (3) Enhanced Vegetation Index (EVI), (4) Normalized Difference Vegetation Index (NDVI), (5) Vegetation Condition Index (VCI), (6) Land Surface Temperature (LST), and (7) Temperature Condition Index (TCI)
|
Exposure
|
(1) Normalized Difference Drought Index (NDDI), (2) Landuse and Landcover (LULC), (3) Average Drought Intensity (ADI), (4) Drought Frequency (DF), (5) Average Drought Duration (ADD), and (6) Peak Intensity (PI)
|
Environmental adaptive capacity
|
(1) Average Groundwater Depth (AGD), (2) Rainfall (RA), (3) Drainage Density (DD), (4) Drainage Buffer (DB), (5) Soil Drainage (SD), (6) Normalized Difference Vegetation Index (NDVI),(7) Aquifer Media (AM), (8) Soil Texture (ST), (9) Soil Depth (SDpt), (10) Relative Relief (RR), (11) Elevation (EL), (12) Slope (SL)
|
Economic adaptive capacity
|
(1) Irrigation Area Density (IAD), (2) Crop Production Density (CPD), (3) Agricultural Area Density (AAD), (4) Livestock Density (LD), (5) Drinking Water Facility (DWF), (6) Piscicultural Area Density (PAD), (7) Road Density (RD), (8) Ratio of Seed Stores (RSS), (9) Ratio of Fertilizer Depots (RFD)
|
Social adaptive capacity
|
(1) Rural Education (RE), (2) Agricultural Labor Density (ALD), (3) Farmer Density (FD), (4) Population Density (PD) (5) Old-Age Dependency Population (ODP), (6) and Rural Health Facility (RHF),
|
AHP technique
The Analytical Hierarchy Process (AHP) is a semi-quantitative comprehensive technique that fulfills the objective (Quantitative) and subjective (qualitative) aspect (Sener et al. 2011). It is a Multi Criteria Decision Analysis (MCDA) approach which has been used to judgment of final outcome through the assigned weights of the parameters with the pair-wise comparison matrix (Bera et al. 2019).
Each individual composite index was developed by following formula:
Where, nCI is the three composite index of vulnerability, Wa is the each factor assigned of weight, and Ra is the Relative rating weights of the pair-wise comparison values under a classified factors.
AHP technique is build up by two leading segments. The primary segment is the prime scheme of Normalized pair-wise comparison matrix and was calculated by the weights for each factor. The secondary segment is calculated by the relative rating weights of all the factors into sub-classes by using pair-wise comparison matrix of each factor. To create a matrix of pair-wise comparison, each criterion is assigned against the other criterion by allocating a relative rank on Satty’s scale (Saaty 1980), between 1 (minimum significant) to 9 (maximum significant) (Table 3). The relative scales of all these factors are given based on different criteria, relative influences, preferences and importance etc. In the Pair-based comparison matrix, each parameter in the row follows the opposite value and its significance with the other parameters. Weights for every factor ware obtained from pair-wise comparison matrix by normalizing the values and this was determined by dividing each cell with corresponding sum of the column and then averaging the rows of each criterion. The general pair-wise comparison matrix P1 is constructed as follows,
At last, the consistency of the Pair-based comparison matrix is assessed by Consistency Ratio (CR). CR is calculated by the following equation:
Consistency Ratio (CR) = CI/RI
Where,
CI =Consistency Index and
RI =Random Index
If, CR is less than or equal to 0.1, the comparison matrix is considered as consistent, else it will be corrected.
The Random Index (RI) value obtained from the Satty’s standard RI table, which is shown in Table 4. The Consistency Index (CI) is applied and it is calculated using the following equation-
Consistency index (CI) = ((γmax - n)∕(n - 1))
Where,
λmax = the principle Eigen value of matrix.
n = Number of parameters used in the analysis.
Table 3 The satty’s 9-point relative scale
Scale’s
|
1
|
2
|
3
|
4
|
5
|
6
|
7
|
8
|
9
|
Degree of importance
|
Equal
|
Weak
|
Slight
|
Moderate
|
Quite
|
Very strong
|
Extreme
|
very strong Extreme
|
Absolute
|
Table 4 Random Index (RI) value (Saaty 1990)
n
|
1
|
2
|
3
|
4
|
5
|
6
|
7
|
8
|
9
|
10
|
11
|
12
|
RI
|
0.0
|
0.0
|
0.58
|
0.90
|
1.12
|
1.24
|
1.32
|
1.41
|
1.45
|
1.49
|
1.51
|
1.48
|
Here, all the index parameters of normalized weights value shown in Table 5 and sub-classes weights value of all the parameters shown in Table 6 & 7.
Table 5 Normalized weights of Exposure index (EI), Sensitivity index (SI) Environmental Adaptive Capacity (EAC) index, Economic Adaptive Capacity (EcAC) and Social Adaptive Capacity (SAC)
Parameter of EI
|
weight
|
Parameter of SI
|
weights
|
Parameter of EAC
|
weights
|
Parameter of SAC
|
weights
|
Parameter of EcAC
|
weights
|
NDDI
|
0.379
|
VHI
|
0.338
|
AGD
|
0.237
|
RE
|
0.379
|
IAD
|
0.306
|
LULC
|
0.248
|
NDWI
|
0.224
|
RA
|
0.199
|
ALD
|
0.248
|
CPD
|
0.218
|
ADI
|
0.160
|
EVI
|
0.143
|
DD
|
0.123
|
FD
|
0.160
|
AAD
|
0.154
|
ADD
|
0.102
|
NDVI
|
0.112
|
DB
|
0.122
|
PD
|
0.102
|
LD
|
0.108
|
DF
|
0.065
|
VCI
|
0.079
|
SD
|
0.089
|
ODP
|
0.065
|
DWF
|
0.076
|
PI
|
0.043
|
LST
|
0.056
|
NDVI
|
0.065
|
RHF
|
0.043
|
PAD
|
0.053
|
|
|
TCI
|
0.045
|
AM
|
0.048
|
|
|
RD
|
0.037
|
|
|
|
|
ST
|
0.033
|
|
|
RSS
|
0.025
|
|
|
|
|
SDpt
|
0.031
|
|
|
RFD
|
0.018
|
|
|
|
|
RR
|
0.021
|
|
|
|
|
|
|
|
|
EL
|
0.012
|
|
|
|
|
|
|
|
|
SL
|
0.012
|
|
|
|
|
Table 6 Normalize assign weight values for all subcategories of Exposure Index (EI) and Sensitivity Index (SI).
Parameter
|
Sub-classes of Exposure Index
(EI)
|
weights
|
Parameter
|
Sub-classes of Sensitivity Index
(SI)
|
weights
|
NDDI
|
0.61-1
|
0.419
|
VHI
|
76-93
|
0.344
|
0.41-0.60
|
0.263
|
61-75
|
0.344
|
0.31-0.40
|
0.160
|
56-60
|
0.177
|
0.01-0.3
|
0.097
|
46-55
|
0.088
|
-1 - 0
|
0.062
|
45-11
|
0.047
|
|
LULC
|
Agricultural land
|
0.427
|
NDWI
|
0.21-04
|
0.386
|
Natural vegetation
|
0.260
|
0.16-0,2
|
0.246
|
Fallow land
|
0.158
|
0.11-0.15
|
0.173
|
Settlement
|
0.096
|
0.01-0.1
|
0.120
|
Water Body
|
0.059
|
-0.1
|
0.075
|
|
ADI
|
-1.37 to -1.35
|
0.471
|
EVI
|
0.41-0.57
|
0.471
|
-.35 to -1.34
|
0.268
|
0.31-0.4
|
0.268
|
-1.33 to -1.35
|
0.143
|
0.21-0.3
|
0.143
|
-1.31 to -1.30
|
0.075
|
0.11-0.2
|
0.075
|
-1.29 to -1.28
|
0.044
|
-0.13
|
0.044
|
|
DD
|
2.71-2.87
|
0.445
|
NDVI
|
0.301-0.4
|
0.492
|
2.53-2.7
|
0.262
|
0.201-0.3
|
0.270
|
2.36-2.52
|
0.153
|
0.101-0.2
|
0.135
|
2.18-2.35
|
0.088
|
0.001-0.1
|
0.065
|
2-2.17
|
0.052
|
-0 -0.0797
|
0.037
|
|
AF
|
13.69-14.9
|
0.419
|
VCI
|
81-100
|
0.419
|
13.39-13.68
|
0.263
|
61-80
|
0.263
|
13.13-13.38
|
0.160
|
41-60
|
0.160
|
12.86-13.12
|
0.097
|
21-40
|
0.097
|
12.50-12.85
|
0.062
|
0-20
|
0.062
|
|
PI
|
-2.72 to -2.56
|
0.419
|
LST
|
30.28-31.86
|
0.365
|
-2.55 to -2.39
|
0.263
|
31.87-32.29
|
0.275
|
-2.38 to -2.22
|
0.160
|
32.3-32.7
|
0.191
|
-2.21 to -2.06
|
0.097
|
32.71-33.28
|
0.106
|
-.05 to -1.89
|
0.061
|
33.29-34.39
|
0.063
|
|
|
TCI
|
81-100
|
0.363
|
61-80
|
0.362
|
41-60
|
0.161
|
21-40
|
0.076
|
0-20
|
0.039
|
Table 7 Normalize assign weight values for all subcategories of environmental adaptive capacity (EAC) index, Economic Adaptive Capacity (EcAC) and Social Adaptive Capacity (SAC)
Parameter of EAC
|
Sub-classes
|
weights
|
Parameter of SAC
|
Sub-classes
|
weights
|
Parameter of EcAC
|
Sub-classes
|
weights
|
Average groundwater depth
|
2.13-3.92
|
0.445
|
Rural Education
|
0.8 - 1
|
0.445
|
Irrigation area density
|
0.8 - 1
|
0.513
|
3.93-4.45
|
0.262
|
0.6 -0.8
|
0.262
|
0.6 -0.8
|
0.262
|
4.46- 4.97
|
0.153
|
0.4 - 0.6
|
0.153
|
0.4 - 0.6
|
0.129
|
4.98-6.05
|
0.089
|
0.2 - 0.4
|
0.089
|
0.2 - 0.4
|
0.063
|
6.05-8.82
|
0.052
|
0 - 0.2
|
0.052
|
0 - 0.2
|
0.033
|
|
Rainfall
|
1585-1631
|
0.418
|
Agricultural
Labour Density
|
0.8 - 1
|
0.513
|
Crop production density
|
0.8 - 1
|
0.471
|
1540-1584
|
0.263
|
0.6 -0.8
|
0.262
|
0.6 -0.8
|
0.268
|
1499-1539
|
0.160
|
0.4 - 0.6
|
0.130
|
0.4 - 0.6
|
0.143
|
1454-1498
|
0.097
|
0.2 - 0.4
|
0.063
|
0.2 - 0.4
|
0.075
|
1398-1453
|
0.062
|
0 - 0.2
|
0.033
|
0 - 0.2
|
0.044
|
|
Drainage density
|
2-3.6
|
0.471
|
Farmer Density
|
0.8 - 1
|
0.471
|
Agricultural area Density
|
0.8 - 1
|
0.419
|
1.5-1.9
|
0.264
|
0.6 -0.8
|
0.268
|
0.6 -0.8
|
0.263
|
1.2-1.4
|
0.143
|
0.4 - 0.6
|
0.143
|
0.4 - 0.6
|
0.160
|
0.73-1.1
|
0.078
|
0.2 - 0.4
|
0.075
|
0.2 - 0.4
|
0.097
|
0.0021-0.72
|
0.044
|
0 - 0.2
|
0.043
|
0 - 0.2
|
0.062
|
|
Drainage buffer
|
100
|
0.471
|
Population Density
|
0.8 - 1
|
0.419
|
Livestock density
|
0.8 - 1
|
0.470
|
300
|
0.268
|
0.6 -0.8
|
0.263
|
0.6 -0.8
|
0.262
|
500
|
0.143
|
0.4 - 0.6
|
0.160
|
0.4 - 0.6
|
0.144
|
1000
|
0.075
|
0.2 - 0.4
|
0.097
|
0.2 - 0.4
|
0.079
|
1500
|
0.044
|
0 - 0.2
|
0.062
|
0 - 0.2
|
0.045
|
|
Soil Drainage
|
Excessive
|
0.350
|
Dependent Population
|
0.8 - 1
|
0.470
|
Lack of water facility mouza
|
0.8 - 1
|
0.445
|
Somewhat Excessive
|
0.276
|
0.6 -0.8
|
0.262
|
0.6 -0.8
|
0.262
|
Well
|
0.159
|
0.4 - 0.6
|
0.145
|
0.4 - 0.6
|
0.152
|
Mod. Well
|
0.096
|
0.2 - 0.4
|
0.079
|
0.2 - 0.4
|
0.089
|
Imperfect Well
|
0.058
|
0 - 0.2
|
0.045
|
0 - 0.2
|
0.052
|
Imperfect Mod
|
0.037
|
|
|
|
|
|
|
Imperfect
|
0.025
|
|
|
|
|
|
|
|
NDVI
|
-0.134
|
0.513
|
Rural Health
|
0.8 - 1
|
0.419
|
Pisicultural density
|
0.8 - 1
|
0.470
|
0.201-0.387
|
0.275
|
0.6 -0.8
|
0.263
|
0.6 -0.8
|
0.262
|
0.101- 0.2
|
0.138
|
0.4 - 0.6
|
0.160
|
0.4 - 0.6
|
0.144
|
0.001- 0.1
|
0.074
|
0.2 - 0.4
|
0.097
|
0.2 - 0.4
|
0.079
|
|
0 - 0.2
|
0.062
|
0 - 0.2
|
0.045
|
Aquifer media
|
Older Alluvium
|
0.415
|
|
Older Alluvium, Sand and Silt
|
0.255
|
|
Road density
|
0.8 - 1
|
0.445
|
Laterite
|
0.153
|
0.6 -0.8
|
0.262
|
Schist
|
0.089
|
0.4 - 0.6
|
0.153
|
Banded Gneissic Complex
|
0.054
|
0.2 - 0.4
|
0.088
|
Basic Intrusives
|
0.034
|
0 - 0.2
|
0.052
|
|
Soil texture
|
Sandy loamy group
|
0.427
|
|
Ration of seed stores
|
0.8 - 1
|
0.418
|
Sandy clay group
|
0.260
|
0.6 -0.8
|
0.263
|
Loamy group
|
0.158
|
0.4 - 0.6
|
0.160
|
Gravelly group
|
0.096
|
0.2 - 0.4
|
0.097
|
Clay group
|
0.058
|
0 - 0.2
|
0.061
|
|
Soil Depth
|
Very Deep
|
0.446
|
|
Ratio of fertilizer depots
|
0.8 - 1
|
0.419
|
Deep
|
0.262
|
0.6 -0.8
|
0.262
|
Moderate
|
0.152
|
0.4 - 0.6
|
0.160
|
Shallow
|
0.089
|
0.2 - 0.4
|
0.097
|
Very Shallow
|
0.052
|
0 - 0.2
|
0.062
|
|
Relative Relief
|
<20
|
0.471
|
|
21-25
|
0.264
|
|
26-35
|
0.143
|
|
36-45
|
0.078
|
|
>46
|
0.044
|
|
|
|
Elevation
|
69-112
|
0.419
|
|
113-140
|
0.263
|
|
141-168
|
0.160
|
|
169-201
|
0.097
|
|
202-255
|
0.062
|
|
|
|
slope
|
0-1
|
0.419
|
|
1.1-3
|
0.263
|
|
3.1- 5
|
0.160
|
|
5.1-7
|
0.097
|
|
>7
|
0.061
|
Synthesizing the composite index
Exposure Index (EI)
Exposure index (EI) is the measurement of the degree of disclosure which received by the drought. Exposure means something particularly embarrassing, damaging or harmful. Here, NDDI, LULC, and Standardized precipitation Index (SPI) based Average Drought Intensity (ADI), Drought Frequency (DF), Drought Duration (DD), and Peak Intensity (PI) indicators are considered (Figure 3) to create the exposure index by using GIS based AHP technique. The Parameters of evaluation exposure indexes have been shown in Table 8.
Table 8 Evaluation Exposure Index (EI) for the agricultural drought vulnerability assessment in the upper Dwarakeshwar river basin.
Index thematic layer
|
Formulating method
|
Description
|
Relation with Exposure
|
Reference
|
NDDI
|
NDDI = ((NDVI - NDWI) / (NDVI + NDWI))
|
NDDI is a satellite-generated and widely known frequently used as agricultural drought indicator. Higher NDDI value indicates higher probability of drought exposure and vice versa.
|
Positive
|
Gu et al.2007
|
LULC
|
Supervised calcification
|
LULC is another important factor for assessing exposure of agricultural drought. Agricultural land, fallow land and natural vegetation are higher probability of drought exposure index relatively water bodies and settlement.
|
Agricultural land is very high exposure, and Water Bodies is very low.
|
Biazina and Sterk 2013
|
Average Drought Intensity
|
ADI=(N1+N2+N3…..+ Nn /Tm)
|
Higher average drought intensity value indicates higher probability of drought exposure and vice versa.
|
Positive
|
Ghosh. 2019
|
Average Drought Duration
|
ADD = (DS/ D)
|
Higher average drought duration value indicates higher probability of drought exposure and vice versa.
|
Positive
|
Ghosh. 2019
|
Drought Frequency
|
DFj.10 = (Nj / j.n) x 100%
|
Higher drought frequency value indicates higher probability of drought exposure and vice versa.
|
Positive
|
Wang et al.2013
|
Peak Intensity
|
Lowest spi value of observational 3-month SPI
|
Higher peak intensity value indicates higher probability of drought exposure and vice versa.
|
Positive
|
Raha and Gayen.2020
|
Sensitivity Index (SI)
Sensitivity in vulnerability assessment is a measure of how much the local climate will change in vulnerability to during drought. Sensitivity is assessment of the susceptibility of moisture stress or water threads for agricultural drought. Vegetation health and freshness, soil moisture, soil temperature, evaporation, and transpiration are all critical factors for assessing agricultural drought sensitivities. Here the Sensitivity index is created using the satellite based remote sensing factor VHI, NDWI, EVI, NDVI, VCI, NDWI, LST, and TCI (Figure 4) and all thematic layers has been prepared and resampling in 30 m spatial resolution through GIS environment. Parameters of evaluation sensitivity indexes have been shown table 9. In addition, a combination of eight different indicators has been developed using data from various sensors like Landsat and Modis in 2016 to better understand the agricultural drought sensitivity.
Table 9 Evaluation Sensitivity Index (SI) for the agricultural drought vulnerability assessment in the upper Dwarakeshwar river basin.
Index thematic layer
|
Formulating method
|
Description
|
Relation With sensitivity
|
Reference
|
VHI
|
VHI = α × VCI + (1 + α) × TCI
|
VHI is a combination of VCI and TCI and is a two-dimensional indicator for assessing the incidence of agricultural drought temporarily and spatially. Higher VHI value represents higher probability of drought sensitivity.
|
Positive
|
sun et al. 2013
|
NDWI
|
NDWI = ((NIR - SWIR)/( NIR + SWIR))
|
NDWI is primarily designed to describe the spatial characteristics of surface open water condition and it used to monitorvegetation and agricultural droughts. That means Higher NDWI value indicates lower probability of drought sensitivity and vice versa.
|
Negative
|
Amalo et al. 2018
|
EVI
|
EVI = 2.5 x ( NIR - RED)/(NIR + 6 x RED - 7.5 x BLUE +1)
|
EVI has proven to be an effective way to assess the long-term trend in vegetation “greening”.So, Higher EVI value indicates higher probability of drought sensitivity and vice versa.
|
Positive
|
Zhu et al.2016
|
NDVI
|
NDVI= (NIR - RED) / (NIR + RED)
|
The seasonal and inter-annual plant growth in a region and Potential cultivable areas can be identified by NDVI. Higher NDVI value indicates higher probability of drought sensitivity and vice versa.
|
Positive
|
Bhavani et al. 2017
|
VCI
|
VCI = ((NDVIi – NDVImin) / (NDVImax – NDVImin)) * 100
|
VCI derived from NDVI isolates short-term weather-related signals from long-term environmental factors and is effective in observing and comparing drought-affected plants across a large area. So Higher VCI value indicates higher probability of drought sensitivity and vice versa.
|
Positive
|
Palchaudhuri and Biswas 2019
|
LST
|
LSTc = (LST × 0.02) – 273.15
|
LST is a significant parameter for the study of drought and environment and Higher VCI value indicates higher probability of drought sensitivity and vice versa.
|
Positive
|
Arekhi et al. 2019
|
TCI
|
TCI = (LSTIi – LSTmin) / (LSTmax – LSTmin)) * 100
|
TCI is involved in the brightness temperature calculated from LST. Higher VCI value indicates higher probability of drought sensitivity and vice versa.
|
Positive
|
Rojas et al. 2011
|
Adaptive Capacity Index (ACI)
Adaptive capacity elaborates the efficiency of acclimatize power. So, Adaptive capacity provides the ability to reconfigure with minimal loss of resilience, environmental, Ecomonic, and human socio-economic system functions. An adaptive capability includes social and technological skills and strategies that allow multiple individuals or groups to adjust the environmental and socio-economic changes. In the context of the food system, adaptive capacity is usually developed or deployed to maintain livelihoods, food production or food access.
In field of drought vulnerability, Adaptive capacity is the inherent strength of the agricultural area to cope with the reduction of the crop productivity and probable loss in the agricultural drought. Here, The ACI is a composite index of three indices, namely Economic Adaptive capacity (EcAC), Environmental Adaptive Capacity (EAC), and Social Adaptive Capacity (SAC).
The SAC and EcAC data was normalized by using the following equations-
If p has positively related to vulnerability then
pn = (pα - pmin)/(pmax - pmin)
And if p has negatively related to vulnerability then used
pn = (pmax - pα )/(pmax - pmin)
Where, pn is normalized parameters, pα is each individual parameter, and pmax and pmin respectively represent maximum and minimum value of each parameter.
Environmental Adaptive Capacity (EAC)
The environmental elements control the amount of potential damage from a potential hazard or disaster and also build the EAC index. To measures EAC index, average groundwater depth, rainfall, drainage density, drainage buffer, soil drainage, NDVI, aquifer media, soil texture, soil depth, relative relief, elevation, slope factors have been used and it has also been attached by AHP technology. All collected data has been thematically mapped in GIS platform at 30 m spatial resolution (Figure. 5) respectively. Parameters of evaluation EAC has been shown in table 10. Finally, the EAC Index is computed by using the GIS overly analysis method based on the weightage of all the parameters.
Table 10 Evaluation Environmental Adaptive Capacity (EAC) index for the agricultural drought vulnerability assessment in the upper Dwarakeshwar river basin.
Index thematic layer
|
Formulating method
|
Description
|
Relation with EAC
|
Average groundwater depth
|
Interpolation (IDW) method
|
Higher average groundwater depth higher probability of drought adaptive capacity.
|
Positive
|
Rainfall
|
Interpolation (IDW) method
|
Higher Rainfall indicates higher probability of drought adaptive capacity.
|
Positive
|
Drainage density
|
Drainage length/area
|
Higher Drainage density indicates higher probability of drought adaptive capacity.
|
Positive
|
Drainage buffer
|
Multiple Buffer
|
Near the drainage indicates higher probability of drought adaptive capacity.
|
Positive
|
Soil drainage
|
Digitalization
|
Higher Soil drainage indicates higher probability of drought adaptive capacity.
|
Positive
|
NDVI
|
(Nir - red)/(Nir + red)
|
Higher value of NDVI indicates higher probability of drought adaptive capacity.
|
Positive
|
Aquifer media
|
Digitalization
|
Higher replaceable recharge indicates higher probability of drought adaptive capacity.
|
Positive
|
Soil texture
|
Digitalization
|
Finer the Soil texture indicates higher probability of drought adaptive capacity.
|
Positive
|
Soil Depth
|
Digitalization
|
Higher Soil Depth indicates higher probability of drought adaptive capacity.
|
Positive
|
Relative Relief
|
From DEM
|
Higher Relative Relief indicates lower probability of drought adaptive capacity.
|
Negative
|
Elevation
|
From DEM
|
Higher Elevation indicates lower probability of drought adaptive capacity.
|
Negative
|
Slope
|
From DEM
|
Higher Slope indicates lower probability of drought adaptive capacity.
|
Negative
|
Social Adaptive Capacity (SAC)
The adaptive capacity of a society is created by bringing together the social elements that empower the society from a single disaster. Social adaptive power controls the severity and duration of any kind of catastrophe. Social infrastructures such as education, health, labor force, unity, technology and productivity have the power to control the consequences of any kind of disaster. Here, to diagnose social adaptive capacity, six parameters have been used, such as, agricultural labor density, farmer density, rural literacy rate, old-age dependency population ratio, rural health facility, and population density (Figure.6). Parameters of evaluation SAC has been shown table 11.
The SAC index has been constructed using the GIS overlay method with AHP based assigned weightage on the thematic layers of all the permits based on their normalized value. Thematic layers are farmers, agricultural labor, rural literacy, population density, old age dependency population and health.
Table 11 Evaluation Social Adaptive Capacity (SAC) index for the agricultural drought vulnerability assessment in the upper Dwarakeshwar river basin.
Index thematic layer
|
Formulating method
|
Description
|
Relation with SAC
|
Rural education
|
Obtained directly from Rural literacy rate
|
Higher Rural education indicates higher probability of drought adaptive capacity.
|
Positive
|
Agricultural labour density
|
No of agricultural labour/Area
|
Higher Agricultural labour density indicates lower probability of drought adaptive capacity.
|
Negative
|
Farmer density
|
No of farmer/Area
|
Higher Farmer density indicates lower probability of drought adaptive capacity.
|
Negative
|
Population density
|
Total population/Area
|
Higher Population density indicates lower probability of drought adaptive capacity.
|
Negative
|
Old age dependent Population
|
No of old age dependency population/Total Population
|
Higher Old age dependant Population indicates lower probability of drought adaptive capacity.
|
Negative
|
Rural health
|
(No of bed/population) x 100
|
Higher Rural health facility indicates higher probability of drought adaptive capacity.
|
Positive
|
Economic Adaptive Capacity (EcAC)
The EcAC Index is formed by the elements that Economically control the ability to adapt any kind of natural phenomena. EcAC index depend on various natural factor which is important index to determine region-based agricultural droughts. These are the road density, drinking water facility, irrigation area, agricultural area, total crop production, ratio of seed stores and livestock for determining the EcAC of agricultural drought in the agro-based upper Dwarakeshwar River Basin which is showing in figure 7. Parameters of evaluation EcAC have been shown in Table. 12.
Table 12 Evaluation Economic Adaptive Capacity (EcAC) index for the agricultural drought vulnerability assessment in the upper Dwarakeshwar river basin.
Index thematic layer
|
Formulating method
|
Description
|
Relation with EcAC
|
Irrigation area density
|
Irrigated area/ total area
|
Higher irrigation density indicates a higher adaptive capacity that means lower vulnerability.
|
Positive
|
crop production density
|
Total crop production/ Total agricultural area
|
A higher crop production indicates higher probability of drought adaptive capacity.
|
Positive
|
Agricultural area Density
|
Total agricultural area/ total area
|
A higher Agricultural area indicates lower probability of drought adaptive capacity.
|
Negative
|
livestock density
|
No of livestock/ total area
|
Livestock is an alternative source of income. So a higher livestock density area indicates higher probability of drought adaptive capacity.
|
Positive
|
Drinking water facility
|
Total mouza – Drinking water facility mouza
|
A higher water facility block indicates higher probability of drought adaptive capacity.
|
Positive
|
Piscicultural Area Density
|
Piscicultural area/ total area
|
Pisciculture is an alternative source of income, so higher Piscicultural density areas indicate higher probability of drought adaptive capacity.
|
Positive
|
Road density
|
Length of road / total area
|
A higher Road density indicates higher probability of drought adaptive capacity.
|
Positive
|
Ration of seed stores
|
No of seed stores/ total area
|
A higher seed stores density area indicates higher probability of drought adaptive capacity.
|
Positive
|
Ratio of fertilizer depots
|
No of fertilizer depots/total area
|
A higher ratio of fertilizer depots indicates higher probability of drought adaptive capacity.
|
Positive
|