71 CHAPTER 4 METHODOLOGY 4.1 GENERAL Drought, a vague phenomenon, has been defined and analyzed in various ways. Drought assessment involves analysis of spatially and temporally varying water related data. GIS is ideally suited for processing and analyzing such kind of data and to produce high quality maps. Using these capabilities, a GIS database can be created and drought severity maps can be produced. Drought assessment has been performed by different researchers using various methods. Drought severity in an area or basin is generally assessed in meteorological, hydrological and agricultural contexts. In this study, meteorological drought assessment was carried out using IMD method. Meteorological Drought Severity Index (MDSI) was developed based on the frequency analysis of IMD method. Agricultural Drought Severity Index (ADSI) was formulated to carry out the agricultural drought assessment using Remote sensing data. A GIS based approach to identify the drought risk area by integrating all the aspects of drought was proposed by developing an Integrated Drought Severity Index (IDSI). The methodology adopted for the analysis is described in detail in the following sections and the same is shown as flowchart in Figure 4.1.
Data collection Primary/ Secondary data Rainfall data Meteorological drought assessment using IMD method Meteorological drought severity index (MDSI) based on frequency analysis of IMD method IRS WiFS Satellite data Generation of crop land and fallow land layers using NDVI analysis Agricultural drought severity index (ADSI) Rainfall Groundwater level Slope Temperature Evaporation Evapotranspiration Relative humidity Drainage condition Surface water storage Land use Income Agriculture dependents Amenities Population density Assign weightings Weighted sum data layer Data Integration in GIS Assign Ratings Integrated drought severity index (IDSI) Meteorological drought severity map Agricultural drought severity map Integrated drought severity map Comparison with the DPAP Figure 4.1 Flow chart showing the methodology used in the study 72
73 4.2 METEOROLOGICAL DROUGHT ASSESSMENT 4.2.1 IMD Method Meteorological drought is a situation when there is a significant decrease in precipitation from the normal over an area. The assessment of drought severity in the meteorological context was carried out by employing IMD method (Report of Irrigation Commission, 1972). In this method, drought is assessed on the basis of percentage deviation of annual rainfall from the long term annual mean rainfall. The percentage deviation (D i ) is given by: D i P P i (4.1) P where P i is the annual rainfall in the year i ; and P is the long term annual mean rainfall. The percentage deviation of rainfall and the category of drought assessment are as given in Table 4.1. Table 4.1 IMD Classification of Drought Sl.No. Range of D i Classification of drought Category 1 >0 M0 No drought 2 0 to 25 M1 Mild drought 3-25 to 50 M2 Moderate drought 4 < -50 M3 Severe drought
74 As stated earlier, there are 15 raingauge stations distributed over the study area. Thirty five years of monthly rainfall data of these stations were analysed for variation in space and time. An attempt was made to carry out the drought assessment for Samba season (major crop season) of the study area for all the fifteen raingauge stations based on IMD method. The deviation of seasonal rainfall from the long term seasonal mean rainfall for all years, for all stations are computed and the same is expressed as percentage deviation. 4.2.2 Meteorological Drought Severity Index Water deficit is the main reason for drought, which depends on rainfall; it may be a wise option to analyse long-term rainfall data and to develop a severity index to view the drought situation in the region. Here, an attempt has been made to develop Meteorological Drought Severity Index (MDSI), which is a single number useful for decision making. This is calculated based on frequency analysis as to the number of times the precipitation is deviated in a given period of time from historically established normal or mean rainfall value. The drought severity classes were found out for each station on a seasonal basis using IMD method. The frequency of various classes of drought severity for each station was found out. The weightages 1, 2, 3 and 4 are assigned to drought severity classes of no, mild, moderate and severe droughts respectively. The probability of drought severity class was calculated. The MDSI of each station is found out by multiplying the frequency of each class of drought severity by the corresponding weightage. Four drought severity classes no drought, mild, moderate and severe were delineated based on the range of drought severity index. The spatial distribution of drought severity was found out using the natural neighbour analysis in GIS environment. The block boundary and drought severity distribution maps are overlaid to represent the spatial drought proneness within the blocks.
75 4.3 AGRICULTURAL DROUGHT ASSESSMENT Agricultural drought occurs when soil moisture and rainfall are inadequate to support crop growth to maturity and cause extreme crop stress leading to the loss of yield. Agricultural drought assessment was carried out based on the deficiency of rainfall for crop production. Many methods, conventional and modern, are available for assessing and monitoring drought. The conventional mechanism for monitoring of drought exists in India with most often point or sample observations (Thiruvengadachari et al 1986). The traditional methods of drought assessment do not provide the actual impact of drought situation and the distress felt by public as these methods are purely based on statistics of rainfall data that are averaged over an area (Nageswara Rao et al 2005). In order to provide spatial information on crop condition and to offer timely, adequate, reliable and unbiased drought surveillance system, the National Agricultural Drought Assessment and Monitoring System (NADAMS, 1990) was established. NADAMS is operated using vegetation index information by the, National Remote Sensing Agency (NRSA). For regional drought monitoring NADAMS uses the WiFS data onboard IRS-1C, IRS-1D and IRS-P3. With 188 m resolution, 800 km swath and high radiometric response for vegetation, WiFS data has evolved as perfect sensor for monitoring the vegetation dynamic at sub-district level (Jeyaseelan 2002). The current satellite based drought assessment and monitoring methodology was developed based on the relationship obtained between previous years Normalised Difference Vegetation Index (NDVI) profiles with the corresponding agricultural performance available at district level and their relative difference in the current year. In this study an attempt was made to develop an index which involves crop land and fallow land that have been delineated by remote sensing data
76 using NDVI analysis. As district is too large a unit with varying covers, soil types and crop types etc., the sub district level assessment in terms of smaller administrative unit like block has been considered for detailed drought assessment in this study. 4.3.1 Creation of Base Map Digital data on block boundaries were collected from the Institute of Remote Sensing, Anna University, Chennai, which were prepared from the mosaic of 1:5,000 scale village map. The resulted block boundary map was used as the base map for overlaying other digital data layers like landuse map, soil map etc., in GIS. The data layers are parameters required for monitoring drought and crop condition assessment. 4.3.2 Geometric Rectification of Satellite Data Even after basic geometric and radiometric corrections, IRS WiFS images for the years 2001 and 2002 Samba season, purchased from NDC, National Remote Sensing Agency, Hyderabad, are not oriented towards true north position. Hence, geometric rectification of satellite data with digital map base is necessary. Geometric rectification of satellite data is essential for transferring ancillary information and crop training sites on to satellite data. It also enables to enumerate crop statistics at various levels and to study the temporal changes in the command area. Satellite data was tied to digital map base by giving sufficient Ground Control Points (GCP) such as linear features (road, rail, river) intersections and other stable topographic features on both the raw satellite data and the digital map base. These GCPs were fitted in second and third
77 order polynomial equations by reducing the root mean square (RMS) error. Thus, the satellite data were rectified. 4.3.3 Agricultural Drought Severity Index (ADSI) The severity of agricultural drought could be monitored direct to the vegetation since the vegetation condition reflects the overall effect of rainfall, soil moisture, weather and agricultural practices (Eleonora Runtunuwu 2005). It has been universally accepted that satellite derived NDVI can be used as an index to assess crop stage/condition (Tucker 1979, Ayyangar et al 1980, Singh et al 2003 and Lei Ji and Peters, 2003). Here an attempt has been made to develop an Agricultural Drought Severity Index (ADSI) which involves crop land and fallow land that were delineated from remote sensing data using NDVI analysis. It is based on the crop condition associated with fallow land that forms the interrelationship with agricultural drought. Use of ADSI for drought analysis is different from the use of rainfall analysis as the vegetation cover find its presence in this index. The total area under agricultural vegetation has been deciphered for the satellite images by eliminating the area under forest, barren lands and land put to non agricultural usage. In eliminating these areas different GIS layers have been used which were taken from the digitized landuse map of the study area. NDVI values have been generated for these satellite images which represent the area under agricultural vegetation using EARDAS IMAGINE 8.5 software package. The NDVI values for vegetation generally range from 0.1 to 0.6. The higher index values being associated with greater leaf area and biomass, and for bare soil and rocks the index values are near zero (Jeyaseelan 2002). All the NDVI values falling between 0.1 and 0.6 were grouped to obtain the spatial extent of vegetation or crop land area and the residual area was considered as fallow land. Fallow land is described as that which is taken up for cultivation but is temporarily allowed to rest, uncropped
78 for one or more seasons, but less than a year. Block boundary map was overlaid on these images to obtain the block wise area of crop land and fallow land. The extent of area under crop land and fallow land associated with each block was used in formulating the ADSI which is given in equation 4.2. Agricultural Drought Severity Index (ADSI) i (Crop land) i (Crop land Fallow land) i (4.2) where i denote the designated block and varies from 1 to 20 since the number of blocks are 20. 4.4 INTEGRATED DROUGHT ASSESSMENT In the above two methods, it is clear that each type of drought indicator accounts only for a few factors of water supply and/or water demand and therefore are in general incomplete in expressing the overall concept of drought and its implication. A drought assessment procedure which will take into account of all the factors, will be of great value for practical use. Also, it will help the Government organizations in making decisions in regard to the drought prone area programmes and to make claims for including a particular area under drought alleviation schemes. Such a technique may be possible if all the aspects of drought are combined to form what can be termed as an integrated drought assessment procedure. In this study, an attempt was made to integrate various parameters affecting drought which comprises of spatial and non-spatial data to come out with an integrated drought severity assessment in GIS environment. The integrated drought severity map was generated season wise by using weighted sum overlay technique in Arc GIS software package. The difficult task in understanding the drought phenomenon is the identification of right parameters that initiate drought. The parameters
79 triggering drought condition in a region are primarily its climatic, geological and topographical variations. Compilation of relevant data is of most important as drought varies with space and time with different intensities. Based on the literature review, a significant number of parameters have been identified and used in this study that apparently triggers drought initiation. Overall fourteen drought parameters have been identified and used for drought analysis in this study. The parameters are rainfall, ground water level, slope, temperature, evaporation, evapotranspiration, relative humidity, drainage condition, surface water storage, landuse, income, agriculture dependents, amenities and population density. Generally, database is the collection of inter-related information. The creation of a clean digital database is the most important and complex task upon which the usefulness of the GIS depends. Drought assessment involves analysis of locational and timely varying water related data. GIS is ideally suited for processing and analyzing such kind of data and producing high quality maps. GIS in conjunction with remote sensing and ancillary data, can be used to identify drought prone areas. Once the droughts have been identified, their representation can be stored conveniently in GIS databases. Using these capabilities, a GIS database can be created and drought severity maps can be produced. For all the fourteen parameters, individual thematic layers were generated and are converted into raster format to a specific grid size of 23.5 m in Arc GIS software package. Having identified a significant number of parameters on the basis of knowledge of drought, a sensible integration of such parameters is a crucial task. GIS provides means of the user to carry out a quick integration of parameters and to see their effect on the output, which is not possible by any amount of human understanding. The purpose of weighting parameters affecting drought is to convey the importance of each parameter relative to
80 other parameter. A weight can be defined as a value assigned to an evaluation criterion that indicates its importance relative to other criteria under consideration (Jacek 1999). The larger the weight, the more important is the criterion in the overall utility. A number of criterion weighting procedures based on the judicious judgment of decision makers either from experts, field observations, previous experience and knowledge have been proposed in the multi-criteria decision literature. The most popular procedures include ranking, rating, pair-wise comparison and trade-off analysis. They differ in terms of their accuracy, degree of easiness to use and understanding on the part of the decision makers. In addition, the weighting assessment methods are discussed in the context of computer software availability and the way they can be incorporated into GIS based multi-criteria decision analysis. Assigning weights to parameters of drought by arbitrary means is likely to induce subjectivity in their weightings which must be removed in order to eliminate biasness. There are several ways to deal with the uncertainty about the relative importance of weightings. Pair-wise comparison method in the context of Analytic Hierarchy Process (AHP) (Saaty 1980), is one among the popular procedures for assigning weighting. 4.4.1 Analytic Hierarchy Process In Analytic Hierarchy Process (AHP), the hierarchy of components of the decisions were used in decision making process. The AHP is essentially an interactive one where a decision maker or group of decision makers relay their preferences to the analyst and can debate or discuss opinions and outcomes (Wendy Proctor, 2000). The AHP is based upon the construction of a series of pair-wise comparison matrices which compares all the criteria to one another. This is done to estimate a ranking or weighting of each of the criteria that describes the importance of each of these criteria in contributing to the overall objective.
81 4.4.2 Saaty s Pairwise Comparison Method Saaty s pair-wise comparison method is based on statistical/heuristic approach. It s advantage is that only two criteria have to be considered at a time. This method involves pairwise comparisons to create a ratio matrix. It takes as an input the pairwise comparisons and produces the relative weights as output. Specifically, the weights are determined by normalizing the eigen vector associated with the maximum eigen value of the (reciprocal) ratio matrix. It is easy to use, has high trustworthiness and quite precise (Jacek 1999). Therefore, Saaty s pair-wise comparison method has been used in this study which is described below. The method consists of three major steps (i) the pairwise comparison matrix generation; (ii) the criterion weights computation; and (iii) the consistency ratio estimation. 4.4.3 Development of the of pairwise comparison matrix The pairwise comparisons are translated from linguistic/verbal terms to numerical numbers using the fundamental Saaty s Scale with values from 1 to 9 to rate the relative preferences for two criteria, as shown in Table 4.2. This procedure is applicable to generate pairwise comparison matrix of order n x n. Using the scale in Table 4.2 the squared matrix A nxn is built as: A= [a ij ] i, j n where a ij represents the comparison between element i and element j.
82 This matrix must have the following properties (Saaty, 1986): Reciprocity: If a ij = x, then a ji = 1/x, with 1/9 x 9. Homogeneity: If the elements i and j are considered to be equally important then: a ij =a ji =1 and a ii =1 for all i. Consistency: a ik * a kj =a ij is satisfied for all 1 i, j, k n. Table 4.2 Fundamental Saaty s Scale for pairwise comparison Intensity of Importance Definition/Verbal Terms Explanation 1 Equal importance 3 Moderate importance 5 Strong importance Two elements have equal importance regarding the element in higher level Experience or Judgment slightly favours one element Experience or Judgment strongly favours one element 7 Very strong importance Dominance of one element proved in practice 9 Extreme importance The highest order dominance of one element over another 2,4,6,8 For compromise between the above values Some times one needs to interpolate a compromise judgment numerically because there is no adequate word to describe it
83 For the property to reciprocate, only n(n-1)/2 comparisons are needed in order to build a matrix with a dimension of n x n. As the comparison matrix is reciprocal i.e. if criterion A is twice as preferred to criterion B, it can be concluded that criterion B is preferred only one-half as much as criterion A. Thus, if criterion A receives a score of 2 relative to criterion B, criterion B should receive a score of ½ when compared to criterion A. Using the same logic, the upper right and lower left side of the matrix of pairwise comparisons are derived. The diagonal elements of the matrix have a value of 1 which represents equally preferred criteria when comparing anything to itself. Thus the pairwise comparison matrix can be completed. The last case or axiom of consistency occurs infrequently due to the innate subjectivity of the decision maker. This subjectivity seeks to objectify the procedure of the paired comparison matrix to the greatest extent possible since the main decision maker must compare the different elements several times in succession, as opposed to just once, in order to build the matrix. This will show any existing inconsistencies in the comparisons. The degree of inconsistency can be measured by calculating the Consistency Ratio (CR) of the matrix A. 4.4.4 Computation of the Criterion Weighting Computing the criteria of weighting involves the following operations: The values in each column of the pairwise comparison matrix are summed. Each element in the matrix is divided by its column total that results in normalized pairwise comparison matrix. The average of the elements in each row of the normalized matrix is calculated. These averages provide an estimate of the relative weightings of the criteria being compared.
84 Using this method, the weightings are interpreted as the average of all possible ways of comparing the criteria. 4.4.5 Estimation of the Consistency Ratio (CR) Determination of the consistency of comparisons involves the following operations: The weighted sum vector is determined by multiplying the weight of the first criterion A by the first column of the original pair-wise comparison matrix, followed by the multiplication of the weight criterion B with the second column, continued for n criterion and finally, summed up for each row. The consistency vector is determined by dividing the weighted sum vector by the criterion weights calculated previously. CR is defined as the ratio of Consistency Index (CI) to Random Index (RI). The value of CR indicates the level of consistency in the pair-wise comparisons. CR less than 0.10, indicates a reasonable level of consistency. CR greater than or equal to 0.10, indicates inconsistent judgments. In such cases, the revision of the original values in the pair-wise comparison matrix is necessary. CI provides a measure of departure from consistency given by, CI= ( -n)/ (n-1) (4.3) where, is the average value of the consistency vector and n represents number of parameters. RI is referred from the Statistical table that is given in Table 4.3.
85 Table 4.3 Random Inconsistency Indices (RI) for n = 1, 2....., 15. n RI n RI n RI 1 0.00 6 1.24 11 1.51 2 0.00 7 1.32 12 1.48 3 0.58 8 1.41 13 1.56 4 0.90 9 1.45 14 1.57 5 1.12 10 1.49 15 1.59 Source: Adopted from Saaty (1980). 4.4.6 Computation of Integrated Drought Severity Index (IDSI) Each parameter considered for drought assessment in this study has its variations within it in expressing the drought intensity, that needs to be given certain ratings. Hence, each parameter has been given 4 ratings that varies from 1 to 4 scales, where 4 indicates a higher influence towards drought and 1 indicates no-drought. The weighted sum overlay approach built into Arc Map Spatial Analyst is adopted for integration of input data layers where the data layers are multiplied by their corresponding weights obtained from Saaty s pair-wise comparison matrix and the same were summed up together to obtain the Integrated Drought Severity Index (IDSI) which is given in equation 4.4. IDSI is the cumulative effect of all parameters for each pixel and is categorized into four drought severity classes viz., Very mild, Mild, Moderate and Severe. Block boundary map is overlaid on the drought severity map to identify the blocks falling under different drought severity classes. IDSI 14 Kij (4.4) K 1 where i,j and K denote weighting, rating and parameter respectively.
86 Blockwise IDSI was calculated to indicate the drought severity. In order to arrive at a single value for each block, the area falling under each drought severity class was multiplied by the corresponding representative IDSI value and the sum was divided by the total area of the block. 4.4.7 Prioritization of drought parameters As discussed earlier, the difficulty involved in understanding the drought phenomenon is the identification of right parameters that initiate drought. Having identified the pertinent data required for drought assessment, the major difficulty that comes across is the availability of the data. Collection of all the data from various departments will be a tedious and time consuming process when the assessment is to be carried out urgently. Keeping this in view, out of fourteen parameters that were already considered, an attempt has been made to compute the IDSI, by choosing less number of parameters. Temperature and relative humidity were eliminated as they are functions of evaporation and evapotranspiration. Surface water storage was eliminated as it is linked with rainfall. Population density and income were eliminated as agricultural dependents and amenities were taken into account. This lead to the IDSI with only nine parameters viz., rainfall, ground water level, slope, evaporation, evapotranspiration, drainage condition, landuse, agriculture dependents and amenities. Further, an attempt has been made to compute the IDSI by eliminating slope and amenities which have only an indirect effect on drought phenomenon. This lead to the IDSI with only seven parameters viz., rainfall, ground water level, evaporation, evapotranspiration, drainage condition, landuse and agriculture dependents. The analysis, results and discussion for meteorological & agricultural drought assessments are presented in Chapter 5. Similar aspects for Integrated drought assessment are presented in Chapter 6.