| dc.description.abstract | In the present work, methodology of statistical analysis of change evolved by Kundzewicz and Robson (204) is revised to obtain a robust methodology named as “Comprehensive Aproach” which addresses research gaps of earlier method, as also those found by literature review. Main aspects of the revised method are: 1) importance of graphical representations as first step, in which, if line spectrum has constant spectral density function then time series is random and no need of further trend detection, 2) importance of computation of statistical parameters of data for deciding type of step change test to be used and for cross checking results of exploratory data analysis (EDA), 3) application of EDA, statistical parameters and checking assumption(s) about the data by statistical test(s) is suggested and also results of these steps can be used to cross check results of each other, 4) suggested basis for selection of step change test(s) i.e. evaluation of two aspects of step change viz. detection and location of step change, 5) suggested basis for selection of trend detection tests i.e. evaluation of all four aspects of trend viz. magnitude, statistical significance, beginning and end of trend and nature of trend, 6) evaluation of regional significance is suggested as essential wherever applicable. The revised method i.e. “Comprehensive Approach” is applied for the trend detection of rainfall of seven homogenous rainfall regions and al India at annual, monthly and seasonal temporal scales for three time periods 1901-203, 1948-203 and 1970-203. Between 100 N to 300 N, there was marked increase in precipitation from 190 to 1950s, but decrease after about 1970 (Trenberth et al., 207). Thus starting years of three time periods are selected as 1901, 1948 and 1970. To have similarity of end year, in analysis periods given in chapters 1, 2 and chapters 3, 4; their end years are kept close to each other i.e. end year of analysis periods is 203 in chapters 1, 2 and end year of analysis periods is 204 in chapters 3, 4. Thus 203 are considered as common end year of three time periods. Burn and Elnur (202) sugested that least number of years required for ensuring statistical validity of results of trend detection are 25 years. So in the third time period (1970-203), the duration is 34 years which is more than 25 years. Three time periods are having data of 103 years (1901-203), 56 years (1948-203) and 34 years (1970- 203) so effect of different time durations on trend detection analysis results is studied. Also temporal scales used in trend detection analysis are annual, monthly and seasonal (4 seasons) thus presence of trend is assessed in these main temporal scales. Results of the analysis showed that, statistically significant trends are found in: 1) winter rainfall time series of peninsular India (PENIN) region for the time period 1901-203, 2) pre-monsoon rainfall time series of north west India (NWIND) and central north east India (CNEIN) regions for the time period 1948-203, 3) monsoon rainfall time series of west central India (WCIND) region for the time period 1948-203, 4) August month rainfall time series of north east India (NEIND) region for the time period 1901-203, 5) June month rainfall time series of NEIND region for the time period 1948-203, 6) Also regionally significant trends are detected in pre- monsoon rainfall time series of five homogeneous regions for the time period 1948-203. Regionally significant trends are detected in pre-monsoon rainfall time series of five homogeneous regions for the time period 1948-203. But effect of cross correlation between rainfall time series of stations of subdivisions and between the sub-divisions in a region is not
accounted in the field/regional significance evaluation and Hegel et al. (207) suggested that reactions to external forcing in trends of regional precipitation trends exhibit weak signal to noise ratios and likely to exhibit strong variations in space because of dependency of precipitation on geographic parameters like pornography and atmospheric circulation. Thus attribution of precipitation is more difficult. Also Saikranthi et al. (2013) suggested that homogeneity of rainfall zones may change in future. So, attribution of trends detected in pre-monsoon rainfall time series of five homogeneous regions was not possible. The results of statistically significant trends are confirmed by smoothing curves, innovative trend analysis plots and Sen.’s slope estimates. Contributions by present trend detection study on rainfall of homogenous regions by using “Comprehensive Approach” method are: 1) modification of guidelines of statistical analysis of change to evolve a robust method termed as “Comprehensive Approach”, 2) systematic trend detection analysis is performed pertaining to the rainfall of core monsoon India (CORIN) region and homogeneous India (HOMIN) region, which was not done earlier, 3) systematic trend detection analysis is performed on the rainfall of al India and seven homogenous regions concurrently for aforesaid temporal scales and time periods (except regional significance evaluation only for five homogeneous regions), which was not done earlier, 4) Man Kendal test with block bootstrapping approach (MKBBS) test (not effected by serial correlation) is used for trend detection of serially correlated data and Man Kendal (MK) test is used for trend detection of serially uncorrelated data. Sen.’s slope is used for evaluation of trend magnitude, 5) evaluation of field/regional significance of trends in rainfall over five homogenous regions is performed, which was not done earlier, 6) Location of beginning, end and progress of trend in rainfall of all India and seven homogenous regions concurrently is performed, which was not done earlier. As mentioned aforesaid, attribution of regionally significant trends detected in pre-monsoon rainfall time series of five homogeneous regions for the time period 1948-203 was not possible because of non-accounting of effects of cross correlation, attribution of rainfall is difficult and homogeneity of rainfall zones may change in future as discussed above in detail. So a thorough investigation about trends in rainfall, three temperatures (minimum, mean and maximum) and stream flow at regional (basin) scale was proposed to be ascertained. As Tapi basin is exposed to occurrence of heavy floods (Joshi and Shah, 2014) and it is climatically sensitive (Bhamare and Agone, 201; Gosain et al. 206; Deshpande et al., 2016), it is considered as study area. The trend detection analysis of gridded data (chapter 4) and regional time series (chapter 3) of rainfall and three temperatures data (1971-204) along with that for station data of stream flow (1979-204) of five gauging stations (chapter 4) is carried out using “Comprehensive Approach” for all temporal scales. Common available end year of data of rainfall, temperature and stream flow was 204 as data after 204 was not available for stream flow for all five gauging stations. Also data of rainfall (0.50 x 0.50) was available from year 1971, which was common starting year among data of rainfall and three temperatures. Also common starting year of stream flow data was 1979. Because of unavailability of rainfall data (0.50 x 0.50) before 1971, the three time periods used in chapters 1 and 2 are not used in chapters 3 and 4, thus only one time period is used for rainfall and three temperatures (1971-204) and stream flow (1979-204).
The analysis has shown the presence of regionally significant rends in the gridded data of annual mean temperature (Tmean) and winter Tmean over Tapi basin apart from significant trends found in regional time series of annual Tmean and winter Tmean of Tapi basin. Monthly, winter and pre- monsoon stream flow volume time series have also shown regionally significant trends over five gauging stations of Tapi basin. Main contributions of the trend detection analysis of hydro- climatic variables of Tapi basin are: 1) grid wise, regional scale and station wise trend detection of three temperatures, rainfall and stream flow respectively is performed, which was not done earlier, 2) regional significance evaluation of gridded data (rainfall and three temperatures) and station data of stream flow (five stream flow gauging stations) is performed, which was not done earlier, 3) all four aspects of trend of hydro-climatic variables are evaluated, which was not done earlier, 4) systematic trend detection study of gridded, regional and station data of hydro-climatic variables is performed in present study which was not done earlier. After detection of regionally significant trends, next step is finding the causal factors through attribution study. Once causal factors of climate change observed in given variable are found, then remedial measures can be carried out for minimizing the effect of these factors on climate change observed in given variable. There are three main methods of attribution found in literature viz. finger printing, optimal finger printing and artificial neural network (ANN) model. In finger printing method only the leading empirical orthogonal function (EOF) is used, so this method is conservative. In optimal finger printing, multivariate regression is used, which has certain assumptions which are difficult to be fulfilled in the case of climate studies as climate is essentially a non-linear dynamic system. ANN being non-linear in nature provides the required solution for the attribution problem related to climate.
Attribution of regionally significant trends detected in monthly, winter and pre-monsoon stream flow volume time series of five gauging stations of Tape basin is not performed because five gauging stations were not representative of entire Tapi basin and two out of the five gauging stations have missing data greater than 15%. Number of significant monotonically increasing trends are more in winter gridded Tmean data as compared to annual gridded Tmean data. Thus attribution analysis of winter gridded Tmean data has given first priority followed by attribution of annual gridded Tmean data. ANN model is developed for the attribution of climate change observed in gridded data of winter Tmean and annual Tmean in three steps: 1) input variable selection (IVS) based on partial mutual information (PMI), 2) data splitting using k-means clustering method and Neyman allocation, 3) ANN model formulation by using best training algorithm among Levenberg-Marquardt (LM) algorithm, scaled conjugate gradient (SCG) algorithm and Broyden, Fletcher, Goldfarb, and Shano (BFGS) algorithm and optimum number of hidden neurons (varying from 1 to 3) corresponding to performance in terms of mean squared error (MSE) and to use these in final ANN model formulation with computation of performance evaluation measures (PEMs). Aforesaid third step is repeated for 50 iterations for each input forcing and given target output to minimize any random variation due to reinitialization of training algorithms. Also random variations due to initialization of ANN model are minimized by keeping initial weights and biases equal to zero. Final PEMs evaluated were the averages of 50 iterations as mentioned aforesaid. Target outputs used in two ANN attribution models are time series of regional winter Tmean and regional annual Tmean. Also in some cases of ANN model formulations, network parameters are kept less than number of data points in the training set for minimizing overriding. Inputs for ANN model were circulation indices and regional, global and national scale input variables. The inputs selected by PMI based input selection (PMIS) algorithm in the step of IVS of both ANN attribution models are seen to be subjected to natural and anthropogenic forcing, which undisputedly shows significant role of anthropogenic activities in observed climate change in aforesaid two gridded temperature variables. Also ranking of input forcing is performed in both the ANN attribution models according to their final PEM values. In the case of ANN attribution model for regional winter Tmean time series, dominant role of natural (‘nat’) input forcing is found behind the given climate change as compared to anthropogenic (‘anth’) input forcing. Among ‘anth’ inputs, effect of land cover (‘Landcover’) input forcing is found to be dominant as compared to green house gases (‘GHgases’) input forcing. Among ‘Landcover’ inputs, urban landcover input was found to be one of the important inputs. In the case of ANN attribution model for regional annual Tmean time series, dominant role of ‘anth’ input forcing is found behind the given climate change as compared to ‘nat’ input forcing. Among ‘anth’ inputs, there is dominant role of ‘Landcover’ input forcing as compared to ‘GHgases’ input forcing. Among ‘Landcover’ inputs, urban landcover input was found to be one of the important inputs. Contributions of attribution study are: 1) checking of input independence and significance by using PMI IVS method, which was not performed earlier, 2) division of data in such a way that al patterns of whole data are present in training, testing and validation subsets and the statistical properties of these subsets are similar to each other and to whole data, which was not performed earlier, 3) using LM, SCG and BFGS algorithms which are converging fatly as compared to Windrow-Hof algorithm and gradient descent algorithm. Also these three algorithms are les liable to be get stuck in local minima, 4) using land cover data as input forcing to ANN model used for attribution of climate change, which was not done earlier. | en_US |
