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1 Modeling of rainfall variability and drought assessment in Sabarmati basin, Gujarat, India Chapter-1 Introduction 1.1 General Many researchers had studied variability of rainfall at spatial as well as at temporal scale relating it with certain weather parameters. Mohapatra and Mohanty (2006) studied rainfall variability over Orissa in relation to low pressure system while Munot and Kothawale (2000) worked on intra-seasonal, inter-annual and decadal scale variability in summer monsoon rainfall over India. The large scale monsoon variability with respect to El-Nino Southern Oscillation (ENSO) had been studied by Kawamura et al (2005). In most of these studies focus was laid on understanding rainfall variability and effect on summer monsoon. Other researchers had studied rainfall and its variability with respect to temperature, crop cover, sea surface temperature, wind velocity and wind directions and weather dependent parameters. Most research contributions require several weather parameters and complex models while data availability for carrying out such research studies at regional and local scale is generally limited. Ghosh et al (2009) in his study on trend analysis of Indian Summer Monsoon Rainfall (ISMR) at different spatial scales found that the conventional method of representing ISMR using a single variable has some limitations and may result in an erroneous outcome for a few places. The results of local-scale studies may not match with those of large-scale analysis. India, being a developing country, is characterized by local-scale changes, viz., population 1
2 growth and urbanization, which have significant impacts on the summer monsoon rainfall pattern. These local changes are obviously not uniform all over India. Therefore, results at a finer scale considering spatial variability are more important and reliable for further use of rainfall data in hydrological applications, water resources management and agricultural water management. 1.2 Motivation for research work There is strong desire and scientific consensus to develop approaches, methods and models based on available data records which are efficient, quick and effective. This research aims to model rainfall variability and drought assessment for an arid basin in India with available data records. Preethi et al (2010) used coupled models to forecast seasonal and inter-annual prediction and found strong dependency of model simulating Indian summer monsoon on initial conditions. Rajeevan et al (2010) in his study used daily gridded rainfall for identifying active and break spells of Indian summer monsoon. Gopinath et al (1990) in his research for predicting ISMR found that rainfall anomaly can be computed knowing the position anomaly of Pacific Ocean Warm Pool (POWP). Gadgil et al (2004) showed a strong relationship between ISMR and a composite index based on ENSO and Equatorial Indian Ocean Oscillation (EQUINO). The variability of ISMR has been observed by Rahman et al (2010) using daily rainfall data from gauge and satellite. Makridakis et al (1978) has opined that rainfall amount and rainfall variability can be used for effective and efficient operations of water resource systems. The effect of rainfall distribution on crop productivity was studied by Wheeler et al. (2005) which showed simulated effect of evenly and unevenly distributed intra-seasonal rainfall. Other researchers such as Sarria -Dodd and Jolliffe, (2001) studied the importance of onset of rainy season (ORS) to determine the planting date. ORS is 2
3 important for crop growth, as planting too early may lead to crop failure whereas planting too late may results in reduced crop production. Hence, understanding of rainfall distribution and variability and prediction of monsoon onset few days ahead are vital for planning irrigation systems, operation of canal systems and assessing the impact of dry spells. The crop production uncertainty is generally defined by the variability of climate, therefore it is important to analyze rainfall variability. The crop yield depends on the rainfall amount and rain duration; hence the study of drought assessment process is essential. In general, drought is determined by comparing the departures and excess amount with the average rainfall over several years. The drought index is useful in identifying the occurrence, extent and severity of drought. Hence, the research attempt to develop a modified drought index for the Sabarmati basin. 1.3 Conventional Methods and their limitations The rainfall variability, distribution and frequency have been analyzed by several researchers using different methodologies. Several weather parameters like rainfall, temperature, evapo-transpiration, relative humidity and atmospheric pressure have been used to analyze the spatial and temporal variability of rainfall. The long term rainfall data are required for analyzing the variability, frequency and trends of rainfall. The statistical tools and soft computing models were used by researchers in the recent past. Several drought indices have been cited in the literature to classify deviation of rainfall from average rainfall. Each drought index has its own applications and limitations. There is no suitable drought index which can be used for every circumstance. Each drought index has parameters like rainfall, soil 3
4 moisture and temperature. Generally, rainfall parameter is most common to calculate drought index from monthly to yearly scale. Although, rainfall data alone may not reflect the range of drought related conditions, but they have been consistently used for field-level monitoring and contingency planning. The most common drought index is Standardized Precipitation Index (SPI), Percent of Normal (PN) and Effective Drought Index (EDI). Generally, all drought Indices use single weather parameter (mostly rainfall) for analysis and classification. PN is quite effective for comparing single season while SPI is useful for carrying out multiple time scale analysis. On the other hand, EDI is useful for carrying out daily scale analysis. Many researchers have used several weather and weather related parameters for predicting the onset of Monsoon. Different types of models like multiple regression models, Autoregressive Integrated Moving Average method (ARIMA), power (nonlinear) regression models and neural network models are used for generating predictions. The official forecast of the IMD is based on the 16 parameter power regression models. Many researchers used Time series methods, Casual methods and Judgemental method for forecasting of rainfall. Autoregressive Moving Average (ARMA), Exponential Smoothing, Extrapolation, Linear Prediction, Trend Estimation, Growth Curve and Box-Jenkins Approach are the methods of Time series modeling which require long term data. Soft computing technologies are the growing technologies for long term rainfall prediction which uses several Fuzzy, regression and neural network based models. 1.4 Objectives of the Study: The main aim of this study is to model rainfall variability, and assessment of drought in Sabarmati basin, Gujarat, India. Following are the objectives of the study. 4
5 1. To determine rainfall variability and rainfall distribution. 2. To develop modified drought index for Sabarmati basin. 3. To develop a reasonable onset definition for Sabarmati basin 4. To predict arrival of monsoon in a region based on arrival of monsoon in neighboring region. 1.5 Methodology: The research methodology has used models, methods and techniques for modeling rainfall variability and drought assessment in Sabarmati basin. The distribution and variability of rainfall has been analyzed using statistical tools. The drought assessment has been carried out by developing a modified drought index. The onset of monsoon has been defined using fuzzy logic approach and prediction of arrival of monsoon has been carried out using linear regression analysis. These techniques are rely on data driven models and statistical equations. The research frame work for the study area is presented below in Figure1.1 In this research study, meteorological and spatial data have been collected from the State Water Data Centre (SWDC), Gandhinagar. The geo-spatial data base for Sabarmati basin has been developed using basin map at 1:250,000 scale, topographical sheets at 1:50,000 scale and satellite imaginary. The satellite images for Landsat-7ETM+ was downloaded from USGS global land cover facility site http\ The images have been classified for land use and agricultural areas. The other data related to culturable area, agricultural yield were obtained from Director of Agriculture, Government of Gujarat. The statistical analysis of rainfall data for rainfall variability has been analyzed and regional classification of basin has been done. The rainfall variability was understood based on spatial, temporal and topographic features. The distribution 5
6 of rainfall and frequency of rainfall has been analyzed. It has been attempted to develop modified drought index which can describe both rainfall amount as well as rainfall duration, as earlier drought indices were based on rainfall amount only. The classification of drought condition has been done based on MDI and results were compared with past records. Figure 1.1 Flow chart on research methodology being adopted for modeling rainfall variability and drought assessment in Sabarmati basin, India A new definition for monsoon onset using a conceptual model has been proposed. It consists of three constraints consisting of total amount of rainfall during ten days period, successive number of rainy days and percentage of stations receiving the rainfall. The onset model uses artificial intelligence technique employing fuzzy logic approach for onset definition and prediction. 6
7 Linear regression equations have been developed to predict the arrival of monsoon in a region based on arrival of monsoon in neighboring region. The validity of time series based ARIMA models for rainfall forecasting have been also studied since long term data records for three stations were available Analysis on rainfall variability and distribution The spatial and temporal variability of monsoon are analyzed based on daily rainfall records for monsoon season for a period from 1 st May to 30 th October. An extended period has been considered to take into account early monsoon onset, late monsoon departure and climate change signals, if any. The available daily rainfall data have been converted into monthly and yearly rainfall records for rainfall amounts and number of rainy days. The few additional parameters such as rainfall departure, rainfall distribution, effect of terrain on rainfall and frequency of rainfall were also analyzed. Later, the yearly station rainfall anomalies were calculated using mean rainfall for the basin to determine the spatial and temporal variability of rainfall Drought Definition and Drought Indices A modified definition of drought index has been proposed based on standardized precipitation index (SPI) and number of rainy days. In the first step of analysis, two drought indices, namely SPI and PN has been computed and drought years has been analyzed. Later, a modified drought index has been computed using SPI and number of rainy days. SPI represents the probability of rainfall while the proposed modified drought index considers the rainfall amount and the duration. This modified index will be superior as it considers rainfall duration on which crop growth depends. Therefore, for the purpose of drought 7
8 planning this modified drought index will offer better insight on drought definition. The drought classes have been defined considering average SPI and average number of rainy days for a given station over long time period. Although none of the drought indices are superior, hence the need of new drought index was felt. Based on the new drought index calculated, drought years were identified and compared with the past records of available data Onset of rainy season The onset of monsoon has been defined considering three constraints namely, total amount of rainfall, number of rainy days and percentage of stations receiving rainfall. An artificial intelligence based fuzzy logic approach has been used to model the onset of rainy season. A fuzzy logic approach is important as it can incorporate the sternness of constraints which have to be fulfilled simultaneously. In this approach, each constraint is attached to a fuzzy membership function using triangular (subscript T) fuzzy numbers. The first two definition constraints are attached to a fuzzy membership function using triangular fuzzy numbers while the third constraint considers the threshold limit. For the first constraint dealing with total amount of rainfall within a 10 days spell, the triangular fuzzy numbers are (18, 25, + ) т. This means that membership grade of rainfall totals less than 18 mm is attached to zero & total larger than 25 mm to one. Between 18 & 25 mm the membership grade is linearly interpolated. For the second constraint dealing with number of rainy day, the triangular fuzzy numbers are (1, 3, + ) т. This means that membership grade of rainy days less than 1 is attached to zero & more than 3 to one. The membership grades between, 1, 1 and 3 are linearly interpolated and appropriate values are assigned. If 2 3 are membership grades for amount of rainfall, number of rainy days and 8
9 percentage of stations receiving rainfall then, the onset date is defined as the first * 1 * day of year where the product 2 3 exceeds a defined threshold value. The membership grades for the first two constraints have been computed with the help of software developed in FOXPRO. A filter was applied on onset date to make prediction relevance, in case the onset definition is beyond the stipulated monsoon duration. A Box-Jenkins approach of Time-series modeling has been used to predict the rainfall for three stations in Sabarmati basin as long term data is available. The ARIMA model of time-series with three parameters namely, p-number of auto regressive term, q- moving average and d-number of non seasonal differences, has been used. The ARIMA Model is tested for different values of parameters p, d and q. The time series models are calibrated for 40 years of period for Ahmedabad and Bhiloda rain gauge stations, and for 33 years for Vadagam station. Vadagam station has short daily rainfall data series available as compared to two other stations. The monthly forecasted rainfall time series is plotted against actual measured rainfall data series. In addition, the distribution of RMSE and SOR is also plotted for each model. The performance analysis of time series models are done using, root means square error (RMSE), sum of residuals (SOR), outliers in each time series, and normal probability plots (NPP). 1.6 Scope of Research work: This study is bounded in scope to the analysis of rainfall variability and assessment of drought in Sabarmati basin. In this study, the basic characteristics of rainfall have been analyzed to understand the rainfall variability at spatial and temporal scale. Regional classification of basin has been carried out. For understanding of drought condition, the study provides a new methodology to 9
10 develop a drought index. It also provides an approach to define the onset of monsoon in Sabarmati basin and prediction of arrival of monsoon in a region based on arrival of monsoon in neighboring region. 1.7 Organization of Thesis The structure of the thesis follows the below mentioned sequence to achieve the objectives of the study. In Chapter 1 the introductory background on the main theme, the research objectives, conceptual framework of the research and scope of research work are presented. The various chapters follow systematically analyze different issues on the basis of the framework. The chapter 2 reviews approaches, models, methods and techniques on modeling basin and station level rainfall variability and drought assessment. Initially, it describes factors influencing crop yield and drought and its dependence on other parameters. A literature review of earlier methods and models developed by researchers to analyze the rainfall variability, to define the onset of monsoon and its forecasting and to compute the drought indices for identifying the distribution, extent and severity of drought has been carried out. The brief over-view of study area as well as the availability and characteristics of data used is described in chapter 3. The chapter 4 describes about the analysis of rainfall done to find out the variability and distribution of rainfall at spatial and temporal level in the Sabarmati basin. In chapter 5, two drought indices using rainfall data are computed. The chapter also describes an approach to develop a modified drought index and to classify drought situation in Sabarmati basin. The Chapter 6 describes an approach based on fuzzy logic to define monsoon onset. It also covers the method used to predict the arrival of monsoon in a region based on arrival of monsoon in neighboring region. The chapter 7 presents summaries and conclusions of the research. It also outlines recommendations for further research. 10
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