Modeling of Rainfall Variability and Drought Assessment in Sabarmati basin, Gujarat, India

Abstract

Agricultural productivity is strongly linked to climate variability over large parts of the tropics. This is especially true in the areas where farmers depends on rain fed agriculture and water scarcity is a major constraint on food production. Indian agriculture has been constantly vulnerable as 68% of cultivable land is prone to drought. The low performance of monsoon rainfall results in low crop productivity, low agriculture investment, out-migration of people from villages to urban areas, poor infrastructure & services. The situation in Sabarmati basin is no different as 58% of land is used for agriculture of which about 50% agriculture is rain fed farming as per ICID (2005) report. Water availability for plants can be associated with rainfall pattern and long term drought signals. The research studies on rainfed farming demonstrate that rainfall amount, rainy days and arrival of monsoon have been important parameters. Therefore, this research work attempts to understand rainfall variability, develop drought indices, and forecast monsoon arrival in Sabarmati basin. Since, the ability to predict rainfall variability a season in advance could have major impacts; the same is attempted in this study. The spatial and temporal variability of monsoon are analyzed based on daily rainfall records for monsoon season for a period from 1st May to 30th October each year, although the IMD considers monsoon season from 1st June to 30th September. However in this research study, an extended monsoon season from 1st May to 30th October has been proposed and considered. This extended period will take into account early monsoon onsets, late monsoon departures and climate change signals if any. The station rainfall data at daily time scale for 26 stations for the year 1961 to 2007 (47 years) has been collected from Government of Gujarat, State Water Data Centre (SWDC). The rainfall data shows that some of these stations do not have continuous daily data series. Therefore, for analysis of rainfall, 20 stations for a period of 1976 to 2007 (32 years) in Sabarmati basin have been selected. The monthly and yearly rainfall data records for rainfall amounts and number of rainy days were obtained from the daily rainfall data series. The basin average and the standard deviation has been calculated using yearly rainfall data. Sub-regional classification of Sabarmati basin has been carried out for rainfall variability analysis. The other parameters such as rainfall departure, rainfall distribution, frequency and probability of rainfall and effect of terrain on rainfall amount were also analyzed for yearly data series. The excessive and deficient rainfall years were identified. The yearly rainfall analysis has been carried out using rainfall anomaly for station level rainfall from a gauged network. The seasonal rainfall analysis for stations during monsoon season shows higher inter-annual variability. The temporal variations of the time series is higher than the spatial variation among stations. Three regions: S-W region, N-W region and N-E region has been identified on the basis of geography and distribution of rainfall. It can be observed that stations in the alluvium plains receive less rainfall as compared to stations in hills. The number of rainy days per season shows altered trend and has been reporting less number of rainy days in alluvium plain and more in the higher altitude. Rainfall intensity found more in N-E region as compared to S-W and N-W. It is found that stations in N-W region show negative anomaly while N-E region (part of Aravali hill range) shows positive anomaly. The S-W region shows anomaly closed to almost zero as there is very little deviation from the normal. A monthly time series model based on Box-Jenkins approach has been developed using STATISTICA software. The model has considered different variable auto regressive p-term, non seasonal difference d-term, and moving average q–term. In total 8-models have been tested on three rain-gauge stations viz. Ahmedabad, Bhiloda and Vadagam where more than 40-years of records for 1st May to 31st October have been available. The results were tested with available data records which do not show coherence. The ARIMA model results shows that all models do not depict any variability. It may be due to large temporal variability in the time series. Hence, ARIMA models for time series forecasting have not been useful for rainfall forecasting in Sabarmati basin. Drought Indices gives quantitative assessment of anomalous climatic condition such as intensity, duration and spatial extent. It simplifies the complex relationships and provide good communication tool. Two drought indices (DI) such as percent of normal (PN) and standardized precipitation index (SPI) based on available data records were identified from the literature. The identified DI’s have limited possibility, therefore the need for new modified drought index (MDI) has been proposed. The modified drought index has been computed using SPI and number of rainy days and is a function of rainfall amount and the duration. The drought classes have been defined considering average SPI and average number of rainy days for a given station over long time period. The drought years have been identified based on MDI, historical data records. The MDI classified drought years have been compared with government declared drought years. The results depict a good fit with past data records. Time series rainfall expressed in terms of SPI & PN shows that the year to year variation in both plots is nearly same. The analysis shows that SPI is better as compared to PN as it shows dry as well as wet conditions of an area. The modified drought index derived has been found to have strong coherence with severe drought and wet years. The analysis shows that when there is a variability of rainfall and rainy days at spatial and temporal scale, MDI may be used for identification of drought at station level. MDI considers rainfall amount and distribution, which may be used at regional level drought classification. The agriculture statistics in Sabarmati basin depicts that major crop production depends on rain-fed farming or rain-related reservoir storages. As discussed, crop yield is highly dependents on arrival of monsoon in addition to rainfall amount and length of dry spells. Therefore, it is necessary to predict the onset of monsoon season at local scale considering a new approach Here, a new approach of monsoon arrival based on fuzzy logic has been proposed considering three constraints namely, total amount of rainfall, number of rainy days and percentage of stations receiving rainfall. A monsoon arrival model based on fuzzy logic approach has been developed for parts of Sabarmati basin. The first two definition constraints are attached to a fuzzy membership function using triangular fuzzy numbers while the third constraint considers the threshold limit. Based on the Fuzzy logic algorithm, software for calculating the values of membership grades has been developed in FOXPRO. The basin has been classified into three regions based on terrain, amount of rainfall received and cluster of stations. The model results showed monsoon onset for region-1 on 14 th June, region-2 on 17 th June, and region-3 on 21th June. The model captures periodic variability in monsoon onset for various years. The model output was verified with Indian Meteorological Department’s dates for monsoon arrival which shows good coherence. The performance of model may be improved using filter(s). The Linear Regression models have been developed to forecast monsoon arrival in region 2 based on onset in region 1 and for region 3 based on monsoon onset in region 2. The model parameters for targeted and independent region have been found to be 0.40 and 0.35 respectively. In this research, a new modified drought index (MDI) has been proposed to classify the drought situation in Sabarmati basin. The MDI showed strong coherence with historical droughts. The prediction of rainfall has been also done using ARIMA methodologies. The research proposed new monsoon onset definition and subsequently fuzzy logic based approach for prediction of monsoon arrival. A linear regression technique has been used for prediction of monsoon arrival in neighboring region. Regional classification of Basin has been done to understand the rainfall variability. Spatial and temporal variability of rainfall over Sabarmati basin has been analyzed. The information about the arrival of rainfall, rainy days during the monsoon season, pattern, anomalies (positive and negative) and frequency of mean seasonal rainfall at station level has been obtained. A new drought index (MDI) based on SPI and number of rainy days for identifying drought has been developed. A data dependent model for defining monsoon onset using FL for Sabarmati basin has been developed. The linear regression models between regions has been developed which can be useful for predicting inter-region monsoon onset.