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 and 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.