HYDROLOGICAL DATA ANALYSIS FOR THE IDENTIFICATION OF DROUGHTS IN AN ANTAPUR DISTRICT, ANDHRA PRADESH

摘要

The agricultural productivity, especially in dry land agriculture, depends upon chiefly the occurrence and distribution of rainfall in a particular region, but due to non-uniform distribution of rainfall and prolonged dry spells during monsoon season and crop period, the dry land agriculture in arid and semiarid regions is becoming difficult. The identification of dry spells and wet spells to find out drought occurring conditions are complex because of the fact that, it requires to analyse the vast hydrological data in a systematic order. The laborious process can be made easy with the advent of using computers and the appropriate data management software. Anantapur is one of the drought-affected districts in Andhra Pradesh due to its location in the rain shadow region of Western Ghats. Due to prolonged dry spells and ill distributed rainfall the district underwent a metamorphosis from drought to desert prone area. Keeping this in view, the present study has been carried out to identify the droughts in Anantapur district by analysing the daily rainfall and evaporation data for a period of 21 years from 1979 to 2000 by the application of Data Base Management System (DBMS) approach by developing programs using Microsoft Visual FoxPro Software. From the data analysis, dry days, dry spells, wet days, wet spells, monthly, seasonal and annual rainfall, and driest and wettest months were obtained. From the results it is possible to identify the likelihood of occurrence of dry spells and wet spells. The onset of monsoons was late and consequently the late sowing of crops which results in crop failure in Anantapur district. Thus, knowledge of likelihood of occurrence of dry spells will greatly help in protection of crop from wilting. The identification of dry spells is, therefore, helpful in agricultural planning, reservoir operations, releasing of water to canals for irrigation and for planning cloud seeding operations.