nrc

Historical trends in averages and extremes of rainfall, temperature and runoff data of Sri Lanka.

NRC Grant:  15-144

Dr. S. Pathmarajah
Department of Agricultural Engineering
University of Peradeniya
Faculty of Agriculture
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Area of Research:Irrigation Engineering and Management
Status:Ongoing

 

objectives

1) To assess the presence or absence in parametric linearity of rainfall and temperature data

2) To find the long-term trends in rainfall, temperature and runoff pattern in Sri Lanka

3) To analyze the averages and extremes of rainfall, temperature and runoff using appropriate statistical models

4) To propose design rainfall for storm water management for selected districts

To map the geostatistical maps and graphs using spatial mapping software

overview

It is understood that climate change has influence on human lives everywhere. Especially extreme climatic events influences a lot. Also it should be noted, changes in rainfall, temperature and runoff records could be associated with natural cycles as well as feedback effects resulting from land use changes. And it becomes too important to find the climatic trend and predict the future extreme events. Climate change investigations should always be preceded by understanding the trends in historical climate and other associated environmental data. Following this guidance, the research associated with this proposal is aimed at finding out historical trends in averages and extremes, based on the baseline hydro-meteorological data available from Meteorological Department of Sri Lanka.

This study aims to investigate the trends in historical data of rainfall, temperature and runoff, focusing on both averages and extremes of those hydro-meteorological data for Sri Lanka. Sri Lanka Meteorological department has the climatic data from the period mostly from 1869 or early 1870s and that is the longest data possible for analysis. Therefore it is expected to collect maximum possible records available for Sri Lanka.

For the trend analysis it is proposed to use comprehensive collection of climate metrics to find the rainfall, temperature and runoff pattern, and further analysis to find out the extremes using statistical modelling. For the statistical analysis the R software will be used and spatial mapping will be performed using R-programming language and ArcGIS platform.