Determining historic (1950 - 2000) average precipitation and temperature for Pakistan by using climate downscaling technique

Abstract

There is an immense hydropower potential in Pakistan. To utilize those resources effectively, it’s compulsory to know about climate history that helps in hydropower project feasibility. For that, there is an assessment required of a specific site from a hydrology perspective i.e. water level or evaporation rate, where spatial data regarding different parameters such as precipitation and temperature is compulsory. Based on this, a climate downscaling technique is used in this paper that requires the input time series data sets of temperature and precipitation, based on 30 arc seconds and 0.5˚ resolution. A downscaled dataset has been generated by using time series input data sets of the Global Precipitation Climatology Centre (GPCC) and Climate Research Unit (CRU) that are of 0.5˚ resolution. Along with such low-resolution datasets, a 30˚ high-resolution time series data sets of WorldClim are also used for temperature and precipitation. A downscaled historical (1950 - 2000) gridded data of precipitation and temperature for Pakistan is generated through the statistical climate downscaling technique to interpolate the coarse resolution data into fine spatial resolution data with the help of Piecewise Cubic Hermite Interpolation Polynomial (PCHIP) interpolation scheme. These downscaled historical precipitation and temperature data in ASCII format are programmed in MATLAB, defining the boundary region of Pakistan to calculate their average annual, seasonal, and monthly temperature and precipitation values. The average annual, seasonal, and monthly precipitation and temperature historical data are plotted for Pakistan using a spreadsheet generated in MATLAB. Such plotted data can be used in the prediction of water stream flow rate and evaporation rate by using hydrology models in the future that help in assessing the hydropower of the Pakistan before commencement of the hydro projects.

Keywords

• climatology
• temperature data
• precipitation data
• downscaling
• weather history