GIS-based high-resolution spatial interpolation of precipitation in mountain–plain areas of Upper Pakistan for regional climate change impact studies

Abstract

In this study, the baseline period precipitation simulation of regional climate model PRECIS is evaluated and downscaled on a monthly basis for northwestern Himalayan mountains and upper Indus plains of Pakistan. Different interpolation models in GIS environment are used to generate fine scale (250×250 m 2 ) precipitation surfaces from PRECIS precipitation data. Results show that the multivariate extension model of ordinary kriging that uses elevation as secondary data is the best model especially for monsoon months. Model results are further compared with observations from 25 meteorological stations in the study area. Modeled data show overall good correlation with observations confirming the ability of PRECIS to capture major precipitation features in the region. Results for low and erratic precipitation months, September and October, are however showing poor correlation with observations. During monsoon months (June, July, August) precipitation pattern is different from the rest of the months. It increases from south to north, but during monsoon maximum precipitation is in the southern regions of the Himalayas, and extreme northern areas receive very less precipitation. Modeled precipitation toward the end of the twenty-first century under A2 and B2 scenarios show overall decrease during winter and increase in spring and monsoon in the study area. Spatially, both scenarios show similar pattern but with varying magnitude. In monsoon, the Himalayan southern regions will have more precipitation, whereas northern areas and southern plains will face decrease in precipitation. Western and south western areas will suffer from less precipitation throughout the year except peak monsoon months. T test results also show that changes in monthly precipitation over the study area are significant except for July, August, and December. Result of this study provide reliable basis for further climate change impact studies on various resources.

Distribución y variabilidad de la precipitación en la Región Pampeana, Argentina

“…La estructura de correlación espacial entre los datos observados permite estimar la distribución espacial de precipitación (Hofstra et al, 2008;Ashiq et al, 2010). Este método es utilizado en particular para la representación mensual y anual de la precipitación (Goovaerts, 2000;Ninyerola et al, 2000;Diodato, 2005;Yang et al, 2011).…”

Geospatial-based soil variability and hydrological zones of Abha semi-arid mountainous watershed, Saudi Arabia

“…Precipitation Precipitation is one of the important climatic variables that are essentially required for numerous applications, particularly in runoff generation and hydrological modeling (Ashiq et al 2010). Therefore, understanding its temporal and spatial distribution is also an essential requirement for undertaking various studies.…”

Future changes in extreme temperature events using the statistical downscaling model (SDSM) in the trans-boundary region of the Jhelum river basin

“…There are two kinds of sub-models, unconditional and conditional, which are used according to the requirement of the predictands. For example, the unconditional sub-model is used for an independent variable like temperature, and the conditional is used for a conditional (dependent) variable like precipitation (Wilby et al, 2002;Ashiq et al, 2010). SDSM has the ability to transform the data into different forms such as the logarithmic, square root, and fourth root to make it normal before the said data can be used in regression equations (Khan et al, 2006).…”

“…The projected values of increase are 0.3-1.7 1C (RCP2.6), 1.1-2.6 1C (RCP4.5), 1.4-3.1 1C (RCP6.0), 2.6-4.8 1C (RCP8.5) for 2081-2100, relative to 1986-2005(IPCC, 2013. Such changes in globle mean temperature can radically disturb human society and the natural environment (Ashiq et al, 2010). However, the changes in extreme temperature events such as heat waves, severe winter and summer storms, hot and cold days, and hot and cold nights (Mastrandrea et al, 2011) can cause more severe impacts on human society and the natural environment.…”