Investigation of impact of environmental changes on precipitation pattern of Pakistan

Assessing the link between land use/land cover changes and land surface temperature in Himalayan urban center, Srinagar

The rapidly growing urbanization and the consequent land use/land cover (LULC) changes have resulted in unsustainable growth of cities in Indian subcontinent especially in Himalayan region which are highly sensitivity to condition like climate change. Using multi-temporal and multi-spectral satellite datasets, this study analyzes the impact of LULC changes on land surface temperature (LST) in the Himalayan city of Srinagar from 1992 to 2020. For LULC classification, the maximum likelihood classifier technique was utilized, and to extract LST from Landsat 5 (TM) and Landsat 8 (TM) (OLI), spectral radiance was employed. The results show that, among various LULC classes, built-up area has seen a maximum increase of 14% while agriculture has decreased by about 21%. On the whole, Srinagar city has witnessed an increase in LST by 4.5 °C with maximum increase of 5.35 °C especially over marshes and a minimum increase of 4 °C on agriculture landscape. Other LULC categories of built-up, water bodies, and plantation saw an increase in LST by 4.19 °C, 4.47 °C, and 5.07 °C, respectively. The transformation of marshes into built-up saw a maximum increase in LST by 7.18 °C followed by water body to built-up (6.96 °C) and water body to agriculture (6.18 °C) while minimum increase was seen in the conversion of agriculture to marshes by about 2.42 °C followed by agriculture to plantation (3.84 °C) and plantation to marshes (3.86 °C). The findings may be useful to urban planners and policymakers in terms of land use planning and city thermal environment control.

Mapping the spatial variability of rainfall from a physiographic-based multilinear regression: model development and application to the Southwestern Iberian Peninsula

A physiographic-based multilinear regression model supported by GIS was developed to estimate spatial rainfall variability in the Southwest Iberian Peninsula. The area study includes a wide diversity of landscape features and comprises four Portuguese regions and one Spanish province (totalizing 28,860 km²). The region suffers a very strong Mediterranean influence, with a major cleavage between winter and summer seasons. Thus, the analysis was carried out separately for the wet (October to March) and dry (April to September) semesters. From an initial set of 10 explanatory physiographic variables, five were selected to be used in the multilinear regression, as they allowed generating models by map algebra that fitted well with the last 40 years of monthly rainfall data records. These records were obtained from 163 weather stations, filtered from an initial set of 230 (142 stations in Portugal and 88 in Spain). The correlation between the physiographic-based multilinear regression model and a model obtained by interpolation from rainfall historical data showed to be good or very good in approximately 75% of the area under study. Results show that physiographic-based models can be effectively used to estimate rainfall where there is a lack of rain gauges, or to densify spatial resolution of rainfall between rain gauges.

A downscaling-disaggregation approach for developing IDF curves in arid regions

Over the past decades, urbanization in Arabian Gulf region expands in flood-prone areas at an unprecedented rate. Chronic water stress and potential changes in extreme rainfall attributed to climate change therefore pose unique challenges in planning and designing water management infrastructures. The objective of this study is to develop a framework to integrate climate change variations into intensity-duration-frequency (IDF) curves in Oman. A two-stage downscaling-disaggregation method was applied with rainfall at Tawi-Atair station in Dhofar region. Potential variations of extreme rainfall in future were examined by eight scenarios composed with two general circulation models (GCMs), two representative concentration pathways (RCPs), and two future periods (2040-2059 and 2080-2099). A stochastic weather generator model was used to downscale rainfall output from GCM grid scale to local scale. Downscaled daily data were then disaggregated to hourly and 5-min series by using K-nearest neighbor (K-NN) technique. Annual maximum rainfall extracted from eight future scenarios and also from present climate (baseline period) was used to develop rainfall intensity-frequency relationships for eight durations range from 5 min to 24 h. Results of the K-NN analysis indicate that the optimum window size of 57 days and 181 h is suitable for hourly and 5-min disaggregation models, respectively. Results also predict that the effects of climate change on the rainfall intensity will be more significant on storms with shorter durations and higher return periods. Moving towards the end of the twenty-first century, the return period of extreme rainfall events is likely to decrease due to intensified rainfall events.