Ghulam Rasul

Changing trends of thermal extremes in Pakistan

Extreme events have gained considerable scientific attention recently due to their potentially catastrophic impacts. Heat waves are thought to be more pronounced now in most parts of the world, and especially in South Asia, but doubts remain. The aim of this study is to calculate the frequency and intensity of heat waves in South Asia, focusing on Pakistan and identifying the regions within Pakistan that are most vulnerable to heat waves. Analyses have been performed both at provincial and country levels from 1961 to 2009. The provincial level analysis shows positive trends for heat waves of magnitudes ≥40°C and ≥45°C for 5 and 7 consecutive days. Events of magnitude ≥40°C and ≥45°C for 10 consecutive days also increased in frequency in Punjab, Sindh, and Balochistan. These regions are therefore considered to be the regions most vulnerable to heat wave events in Pakistan. The Balochistan region shows a consistently increasing trend throughout the study period, which may lead to more frequent drought in the future. The country level analysis indicates an increase in the frequency of 5 and 7 consecutive days heat waves at all defined temperature thresholds. The 10-days heat waves spells show a slight increase at ≥40°C and no significant change at ≥45°C. The Gilgit Baltistan and Azad Jammu & Kashmir areas reported no events at ≥45°C for 5, 7 and 10 continuous days. It is anticipated that with a long term rise in temperatures around the globe, heat waves will become more frequent and intense in all parts of the world, including Pakistan.

Integrated simulation of snow and glacier melt in water and energy balance‐based, distributed hydrological modeling framework at Hunza River Basin of Pakistan Karakoram region

© 2015 American Geophysical Union. All Rights Reserved. Energy budget-based distributed modeling of snow and glacier melt runoff is essential in a hydrologic model to accurately describe hydrologic processes in cold regions and high-altitude catchments. We developed herein an integrated modeling system with an energy budget-based multilayer scheme for clean glaciers, a single-layer scheme for debris-covered glaciers, and multilayer scheme for seasonal snow over glacier, soil, and forest within a distributed biosphere hydrological modeling framework. Model capability is demonstrated for Hunza River Basin (13,733km2) in the Karakoram region of Pakistan on a 500m grid for 3 hydrologic years (2002-2004). Discharge simulation results show good agreement with observations (Nash-Sutcliffe efficiency=0.93). Flow composition analysis reveals that the runoff regime is strongly controlled by the snow and glacier melt runoff (50% snowmelt and 33% glacier melt). Pixel-by-pixel evaluation of the simulated spatial distribution of snow-covered area against Moderate Resolution Imaging Spectroradiometer-derived 8day maximum snow cover extent data indicates that the areal extent of snow cover is reproduced well, with average accuracy 84% and average absolute bias 7%. The 3year mean value of net mass balance (NMB) was estimated at +0.04myr-1. It is interesting that individual glaciers show similar characteristics of NMB over 3years, suggesting that both topography and glacier hypsometry play key roles in glacier mass balance. This study provides a basis for potential application of such an integrated model to the entire Hindu-Kush-Karakoram-Himalaya region toward simulating snow and glacier hydrologic processes within a water and energy balance-based, distributed hydrological modeling framework.

Statistical downscaling and future scenario generation of temperatures for Pakistan Region

Finer climate change information on spatial scale is required for impact studies than that presently provided by global or regional climate models. It is especially true for regions like South Asia with complex topography, coastal or island locations, and the areas of highly heterogeneous land-cover. To deal with the situation, an inexpensive method (statistical downscaling) has been adopted. Statistical DownScaling Model (SDSM) employed for downscaling of daily minimum and maximum temperature data of 44 national stations for base time (1961–1990) and then the future scenarios generated up to 2099. Observed as well as Predictors (product of National Oceanic and Atmospheric Administration) data were calibrated and tested on individual/multiple basis through linear regression. Future scenario was generated based on HadCM3 daily data for A2 and B2 story lines. The downscaled data has been tested, and it has shown a relatively strong relationship with the observed in comparison to ECHAM5 data. Generally, the southern half of the country is considered vulnerable in terms of increasing temperatures, but the results of this study projects that in future, the northern belt in particular would have a possible threat of increasing tendency in air temperature. Especially, the northern areas (hosting the third largest ice reserves after the Polar Regions), an important feeding source for Indus River, are projected to be vulnerable in terms of increasing temperatures. Consequently, not only the hydro-agricultural sector but also the environmental conditions in the area may be at risk, in future.

Quantification of Climate Warming and Crop Management Impacts on Cotton Phenology

Understanding the impact of the warming trend on phenological stages and phases of cotton (Gossypium hirsutum L.) in central and lower Punjab, Pakistan, may assist in optimizing crop management practices to enhance production. This study determined the influence of the thermal trend on cotton phenology from 1980–2015 in 15 selected locations. The results demonstrated that observed phenological stages including sowing (S), emergence (E), anthesis (A) and physiological maturity (M) occurred earlier by, on average, 5.35, 5.08, 2.87 and 1.12 days decade−1, respectively. Phenological phases, sowing anthesis (S-A), anthesis to maturity (A-M) and sowing to maturity (S-M) were reduced by, on average, 2.45, 1.76 and 4.23 days decade−1, respectively. Observed sowing, emergence, anthesis and maturity were negatively correlated with air temperature by, on average, −2.03, −1.93, −1.09 and −0.42 days °C−1, respectively. Observed sowing-anthesis, anthesis to maturity and sowing-maturity were also negatively correlated with temperature by, on average, −0.94, −0.67 and −1.61 days °C−1, respectively. Applying the cropping system model CSM-CROPGRO-Cotton model using a standard variety in all locations indicated that the model-predicted phenology accelerated more due to warming trends than field-observed phenology. However, 30.21% of the harmful influence of the thermal trend was compensated as a result of introducing new cotton cultivars with higher growing degree day (thermal time) requirements. Therefore, new cotton cultivars which have higher thermal times and are high temperature tolerant should be evolved.

Risk assessment of maize drought disaster in southwest China using the Environmental Policy Integrated Climate model

The East Asian monsoon has a tremendous impact on agricultural production in China. An assessment of the risk of drought disaster in maize-producing regions is therefore important in ensuring a reduction in such disasters and an increase in food security. A risk assessment model, EPIC (Environmental Policy Integrated Climate) model, for maize drought disasters based on the Erosion Productivity Impact Calculator crop model is proposed for areas with the topographic characteristics of the mountainous karst region in southwest China. This region has one of the highest levels of environmental degradation in China. The results showed that the hazard risk level for the maize zone of southwest China is generally high. Most hazard index values were between 0.4 and 0.5, accounting for 47.32% of total study area. However, the risk level for drought loss was low. Most of the loss rate was <0.1, accounting for 96.24% of the total study area. The three high-risk areas were mainly distributed in the parallel ridge-valley areas in the east of Sichuan Province, the West Mountain area of Guizhou Province, and the south of Yunnan Province. These results provide a scientific basis and support for the reduction of agricultural drought disasters and an increase in food security in the southwest China maize zone.

Inventory of glaciers and glacial lakes of the Central Karakoram National Park (CKNP – Pakistan)

ABSTRACT This study presents a map reporting valuable information on the cryosphere of the Central Karakoram National Park (CKNP, the largest protected area of Pakistan and the highest park in the world). All the information is provided considering the CKNP as a whole, and in detail by dividing it into five basins (i.e. Shigar, Hunza, Shyok, Upper Indus, and Gilgit). The glacier inventory reports 608 ice bodies covering 3680 km2 (∼35% of the CKNP area), with a total glacier volume of ca. 532 km3. In addition, we modeled the meltwater from glacier ice ablation over the period 23 July to 9 August 2011. The total melt amount is ca. 1.5 km3. Finally, we considered glacial lakes (202 water-bodies, covering 4 km2). For these latter glacier features, we also analyzed their potentially dangerous conditions and two lakes were found having such conditions.

Air Quality Measurements at Multan, Pakistan

Urban outdoor air pollution is estimated by the World Health Organization (WHO) to cause 1.3 million deaths worldwide per year. By reducing air pollution levels, it is possible to reduce the global burden of disease from respiratory infections, heart disease, and lung cancer. Air pollution is a major environmental health problem able to produce serious risks to health from exposure to particulate matter (PM) and ozone (O3) in many cities, including Multan, Pakistan. At present there are no national inventories that estimate air pollutant emissions in Pakistan, and regular monitoring of environmental air quality is still not systematic in this country. According to the Pakistan Environmental Protection Agency (Pak-EPA), a major share of the emissions load from motor vehicles, although not quantified, can be attributed to a relatively small number of smoky diesel and two-stroke vehicles found in many Pakistani cities. The high levels of sulfur in automotive diesel (0.5–1 %) and furnace oil (1–3.5 %) are seen as a major contributor PM in ambient air. Emissions from large-scale facilities and a wide range of small-to-medium-scale industries (brick kilns, steel rerolling, steel recycling, plastic molding, etc.) cause a disproportionate share of pollution through their use of dirty “waste” fuels (i.e., old tires, paper, wood, and textile waste) and the use of diesel electric generators in commercial and residential areas.