Institute of Geographic Sciences and Natural Resources Research

The present study investigated future temporal and spatial changes in maximum temperature, minimum temperature, and precipitation in two sub-basins of the Jhelum River basin—the Two Peak Precipitation
basin (TPPB) and the One Peak Precipitation basin (OPPB)—and in the Jhelum River basin on the whole, using the statistical downscaling model, SDSM. The Jhelum River is one of the biggest tributaries of the
Indus River basin and the main source of water for Mangla reservoir, the second biggest reservoir in Pakistan. An advanced interpolation method, kriging, was used to explore the spatial variations in the study area. Validation results showed a better relationship between simulated and observed monthly time series as well as between seasonal time series relative to daily time series, with an average R2 of 0.92–0.97 for temperature and 0.22–0.62 for precipitation.
Mean annual temperature was projected to rise significantly in the entire basin under two emission scenarios of HadCM3 (A2 and B2). However, these changes in mean annual temperature were predicted to be higher in the TPPB than the OPPB. On the other hand, mean annual precipitation showed a distinct increase in the TPPB and a decrease in the OPPB under both scenarios. In the case of seasonal changes, spring in the TPPB and autumn in the OPPB were projected to be the most affected seasons, with an average increase in temperature of 0.43–1.7 °C in both seasons relative to baseline period. Summer in the TPPB and autumn in the OPPB were projected to receive more pre-
cipitation, with an average increase of 4–9% in both seasons, and winter in the TPPB and spring in the OPPB were predicted to receive 2–11% less rainfall under both future scenarios, relative to the baseline
period. In the case of spatial changes, some patches of the basin showed a decrease in temperature but most areas of the basin showed an increase. During the 2020s (2011–2040), about half of the basin showed a decrease in precipitation. However, in the 2080s (2071–2099), most parts of the basin were projected to have decreased precipitation under both scenarios.

In this study, a linear scaling method, precipitation extended linear scaling (PELS), is proposed to correct precipitation simulated by GCMs. In this method, monthly scaling factors were extended to daily scaling factors (DSFs) to improve the daily variation in precipitation. In addition, DSFs were also checked for outliers and smoothed with a smoothing filter to reduce the effect of noisy DSFs before correcting the GCM's precipitation. This method was evaluated using the observed precipitation of 21 climate stations and five GCMs in the Jhelum River basin, Pakistan and India, for the period of 1986–2000 and also compared with the original linear scaling (OLS) method. The evaluation results showed substantial improvement in the corrected GCM precipitation, especially in case of mean and standard deviation values. Although PELS and OLS showed comparable results, the overall performance of PELS was better than OLS. After Evaluation, PELS was applied to the future precipitation from five

The study evaluates statistical downscaling model (SDSM) developed by annual and monthly sub-models for downscaling maximum temperature, minimum temperature , and precipitation, and assesses future changes in climate in the Jhelum River basin, Pakistan and India. Additionally, bias correction is applied on downscaled climate variables. The mean explained variances of 66, 76, and 11 % for max temperature, min temperature, and precipitation , respectively, are obtained during calibration of SDSM with NCEP predictors, which are selected through a quantitative procedure. During validation, average R 2 values by the annual sub-model (SDSM-A)—followed by bias correction using NCEP, H3A2, and H3B2—lie between 98.4 and 99.1 % for both max and min temperature, and 77 to 85 % for precipitation. As for the monthly sub-model (SDSM-M), followed by bias correction, average R 2 values lie between 98.5 and 99.5 % for both max and min temperature and 75 to 83 % for precipitation. These results indicate a good applicability of SDSM-A and SDSM-M for downscaling max temperature, min temperature, and precipitation under H3A2 and H3B2 scenarios for future periods of

In the 21st century, climate change is considered to be one of the greatest environmental threats to the world, and the changes in climate extremes are estimated to have greater negative impacts on human society and the natural environment than the changes in mean climate. This study presents the projections of future changes in extreme temperature events under A2 and B2 SRES scenarios using the statistical downscaling model (SDSM) in the trans-boundary region of the Jhelum River basin. This area is located in Pakistan and India. In order to get realistic results, bias correction was also applied to downscale the daily maximum and minimum temperature values before calculating 8 intensity and 4 frequency indices. Validation (1991–2000) showed great reliability of SDSM in ascertaining changes for the periods 2011–2040, 2041–2070 and 2071–2099, relative to 1961–1990. The intensity of the highest and the lowest night time temperatures is simulated to be higher than the highest and lowest day time temperatures. In contrast, the intensity of high night time temperature (hot nights) is projected to be lower than high day time temperature (hot days). The number of hot days and hot nights is predicted to increase, and by contrast, the frequency of cold days and cold night is predicted to decrease in all three future periods. Almost all the seasons will witness warming effects in the basin. However, these effects are much more serious in spring (hot days and nights) and in winter (cold and frosty days). On the whole in the Jhelum basin, the intensity and frequency of warm temperature extremes are likely to be higher and the intensity and frequency of cold temperature extremes to be lower in the future.

The present study examined the hydro-meteorological trends and their magnitudes using the Mann–Kendall, Sen’s slope, and linear regression methods in the Jhelum River basin. Maximum and minimum temperatures showed increasing trends in the basin. However, the increasing trends of maximum temperature in all seasons as well as in annual datasets were stronger and statistically more significant than minimum temperature. Precipitation showed non-significant increasing and  decreasing trends spread evenly throughout the basin. However, decreasing trends dominated in the
basin, except in winter, with an average annual decrease of 3.3 mm. In case of
streamflow, seasonal and annual decreasing trends dominated in the basin. Summer showed stronger and significant decreasing trends at most of the hydrometric stations in the basin. An annual decrease of 8 mm was observed at Azad Pattan. These decreasing trends are most probably due to decreasing trends in precipitation and increasing trends in temperature, though other factors such as land use changes, industrialization, and urbanization can also affect the changes in streamflow. These decreasing trends in precipitation and stream flow can have some serious implications in the reduction of water availability to the Mangla reservoir, thus producing many challenges for efficient reservoir operation
and management.

Pakistan is one of the most highly water-stressed countries in the world and its water resources are greatly vulnerable to changing climatic conditions. The present study investigates the possible impacts of climate change on the water resources of the Kunhar River basin, Pakistan, under A2 and B2 scenarios of HadCM3, a global climate model. After successful development of the hydrological modeling system (HEC-HMS) for the basin, streamflow was simulated for three future periods (2011–2040, 2041–2070, and 2071–2099) and compared with the baseline period (1961–1990) to explore the changes in different flow indicators such as mean flow, low flow, median flow, high flow, flow duration curves, temporal shift in peaks, and temporal shifts in center-of-volume dates. From the results obtained, an overall increase in mean annual flow was projected in the basin under both A2 and B2 scenarios. However, while summer and autumn showed a noticeable increase in
streamflow, spring and winter showed decreased streamflow. High and median flows were predicted to increase, but low flow was projected to decrease in the future under both scenarios. Flow duration curves showed that the probability of occurrence of flow is likely to be more in the future. It was also noted that peaks were predicted to shift from June to July in the future, and the center-of-volume date—the date at which half of the annual flow passes—will be delayed by about 9–17 days in the basin, under both A2 and B2 scenarios. On the whole, the Kunhar basin will face more floods and droughts in the future due to the projected increase in high flow and decrease in low flow and greater
temporal and magnitudinal variations in peak flows. These results highlight how important it is to take cognizance of the impact of climate change on water resources in the basin and to formulate suitable policies for the proper utilization and management of these resources.

Pakistan’s economy is significantly reliant on agriculture. However, Pakistan is included in the most water-stressed countries in the world, and its water resources are considerably vulnerable to climate variability and climate change. Therefore, in the present study, the water resources of the Jhelum River basin, which provides water to 6 million hectares of land of Pakistan and hydropower production, were assessed under the scenarios A2 and B2 of HadCM3. A hydrological model, Hydrologic Modeling System (HEC-HMS), was set up, calibrated, and validated for the Jhelum basin, and then streamflow was simulated for three future periods: 2011–2040, 2041–2070, and 2071–2099. The simulated streamflow of each period was compared with the simulated streamflow of the baseline period (1971–2000) to find the changes in the following indicators: mean flow, low flow, median flow, high flow, and center-of-volume dates (CVDs). The results of the study showed an increase
of 10%–15% in the mean annual flow as compared to the baseline flow at the end of this century. Winter, spring, and autumn showed an increase in streamflow at most of the sites in all three periods.
However, summer (the monsoon season in the basin) showed decreased streamflow at most of the sites. Maximum increase at Azad Pattan was projected in winter in the 2080s, with about 37%–39% increase in flow under both scenarios. Low and median flows were projected to increase, but a decline in high flow was detected in the future under both scenarios. It was also concluded that half of the annual flow in the basin will pass by the Azad Pattan site one week earlier than it does now. On the whole, the Jhelum basin would face more temporal and magnitudinal variations in high, low, and mean flows relative to present conditions. This shows that without a consideration of climate change impacts, proper utilization and management of water resources in the basin will be more difficult.

A B S T R A C T The mineralization (or decomposition) processes of soil organic matter (SOM), from organic to inorganic, play important roles in supplying most of nutrient for plant growth. Thus, understanding the spatial pattern and regulating mechanisms of soil nitrogen mineralization rate (N min) and its temperature sensitivity (Q 10) is of great significance for evaluating ecosystem productivity and the supply of soil available nitrogen (N). Here, data derived from 212 published papers in Chinese terrestrial ecosystems were used to explore the spatial pattern and regulating mechanisms of N min and Q 10. The results showed that the average N min was 2.78 AE 0.18 mg N kg À1 d À1 at an incubation conditions of 25 C across Chinese terrestrial ecosystems, and it decreased gradually with increasing latitude (P < 0.01). The N min values were the highest in farmlands (3.08 AE 0.20 mg N kg À1 d À1), followed by forests (2.35 AE 0.42 mg N kg À1 d À1) and grasslands (0.57 AE 0.13 mg N kg À1 d À1). The Q 10 values differed significantly among different ecosystems (P < 0.01), with an average of 1.58. The Q 10 values increased significantly with increasing latitude, supporting the hypothesis that the Q 10 values are higher in colder regions. Path analysis showed that N min is mainly affected by mean annual temperature and SOM content, whereas Q 10 is primarily affected by soil total nitrogen. The activation energy (E a) of N min and substrate quality index (A) was strongly negatively correlated, indicating that the carbon-quality temperature hypothesis is applicable to N min at a large spatial scale. These findings provide new evidence that soil N availability in colder regions is likely to increase due to higher Q 10 under global warming scenarios, which may promote ecosystem productivity at higher latitudes.

Purpose The current study was carried out in Gadoon Amazai
industrial estate and surrounding areas in order to investigate
environmental pollution resulted from various industries. The
industries flush their effluents in the surrounding areas without
treatment, which can contribute toxic elements and compounds
to the soil and also to groundwater of nearby areas.
The toxic metals have high potential ecological risk to surrounding
environment.