• Energy Research
• environmental engineering close
• PK close

AbstractThe identification of projected changes in temperature caused by global warming at a fine-scale spatial resolution is of great importance for the high-altitude glacier and snow covered Upper Indus Basin. This study used a multimodel ensemble mean bias-correction technique which uses the ensemble empirical mode decomposition method to correct the bias of ensemble mean of seven CMIP6 GCMs outputs with reference to the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5). The bias-corrected data have a nonlinear trend of seven GCMs but interannual variance and mean climate of ERA5 dataset. The dataset spans from 1985 to 2100 for historical and future climate scenarios (SSP126, SSP245, SSP370, and SSP585) at daily time intervals with a 1 km grid resolution. The result of different scenarios indicates that the increase in maximum (Tmax) and minimum temperature (Tmin) ranging from 1.5 to 5.4 °C and 1.8 to 6.8 °C from 2015 to 2100, respectively. Similarly, elevation-dependent warming is identified in Tmin from 1.7 to 7.0 °C at elevations <2,000 to 6,000 m asl, while the contrary relationship in Tmax is projected under different scenarios from 2015 to 2100. This study provides an insight into how to improve the GCMs projections and can be helpful for further climate change impact studies.

The over-exploitation of groundwater resources is a significant concern due to the potential risks associated with the depletion of this valuable freshwater source. Future planning must consider changes in groundwater availability and urban expansion which are critical for understanding urban growth patterns. This study aims to investigate the impact of land cover change on groundwater depletion. Further, the Land surface temperature (LST) analysis has been performed to find the spatial spread of urbanization and its impact on surface temperature. The Gravity Recovery and Climate Experiment (GRACE) data for groundwater storage monitoring and Landsat data for land cover and LST mapping have been used. The GRACE-based Groundwater Storage (GWS) anomaly has been correlated with Tropical Rainfall Measuring Mission (TRMM)-based precipitation data. The GWS is further cross validated with the groundwater monitoring stations in the study area and the correlation of 0.7 is found. The time series analysis of GWS and the land cover maps with a decadal interval from 1990 to 2020 has been developed to find the impact of groundwater change due to urbanization. The results demonstrate a rapid increase in groundwater depletion and urbanization rates over the past decade. The LST spatial pattern is increasing similarly with the study area’s urban expansion, indicating the temperature rise due to urbanization. The study highlights the limitation of effective policies to regulate groundwater extraction in urban areas and the importance of proper planning to ensure the long-term sustainability of freshwater resources.

La production d'hydroélectricité est l'un des éléments clés de l'économie d'un pays. La présente étude se concentre sur la production optimale d'électricité à partir du réservoir de Mangla au Pakistan. Un modèle mathématique a été développé pour la centrale hydroélectrique de Mangla et des techniques d'optimisation de l'algorithme génétique et de l'essaim de particules ont été appliquées à ce modèle pour une production d'électricité optimale. Les résultats ont révélé que la production d'électricité augmente avec l'application de techniques d'optimisation au modèle mathématique proposé. L'algorithme génétique peut produire un maximum d'électricité que l'optimisation de l'essaim de particules, mais le temps d'exécution de l'optimisation de l'essaim de particules est bien inférieur à celui de l'algorithme génétique. La centrale hydroélectrique de Mangla peut produire jusqu'à 59*109 kWh d'électricité en utilisant les flux de manière optimale par rapport à la production de 47*108 kWh des méthodes traditionnelles. La generación hidroeléctrica es uno de los elementos clave en la economía de un país. El presente estudio se centra en la generación óptima de electricidad a partir del embalse de Mangla en Pakistán. Se ha desarrollado un modelo matemático para la central hidroeléctrica de Mangla y se aplicaron técnicas de optimización de enjambres de partículas y algoritmos genéticos en este modelo para la generación óptima de electricidad. Los resultados revelaron que la producción de electricidad aumenta con la aplicación de técnicas de optimización en el modelo matemático propuesto. El algoritmo genético puede producir la máxima electricidad que la optimización del enjambre de partículas, pero el tiempo de ejecución de la optimización del enjambre de partículas es mucho menor que el algoritmo genético. La central hidroeléctrica de Mangla puede producir hasta 59*109 kWh de electricidad utilizando los flujos de manera óptima que la producción de 47*108 kWh de los métodos tradicionales. Hydropower generation is one of the key element in the economy of a country. The present study focusses on the optimal electricity generation from the Mangla reservoir in Pakistan. A mathematical model has been developed for the Mangla hydropower station and particle swarm and genetic algorithm optimization techniques were applied at this model for optimal electricity generation. Results revealed that electricity production increases with the application of optimization techniques at the proposed mathematical model. Genetic Algorithm can produce maximum electricity than Particle swarm optimization but the time of execution of particle swarm optimization is much lesser than the Genetic algorithm. Mangla hydropower station can produce up to 59*109 kWh electricity by using the flows optimally than 47*108 kWh production from traditional methods. توليد الطاقة الكهرومائية هو أحد العناصر الرئيسية في اقتصاد أي بلد. تركز هذه الدراسة على توليد الكهرباء الأمثل من خزان مانغلا في باكستان. تم تطوير نموذج رياضي لمحطة الطاقة الكهرومائية Mangla وتم تطبيق تقنيات سرب الجسيمات وتحسين الخوارزمية الجينية في هذا النموذج لتوليد الكهرباء الأمثل. كشفت النتائج أن إنتاج الكهرباء يزداد مع تطبيق تقنيات التحسين في النموذج الرياضي المقترح. يمكن للخوارزمية الوراثية أن تنتج أقصى قدر من الكهرباء من تحسين سرب الجسيمات ولكن وقت تنفيذ تحسين سرب الجسيمات أقل بكثير من الخوارزمية الوراثية. يمكن لمحطة مانغلا للطاقة الكهرومائية أن تنتج ما يصل إلى 59*109 كيلو واط في الساعة من الكهرباء باستخدام التدفقات على النحو الأمثل من إنتاج 47*108 كيلو واط في الساعة من الطرق التقليدية.

With the impacts of climate disruption becoming more evident there has been an increase in the uptake of climate change adaptation "toolkits" to assist local governments build community resilience and adapt to the impacts of climate change. There is increasing attention and call for practitioners to adopt proactive and participatory approaches to help in the adaptive response planning process. One such toolkit is the International Council for Local Environmental Initiatives (ICLEI) Asian Cities Climate Change Resilience Network (ACCRN) Process (IAP). This is a simple but rigorous toolkit developed to help local governments in Asian cities build resilience to the impacts of climate change. This paper outlines the application of the toolkit to determine its versatility in the rural context and was trialled in the Himalayan rural enclave of Ramgad in the Indian state of Uttarakhand. Given the differences between urban and rural environments, the outcomes highlighted the need for further investigation and analysis into the process to ensure that the methodology truly reflects the nature of rural systems and their level of vulnerability and adaptive capacity. Overall, the toolkit proved to be a simple but versatile toolkit to assess the vulnerability and adaptive capacity of communities in rural Himalaya. Over 40 resilience intervention strategies were developed for the Ramgad enclave and these were prioritized according to their technical, political, social and economic feasibility.

Here, we provided a comprehensive analysis of long-term drought and climate extreme patterns in the agro ecological zones (AEZs) of Pakistan during 1980–2019. Drought trends were investigated using the standardized precipitation evapotranspiration index (SPEI) at various timescales (SPEI-1, SPEI-3, SPEI-6, and SPEI-12). The results showed that droughts (seasonal and annual) were more persistent and severe in the southern, southwestern, southeastern, and central parts of the region. Drought exacerbated with slopes of −0.02, −0.07, −0.08, −0.01, and −0.02 per year. Drought prevailed in all AEZs in the spring season. The majority of AEZs in Pakistan’s southern, middle, and southwestern regions had experienced substantial warming. The mean annual temperature minimum (Tmin) increased faster than the mean annual temperature maximum (Tmax) in all zones. Precipitation decreased in the southern, northern, central, and southwestern parts of the region. Principal component analysis (PCA) revealed a robust increase in temperature extremes with a variance of 76% and a decrease in precipitation extremes with a variance of 91% in the region. Temperature and precipitation extremes indices had a strong Pearson correlation with drought events. Higher temperatures resulted in extreme drought (dry conditions), while higher precipitation levels resulted in wetting conditions (no drought) in different AEZs. In most AEZs, drought occurrences were more responsive to precipitation. The current findings are helpful for climate mitigation strategies and specific zonal efforts are needed to alleviate the environmental and societal impacts of drought.

The dynamic nature and unsustainable exploitation of groundwater aquifers pose a range of management challenges. The accurate basin-wide hydrological assessment is very critical for the quantification of abstraction rates, spatial patterns of groundwater usage, recharge and discharge processes, and identification of critical areas having groundwater mining. This study provides the appraisal of remote sensing technology in comparison with traditionally prevailing tools and methodologies and introduces the practical use of remote sensing technology to bridge the data gaps. It demonstrates the example of Gravity Recovery and Climate Experiment (GRACE) satellite inferred Total Water Storage (TWS) information to quantify the behavior of the Upper Indus Plain Aquifer. The spatio-temporal changes in aquifer usage are investigated particularly for irrigation and anthropogenic purposes in general. The GRACE satellite is effective in capturing the water balance components. The basin-wide monthly scale groundwater storage monitoring is a big opportunity for groundwater managers and policymakers. The remote sensing integrated algorithms are useful tools to provide timely and valuable information on aquifer behavior. Such tools are potentially helpful to support the implementation of groundwater management strategies, especially in the developing world where data scarcity is a major challenge. Groundwater resources have not grown to meet the growing demands of the population, consequently, overexploitation of groundwater resources has occurred in these decades, leading to groundwater decline. However, future developments in the field of space technology are envisioned to overcome the currently faced spatio-temporal challenges.

This paper investigates the annual and seasonal variations in the minimum and maximum air temperature (Tmin and Tmax) and precipitation over Kashmir valley, Northwestern Himalayas from 1980–2019 by using the innovative trend analysis (ITA), Mann-Kendall (MK), and Sen’s slope estimator methods. The results indicated that the annual and seasonal Tmin and Tmax are increasing for all the six climatic stations, whereas four of them exhibit significant increasing trends at (α = 0.05). Moreover, this increase in Tmin and Tmax was found more pronounced at higher altitude stations, i.e., Pahalgam (2650 m asl) and Gulmarg (2740 m asl). The annual and seasonal precipitation patterns for all climatic stations showed downward trends. For instance, Gulmarg station exhibited a significant downward trend for the annual, spring, and winter seasons (α = 0.05). Whereas, Qazigund showed a significant downward trend for the annual and spring seasons (α = 0.05). The overall analysis revealed that the increased Tmin and Tmax trends during the winter season are one of the reasons behind the early onset of melting of snow and the corresponding spring season. Furthermore, the observed decreased precipitation trends could result in making the region vulnerable towards drier climatic extremes. Such changes in the region’s hydro-meteorological processes shall have severe implications on the delicate ecological balance of the fragile environment of the Kashmir valley.

Urban aquifers are experiencing increasing pressures from climate change, land-use change, and abstraction, consequently, altering groundwater levels and threatening sustainable water availability, consumption, and utilization. Sustainability in such areas requires the adaptation of groundwater resources to these stressors. Consequently, this research made projections about future climate, land use, and abstraction, examines how these drives will affect groundwater levels, and then proposes adaptation strategies to reduce the impact on Lahore’s groundwater resources. The objectives are achieved using an integrated modeling framework involving applications of Soil Water Assessment Tool (SWAT) and MODFLOW models. The results indicated a projected rise in Tmin by ~2.03 °C and Tmax by ~1.13 °C by 2100 under medium (RCP 4.5) and high-end (RCP 8.5) scenarios, respectively. Future precipitation changes for mid, near and far periods are projected to be −1.0%, 25%, and 24.5% under RCP4.5, and −17.5%, 27.5%, and 29.0% under RCP8.5, respectively. The built-up area in the Lahore division will dominate agricultural land in the future with an expansion from 965 m2 to 3716 km2 by the year 2100 under R1S1 (R2S2) land-use change scenarios (significant at p = 5%). The future population of the Lahore division will increase from 6.4 M to 24.6 M (28.7 M) by the year 2100 under SSP1 (SSP3) scenarios (significant at p = 5%). Groundwater level in bult-up areas will be projected to decline from 185 m to 125 m by 2100 due to increasing groundwater abstraction and expansion in the impermeable surface under all scenarios. In contrast, agricultural areas show a fluctuating trend with a slight increase in groundwater level due to decreasing abstraction and multiple recharge sources under combined scenarios. The results of this study can be a way forward for groundwater experts and related institutions to understand the potential situation of groundwater resources in the Lahore division and implement adaptation strategies to counteract diminishing groundwater resources.