ORCID Profile
0000-0003-0771-4498
Current Organisation
Brunel University London
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Publisher: MDPI AG
Date: 24-01-2022
Abstract: Inter alia, inter-annual and spatial variability of climate, particularly rainfall, shall trigger frequent floods and droughts in Pakistan. Subsequently, a higher proportion of the country’s population will be exposed to water-related challenges. This study analyzes and projects the long-term spatio-temporal changes in precipitation using the data from 2005 to 2099 across two large river basins of Pakistan. The plausible precipitation data to detect the projected trends seems inevitable to study the future water resources in the region. For, policy decisions taken in the wake of such studies can be instrumental in mitigating climate change impacts and shape water management strategies. Outputs of the Coupled Model Intercomparison Project 5 (CMIP5) climate models for the two forcing scenarios of RCP 4.5 and RCP 8.5 have been used for the synthesis of projected precipitation data. The projected precipitation data have been synthesized in three steps (1) iding the area in different climate zones based on the similar precipitation statistics (2) selection of climate models in each climate zone in a way to shrink the ensemble to a few representative members, conserving the model spread and accounting for model similarity in a baseline period of 1971–2004 and the projected period of 2005–2099 and (3) combining the selected model’s data in mean and median combinations. The future precipitation trends were detected and quantified, for the set of four scenarios. The spatial distribution of the precipitation trends was mapped for better understanding. All the scenarios produced consistent increasing or decreasing trends. Significant declining trends were projected in the warm wet season at 0.05% significance level and the increasing trends were projected in cold dry, cold wet and warm dry seasons. Framework developed to project climate change trends during the study can be replicated for any other area. The study therefore can be of interest for researchers working on climate impact modeling.
Publisher: Informa UK Limited
Date: 18-08-2021
Publisher: MDPI AG
Date: 15-02-2020
Abstract: Water is essential for life, agriculture, and industrialization however, a rapid increase in population is constantly causing water scarcity and pollution in Pakistan. Mining activities produce the potential toxic element (PTE) accumulation, which lead to unnatural enrichment, ecological pollution, and environmental degradation. The ecological resources impeded by the PTEs cause serious abnormalities in the population through dermal contact, inhalation, and digestion. Mining induced anthropogenic activities are well-known causes of contamination of ecological resources. The produced effluents have drastic effects by changing the physical, chemical, and biological properties of the concerned resources. The Central Indus Basin is a well-known coal regime, where more than 160 mines are active at present. The s les that were collected from the mine water, groundwater, surface water, and the soil were analyzed by atomic absorption and elemental determination analysis (EDA) for an assessment of their quality and the presence of PTEs. The results were correlated with available quality standards, including the World Health Organization (WHO), National Standard of Drinking Water Quality (NSDWQ), World Wildlife Fund (WWF), and Sediment Quality Guidelines (SQGs). These analyses showed the noticeable anthropogenic concentration of PTEs, like iron, cadmium, sulphur, and copper, which can degrade the quality of resources in the Central Indus Basin and have adverse effects on human health. An excessive amount of acid mine drainage (AMD) draws attention to some suitable active or passive treatments for disposal from mines to avoid degradation of ecological resources in the Central Indus Basin of Pakistan.
Publisher: MDPI AG
Date: 10-2020
DOI: 10.3390/APP10196878
Abstract: Climatic data archives, including grid-based remote-sensing and general circulation model (GCM) data, are used to identify future climate change trends. The performances of climate models vary in regions with spatio-temporal climatic heterogeneities because of uncertainties in model equations, anthropogenic forcing or climate variability. Hence, GCMs should be selected from climatically homogeneous zones. This study presents a framework for selecting GCMs and detecting future climate change trends after regionalizing the Indus river sub-basins in three basic steps: (1) regionalization of large river basins, based on spatial climate homogeneities, for four seasons using different machine learning algorithms and daily gridded precipitation data for 1975–2004 (2) selection of GCMs in each homogeneous climate region based on performance to simulate past climate and its temporal distribution pattern (3) detecting future precipitation change trends using projected data (2006–2099) from the selected model for two future scenarios. The comprehensive framework, subject to some limitations and assumptions, provides isional boundaries for the climatic zones in the study area, suitable GCMs for climate change impact projections for adaptation studies and spatially mapped precipitation change trend projections for four seasons. Thus, the importance of machine learning techniques for different types of analyses and managing long-term data is highlighted.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Muhammad Shahid.