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The Impact of Climate Change and Land Use on Water Resources—an Issue of Environmental Global Safety

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water and Climate Change".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 6447

Special Issue Editor

Dr. Ana-Maria Ciobotaru

E-Mail Website
Guest Editor
“Gheorghe Bals” Technical College, 107 Republicii Street, 625100 Adjud, Vrancea, Romania
Interests: deforestation; economic geography; human geography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I am pleased to announce a new Special Issue entitled “The Impact of Climate Change and Land Use on Water Resources—an Issue of Environmental Global Safety”.

Water is a basic element for the optimal functioning of the environment at local, regional, and global levels. The impact of climate change, anthropogenic activities, rapid urbanization, desertification, and deforestation has led to imbalances and combined effects on the environment: a greater pressure on water resources in arid areas or fully affected by the increase in global temperature and the number of inhabitants.

The State of Food Security and Nutrition in the World 2023 stated that good quality water is so important in urban and peri-urban areas because almost 2.5 billion people consume street foods. Additionally, the policies and solutions for healthy diets in rural and urban areas are a significant issue. The damage to human health implies the instability in social and economic systems and the need for more financial resources.

Water scarcity and chemical pollution are key global food and environmental safety elements. Water availability is important in agriculture to maintain water resources and mitigate climate change impacts on land use and land cover. Good water resource management helps areas affected by desertification, dry landscapes, and droughts. Water resources are the base for regulating and supporting services and cultural services.

This Special Issue aims to publish original research and review papers about climate change, land use, and land cover changes. Thus, papers with a theoretical background are welcomed, especially the applicated papers.

Dr. Ana-Maria Ciobotaru
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • water resources
  • environmental safety
  • impact of climate change
  • management of water resources

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Published Papers (4 papers)

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Research

29 pages, 13375 KiB  
Article
Assessing Seasonal Biogeochemical Variations in the Mun River Watershed Using Water Quality Data and the Geochemical Mass Balance Method
by Supanut Suntikoon, Pee Poatprommanee, Sutthipong Taweelarp, Morrakot Khebchareon and Schradh Saenton
Water 2025, 17(7), 985; https://doi.org/10.3390/w17070985 - 27 Mar 2025
Viewed by 227
Abstract
The Mun River watershed, a vital water resource in Northeastern Thailand and a major tributary of the Mekong River, faces significant water quality challenges driven by climate change and human activities. This study examines seasonal biogeochemical variations in the watershed, with a focus [...] Read more.
The Mun River watershed, a vital water resource in Northeastern Thailand and a major tributary of the Mekong River, faces significant water quality challenges driven by climate change and human activities. This study examines seasonal biogeochemical variations in the watershed, with a focus on how climate fluctuations affect water quality and geochemical processes. Water samples were collected from 19 surface sites during the dry and wet seasons of 2024 and analyzed for major dissolved ions. Using the geochemical mass balance method, we quantified rates of mineral weathering and biomass degradation. Our findings reveal a notable shift in hydrochemical facies from Na-Cl dominance in the dry season to Ca-HCO3 dominance in the wet season, indicating reduced salinity and changes in geochemical conditions. Wet season mineral weathering rates averaged 300–700 µmol m−2 d−1, approximately 10–20 times higher than those in the dry season. The highest weathering and biomass degradation rates, ranging from 900 to 1200 µmol m−2 d−1, were observed in the northern subwatersheds, likely due to intensified agricultural practices and underlying geological conditions. These results highlight the urgent need for adaptive watershed management strategies to address the growing impact of climate change on regional water quality. Full article
(This article belongs to the Special Issue The Impact of Climate Change and Land Use on Water Resources—an Issue of Environmental Global Safety)
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17 pages, 1674 KiB  
Article
Optimizing Rice Field Yield with Deficit Irrigation to Support Fish Populations in River Ecosystems
by Mahdi Sedighkia and Bithin Datta
Water 2025, 17(4), 535; https://doi.org/10.3390/w17040535 - 13 Feb 2025
Viewed by 430
Abstract
This study presents a simulation–optimization framework that integrates deficit irrigation strategies with ecological considerations to mitigate the impact of water abstraction on potential fish populations in river ecosystems. The framework addresses two primary objectives: minimizing fish population loss, an ecological index reflecting environmental [...] Read more.
This study presents a simulation–optimization framework that integrates deficit irrigation strategies with ecological considerations to mitigate the impact of water abstraction on potential fish populations in river ecosystems. The framework addresses two primary objectives: minimizing fish population loss, an ecological index reflecting environmental impacts, and minimizing the yield reduction of rice crops caused by deficit irrigation. Regression models and adaptive neuro-fuzzy inference systems were employed to simulate the physical and water quality parameters of the river. Additionally, a multivariate linear regression model was developed to estimate potential fish populations using combined physical and water quality indices as inputs. Multi-objective particle swarm optimization was applied to achieve the defined objectives. Results from the case study demonstrate the model’s ability to balance ecological requirements with rice production through deficit irrigation. The ecological degradation of river ecosystems was found to be comparable during dry and normal years, while rice yield decreased by approximately 10% in dry years. Comparisons with unsustainable practices, where ecological flow was disregarded, revealed that significant reductions in rice production are inevitable to sustain river ecosystems. The proposed method provides a practical approach for achieving a fair balance between agricultural benefits and environmental sustainability in river basins, making it a valuable tool for water resource management. Full article
(This article belongs to the Special Issue The Impact of Climate Change and Land Use on Water Resources—an Issue of Environmental Global Safety)
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23 pages, 6277 KiB  
Article
Land Reforestation and Its Impact on the Environmental Footprints Across Districts of Khyber Pakhtunkhwa in Pakistan
by Muhammad Ali, Khalil Ur Rahman, Hidayat Ullah, Songhao Shang, Deqiang Mao and Mei Han
Water 2024, 16(20), 3009; https://doi.org/10.3390/w16203009 - 21 Oct 2024
Cited by 1 | Viewed by 2425
Abstract
This study integrates various remote sensing datasets to analyze environmental changes and their impacts on ecosystems across Pakhtunkhwa Province in Pakistan. Precipitation data from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) dataset, along with vegetation health assessments using Normalized Difference [...] Read more.
This study integrates various remote sensing datasets to analyze environmental changes and their impacts on ecosystems across Pakhtunkhwa Province in Pakistan. Precipitation data from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) dataset, along with vegetation health assessments using Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data from the Landsat dataset, were used to comprehensively analyze the impact of vegetation dynamics on environmental footprints (i.e., temperature, precipitation, and LST). Land use maps, generated through supervised classification of Landsat images from 1985 to 2023, highlight significant changes in different land use classes, including vegetation and forest cover. Bayesian Network Modelling (BNM) and Dummy Variable Regression (DVR) methods were employed to assess the impact of vegetation (using NDVI time series) on environmental footprint and forest cover in particular. The results suggest that the NDVI generally increase the cooling effect across most of the study area, indicating that higher vegetation density is linked to a decrease in temperatures. This inverse relationship is also apparent in the connection between the NDVI and the LST, depicting a negative trend in surface temperature over most of the pixels/districts. The regression coefficients for the NDVI and the LST vary across different pixels, ranging from −5.3839 °C to 5.2697 °C, with standard deviations from 2.057 °C to 5.138 °C, reflecting a variability in the strength of this cooling effect. Similarly, for the relationship between the NDVI and the LST, coefficients range from −7.1513 °C to 6.6322 °C, with standard deviations between 1.612 °C and 4.155 °C. In contrast, NDVI and precipitation show a positive relationship, with regression coefficients ranging from 4.1686 °C to 44.3932 °C and standard deviations between 2.242 °C and 8.224 °C, suggesting greater variability in precipitation corresponding to vegetation dynamics. Additionally, forest cover generally correlates positively with precipitation in most pixels, but the variability across pixels emphasizes the complex nature of these relationships. The study identified substantial fluctuations in land use categories over the decades, indicating environmental shifts driven by both natural and human factors. BNM demonstrated a positive impact of vegetation dynamics on precipitation and a negative impact on both temperature and LST. On the other hand, the increase in forest cover, particularly due to the Billion Tree Tsunami Project, has a significant impact on the environmental footprint identified through DVR. By combining high-resolution datasets with advanced statistical techniques, this study offers key insights into the dynamic interactions between land cover, vegetation, and climate in the study region, providing valuable information for sustainable environmental management. Full article
(This article belongs to the Special Issue The Impact of Climate Change and Land Use on Water Resources—an Issue of Environmental Global Safety)
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25 pages, 2419 KiB  
Article
Decision Support Framework for Water Quality Management in Reservoirs Integrating Artificial Intelligence and Statistical Approaches
by Syeda Zehan Farzana, Dev Raj Paudyal, Sreeni Chadalavada and Md Jahangir Alam
Water 2024, 16(20), 2944; https://doi.org/10.3390/w16202944 - 16 Oct 2024
Cited by 2 | Viewed by 2362
Abstract
Planning, managing and optimising surface water quality is a complex and multifaceted process, influenced by the effects of both climate uncertainties and anthropogenic activities. Developing an innovative and robust decision support framework (DSF) is essential for effective and efficient water quality management, so [...] Read more.
Planning, managing and optimising surface water quality is a complex and multifaceted process, influenced by the effects of both climate uncertainties and anthropogenic activities. Developing an innovative and robust decision support framework (DSF) is essential for effective and efficient water quality management, so it can provide essential information on water quality and assist policy makers and water resource managers to identify potential causes of water quality deterioration. This framework is crucial for implementing actions such as infrastructure development, legislative compliance and environmental initiatives. Recent advancements in computational domains have created opportunities for employing artificial intelligence (AI), advanced statistics and mathematical methods for use in improved water quality management. This study proposed a comprehensive conceptual DSF to minimise the adverse effects of extreme weather events and climate change on water quality. The framework utilises machine learning (ML), deep learning (DL), geographical information system (GIS) and advanced statistical and mathematical techniques for water quality management. The foundation of this framework is the outcomes from our three studies, where we examined the application of ML and DL models for predicting water quality index (WQI) in reservoirs, utilising statistical and mathematical methods to find the seasonal trend of rainfall and water quality, exploring the potential connection between streamflow, rainfall and water quality, and employing GIS to show the spatial and temporal variability of hydrological parameters and WQI. Three potable water supply reservoirs in the Toowoomba region of Australia were taken as the study area for practical implementation of the proposed DSF. This framework can serve as a comprehensive mechanism to identify distinct seasonal characteristics and understand correlations between rainfall, streamflow and water quality. This will enable policy makers and water resource managers to enhance their decision making processes by selecting the management priorities to safeguard water quality in the face of future climate variability, including prolonged droughts and flooding. Full article
(This article belongs to the Special Issue The Impact of Climate Change and Land Use on Water Resources—an Issue of Environmental Global Safety)
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