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GIS and Geocomputation for Water Resource Science and Engineering not only provides a comprehensive introduction to the fundamentals of geographic information systems but also demonstrates how GIS and mathematical models can be integrated to develop spatial decision support systems to support water resources planning, management and engineering. The book uses a hands-on active learning approach to introduce fundamental concepts and numerous case-studies are provided to reinforce learning and demonstrate practical aspects. The benefits and challenges of using GIS in environmental and water resources fields are clearly tackled in this book, demonstrating how these technologies can be used to harness increasingly available digital data to develop spatially-oriented sustainable solutions. In addition to providing a strong grounding on fundamentals, the book also demonstrates how GIS can be combined with traditional physics-based and statistical models as well as information-theoretic tools like neural networks and fuzzy set theory.

This book provides the necessary elements to determine exactly what information should be collected to make the collected information relevant for policy makers. It highlights the dissatisfaction of information users about the information they get and the reasons for this dissatisfaction. It also discusses general issues around the role and use of information in policy making. The text then describes the how to develop a full understanding of the policy makers' information needs and will describe how policy makers can be included in the process. Finally, the book describes how the results from this process are input for the information production process.

Using technological advancements and analyzing urban water consumption patterns, this article employs GRACE satellite data and statistical records to conduct a comprehensive assessment and evaluation of water resource utilization efficiency across 34 prefecture-level cities in China’s three northeastern provinces—Liaoning, Jilin, and Heilongjiang—over the period spanning from 2003 to 2020. By utilizing the sophisticated Super-SBM model, the study delves into the spatial and temporal variations in terrestrial water reserves and green water usage efficiency. Additionally, the Tobit model is introduced to investigate the influencing factors of water resource utilization efficiency. The primary findings of the study are outlined below: The spatial distribution of terrestrial water resources in the three northeastern provinces reveals a clear north-south gradient, with abundant resources in the northern regions and scarcity in the southern parts. Seasonal fluctuations, albeit present, are relatively modest, with higher water storage levels typically observed in spring and summer, and lower levels in autumn and winter. Regarding the static water use efficiency among the 34 prefecture-level cities, Panjin stands out with the highest efficiency, whereas Qiqihar ranks lowest. Notably, 91.18% of the cities exhibit medium to high efficiency levels, reflecting commendable performance in water utilization throughout the region. Almost half of the cities have experienced an improvement in their water use efficiency compared to the previous year, signaling a gradual enhancement in water utilization capabilities. The average total factor productivity across the three northeastern provinces stands at 1.012, representing an annual growth rate of 1.2%. The efficiency of water resource utilization in these provinces is intricately linked to the technological progress index. To enhance water resource utilization efficiency, it is imperative to introduce advanced technologies, increase research investments, and foster technological advancements.

Global freshwater resources are under increasing pressure from economic development, population growth, and climate change. The international trade of water-intensive products (e.g., agricultural commodities) or virtual water trade has been suggested as a way to save water globally. We focus on the virtual water trade network associated with international food trade built with annual trade data and annual modeled virtual water content. The evolution of this network from 1986 to 2007 is analyzed and linked to trade policies, socioeconomic circumstances, and agricultural efficiency. We find that the number of trade connections and the volume of water associated with global food trade more than doubled in 22 years. Despite this growth, constant organizational features were observed in the network. However, both regional and national virtual water trade patterns significantly changed. Indeed, Asia increased its virtual water imports by more than 170%, switching from North America to South America as its main partner, whereas North America oriented to a growing intraregional trade. A dramatic rise in China's virtual water imports is associated with its increased soy imports after a domestic policy shift in 2000. Significantly, this shift has led the global soy market to save water on a global scale, but it also relies on expanding soy production in Brazil, which contributes to deforestation in the Amazon. We find that the international food trade has led to enhanced savings in global water resources over time, indicating its growing efficiency in terms of global water use.

In Saudi Arabia, water pollution and drinking water scarcity pose a major challenge and jeopardise the achievement of sustainable development goals. The urgent need for rapid and accurate monitoring and assessment of water quality requires sophisticated, data-driven solutions for better decision-making in water management. This study aims to develop optimised data-driven models for comprehensive water quality assessment to enable informed decisions that are critical for sustainable water resources management. We used an entropy-weighted arithmetic technique to calculate the Water Quality Index (WQI), which integrates the World Health Organization (WHO) standards for various water quality parameters. Our methodology incorporated advanced machine learning (ML) models, including decision trees, random forests (RF) and correlation analyses to select features essential for identifying critical water quality parameters. We developed and optimised data-driven models such as gradient boosting machines (GBM), deep neural networks (DNN) and RF within the H2O API framework to ensure efficient data processing and handling. Interpretation of these models was achieved through a three-pronged explainable artificial intelligence (XAI) approach: model diagnosis with residual analysis, model parts with permutation-based feature importance and model profiling with partial dependence plots (PDP), accumulated local effects (ALE) plots and individual conditional expectation (ICE) plots. The quantitative results revealed insightful findings: fluoride and residual chlorine had the highest and lowest entropy weights, respectively, indicating their differential effects on water quality. Over 35% of the water samples were categorised as ‘unsuitable’ for consumption, highlighting the urgency of taking action to improve water quality. Amongst the optimised models, the Random Forest (model 79) and the Deep Neural Network (model 81) proved to be the most effective and showed robust predictive abilities with R 2 values of 0.96 and 0.97 respectively for testing dataset. Model profiling as XAI highlighted the significant influence of key parameters such as nitrate, total hardness and pH on WQI predictions. These findings enable targeted water quality improvement measures that are in line with sustainable water management goals. Therefore, our study demonstrates the potential of advanced, data-driven methods to revolutionise water quality assessment in Saudi Arabia. By providing a more nuanced understanding of water quality dynamics and enabling effective decision-making, these models contribute significantly to the sustainable management of valuable water resources.

Surface water is important for the urban and agriculture ecosystem, the accurate and very easy to detect and analysis of the surface water based on the remote sensing data and google earth engine platform. It is a very much important for irrigation and water resource management during the dry period and rabi with summer season. In this paper, we have extracted the surface water analysis from Sentinel-2A images using a new technique of Google Earth Engine (GEE) and machine learning coding. Monitoring the dynamics of surface water is a very helpful to study of the irrigation, and drinking water requirement, which climate change factors most damages on the surface water, natural environmental health and understand the impacts of global changes and human actions on the planning and development of the water resources in the semi-arid region. Currently, so much research has focused on the surface water extraction and dynamic monitoring using remote sensing imagery and softwares. But downloading a big number of remote sensing images are covering that area and then process in the remote sensing and GIS software as per the traditional method. The traditional process is much lengthy, also very time-wasting for such kind of time series dataset analysis. In this view, GEE platform has been given easier to access any satellite data within less time. The GEE is a total cloud-based platform enthusiastic to satellite image data processing based on the machine learning coding. So for many excellent remote sensing image processing coding, algorithms have been integrated in the platform of Google Earth Engine (GEE). These data do not require to save images and collection, which are very easy to doing satellite data processing and effective output capability. The results show that 2019 year has observed that there are increased water area, i.e. 202.4871 km 2 and for the year of 2019, water spread-out area is 410.9113 km 2 in dam. To compare the results of 2015–2019, there is much increase in the water area due to various reasons like pumping, heavy rain, etc. in the Jayakwadi dam. This water can use for various purposes and irrigation water management and drinking purposes during the drought condition. The developed algorithms have given better information and results of water spread-out by google earth engine. The current techniques are reliable, novel, and quick to get the maximum and minimum extent of the surface water. The results can be very useful for the surface water planning and management in the study area.

Unplanned urbanization and economic development can deteriorate water quality (WQ) and alter its beneficial usage. Continuous monitoring of biotic and abiotic parameters describing the WQ is essential to track changes and classify water resources to protect public health. Various invest significant effort, money, and time in monitoring programs. Using data from those sources is challenging due to the large number of observations, and inconsistencies in sampling time, date, stations, and gaps. This study aims to design different water quality index (WQI) models to provide policymakers, stakeholders, and water managers with a more comprehensive assessment by converting complex datasets from over 10 years, processed with the statistical software R, into consistent data sets. These datasets are then transformed into small principal components. WQ datasets of lakes and reservoirs in the western USA were chosen as case studies. The strategy of data processing is explained, and the results organized as a descriptive summary of the 12,000 observations for 31 parameters are discussed. Outputs of principal component analysis (PCA) are used to create relative and absolute WQI models for water irrigation usage and protecting cold- and warm-water species of game fish. Weighted arithmetic water quality indices are applied, and the relation between different models is examined.

A great majority of the environmental Kuznets curve (EKC) literature use CO 2 emissions to proxy for environmental degradation. However, this is an important shortage in application of the EKC concept because environmental degradation cannot be captured by CO 2 emissions only. By using a broader proxy, ecological footprint, this study aims to investigate the presence of environmental Kuznets curve hypothesis for the EU countries. The annual data from 1980 to 2013 is examined with second generation panel data methodologies which take into account the cross-sectional dependence among countries. The results show that there is U-shaped relationship between the real income and ecological footprint. In addition, non-renewable energy increases the environmental degradation while renewable energy and trade openness decrease the environmental degradation in the EU countries. Policy implications are further discussed.

This book shows how compliance modelling has been used to very good effect in the optimisation of plumbosolvency control in the United Kingdom, particularly in the optimisation of orthophosphate dosing. Over 100 water supply systems have been modelled, involving 30% of the UKs water companies. This proof-of-concept project has the overall objective of demonstrating that these modelling techniques could also be applicable to the circumstances of Canada and the United States, via three case studies.