Search Results (250)

Precipitation, as a key input in the water cycle, directly influences the formation and change process of runoff. Meanwhile, the return runoff intuitively reflects the available quantity of water resources in a river basin. An in-depth analysis of the evolution laws and response relationships between precipitation and return runoff over a long time scale serves as an important support for exploring the evolution of hydrometeorological conditions and provides an accurate basis for the scientific planning and management of water resources. Taking Lanzhou Station on the upper Yellow River as a typical case, this study proposes the VSSL (LSTM Fusion Method Optimized by SSA with VMD Decomposition) deep learning precipitation element series extension method and the SSVR (SVR Fusion Method Optimized by SSA) machine learning runoff element series extension method. These methods achieve a reasonable extension of the missing data and construct 100-year precipitation and return runoff series from 1921 to 2020. The research results showed that the performance of machine learning and deep learning methods in the precipitation and return runoff test sets is better than that of traditional statistical methods, and the fitting effect of return runoff is better than that of precipitation. The 100-year precipitation and return runoff series of Lanzhou Station from 1921 to 2020 show a non-significant upward trend at a rate of 0.26 mm/a and 0.42 × 10⁸ m³/a, respectively. There is no significant mutation point in precipitation, while the mutation point of return runoff occurred in 1991. The 100-year precipitation series of Lanzhou Station has four time-scale alternations of dry and wet periods, with main periods of 60 years, 20 years, 12 years, and 6 years, respectively. The 100-year return runoff series has three time-scale alternations of dry and wet periods, with main periods of 60 years, 34 years, and 26 years, respectively. During the period from 1940 to 2000, an approximately 50-year cycle, precipitation and runoff not only have strong common-change energy and significant interaction, but also have a fixed phase difference. Precipitation changes precede runoff, and runoff responds after a fixed time interval. Full article

According to Victor Ernest Shelford’s ‘Law of Tolerance,’ organisms within ecosystems thrive optimally when environmental conditions are favorable. Applying this principle to ecosystems and agro-ecosystems facing water scarcity or environmental challenges can significantly enhance their productivity. In these ecosystems, phytocenosis adjusts its conditions by utilizing water with varying salinity levels. Moreover, establishing optimal drinking water conditions for human populations within an ecosystem can help mitigate future negative succession processes. The purpose of this study is to evaluate the quality of two distinct water sources in the Amudarya district of the Republic of Karakalpakstan, Uzbekistan: collector-drainage water and groundwater at depths of 10 to 25 m. This research is highly relevant in the context of climate change, as improper management of water salinity, particularly in collector-drainage water, may exacerbate soil salinization and degrade drinking water quality. The primary methodology of this study is as follows: The Food and Agriculture Organization of the United Nations (FAO) standard for collector-drainage water is applied, and the water quality index is assessed using the CCME WQI model. The Canadian Council of Ministers of the Environment (CCME) model is adapted to assess groundwater quality using Uzbekistan’s national drinking water quality standards. The results of two years of collected data, i.e., 2021 and 2023, show that the water quality index of collector-drainage water indicates that it has limited potential for use as secondary water for the irrigation of sensitive crops and has been classified as ‘Poor’. As a result, salinity increased by 8.33% by 2023. In contrast, groundwater quality was rated as ‘Fair’ in 2021, showing a slight deterioration by 2023. Moreover, a comparative analysis of CCME WQI values for collector-drainage and groundwater in the region, in conjunction with findings from Ethiopia, India, Iraq, and Turkey, indicates a consistent decline in water quality, primarily due to agriculture and various other anthropogenic pollution sources, underscoring the critical need for sustainable water resource management. This study highlights the need to use organic fertilizers in agriculture to protect drinking water quality, improve crop yields, and promote soil health, while reducing reliance on chemical inputs. Furthermore, adopting WQI models under changing climatic conditions can improve agricultural productivity, enhance groundwater quality, and provide better environmental monitoring systems. Full article

Peatland research has expanded rapidly in the last two decades encompassing a diverse, multi-disciplinary evidence base, as countries seek to manage this resource sustainably along with meeting climate and biodiversity targets. There is growing global interest in the role of peatlands in carbon and water cycles, leading to more interdisciplinary research that applies ecosystem services and other integrative frameworks to generate knowledge and provide guidance for action. These trends have been replicated in Ireland with increasing research in peatland science, applied work on these degraded ecosystems, and a growing interest from civil society, landowners, and communities in the stewardship of this resource. This paper presents evidence-based insights from over two decades of Irish peatland research, with practical lessons for peatland policy and management in other national contexts. Analyses of the evidence from the literature, specialist expertise, and stakeholder knowledge were carried out under ten themes: biodiversity, soil, climate change, water, archaeology and palaeoenvironment, technology and mapping, society and culture, management, growing media and policy and law. The research identified four foundational pillars (accountability, longevity, equity and holistic knowledge) as critical to achieving sustainable peatland management in Ireland, with broader application to other regions. Peatland restoration is widely recognised across research disciplines as a key tool to meet regulatory targets related to climate, biodiversity, and water quality, while also delivering societal benefits. The findings of this research provide accessible, reliable and up-to-date evidence for sustainable peatland management. This study addresses a critical global knowledge gap by developing a novel, interdisciplinary evidence synthesis framework—applied here to Ireland but replicable worldwide—that systematically integrates 20 years of multi-disciplinary peatland research, expert insights, and stakeholder perspectives across ten thematic pillars. Full article

Based on the long-term daily historical rainfall data, this study analyzes the seasonal differences in extreme rainfall in the monsoon region with frequent extreme rainfall in China over the past 40 years. From the detailed analysis of extreme rainfall indicators, the spatial and temporal variation in extreme rainfall indicators in the monsoon region of China from 1980 to 2020 is explored. Through Mann–Kendall test and multi-index spatial and temporal analysis, the spatial and temporal evolution law and seasonal differentiation characteristics of extreme precipitation events are revealed. The results show the following: (1) The precipitation change presents a dipole pattern of southeast–northeast enhancement, northwest–central attenuation. (2) The precipitation intensity showed the spatial heterogeneity of latitude differentiation of “strong in summer and weak in winter, strong in south and weak in north”, and generally attenuated in winter after reaching the peak in summer. (3) There were significant dry and wet differences between continuous drought days (CDDs) and wet days (CWDs), reflecting the characteristics of “dry in winter and wet in summer”, and the seasonal differentiation of cumulative precipitation (PRCPTOT) was significant. (4) The extreme precipitation threshold is strengthened in winter, and the frequency shows the characteristics of “high in winter and spring, low in summer and autumn”. Studies have shown that extreme precipitation in the monsoon region of China has seasonal redistribution characteristics, which may aggravate the challenge of water resources management. It is necessary to further analyze its driving factors in combination with a dynamic climate mechanism. Full article

Ecological restoration and brownfield reuse are important issues in the current field of urban sustainable development and environmental protection. This paper adopts bibliometric and network analysis methods, using more than 600 literature from Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases as research objects, to analyze the research trends, main researchers, contributions of different disciplines, and research hotspots in ecological restoration and brownfield reuse. Based on the results of quantitative analysis, this paper reviews the main research theories, methods, and technologies of ecological restoration and brownfield reuse at home and abroad, as well as response strategies in different regions and future prospects under challenges. Among the top 10 disciplines in terms of disciplinary contribution, the discipline with the highest relevance in the research articles of CNKI is “Environmental Science and Technology”, accounting for approximately 47.24%. The discipline with the highest relevance in the research articles of WOS is “Building Science and Engineering”, accounting for approximately 61.21%. In terms of research theories and methods, emphasis is placed on the application of ecological engineering, landscape ecology, land economics, and sustainable development methods. At the same time, adaptive management methods are emphasized, aiming to achieve a balance between ecological protection and urban development. In terms of response strategies for different regions, the main strategies for ecological restoration and brownfield reuse are proposed from five main aspects: reducing soil erosion and controlling water pollution, restoring ecological communities and enhancing biodiversity, landscape reshaping and spatial transformation, tourism development, and leisure space design. Based on the current challenges in technology, law, funding, management, and society, research prospects for strengthening interdisciplinary integration, digital drive, interdisciplinary collaboration, and multi-party cooperation in the future are proposed. It can be seen that research in this field is no longer just a simple ecological issue, but a comprehensive social problem. Full article

In well drilling operations, interactions between drilling fluid water-based and the well-bore present significant challenges, often escalating project costs and timelines. Particularly, fractures (both induced and natural) and permeable zones at the wellbore can result in substantial mud loss or increased filtration. Addressing these challenges, our research introduces a novel coupled numerical model designed to precisely calculate fluid losses in fractured and permeable zones. For the permeable zone, fundamental variables such as filtration velocity, filtrate concentration variations, permeability reduction, and fluid cake growth are calculated, all based on the law of continuity and convection-dispersion theory. For the fracture zone, the fluid velocity profile is determined using the momentum balance equation and both Newtonian and non-Newtonian rheology. The model was validated against laboratory data and physical models, and adapted for field applications. Our findings emphasize that factors like mud particle size, shear stress, and pressure differential are pivotal. Effectively managing these factors can significantly reduce fluid loss and mitigate formation damage caused by fluid invasion. Furthermore, the understanding gathered from studying mud behavior in both permeable and fractured zones equips drilling personnel with valuable information related to the optimal rheological properties according to field conditions. This knowledge is crucial for optimizing mud formulations and strategies, ultimately aiding in the reduction of non-productive time (NPT) associated with wellbore stability issues. Full article

The relationship between wind and waves has been extensively studied over time. However, understanding the local wind and wave relationship remains crucial for advancing renewable energy development and optimizing ocean management strategies. This study used wind and wave data collected by the ten weather buoys in the waters surrounding Taiwan to analyze regional sea states. The relationship between wind speed and significant wave height (SWH) was examined using regression analysis. Additionally, machine learning techniques were employed to assess the relative importance of features contributing to SWH growth. The regression analysis revealed that SWH in the waters surrounding Taiwan was not fully developed, with notable discrepancies observed between the waters east and west of Taiwan. According to the power law formula describing the relationship between wind speed and SWH, the eastern waters exhibited a larger prefactor coupled with a smaller scaling exponent, while the western waters manifested a converse parametric configuration. Through an evaluation of four machine learning algorithms, it was determined that wind speed is the most influential factor driving these regional differences, especially in the waters west of Taiwan. Beyond wind speed, air pressure or temperature emerged as the secondary feature factor governing wind–wave interactions in the waters east of Taiwan. Full article

Soil particle size distribution is a critical parameter in geotechnical and hydraulic engineering, particularly in applications such as dam seepage monitoring, building foundation assessments, and sediment transport. This study presents a novel algorithm for estimating soil particle sizes by analyzing their falling velocities in water, combining optical flow computation with chaotic motion analysis. To address the limitations of the classical Horn and Schunck method, particularly its sensitivity to large displacements and brightness variations, we introduced a coarse-to-fine warping strategy, an image decomposition step to separate dominant structures from fine textures, and the Charbonnier penalty function. The improved model achieved competitive accuracy compared to advanced optical flow algorithms. To manage turbulence and motion noise during particle settling, we incorporated a global flow analysis framework using streaklines, streak flow, and potential functions. This enabled the segmentation of laminar, turbulent, and rebound flow regions without requiring individual particle tracking. Soil particle sizes were then back-calculated from laminar flow velocities using Stokes’ Law. Experimental results confirmed the method’s accuracy for particle sizes ranging from 20 mm to 0.7 mm, significantly extending the measurable range of Sedimaging systems. The proposed approach shows strong potential for integration into dam-related particle monitoring applications and building-related monitoring systems requiring fine-resolution analysis. Full article

With the acceleration of urbanization, the diffusion mechanism of urban non-point source (NPS) pollution caused by extreme rainfall is not clear, which leads to high cost and difficulty in water environment treatment. In view of the shortcomings of dynamic diffusion simulations of mesoscale pollution, this paper proposes a simulation framework based on cellular automata, GIS geographic technology, and a two-dimensional shallow water model. Taking the 500 m × 500 m grid as the unit, we explore the migration laws of nitrogen and phosphorus pollutants and the response relationship between pollutant diffusion and land use under extreme rainfall scenarios. The results show that (i) the pollution risk increases significantly with diffusion, with the maximum pollution load in high-risk areas increasing by 181%, and the diffusion rate is positively correlated with the rate of change in rainfall intensity; (ii) forest land has the highest grid pollution load loss rate, whereas the water grid has the highest accumulation rate; (iii) this method can accurately identify the hot spots of pollution diffusion, providing a basis for the precise control of high-risk areas. This study can support the targeted governance of pollution sources and land planning optimization in urban storm and flood management, and help reduce environmental health risks in extreme climates. Full article

Among its various targets on restoring natural habitats and ecosystems in the EU, the recently adopted Nature Restoration Law (NRL) introduces ambitious targets for restoring surface water bodies (SWBs) as well. Simultaneously, the Italian CAP Strategic Plan for the implementation of the Common Agricultural Policy 2023–2027 has been designed to enhance sustainable agricultural practices, including water resource management. This paper provides a comparative analysis of the synergies, gaps, and challenges between these two regulatory frameworks, focusing on sustainable water use in Italian agriculture. A two-level comparative matrix methodology is employed to evaluate the alignment between the NRL’s objectives for freshwater ecosystems and the measures taken by the Italian CAP Strategic Plan on water resources. The results highlight key areas of convergence, existing shortcomings, and necessary steps for aligning Italian agricultural policies with the EU’s water restoration goals. The findings offer insights for policymakers, researchers, and stakeholders engaged in water governance, biodiversity conservation, and agricultural sustainability. Full article

This Study delves on changes in the extent of mangroves over a 42 years span in the Douala-Edea NP, Cameroon. Mangroves are valuable ecosystems that provide significant biological, environmental, ecological, and cultural functions. To inform the development of management plans for the ecosystem’s sustainability, it is crucial to evaluate how their land cover, levels of degradation, and phases of restitution have changed. GIS and remote sensing techniques were used to classify and analyze Landsat images from 1980 to 2022 categorized into nine classes: bare ground, Nypa palms, settlements, coastal sedimentation, river sedimentation, regeneration, matured mangroves, dense forest, and water body. Using the Markovian chain approach, the changes noted during the period were utilized to forecast future trends up to 2052. Findings demonstrated that the mature mangrove area decreased throughout the study. The surface area covered by mature mangroves was 80,628.78 hectares in 1980, which decreased by 7.31%, 1.51%, 3.70%, and by 17% for the overall period of 42 years. Additionally, a gain of 6.84% from 1980 to 2022 was observed, probably from artificial mangrove regeneration. Settlements, invasive Nypa palms, bare ground (resulting from over-exploitation), and the sedimentation of rivers and coast primarily replaced mangroves. The prediction derived indicated the continuous decline in mangroves if not fully protected by law. The gazettement to National Park and recent promulgation of two laws are steps in providing the needed protection. These results provide vital information to direct future mangrove conservation actions in the recently gazetted Douala-Edea National Terrestrial and Marine Park and other mangrove blocks along the Gulf of Guinea. Full article

This paper critically examines Brazil’s legislative framework for pesticide waste management, highlighting recent advancements and persistent challenges. The introduction of Law 14785/2023 marks a pivotal shift in pesticide regulation, streamlining registration processes and enhancing safety measures. This law consolidates various aspects of pesticide management, from research to disposal, aiming for more efficient oversight. Complementary to this, foundational laws such as the Federal Constitution (1988), Law 6938/1981 (National Environmental Policy), Law 12651/2012 (New Forest Code), and Law 13123/2015 (Biodiversity Law) establish broad guidelines for environmental protection and the sustainable use of resources. Specific regulations, including the Environmental Crimes Act (Law 9605/1998) and CONAMA Resolutions No. 465/2014 and No. 420/2009, address critical aspects of pesticide waste, focusing on proper disposal and soil quality management. Despite these advancements, challenges persist in the effective implementation of these laws. Inadequate monitoring systems and limited stakeholder awareness hinder regulatory enforcement. Strategic recommendations to address these issues include enhancing monitoring technologies, strengthening educational initiatives, fostering stakeholder collaboration, and adapting legislation to keep pace with technological advancements. A robust pesticide waste management system is essential for ensuring long-term sustainability in agricultural production. Inefficient disposal practices can contaminate soil and water, posing risks to biodiversity and public health. By reinforcing regulatory enforcement and integrating sustainable waste management strategies, Brazil can mitigate the environmental impact of pesticide use while promoting safer agricultural practices. The ongoing evolution of legislation reflects a commitment to sustainability; however, continuous efforts are necessary to fully realize these goals and align agricultural development with environmental conservation. Full article

In forested watersheds, suspended sediment concentration (SSC) is an important parameter that impacts water quality and beneficial use. Water quality also has impacts beyond the stream channel, as elevated SSC can violate Indigenous sovereignty, treaty rights, and environmental law. To address elevated SSC, watershed partners must understand the dynamics of the sediment regime in the basins they steward. Collection of additional data is expensive, so this study presents modeling and analysis techniques to leverage existing data on SSC. Using data from the South Fork Clearwater River in Idaho County, Idaho, USA, we modeled SSC over water years 1986–2011 and we applied regression techniques to evaluate correlations between SSC and natural disturbances (channel-building flow events) and anthropogenic disturbances (timber harvesting, hazardous fuel management, controlled burns, and wildfire). Analysis shows that SSC did not change over the period of record. This study provides a monitoring program design to support future decision making leading to reductions in SSC. Full article

Oil sands tailings consist of a combination of sand, fine particles, water, and residual unextracted bitumen in varying ratios. The management of these mine waste tailings is largely influenced by their consolidation behavior. Large strain consolidation testing, such as the multi-step large strain consolidation (MLSC) test, is commonly used to determine consolidation properties but requires considerable time. A benchtop centrifuge (BTC) apparatus was proposed to derive the consolidation parameters of the following three clay-rich oil sands tailings slurries: two samples of high-plasticity fluid fine tailings (FFT) and one of low-plasticity FFT. Comparison with the MLSC tests illustrates that the BTC-derived compressibility data closely matched the MLSC test’s compressibility curve within the BTC stress range. However, the hydraulic conductivity from the BTC test was an order of magnitude higher than that from the MLSC test. The consistency of the BTC method and the validation of scaling laws were confirmed through modeling-of-models tests, showing a consistent average void ratio regardless of the specimen height or gravity scale. The influence of the small radius of the BTC was found to be minimal. The limitations of the BTC in the physical modeling of the consolidation behavior are discussed and their impact on the interpretation of the observed consolidation behavior is addressed. Overall, the BTC test provides a rapid method to gain insight on high-water-content slurries’ large strain consolidation behavior. Full article

Mining activities generate substantial amounts of waste rock, which are often disposed of in waste rock piles. Drainage from these piles can pose serious environmental risks. It is crucial to reliably predict drainage properties in order to effectively manage them. In previous work, we developed a machine learning model to predict waste rock drainage quantity using weather monitoring data as the input and drainage flow rate as the output. However, this model lacked physical constraints, limiting its interpretability, reliability, and applicability. In this study, we introduced a new machine learning model designed with physical constraints to improve the predictions of drainage quantity. This new model incorporates a weather refining sub-model and integrates physical constraints to enhance the overall reliability of the model predictions. The weather refining sub-model transforms primary weather features (total precipitation and temperature) into secondary features (rainfall, snowmelt, and evaporation) through established mathematical relationships. These secondary features were then used as inputs for the machine learning model to predict drainage quantity. To embed physical principles within the machine learning model, we integrated a water balance equation into the neural network architecture and modified the loss function accordingly. In addition, we included an adjustable bias term to optimize the balance between model performance and interpretability. Compared with our previous model, the incorporation of physical constraints into the machine learning model improved the accuracy of the drainage quantity predictions. More importantly, this approach ensures that the model outputs adhere to physical laws, thereby enhancing its interpretability, reliability, and applicability. Full article