The impact of land use and spatial mediated processes on the water quality in a river system

Modelling relationships between land use and water quality using statistical methods: A critical and applied review

Land use/land cover (LULC) can have significant impacts on water quality and the health of aquatic ecosystems. Consequently, understanding and quantifying the nature of these impacts is essential for the development of effective catchment management strategies. This article provides a critical review of the literature in which the use of statistical methods to model the impacts of LULC on water quality is demonstrated. A survey of these publications, which included hundreds of original research and review articles, revealed several common themes and findings. However, there are also several persistent knowledge gaps, areas of methodological uncertainty, and questions of application that require further study and clarification. These relate primarily to appropriate analytical scales, the significance of landscape configuration, the estimation and application of thresholds, as well as the potentially confounding influence of extraneous variables. Moreover, geographical bias in the published literature means that there is a need for further research in ecologically and climatically disparate regions, including in less developed countries of the Global South. The focus of this article is not to provide a technical review of statistical techniques themselves, but to examine important practical and methodological considerations in their application in modelling the impacts of LULC on water quality.

Managing multi-functional peri-urban landscapes: Impacts of horse-keeping on water quality

Eutrophication assessments in water management to quantify nutrient loads and identify mitigating measures seldom include the contribution from horse facilities. This may be due to lack of appropriate methods, limited resources, or the belief that the impact from horses is insignificant. However, the recreational horse sector is growing, predominantly in multi-functional peri-urban landscapes. We applied an ecosystem management approach to quantify nutrient loads from horse facilities in the Stockholm Region, Sweden. We found that horses increased the total loads with 30-40% P and 20-45% N, with average area-specific loads of 1.2 kg P and 7.6 kg N ha-1 year-1. Identified local risk factors included manure management practices, trampling severity, soil condition and closeness to water. Comparisons of assessment methods showed that literature standard values of area-specific loads and water runoff may be sufficient at the catchment level, but in small and more complex catchments, measurements and local knowledge are needed.

Water quality assessment for organic matter load in urban rivers considering land cover dynamics

The strategy of considering a model that is comparable to the Soil Conservation Service Curve-Number (SCS-CN) method that employs land use maps to estimate the effects of land use on the water quality has considerable potential for application. This paper presents the LUPC (Land Use Pollutant Contribution) Model to estimate water pollution from the watershed land use obtained by satellite image classification (Sentinel-2). It defines that each land use produces a specific pollutant load per unit area, called Pollutant Standard Index (PSI), which undergoes degradation and/or retention until it reaches the river. This decay estimate is based on a Kernel Function. Organic matter (OM) was the pollutant chosen for the definition of the LUPC model and fractions of labile and refractory organic matter (LOM, ROM). The model was applied to the Barigüi River basin, and five samples were collected at 12 points along the river. Water quality parameters such as dissolved organic carbon (DOC) and UV-Visible absorbance in addition to chemical and biological oxygen demand (COD and BOD), dissolved oxygen (DO), and nitrogen and phosphorus fractions were the reference for modeling purposes. The results indicate that organic loads can be estimated from watershed characteristics, despite influence from seasonal influences captured by the PSI values and the basin shape parameter. Considering its versatile response, the LUPC model can be used for integrated water resources and land use planning and management and be indicator of the potential pollution of rivers by OM.

Effects of land use and slope on water quality at multi-spatial scales: a case study of the Weihe River Basin

Exploring the impact of land use and slope on basin water quality can effectively contribute to the protection of the latter at the landscape level. This research concentrates on the Weihe River Basin (WRB). Water samples were collected from 40 sites within the WRB in April and October 2021. A quantitative analysis of the relationship between integrated landscape pattern (land use type, landscape configuration, slope) and basin water quality at the sub-basin, riparian zone, and river scales was conducted based on multiple linear regression analysis (MLR) and redundancy analysis (RDA). The correlation between water quality variables and land use was higher in the dry season than in the wet season. The riparian scale was the best spatial scale model to explain the relationship between land use and water quality. Agricultural and urban lands had a strong correlation with water quality, which was most affected by land use area and morphological indicators. In addition, the greater the area and aggregation of forest land and grassland, the better the water quality, while urban land presented larger areas with poorer water quality. The influence of steeper slopes on water quality was more remarkable than that of plains at the sub-basin scale, while the impact of flatter areas was greater at the riparian zone scale. The results indicated the importance of multiple time-space scales to reveal the complex relationship between land use and water quality. We suggest that watershed water quality management should focus on multi-scale landscape planning measures.

Effects of detection limits on spatial modeling of water quality in lakes

Identifying sources and fate of nutrients and pollutants in lake waters is often difficult when key analytes (e.g., dissolved phosphate) are frequently below analytical detection limits (non-detects). One way of dealing with this problem in water quality data is to replace non-detects with "fill-in" values using imputation methods (IMs). While their performance for estimating descriptive statistics (e.g., mean and variance) has been evaluated comprehensively for many environmental variables, whether IMs can reconstruct spatial patterns using long-term water quality data with non-detects under different magnitudes of spatial variation remains under-studied. We developed an integrative framework, combining numerical simulations with univariate and multivariate approaches, to compare performance of nine IMs in recovering spatial patterns of water quality data with different degrees of spatial heterogeneity. We applied this framework to a 12-year water quality dataset sampled from the nearshore region of Lake Ontario near Pickering and Ajax to show the usefulness of IMs in estimating water quality spatial variation. Firstly, in the simplest modeling scenario, we found that most IMs reproduced spatial patterns of univariate data well with ≤30 % non-detects in the dataset. Secondly, when spatial patterns were heterogeneous (e.g., when weak water mixing in nearshore regions limited nutrient transport from input sources to offshore regions), most IMs also performed well by recovering spatial variation in multivariate data with ≤80 % non-detects. Thirdly, when spatial distributions were homogeneous (e.g., when strong water mixing increased transport of nutrients from input sources to other lake areas), only weighted quantile sum regression (WQSR) performed well in reconstructing spatial multivariate data trends with ≤10 % non-detects. Our study highlighted that IMs (especially WQSR) are useful for reconstructing spatial trends of water quality in large lakes. However, potential interactions between spatial heterogeneity and non-detect frequency must be considered when selecting an appropriate IM procedure to accurately model spatial patterns in water quality.

Land use, hydrology, and climate influence water quality of China's largest river

Influences of multiple environmental factors on water quality patterns is less studied in large rivers. Landscape analysis, multiple statistical methods, and the water quality index (WQI) were used to detect water quality patterns and influencing factors in China's largest river, the Yangtze River. Compared with the dry season, the wet season had significantly higher total phosphorus (TP), chemical oxygen demand (COD), total suspended solids (TSS), and turbidity (TUR). The WQI indicated "Moderate" and "Good" water quality in the wet and dry seasons, respectively. Compared with other sites, the upper reach sites that immediately downstream of the Three Gorges Dam had lower TP, TN, TSS and TUR in both seasons, and had lower and higher water temperature in the wet and dry seasons, respectively. Water quality patterns were mainly driven by heterogeneity in land use (i.e., wetland, cropland, and urban land), hydrology (i.e., water flow, water level), and climate (i.e., rainfall, air temperature). Water quality in the wet season was primarily driven by land use while the joint effect of land use and hydrology primarily drove in the dry season. Decision-makers and regulators of large river basin management may need to develop programs that consider influences from both human and natural drivers for water quality conservation.

Influence of socioeconomic development on river water quality: a case study of two river basins in China

Social and economic development processes require large amounts of natural resources and in some cases seriously deteriorate river water quality. Since the reform and expansion era began, China has vigorously pursued socioeconomic development but neglected environmental protection. However, in recent years, improvements in environmental awareness and the implementation of environmental protection measures have led to a balanced relationship between economic development and the environment. In this study, the Yangtze River Basin and the Yellow River Basin were selected as research areas. We used a combination of canonical correlation analysis (CCA) and a distance-based influence assessment method to quantitatively assess the influence of socioeconomic development on river water quality. The results revealed a strong correlation between socioeconomic development and river water quality. The degree of influence of socioeconomic development on water quality varied not only temporally but also spatially due to differences in socioeconomic development and hydrometeorology in the two basins in North and South China. The average degree of influence in the Yangtze River Basin was between 0.22 and 0.27, and that in the Yellow River Basin was between 0.2 and 0.36. Moreover, the degree of influence in the Yangtze River Basin in the wet season was greater than that in the dry season, whereas the opposite pattern was observed in the Yellow River Basin. The degree of influence in both basins gradually declined after 2011, indicating that the coupling and coordination between socioeconomic development and environmental protection have continuously improved and that the water quality has gradually improved. By analysing the influences of various socioeconomic indicators on water quality, we found that the main factors that influence water quality are per capita GDP and urbanization rate in the Yangtze River Basin and urbanization rate in the Yellow River Basin. The results provide a basis for future sustainable development in the Yangtze River Basin and the Yellow River Basin.

Understanding the spatiotemporal pollution dynamics of highly fragile montane watersheds of Kashmir Himalaya, India

Pollution of riverine ecosystems through the multidimensional impact of human footprints around the world poses a serious challenge. Research studies that communicate potential repercussions of landscape structure metrics on snowmelt riverine water quality particularly, in climatically fragile Himalayan watersheds are very scarce. Though, worldwide, grasping the influence of land-use practices on water quality (WQ) has received renewed attention yet, the relevance of spatial scale linked to landscape pattern is still elusive due to its heterogenic nature across diverse geomorphic regions. In this work, therefore, we tried to capture the insights on landscape-aquascape interface by juxtapositioning the impacts of landscape structure pattern on snowmelt stream WQ of the whole Jhelum River Basin (JRB) under three varying spatial scales viz., watershed scale, riparian corridor (1000 m wide) and reach buffer (500 m wide). The percentage of landscape pattern composition and configuration metrics in the JRB were computed in GIS utilizing Landsat-8 OLI/TIRS satellite image having 30 m resolution. To better explicate the influence of land-use metrics on riverine WQ with space and time, we used Redundancy analysis (RDA) and multilinear regression (MLR) modeling. MLR selected land-use structure metrics revealed the varied response of WQ parameters to multi-scale factors except for total faecal coliform bacteria (TC) which showed perpetual presence. The reach-scale explained slightly better (76%) variations in WQ than riparian (75%) and watershed (70%) scales. Likewise, across seasonal scale, autumn (75%), winter (83%), and summer (77%) captured the most WQ variation at catchment, riparian, and reach scales respectively. We observed impairing WQ linkages with agriculture, built-up and barren rocky areas across watersheds, besides, pastures in riparian buffer areas, and fragmentation of landscape patches at the reach scale. Due to little appearance of spatial scale differences, a multi scale perspective landscape planning is emphasized to ensure future sustainability of Kashmir Himalayan water resources.

Spatio-seasonal variation of water quality influenced by land use and land cover in Lake Muhazi

Understanding the influence of land use/land cover (LULC) on water quality is pertinent to sustainable water management. This study aimed at assessing the spatio-seasonal variation of water quality in relation to land use types in Lake Muhazi, Rwanda. The National Sanitation Foundation Water Quality Index (NSF-WQI) was used to evaluate the anthropogenically-induced water quality changes. In addition to Principal Components Analysis (PCA), a Cluster Analysis (CA) was applied on 12-clustered sampling sites and the obtained NSF-WQI. Lastly, the Partial Least Squares Path Modelling (PLS-PM) was used to estimate the nexus between LULC, water quality parameters, and the obtained NSF-WQI. The results revealed a poor water quality status at the Mugorore and Butimba sites in the rainy season, then at Mugorore and Bwimiyange sites in the dry season. Furthermore, PCA displayed a sample dispersion based on seasonality while NSF-WQI's CA hierarchy grouped the samples corresponding to LULC types. Finally, the PLS-PM returned a strong positive correlation (+ 0.831) between LULCs and water quality parameters in the rainy season but a negative correlation coefficient (- 0.542) in the dry season, with great influences of cropland on the water quality parameters. Overall, this study concludes that the lake is seasonally influenced by anthropogenic activities, suggesting sustainable land-use management decisions, such as the establishment and safeguarding protection belts in the lake vicinity.

Linking water environmental factors and the local watershed landscape to the chlorophyll a concentration in reservoir bays

The frequency of harmful algal blooms caused by eutrophication is increasing globally, posing serious threats to human health and economic development. Reservoir bays, affected by water environment and local watershed landscape, are more prone to eutrophication and algal blooms. The chlorophyll a (Chl a) concentration is an important indicator for the degree of eutrophication and algal bloom. Exploring the complex relationships between water environment and landscape background, and Chl a concentration in the reservoir bays are crucial for ensuring high-quality drinking water from reservoirs. In this study, we monitored Chl a concentrations of 66 bays in Danjiangkou Reservoir and the related water quality parameters (e.g., water temperature, turbidity, nutrients) in waterbodies of these reservoir bays in the storage and discharge periods from 2015 to 2018. Partial least squares-structural equation modeling (PLS-SEM) was used to quantify the relationship between water environmental factors and watershed landscapes, and Chl a concentrations in reservoir bays. The results showed that mean Chl a concentration was higher in storage period than that in discharge period. Two optimal PLS-SEMs explained 66.8% and 53.6% of Chl a concentration variation in the storage and discharge periods, respectively. The net effect of water chemistry on Chl a concentration was more pronounced during the discharge period (total effect = 0.61, 37% of the total effect on Chl a), while the net effect of land-use composition on Chl a concentration was more significant during the storage period (total effect = 0.57, 30% of the total effect on Chl a). The landscape pattern had significant indirect effects on Chl a concentration, especially during the discharge period (indirect effect = -0.31, 19% of the total effect on Chl a). Our results provide valuable information for managers to make rational decisions, thereby contributing to the prevention of eutrophication and algal blooms in reservoir bays.

Comparison between water quality indices in watersheds of the Southern Bahia (Brazil) with different land use

The present study evaluated the influence of land use and occupation on water quality indices (WQI); the WQI developed by the National Sanitation Foundation (NSF), the WQI adapted by the Environmental Company of the São Paulo State (CETESB), WQI proposed by Bascarón and the Canadian Council of Ministers of the Environment (CCME) WQI, obtained for watersheds located in the Eastern Water Planning and Management Region (BA). The study also analyzed the divergences and similarities of these WQI methods. Water quality data were obtained from the Monitoring Program (Monitora) of Environment and Water Resources Institute of Bahia (INEMA), covering the period from 2008 to 2015, at thirteen (13) sampling sites, with quarterly collections, as well as land use and occupation data. The influence of land use and occupation on water quality indices was assessed by principal component analysis (PCA). The PCA showed that urban and agricultural/pasture areas were influencing factors on water quality variables, such as total phosphorus, biochemical oxygen demand, total nitrogen, turbidity total residues and consequently lower WQI values in the Cachoeira watershed. Among the tested methods to evaluate the water quality of watersheds in the study area, the most similar were the NSF WQI, CETESB WQI, and Objective Bascarón WQI.

The direct and indirect effects of land use and water quality on phytoplankton communities in an agriculture-dominated basin

Understanding the factors that control biodiversity in rivers is challenging due to the variety of potential sources, linkages, and processes. This research assesses the effects of land use on phytoplankton communities across water quality gradients. By employing abiotic and biotic datasets of 149 catchments in Lake Chaohu basin, China, and a structural equation model (SEM), the direct and indirect effects of land use and water quality on phytoplankton dynamic were analyzed. Both land use and water quality had statistically significant direct effects on phytoplankton community attribute and diversity, although these effects differed among these indices. For instance, farmland was found to positively affect the abundance and diversity indices, while total nitrogen (TN) had significant positive effects on species richness and abundance. Importantly, the average indirect effects strengthened the effects of land use (e.g., built-up land and woodland) up to 82.4% mainly through nutrients, while the average indirect effects weakened the effects of land use (e.g., farmland) by as much as 49.9% mainly due to nutrients, thus indicating the prevailing role of the effects of land use on phytoplankton based on nutrient concentrations. The results suggest that nutrients can regulate the effect of land use on phytoplankton community attribute and diversity indices. This study highlights the advantages of using an SEM because the potential linkages for phytoplankton diversities are more likely to be identified with this method than with a classical linear regression model. Therefore, SEM has wide application prospects in the field of the conservation of biodiversity in freshwater rivers.

Assessment of Water Quality Using Chemometrics and Multivariate Statistics: A Case Study in Chaobai River Replenished by Reclaimed Water, North China

Dry rivers could be effectively recovered by reclaimed water in North China, while river water quality would be an important issue. Therefore, it is important to understand the spatiotemporal variation and controlling factors of river water. Water samples were collected during March, May, July, September, and November in the year 2010, then 20 parameters were analyzed. The water environment was oxidizing and alkaline, which was beneficial for nitrification. Nitrate was the main nitrogen form. Depleted and enriched isotopes were found in reclaimed water and river water, respectively. Total nitrogen (TN) and total phosphorus (TP) of reclaimed water exceed the threshold of reclaimed water reuse standard and Class V in the surface water quality criteria. Most river water was at the severe eutrophication level. The sodium adsorption ratio indicated a medium harmful level for irrigation purpose. Significant spatial and temporal variation was explored by cluster analysis. Five months and nine stations were both classified into two distinct clusters. It was found that 6 parameters (chloride: Cl−, sulphate: SO42−, potassium: K+, sodium: Na+, magnesium: Mg2+, and total dissolved solids: TDS) had significant upward temporal variation, and 12 parameters (dissolved oxygen: DO, electric conductivity: EC, bicarbonate: HCO3−, K+, Na+, Ca2+, TDS, nitrite-nitrogen: NO2-N, nitrate nitrogen: NO3-N, TN, TP, and chlorophyll a: Chl.a) and 4 parameters (Mg2+, ammonia nitrogen: NH3-N, and the oxygen-18 and hydron-2 stable isotope: δ18O and δ2H) had a significant downward and upward spatial trend, respectively. The Gibbs plot showed that river water chemistry was mainly controlled by a water–rock interaction. The ionic relationship and principal component analysis showed that river water had undergone the dissolution of carbonate, calcite, and silicate minerals, cation exchange, a process of nitrification, photosynthesis of phytoplankton, and stable isotope enrichment. In addition, gypsum and salt rock have a potential dissolution process.

Influences of Land-Use Dynamics and Surface Water Systems Interactions on Water-Related Infectious Diseases—A Systematic Review

Human interactions with surface water systems, through land-use dynamics, can influence the transmission of infectious water-related diseases. As a result, the aim of our study was to explore and examine the state of scientific evidence on the influences of these interactions on water-related infectious disease outcomes from a global perspective. A systematic review was conducted, using 54 peer-reviewed research articles published between 1995 and August 2019. The study revealed that there has been an increase in the number of publications since 2009; however, few of these publications (n = 6) made explicit linkages to the topic. It was found that urban and agricultural land-use changes had relatively high adverse impacts on water quality, due to high concentrations of fecal matter, heavy metals, and nutrients in surface water systems. Water systems were found as the common “vehicle” for infectious disease transmission, which in turn had linkages to sanitation and hygiene conditions. The study found explicit linkages between human–surface water interaction patterns and the transmission of water-based disease. However, weak and complex linkages were found between land-use change and the transmission of water-borne disease, due to multiple pathways and the dynamics of the other determinants of the disease. Therefore, further research studies, using interdisciplinary and transdisciplinary approaches to investigate and enhance a deeper understanding of these complexities and linkages among land use, surface water quality, and water-related infectious diseases, is crucial in developing integrated measures for sustainable water quality monitoring and diseases prevention.

Assessing Anthropogenic Impacts on Chemical and Biochemical Oxygen Demand in Different Spatial Scales with Bayesian Networks

In order to protect the water environment in seriously polluted basins, the impacts of anthropogenic activities (sewage outfalls and land use) on water quality should be assessed. The Bayesian network (BN) provides a convenient way to model these complex processes. In this study, anthropogenic impacts on chemical oxygen demand (COD) and biochemical oxygen demand (BOD) were evaluated in the Huaihe River basin (HRB) considering dry and wet seasons and different spatial scales. The results showed that anthropogenic activities had the most significant impacts on COD and BOD at the catchment scale. In dry seasons, sewage outfalls played an important role in organic pollution. Farmland became the most important source in wet seasons although it had a “sink” process in dry seasons. Intensive human activities in urban made significant contributions to increased COD levels. Grassland had a negative relationship with organic pollution, especially in dry seasons. Therefore, governments should implement strategies to control organic matters transported from urban and farmland regions. Increasing the efficiency of wastewater treatments and the percentage of grassland in the riparian zone could improve water quality. These results can enhance understanding of anthropogenic impacts on water quality and contribute to efficient management for river basins.

Land use impact on the water quality of large tropical river: Mun River Basin, Thailand

Globally, rivers and streams are experiencing declining water quality. Anthropogenic activities largely contribute to surface water pollution. Understanding human-induced influence on river water quality remains a challenge owing to spatiotemporal variations. In this study, we assessed the influence of various land uses (LU) on 16 water quality parameters of the Mun River, a tributary of the Mekong River, at different scales. Water quality was statistically analyzed both spatially and temporally (1995-2010). Seasonal and annual effect of LU on water quality was evaluated at buffer zone scale and sub-basin scale (i.e., catchment scale) using multiple regression analysis. The result showed that urban LU extensively adds to the nutrient concentration [i.e., total phosphorus (TP), ammonia nitrogen (NH₃-N)] followed by agriculture LU at the sub-basin scale. Site-specific variability of TP is explained by urban LU and biological oxygen demand (BOD) by agriculture LU at the 5-km buffer in Upper and Middle Mun whereas at Lower Mun, the 20-km buffer explains the variability of suspended solids (SS) and total suspended solids (TSS), suggesting a more localized effect on the parameters upstream. The high concentration of parameters was noted in the dry season whereas the opposite was true for fecal coliform bacteria (FCB), SS, and TP. The maximum parameter concentration of NH₃-N, FCB, and total coliform bacteria exceeds the permissible surface water quality standards of the Pollution Control Department (PCD) of Thailand in all three sub-basins. The study suggests the need for multi-scale interventions and effective pollution control measures focusing on nutrient, pathogenic bacteria, and solids pollution to improve the river water quality of large river basin.

Tracing controlling factors of riverine chemistry in a headwater tributary of the Yangtze River, China, inferred from geochemical and stable isotopic signatures

The Jialing River is the second largest headwater tributary of the Yangtze River in China, therefore, the river water has been contaminated and water quality is deteriorated. Hence, this study aims to find the main controling factors of riverine chemistry. 52 water samples were collected for the determination of major ions and environmental isotopes of δ¹⁸O and δ²H. Stoichiometry of geochemical data with mixing end members and multivariate statistical analysis were employed with integrated GIS approach for data interpretations. The δ¹⁸O and δ²H of the Jialing River Basin (JRB) were used to define the origin of river water from meteoric water and water in the spring season is affected by high evaporation and evaporates dissolution. The average TDS 301 mg/L that is higher than the Yangtze River. In the JRB, 80% of the anion in water samples represented HCO₃^- (207 mg/L) and SO₄^2- (80 mg/L) while 80% of the cations were accounted by Ca²⁺ (59.8 mg/L) and Mg²⁺ (17.9 mg/L). The water chemistry mainly derived from the water rock interaction. Piper plot indicated that Ca-Mg-HCO₃^- was the most dominant water type and most ions derived from carbonate weathering by H₂SO₄ and H₂CO₃. The stoichiometry results further confirmed carbonate weathering is dominant than silicate weathering. Evaporate ions were modified by anthropogenic sources. Agricultural inputs are higher than the industry and atmospheric inputs. Redundancy analysis showed that most contributive land-use type in explaining riverine chemistry was the cultivate land (62.6, 66.4, and 67.9%) at all buffer scales of 30, 20, and 10 km, respectively. Forest and grasslands mostly correlate with Ca²⁺, Mg²⁺, Cl^-, SO₄^2-, EC, pH, and HCO₃^- while anthropogenic land-use types such as cultivated and construction lands correlate with Na⁺, K⁺, Cl^-, and NO₃^-. These results revealed that the lithology of the basin mainly controlled the upstream water chemistry while downstream riverine chemistry was controlled by both lithology and anthropogenic inputs. Nevertheless, this study suggested that explicitly determining the controlling factors of riverine chemistry involves a complex process and combination of different chemical constituents and factors on river water. However, this study managed to provide useful information to further understanding of the geochemical process in JRB.

Correlations between water quality and the structure and connectivity of the river network in the Southern Jiangsu Plain, Eastern China

Incorporating the structure and connectivity of the river network to seasonal variations and different land use patterns can help improve the understanding the complex relationship between water quality and environmental factors. The present study first employed the grey relational analysis (GRA) to examine any existing correlations between the water quality and the structure and connectivity of river networks in the Southern Jiangsu Plain in Eastern China. All grey relational degree results were greater than the distinguishing coefficient (ρ = 0.5), and their average value was 0.7551. The average grey relational degrees of the water quality parameters varied between 0.7389 and 0.7744, and those of the characteristic indicators of the river network ranged from 0.6874 to 0.8850. Seasonal variations and different land use patterns were then employed to further analyze these relationships. The average grey relational degrees in the urban, rural, and fringe regions were calculated to be 0.7231, 0.7530, and 0.7124 during the flood season, respectively, and 0.7331, 0.7432, and 0.7052 during the non-flood season. The results suggest strong correlations between the water quality and the structure and connectivity of the river network. The preponderance of the urban land weakened the original correlations more than that of the cultivated land, while the seasonal interactions of the cultivated and urban lands presented opposite. The GRA can be employed as an effective supplement for numerical modeling and statistical analysis of the incomplete data. In addition, the structure and connectivity of the river network should be taken in account to improve water quality.

The impact of socioeconomic system on the river system in a heavily disturbed basin

The quantitative assessment of the impact of socioeconomic development on river water environment is important to the scientific management of river basins. However, current methods have high data requirements or are difficult to deal with the impact between systems (which is defined by a collection of indicators). This paper first uses canonical correlation analysis (CCA) to understand the relationship between socialeconomic system (defined by a set of indicators reflecting socioeconomic development) and river system (defined by a set of indicators reflecting river water environment), and then proposes a method to assess the impact of socioeconomic system on river system by integrating CCA and the degrees of influence of river system indicators. The proposed method and framework are applied to the Shaying River Basin with the characteristics of multi-sluices, high pollution, and dense population based on data from 2000 to 2015. Results indicate that socioeconomic and river systems are highly related to each other with the average influence degree of greater than 0.9, indicating very close relationships between socioeconomic and river systems. The changes of influence degree vary between 0.19 and 0.79 with a turning point in 2010. Most of the influence levels are "moderate" (influence degree between 0.4 and 0.6) or "high" (influence degrees between 0.6 and 0.8) before 2010 but become to "low" (influence degrees between 0.2 and 0.4) since then. In addition, the influence degree shows a significant increase from upstream to downstream with Zhoukou Station as the turning point, meaning that the stronger the human activity is, the greater the impact of the socioeconomic system on the river system is. The main influential factors are population density and sewage treatment rate. The proposed method contributes to the research in river management with limited data availability and the results can serve as an important reference for basin management.

The Spatial and Temporal Research on the Coupling and Coordinated Relationship between Social Economy and Energy Environment in the Belt and Road Initiatives

The Belt and Road Initiative (BRI) was presented by China’s central government to achieve regional economic cooperation under the background of economic globalization. Energy and environmental problems, as by-products of economic success, are fundamental concerns in this grand project. An integrated method that combined the coupling coordinated degree model, Moran’s I, and the spatial panel data model was used in the research zone. Results of the coupling coordinated degree model indicated that the statuses of the social economy and the energy environment of the countries in the research zone improved from 1997 to 2014, with the changes in China and Eastern Europe being particularly significant. From the spatial econometric perspective, the coupling coordinated degrees of the countries in the research zone appeared in the spatial cluster in accordance with Moran’s I. The result of the spatial panel data model indicated that the coupling coordinated degree of one country positively affected those of its neighboring countries.

Effects of land use and spatial processes in water and surface sediment of tropical reservoirs at local and regional scales

The effects of land use and connectivity on the characteristics of aquatic ecosystems are thought to be scale-dependent. This study aimed to evaluate the relationships between land use and reservoir characteristics at two spatial scales, after controlling for spatial processes. Water and surface sediment samples were collected from 31 sites (7 reservoirs) in the Paiva Castro and Piracicaba River basins (Cantareira System, São Paulo State, Brazil), during austral summer and winter. The dataset included 15 water quality variables and 6 surface sediment variables. Land use variables (natural areas, pasture, agriculture and urban areas) were obtained at two spatial scales (buffer and watershed) in each reservoir. Spatial variables were calculated using Moran's Eigenvectors Maps and Asymmetric Eigenvector Maps. The strengths of the relationships between land use and sediment variables were stronger than those between land use and water quality variables. The strengths of some of the relationships were scale-dependent. Finally, spatial processes, mostly hydrological connectivity, play an important role in water-sediment quality and should be considered in landscape management programs.

Characterisation of spatial variability in water quality in the Great Barrier Reef catchments using multivariate statistical analysis

Water quality monitoring is important to assess changes in inland and coastal water quality. The focus of this study was to improve understanding of the spatial component of spatial-temporal water quality dynamics, particularly the spatial variability in water quality and the association between this spatial variability and catchment characteristics. A dataset of nine water quality constituents collected from 32 monitoring sites over a 11-year period (2006-2016), across the Great Barrier Reef catchments (Queensland, Australia), were evaluated by multivariate techniques. Two clusters were identified, which were strongly associated with catchment characteristics. A two-step Principal Component Analysis/Factor Analysis revealed four groupings of constituents with similar spatial pattern and allowed the key catchment characteristics affecting water quality to be determined. These findings provide a more nuanced view of spatial variations in water quality compared with previous understanding and an improved basis for water quality management to protect nearshore marine ecosystem.

Spatiotemporal variations of hydrogeochemistry and its controlling factors in the Gandaki River Basin, Central Himalaya Nepal

The characterization and assessment of water quality in the head water region of Himalaya is necessary, given the immense importance of this region in sustaining livelihoods of people and maintaining ecological balance. A total of 165 water samples were collected from 55 sites during pre-monsoon, monsoon and post-monsoon seasons in 2016 from the Gandaki River Basin of the Central Himalaya, Nepal. The pH, EC values and TDS concentrations were measured in-situ and the concentrations of major ions (Ca²⁺, Mg²⁺, K⁺, Na⁺, Cl^-, SO₄^2-, NO₃^-) and Si were analyzed in laboratory. Correlation matrices, paired t-test, cluster analysis, principal component analysis (PCA), the Piper, Gibbs, and Mixing plots, and saturation index were applied to the measurements for evaluating spatiotemporal variation of the major ions. The results reveal mildly alkaline pH values and the following pattern of average ionic dominance: Ca²⁺>Mg²⁺>Na⁺>K⁺ for cations and HCO₃^->SO₄^2->Cl^->NO₃^- for anions. The results of PCA, Gibbs plot and the ionic relationships displayed the predominance of geogenic weathering processes in areas with carbonate dominant lithology. This conclusion is supported by geochemically different water facies identified in the Piper plot as Ca-HCO₃ (83.03%), mixed Ca-Mg-Cl (12.73.0%) and Ca-Cl (4.24%). Pronounced spatiotemporal heterogeneity demonstrates the influence of climatic, geogenic and anthropogenic conditions. For instance, the Ca²⁺-SO₄^2-, Mg²⁺-SO₄^2- and Na⁺-Cl^- pairs exhibit strong positive correlation with each other in the upstream region, whereas relatively weak correlation in the downstream region, likely indicating the influence of evapo-crystallization processes in the upstream region. Analyses of the suitability of the water supply for drinking and irrigation reveal that the river has mostly retained its natural water quality but poses safety concern at a few locations. Knowledge obtained through this study can contribute to the sustainable management of water quality in the climatically and lithologically distinct segments of the Himalayan river basins.

Agriculture and elevation are the main factors for Pampasic stream habitat and water quality

Streams of the Pampasic plain in Southeastern South America are ecosystems affected by both water pollution and habitat alteration mainly due to agricultural activity. Water quality is influenced by the quality of habitats and both depend on land use and watershed morphology. The objective of this study was to determine the relationship between the variables of four factors: (1) the morphology of the watershed, (2) land use in the watershed, (3) river habitat, and (4) water quality of wadeable streams in Uruguay, as well as to determine the most representative variables to quantify such factors. We studied 28 watersheds grouped into three ecoregions and four principal activities, which generated seven zones with three to five streams each. Correlations between the variables of each factor allowed reducing the total number of variables from 57 to 32 to perform principal component analyses (PCA) by factor, reducing the number of variables to 18 for a general PCA. The first component was associated with water quality and elevation. The second was associated with the stream and watershed size, the third with habitat quality, and the fourth to the use of neighboring soils and objects in the channel. Our results indicate that agricultural intensity and elevation are the main factors associated with the habitat and water quality of these lowland streams. These factors must be especially considered in the development of water quality monitoring programs.

Evaluating the relationship between temporal changes in land use and resulting water quality

Changes in land use have a direct impact on receiving water quality. Effective mitigation strategies require the accurate prediction of water quality in order to enhance community well-being and ecosystem health. The research study employed Bayesian Network modelling to investigate the validity of using cross-sectional and longitudinal data on water quality and land use for predicting water quality in a mixed use catchment and the role it plays in the generation of blue-green algae in the receiving marine environment. Bayesian Network modelling showed that cross-sectional and longitudinal data analyses generate contrasting information about the influence of different land uses on surface water pollution. The modelling outcomes highlighted the lack of reliability in cross-sectional data analysis, based on the indication of spurious relationships between water quality and land use. On the other hand, the longitudinal data analysis, which accounted for changes in water quality and land use over a ten-year period, informed how catchment water quality varies in response to temporal changes in land use. The longitudinal data analysis further revealed that the types of anthropogenic activities have a more significant influence on pollutant generation than the change in the area extent of different land uses over time. Therefore, the careful interpretation of the findings derived solely from cross-sectional data analysis is important in the design of long-term strategies for pollution mitigation.