Satellite-based assessment of mining-related sediment influence on water quality in an extractive river basin.

Changes in land use/land cover (LULC) directly or indirectly affect water quality in watercourses and impoundments. Sustainable management strategies aimed to enhance ecosystem health and community well-being require an accurate water-quality evaluation. This study looks into the correlation between temporal changes in LULC, represented by selected landscape variables (land cover area and proportion, patch density, Euclidean nearest-neighbor distance, mean shape index, and Shannon index), and water quality variables (nitrate, total phosphorus, and total suspended solids) at catchment scale. To compare the watershed-size influence, this analysis was performed at two different spatial scales represented by two Uruguayan basins of different sizes, San Salvador (3,118 km2) and Del Tala (160 km2). Partial Least Squares and Random Forest unsupervised machine-learning models were employed for this analysis. By exploiting a non-model-biased method based on game theory (SHAP), the LULC characteristics were quantified and ranked based on their level of importance in the water-quality evaluation. The main outcomes of this study proved that patch density is one of the most influencing metrics in both watersheds and for both models. Agricultural land use is the most critical one at both catchments and agricultural with a forage crop land uses are the most important ones for both algorithms. Furthermore, it is possible to state that the adopted techniques are valuable tools that can provide an adequate overview of the water‐quality behavior in space and time and the correlations between water-quality variables and LULC.

Quantifying the effects of land use change and aggregate stormwater management practices on fecal coliform dynamics in a temperate catchment.

Evaluating the joint effects of climate and land use change on runoff and pollutant loading in a rapidly developing watershed

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.

The study aims to assess spatial and temporal water quality variations in the upper reaches of the Vietnamese Mekong Delta. Thirty-one water monitoring samples of the two main rivers (Tien and Hau Rivers) and six canals flowing through An Giang Province were collected in the dry season (March) and the rainy season (September) from 2009 to 2019. Seven physicochemical parameters were analyzed including temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), total suspended solids (TSS), orthophosphate (P-PO43-), and coliforms. Water quality index (WQI), cluster analysis (CA), and discriminant analysis (DA) were applied to evaluate water quality, spatial and temporal variations, and seasonal discriminant water variables. WQI values (15–71) indicated surface water quality was very bad to medium in which the water quality in larger and in smaller rivers in the dry season was less polluted than that in the rainy season due to erosion and runoff water containing waste materials in the wet season. CA grouped the water quality in the dry and rainy seasons into four clusters mainly due to BOD and coliforms in the dry season; TSS and coliforms in the rainy season. Discriminant analysis revealed that DO, TSS, coliforms, temperature and BOD significantly contributed to seasonal variations in water quality. Therefore, water quality monitoring in the surveyed area could only focus on DO, TSS, coliforms, temperature and BOD to reduce monitoring cost.

Lakes play an important role in socioeconomic development and ecological balance in China, but their water quality has deteriorated considerably in recent decades. In this study, we investigated the spatial-temporal variations of eutrophication parameters (secchi depth, total nitrogen, total phosphorus, chemical oxygen demand, chlorophyll-a, trophic level index, and trophic state index) and their relationships with lake morphology, watershed land use, and socioeconomic factors in the Yunnan Plateau lakes. Results indicated that about 77.8% of lakes were eutrophic according to trophic state index. The plateau lakes showed spatial variations in water quality and could be classified into high-nutrient and low-nutrient groups. However, because watersheds were dominated by vegetation, all eutrophication parameters except chlorophyll-a showed no significant differences between the wet and dry seasons. Lake depth, water residence time, volume, and percentage of built-up land were significantly related to several eutrophication parameters. Agricultural land use and social-economic factors had no significant correlation with all eutrophication parameters. Stepwise regression analyses demonstrated that lake depth and water residence time accounted for 73.8% to 87.6% of the spatial variation of single water quality variables, respectively. Redundancy analyses indicated that lake morphology, watershed land use, and socioeconomic factors together explained 74.3% of the spatial variation in overall water quality. The results imply that water quality degradation in the plateau lakes may be mainly due to the domestic and industrial wastewaters. This study will improve our understanding of the determinants of lake water quality and help to design efficient strategies for controlling eutrophication in the plateau region.

<div> <div> <div> <div>Our current capacity to model stream water quality is limited particularly at large spatial scales across multiple catchments. To address this, we developed a Bayesian hierarchical statistical model to simulate the spatio-temporal variability in stream water quality across the state of Victoria, Australia. The model was developed using monthly water quality monitoring data over 21 years, across 102 catchments, which span over 130,000 km<sup>2</sup>. The modelling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate-nitrite (NO<sub>x</sub>), and electrical conductivity (EC). The model structure was informed by knowledge of the key factors driving water quality variation, which had been identified in two preceding studies using the same dataset. Apart from FRP, which is hardly explainable (19.9%), the model explains 38.2% (NO<sub>x</sub>) to 88.6% (EC) of total spatio-temporal variability in water quality. Across constituents, the model generally captures over half of the observed spatial variability; temporal variability remains largely unexplained across all catchments, while long-term trends are well captured. The model is best used to predict proportional changes in water quality in a Box-Cox transformed scale, but can have substantial bias if used to predict absolute values for high concentrations. This model can assist catchment management by (1) identifying hot-spots and hot moments for waterway pollution; (2) predicting effects of catchment changes on water quality e.g. urbanization or forestation; and (3) identifying and explaining major water quality trends and changes. Further model improvements should focus on: (1) alternative statistical model structures to improve fitting for truncated data, for constituents where a large amount of data below the detection-limit; and (2) better representation of non-conservative constituents (e.g. FRP) by accounting for important biogeochemical processes.</div> </div> </div> </div>

Abstract. Our current capacity to model stream water quality is limited – particularly at large spatial scales across multiple catchments. To address this, we developed a Bayesian hierarchical statistical model to simulate the spatiotemporal variability in stream water quality across the state of Victoria, Australia. The model was developed using monthly water quality monitoring data over 21 years and across 102 catchments (which span over 130 000 km2). The modeling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate–nitrite (NOx) and electrical conductivity (EC). The model structure was informed by knowledge of the key factors driving water quality variation, which were identified in two preceding studies using the same dataset. Apart from FRP, which is hardly explained (19.9 %), the model explains 38.2 % (NOx) to 88.6 % (EC) of the total spatiotemporal variability in water quality. Across constituents, the model generally captures over half of the observed spatial variability; the temporal variability remains largely unexplained across all catchments, although long-term trends are well captured. The model is best used to predict proportional changes in water quality on a Box–Cox-transformed scale, but it can have substantial bias if used to predict absolute values for high concentrations. This model can assist catchment management by (1) identifying hot spots and hot moments for waterway pollution; (2) predicting the effects of catchment changes on water quality, e.g., urbanization or forestation; and (3) identifying and explaining major water quality trends and changes. Further model improvements should focus on the following: (1) alternative statistical model structures to improve fitting for truncated data (for constituents where a large amount of data fall below the detection limit); and (2) better representation of nonconservative constituents (e.g., FRP) by accounting for important biogeochemical processes.

Studies on Land use/land cover (LULC) changes from 2015 to 2023 were analyzed to understand the spatial variation in water quality within the Athi River Basin. Data was extracted from Landsat 8 imagery from the USGS archive and analyzed using Google Earth Engine. Land use land cover (LULC) changes analyzed include six categories namely Bare-lands, Built-up, Farmlands, Forestlands, Grasslands, and Open-waters. Pearson correlation analysis was employed to assess the spatial LULC differences in water quality at different sampling stations within the mid-reaches of the Athi River Basin. Ground truthing surveys involving interviews were conducted to determine land use activities influencing water quality. The findings revealed significant LULC changes between 2015 and 2023. Barelands decreased by 7.06%, while built-up areas rose slightly by 0.29%. Farmland grew by 0.52%, forestlands by 4.54%. Grasslands increased by 2.77%, while open waters declined by 1.24% from 2015 to 2023. The result on spatial LULC differences indicated significant influence on water quality. Urbanization and agricultural activities generate pollutants such as Total Dissolved solids (TDS), Electrical Conductivity (EC), Biological Oxygen Demand (BOD5), cadmium, and chromium across the stations. Drought in open water with a -0.85 correlation result increases pollutants and dilution effect which worsens the water quality over time. The interview survey identified four land use drivers and a natural factor affecting water quality. Respondents cited climatic factors, agriculture, and settlement as primary drivers of water quality degradation, with industry and commercial activities as secondary drivers in the Athi River Basin. Climatic factors were associated with Grasslands and Farmlands. Agriculture impacted Forestlands and open waters, and Settlement influenced Bare-lands, Grasslands, and Forestlands. Industry affected Built-up/others and open waters, while commercial activities relate to Built-up. In conclusion, the Government of Kenya should enforce the regulations on environmental management, water resource conservation, sustainable land use, public health protection, irrigation control, forest preservation, and aquatic ecosystem conservation to safeguard the water quality of the Athi River Basin.

Spatial and temporal variations in surface water quality of the dam reservoirs in the Tigris River basin, Turkey

This study evaluated surface water quality changes in Ben Tre province using multivariate statistical analyses. The water monitoring data were collected from the Department of Natural Resources and Environment of Ben Tre province in 2020, which 13 water parameters have been measured, including pH, water temperature (T), salinity (Sal), turbidity (Turb), total suspended solids (TSS), dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammonium (NH4+_N), nitrate (NO3-_N), orthophosphate (PO43-_P), iron (Fe) and coliform bacteria. Water quality was assessed using national technical regulations on surface water quality of QCVN 08-MT: 2015/BTNMT. Spatiotemporal variation of water quality was evaluated using cluster analysis (CA) while potential pollution sources and key water variables influencing water quality were evaluated using principal component analysis (PCA). The findings showed that the water parameters of turbidity, salinity, TSS, DO, BOD, COD, NH4+_N, PO43-_P, Fe and coliform exceeded the allowable limits of QCVN 08-MT:2015/BTNMT. The water parameters of BOD, COD, NO3-_N, PO43-_P in the rainy season tended to be higher than those in the dry season. Cluster analysis divided surface water quality into 7 clusters, thus reducing 8 sampling sites, and 2 monitoring times of frequency. Principal component analysis identified 13 potential pollution sources affecting surface water quality in the study area, in which, 76.10 % of the variation in surface water quality were contributed by PC1, PC2, PC3, PC4 and PC5. PCA results also showed that 13 observed water parameters significantly contribute to the variation in water quality. The current study results could be very useful in reducing sites and frequency of surface water quality monitoring in Ben Tre province. HIGHLIGHTS The water quality in the study area was polluted by suspended particulate matters, organics, nutrients, heavy metals and salinity. Thirteen potential water pollution sources have been identified by principal component ananlysis (PCA), in which, 76.1 % of surface water quality variations were explained by five key sources sources of PC1-PC5 The water quality parameters of BOD, COD, NO3-_N, PO43-_P in the rainy season tended to be higher than those in the dry season Cluster analysis (CA) suggested the current surface water monitoring sites and frequency of the sampling could be reduced by 8 and 2, respectively, thus, saving the monitoring cost GRAPHICAL ABSTRACT

Hawaiian watersheds are small, steep, and receive high intensity rainfall events of non-uniform distribution. These geographic and weather patterns result in flashy streams of strongly variable water quality even within various stream segments. Total suspended solids (TSS) and total dissolved solids (TDS) were used to investigate the variability in water quality in the upper part of Manoa Stream in Honolulu, Hawaii. With a few interruptions, water samples were taken on a daily basis between September 2005 and June 2006. The samples were analyzed for TSS and TDS, and varied from almost 0 to 724 and to 302 mg L-1, respectively. During the raining season (October through March) TSS and TDS were more variable, and TSS was higher than in the dry season (April through June). No relation was observed between TSS and TDS and discharge. This may be explained by the heterogeneous rainfall distribution which causes varying contributions from different sources. During one rainfall event TSS and TDS also varied considerably in time. Both TSS and TDS showed increasing trends going downstream suggesting that the urbanized area generates more suspended and dissolved matter than the forested conservation area upstream. However, given the large variability in TSS and TDS, the increasing trend downstream is associated with high uncertainty. The results of this study stress the necessity of recognizing the variability in water quality of small streams for setting up a monitoring strategy, adopting a modeling approach to predict water quality or extrapolating data from limited samples to annual loads in coastal regions.

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.

Influence of land use and land cover patterns on seasonal water quality at multi-spatial scales

Using radical terraces for erosion control and water quality improvement in Rwanda: A case study in Sebeya catchment