Effects of future climate and land use scenarios on riverine source water quality

Effects of landscape changes on water quality: A global meta-analysis

Landscape changes resulting from anthropogenic activities and climate changes severely impact surface water quality. A global perspective on understanding their relationship is a prerequisite for pursuing equity in water security and sustainable development. A sequent meta-analysis synthesizing 625 regional studies from 63 countries worldwide was conducted to analyze the impacts on water quality from changing landscape compositions in the catchment and explore the moderating factors and temporal evolution. Results exhibit that total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD) in water are mostly concerned and highly responsive to landscape changes. Expansion of urban lands fundamentally degraded worldwide water quality over the past 20 years, of which the arid areas tended to suffer more harsh deterioration. Increasing forest cover, particularly low-latitude forests, significantly decreased the risk of water pollution, especially biological and heavy metal contamination, suggesting the importance of forest restoration in global urbanization. The effect size of agricultural land changes on water quality was spatially scale-dependent, decreasing and then increasing with the buffer radius expanding. Wetland coverage positively correlated with organic matter in water typified by COD, and the correlation coefficient peaked in the boreal areas (r=0.82, p<0.01). Overall, the global impacts of landscape changes on water quality have been intensifying since the 1990s. Nevertheless, knowledge gaps still exist in developing areas, especially in Africa and South America, where the water quality is sensitive to landscape changes and is expected to experience dramatic shifts in foreseeable future development. Our study revealed the worldwide consistency and heterogeneity between regions, thus serving as a research roadmap to address the quality-induced global water scarcity under landscape changes and to direct the management of land and water.

Temporal and spatial changes of water quality in intensively developed urban rivers and water environment improvement: a case study of the Longgang River in Shenzhen, China

The water quality status, spatial and temporal change processes, and water environment improvement process of urban rivers are valuable lessons to be learned under the sustainable development strategy. This study aims to reveal the water environment improvement process of intensively developed urban rivers, elucidate the spatial and temporal distribution characteristics of major pollutants, and provide recommendations for their water environment management. Water quality data from eight monitoring sites (2007-2020) in the Longgang River basin in Shenzhen, China, and comprehensive pollution index method (CPI), modified comprehensive pollution index method (M-CPI), and Pearson correlation analysis method were used for comprehensive analysis. The study shows that TN, TP, NH3-N, and COD have the greatest influence on the water quality of Longgang River, with the average pollution contribution of 53.39%, 14.49%, 11.66%, and 4.92%, in order. In 2015-2020, the water quality of the main stream of the Longgang River in the wet season was worse than that in the dry season, while the water quality of the tributaries Dingshan River and the Huangsha River in the dry season was worse than the wet season. The spatial distribution characteristics based on M-CPI indicate that the water quality of the lower reaches of Longgang River, the tributaries Dingshan River and Huangsha River, is relatively poor. In addition, the water environment improvement process of Longgang River can be divided into 3 stages: engineering stage (2007-2013, rating changed from heavily polluted to basically qualified), bottleneck stage (2013-2017, rating fluctuated slightly above and below basically qualified), and ecological restoration stage (2017-2020, rating reached qualified in 2019).

Flow Cytometry and Fecal Indicator Bacteria Analyses for Fingerprinting Microbial Pollution in Karst Aquifer Systems

Microbial pollution of aquifers is a persistent water quality problem globally which poses significant risks to public health. Karst aquifer systems are exceptionally vulnerable to pollution from fecal contamination sources as a result of rapid recharge of water from the surface via discrete pathways linked to highly conductive, solutionally enlarged conduits alongside strong aquifer heterogeneity. Consequently, rapid changes in microbial water quality, which are difficult to monitor with expensive and time-consuming conventional microbiological methods, are a major concern in karst environments. This study examined flow cytometric (FCM) fingerprinting of bacterial cells in groundwater together with fecal indicator bacteria (FIB) at nine separate karst springs of varying catchment size over a 14 month period in order to assess whether such a technique can provide faster and more descriptive information about microbial pollution through such karst aquifer systems. Moreover, the data have also been evaluated with respect to the potential of using turbidity as an easy-to-measure proxy indicator of microbial pollution in a novel way. We argue that FCM provides additional data from which enhanced insights into fecal pollution sources and its fate and transport in such karst catchments can be gained. We also present valuable new information on the potential and limitations of turbidity as an indicator of fecal groundwater contamination in karst. FCM has the potential to become a more widely used tool in the field of contaminant hydrogeology.

Assessment of the impact of climate change on cities livability in China

As global warming worsens, climatic conditions in many regions are undergoing profound change, which could influence certain industries, such as agriculture and transportation, and affect the livability of cities. In this study, cities statistics and meteorological station data of the past decade in China were used to analyze the effect of climate change on cities livability. The livability of 288 Chinese cities during 2006-2016 was assessed using an analytic hierarchy process method. Results showed the mean cities livability index in China has improved about 12% throughout the past decade. Moreover, the Moran's I statistic and local indicators of spatial association revealed that the distribution of cities livability reflects a trend of gradual spatial agglomeration. In addition, geostatistical analysis was used to evaluate the impact of extreme weather events on cities livability. It was established that heatwaves and extreme precipitation events have significant impact on the livability of cities in southern China, whereas freezing weather drives the change of urban livability in northern areas. Combinations of different extreme weather conditions will have greater impact on urban livability. Based on mobile phone user data, the relationship between livability change and climate change in Chinese cities was elucidated to provide reference for urban environmental management.

Numerical Modeling of Microbial Fate and Transport in Natural Waters: Review and Implications for Normal and Extreme Storm Events

Degradation of water quality in recreational areas can be a substantial public health concern. Models can help beach managers make contemporaneous decisions to protect public health at recreational areas, via the use of microbial fate and transport simulation. Approaches to modeling microbial fate and transport vary widely in response to local hydrometeorological contexts, but many parameterizations include terms for base mortality, solar inactivation, and sedimentation of microbial contaminants. Models using these parameterizations can predict up to 87% of variation in observed microbial concentrations in nearshore water, with root mean squared errors ranging from 0.41 to 5.37 log10 Colony Forming Units (CFU) 100 mL−1. This indicates that some models predict microbial fate and transport more reliably than others and that there remains room for model improvement across the board. Model refinement will be integral to microbial fate and transport simulation in the face of less readily observable processes affecting water quality in nearshore areas. Management of contamination phenomena such as the release of storm-associated river plumes and the exchange of contaminants between water and sand at the beach can benefit greatly from optimized fate and transport modeling in the absence of directly observable data.

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.

Climate and Land Use Influences on Bacteria Levels in Stormwater

The influence of climatic variables and land use on fecal coliform (FC) levels in stormwater collected from outfalls throughout southern Vancouver Island between 1995 and 2011 are examined through statistical analyses, Fourier analysis, Multiple Linear Regression (LR) and Multivariate Logistic Regression (MLR). Kendall’s τ-b demonstrated that FC levels were significantly and positively correlated with the amount of residential area within a drainage catchment generating the runoff, and that FC levels were location dependent. Climatic variables of temperature and antecedent dry period length were significantly and positively correlated with FC levels at both the sampling location level and across the region overall. Precipitation and flowrates were negatively correlated with FC levels. Fourier analysis showed that monthly FC levels shared the same 12 month cycle (peaking in July) as precipitation and temperature. MLR modelling was applied by aggregating the LogFC data by order of magnitude. The MLR model shows that the data are subject to different influences depending on the season and as well, the month of the year. The land use and climate analyses suggest that future climate change impact studies attempted on nearshore bacterial water quality should be conducted at the urban catchment scale.

Modelling regional cropping patterns under scenarios of climate and socio-economic change in Hungary

Impacts of socio-economic, political and climatic change on agricultural land systems are inherently uncertain. The role of regional and local-level actors is critical in developing effective policy responses that accommodate such uncertainty in a flexible and informed way across governance levels. This study identified potential regional challenges in arable land use systems, which may arise from climate and socio-economic change for two counties in western Hungary: Veszprém and Tolna. An empirically-grounded, agent-based model was developed from an extensive farmer household survey about local land use practices. The model was used to project future patterns of arable land use under four localised, stakeholder-driven scenarios of plausible future socio-economic and climate change. The results show strong differences in farmers' behaviour and current agricultural land use patterns between the two regions, highlighting the need to implement focused policy at the regional level. For instance, policy that encourages local food security may need to support improvements in the capacity of farmers to adapt to physical constraints in Veszprém and farmer access to social capital and environmental awareness in Tolna. It is further suggested that the two regions will experience different challenges to adaptation under possible future conditions (up to 2100). For example, Veszprém was projected to have increased fallow land under a scenario with high inequality, ineffective institutions and higher-end climate change, implying risks of land abandonment. By contrast, Tolna was projected to have a considerable decline in major cereals under a scenario assuming a de-globalising future with moderate climate change, inferring challenges to local food self-sufficiency. The study provides insight into how socio-economic and physical factors influence the selection of crop rotation plans by farmers in western Hungary and how farmer behaviour may affect future risks to agricultural land systems under environmental change.

Spatial scale and seasonal dependence of land use impacts on riverine water quality in the Huai River basin, China

Land use pattern is an effective reflection of anthropic activities, which are primarily responsible for water quality deterioration. A detailed understanding of relationship between water quality and land use is critical for effective land use management to improve water quality. Linear mixed effects and multiple regression models were applied to water quality data collected from 2003 to 2010 from 36 stations in the Huai River basin together with topography and climate data, to characterize the land use impacts on water quality and their spatial scale and seasonal dependence. The results indicated that the influence of land use categories on specific water quality parameter was multiple and varied with spatial scales and seasons. Land use exhibited strongest association with dissolved oxygen (DO) and ammonia nitrogen (NH₃-N) concentrations at entire watershed scale and with total phosphorus (TP) and fluoride concentrations at finer scales. However, the spatial scale, at which land use exerted strongest influence on instream chemical oxygen demand (COD) and biochemical oxygen demand (BOD) levels, varied with seasons. In addition, land use composition was responsible for the seasonal pattern observed in contaminant concentrations. COD, NH₃-N, and fluoride generally peaked during dry seasons in highly urbanized regions and during rainy seasons in less urbanized regions. High proportion of agricultural and rural areas was associated with high nutrient contamination risk during spring. The results highlight the spatial scale and seasonal dependence of land use impacts on water quality and can provide scientific basis for scale-specific land management and seasonal contamination control.

Spatial Regression and Prediction of Water Quality in a Watershed with Complex Pollution Sources

Fast economic development, burgeoning population growth, and rapid urbanization have led to complex pollution sources contributing to water quality deterioration simultaneously in many developing countries including China. This paper explored the use of spatial regression to evaluate the impacts of watershed characteristics on ambient total nitrogen (TN) concentration in a heavily polluted watershed and make predictions across the region. Regression results have confirmed the substantial impact on TN concentration by a variety of point and non-point pollution sources. In addition, spatial regression has yielded better performance than ordinary regression in predicting TN concentrations. Due to its best performance in cross-validation, the river distance based spatial regression model was used to predict TN concentrations across the watershed. The prediction results have revealed a distinct pattern in the spatial distribution of TN concentrations and identified three critical sub-regions in priority for reducing TN loads. Our study results have indicated that spatial regression could potentially serve as an effective tool to facilitate water pollution control in watersheds under diverse physical and socio-economical conditions.

Farmland-atmosphere feedbacks amplify decreases in diffuse nitrogen pollution in a freeze-thaw agricultural area under climate warming conditions

Although climate warming and agricultural land use changes are two of the primary instigators of increased diffuse pollution, they are usually considered separately or additively. This likely lead to poor decisions regarding climate adaptation. Climate warming and farmland responses have synergistic consequences for diffuse nitrogen pollution, which are hypothesized to present different spatio-temporal patterns. In this study, we propose a modeling framework to simulate the synergistic impacts of climate warming and warming-induced farmland shifts on diffuse pollution. Active accumulated temperature response for latitudinal and altitudinal directions was predicted based on a simple agro-climate model under different temperature increments (△T₀ is from 0.8°C to 1.4°C at an interval of 0.2°C). Spatial distributions of dryland shift to paddy land were determined by considering accumulated temperature. Different temperature increments and crop distributions were inserted into Soil and Water Assessment Tool model, which quantified the spatio-temporal changes of nitrogen. Warming led to a decrease of the annual total nitrogen loading (2.6%-14.2%) in the low latitudes compared with baseline, which was larger than the decrease (0.8%-6.2%) in the high latitudes. The synergistic impacts amplified the decrease of the loading in the low and high latitudes at the sub-basin scale. Warming led to a decrease of the loading at a rate of 0.35kg/ha/°C, which was lower than the synergistic impacts (3.67kg/ha/°C) at the watershed level. However, warming led to the slight increase of the annual averaged NO3 (LAT) (0.16kg/ha/°C), which was amplified by the synergistic impacts (0.22kg/ha/°C). Expansion of paddy fields led to a decrease in the monthly total nitrogen loading throughout the year, but amplified an increase in the loading in August and September. The decreased response in spatio-temporal nitrogen patterns is substantially amplified by farmland-atmosphere feedbacks associated with farmland shifts in response to warming.

Riparian responses to extreme climate and land-use change scenarios

Climate change will induce alterations in the hydrological and landscape patterns with effects on riparian ecotones. In this study we assess the combined effect of an extreme climate and land-use change scenario on riparian woody structure and how this will translate into a future risk of riparian functionality loss. The study was conducted in the Tâmega catchment of the Douro basin. Boosted Regression Trees (BRTs) were used to model two riparian landscape indicators related with the degree of connectivity (Mean Width) and complexity (Area Weighted Mean Patch Fractal Dimension). Riparian data were extracted by planimetric analysis of high spatial-resolution Word Imagery Layer (ESRI). Hydrological, climatic and land-use variables were obtained from available datasets and generated with process-based modeling using current climate data (2008-2014), while also considering the high-end RCP8.5 climate-change and "Icarus" socio-economic scenarios for the 2046-2065 time slice. Our results show that hydrological and land-use changes strongly influence future projections of riparian connectivity and complexity, albeit to diverse degrees and with differing effects. A harsh reduction in average flows may impair riparian zones while an increase in extreme rain events may benefit connectivity by promoting hydrologic dynamics with the surrounding floodplains. The expected increase in broad-leaved woodlands and mixed forests may enhance the riparian galleries by reducing the agricultural pressure on the area in the vicinity of the river. According to our results, 63% of river segments in the Tâmega basin exhibited a moderate risk of functionality loss, 16% a high risk, and 21% no risk. Weaknesses and strengths of the method are highlighted and results are discussed based on a resilience perspective with regard to riparian ecosystems.

Influence of Climate Extremes and Land Use on Fecal Contamination of Shallow Tubewells in Bangladesh

Climate extremes in conjunction with some land use practices are expected to have large impacts on water quality. However, the impacts of land use and climate change on fecal contamination of groundwater has not been well characterized. This work quantifies the influences of extreme weather events and land use practices on Escherichia coli presence and concentration in groundwater from 125 shallow wells, a dominant drinking water resource in rural Bangladesh, monitored over a 17 month period. The results showed that E. coli presence was significantly associated with the number of heavy rain days, developed land and areas with more surface water. These variables also had significant impacts on E. coli concentration, with risk ratios of 1.38 (95% CI = 1.16, 1.65), 1.07 (95% CI: 1.05, 1.09), and 1.02 (95% CI = 1.01, 1.03), respectively. Significant synergistic effects on E. coli presence and concentration were observed when land use and weather variables were combined. The findings suggest that climate extremes and land use practices, particularly urbanization, might promote fecal contamination of shallow well water, thus increasing the risk of diarrheal diseases.

Modelling the spatial and seasonal variability of water quality for entire river networks: Relationships with natural and anthropogenic factors

We model the spatial and seasonal variability of three key water quality variables (water temperature and concentration of nitrates and phosphates) for entire river networks in a large area in northern Spain. Models were developed with the Random Forest technique, using 12 (water temperature and nitrate concentration) and 15 (phosphate concentration) predictor variables as descriptors of several environmental attributes (climate, topography, land-uses, hydrology and anthropogenic pressures). The effect of the different predictors on the response variables was assessed with partial dependence plots and partial correlation analysis. Results indicated that land-uses were important predictors in defining the spatial and seasonal patterns of these three variables. Water temperature was positively related with air temperature and the upstream drainage area, whereas increases in forest cover decreased water temperature. Nitrate concentration was mainly related to the area covered by agricultural land-uses, increasing in winter, probably because of catchment run-off processes. On the other hand, phosphate concentration was highly related to the area covered by urban land-uses in the upstream catchment and to the proximity of the closest upstream effluent. Phosphate concentration increased notably during the low flow period (summer), probably due to the reduction of the dilution capacity. These results provide a large-scale continuous picture of water quality, which could help identify the main sources of change in water quality and assist in the prioritization of river reaches for restoration projects.