Water Quality Stations Research Articles

Seasonal differences in water quality of highland lakes and its influencing factors: A case study of Dianchi Lake

Abstract Water quality is of great significance to human survival and development. It is of good theoretical value to study seasonal water quality variation and driving factors in plateau lakes. This paper studied the variation of water quality parameters in different seasons and their relationship with land use and climate factors in Dianchi Lake, Yunnan Province from 2001 to 2019. The results show that the factors restricting water environment in different seasons are different, but basically close. The water quality parameters of multiple water quality monitoring stations in different seasons showed a decreasing or insignificant trend. In the rainy season, the temperature and precipitation were positively correlated with most water quality parameters, while the driving effect of climate factors was not significant in the dry season. Different land types have different effects on water quality indexes in different seasons. The water quality score was higher in the rainy season than in the dry season. The scores of water quality stations in the south and middle of Dianchi Lake are higher than those in the north. In this study, the relationship between climate, land, and water quality is established, which provides a way for the research of water quality parameters.

Effects of landscape characteristics, anthropogenic factors, and seasonality on water quality in Portland, Oregon

Urban areas often struggle with deteriorated water quality because of complex interactions between landscape factors and climatic variables. However, few studies have considered the effects of landscape variables on water quality at a sub-500-m scale. We conducted a spatial statistical analysis of six pollutants for 128 water quality stations in four watersheds around Portland, Oregon, using data from 2015 to 2021 for the wet season at two microscales (100m and 250m buffers). E. coli was associated with land cover, soil type, topography, and pipe length, while lead variations were best explained by topographic variables. Developed land cover and impervious surface explained variations in nitrate, while orthophosphate was associated with mean elevation. Models for zinc included land cover and topographic variables in addition to pipe length. Spatial regression models better explain variations in water quality than ordinary least squares models, indicating strong spatial autocorrelation for some variables. Our findings provide valuable insights to city planners and researchers seeking to improve water quality in metropolitan areas by manipulating city landscapes.

The spatial organization of CAFOs and its relationship to water quality in the United States

• Applied Nearest Neighbor Index and Moran’s I to quantify the spatial pattern of AFOs. • Watersheds with higher AFO NNI were associated with elevated nutrient levels. • Clustering of smaller farms (less regulated) also corresponded to high nutrients. • Results help to inform management of AFOs and other pollutant sources. Large animal operations in the United States (US) have expanded in recent decades as a solution for meeting increasing food demand. These operations can release large amounts of organic matter and nutrients into the environment, potentially impacting water quality conditions of nearby waterbodies. Often referred to as animal feeding operations (AFOs) or CAFOs (concentrated AFOs) based on nomenclature in federal regulations, these operations are usually clustered in space, which may intensify the impacts observed. However, the influence of the known spatial aggregation of CAFOs on water quality conditions has not been evaluated. This study investigates the spatial organization of CAFOs and its relationship to water quality conditions. We hypothesized that watersheds with clustered CAFOs are likely to be associated with higher concentrations of Total Phosphorus (TP) and Total Nitrogen (TN) in the US. We gathered and digitized CAFO locations in 16 US states and used TP and TN concentration data to evaluate our hypothesis. We calculated the flow-weighted mean concentration of TN and TP from 2005 to 2014 and averaged those values per season. We used the nearest neighbor index (NNI) to determine the spatial pattern (i.e., clustering or dispersed) of CAFOs per watershed and its significance level. Additionally, for states with animal number information per farm, we performed a Local Moran’s I analysis to understand if the clustering of CAFOs with a low or high number of animals relates to the TP and TN concentrations of each nearest water quality station. Overall, we found that watersheds with significant clustering patterns were associated with higher TP and TN levels. Seasonally, this finding also held principally during spring and summer seasons, when manure tends to be land applied. Our Local Moran’s I analysis also revealed that the clustering of smaller and medium-sized farms, usually less regulated, is associated with elevated TP and TN concentrations. This reveals that the small, yet potentially unregulated farms could be highly impactful to the environment, which warrants further study. Given the lack of information on the influence of CAFO clustering (and other pollutant source clustering) on water quality and that most states do not regulate emitters based on this aspect, our outcomes advance the knowledge of how these entities may be driving changes to the environment. This study also brings insights into new water quality modeling approaches and supports future policy decisions.

Water-Quality Assessment and Pollution-Risk Early-Warning System Based on Web Crawler Technology and LSTM.

The openly released and measured data from automatic hydrological and water quality stations in China provide strong data support for water environmental protection management and scientific research. However, current public data on hydrology and water quality only provide real-time data through data tables in a shared page. To excavate the supporting effect of these data on water environmental protection, this paper designs a water-quality-prediction and pollution-risk early-warning system. In this system, crawler technology was used for data collection from public real-time data. Additionally, a modified long short-term memory (LSTM) was adopted to predict the water quality and provide an early warning for pollution risks. According to geographic information technology, this system can show the process of spatial and temporal variations of hydrology and water quality in China. At the same time, the current and future water quality of important monitoring sites can be quickly evaluated and predicted, together with the pollution-risk early warning. The data collected and the water-quality-prediction technique in the system can be shared and used for supporting hydrology and in water quality research and management.

Water temperature variability in the Coos Estuary and its potential link to eelgrass loss

Subtidal water temperatures in estuaries influence where organisms can survive and are determined by oceanic, atmospheric and riverine heat fluxes, modulated by the distinct geometry and bathymetry of the system. Here, we use 14 years of data from the Coos Estuary, in southwest Oregon, USA, to explore the impact of anomalously warm oceanic and atmospheric conditions during 2014-2016 on the estuary temperature. The arrival of a marine heatwave in September 2014 increased water temperature in the greater Pacific Northwest region until March 2015, and again from July to August 2015. Additionally, in 2014-2016, the Equatorial Pacific showed increased temperatures due to El Niño events. In the Coos Estuary, this warming was observed at all the water quality stations, producing more than 100 days with temperatures at least 1.5°C warmer than normal, and notably, a higher prevalence during Fall and Winter seasons. Larger temperature variations occurred at shallower stations located further away from the mouth of the estuary, changing the along-estuary temperature gradient and potentially the advection of heat through the estuary. After the onset of these increased temperatures, eelgrass declined sharply, but only in certain stations in the shallow estuary South Slough and has not yet returned to long term average values. As global temperatures continue rising due to climate change, increased numbers of marine heatwaves and El Niño events are expected, leading to higher temperature stress on the marine ecosystem within estuaries.

Rainstorms Inducing Shifts of River Hydrochemistry during a Winter Season in the Central Appalachian Region

Rainstorms rapidly change catchment conditions which can alter river flow and water constituents due to the transport and fate of suspended and dissolved solids and the river water chemistry. To understand river water chemistry changes, this investigation relies on field data collected during a winter season. The Kanawha River in West Virginia was monitored using grab water samples and continuous readings from two water quality stations (Q1 and Q2) separated by 23.5 km. Water samples allowed the identification of water chemistry, whereas the two stations retrieved hourly measurements of temperature, turbidity, NO3−, Cl− and pH to capture transient rainstorm responses. It was found through the Piper diagram that water type was mainly calcium-chloride, whereas the Gibbs diagram identified that the dominant geochemical process was rock weathering. On the other hand, during transient rainstorms responses, we found that concentrations of HCO3−, NO3− and Cl− changed from bicarbonate type to no dominant type. Furthermore, hysteretic effects of rainstorms were influenced by the soil moisture of the catchment area. Additionally, HCO3− and NO3− had different hysteretic loop directions between Q1 and Q2. This approach proved that river water chemistry adjustments caused by rainstorms were successfully identified by relying on grab water samples and continuous measurements.

Application of machine learning approaches in predicting estuarine dissolved oxygen (DO) under a limited data environment

Abstract The application of machine learning (ML) approaches to predict estuarine dissolved oxygen (DO) from a set of environmental covariates including nutrients remains unexplored due to nutrient data unavailability. Employing data from 12 southwest coastal Florida water quality stations, the applicability of four ML models – support vector machine (SVM), random forest (RF), decision tree, and Wang–Mendel – was examined in predicting DO under a limited nutrient data environment. Monthly water temperature, pH, salinity, total nitrogen (TN), and total phosphorus (TP) data were used for model development. The multiple linear regression model was trained as benchmarks to compare the ML model performances. The site-specific RF and SVM showed superior model efficiency (Nash–Sutcliffe Efficiency > 0.80) when all the predictor variables were used for model development. However, models trained without nutrients demonstrated reduced prediction accuracy. Modeling by synthesizing all site data under TN-limited, TP-limited, and TN- & TP-co-limited regimes illustrated a preferable performance of RF. Overall, the study rendered two crucial conclusions that could complement the existing approaches to estimate total daily loads for environmental management: (1) nutrients serve as a necessary predictor of estuarine DO dynamics and (2) RF performs better among the ML methods under a limited data environment.

Satellite imagery: a way to monitor water quality for the future?

Monitoring water at high spatial and temporal resolutions is important for maintaining water quality because the cost of pollution remediation is often higher than the cost of early prevention or intervention. In recent decades, the availability and affordability of satellite images have regularly increased, thus supporting higher-frequency and lower-cost alternative methods for monitoring water quality. The core step in satellite remote sensing detection is inverse modeling, which is used to calibrate model parameters and enhance the similarity between the model and the real system being simulated. The reflectance values measured at water quality stations are extracted from atmosphere-corrected satellite imagery for analysis. However, various external environmental, hydrological, and meteorological factors affect the evaluation results, and the results obtained with different parameters can vary. This literature review shows that nonpoint-source pollution caused by stormwater runoff can also be monitored using satellite imagery. To improve the accuracy of satellite-based water quality prediction, the temporal resolution of field measurements can be increased, thus better considering the influence of seasonality. Then, the atmospheric correction module can be improved by using available atmospheric water content products. Moreover, because water surface ripples affect reflectance, wind speed and direction should be considered when comparing water quality scenes.

Restoring a Degraded, Sentinel New England Salt Marsh to Mid-20th Century Conditions (Flax Pond, New York, U.S.A.)

Swanson, R.L.; Giglio, K.W., and Chi, L., 2021. Restoring a degraded, sentinel New England salt marsh to mid-20th century conditions (Flax Pond, New York, U.S.A.). Journal of Coastal Research, 37(5), 993–1011. Coconut Creek (Florida), ISSN 0749-0208. Flax Pond, located between Crane Neck and Old Field Point on the north shore of Long Island, is designated as a Significant Coastal Fish and Wildlife Habitat by the U.S. Fish and Wildlife Service and as a Scenic Recreational Reserve by the New York State Department of Environmental Conservation. The pond's tidal prism has been reduced about 18% over the past half-century as its inlet has shoaled and its tidal range diminished 33%, 0.6 m, all in low water. Reduced flushing has led to loss of wetlands, reduced water quality, increased pond salinity, and nighttime summertime hypoxia with concomitant reduction in pH. Historic charts, topographic surveys, and vertical aerial photographs were used to study the evolution of the pond's inlet from Long Island Sound (LIS). Tide, tidal current, and water quality observations were made and/or historical measurements were acquired at these locations: the pond's inlet, a proposed seawater intake for the Flax Pond Marine Laboratory, and the U.S. Geological Survey (USGS) water quality station some 650 m into the pond. The flux of water through the inlet from LIS and the change in bed slope through the pond inlet were measured. USGS data within the pond were used to determine when hypoxic conditions are likely to occur: an increase in bottom-water salinity of 2.1 in 9 years and in the elevation of low water of 18 mm y–1 from 2008–14. The environmental benefits of reconfiguring the inlet to increase the tidal prism to that of the 1970s were explored. This would reduce the existing flushing time by more than 50%. The design of the cross-sectional shape for the inlet is based on this tidal prism and a compatible depth entering LIS; it should stabilize the inlet opening and improve much needed pond flushing.

Spatiotemporal Characteristics of the Water Quality and Its Multiscale Relationship with Land Use in the Yangtze River Basin

The spatiotemporal characteristics of river water quality are the key indicators for ecosystem health evaluation in basins. Land use patterns, as one of the main driving forces of water quality change, affect stream water quality differently with the variations in the spatiotemporal scales. Thus, quantitative analysis of the relationship between different land cover types and river water quality contributes to a better understanding of the effects of land cover on water quality, the landscape planning of water quality protection, and integrated water resources management. Based on water quality data of 2006–2018 at 18 typical water quality stations in the Yangtze River basin, this study analyzed the spatial and temporal variation characteristics of water quality by using the single-factor water quality identification index through statistical analysis. Furthermore, the Spearman correlation analysis method was adopted to quantify the spatial-scale and temporal-scale effects of various land uses, including agricultural land (AL), forest land (FL), grassland (GL), water area (WA), and construction land (CL), on the stream water quality of dissolved oxygen (DO), chemical oxygen demand (CODMn), and ammonia (NH3-N). The results showed that (1) in terms of temporal variation, the water quality of the river has improved significantly and the tributaries have improved more than the main rivers; (2) in the spatial variation respect, the water quality pollutants in the tributaries are significantly higher than those in the main stream, and the concentration of pollutants increases with the decrease of the distance from the estuary; and (3) the correlation between DO and land use is low, while that between NH3-N, CODMn, and land use is high. CL and AL have a negative effect on water quality, while FL and GL have a purifying effect on water quality. In particular, AL and CL have a significant positive correlation with pollutants in water. Compared with NH3-N, CODMn has a higher correlation with land use at a larger scale. The results highlight the spatial scale and seasonal dependence of land use on water quality, which can provide a scientific basis for land management and seasonal pollution control.

Assessment of Kelantan River water quality using water quality index (WQI)

Kelantan River is the main river in Kelantan, and it has been used for water sources for irrigation, small-scale fishing industries, transportation, and sand mining. WQI is a comprehensive method for displaying water quality status and achieving good water quality. This study is aims to identify the significant difference in water quality index (WQI) parameters and the correlation between WQI parameters in urban, suburban, and residential area along the Kelantan River from 2015 until 2019. Water quality stations in urban, suburban, and residential areas were selected with facilitation of ArcMap 10.0 software. In this study, Kota Bharu represented urban area, Kuala Krai represented suburban area and Tanah Merah represented residential area. The significant difference between WQI value with different stations were determined using analysis of variance (ANOVA). Meanwhile the correlation between WQI parameters were determined using Pearson correlation. From this research, there is no significant difference in water quality between urban, suburban, and residential areas along Kelantan River. The correlation of water quality parameters varies between locations which indicates different water pollution contributors except correlation between biochemical oxygen demand (BOD) and chemical oxygen demand (COD), pH and ammoniacal nitrogen (AN), and total suspended solid (TSS) and WQI. WQI in urban, suburban, and residential areas were classified in Class II from 2015 until 2019 except WQI in residential area in 2019 which was classified in Class III. This study will provide scientific reference for future use to protect local aquatic environment in Kelantan River and can be used to manage the river basin development in future.

Quantification of land use/land cover impacts on stream water quality across Taiwan

Change in water quality is seen as a reflection of change in terrestrial and aquatic ecosystems that are degraded by anthropogenic activities, including inappropriate land use management, deforestation, and urbanization. As the inter-correlations among landscape structure and ecological process have great influences on the hydrological process, energy flow, and nutrient cycles, it is important to evaluate the impact of landscape configuration on water quality for individual basins. Spatio-temporal variations of water quality and correlations among land use/land cover and water quality across Taiwan have not been thoroughly investigated. In this study, 48 water quality stations within 10 basins across Taiwan are investigated to identify the relationships among various water quality indices, land use/land cover, and landscape metrics at two different time periods. A total of 12 water quality parameters are included: water temperature, pH value, electrical conductivity, dissolved oxygen, biological oxygen demand, chemical oxygen demand, total suspended sediment, total phosphorus, ammonia, nitrate, nitrite, and total nitrogen. Two years (1994 and 2007) of land use/land cover were characterized by using landscape metrics to quantify change in landscape configuration. The hypothesis is that individual basin has different characteristics resulting in different water quality – landscape metric relationships. The results showed that high degrees of landscape fragmentation and interspersion are correlated with degradation of water quality, in terms of high biological and chemical oxygen demands, and nitrogen loads, and such significant relationships are found in certain land use types as pollution sources. Only 3 out of 10 basins have strong or moderate impacts of landscape variation on water quality, indicating these findings could provide reference for developing an effective watershed planning and management with further investigations of landscape configuration at the class level.

Improved Microbial Water Quality Associated with Best Management Practices on Coastal Dairies and Livestock Grazing Operations

Dairy and livestock grazing operations can introduce pollutants such as fecal coliform to surface waters, posing risk to human health and ecological conditions. Agricultural best management practices (BMPs) that increase control of manure, runoff, and animal access to waterways can reduce deposition of microbial pollution into streams. Between 2000 and 2013, we monitored water quality (fecal indicator bacteria [FIB] consisting of fecal coliform [FC] and Escherichia coli [EC]) in three coastal watersheds that encompass intensively managed dairies, beef cattle grazing operations, and public use at Point Reyes National Seashore in Marin County, California. Concurrently, approximately 30 BMPs were implemented to manage cattle and improve dairy infrastructure. We also monitored a fourth adjacent watershed without any BMPs and limited direct livestock influence. The primary FIB parameter was changed from FC to EC midway through the study in 2007, so we combined FC data from 2000 to 2006 with EC data from 2007 to 2013 using previously published EC/FC ratios and data from this study to create a continuous time series. Competing Bayesian generalized linear mixed models examined whether FIB was best explained by year, 24-h rainfall, season, or annual rainfall and compared results with numeric regulatory objectives for surface waters. FIB from 2000 to 2013 declined at all 13 water quality stations that were downstream of BMPs implemented during the study, while there was a slight positive trend in the adjacent watershed without BMPs and limited livestock influence. There was a 54−99% reduction in FIB with a sixfold increase in the frequency of samples meeting regulatory criteria over the study period, and results were robust to varying assumptions of the relationship between FC and EC. These findings indicate that targeted BMPs can effectively reduce FIB, increasing the probability of meeting water quality objectives across varying types of livestock operations.

Multivariate statistics for spatial and seasonal quality assessment of water in the Doce River basin, Southeastern Brazil.

This study employed multivariate statistical techniques in one of the main river basins in Brazil, the Doce River basin, to select and evaluate the most representative parameters of the current water quality aspects, and to group the stations according to the similarity of the selected parameters, for both dry and rainy seasons. Data from 63 qualitative monitoring stations, belonging to the Minas Gerais Water Management Institute network were used, considering 38 parameters for the hydrological year 2017/2018. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to reduce the total number of variables and to group stations with similar characteristics, respectively. Using PCA, four principal components were selected as indicators of water quality, explaining the cumulative variance of 68% in the rainy season and 65% in the dry season. The HCA grouped the stations into four groups in the rainy season and three groups in the dry season, showing the influence of seasonality on the grouping of stations. Moreover, the HCA made it possible to differentiate water quality stations located in the headwaters of the basin, in the main river channel, and near urban centers. The results obtained through multivariate statistics proved to be important in understanding the current water quality situation in the basin and can be used to improve the management of water resources because the collection and analysis of all parameters in all monitoring stations require greater availability of financial resources.

Water quality evaluation of Xiaoshan water quality station in eastern Zhejiang Water Diversion Project Based on BP network

East Zhejiang water diversion project is a major water resources allocation project to ensure the sustainable economic and social development of xiaoshaoningzhou area in eastern Zhejiang. The project task is to introduce Qiantang River water to provide domestic, industrial and agricultural irrigation water to xiaoshaoning plain and Zhoushan City, and to improve water environment. Its head project is Xiaoshan hub project. In order to ensure the safety of water quality along the water diversion line, it is very important to strengthen the monitoring and evaluation of water quality at the source of eastern Zhejiang. In this paper, BP network algorithm based on error back propagation is used to evaluate the water quality station of Xiaoshan junction. The evaluation results show that most of the water quality in Xiaoshan water diversion project is class II water and class III water.

Scenario-based pollution discharge simulations and mapping using integrated QUAL2K-GIS

Malaysia is a tropical country that is highly dependent on surface water for its raw water supply. Unfortunately, surface water is vulnerable to pollution, especially in developed and dense urban catchments. Therefore, in this study, a methodology was developed for an extensive temporal water quality index (WQI) and classification analysis, simulations of various pollutant discharge scenarios using QUAL2K software, and maps with NH3–N as the core pollutant using an integrated QUAL2K-GIS. It was found that most of the water quality stations are categorized as Class III (slightly polluted to polluted). These stations are surrounded by residential areas, industries, workshops, restaurants and wet markets that contribute to the poor water quality levels. Additionally, low WQI values were reported in 2010 owing to development and agricultural activities. However, the WQI values improved during the wet season. High concentrations of NH3–N were found in the basin, especially during dry weather conditions. Three scenarios were simulated, i.e. 10%, 50% and 70% of pollution discharge into Skudai river using a calibrated and validated QUAL2K model. Model performance was evaluated using the relative percentage difference. An inclusive graph showing the current conditions and pollution reduction scenarios with respect to the distance of Skudai river and its tributaries is developed to determine the WQI classification. Comprehensive water quality maps based on NH3–N as the core pollutant are developed using integrated QUAL2K-GIS to illustrate the overall condition of the Skudai river. High NH3–N in the Skudai River affects water treatment plant operations. Pollution control of more than 90% is required to improve the water quality classification to Class II. The methodology and analysis developed in this study can assist various stakeholders and authorities in identifying problematic areas and determining the required percentage of pollution reduction to improve the Skudai River water quality.

Variations of water quality in the monitoring network of a tropical river

Rapid development and population growth have resulted in an ever-increasing level of water pollution in Malaysia. Therefore, this study was conducted to assess water quality of Selangor River in Malaysia. The data collected under the river water quality monitoring program by the Department of environment from 2005 to 2015 were used for statistical analyses. The local water quality indices were computed and a trend detection technique and cluster analysis were applied, respectively, to detect changes and spatial disparity in water quality trends. The results showed that the river water is of good quality at all stations, with the exception of 1SR01 and 1SR09 located upstream, which recorded moderate water quality indices of 68 and 71, respectively. The results of trend analysis showed downward trends in dissolved oxygen, biochemical oxygen demand and ammonia nitrogen, for most water quality stations, as well as increasing trends in chemical oxygen, suspended solids, pH and temperature for most stations. In addition, the results of cluster and time series analyses showed that the trend variation in dissolved oxygen, pH, and temperature between the station clusters is relatively low as compared to chemical oxygen demand, biochemical oxygen demand, suspended solids, and ammonia nitrogen. With the peak concentration of 13 mg/L for dissolved oxygen observed in cluster 2 in 2014, and the highest decrease in suspended solids (8 mg/L) observed in cluster 1 for 2015. This finding demonstrates that these combined statistical analyses can be a useful approach for assessing water quality for adequate management of water resources.

Water Quality Modeling of Phytoplankton and Nutrient Cycles of a Complex Cold-Region River-Lake System

Linking elemental processes in integrated modeling provides a means of handling emerging issues related to aquatic system management. In this study, an integral modeling strategy was used to develop a water quality model of the Qu’Appelle River, particularly for under-ice conditions, which is a topic that has not received much attention in the literature. Phytoplankton and nutrient cycles of the river and lake were coupled using the Water Quality Analysis Simulation Program (WASP) 7.52. Model calibration and validation were based on samples taken from six water quality stations during the period 2013 to 2016. The calibration and validation results show good agreement between model predictions and observed data for oxygen, organic nitrogen, and ammonium. This study demonstrates the development and use of a model connecting riverine and lake sections to study under-ice conditions and can be applied to similar systems in cold regions.

Water quality trend assessment in Jakarta: A rapidly growing Asian megacity.

Megacities are facing serious water pollution problems due to urbanization, rapid population growth and economic development. Water is an essential resource for human activities and socio-economic development and water quality in urban settings has important implications for human and environmental health. Urbanization and lack of sewerage has left the water in Jakarta, Indonesia in a heavily polluted condition. Rigorous assessment of urban water quality is necessary to understand the factors controlling water quality conditions. We use trend analysis to assess the current water quality conditions in Jakarta, focusing on Biochemical Oxygen Demand (BOD), Dissolved Oxygen (DO), and Total Suspended Solids (TSS). In most monitoring stations analyzed, BOD and TSS concentrations have decreased over time, but from large starting concentrations. DO in most monitoring stations has increased. Although Jakarta’s water quality has shown some improvement, it remains heavily impaired. The average value of BOD is low in upper stream stations compared to middle and lower stream stations. BOD and TSS trends of some water quality stations in middle and lower streams show increasing trends. Cluster analysis results suggest three groups for BOD and TSS, and four groups for DO. Understanding water quality conditions and factors that control water quality suggest strategies for improving water quality given current trends in climate, population growth and urban development. Results from this study suggest research directions and management strategies to address water quality challenges.

Distributed source pollutant transport module based on BTOPMC: a case study of the Laixi River basin in the Sichuan province of southwest China

Abstract. Nitrogen and phosphorus concentrations in Chinese river catchments are contributed by agricultural non-point and industrial point sources causing deterioration of river water quality and degradation of ecosystem functioning for a long distance downstream. To evaluate these impacts, a distributed pollutant transport module was developed on the basis of BTOPMC (Block-Wise Use of TOPMODEL with Muskingum-Cunge Method), a grid-based distributed hydrological model, using the water flow routing process of BTOPMC as the carrier of pollutant transport due a direct runoff. The pollutant flux at each grid is simulated based on mass balance of pollutants within the grid and surface water transport of these pollutants occurs between grids in the direction of the water flow on daily time steps. The model was tested in the study area of the Lu county area situated in the Laixi River basin in the Sichuan province of southwest China. The simulated concentrations of nitrogen and phosphorus are compared with the available monthly data at several water quality stations. These results demonstrate a greater pollutant concentration in the beginning of high flow period indicating the main mechanism of pollution transport. From these preliminary results, we suggest that the distributed pollutant transport model can reflect the characteristics of the pollutant transport and reach the expected target.