Modeling water quantity and quality for a typical agricultural plain basin of northern China by a coupled model

Hybrid deep learning based prediction for water quality of plain watershed

Establishing a highly reliable and accurate water quality prediction model is critical for effective water environment management. However, enhancing the performance of these predictive models continues to pose challenges, especially in the plain watershed with complex hydraulic conditions. This study aims to evaluate the efficacy of three traditional machine learning models versus three deep learning models in predicting the water quality of plain river networks and to develop a novel hybrid deep learning model to further improve prediction accuracy. The performance of the proposed model was assessed under various input feature sets and data temporal frequencies. The findings indicated that deep learning models outperformed traditional machine learning models in handling complex time series data. Long Short-Term Memory (LSTM) models improved the R2 by approximately 29% and lowered the Root Mean Square Error (RMSE) by about 48.6% on average. The hybrid Bayes-LSTM-GRU (Gated Recurrent Unit) model significantly enhanced prediction accuracy, reducing the average RMSE by 18.1% compared to the single LSTM model. Models trained on feature-selected datasets exhibited superior performance compared to those trained on original datasets. Higher temporal frequencies of input data generally provide more useful information. However, in datasets with numerous abrupt changes, increasing the temporal interval proves beneficial. Overall, the proposed hybrid deep learning model demonstrates an efficient and cost-effective method for improving water quality prediction performance, showing significant potential for application in managing water quality in plain watershed.

Impact on urban river water quality and pollution control of water environmental management projects based on SMS-Mike21 coupled simulation

To explore the impact of expanding Nanyang Sewage Purification Center (NSPC) on the main sewage discharge area of Bai River, we constructed a 2D hydrodynamic-water quality model based on surface water modeling system (SMS) and Mike21. Simulating three sewage discharge conditions in wet, normal, and dry season, we evaluated three indicators (COD, NH3-N, and BOD5) by the single-factor pollution index and provided recommendations for water environment management. The results showed that, maximum absolute error of water level was 0.08 m, percentage bias coefficient of COD, NH3-N and BOD5 were 19.3%, 16.2% and 23.1%, indicating the SMS and Mike21 coupling model was applicable; water quality of the assessment section were upgraded from the original class IV, V, V (Condition 1) to class IV, III, II (Condition 2) and class IV, III, III (Condition 3) in the wet, normal and dry season, indicating that NSPC's expansion had improved the water quality of the assessment section; as the primary pollutant, BOD5 concentration in the downstream was lower than the upstream, which was due to the dilution effect of river. Therefore, on the basis of expanding NSPC, we recommend to remediation of BOD5 by physical, chemical, and biological methods. This study broadens new ideas for the application of Mike21, and provide a reference for the prevention and improvement of river water pollution in urban areas.

Mapping and assessment of river water quality under varying hydro-climatic and pollution scenarios by integrating QUAL2K, GEFC, and GIS

Water quality modelling has proved to be effective method for managing river water quality. But the most effective and comprehensive approach involving integration of river water quality simulation and pollution visualization with the objective of pollution reduction and maintenance of environmental flow strategies has gained less attention. Thus, the objective of this study was to employ an integrated approach for mapping and analysing river water quality under various hydro-climatic and pollution scenarios. Specifically, this approach involved the integration of a river water quality simulation model, QUAL2K, Global Environmental Flow Calculator (GEFC), and Geographical Information System (GIS) to develop water quality index (WQI) based map charts of water quality. The calibrated QUAL2K model was utilized to simulate WQI parameters including water temperature, pH, electrical conductivity, dissolved oxygen (DO), biological oxygen demand (BOD), nitrates (NO3), ammonia (NH4), and alkalinity. To analyse the WQI, the Weighted Arithmetic-Water Quality Index (WA-WQI) method was employed for various individual and combined pollution scenarios, environmental flow (Eflow), and climate change scenarios. The developed integrated approach was applied to the Bhadravati segment of Bhadra River, India. The findings revealed that the prevailing WQI status of the study stretch ranged from poor to unsuitable for drinking purposes. This deterioration can be attributed to the impact of both industrial and municipal effluents. By maintaining the effective Environmental Management Class (EMC) flow rates (class C flowrate of EMC (40.32 m3/s)) in conjunction with appropriate Pollution Reduction (PR) level (10% PR) at headwater and incoming drains, the stream self-purification capacity was enhanced resulting in the Bhadravati River stretch water quality transitioning to favourable water quality condition.

Research on water quality improvement of plain irrigation area based on multi-scenario simulation

Water diversion projects have proven to be effective interventions to improve water quality in irrigation ditches. This study focused on quantifying the water quality improvement by utilizing a hydrodynamic water quality model in Funing County, Yancheng City. The model performed a spatial analysis of pollution concentrations across the study area. Various optimization scenarios were designed based on the diversion project and hydrological structure connectivity. The model was used to simulate changes in nutrient concentrations under different scenarios. The findings of this study were as follows: (1) Rural areas had lower nutrient concentrations and superior hydrological connectivity than urban areas. (2) The effect of water quality improvement correlated positively with increased flow rates introduced by the diversion project. Specifically, when the flow rate increased by 50%, the average reductions were 20% for NH4+, 5.2% for TN, and 5.1% for TP. Furthermore, introduced clean water led to more pronounced improvements in the overall regional water quality. (3) Although increasing the number of ditches improved water pollution concentration, the impact was not significant. (4) Model simulation results showed that 18 to 45% water diversion intensity effectively improved water quality, and the optimal water diversion intensity was 27 to 30%. The optimal water diversion intensities offered valuable insights for managing this region. The study's methods contributed to the promotion of sustainable development in regional water resources and the integrated management of the water environment.

Pollution source identification and abatement for water quality sections in Huangshui River basin, China

Accurately obtaining the pollution sources and their contribution rates is the basis for refining watershed management. Although many source analysis methods have been proposed, a systematic framework for watershed management is still lacking, including the complete process of pollution source identification to control. We proposed a framework for identification and abatement of pollutants and applied in the Huangshui River Basin. A newer contaminant flux variation method based on a one-dimensional river water quality model was used to calculate the contribution of pollutants. The contributions of various factors to the over-standard parameters of water quality sections at different spatial and temporal scales were calculated. Based on the calculation results, corresponding pollution abatement projects were developed, and the effectiveness of the projects was evaluated through scenario simulation. Our results showed that the large scale livestock and poultry farms and sewage treatment plants were the largest sources of total nitrogen (TP) in Xiaoxia bridge section, with contribution rates of 46.02% and 36.74%, respectively. Additionally, the largest contribution sources of ammonia nitrogen (NH3-N) were sewage treatment plants (36.17%) and industrial sewage (26.33%). Three towns that contributed the most to TP were Lejiawan Town (14.4%), Ganhetan Town (7.3%) and Handong Hui Nationality town (6.6%), while NH3-N mainly from the Lejiawan Town (15.9%), Xinghai Road Sub-district (12.4%) and Mafang Sub-district (9.5%). Further analysis found that point sources in these towns were the main contributor to TP and NH3-N. Accordingly, we developed abatement projects for point sources. Scenario simulation indicated that the TP and NH3-N could be significantly improved by closing down and upgrading relevant sewage treatment plants and building facilities for large scale livestock and poultry farms. The framework adopted in this study can accurately identify pollution sources and evaluate the effectiveness of pollution abatement projects, which is conducive to the refined water environment management.

The evaluation of N/P fate using the SPARROW model: a case study in an arid and semi-arid region, northern China

The assessment of nutrients' fate from source to sink is critical to water quality control. As an important ecological reserve in the arid and semi-arid regions of China, the Luanhe River Basin (LRB) has suffered from the deterioration of water quality, thus leading to the urgent management and control. However, few studies have devoted to exploring the fate of N/P contaminations for the entire watershed, due possibly to the large drainage area and heterogeneous watershed composition. Here, we attempt to illustrate N/P contaminations delivery and retention processes using the SPAtially Referenced Regression On Watershed attributes (SPARROW) model. The model reveals 97% of the spatial variability in the TN load and 81% in the TP load, verifying its availability and credibility. The results indicate that anthropogenic sources are dominating the N/P load, which account for 68.5% of N and 74.6% of P inputs. The results highlight the significant retention effects of streams and reservoirs, with 16.4% of N and 13.4% of P removals by streams and 24.3% of N and 10.7% of P removals by reservoirs, respectively. Ultimately, only 49,045.2 t yr^-1 (or 16.9%) of N and 1668.7 t yr^-1 (or 17.1%) of P being transported to the Bohai Sea. In addition, the analysis of influencing factors showed that regional characteristics (e.g., topography, rainfall), stream size, and delivery distance are potential factors affecting the riverine transport, whereas flow rate and surface area are primarily affecting the reservoirs attenuation. In the future, the watershed water quality management should pay more attention to source management and pollution legacy risks to achieve sustainable and healthy watershed development.

Water quality status response to multiple anthropogenic activities in urban river

Water quality evaluation and degrading factors identification are crucial for predicting water quality evolution trends in an urban river. However, under the coupling of multiple factors, these targets face great challenges. The water quality status response to multiple anthropogenic activities in an urban river was evaluated and predicted based on comprehensive assessment methods and random forest (RF) model. We found that the distribution of each physicochemical parameter exhibits an obvious spatial clustering. The mean pollution level and trophic status of the urban river are medium pollution (water quality index = 59.79; Nemerow's pollution index = 2.00) and light eutrophication (trophic level index = 57.30). The water quality status is sensitive to anthropogenic activities, showing the following order of TLI and NPI values: residential district > industrial district > agricultural district and downtown > suburbs > countryside. According to the redundancy analysis, constructed land (F = 15.90, p < 0.01) and domestic sewage (F = 14.20, p < 0.01) evinced as the crucial factors that aggravated the water quality pollution level. Based on the simulation results of the RF model (variation explained = 94.91%; R² = 0.978), improving domestic sewage treatment standards is the most effective measure to improve the water quality (increased by 40.3-49.3%) in residential and industrial districts. While in a suburban district, improving the domestic sewage collection rate has more effectively (23%) than those in the residential and industrial districts. Conclusively, reducing exogenous pollution input and improving domestic sewage treatment standards are vital to urban river restoration. Clinical trial registration Not applicable.

Improving the accuracy of nonpoint-source pollution estimates in inland waters with coupled satellite-UAV data

Quantitatively and accurately analyzing nonpoint-source (NPS) pollution is essential for efficiently preventing the input of NPS loads into inland waters. However, the accuracy of previous NPS pollution models is limited by the accuracy of ground parameter data. In addition, there are few effective methods that thoroughly verify modeling results at large scales. This paper presents a framework for accurate NPS pollution estimation by coupling satellite and unmanned aerial vehicle (UAV) monitoring data, and the results are verified by both field sampling and a newly developed inlet NPS pollution "observation" simulation method. Fractional vegetation coverage (FVC) data obtained by satellite were used to improve the accuracy of the runoff module of the framework. Satellite and UAV data were coupled to acquire livestock data, determine inlets, and identify reservoir buffer zones and vegetation types. These new data were then used to improve the accuracy of the livestock and runoff modules in the framework. The results show that the estimation accuracy of total nitrogen, total phosphorus, ammonia nitrogen, and chemical oxygen demand with FVC were improved by 39.96%, 69.29%, 54.05% and 47.22% (in relative error), respectively. The high-resolution livestock data acquisition improved the estimation accuracy of the NPS pollution load by 7-53%. The high-resolution inlet extraction improved the accuracy by 3-24%. The high-resolution buffer zone identification improved the accuracy with the estimated NPS pollutant concentration into reservoir decreasing by 60-99%. Finally, the high-resolution vegetation type identification improved the accuracy by 10-72%. The framework performs satisfactorily, which was verified based on the simulated NPS observations with an average relative error of 11.54-24.31%. We found that the FVC, livestock number, and inlet number are key parameters for NPS pollution modeling; the introduction of monthly variation in the FVC makes the modeled NPS pollution load much higher in areas with mature complex forested ecosystems or densely distributed vegetation but much lower in areas with sparsely distributed vegetation. The above methods provide a scientific reference for high-efficiency NPS pollution prevention in inland waters, laying a solid basis for decision-making regarding water quality management in data-scarce regions around the world.

Autochthonous sources and drought conditions drive anomalous oxygen-consuming pollution increase in a sluice-controlled reservoir in eastern China

Freshwater reservoirs are an important type of inland waterbody. However, they can suffer from oxygen-consuming pollution, which can seriously threaten drinking water safety and negatively impact the health of aquatic ecosystems. Oxygen-consuming pollutants originate from both allochthonous and autochthonous sources, and have temporally and spatially heterogeneous drivers. Datanggang Reservoir, China, is located in a small agricultural watershed; it is controlled by multiple sluice gates. Anomalously high oxygen consumption indicators were observed in this reservoir in March 2021. Here, it was hypothesized that autochthonous sources were the primary drivers of oxygen-consuming pollution in the reservoir under drought conditions. Datasets of water quality, precipitation, primary productivity, and sediment were used to analyze water quality trends in the reservoir and inflow rivers, demonstrating the effects of allochthonous inputs and autochthonous pollution. No correlation was found between reservoir oxygen consumption indicators and allochthonous inputs; reservoir oxygen consumption indicators and chlorophyll-a concentration were significantly positively correlated (p < 0.05). Substantially lower precipitation and higher water temperature and pH (compared to historical levels) were also observed before the pollution event. Therefore, during this period the hydrological conditions, water temperature, pH, and other variables caused by short-term drought conditions may have facilitated phytoplankton growth in the reservoir. This contributed to a large increase in autochthonous oxygen-consuming pollutants, as reflected by the abnormally high indicators. Sediments contaminated with organic matter may also have been an important contributor. As the effects of environmental management and pollution control continue to emerge, exogenous pollutants imported from the land to reservoirs are currently effectively controlled. However, endogenous pollutants driven by a variety of factors, such as meteorology and hydrology, will likely become the main drivers of short-term changes in oxygen-consuming pollution in freshwater reservoirs in the foreseeable future.

Evaluating the risks of spatial and temporal changes in nonpoint source pollution in a Chinese river basin

In watershed management, it is of great importance to evaluate the risks of nonpoint source (NPS) pollution. In this study, the Nonpoint Source Pollution Risk Index (NSPRI), a multi-factor NPS risk assessment model that was based on the source-sink landscape theory, was proposed and applied in Muzhuhe River Basin, Shandong, China to (1) highlight spatial and temporal variations in the risks from nitrogen and phosphorus losses, and (2) identify how the basin characteristics influenced the risk of nutrient loss. According to the analysis on land use change, the study area is featured with high proportions of forest and agricultural land uses; the area of urban and industrial land had increased considerably from 2000 and 2018. Based on the division of the calculated risk indices on subbasin scale, the area with extremely high risks has decreased from 56,442 ha to 43,922 ha. The average and coefficient of variation (CV) values of NSPRI in the river basin have dropped from 1.3 to 1.1, and from 78.2% to 48.9%, respectively. The distribution of NSPRI suggested an increase in spatial clustering and improvements in the ecological balance. Correlation analysis of the Soil and Water Assessment Tool (SWAT) model (R² > 0.68, ENS > 0.59) and NSPRI indicated the applicability of the method used (r > 0.84, p < 0.01). Analysis on the impact of metrics of land use composition, landscape, and environmental settings on NSPRI indicated that the water quality was more significantly correlated with land use composition, landscape pattern and vegetation cover than with flow path distance, soil erodibility, and rainfall erosivity. Moreover, results of redundancy analysis revealed that nutrient loss risk was better explained by land use compositions than by landscape configuration. The assessment method provided scientific support for NPS pollution control from the perspective of source-sink landscape theory.

Ammonium Nitrogen Streamflow Transport Modelling and Spatial Analysis in Two Chinese Basins

Ammonium nitrogen (NH4+-N), which naturally arises from the decomposition of organic substances through ammonification, has a tremendous influence on local water quality. Therefore, it is vital for water quality protection to assess the amount, sources, and streamflow transport of NH4+-N. SPAtially Referenced Regressions on Watershed attributes (SPARROW), which is a hybrid empirical and mechanistic modeling technique based on a regression approach, can be used to conduct studies of different spatial scales on nutrient streamflow transport. In this paper, the load and delivery of NH4+-N in Poyang Lake Basin (PLB) and Haihe River Basin (HRB) were estimated using SPARROW. In PLB, NH4+-N load streamflow transport originating from point sources and farmland accounted for 41.83% and 32.84%, respectively. In HRB, NH4+-N load streamflow transport originating from residential land and farmland accounted for 40.16% and 36.75%, respectively. Hence, the following measures should be taken: In PLB, it is important to enhance the management of the point sources, such as municipal and industrial wastewater. In HRB, feasible measures include controlling the domestic pollution and reducing the usage of chemical fertilizers. In addition, increasing the vegetation coverage of both basins may be beneficial to their nutrient management. The SPARROW models built for PLB and HRB can serve as references for future uses for different basins with various conditions, extending this model’s scope and adaptability.