Watershed Water Quality Research Articles

Uncertainty analysis of hydrological parameters of the APEXgraze model for grazing activities

Researchers in hydrological sciences have developed agro-hydrological models to study water quantity and quality in small-scale watersheds. These models, however, often exhibit significant uncertainty in both parameters and response variables. The study aims to address limited research on the uncertainty range of runoff-related parameters in watershed models, particularly those analyzing the impact of grazing operations. It also seeks to improve existing uncertainty analysis protocols because these protocols rely on parameter distributions, which are often difficult to determine. A generalized uncertainty analysis protocol that statistically considers multiple acceptable solutions from calibrated agro-hydrological models was developed. This approach employed a variant of the Agricultural Policy eXtender (APEX) model with an expanded grazing module called APEXgraze to perform uncertainty analysis of runoff and sediment-related parameters. Four small-scale watershed models were developed for calibration: a) native prairie, b) native prairie under grazing operations, c) cereals (winter wheat and one season of oats), and d) the same cereals under grazing operations in a semi-arid region of Oklahoma, United States. This work demonstrated that a simplified uncertainty analysis approach effectively captured the internal dynamics of hydrological processes within a statistically significant range of parameters. This observation was evidenced by a small range of water balance in both magnitude and percentage. The procedure also helped identify redundant parameters in sensitivity and uncertainty analyses. The proposed generalized uncertainty analysis protocol offers a reliable method for assessing hydrological models' internal dynamics and identifying critical parameters. This approach can enhance the accuracy of watershed models, particularly in regions with grazing operations.

Assessing the Impact of Riparian Buffer Systems on Water Quality Improvement and Infiltration Enhancement: Integration of SWAT-MODFLOW and SWAT-REMM

Objectives:This study aims to assess the effect of riparian buffer system on the water quality improvement and infiltration enhancement in the upper Sinduncheon watershed by integrating the SWAT-MODFLOW and SWAT- REMM models.Methods:The study focused on three subbasins in the upper Sinduncheon watershed. Streamflow and groundwater level were simulated and relevant parameters were calibrated using SWAT-MODFLOW. Subsequently, the calibrated parameters were applied to SWAT-REMM to evaluate the effects of riparian buffer systems on the reduction of NO3-N and Suspended Solids (SS) and the enhancement of water infiltration. Riparian buffer scenarios with varying widths (9m, 21m, 27m). Results and Discussion:The results showed that the riparian buffer system led to significant reductions in NO3-N and SS. Specifically, a subbasin with the widest buffer (27m) showed the highest reduction efficiency (82.6% for NO3-N and 99.6% for SS). However, in a subbasin, despite having a wider buffer than other subbasin, the reduction efficiency was lower due to the presence of channelized pollutant inflow areas. Additionally, the riparian buffer increased rainfall infiltration by approximately 13%, contributing to the stabilization of baseflow.Conclusion:This study demonstrates that riparian buffer systems can significantly improve water quality and increase water infiltration in the upper Sinduncheon watershed. Future research should focus on developing tailored riparian buffer strategies that consider regional characteristics such as pollutant inflow patterns, land cover, and topography.

Modeling the effect of land-use change on runoff water quality in the urban watershed: a case study of Settat city, Morocco

ABSTRACT The purpose of this study is to create a model of the quantity and quality of runoff from the urban watershed. The modeling was performed using a personal computer stormwater management model (PCSWMM) tool and was based on washoff and buildup for total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP) measured in the field. In addition, low impact development (LID) was used under four different scenarios to improve runoff control of the urban hydrologic cycle and reduce pollutant concentrations. Model performance was analyzed using the R2 and NSE goodness-of-fit indices. Thus, three typical rainfall events, as well as field measurement parameters (TSS, TN, and TP), were used for the model's calibration and validation. The base scenario quantified the runoff volume and pollutant load without any intervention in the runoff process control. Scenarios S2 (BR: bioretention) and S3 (PP: permeable pavement) contributed, separately, in reducing runoff and pollutant load but with relatively small percentages. In addition, the results show that scenario S4 (BR and PP) has the most significant impact on flow reduction, by about 10% compared with the base scenario, as well as on the pollutant load reduction, by about 31, 29, and 40% for the parameters TSS, TN, and TP, respectively.

How uncertainty in calibration data affects the modeling of non-point source pollutant loads in baseflow

Baseflow is a major transport pathway for non-point source (NPS) pollutants. Watershed water quality (WWQ) models calibrated by low-quality data may produce misleading predictions of baseflow NPS pollutant loads, resulting in poor management decisions. We evaluated how models of the baseflow nitrate loads in the Huron River basin, southwest of Lake Erie, were affected by uncertainty in the calibration data. Based on a five-year time series of daily streamflow, nitrate concentration, and specific conductance, two sets of “observed” baseflow nitrate load data that include uncertainty were estimated using various tracer-based and non-tracer-based hydrograph separation methods, in conjunction with assumptions regarding baseflow nitrate concentrations. We calibrated the Soil and Water Assessment Tool plus (SWAT+) model with the two “observed” data sets and used the Generalized Likelihood Uncertainty Estimation (GLUE) approach to quantify parameter and predictive uncertainties. The results showed that baseflow accounted for 26 %–34 % of the mean annual total streamflow (11.8 m3/s) and 8 %–37 % of the mean annual total nitrate load (14.3 kg·ha−1·year−1) in the Huron River basin. The baseflow and nitrate load estimates from the non-tracer-based methods resembled those from the tracer-based method but had greater uncertainty. The posterior parameter distributions, as well as the weighted means and 90 % prediction intervals of the simulated baseflow nitrate loads, exhibited minimal variation when different calibration data sets for SWAT+ and different threshold likelihood values for GLUE were used. Our analysis emphasizes the necessity of calibrating WWQ models with baseflow pollutant loads/concentrations when addressing water quality issues related to baseflow. It also demonstrates the feasibility of utilizing multiple non-tracer-based hydrograph separation methods to estimate baseflow NPS pollutant loads. These non-tracer-based methods offer a simplicity and broader applicability compared to tracer-based methods. This study has provided insights into how calibration data uncertainty impacts the modeling of NPS pollution in baseflow and highlights the practical value of non-tracer-based hydrograph separation methods.

Quantitative prediction of water quality in Dongjiang Lake watershed based on LUCC

Land Use/ Cover Change (LUCC) plays a crucial role in influencing hydrological processes, nutrient cycling, and sediment transport in watersheds, ultimately impacting water quality on both spatial and temporal scales. Accurately predicting changes in watershed water quality is beneficial for the sustainable management of water resources. Current models often lack the ability to effectively predict water quality changes in a dynamic spatio-temporal context, particularly in complex watershed environments. The overall purpose of the study is to establish a comprehensive and dynamic modeling framework that links LUCC with water quality, allowing for accurate predictions of future water quality under varying land use scenarios. The model, which uses water quality as the dependent variable and LUCC as the independent variable, was developed to quantitatively predict changes in watershed water quality. To achieve this, annual multi-period remote sensing images from Landsat-5, Landsat-8 or Sentinel-2 satellites spanning from 1992 to 2022 were analyzed. Random Forest (achieving a Kappa coefficient of 0.9468) were employed to classify land use within the watershed. Based on classification results, a Cellular Automata-Markov chain model (CA-Markov) was constructed to simulate and predict the spatio-temporal patterns of land use, incorporating driving factors such as proximity to water systems, roads, elevation, and slope. Validation of the model using LUCC data from 2020 yielded a high prediction accuracy with a Kappa coefficient of 0.9505. The CA-Markov model was further utilized to project LUCC under three different scenarios—natural development, ecological protection, and arable land protection—between 2023 and 2033. Based on these projections, the coupled water quality and LUCC model was employed to predict water quality changes in the watershed over the same period. Key findings indicate that water quality is likely to improve under ecological protection scenario, while deterioration is expected under natural development scenario and cropland protection scenario due to urban expansion, agricultural practices, and water diversion for irrigation. This study provides a robust framework for watershed management, offering scientific guidance for source management and water purification efforts, thereby contributing significantly to the sustainable development of water resources.

A hybrid approach to improvement of watershed water quality modeling by coupling process-based and deep learning models.

Watershed water quality modeling to predict changing water quality is an essential tool for devising effective management strategies within watersheds. Process-based models (PBMs) are typically used to simulate water quality modeling. In watershed modeling utilizing PBMs, it is crucial to effectively reflect the actual watershed conditions by appropriately setting the model parameters. However, parameter calibration and validation are time-consuming processes with inherent uncertainties. Addressing these challenges, this research aims to address various challenges encountered in the calibration and validation processes of PBMs. To achieve this, the development of a hybrid model, combining uncalibrated PBMs with data-driven models (DDMs) such as deep learning algorithms is proposed. This hybrid model is intended to enhance watershed modeling by integrating the strengths of both PBMs and DDMs. The hybrid model is constructed by coupling an uncalibrated Soil and Water Assessment Tool (SWAT) with a Long Short-Term Memory (LSTM). SWAT, a representative PBM, is constructed using geographical information and 5-year observed data from the Yeongsan River Watershed. The output variables of the uncalibrated SWAT, such as streamflow, suspended solids (SS), total nitrogen (TN), and total phosphorus (TP), as well as observed precipitation for the day and previous day, are used as training data for the deep learning model to predict the TP load. For the comparison, the conventional SWAT model is calibrated and validated to predict the TP load. The results revealed that TP load simulated by the hybrid model predicted the observed TP better than that predicted by the calibrated SWAT model. Also, the hybrid model reflects seasonal variations in the TP load, including peak events. Remarkably, when applied to other sub-basins without specific training, the hybrid model consistently outperformed the calibrated SWAT model. In conclusion, application of the SWAT-LSTM hybrid model could be a useful tool for decreasing uncertainties in model calibration and improving the overall predictive performance in watershed modeling. PRACTITIONER POINTS: We aimed to enhance process-based models for watershed water-quality modeling. The Soil and Water Assessment Tool-Long Short-Term Memory hybrid model's predicted and total phosphorus (TP) matched the observed TP. It exhibited superior predictive performance when applied to other sub-basins. The hybrid model will overcome the constraints of conventional modeling. It will also enable more effective and efficient modeling.

Determining Water Quality Status and Assimilation Capacity of Pollutant Loads in West Lombok Regency

Babak watershed is one of the essential watersheds in Lombok Island providing water supply for irrigation, fish pool, drinking water supply, and micro-hydro power plant. Excessive human activities degrade the water quality of Babak watershed, and more pollutants are present in the water column. Thus, this study aims to determine Babak watershed's water quality status and assimilating capacity to plan effective and holistic mitigation measures properly. The study calculates the pollution index (PI) using a formula based on the Minister of Environment No.115/2003 Decree and assimilating capacity (AC) based on the Minister of Environment No.110/2003 Decree. The results found that Babak watershed is lightly polluted, defined by exceeding chemical parameters such as DO, total phosphate, nitrite, and dissolved iron (Fe). Similarly, these parameters (total phosphate, nitrite, and Dissolved iron (Fe) also reached the maximum assimilation capacity of Babak watershed to contain and self-purify these pollutants from the water column. Thus, planning mitigation and adaptation measures are required to prevent further contaminations in this watershed.

Water quality management in a tropical karstic system influenced by land use in Chiapas, Mexico

Urbanization and agricultural activities are increasingly threatening karstic systems and the water resources they provide, which are crucial for the livelihood of rural communities. Over the past decade, the Río Grande de Comitán-Lagos de Montebello (RGC-LM) watershed in Chiapas, Mexico, has experienced significant deterioration in water quality. The objective of this study was to analyze the water quality dynamics within the RGC-LM watershed and their potential interaction with land use change. We conducted assessments of the physicochemical and microbiological parameters across the lotic and lentic systems within the watershed over eight monitoring campaigns (from 2013 to 2020). Cluster Analysis (CA) and Principal Component Analysis (PCA) statistical approaches were performed to identify the main drivers of surface water quality variability. This approach aims to enhance water quality assessments within environmental management strategies, with the goal of promoting sustainable land-use practices aimed at protecting and improving water resources within the watershed. Our findings indicate that there is a substantial difference between the upper and lower watershed water quality. The good water quality conditions observed in the higher altitude lakes (mountain lakes) can be attributed to favorable topographical features, specific land use patterns, and effective conservation measures. In contrast, the lotic system in the middle watershed and the lentic water bodies located at lower altitudes (plain lakes) exhibit signs of water quality degradation due to inefficient wastewater treatment and agricultural runoff. This study highlights the importance of implementing adequate environmental management strategies to address these water quality challenges effectively. Given the pressing issue of water quality degradation in karstic environments, we emphasize on the need for increased monitoring frequency of both physicochemical and microbiological parameters to capture seasonal variations and to further understand the vulnerability of water resources in karstic environments. Future assessment of nutrient concentrations, pesticide levels, and blue-green algae populations will also be crucial for evaluating the trophic state at a watershed scale.

Spatio-Temporal Dynamics Coupling between Land Use/Cover Change and Water Quality in Dongjiang Lake Watershed Using Satellite Remote Sensing

With rapid social and economic development, land use/land cover change (LUCC) has intensified with serious impacts on water quality in the watershed. In this study, we took Dongjiang Lake watershed as the study area and obtained measured data on water quality parameters from the watershed’s water quality monitoring stations. Based on Landsat-5, Landsat-8, or Sentinel-2 remote sensing data for multiple periods per year between 1992 and 2022, the sensitive satellite bands or band combinations of each water quality parameter were determined. The Random Forest method was used to classify the land use types in the watershed into six categories, and the area proportion of each type was calculated. We established machine learning regression models and polynomial regression models with WQI as the dependent variable and the area proportion of each land use type as the independent variable. Accuracy test results showed that, among them, the quadratic cubic polynomial regression model with grassland, forest land, construction land, and unused land as its independent variables was the best model for coupling watershed water quality with LUCC. This study’s results provide a scientific basis for monitoring spatial and temporal changes in water quality caused by LUCC in the Dongjiang Lake watershed.

Is comprehensive event sampling necessary for constraining process models of water quality? A comparison of high and low frequency phosphorus sampling programs for constraining the HYPE water quality model

Model parameter calibration is an important step in process-based watershed water quality modelling. Calibration is commonly performed using readily available water quality data from long-term monitoring programs that collect samples periodically at a relatively low frequency (i.e. monthly). Higher frequency synoptic and targeted event data is becoming available from more intensive monitoring programs, yet there remains little consensus on whether relatively lower frequency data are sufficient to constrain process-based watershed water quality models. To investigate the effects of using water quality data from differing sampling regimes for calibration, we use an implementation of HYPE (HYdrological Predictions for the Environment), a process-based watershed water quality model, in Southern Ontario, Canada. HYPE was calibrated with two stream water quality datasets, one associated with the long-term Ontario Provincial (Stream) Water Quality Monitoring Network and characterized by routine approximately monthly sampling frequency, and one associated with the Multi-Watershed Nutrient Study and characterized by semi-regular synoptic sampling during baseflow and at increased frequency (∼4 to 8 h) during event flow. Performances varied widely between sites, with validation ranges of daily predictions having Nash-Sutcliffe Efficiencies (NSE) from > −1 to 0.48 for flow and > −1 to 0.98 for total Phosphorus. Medians for daily data during validation ranged from 0.15 to 0.24 for flow and from 0.19 to 0.36 for total phosphorus. We found that model performance and simulated total phosphorus loads were similar for the two calibration datasets, potentially suggesting that the dataset with approximately monthly water quality sampling was adequate to constrain the HYPE model. We attribute this to a combination of similar levels of statistical variability in the calibration datasets and prior knowledge in the HYPE model structure. Furthermore, while model performance was similar when HYPE was calibrated with datasets of differing sampling strategies, there was a large range in model performance within each modelling domain. Results suggest that this range in performance could be due in part to poor representation of cold-weather processes in the HYPE model, as sites with higher mean annual temperature and fewer freezing days had better model performance.

Evaluation of the Best Management Practices for Reducing Phosphorus Load in a Watershed in Terms of Cost and Greenhouse Gas Emissions

Effective management of water quality in watersheds is crucial because it is directly linked to the sustainability of aquatic ecosystems. In conventional watershed management, best management practices (BMPs) have been instrumental in addressing deteriorating water quality issues caused by non-point source pollution. Greenhouse gas (GHG) emissions have emerged as a global concern, necessitating immediate and diverse environmental actions to mitigate their impacts. This study aims to explore BMPs that maximize total phosphorus (TP) load removal efficiencies, while minimizing costs and GHG emissions within watersheds, using the Soil and Water Assessment Tool (SWAT) and non-dominated sorting genetic algorithm III (NSGA-III). The Yeongsan River Watershed between 2012 and 2021 was selected as the study area. Hydrological and BMP data were analyzed. Applying identical BMPs to the watershed showed that the BMPs with high TP removal efficiency may not be effective in terms of cost and GHG emissions. Therefore, the optimal combination of BMPs for the Yeongsan River Watershed was determined using NSGA-III considering TP removal efficiency, cost, and GHG emissions. This study is the first to consider GHG emissions at the watershed level when applying BMPs and is expected to contribute to the development of BMP implementation incorporating GHG emissions.

Geological and anthropogenic contributions of metal(loid)s in the artisanal and small-scale mining-impacted Ocoña watershed of Arequipa, Peru

Strategies to resolve conflicts around water supply and quality in mining-impacted communities include a better understanding of the role and mitigation of mining activities proximal to surface water bodies. The Ocoña watershed of Arequipa (Peru) is one such region where there are conflicts between artisanal miners, fishers, and agriculture over local water resources. The aim of this study is to develop a workflow that leverages existing water quality monitoring and geological mapping to identify major processes that adversely impact water quality with a focus on the relative contribution of mining-related activities on the Ocoña watershed. This study integrates a statistical evaluation of water chemistry data, concentration-discharge relationships, and detailed mineralogy with geochemical modeling to identify sources and mechanisms of contaminant contributions to surface waters in the Ocoña watershed. This revealed that primary deposit mineralogy and seasonal variations exert direct effects on water quality in this mining-active watershed. Multivariate statistical analysis reveals two distinct hydrogeochemical signatures in Ocoña River water that likely represent natural geothermal activity in the northern portion and acid mine drainage contribution in the southern portion of the watershed. In both cases, anthropogenic activities such as mining and agricultural may increase contaminant mobilization. Interestingly, despite perceived challenges, water quality in the Ocoña watershed is better than that of neighbors potentially due to pH-controlled precipitation of Fe and resultant sorption of metals onto precipitants. However, copper released from acid mine drainage, and in particular peak concentrations during the high flow season, exert a potential for adverse ecotoxicological effects. This workflow could potentially be extrapolated to other regions with similar available data to better identify the relative contributions of natural and anthropogenic pressures to the release of toxic compounds in watersheds of concern.

Spatial scale effects of landscape patterns on non-point source pollution: A case study of Taizi River Basin in Northeast China

River water quality is influenced by natural processes and human activities. Multi-scale landscape patterns can affect river water quality by altering the generation and transport processes of pollutants at different spatial scales. Taking Taizi River Basin in Northeast China as an example, we analyzed the relationship between landscape patterns and non-point source pollution in rivers based on water quality monitoring data and land use data by using correlation analysis and redundancy analysis methods. We aimed to determine the key spatial scales for the responses of landscape patterns to non-point source pollution and identify the key landscape indices influencing river non-point source pollution. The results showed that water quality of Taizi River Basin had seasonal differences, with better water quality during the flood season than non-flood season. Spatially, total nitrogen (TN) and total phosphorus (TP) were higher at the confluence points of tributaries and downstream areas. The impact of landscape patterns on non-point source pollution was stronger during the non-flood season than the flood season, while the influence on TN was stronger than on TP. At the spatial scale of within 500 m buffer zone during the flood season and at the sub-watershed scale during the non-flood season, landscape patterns showed the highest explanatory power for the variations of TN and TP. At the type level, built-up land, cropland, and bare land were positively correlated with TN and TP, while forest was negatively correlated with TN and TP, which were the key types influencing non-point source pollution. At the landscape level, patch density, percentage of like adjacencies, and contagion index were key indicators affecting watershed water quality. Lower patch density was associated with better connectivity and aggregation of "sink" landscapes, leading to better purification effects on TN, but more pronounced retention effects on TP. Conversely, higher landscape diversity and denser pattern of multiple types would cause the deterioration of water quality. Our results suggested that rational allocation of landscape types within the watershed and riparian buffer zones, appropriately enriching landscape diversity, and optimizing landscape aggregation and connectivity would be effective measures for improving water quality and achieving sustainable ecological management.

Nutrient transformations based on sampling scheme and cropping system following subsurface‐banded poultry litter

AbstractPoultry litter (PL) is an excellent source of micro‐ and macronutrients. However, surface applications result in greater nutrient runoff and nitrogen loss via ammonia volatilization. Subsurface banding PL is a promising technology for combating these challenges, but scant information exists on proper soil sampling techniques and management recommendations for subsurface‐applied PL. Therefore, objectives were to quantify the nutrient status based on sampling depth (0–15 cm and 15–30 cm) and schema (systematic [0, 3, 7, and 10 cm from PL bands] and composite) to develop subsurface PL recommendations per system (annual cropping and perennial pasture). Soil samples were collected during PL application (subsurface and surface) and 1, 6, 12, 18, and 24 months thereafter. On average, total N, Mehlich‐3 extractable P, and Mehlich‐3 extractable K were 15%, 96%, and 72% greater, respectively, for subsurface compared to surface applications. Further, Mehlich‐3 and water‐soluble P at the 0‐ to 15‐cm depth were 4–5 and 2–3 times greater in soils receiving subsurface PL in perennial pasture and row crop systems, respectively, compared to surface applications, likely owing to lesser nutrient losses to the air, soil, and water under subsurface PL systems. Compared to surface applications, subsurface PL increased (p < 0.05) N, P, and K crop removal by 75%, 70%, and 72%, respectively, and resulted in 80% and 78% yield increases and N‐use efficiency, respectively. Consequently, subsurface PL conserved greater N, P, and K at the 0‐ to 15‐cm depth, thus increasing nutrient‐use efficiency in row crop systems and improving water quality in sensitive watersheds.

Spatial Correlations between Nitrogen Budgets and Surface Water and Groundwater Quality in Watersheds with Varied Land Covers

Anthropogenic nitrogen (N) inputs can have detrimental environmental effects, necessitating a comprehensive understanding of the nitrogen budget (NB) and its spatial correlation with the water quality. This study, utilizing a 2016 dataset, scrutinized 850 subwatersheds with diverse land covers across the Republic of Korea (ROK). Employing Geographically Weighted Regression (GWR), it examined the spatial correlations between the NBs and the quality of the groundwater and river water at the watershed scale. Robust correlations (R2 = 0.87) were observed between the groundwater quality and NBs, surpassing those of the surface water (R2 = 0.48). Sensitivity analyses highlighted the importance of high-resolution spatial data in capturing nuances within complex land covers. The integration of such data led to increases in the spatial correlations between the groundwater and river water quality of approximately 0.6–0.9 and 0.3–0.5, respectively. Notably, when the agricultural land cover exceeded 10%, significant enhancements in the spatial correlations were observed, emphasizing the pivotal role of agriculture in nutrient and water quality. At a 10% cropland ratio, the spatial correlations between the watershed-scale NBs and river/groundwater quality increased by approximately 76% and 501%, respectively. This study provides novel insights into the spatial relationships among NBs, water quality, and land use, highlighting the significance of high-resolution data and the impact of agricultural practices on watershed management. These findings contribute valuable information for developing strategies to mitigate nitrogen pollution.

Assessing ammonium pollution and mitigation measures through a modified watershed non-point source model

Watershed water quality modeling is a valuable tool for managing ammonium (NH4+) pollution. However, simulating NH4+ pollution presents unique challenges due to the inherent instability of NH4+ in natural environment. This study modified the widely-used Soil and Water Assessment Tool (SWAT) model to simulate non-point source (NPS) NH4+ processes, specifically incorporating the simulation of land-to-water NH4+ delivery. The Jiulong River Watershed (JRW) is the study area, a coastal watershed in Southeast China with substantial sewage discharge, livestock farming, and fertilizer application. The results demonstrate that the modified model can effectively simulate the NPS NH4+ processes. It is recommended to use multiple sets of observations to calibrate NH4+ simulation to enhance model reliability. Despite constituting a minor proportion (5.6 %), point source inputs significantly contribute to NH4+ load at watershed outlet (32.4∼51.9 %), while NPS inputs contribute 15.3∼17.3 % of NH4+ loads. NH4+ primarily enters water through surface runoff and lateral flow, with negligible leaching. Average NH4+ land-to-water delivery rate is about 2.35 to 2.90 kg N/ha/a. High delivery rates mainly occur at agricultural areas. Notably, proposed NH4+ mitigation measures, including urban sewage treatment enhancement, livestock manure management improvement, and fertilizer application reduction, demonstrate potential to collectively reduce the NH4+ load at watershed outlet by 1/4 to 1/3 and significantly enhance water quality standard compliance frequency. Insights gained from modeling experience in the JRW offer valuable implications for NH4+ modeling and management in regions with similar climates and significant anthropogenic nitrogen inputs.

Impacts of irrigation development on water quality in the San Salvador watershed (Part 2)

Intensive agricultural activities pose a significant threat to water quality as critical non-point sources of pollution. Effective mitigation strategies demand understanding the causes and processes of water pollution. This study aimed to quantify the impacts of irrigation development on water quality and assess best management practices for sustainable agriculture intensification. Employing the calibrated SWAT model for the San Salvador watershed (baseline scenario), two scenarios were implemented and evaluated: the first one depicted irrigation development from a future reservoir, and the second integrated riparian buffer zones to minimize nutrient and sediment losses. Notably the baseline scenario did not achieve nutrient water quality objectives. Results revealed that irrigation development increases nutrient yields, driving the future reservoir toward eutrophication. Implementing riparian buffer zones reduced nutrient loss, but additional measures are necessary for sustainable environmental goals at the basin scale. This research contributes with valuable insights for formulating effective management strategies to minimize nutrient pollution in water and safeguard water quality and biodiversity in the basin.

Impacts of irrigation development on water quality in the San Salvador watershed (Part 1)

The development of irrigation involves a change in land use and management and has implications for water quality and quantity. It is critical to design conservation practices and best management practices consistent with sustainable agricultural intensification. The objective of this work was to understand and characterize key processes affecting hydrology, nutrient export and transport, and quantify impacts in the San Salvador watershed. For such purpose, the Soil & Water Assessment Tool (SWAT) was implemented, calibrated for water quantity, and water quality was adjusted using soft calibration techniques. The model reproduces water quantity and nutrient balance, and aids in characterizing the nutrient delivery and transport in the watershed. The magnitude of runoff affects the balance of nutrients. In high flows, diffuse sources are more prevalent, while in low flows, point sources and direct livestock manure to the river are more significant. The main outcomes of this work contribute to the design of strategies to achieve sustainable agricultural intensification. It also describes a new modeling tool freely available that could be used in further studies.

Water quality monitoring of welang river basin

The Welang watershed, located in Malang Regency and Pasuruan Regency, has diverse human activities that can affect watershed hydrology, one of which is related to water quality. The purpose of this research is to determine the current water quality of the Welang watershed. This research was conducted in March 2023 - June 2023, which was conducted using random purposive samples with parameters of sunlight intensity, humidity, air temperature, water temperature, pH, TDS, brightness, chlorine, bromine, lead, nitrate, nitrite, fluoride, iodine, cyanuric acid, carbonate root, and total alkalinity. From this study, it was obtained that the upstream part of the Welang River on the Arjuno side and Bromo side contained Chlorine (Cl), Bromine (Br), Iron (Fe), Lead (Pb), Nitrate (NO3-), Nitrite (NO2-), Fluoride (F-), and Carbonate Root (CO3 2–). Meanwhile, the middle part of the Welang River dominantly has the same content, but no Iron (Fe) was found but instead Cyanuric Acid C3N3(OH)3. Meanwhile, the downstream part of the Welang River contained Chlorine (Cl), Copper (Cu), Lead (Pb), and Fluoride (F-).

Assessing wet season water quality and health risks of Chaobai River: impacts of natural weathering and anthropogenic activities in a small urban watershed

ABSTRACT Small urban watersheds are susceptible to external influences, facing increased solute inputs during high-flow periods without sufficient dilution. This study determined the sources of major ions in the Chaobai River, which is the essential surface water source of North China. The dominant river water type was HCO3-Ca·Mg based on the Piper diagram. PCA and ion ratios revealed that natural weathering (evaporate dissolution, carbonate, and silicate weathering by carbonic and sulfuric acid) and anthropogenic activities (urban domestic sewage and industrial effluent) were the dominant factors affecting the chemical composition. Sodium adsorption ratio (SAR), soluble sodium percentage (Na%), and residual sodium carbonate (RSC) assessments indicated potential sodium and moderate salinity hazards. Meanwhile, the non-carcinogenic health risks of riverine solutes evaluated by health risk assessment need to be noticed, particularly for children. The limitation of urban sewage discharge is crucial for improving water quality in urban watershed and needs further studies.