Characterizing the impacts of land use on nitrate load and water yield in an agricultural watershed in Atlantic Canada

Seasonal dynamics of dissolved inorganic nitrogen in groundwater: Tracing environmental controls and land use impact

High levels of dissolved inorganic nitrogen (DIN) in groundwater pose challenges for regions like northern Anhui Province, China, where groundwater is a crucial domestic resource. This study utilized modern geostatistics to explore the spatial and temporal dynamics of DIN in groundwater. Significant seasonal influences on DIN concentrations were identified: ammonium peaks during wet season driven by agricultural activities, while nitrate peaks during the dry season primarily influenced by municipal inputs. This study established a Bayesian Maximum Entropy - Random Forest (BME-RF) model based on Land Use/Land Cover data to infer the spatio-temporal performance of DIN, achieving an interpretation rate above 90 %. It also highlighted the role of hydrogeological conditions and aquifer types in the evolution of DIN. By employing a DIN environmental interaction model, it further analyzed the eco-hydrological drivers and seasonal trends affecting DIN variability, enhancing the understanding of groundwater nitrogen dynamics and their link to environmental factors with low consumption. SYNOPSIS: This study reveals seasonal shifts in groundwater DIN, links them to human activity, and uses the BME model to guide targeted nitrogen fluctuation.

Comparative efficiency of the SWAT model and a deep learning model in estimating nitrate loads at the Tuckahoe creek watershed, Maryland

Modeling nitrate fate and transport in water sources is an essential component of predictive water quality management. Both mechanistic and data-driven models are currently in use. Mechanistic models, such as SWAT, simulate daily nitrate loads based on the results of simulating water flow. Data-driven models allow one to simulate nitrate loads and water flow independently. Performance of SWAT and deep learning model was evaluated in cases when deep learning model is used in (a) independent simulations of flow series and nitrate concentration series, and (b) in both flow rate and concentration simulations to obtain nitrate load values. The data were collected at the Tuckahoe Creek watershed in Maryland, United States. The data-driven deep learning model was built using long-short-term-memory (LSTM) and three-dimensional convolutional networks (3D Convolutional Networks) to simulate flow rate and nitrate concentration using weather data and imagery to derive leaf area index according to land use. Models were calibrated with data over training period 2014-2017 and validated with data over testing period. SWAT Nash-Sutcliffe efficiency (NSE) was 0.31 and 0.40 for flow rate and -0.26 and -0.18 for the nitrate load rate over training and testing periods, respectively. Three data-driven modeling scenarios were implemented: (1) using the observed flow rate and simulated nitrate concentration, (2) using the simulated flow rate and observed nitrate concentration, and (3) using the simulated flow rate and nitrate concentration. The deep learning model performed better than SWAT in all three scenarios with NSE from 0.49 to 0.58 for training and from 0.28 to 0.80 for testing periods with scenario 1 showing the best results. The difference in performance was most pronounced in fall and winter seasons. The deep learning modeling can be an efficient alternative to mechanistic watershed-scale water quality models provided the regular high-frequency data collection is implemented.

A machine learning framework for spatio-temporal vulnerability mapping of groundwaters to nitrate in a data scarce region in Lenjanat Plain, Iran

The temporal aspect of groundwater vulnerability to contaminants such as nitrate is often overlooked, assuming vulnerability has a static nature. This study bridges this gap by employing machine learning with Detecting Breakpoints and Estimating Segments in Trend (DBEST) algorithm to reveal the underlying relationship between nitrate, water table, vegetation cover, and precipitation time series, that are related to agricultural activities and groundwater demand in a semi-arid region. The contamination probability of Lenjanat Plain has been mapped by comparing random forest (RF), support vector machine (SVM), and K-nearest-neighbors (KNN) models, fed with 32 input variables (dem-derived factors, physiography, distance and density maps, time series data). Also, imbalanced learning and feature selection techniques were investigated as supplementary methods, adding up to four scenarios. Results showed that the RF model, integrated with forward sequential feature selection (SFS) and SMOTE-Tomek resampling method, outperformed the other models (F1-score: 0.94, MCC: 0.83). The SFS techniques outperformed other feature selection methods in enhancing the accuracy of the models with the cost of computational expenses, and the cost-sensitive function proved more efficient in tackling imbalanced data issues than the other investigated methods. The DBEST method identified significant breakpoints within each time series dataset, revealing a clear association between agricultural practices along the Zayandehrood River and substantial nitrate contamination within the Lenjanat region. Additionally, the groundwater vulnerability maps created using the candid RF model and an ensemble of the best RF, SVM, and KNN models predicted mid to high levels of vulnerability in the central parts and the downhills in the southwest.

Assessing watershed-scale impacts of best management practices and elevated atmospheric carbon dioxide concentrations on water yield

Changes in water yield are influenced by many intersecting biophysical elements, including climate, on-land best management practices, and landcover. Large-scale reductions in water yield may present a significant threat to water supplies globally. Many of these intersecting factors are intercorrelated and confounded, making it challenging to separate the factors' individual contributions to shaping local streamflow dynamics. Comprehensive hydrological models constructed based on a well-established understanding of biophysical processes are often employed to address these matters. However, these models rarely incorporate all relevant factors influencing local hydrological processes, due to the reliance of these models on the latest, albeit limited, state-of-the-art research. For instance, complexities inherent in watershed hydrology, which involve multilayered interactions among potentially many biophysical factors, leave the direct analysis of subtle impacts on water yields measured in-situ largely intractable. Therefore, we propose an innovative approach to assess impacts of elevated atmospheric CO2 concentrations and flow diversion terraces (FDTs) on stream discharge rates at the watershed scale. Initially, we use a comprehensive hydrological model to account for the impacts of major climatic and landuse/landcover factors on changes in field-acquired measurements of water yield. Next, we employ conventional and advanced statistical methods to decompose the residuals of model predictions to facilitate the identification of subtle influences promoted by increases in atmospheric CO2 concentrations and the application of FDTs in an agriculture-dominated watershed. Through this innovative approach, we find that FDTs contributed to a watershed-wide, net water-yield reduction of 188.0 mm (or 28.9 %) from 1992 to 2014. Ongoing increases in ambient CO2 concentrations, which are responsible for an overall reduction in a watershed-level assessment of stomatal conductance, have led to a minor increase in stream discharge rates during the same 23-year period, i.e., 0.45 mm of water yield per year, or 1.6 % overall. Streamflow reductions explicitly caused by regional warming in the area alone, on account of increased evapotranspiration, may be overestimated due to the opposing, synergistic effects on water yield associated with CO2-enrichment of the lower atmosphere and the annual application of FDTs.

Application of classification machine learning algorithms for characterizing nutrient transport in a clay plain agricultural watershed

Excess nutrients in surface water and groundwater can lead to water quality deterioration in available water resources. Thus, the classification of nutrient concentrations in water resources has gained significant attention during recent decades. Machine learning (ML) algorithms are considered an efficient tool to describe nutrient loss from agricultural land to surface water and groundwater. Previous studies have applied regression and classification ML algorithms to predict nutrient concentrations in surface water and/or groundwater, or to categorize an output variable using a limited number of input variables. However, there have been no studies that examined the application of different ML classification algorithms in agricultural settings to classify various output variables using a wide range of input variables. In this study, twenty-four ML classification algorithms were implemented on a dataset from three locations within the Upper Parkhill watershed, an agricultural watershed in southern Ontario, Canada. Nutrient concentrations in surface water were classified using geochemical and physical water parameters of surface water and groundwater (e.g., pH), climate and field conditions as the input variables. The performance of these algorithms was evaluated using four evaluation metrics (e.g., classification accuracy) to identify the optimal algorithm for classifying the output variables. Ensemble bagged trees was found to be the optimal ML algorithm for classifying nitrate concentration in surface water (accuracy of 90.9%), while the weighted KNN was the most appropriate algorithm for categorizing the total phosphorus concentration (accuracy of 87%). The ensemble subspace discriminant algorithm gave the highest overall classification accuracy for the concentration of soluble reactive phosphorus and total dissolved phosphorus in surface water with an accuracy of 79.2% and 77.9%, respectively. This study exemplifies that ML algorithms can be used to signify exceedance of recommended concentrations of nutrients in surface waters in agricultural watersheds. Results are useful for decision makers to develop nutrient management strategies.

Simulating agroecosystem soil inorganic nitrogen dynamics under long-term management with an improved SWAT-C model

Despite the extensive application of the Soil and Water Assessment Tool (SWAT) for water quality modeling, its ability to simulate soil inorganic nitrogen (SIN) dynamics in agricultural landscapes has not been directly verified. Here, we improved and evaluated the SWAT-Carbon (SWAT-C) model for simulating long-term (1984-2020) dynamics of SIN for 40 cropping system treatments in the U.S. Midwest. We added one new nitrification and two new denitrification algorithms to the default SWAT version, resulting in six combinations of nitrification and denitrification options with varying performance in simulating SIN. The combination of the existing nitrification method in SWAT and the second newly added denitrification method performed the best, achieving R, NSE, PBIAS, and RMSE of 0.63, 0.29, -4.7 %, and 16.0 kg N ha^-1, respectively. This represents a significant improvement compared to the existing methods. In general, the revised SWAT-C model's performance was comparable to or better than other agroecosystem models tested in previous studies for assessing the availability of SIN for plant growth in different cropping systems. Sensitivity analysis showed that parameters controlling soil organic matter decomposition, nitrification, and denitrification were most sensitive for SIN simulation. Using SWAT-C for improved prediction of plant-available SIN is expected to better inform agroecosystem management decisions to ensure crop productivity while minimizing the negative environmental impacts caused by fertilizer application.

A deep learning-based novel approach to generate continuous daily stream nitrate concentration for nitrate data-sparse watersheds

High-frequency stream nitrate concentration provides critical insights into nutrient dynamics and can help to improve the effectiveness of management decisions to maintain a sustainable ecosystem. However, nitrate monitoring is conventionally conducted through lab analysis using in situ water samples and is typically at coarse temporal resolution. In the last decade, many agencies started collecting high-frequency (5-60 min intervals) nitrate data using optical sensors. The hypothesis of the study is that the data-driven models can learn the trend and temporal variability in nitrate concentration from high-frequency sensor-based nitrate data in the region and generate continuous nitrate data for unavailable data periods and data-limited locations. A Long Short-Term Memory (LSTM) model-based framework was developed to estimate continuous daily stream nitrate for dozens of gauge locations in Iowa, USA. The promising results supported the hypothesis; the LSTM model demonstrated median test-period Nash-Sutcliffe efficiency (NSE) = 0.75 and RMSE = 1.53 mg/L for estimating continuous daily nitrate concentration in 42 sites, which are unprecedented performance levels. Twenty-one sites (50 % of all sites) and thirty-four sites (76 % of all sites) demonstrated NSE > 0.75 and 0.50, respectively. The average nitrate concentration of neighboring sites was identified as a crucial determinant of continuous daily nitrate concentration. Seasonal model performance evaluation showed that the model performed effectively in the summer and fall seasons. About 26 sites showed correlations >0.60 between estimated nitrate concentration and discharge. The concentration-discharge (c-Q) relationship analysis showed that the study watersheds had four dominant nitrate transport patterns from landscapes to streams with increasing discharge, including the flushing pattern being the most dominant one. Stream nitrate estimation impedes due to data inadequacy. The modeling framework can be used to generate temporally continuous nitrate at nitrate data-limited regions with a nearby sensor-based nitrate gauge. Watershed planners and policymakers could utilize the continuous nitrate data to gain more information on the regional nitrate status and design conservation practices accordingly.

Urbanization and agriculture intensification jointly enlarge the spatial inequality of river water quality

Rivers are severely polluted by multiple anthropogenic stressors. An unevenly distributed landscape pattern can aggravate the deterioration of water quality in rivers. Identifying the impacts of landscape patterns on the spatial characteristics of water quality is helpful for river management and water sustainability. Herein we quantified the nationwide water quality degradation in China's rivers and analyzed its responses to spatial patterns of anthropogenic landscapes. The results showed that the spatial patterns of river water quality degradation had a strong spatial inequality and worsened severely in eastern and northern China. The spatial aggregation of agricultural/urban landscape and the water quality degradation exhibits high consistency. Our findings suggested that river water quality would further deteriorate from high spatial aggregation of cities and agricultures, which reminded us that the dispersion of anthropogenic landscape patterns might effectively alleviate water quality pressures.

Identifying the impact of land use land cover change on streamflow and nitrate load following modeling approach: a case study in the upstream Dong Nai River basin, Vietnam

Tri An Reservoir is a vital source of water for agriculture, industry, hydropower, and public usage in Southern Vietnam. Due to human activities, water eutrophication has become a serious problem in recent decades. This study investigated for the first time the impact of land use and land cover (LULC) change on streamflow and nitrate load from the upstream Dong Nai River basin, which is the largest watershed of the reservoir. The study utilized several LULC scenarios, including LULC 2000, 2010, and 2020. The SWAT model was applied to model the watershed during the period 1997-2009. Results showed that the hydrological model performed satisfactorily based on the Nash-Sutcliffe efficiency (NSE) coefficient, the root mean square error observations standard deviation ratio (RSR), and the percent bias (PBIAS). The average simulated values of monthly streamflow and nitrate load were 453.7, 450.0, 446.7 m³/s and 17,699.43, 17,869.13, 17,590.81 tonnes for the LULC 2000, 2010, and 2020 scenarios, respectively. There were no significant differences in streamflow and nitrate load at the basin level under the different LULC scenarios. However, when looking at the subbasin level, there were differences in nitrate load among the scenarios. This suggests that the impacts of LULC on nitrate load may be more pronounced at smaller scales. Overall, our finding underscores the importance of modeling techniques in predicting the impacts of LULC change on streamflow and water quality, which can ultimately aid in the sustainable management of water resources.

Evaluation of Nitrogen Fertilizer Fates and Related Environmental Risks for Main Cereals in China's Croplands from 2004 to 2018

Assessment of the nitrogen (N) inputs and outputs in croplands would help effectively manage the distribution of N to improve crop growth and environmental sustainability. To better understand the N flow of the main cereal systems in China, soil N balance, N use efficiency (NUE), N losses and the potential environmental impacts of maize, wheat and rice cropping systems were estimated at the regional and national scales from 2004 to 2018. Nationally, the soil N balance (N inputs-N outputs) of maize, wheat, single rice and double rice decreased by 28.8%,13.3%, 30.8% and 34.1% from 2004-2008 to 2014-2018, equivalent to an average of 33.3 to 23.7 kg N ha-1, 82.4 to 71.4 kg N ha-1, 93.6 to 64.8 kg N ha-1 and 51.8 to 34.1 kg N ha-1, respectively. The highest soil N balance were observed in Southeast (SE) region for maize and double rice, North central (NC) region for wheat single rice and Northwest region for wheat, whereas Northeast (NE) region had the lowest N balance for all crops. The NUE increased from 49.8%, 41.2%, 49.7% and 53.7% in 2004-2008 to 54.8%, 45.9%, 55.5% and 56.5% in 2014-2018 for maize, wheat, single rice and double rice, respectively. The fertilizer N losses (i.e., N2O emission, NO emission, N2 emission, NH3 volatilization, N leaching and N runoff) were estimated as 43.7%, 38.3%, 40.2% and 36.6% of the total N inputs for maize, wheat, single rice and double rice, respectively in 2014-2018. Additionally, the highest global warming potential and acidification effects were found in NE and NC regions for maize, NC region for wheat, the middle and lower reaches of Yangtze River for single rice and SE region for double rice, respectively. The highest risk of water contamination by N leaching and surface runoff was observed in NC region for all crops mainly due to high N fertilizer input. Furthermore, the dynamics of N balance for all crops were closely tied with grain yields, except for single rice, the N balance of which was mainly correlated with N fertilizer input. Our results could help researchers and policy makers effectively establish optimized fertilization strategies and adjust the regional allocation of grain cropping areas in response to environmental risks and climate change caused by food crop cultivation in China.

Quantifying the contribution of direct runoff and baseflow to nitrogen loading in the Western Lake Erie Basins

Soluble nitrogen is highly mobile in soil and susceptible to leaching. It is important to identify nitrogen transport pathways so that the sources can be efficiently targeted in environment management. This study quantified the contribution of direct runoff and baseflow to nitrate + nitrite loading by separating flow and nitrate + nitrite concentration measurements into two periods depending on whether only baseflow was present or not using baseflow separation methods. When both direct runoff and baseflow were present in streamflow, their nitrate + nitrite concentrations were assumed based on the hydrological reasoning that baseflow does not change rapidly, and streamflow mostly consists of direct runoff within a rainfall event. For this study, we obtained and investigated daily flow and nitrate + nitrite concentration observations made at the outlets of 22 watersheds located in the Western Lake Erie area. Results showed that baseflow was responsible for 26 to 77% of the nitrate + nitrite loads. The relative nitrate + nitrite load contributions of direct runoff and baseflow substantially varied with the sizes of drainage areas and agricultural land uses. Increases in drainage areas tend to prolong the travel time of surface runoff and thus help its reinfiltration into soil, which then could increase the baseflow contribution. In addition, the artificial drainage networks common in the agricultural fields of the study areas would promote the drainage of nutrient-laden excess water from soils. Such findings suggest the need for environmental management customized considering nitrogen transport pathways.

Impact assessment of on-site swine wastewater treatment facilities on spatiotemporal variations of nitrogen loading in an intensive livestock farming watershed

Excess nitrogen (N) resulting from human activity causes environmental issues, including eutrophication in agricultural watersheds with intensive livestock farming. Among the N sources in Korea, on-site swine wastewater treatment facilities (OSWTFs) tend to be densely distributed in watersheds with intensive livestock farming. Therefore, it is critical to sustainably manage livestock excreta. This study used the Soil and Water Assessment Tool (SWAT) to investigate the effects of various pollution sources, including OSWTFs, on N loads in rivers in the Cheongmi watershed, which is an intensive livestock farming and agricultural area in Korea. The simulated hydrological and water quality outputs were calibrated and validated for 2012-2019 using Sequential Uncertainty Fitting ver. 2 in the SWAT-Calibration and Uncertainty Program. The hydrological simulations agreed with the observations, with a correlation coefficient (R²) of ≥ 0.8 and Nash-Sutcliffe coefficient of 0.67-0.86. The simulated total N (TN) was also strongly correlated with the observed monthly average loading (R², 0.36-0.73) and annual average concentration (R² ≥ 0.5), demonstrating the reliability of the model constructed herein. A simulation of management scenarios indicates that, if the permissible N concentration in effluent from OSWTFs was reduced to 60 mg N/L, the TN concentrations in rivers would decrease by up to 50%. The findings of this study indicate that more stringent effluent water quality standards are required for OSWTFs to protect water quality and aquatic ecosystems in intensive swine farming watersheds.

Long-term water quality assessments under changing land use in a large semi-arid catchment in South Africa

Increasing nutrient loads from land use and land cover (LULC) change degrade water quality through eutrophication of aquatic ecosystems globally. The Vaal River Catchment in South Africa is an agriculturally and economically important area where eutrophication has been a problem for decades. Effective mitigation strategies of eutrophication in this region require an understanding of the relationship between LULC change and water quality. This study assessed the long-term impacts of LULC changes on nitrate (NO₃-N) and orthophosphate (PO₄-P) pollution in the lower Vaal River Catchment between 1980 and 2018. Multi-year LULC was mapped from Landsat imagery and changes were determined. Long-term trends in NO₃-N and PO₄-P loads and concentrations in river water samples were analysed, while multi-year LULC data were ingested into the Soil and Water Assessment Tool (SWAT) to simulate the impacts of LULC changes in NO₃-N and PO₄-P loads. Main LULC changes included an increase in the irrigated area by 262% and in built-up area by 33%. This occurred at the expense of cultivated dryland fields and rangelands. In situ data analysis showed that at the catchment inlet, PO₄-P concentration and loads significantly increased, while NO₃-N concentration and loads decreased between 1980 and 2018. At the catchment outlet, only PO₄-P loads increased, while NO₃-N loads and concentrations remained the same. SWAT simulations at the Hydrologic Response Unit scale showed that irrigated land was the largest contributor to NO₃-N leaching per ha. Aggregation of nutrient loads by LULC type showed increased nutrient loads from irrigated and built-up areas over time, while loads from dryland areas decreased. At catchment scale, dryland remained an important contributor of the annual nutrient loads total because of its large area. In future, research efforts should focus on crop management practices to reduce nutrient loads.

Effects of climate and land use changes on water quantity and quality of coastal watersheds of Narragansett Bay

Land use is rapidly changing in coastal watersheds, with implications on eutrophication of coastal watersheds. The long-term consequences of climate change on these impacts are critical to watershed management. With coastal watersheds facing frequent hypoxic events and cultural eutrophication, the coupled influence of land use and climate change can lead to policies under nonstationarity assumptions. This study aims to model a regional coastal watershed system using a dynamic simulation with future land use and climate stressors for watershed sustainability. The efficacy of current nutrient management efforts may be limited or undone if future changes in climate or land use increase nutrient and sediment loads to the Narragansett Bay. The baseline model was calibrated and validated to accurately reflect watershed processes to simulate water quantity and quality under the independent and combined influence of future climate and land scenarios. Results show significant effects of climate change and land-use change on the watershed, with demonstrated impacts on sediment loading, organic N, organic P, and nitrates. Climate impacts were much more significant than land-use effects, but land-use impacts displayed greater regional variation. The results from combined simulations indicate that future climate and land-use change will likely negatively impact the coastal system and need restoration efforts that consider nonstationarity. However, the results also highlight the potential to utilize land use to mitigate and adapt to climate change impacts.

Impact of Land Use Changes on the Surface Runoff and Nutrient Load in the Three Gorges Reservoir Area, China

Dramatic changes in land use/cover (LULC) patterns have taken place in the Three Gorges Reservoir Area (TGRA) after the construction of the Three Gorges Dam, which have led to hydrological and environment alterations. In this study, eight land use scenarios from 1980 to 2018 were used to evaluate the impact of LULC changes on runoff and nutrient load in the TGRA, using a validated version of the Soil & Water Assessment Tool (SWAT) model. Firstly, we analyzed the LULC characteristic. During the 38-year period, the LULC pattern showed an increase in forestland and a decrease in cropland. The cropland mainly changed into forestland. Construction land realized growth by encroaching mainly on cropland and forestland. Secondly, the temporal–spatial characteristics of runoff and nutrient load were analyzed. In the TGRA, surface runoff and nutrient load exhibited significant tempo-spatial heterogeneity. The runoff depth and the total nitrogen (TN) and total phosphorus (TP) loads increased through 1980 to 2018, and 2005 was a turning point. After 2005, the annual average change rate was larger than before 2005. The area with a larger runoff depth was mainly distributed in the head and middle region as well as on the left bank of the TGRA. The middle and tail region of the TGRA generated relatively higher TN and TP loads. Lastly, the contributions of LULC types on runoff and nutrient load were explored. Forestland had the highest contribution rate to surface runoff, followed by cropland. Cropland had the highest contribution rate to TN and TP, follow by forestland. This study can provide a better understanding of the hydrological consequences of LULC changes and help watershed management in the TGRA.

Comparative health risk assessment of nitrate in drinking groundwater resources of urban and rural regions (Isfahan, Iran), using GIS

Infantile methemoglobinemia, thyroid disorders, and probably some carcinogenic effects are health concerns associated with dietary nitrate. Isfahan province has a dry and semi-arid desert climate such that the main source of various applications in this province is groundwater resources. This study evaluated spatial analysis of the groundwater NO₃^- concentrations and its possible health risk to residents. Method 8171 Hach was used for nitrate measurement of 1319 groundwater samples from March 2018 to February 2019. Non-carcinogenic risk due to NO₃^- exposure through consumption of drinking water was assessed, and the associated zoning maps were presented using geographic information system (GIS). Nitrate concentrations in the rural and urban areas were within 0.4-137 mg/L NO₃^- and 2.9-209 mg/L NO₃^-, respectively. Also, 226 (25%) and 104 (24%) of samples in the rural and urban areas, respectively, were detected above the Iran and WHO guideline NO₃^- values of 50 mg/L. The highest levels of NO₃^-, which were found in the western and central groundwater resources, occurred in the agricultural and residential areas. The NO₃^- concentrations were higher in urban than rural areas in the many studied counties. Also, nitrate was higher in wet seasons than in dry ones. Infants' non-carcinogenic risks were higher than the other groups. Infants (HQ > 1) were the most vulnerable group compared with the other groups in some counties. Thus, there are potential risks of methemoglobinemia, especially for infants. It is critical to adopt specific strategies to reduce the nitrate concentration in the studied groundwater.

Three decades of regulation of agricultural nitrogen losses: Experiences from the Danish Agricultural Monitoring Program

Excess nitrogen (N) losses from intensive agricultural production are a world-wide problem causing eutrophication in vulnerable aquatic ecosystems such as estuaries. Therefore, Denmark as one of the most intensively farmed countries in the world has enforced mandatory regulations on agricultural production since the late 1980s. We demonstrate the outcome of the regulations imposed on agriculture by analyzing decadal trends in nitrate (NO₃^-) concentrations and loads in streams using 29 years of detailed monitoring data and survey information on agricultural practices at field level from five intensively cultivated headwater catchments. The analysis includes the importance of four main drivers (climate, land use, agricultural practices, and biogeophysical properties of catchments), each divided into different factors that may influence stream NO₃^- loads during three subperiods defined by the time of introduction of different mitigation measures: i) 1990-1998, ii) 1999-2007, and iii) 2008-2018. Significant correlations with annual flow-weighted stream NO₃^- concentrations and/or loads were found for factors representing all of the four main drivers including precipitation, large scale climate fluctuations, runoff, previous year's runoff, baseflow index, number of annual frost days, agricultural area, livestock density, field N surplus, catch crop cover, manure storage capacity, method and time of manure spreading, and time of soil tillage. Changes in the four drivers were reflected by the load-runoff (L-Q) relationships for each of the three subperiods within each of the five headwater catchments. The five catchments experienced large but catchment-specific downward shifts in the L-Q relationship attributable to changes in land use and agricultural management within the catchments. The documented large downward shifts in NO₃^- loads demonstrated for the five catchments (30-52%) as a consequence of mandatory regulation over a period of nearly three decades are a unique example of how agriculture can reduce its environmental impact.

Spatial-temporal analysis and background value determination of molybdenum concentration in basins with high molybdenum geochemical background - A case study of the upper Yi River basin

The environmental background value of the river section is important. It can be used to evaluate the effect of pollution control of the upstream of that river section, analyze the trend of environmental pollution, and assist the government to make decisions. Yi river is the main tributary of the Yellow River. In the headwaters of the Yi river, there are two very large molybdenum mines with a history of mining and smelting of many years. This area is also a region with a high molybdenum geochemical background. Using the collected regional molybdenum geochemical map, historical monitoring data, sampling data, remote sensing image, and spatial information of mineral enterprises, we found two reasons of why the molybdenum concentration is unusual in the basin. The first reason is the area is a high molybdenum region. The second reason is that the inherent solubility of molybdenum in the soil is changed due to human engineering activities. In this paper, we did a linear fitting on the soil samples and water samples collected from the natural areas and areas affected by human mining activities, and established a leaching model. By comparing the leaching capability of molybdenum in the soil of different areas, we found that the molybdenum release capability in areas affected by human mining was much higher than that in natural areas. Finally, this paper proposed a method to analyze the contribution rate of molybdenum concentration of this river section, using a combination of the leaching model and the D8 algorithm. The experimental results showed that the contribution rate of natural factors and human influence factors at the exit section of Yi River was 81.38% and 18.62% respectively. The background molybdenum concentration in this section was 0.16 mg/L.

Cleaner agricultural production in drinking-water source areas for the control of non-point source pollution in China

With continuous population growth and acceleration of urbanization in China, environmental problems in drinking-water source areas have become increasingly prominent. In some places, domestic wastewater and aquaculture sewage are directly discharged into water bodies without any treatment. Also, large amounts of domestic garbage and aquaculture waste are often randomly stacked, seriously polluting the surrounding groundwater and surface water and deteriorating the water quality. Notably, some agricultural production activities can also cause non-point source pollution, resulting from eutrophication of water bodies. In some instances, these activities can lead to nitrogen losses of 0.7%-83.9% and phosphorus losses of 0.6%-82.8%. In view of this situation, the implementation of cleaner agricultural production is of great significance for protecting the environment in drinking-water source areas and maintaining drinking-water safety. Specific practicable measures include formula fertilization through soil testing, integrated pest management, and water-saving irrigation technology. For the livestock- and poultry-breeding industry, it is necessary for large-scale farms to construct excreta discharge treatment facilities, carry out harmless treatment and resource utilization of organic wastes, establish rural biogas septic tanks, and make use of domestic-sewage and livestock-breeding wastewaters. Also, fixed garbage-dumping sites should be built in rural water-source areas, and a unified garbage-disposal station set up to reduce the pollution discharge of domestic garbage. Moreover, it is crucial to strictly control the development and utilization of hillsides in the middle and upper reaches of the drinking-water source area, as well as strengthen the restoration of vegetation and the construction of soil and water conservation forests in these areas.

Management optimization of nonpoint source pollution considering the risk of exceeding criteria under uncertainty

Management of nonpoint source (NPS) pollution is highly important in watershed water environmental and ecological security. However, the many complexities and uncertainties that exist in the processes of export and management of NPS pollution exert substantial influences on the reliability of multiple management practices. This study developed an inexact multiobjective possibilistic mean-variance mixed-integer programming (IMPMMP) model for NPS pollution management through optimization of watershed land use pattern and livestock production structure. By coupling interval parameter programming, mixed-integer programming, multiobjective programming, and an export coefficient model within a general possibilistic mean-variance model framework, the IMPMMP model deals effectively with system uncertainties and complexities. Moreover, the risk of exceeding criteria (REC) in NPS pollution management systems can be considered. The proposed IMPMMP model was applied to a real-world case study in the Xinfengjiang Reservoir watershed in South China. Results showed that the preference of decision makers regarding land use adjustment plays a decisive role in determining model feasibility. The area provided for each land use type that could be adjusted has to reach a certain threshold to achieve the goals of reduced pollution load and REC control. The NPS pollution loads after optimization would be exported primarily from different land uses and the human population. Compared with NPS nitrogen pollution management, it is more difficult to reduce the NPS phosphorus load and to manage the corresponding REC through adjustment of the land use pattern and livestock production structure. Moreover, it is difficult to simultaneously reduce the NPS nitrogen and phosphorus pollution loads and REC in each subbasin. The model, which can provide policy makers with a series of schemes for optimization of land use pattern and livestock production structure, has satisfactory applicability and could be used for watershed NPS pollution management.

Multi-Time Scale Evaluation of Forest Water Conservation Function in the Semiarid Mountains Area

Forest water conservation function is an important part of forest ecosystem services. The discontinuous distribution of forests in semiarid areas brings difficulties to the quantitative evaluation of forest water conservation functions at the basin scale. In this paper, we took the upstream of Xiong’an New Area (Zijingguan—ZJG, Zhongtangmei—ZTM and Fuping—FP basins) as an example and combine the soil and water assessment tool (SWAT) and the water balance method to calculate the amount of forest water conservation (AFWC) at annual, monthly and daily scales from 2007 to 2017, and analyzed the changes of AFWC. The results showed that the hydrological response unit (HRU) generated with the threshold area zero can accurately reflect the forest patch distribution in the three basins. On an annual scale, the annual AFWC were all positive in ZJG and ZTM basins from 2007 to 2017. While, the annual AFWC in the FP basin was negative in 2009, 2013, 2014 and 2017. On a monthly scale, the positive values of AFWC mainly appear from June to September, and the negative values of AFWC mainly appear from December to March. On a daily scale, the AFWC during extreme precipitation was positive, while that was negative during extreme drought. The annual and monthly AFWC in the three basins was positively correlated with the wetness index, and FP basin needs more humid climate conditions than ZJG and ZTM basins to make the forest store water and keep in a stable water storage state. The above results can not only provide important insight into sustainable forest and water resources management in the region, but also serve as reference cases for other regions to carry out relevant research work.

Spatiotemporal evolution of groundwater nitrate nitrogen levels and potential human health risks in the Songnen Plain, Northeast China

As one of the most widespread pollutants worldwide, nitrogen has long been a concern in the environment, including groundwater. However, due to the limitations of investigations and study progress, there is still a poor understanding of groundwater nitrogen pollution and its potential effects on human health in many areas, particularly in developing countries. The spatiotemporal evolution of groundwater nitrate nitrogen levels and potential human health risks in the Songnen Plain, Northeast China were comprehensively studied based on both our own test data and available published data that were collected by us over a study period from 1995 to 2015. Groundwater nitrate nitrogen concentrations exhibited significant temporal and spatial differences: there was an increasing trend with time; and the distribution of high concentration areas expanded from the central and western areas to the east with time. The similar pattern existed in the potential health risks posed to the residents considering the two exposure pathways including drinking water and dermal contact. The effects of groundwater nitrate nitrogen on human health depend on the nitrate concentration but there were also age differences, namely, in the order of infants > children > adult females ≈ adult males, according to the hazard quotient (HQ) used in the human health risk assessment (HHRA) model. The spatiotemporal evolution of groundwater nitrate nitrogen levels and potential human health risks indicate that the issue of nitrogen pollution in groundwater in the study area is worsening and needs further attention. The drivers that increased nitrate nitrogen concentrations in the groundwater of the study area were the increased fertilizer use due to the increased cultivated land area and implementation of a land fertility policy by the local government. It should be acknowledged that the results have uncertainties that not only come from the layout of sampling points and selection of spatial interpolation methods but also come from the parameter settings in the assessment model and assumptions of drinking water scenarios. However, the conclusions still have important reference value for groundwater pollution control and management and human health risk supervision and early warning.