Typical Agricultural Watershed Research Articles

Multi-medium residues and ecological risk of herbicides in a typical agricultural watershed of the Mollisols region, Northeast China

The widespread use of herbicides impacts non-target organisms, promotes weed resistance, posing a serious threat to the global goal of green production in agriculture. Although the herbicide residues have been widely reported in individual environmental medium, their presence across different media has received scant attention, particularly in Mollisols regions with intensive agricultural application of herbicides. A systematic investigation was conducted in this study to clarify the occurrence of herbicide residues in soil, surface water, sediments, and grains from a typical agricultural watershed in the Mollisols region of Northeast China. Concentrations of studied herbicides ranged from 0.30 to 463.49 μg/kg in soil, 0.31–29.73 μg/kg in sediments, 0.006–1.157 μg/L in water, and 0.32–2.83 μg/kg in grains. Among these, Clomazone was the most priority herbicide detected in soil, sediments, and water, and Pendimethalin in grains. Crop types significantly affected the residue levels of herbicides in grains. Clomazone posed high ecological risks in soil and water, with 86.4 % of water samples showing high risks from herbicide mixtures (RQ > 1). These findings aid in enhancing our comprehension of the pervasive occurrence and potential ecological risks of herbicides in different media within typical agricultural watersheds, providing detailed data to inform the development of targeted mitigation strategies.

Variability of CO<sub>2</sub> emission from different types of ponds in a typical agricultural watershed

Variability of CO<sub>2</sub> emission from different types of ponds in a typical agricultural watershed

Crop production and water quality under 1.5 °C and 2 °C warming: Plant responses and management options in the mid-Atlantic region

The 2015 “Paris Agreement” aims to limit the global average temperature rise to significantly less than 2 °C, preferably within 1.5 °C above pre-industrial levels. A multitude of studies have focused on evaluating how different sectors respond to such levels of warming. Nonetheless, most of these studies fail to provide a clear roadmap to mitigate these impacts. A case in point is the anticipated decline in corn and soybean yields and increased phosphorus (P) and nitrogen (N) discharge into water bodies, a trend linked to past agricultural practices and climate change. In this research, we employ a novel assessment of how existing management practices under 1.5 °C and 2 °C global warming (GW) scenarios can affect nutrient availability in time and space as well as crop yield in a typical agricultural watershed in the Mid-Atlantic Region, specifically the Upper Maurice River Watershed (UMRW) in New Jersey.Using the Soil and Water Assessment Tool (SWAT) with multiple Global Climate Model (GCM) projections, we found that compared to 1.5 °C, a 2 °C GW scenario would exacerbate runoff, leading to amplified nutrient leaching. These losses decrease nutrient availability during the crop growing season. Moreover, a mismatch between the timing of fertilizer application and crop nutrient absorption caused nutrient-related stress. This nutrient and anticipated temperature stress resulted in a more significant decrease in crop yields under the 2 °C GW scenario than the 1.5 °C scenario. We have designed a management scenario to reduce future nutrient losses while increasing crop yields. The strategy involves altering the timing of planting/harvesting and the fertilizer application rate in response to a warming climate. This approach is projected to increase corn and soybean yields by +39 % (+21 %) and +2 % (+17 %), respectively, under the 1.5 °C (2.0 °C) GW scenario for the RCP-4.5 pathway. Simultaneously, it is expected to decrease the N and P loads at 1.5 °C (2.0 °C) GW. Comparable projections are also observed under the RCP-8.5 pathway.

Landscape pattern exhibits threshold-driven effect on nitrogen export of typical land use in subtropical hilly watershed under specific hydrological regimes

Landscape pattern can affect the export of non-point source nitrogen (N) in watersheds by regulating hydrological and biogeochemical processes. However, due to the inherent spatiotemporal variability of N exports under different land uses and hydrological regimes, it is essential to quantitatively evaluate the impact of landscape pattern on total N export coefficients (TNECs) and identify the threshold effect of TNECs between key landscape metrics. Based on river monitoring data for eight years in nine catchments of a typical agricultural watershed in the central subtropical region of China, we estimated the spatiotemporal variability and uncertainty of typical land-use TNECs (cropland, forest, and tea gardens) by combining an improved pollutant export coefficient model and Bayesian statistics. Subsequently, the contribution of landscape pattern to the variability in land use TNECs under different hydrological regimes was quantified, and the abrupt change points of TNECs along with the gradient of key landscape metrics were further determined. The results revealed that landscape pattern only had a significant effect on TNECs under medium-flow (representing discharge values within the 30–70th percentile range) and low-flow hydrological regimes (70–100th percentile range), explaining 48.1–54.7% and 55.2–69.3% of the variability, respectively. Threshold effects were observed between key landscape metrics representing different landscape categories (area edge, shape, and aggregation) and TNECs for each land use, and the threshold intervals corresponding to the TNEC abrupt change points were relatively consistent under medium- and low-flow regimes. These results serve as a critical reference for land-use planning and management to improve water quality, the approaches developed can be broadly extended to other regions with diverse land-use types and pollutant sources.

Dissolved Organic Carbon Dynamics Variability from Ponds Draining Different Landscapes in a Typical Agricultural Watershed

Dissolved organic carbon (DOC) in inland waters (rivers, reservoirs, lakes, and small ponds) plays a significant role in the global carbon cycle and affects global climate change. In addition, DOC is also a vital indicator of the water environment due to its multiple physical, chemical, and ecological roles. Lakes and ponds of small sizes are abundant on a global and regional scale, and a large increase in ponds is expected with global agricultural land expansion. However, the DOC characteristics of ponds in agricultural watersheds are still unclear, posing a challenge to better understanding the carbon cycle of inland waters. In this study, we explored the DOC variability and their influencing factors in ponds draining different landscapes in a typical agricultural watershed to address the issue. The field measurements over a year showed the DOC concentration varied among ponds draining different landscapes. Specifically, the mean DOC concentrations in the natural pond, sewage pond, aquaculture pond, and irrigation pond were (6.17 ± 1.49) mg/L, (12.08 ± 2.92) mg/L, (9.36 ± 2.92) mg/L, and (8.91 ± 2.71) mg/L, respectively. Meanwhile, monthly measurements found the DOC varied across sampling dates. The DOC variability was positively correlated with nutrients, primary production, and precipitation, suggesting anthropogenic loadings, an internal production rate, and hydrological regime that regulated the substantial variability of DOC in these ponds at the watershed scale. Further, large pollutant discharge and high primary production led to peak DOC occurring in the sewage pond. Our results implied that more attention should be paid to ponds in agricultural watersheds to better understand the roles of inland waters in the global carbon cycle.

Spatial optimization of ecological ditches for non-point source pollutants under urban growth scenarios.

Non-point source (NPS) pollution is regarded as the major threat to water quality worldwide, and ecological ditches (EDs) are considered an important and widely used method to collect and move NPS pollutants from fields to downstream water bodies. However, few studies have been conducted to optimize the spatial locations of EDs, particularly when the watershed experiences urbanization and rapid land-use changes. As land-use patterns change the spatial distribution of NPS loads, this study used a cellular automata-Markov method to simulate future land-use changes in a typical agricultural watershed. Three scenarios are included as follows: historical trend, rapid urbanization, and ecological protection scenarios. The spatial distributions of particulate phosphorus loads were simulated using the revised universal soil loss equation and sediment transport distribution model. The results suggested that the total particulate phosphorus (TP) load in the Zhuxi watershed decreased by 10,555.2kg from 2000 to 2020, primarily because the quality and quantity of forests in Zhuxi County improved over the last 20years. The TP load in Zhuxi watershed would be 2588.49, 2639.15, and 2553.32kg in 2040 in historical trend, rapid urbanization, and ecological protection scenarios, respectively, compared with 2308.1kg in 2020. This indicated that urban expansion increases the TP load, and the faster the expansion rate, the more the TP load. Consequently, the optimal locations of EDs were determined based on the intercepted loads and the period during which they existed during land-use changes. The results suggested that rapid urbanization would consequently reduce the space available for building EDs and also increase the cost of building EDs to control the NPS pollution in the watershed.

Quantifying groundwater phosphorus flux to rivers in a typical agricultural watershed in eastern China.

Increasing evidence indicates that groundwater can contain high dissolved phosphorus (P) concentrations, thereby contributing as a potential pollution source for surface waters. However, limited quantitative knowledge is available concerning groundwater P fluxes to rivers. Based on monthly hydrochemical monitoring data for rivers and groundwater in 2017-2020, this study combined baseflow separation methods and a load apportionment model (LAM) to quantify contributions from point sources, surface runoff, and groundwater/subsurface runoff to riverine P pollution in a typical agricultural watershed of eastern China. In the studied Shuanggang River, most total P (TP) and dissolved P (DP) concentrations exceeded targeted water quality standards (i.e., TP ≤ 0.2mg P L-1, DP ≤ 0.05mg P L-1), with DP (76 ± 20%) being the major riverine P form. Observed DP concentrations in groundwater were generally higher than those of river waters. There was a strong correlation between river and groundwater P concentrations, implying that groundwater might be a considerable P pollution source to rivers. The nonlinear reservoir algorithm estimated that baseflow/groundwater contributed 66-68% of monthly riverine water discharge on average, which was consistent with results estimated by an isotope-based sine-wave fitting method. The LAM incorporating point sources, surface runoff, and groundwater effectively predicted daily riverine TP [calibration: coefficient of determination (R2) = 0.76-0.82, Nash-Sutcliffe Efficiency (NSE) = 0.61-0.77; validation: R2 = 0.88-0.98, NSE = 0.54-0.64] and DP loads (calibration: R2 = 0.73-0.84, NSE = 0.67-0.72; validation: R2 = 0.88-0.97, NSE = 0.56-0.83). The LAM estimated point source, surface runoff, and groundwater contributions to riverine loads were 15-18%, 14-35%, and 46-70% for TP loads and 7-9%, 10-32%, and 59-82% for DP loads, respectively. Groundwater was the dominant riverine P source due to long-term accumulation of P from excess fertilizer and farmyard manure applications. The developed methodology provides an alternative method for quantifying P pollution loads from point sources, surface runoff, and groundwater to rivers. This study highlights the importance of controlling groundwater P pollution from agricultural lands to address riverine water quality objectives and further implies that decreasing fertilizer P application rates and utilizing legacy soil P for crop uptake are required to reduce groundwater P loads to rivers.

A novel irrigation canal scheduling model adaptable to the spatial-temporal variability of water conveyance loss

The water conveyance loss of irrigation canals shows significant spatial-temporal variability both along the canal reach and over time. An accurate representation of these variances in irrigation canal scheduling is essential to reduce water conveyance loss. However, the existing optimal irrigation scheduling models (OISMs) still have deficiencies in dealing with spatial-temporal variability of water conveyance losses, which seriously limits the model performance in many irrigation areas with high spatial-temporal variability. Borrowing a concept from hydrology, we propose a Dynamic Calculation Method of canal water conveyance loss (DMWCL), in which antecedent water conveyance index (AWCI) is defined to represent the effects of antecedent soil water content upon infiltration losses, and the integral-flow balance method is adopted to describe the characteristics of canal discharge decreasing along the canal reach. Based on the DMWCL, we present a novel canal scheduling model with the potential to adapt to spatial-temporal variability (DMWCL-CS). The new model is applied to the Hetao Irrigation District, Inner Mongolia, a typical agricultural watershed in China. Our results across the case studies show that the canal water delivery schedule derived by the DMWCL-CS model reduces water conveyance loss and improves irrigation performance, ranging from 2.49 million m3 to 10.60 million m3 in 2014 and from 0.60 million m3 to 6.17 million m3 in 2016, respectively, compared to that of three additional models. Interestingly, we find that the water delivery schedule of the DMWCL-CS model accelerates the wetting process of the canal bed soil to reduce the water conveyance loss and increases the discharge to adapt to the characteristics that the discharge decreases along the canal reach. The DMWCL-CS model provides essential insights for irrigation managers to formulate a canal water delivery schedule that adapts to spatial-temporal variability. The proposed framework is generic and can be integrated into any OISM.

Future warming-induced phosphorus loss mitigated by land conversion and degradation

Under the context of global climate change, it is critical to understand future soil organic carbon (SOC) dynamics and the corresponding diffuse phosphorus pollution, and to explore their driving factors to facilitate sustainable soil and water management in agricultural watersheds. With the integrated application of the Denitrification-Decomposition (DNDC) and Soil and Water Assessment Tool (SWAT) models, we simulated the long-term changes in SOC and diffuse phosphorus loss during the 2011–2100 period in Naoli River watershed, which is a typical agricultural watershed in northeastern China. The results showed that approximately 14.71 % of the study area will be converted from natural land (i.e., forest, pasture, and wetland) into agricultural land, and 23.50 % of the uplands will be converted into paddy fields under future climate change conditions. Without additional carbon input, the SOC will decrease at an annual rate of 0.37 % from 24.48 g/kg in 2011 to 17.77 g/kg in 2100, indicating serious land degradation in the future. As a result, watershed diffuse phosphorus loss will increase in the first 40 years (from 2011 to 2050), but show a decreasing trend in the later 50 years (from 2051 to 2100). Overall, watershed diffuse phosphorus loss will decrease from 2.75 kg/ha to 2.24 kg/ha during the 2011–2100 period. According to the scenario comparison, the logarithmic mean Divisia index and structural equation model analysis indicated that climate-induced land-use changes was the dominant factor causing SOC changes and diffuse phosphorus pollution, rather than climate change alone. The expansion of paddy fields could mitigate diffuse phosphorus pollution in the future, and improving agronomic management, such as increasing carbon inputs and integrating water and fertilizer management, are suggested to be adopted to mitigate land degradation by increasing SOC sequestration.

Residential and agricultural soils dominate soil organic matter loss in a typical agricultural watershed of subtropical China

Soil organic matter (SOM) loss in agricultural watersheds driven by hydrology threatens the aquatic environment, and increases the risk of greenhouse gas emissions from aquatic systems, and will be detrimental to both the green development of agriculture and the achievement of the national “carbon peaking and carbon neutrality” goal of China. Consequently, we explored the use of δ13C, δ15N, and the carbon to nitrogen ratio (C/N) as end-members and combined this with a Bayesian stable-isotope mixed model to quantitatively estimate the contribution of typical land-use types (i.e., forest, rice, vegetable, tea, and residential soils) to sedimentary organic matter in a typical agricultural watershed in subtropical China. The results indicated that there was significant spatial variation of δ13C, δ15N, and the C/N in the target sediment mixture and its sources. The range of sediment δ13C (−26.2‰ to −23.8‰) and δ15N (3.9–7.9‰) were within the ranges of the sources, with δ13C and δ15N ranges of − 29.3‰ to − 20‰ and − 1.2‰ to 9‰, respectively. The average C/N of sediment was 11.1 ± 0.52, which was significantly higher than all source samples. The spatial distribution of δ13C and δ15N indicated that SOM loss in this catchment may primarily be attributed to vegetable and residential soils. According to the qualitative identification from dual fingerprint mark composite graphs of the C/N and δ15N, as well as δ13C and δ15N, the sedimentary SOM in the Tuojia watershed was mainly derived from sewage. Furthermore, Bayesian stable-isotope mixed model analysis indicated that residential soil was the main contributor to the SOM loss in the watershed, with a contribution rate of 75.8 %, followed by agricultural land (rice and vegetable soils) with a combined input of 12 %. The findings highlight that anthropogenic life and agricultural production have a vital impact on the carbon and nitrogen cycle in this agricultural catchment, and improving the rural habitation environment may play a key role in the control of SOM loss in subtropical agricultural watersheds.

Hydro-conditioning: Advanced approaches for cost-effective water quality management in agricultural watersheds

Accurate simulation of landscape hydrological connectivity is pivotal for planning practices required for treating agricultural farm pollution. This study assesses the role of an advanced geospatial approach, namely, ‘hydro-conditioning’ employed for modifying Digital Elevation Models, termed hDEMs to replicate landscape hydrology by simulating continuous downslope flow through drainage structures such as bridges and culverts. The capabilities of manual and automated hDEMs in delineating optimal locations and water treatment potential of Best Management Practices (BMPs) in a typical agricultural watershed were evaluated. Parallel processing of both hDEMs revealed that ‘ground truthing’ plays a critical role in the accurate placement of breach lines for allowing water movement through digitally elevated surfaces. Outcomes guide the practitioners in selecting appropriate hDEM (manual or automated) depending on the complexity of modeled hydrological pathways, which is essential for planning BMPs in a cost-effective manner at different spatial scales. Modeling results show that hDEMs greatly influence hydrological connectivity, catchment boundaries, BMP locations, treatment capacities, and related costs. The accuracy of hDEMs was verified using a robust sub-basin scale validation approach. The study recommends a hybrid approach for utilizing the strengths of both, automated and manual hDEMs for efficient agricultural farm pollution in an economical manner.

Sources and transformations of nitrogen in an agricultural watershed on the Jianghan Plain, China: an integration of δ15N–NH4+, δ15N–NO3-, δ18O–NO3- and a Bayesian isotope mixing model

Due to the complexity of nitrate sources and transformation processes under extensive human activities and climate change, there are a great number of uncertainties associated with ascertaining nitrogen sources and transformations in agricultural watersheds. In this study, multiple isotopes of ammonium (δ15N–NH4+), nitrate (δ15N–NO3-, δ18O–NO3-), water (δ18O–H2O) and hydrogeochemical data were integrated to identify nitrogen sources and elucidate the mechanisms of nitrogen transformation in the Quanshui River watershed, a typical agricultural watershed in central China. The characteristics of nitrate nitrogen and oxygen isotopes, lower δ15N–NO3- values (<9.73‰), and the results from the Bayesian isotope mixing model showed that the main sources of nitrogen in the watershed were mostly ammonium fertilizers (AF), nitrate fertilizer (NF) and soil organic nitrogen (SON). In the fertilization season, the enrichment of δ15N–NH4+ and the negative correlation between δ15N–NH4+ and δ15N–NO3- indicated that the volatilization and partial nitrification of nitrate in chemical fertilizers were the main factors affecting nitrogen isotope changes in the water. Seasonal changes in δ15N–NH4+ and δ15N–NO3- values can provide more direct isotopic evidence for the occurrence of nitrification, which may provide more useful insights into nitrogen transformation in future studies. Seasonal variations in δ18O–NO3- can effectively indicate the impact of rainfall events on the nitrogen cycle.

Soil acidification and loss of base cations in a subtropical agricultural watershed

Soil acidification along with base cations loss degrades soil quality and is a major environmental problem, especially in agroecosystems with extensive nitrogen (N) fertilization. So far, the rates of proton (H+) production and real soil acidification (loss of base cations) remain unclear in subtropical agricultural watersheds. To assess the current status and future risk of soil acidification in subtropical red soil region of China, a two-year monitoring was conducted in a typical agricultural watershed with upland, paddy fields, and orchards where high N fertilizers are applied (320 kg N ha−1 yr−1). H+ production, neutralization and base cations losses were quantified based on the inputs (rainwater, inflow of water, and fertilizer) and outputs (outflow of water, groundwater drainage, and plant uptake) of major elements (K+, Ca2+, Na+, Mg2+, Al3+, NH4+, NO3−, SO42−, Cl−, and H+). The result showed that total H+ production in the watershed was 5152 molc ha−1 yr−1. N transformation was the most important H+ source (68%), followed by excess plant uptake of cations (25%) and H+ deposition (7%). Base cations exchange and weathering of minerals (3842 molc ha−1 yr−1) dominated H+ neutralization, followed by SO42− adsorption (1081 molc ha−1 yr−1), while H+ and Al3+ leaching amounted to 431 molc ha−1 yr−1, only. These results state clearly that despite significant soil acidification, the acidification of surface waters is minor, implying that soils have buffered substantially the net H+ addition. As a result of soil buffering, there was abundant loss of base cations, whose rate is significantly higher than the previously reported weathering rate of minerals in red soils (3842 vs 230–1080 molc ha−1 yr−1). This suggests that the pool of exchangeable base cations is being depleted in the watershed, increasing the vulnerability of the watershed, and posing a serious threat to future recovery of soils from acidification.

Impacts of parameter uncertainty on baseflow separation by a two‐parameter recursive digital filter

AbstractAccurate baseflow quantification is an important step in the survey and evaluation of water resources, non‐point source pollution quantification, and optimization of water resource allocation. We selected a typical agricultural watershed with high precipitation rates in eastern China as the study area and investigated the impact of the combination of a recession constant (on an annual, monthly, or daily scale) and the maximum baseflow index (BFImax; empirical value vs. optimal value) on the accuracy and reliability of baseflow separated using a two‐parameter recursive digital filter (TPRDF). It was observed that the accuracy and reliability of the TPRDF‐separated baseflow varied with the time scale of the recession constant. Specifically, they decreased in the following order: baseflow estimated using daily recession constants (EBSD) (NSE = 0.797, R2 = 0.819) &gt; baseflow estimated using monthly recession constants (EBSM; NSE = 0.779, R2 = 0.801) &gt; baseflow estimated using annual recession constants (EBSY; NSE = 0.666, R2 = 0.675), where ‘NSE’ is the Nash–Sutcliffe efficiency coefficient. The empirical value setting of BFImax was another important reason for the uncertainties in the TPRDF‐separated baseflow. The NSE values of EBSY, EBSM, and EBSD increased from 0.666, 0.779, and 0.797–0.785, 0.859, and 0.876, respectively, after replacing the empirical values with numerically optimized values. The combination of BFImax with recession constants on various time scales significantly affected the annual total TPRDF‐separated baseflow. It also affected the overall variation characteristics of the baseflow time series, composition of different magnitude (frequency) components, and sensitivity of baseflow time series to rainfall events.

Effects of different cropping systems on ammonia nitrogen load in a typical agricultural watershed of South China

The excessive application of agricultural irrigation water and chemical fertilizer has increased crop yields to help meet the demand for food, but it has also led to major water environment problem, i.e. non-point source (NPS) pollution, which needs to be addressed to achieve sustainable development targets. Although numerous studies have focused on the control and reduction of agricultural NPS pollution from the perspective of irrigation and fertilizer, the effects of different cropping systems on NPS pollution (ammonia nitrogen (NH3−N)) in the Dongjiang River Basin (DRB) were seldom assessed. Specifically, variation in the NH3-N load was simulated and analyzed at the annual and semi-annual scales under ten different cropping systems using the Soil and Water Assessment Tool (SWAT) model, which was calibrated and validated with satisfactory statistical index values in the DRB. The results indicated that the NH3-N load decreased, distinctly increased, slightly decreased when sweet potato, peanut, and rice were planted, respectively. Compared with mono-cropping, crop rotation could reduce the NH3-N load, and the planting sequence of crops could affect the NH3-N load to a certain extent. Planting peanuts in spring would dramatically increase NH3-N load. To evaluate NH3-N pollution, a critical threshold of NH3-N emission (5.1 kg·ha−1·year−1) was proposed. Meeting the NH3-N emission threshold cannot be achieved by altering the cropping system alone; additional measures are needed to reduce agricultural NPS pollution. This study facilitates the development of cropping systems and provides relevant information to aid the sustainable development of agriculture in the DRB.

Spatiotemporal distribution characteristics and key sources of nitrogen pollution in a typical agricultural watershed based on SWAT model

水资源恶化、水体富营养化严重威胁生态环境健康，农业活动所产生的氮是造成水污染的主要原因之一.本研究以句容水库农业流域为研究对象，基于实地监测数据验证了SWAT模型模拟当地氮污染的适用性，并分析了氮素负荷的时空分布特征及其关键源.结果表明：硝态氮（NO₃^--N）和总氮（TN）的年均入库量分别为9.98和27.22 t.时间尺度上，入库氮素负荷量年际呈现“M”型波动趋势；年内主要集中在汛期（6—9月），汛期氮素负荷量占到全年的84%以上.空间尺度上，径流NO₃^--N负荷量和TN负荷量均呈现“局部集中，临近水体”的分布特征，主要集中在农田占比高的地区.流域入库TN负荷量主要来源是农田施肥，贡献率达49.60%.研究结果可为当地流域氮污染控制提供理论依据.;The deterioration of water resources and surface water eutrophication threaten the health of the ecological environment seriously. Nitrogen (N) pollution from agricultural activities is an important source of water pollution. In this study, the Jurong Reservoir agricultural watershed was taken as the research object, and the local applicability of the SWAT model for N pollution was validated based on field monitoring data. In addition, the spatial and temporal distribution characteristics of N load and key pollution sources were analyzed. During the monitoring period, the annual N load showed an M type fluctuation trend, with a high N load occurred in the flood season from June to September, which accounted for more than 84% of the total N load of the whole year. Moreover, the spatial distribution of nitrate nitrogen (NO₃^--N) and total nitrogen(TN) load showed a trend of local concentration, adjacent to water body, mainly concentrated in the area with a high proportion of farmland. Farmland fertilizer is the main source of TN load in this watershed, contributing to 49.60% of total N load and the average annual storage capacity of NO₃^--N and TN was 9.98 t and 27.22 t, respectively. Our results can serve as a theoretical basis for the control of N pollution at the watershed scale.

Electrical conductivity and dissolved oxygen as predictors of nitrate concentrations in shallow groundwater in Erhai Lake region

Elevated nitrogen (N) concentration in shallow groundwater is becoming increasingly problematic, putting water resources under pressure. For more effective management of such a resource, more precise predictors of N level in groundwater using smart monitoring networks are needed. However, external factors such as land use type, rainfall, and N loads from multiple sources (residential and agricultural) make it difficult to accurately predict the spatial and temporal variations of N concentration. In order to identify the key factors affecting spatial and temporal N concentration in shallow groundwater and develop a predictive model, 635 groundwater samples from drinking wells in residential areas and agricultural wells in croplands of a typical agricultural watershed in the Erhai Lake Basin, southwest China, in the period from 2018 to 2020, were collected and analyzed. The results showed that the type of land use and seasonal variations significantly affected the N forms and their concentrations in the shallow groundwater, as the ratios of ON and NO3−-N to TN were 30%–39% and 52%–59% for the two land uses and 25%–44% and 46%–66% for seasonal changes. Their variations were reflected by electrical conductivity (EC) and redox environment. EC and dissolved oxygen (DO) had a positive non-linear relationship with the concentrations of total nitrogen (TN) and nitrate (NO3−-N). The fitted non-linear quantitative models were established separately to predict TN and NO3−-N concentrations in groundwater using easily available indictors (EC and DO). The high accuracy and performance of the models were investigated and approved by rRMSE, MAE, and 1:1 line. These findings can provide technical support for the rapid prediction and evaluation of N pollution in shallow groundwater through easily available indicators.

Tracing riverine sulfate source in an agricultural watershed: Constraints from stable isotopes

The sulfate pollution in water environment gains more and more concerns in recent years. The discharge of domestic, municipal, and industrial wastewaters increases the riverine sulfate concentrations, which may cause local health and ecological problems. To better understand the sources of sulfate, this study collected water samples in a typical agricultural watershed in East Thailand. The source apportionment of sulfide was conducted by using stable isotopes and receptor models. The δ34SSO4 value of river water varied from 1.2‰ to 16.4‰, with a median value of 8.9‰. The hydrochemical data indicated that the chemical compositions of Mun river water were affected by the anthropogenic inputs and natural processes such as halite dissolution, carbonate, and silicate weathering. The positive matrix factorization (PMF) model was not suitable to trace source of riverine sulfate, because the meaning of the extracted factors seems to be vague. Based on the elemental ratio and isotopic composition, the inverse model yielded the relative contribution of sulfide oxidation (approximately 46.5%), anthropogenic input (approximately 41.5%), and gypsum dissolution (approximately 12%) to sulfate in Mun river water. This study indicates that the selection of models for source apportionment should be careful. The large contribution of anthropogenic inputs calls an urgent concern of the Thai government to establish effective management strategies in the Mun River basin.

A novel irrigation canal scheduling approach without relying on a prespecified canal water demand process

The creation of an efficient and environmentally friendly irrigation canal schedule strongly depends on the accuracy of the prespecified water demand (PSWD) process in existing scheduling practices and modeling studies. Overestimating water demand would result in spillage losses from over-irrigation, while underestimating water demand would lead to crop yield losses due to deficit water supply. However, prespecifying an accurate water demand often poses various challenges for both farmers and canal administrators, as it involves the temporal variability in agrometeorological conditions, the spatial diversity of soil properties, and the sensitivity of crops to water stress at different growth stages. Therefore, to reduce the uncertainty of prespecifying water demand and avoid the adverse effects of an inaccurate water demand on canal water allocation, this paper proposes an alternative canal scheduling model that maximizes canal scheduling service quality by targeting the ultimate objectives of the irrigation system (UOIS) without considering the prespecified water demand as the model input. The UOIS canal scheduling (UOIS-CS) model aims to optimize the canal water delivery schedule via simulation of soil water and salt migration using the Simultaneous Heat and Water (SHAW) model, to provide the most suitable soil moisture content and soil salinity for crops. The UOIS-CS model is used for the Hetao Irrigation District, Inner Mongolia, a typical agricultural watershed in China, where supplementary irrigation is performed after the autumn harvest (i.e., autumn irrigation) to leach excessive soil salt and preserve a moderate amount of soil moisture for the beginning of the next spring sowing period. To evaluate the irrigation performance of the UOIS-CS model further, a comparative analysis between the UOIS-CS model and the widely used PSWD-based canal scheduling (PSWD-CS) model is conducted. Compared with the PSWD-CS model, the proposed UOIS-CS model exhibits a superior irrigation performance in terms of leaching soil salt and preserving soil water, and a lower risk of canal overload operation during a higher water demand period. The improved performance of the UOIS-CS model is attributed to its greater adaptability to the spatial diversity of soil properties and the temporal variability in agrometeorological conditions, which originates from the full use of the equifinality of model performance metrics and model inputs. The UOIS-CS model provides managers with an effective approach to formulate a canal water delivery schedule with improved irrigation efficiency and lower overload risk. The proposed framework is generic and can be applied to non-crop growing seasons.

Trade-off analyses and optimization of water-related ecosystem services (WRESs) based on land use change in a typical agricultural watershed, southern China

As the direct manifestation of human activities, land use change is the main driving factor in variations in ecological processes and ecosystem services (ESs). For better land use management in the future, it is essential to evaluate the changes of ESs affected by land use change and identify the relationships among ESs. In this study, four primary water-related ecosystem services (WRESs), including water yield, food production, water purification and soil conservation, were selected to assess the effects of past land use change (1980–2015) on WRESs in the Xiangjiang River Basin, a typical agricultural watershed in southern China. From 1980 to 2015, water yield, food production increased by 1.58% and 44.78%, respectively. And soil conservation was improved with a 13.11% decrease in sediment export. Because of the rapid expansion of agriculture and urbanization from 2000 to 2015, water purification was degraded significantly with the increase in nitrogen export (+22.56%) and phosphorus export (+25.85%). Trade-offs mainly occurred between water purification and other WRESs. Based on the existing ecological problem, four alternative land use scenarios were designed to explore the underlying effects of various land use polices. Among four scenarios, the combined scenario was selected as the optimal scenario because of the greatest improvement in water purification and small negative effect on food production. Our research provides an integrated WRESs trade-offs assessment framework. Moreover, the establishment of multiple land use scenarios based on land suitability evaluation could inform future land use decision-making and water conservation management in the agricultural watershed.