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Nutrient runoff from agricultural regions of the midwestern U.S. corn belt has degraded water quality in many inland and coastal water bodies such as the Great Lakes and Gulf of Mexico. Under current climate, observational studies have shown that winter cover crops can reduce dissolved nitrogen and phosphorus losses from row-cropped agricultural watersheds, but performance of cover crops in response to climate variability and climate change has not been systematically evaluated. Using the Soil & Water Assessment Tool (SWAT) model, calibrated using multiple years of field-based data, we simulated historical and projected future nutrient loss from two representative agricultural watersheds in northern Indiana, USA. For 100% cover crop coverage, historical simulations showed a 31–33% reduction in nitrate (NO3−) loss and a 15–23% reduction in Soluble Reactive Phosphorus (SRP) loss in comparison with a no-cover-crop baseline. Under climate change scenarios, without cover crops, projected warmer and wetter conditions strongly increased nutrient loss, especially in the fallow period from Oct to Apr when changes in infiltration and runoff are largest. In the absence of cover crops, annual nutrient losses for the RCP8.5 2080s scenario were 26–38% higher for NO3−, and 9–46% higher for SRP. However, the effectiveness of cover crops also increased under climate change. For an ensemble of 60 climate change scenarios based on CMIP5 RCP4.5 and RCP8.5 scenarios, 19 out of 24 ensemble-mean simulations of future nutrient loss with 100% cover crops were less than or equal to historical simulations with 100% cover crops, despite systematic increases in nutrient loss due to climate alone. These results demonstrate that planting winter cover crops over row-cropped land areas constitutes a robust climate change adaptation strategy for reducing nutrient losses from agricultural lands, enhancing resilience to a projected warmer and wetter winter climate in the midwestern U.S.

Classification of remotely sensed imagery for reliable land use and land cover (LULC) remains a challenge in areas where spectrally similar LULC features occur. For example, bare soils of harvested crop fields in agricultural watersheds exhibit spectral characteristics similar to high-intensity developed regions and impede an accurate classification. The goal of this study is to improve the accuracy of LULC classification of satellite imagery for the Big Sunflower River Watershed, Mississippi using ancillary data, multiple classification methods, and a post-classification correction (PCC). To determine the best approach, the methodology was applied to Landsat 8 Operational Land Imager (OLI) imagery during the growing season and post-harvest. Imagery for the growing season was acquired on 25 August 2015, and post-harvest was acquired on 7 January 2018. Three classification methods were applied: maximum likelihood (ML), support vector machine (SVM), and random forest (RF). LULC imagery was classified as open water, woody wetlands, harvested crop, rangeland, cultivated crop, high-intensity developed, and mid-low intensity developed areas. Ancillary data such as normalized difference vegetation index (NDVI), thematic maps of urban areas, river networks, transportation networks, high-resolution National Agriculture Imagery Program (NAIP) imagery, Google Earth time-series data, and phenology were used to determine the training dataset. Initially none of the three classification methods performed adequately. Hence, a post-classification correction (PCC) was implemented by masking and applying a majority filter using thematic maps of urban areas. Once PCC was implemented, the accuracies from each of the classification methods increased significantly with the SVM classification method performing best in both the growing season and post-harvest with an overall classification accuracy of 93.5% with a Kappa statistic of 0.88 in the post-harvest imagery and an overall classification accuracy of 84% with a Kappa statistic of 0.789 in the imagery from the growing season. It was found that SVM was the best classification method while PCC is an effective strategy to implement when dealing with spectrally similar LULC features. The use of SVM together with PCC increased the reliability of the information extracted. Strategies from this study can help to evaluate the LULC in agricultural and other watersheds.

Artificial drainage systems comprising subsurface networks of perforated pipes (tile drains) and engineered surface ditches are widely used to remove excess water from poorly drained agricultural regions. Artificial drainage lowers the water table by design but also has inadvertent effects on the watershed-scale hydrologic response with important implications for flood risk and nutrient exports. We investigated the effects of tile drains on watershed-scale hydrologic response in the Portage River, OH, Watershed using a high-resolution physically based integrated surface/subsurface hydrology model with recently developed capabilities to represent artificial drainage. Tile drains were found to enhance streamflow during times of low flow, generally consistent with previous studies. Streamflow flashiness was found to have a non-monotonic dependence on tile spacing with a minimum at intermediate spacings (∼50 m). Flashiness and the event hydrographs for small tile spacing were similar to the situation with no tiles, but flow paths from farm to stream were very different for those two end member cases, emphasizing the limitations of the stream hydrograph in characterizing hydrologic response. For typical tile spacings, peak flow can either be enhanced or attenuated by the presence of tiles, depending on the size of the event and the antecedent meteorological conditions. Tiles enhance peak flow when the event is below a threshold of ∼25 mm or when events arrive in dry conditions. Peak flow is reduced by tiles when events are large and arrive in conditions that are not overly dry. The dependence on event size and antecedent conditions is explained by differences in available storage and flow paths to the streams. These results provide additional insights into how tile drainage modulates event-scale hydrologic response, an important control on flood generation mechanisms and nutrient exports.

The main objective of this paper is to analyze the impact of climate change on water resources management and groundwater quantity and quality in the coastal agricultural Almyros Basin, in Greece. Intensive groundwater abstractions for irrigation and nitrogen fertilization for crop production maximization, have caused a large water deficit, nitrate pollution, as well as seawater intrusion in the Almyros aquifer system. Multi-model climate projections for Representative Concentration Pathways (RCPs 4.5 and 8.5) from the Med-CORDEX database for precipitation and temperature have been used to evaluate the impacts of climate change on the study area. The multi-model climate projections have been bias-corrected with Delta, Delta change of Mean and Variance, Quantile Delta Change, Quantile Empirical Mapping, and Quantile Gamma Mapping methods, and statistically tested to find the best GCM/RCM multi-model ensemble. Simulation of coastal water resources has been performed using an Integrated Modelling System (IMS) that contains connected models of surface hydrology (UTHBAL), groundwater hydrology (MODFLOW), nitrate leaching/crop growth (REPIC), nitrate pollution (MT3DMS), and seawater intrusion (SEAWAT). The results indicate that the best climate multi-model ensemble consists of three (3) climate models for both RCP4.5 and RCP8.5 using the Quantile Empirical Mapping bias-correction method. The IMS was applied for historical and future periods with observed and simulated meteorological inputs (e.g. precipitation and temperature) and various irrigation and agronomic scenarios and water storage works development (i.e. reservoirs). The results indicate that at least deficit irrigation and deficit irrigation along with rain-fed cultivation schemes, combined with or without the development and operation of reservoirs, should be applied to overcome the degradation of groundwater quality and quantity in the study basin. Based on the findings of this work, the water resources management should be adaptive to tackle the water resources problems of the Almyros Basin.

Small waters, like ponds, are the most abundant freshwater environments, and are increasingly recognized for their function in ecosystem service delivery. In agricultural watershed, artificial ponds play an essential role in reducing nitrogen pollution. However, until now artificial ponds remain the least investigated part of water environments. The importance of microbial activities has seldom been discussed, which makes the microbial pathways and processes rates in nitrogen removal poorly understood. To illustrate the role of artificial ponds in microbial nitrogen removal in agricultural water-sheds, 21 pond sediments and 11 soils are collected in an agricultural watershed of China. Results show that surface sediments in ponds carry significantly higher dissolved inorganic nitrogen (9.1–21.9 mg/kg) and total organic matter (64.8–113.0 g/kg) compared to the surrounding agricultural soils. High rates of microbial nitrogen removal in ponds (12.4–25.5 nmol N g⁻¹ h⁻¹) are observed, which are 2–9 times higher than those in dryland soils. In pond sediments, denitrification dominates (>90% N-loss) the microbial nitrogen removal process with only a minor contribution of anaerobic ammonium oxidation. A high potential of N₂O production (up to 9.4 nmol N g⁻¹ h⁻¹) occurs in ponds along with the rapid nitrogen removal. For denitrifier genes, nir gene are always more abundant than nosZ gene. Additionally, the nirS gene is more abundant under flooded conditions, while nirK gene prefers higher dissolved oxygen and NO₃⁻ in drylands. These findings highlight the ecosystem function of ponds in agricultural watersheds, and provide new ideas on pollution control and global nitrogen cycling.

Agriculturally-driven land use change and hydrologic modifications have influenced solute transport in midwestern U.S. streams. A clear understanding of the mechanisms driving nutrient export from agricultural watersheds will be critical in mitigating diffuse nutrient pollution, given anticipated shifts in hydrology associated with a changing climate. Specifically, more frequent, intense precipitation and altered snow patterns are predicted for the upper Midwest. We used four years of high-frequency nitrate (NO₃⁻–N) sensor data from two tile-drained, agricultural watersheds in Indiana to explore NO₃⁻–N export for 200 storms. We used concentration-discharge (C–Q) relationships and two indices, the hysteresis index (HI) and flushing index (FI), to understand physicochemical controls of NO₃⁻–N export across time scales. On both annual and seasonal time scales, we found NO₃⁻–N concentrations were largely chemostatic; however, patterns in FI suggested C–Q relationships for individual storms were highly variable, which may influence estimates of watershed-scale NO₃⁻–N export. We also found storm NO₃⁻–N export was strongly driven by mobilization of distal sources, given the predominance of counterclockwise hysteresis. In both watersheds, HI and FI values varied seasonally and with storm size, and patterns were linked to changes in hydrologic connectivity related to variation in seasonal tile drain flow. Variation in storm-specific NO₃⁻–N yields was driven by event runoff, storm duration, and antecedent basin moisture, rather than antecedent precipitation. Overall, we found that high-frequency NO₃⁻–N data accurately documented the magnitude of the ecological challenge presented by storm-driven nutrient export in agricultural watersheds.

Despite numerous published literature on the impacts of agriculture on water quality, knowledge gaps persist regarding which farming systems are of most concern for their impact on water quality, which would allow water resource planners to better target water management efforts. Seeking to understand how these relationships vary across different farming systems, we used data on water quality status in watersheds of an agricultural region in southern Portugal and overlaid it with a map of farming systems for the same region provided by a previous study. By intersecting both data layers, we characterized the areal shares of the farming systems in each watershed and inspected how these shares relate to water quality status using logistic regression. The results show that the impact of agriculture on water quality is primarily related to specific farming systems. We believe this type of information can be of significant interest to agricultural planners and policymakers interested in meeting water quality standards, and we conclude this study by suggesting innovative policy options based on payments to farmers operating selected farming systems as a cost-effective way to reconcile agricultural and environmental policy objectives.

Agricultural watersheds must simultaneously support multiple Ecosystem Services (ESs), yet the coordination between Ecosystem Service Value (ESV) growth and synergies of ESs remains poorly understood. Taking the Liaohe River mainstream Basin (LRMB), a typical agricultural watershed, as a case, this study investigates the spatiotemporal dynamics of ESV and trade-offs among ESs, along with their driving factors. Five key ESs—Food Production (FP), Water Conservation (WC), Water Purification (WP), Soil Conservation (SC), and Landscape Aesthetics (LA)—were selected. The InVEST model, Function-based Valuation Method, Root Mean Square Deviation (RMSD), and Coupling Coordination Degree (CCD) were comprehensively applied to assess the spatiotemporal variations in ESV, trade-off intensity, and their coupling coordination degree in the watershed from 2000 to 2023. Furthermore, the Optimal Parameters-based Geographical Detector (OPGD) and Multiscale Geographically Weighted Regression with Spatial Auto-correlation (MGWR-SAR) were employed to explore the driving mechanisms underlying changes in ESV and trade-off intensity, and to identify the major driving factors and their spatial heterogeneity. The results reveal the following: (1) From 2000 to 2023, total ESV in the LRMB increased by 69.5% from 77.66 to 131.59 billion yuan, with WC and FP accounting for 42.8% and 41.9% of this growth. Spatially, ESV shifted from a west-to-east increasing gradient to a U-shaped pattern, with high values concentrated in mountainous areas and low values along the mainstream. (2) Mean trade-off intensity remained stable at approximately 0.29, yet exhibited pronounced spatial polarisation. High trade-off zones shifted from the southwestern estuary toward the mainstream corridor, driven primarily by intensifying conflicts between FP and other ESs. (3) Despite a stable watershed-average CCD of 0.71–0.73, the CCD along the Liaohe River mainstream declined by over 15%, forming a corridor of coordination decay and revealing that ESV growth occurs at the expense of internal synergy. (4) Nonlinear interactions dominated ES dynamics, with the interaction of precipitation and human disturbance intensity exhibiting the highest explanatory power (q-values of 0.61 for ESV and 0.58 for RMSD). (5) Natural climatic factors (precipitation, temperature) predominantly enhanced synergy in mountainous areas, whereas human and landscape factors (human disturbance intensity, Shannon’s Diversity Index, PLAND of water) intensified trade-offs along the mainstream and central plains. This study establishes an integrated “ESV–trade-off–CCD” diagnostic framework and proposes a differentiated management strategy, offering a potentially transferable paradigm for sustainable governance in agricultural watersheds.

Nitrate contamination of freshwater systems is common in agricultural watersheds, leading to human and environmental health concerns. The Bells Creek watershed, located in central Prince Edward Island (PEI), Canada, is dominated by agricultural land use and has elevated nitrate concentrations in groundwater and surface water. Beneficial Management Practices (BMPs), such as constructed wetlands, may provide a means of mitigating such contamination; however, the significance of small wetlands in regulating or attenuating nitrate in PEI has yet to be investigated. In this year-long (August 2023 – July 2024) study, the nitrate loads delivered to a small (1.2 ha) groundwater-influenced, stream-wetland system were quantified using high-frequency monitoring techniques. Results indicate that discharge variability is the primary control on the observed variability of surface and subsurface nitrate loads. The total annual nitrate load to the wetland was approximately 30,000 kg NO 3 -N/yr. Groundwater discharge, which bypasses the wetland riparian zone, contributed approximately 67% of the total load to the wetland. Weekly flux calculations revealed that the wetland behaved as a nitrate sink for 49 out of 52 weeks of the year. Overall, the wetland attenuated 39% of the imported annual nitrate load which, on a wetland catchment area basis, is equivalent to 21.1 kg NO 3 -N/ha catchment/yr. A positive, non-linear trend between the imported nitrate load and the percent change in nitrate load revealed that the wetland attenuated disproportionately more nitrate during periods of high imported loads. These findings indicate that small, constructed wetlands may be an effective component of BMPs aimed at reducing nitrate loads in agriculturally dominated watersheds like those in Prince Edward Island.

Accurate identification of nitrate (NO 3 − ) sources is the premise of non-point source pollution control in watersheds. The multiple isotope techniques (δ 15 N-NO 3 − , δ 18 O-NO 3 − , δ 2 H-H 2 O, δ 18 O-H 2 O), combined with hydrochemistry characteristics, land use information, and Bayesian stable isotope mixing model (MixSIAR), were used to identify the sources and contributions of NO 3 − in the agricultural watershed of the upper Zihe River, China. A total of 43 groundwater (GW) and 7 surface water (SFW) samples were collected. The results showed that NO 3 − concentrations of 30.23% GW samples exceeded the WHO maximum permissible limit level, whereas SFW samples did not exceed the standard. The NO 3 − content of GW varied significantly among different land uses. The averaged GW NO 3 − content in livestock farms (LF) was the highest, followed by vegetable plots (VP), kiwifruit orchards (KF), croplands (CL), and woodlands (WL). Nitrification was the main transformation process of nitrogen, while denitrification was not significant. Hydrochemical analysis results combined with NO isotopes biplot showed that manure and sewage (M&S), NH 4 + fertilizers (NHF), and soil organic nitrogen (SON) were the mixed sources of NO 3 − . The MixSIAR model summarized that M&S was the main NO 3 − contributor for the entire watershed, SFW, and GW. For contribution rates of sources in GW of different land use patterns, the main contributor in KF was M&S (contributing 59.00% on average), while M&S (46.70%) and SON (33.50%) contributed significantly to NO 3 − in CL. Combined with the traceability results and the situation that land use patterns are changing from CL to KF in this area, improving fertilization patterns and increasing manure use efficiency are necessary to reduce NO 3 − input. These research results will serve as a theoretical foundation for controlling NO 3 − pollution in the watershed and adjusting agricultural planting structures. Graphical abstract