The synergistic impacts of land use/land cover (LULC) transformations and weather pattern variabilities (WPV) represent a primary driver of hydro-geological instability, threatening agricultural productivity, soil conservation, and water quality. Disentangling the discrete contributions of these stressors to runoff and sediment yield (SY) remains a significant challenge, particularly in complex, confluence-proximal watersheds lacking major hydraulic regulations. This study investigates the Tirumakudalu Narasipura watershed in Karnataka, India, an agriculturally intensive system undergoing rapid peri-urbanization. Leveraging the process-based geospatial interface of the Water Erosion Prediction Project (GeoWEPP), we analyzed hydrological responses over a 24-year period (2000-2023) and projected future trajectories through 2030. To overcome the traditional constraints of GeoWEPP, which was developed for small-scale watersheds (<260 ha), we present a novel upscaling framework utilizing a multi-site multivariate temporal calibration of hydrological response variables to exploit its process-based precision in capturing distributed soil erosion and landscape heterogeneity. This approach is further reinforced by an ancillary data validation to minimize error propagation while model-upscaling. Our findings reveal projected increases in runoff and SY of 14.69% and 49.23%, respectively, between 2000 and 2030. Notably, the sub-decadal acceleration from 2023 to 2030 (17.32% for runoff and 18.51% for SY) underscores a shifting dominance where LULC-driven surface modifications now outweigh climatic variance in forcing hydrologic change. Furthermore, the study quantifies how anthropogenic interventions such as strategic crop selection, tillage intensity, and irrigation regimes act as critical determinants of topsoil preservation. These results provide a scalable, economically feasible framework for precision land stewardship and sustainable watershed management in rapidly developing tropical landscapes.

Glyphosate, a non-selective herbicide, is widely used in US agriculture, raising concerns about its off-site transport into streams. This study investigated glyphosate concentrations in streams within nested agricultural watersheds in the US Midwest, dominated by corn (Zea mays)-soybean (Glycine max) rotations. Over a 7-year period (2005-2011), water samples from three nested watersheds were collected daily or every other day and analyzed for glyphosate. Results showed no detectable glyphosate in over 80% of samples (detection limit: 0.62 µg L- 1), with the highest concentrations observed during rainstorms. The maximum glyphosate concentration observed (102 µg L- 1) was below the US Environmental Protection Agency drinking water standard (700 µg L- 1) and the threshold for protecting aquatic life (12,000 µg L- 1). Regressions indicated that glyphosate concentrations correlated strongly with phosphate concentrations, precipitation, and seasonal variations. Additionally, the watershed with the most susceptibility to erosion showed the highest glyphosate off-site transport. These findings highlight the impact of hydrological and chemical factors on glyphosate transport under the conditions evaluated in this study.

Farming activities contribute to soil antibiotic pollution, posing health risks for rural farm workers, especially on small farms in impoverished regions. The effectiveness of large farms in reducing poverty-induced soil antibiotic exposure risk (SABER) remains uncertain. Here we integrate global datasets on concentration of soil antibiotics, rural farm-worker employments and on-farm working hours to quantify SABER. We find that exposure-weighted relative populations are concentrated in underdeveloped regions, particularly East Africa and South and Southeast Asia. A 1,000 ha farm is optimal for SABER reduction, farm employment and working hours, outperforming both smaller and larger farms. Establishing large farms in the top 20% of priority areas can cover 47.3-75.5% of SABER hotspots, while establishing large farms in the top 44% of priority areas achieves the highest coverage of SABER hotspots without substantial declines in rural employment. This approach offers practical strategies to mitigate SABER while maintaining rural farm-worker employment.

Engineering practices can efficiently mitigate river pollution by activities such as controlling pollution sources, excavating contaminated riverbed sediments, and remediating contaminated surface water. However, their effects on the improvement of watershed-scale groundwater quality may be significantly delayed. This study investigated the groundwater quality in response to the engineering practices for river restoration using ammonia nitrogen (NH4+-N) as an example contaminant in a megacity Shenzhen, Southern China, which is bedevilled by water pollution. The monitoring results of water quality from 2017 to 2019 showed a significant reduction in NH4+-N concentration in river water as a result of watershed-scale river restoration starting in 2018, while no obvious changes were found in groundwater, showing apparent asynchronism between the improvements of river and groundwater quality. The analysis of NH4+-N load at the watershed scale, and groundwater numerical simulation together with continuous water quality monitoring, indicated that the slow response of the groundwater quality mainly resulted from (1) the large NH4+-N pool that had historically accumulated in the groundwater system, (2) slow groundwater-surface water exchanges in the watershed that prolonged contaminant residence time, and (3) reduced redox conditions in the aquifers that diminished NH4+-N oxidation and removal. Analysis of future NH4+-N changes in the watershed indicated that a decrease in NH4+-N discharges from municipal wastewater treatment plants, such as through enforcing stricter discharge criteria, can significantly reduce NH4+-N discharge into the groundwater system.

While the Water Erosion Prediction Project (WEPP) provides a robust process-based modeling framework since 1985 by the United States Department of Agriculture (USDA), applications could be fairly challenging for those who do not possess professional training. In 2013, the USDA-Natural Resources Conservation Service (NRCS) initiated collaborations with the USDA-Agricultural Research Service (ARS), Purdue University, the University of Idaho, and Colorado State University to build an interface, dubbed WEPP-COMPARE (WEPP-Comprehensive Operation Management Practice Assessment and Rotation Engine). WEPP-COMPARE was developed to fulfill requirements as a web-based, spatial-distributed, and user-friendly decision support system that can be implemented to derive relevant and visualized soil erosion predictions for the entire United States. In addition, WEPPCOMPARE is available to the public and accessible anywhere at the county level in all 50 states and five major territories within a few minutes and doesn't require much knowledge of modeling or analytical data in advance. Wide varieties of long-term crop growth and conservation scenarios are ready to derive relevant and visualize outputs. Furthermore, a detailed list of management practices available for users to make decisions on crops to be planted to get maximum yield with corresponding soil loss map. One can take advantage of this tool and make timely preliminary comparisons before further investment of detailed investigation.

We quantify the relationship between nitrogen (N) runoff to the Gulf of Mexico, U.S. agricultural production, and exports to China using an integrated assessment model. We show that a 25% Chinese tariff on U.S. soybean and corn increases annual N runoff to the Gulf by 800 metric tons (0.2%) as soybean production in the Mississippi River Basin is displaced with more N-intensive crops. Results also indicate that reducing N runoff to the Gulf by 10% decreases U.S. corn export to China by 14.5%, similar to the effect of a 25% Chinese tariff on corn and soybeans.

The difference in the influencing pattern of extreme rainfall and artificial recharge events on hydrological processes remains elusive. To address this question, this study implemented a high-resolution synchronous monitoring program by utilizing stable hydrogen-oxygen and nitrogen-oxygen isotopes across multiple components, including river water, groundwater, soil water, and rainfall in a typical region of China. The results indicated a significant positive correlation between soil moisture and artificial recharge, soil moisture increased exclusively at depths of 100 cm and 120 cm during artificial recharge. The artificial recharge showed a "Down-Top" influencing pattern on soil hydrological process, i.e., the artificial recharge first increased the groundwater table and afterwards enhanced the soil moisture. A "Top-Down" pattern occurred in the influence of extreme rainfall on soil hydrological process, i.e., the extreme rainfall moved the water from top soil to down soil, and consequently raised the groundwater table. Rainfall and ecological water recharge also both lead to migration of nitrate nitrogen mainly in the upper and middle soil layers. Extreme heavy rainfall events cause a reduction of nitrate nitrogen content in the 0-120 cm soil layer of farmland by 32.29-160.90 mgkg(-1), and the nitrate nitrogen accumulation peak shifts from 40 to 60 cm to 80-100 cm. Ecological water recharge mainly affected the migration of nitrate nitrogen in the middle and lower soil layers of farmland. In addition, the influence of ecological water recharge on nitrate nitrogen migration also showed obvious seasonal differences, with summer > spring > winter. The results of this study would provide basis for understanding the water cycling under Climate change and human activities.

The quality of calibration datasets is critical for establishing well-calibrated models for reliable decision-making support. However, the analysis of the influence of calibration dataset quality and the discussion on how to use flawed and/or incomplete datasets are still far from sufficient. An evaluation framework for the impact of model calibration data on parameter identifiability, sensitivity, and uncertainty (ISU) was established. Three quantitative and normalized indicators were designed to describe the magnitude of ISU. With the case study of the upper Daqing River watershed, China and the model SWAT (Soil and Water Assessment Tool), one ideal dataset without quality flaws and 79 datasets with different types of flaws including observation error, low monitoring frequency, short data duration and low data resolution were evaluated. The result showed that 4 of 13 parameters that control canopy, groundwater and channel processes have higher ISU values, indicating the high identifiability, high sensitivity, and low uncertainty. The largest gap of parameter ISU between dataset with quality flaw and ideal dataset was 0.61 due to short data duration, while the smallest gap was -0.28 due to low monitoring data frequency. Although some defective datasets caused unacceptable calibration results and model output, some defective datasets can still be valuable for model calibration which depends on the hydrological processes of interest when applying the model. Equivalent calibration results were yielded by the datasets with similar statistical properties. When using datasets with traditional defective issues for calibration, a new step checking the consistency among decision goal, representative system process, determinative parameters and calibration datasets is suggested. Practices including process-related data selection, dataset regrouping and risk self-reporting when using low-quality datasets are encouraged to increase the reliability of model-based watershed management.

Canopy evaporation (Ei) is a vital process in forest ecosystems impacting hydrology and biogeochemistry through the redistribution of gross rainfall and gradual infiltration of water into the soil profile. Inaccurate representation of Ei in models may lead to flawed predictions of ecohydrological processes such as water availability, soil erosion, nutrient transport, and ecosystem productivity, thus compromising the reliability of model outputs. The Soil and Water Assessment Tool (SWAT) ecohydrological model has been widely used for various purposes worldwide. However, SWAT has shown limitations in forest ecosystems. SWAT employs a single equation to calculate canopy evaporation for crops and trees, which may not accurately account for the differences in ecophysiology and aerodynamic resistance between short and tall vegetation. In SWAT, canopy interception is calculated as a function of canopy storage and is normalized by the maximum plant leaf area index (LAI). Here we present an alternative approach to simulate forest canopy interception and evaporation with SWAT. Under our proposed approach, the LAI normalization is eliminated, and canopy storage is computed as a linear function of daily LAI and a user -defined parameter. We used remote -sensing (R -S) estimates of Ei to accurately parameterize forest canopy evaporation in the modified and default models. The Alabama-Coosa-Tallapoosa, a large (55,000 km2) and forested watershed system in the Southeast United States, is utilized as testbed. Results showed that the default SWAT largely underestimated (> 70%) forest Ei across our study domain. The modified model better matched R -S estimates of Ei, showing a mere 2% overestimation. Additionally, the modified model yielded better agreement with R -S transpiration and total evapotranspiration compared to the default model. Our alternative approach positively affected the model simulation of daily streamflow and ecologically relevant flow metrics, reducing model overestimations and leading to better agreement with observations. Also, the modified model led to reduced sediment, nitrate, and organic nitrogen loadings, with sediment and organic nitrogen being particularly affected, witnessing reductions of 13 and 11%, respectively, compared to the default model. Finally, our proposed approach resonated in better agreement between simulated net primary productivity (NPP) and R -S estimates. Although our study is in the context of SWAT, our findings can be useful to the broader modeling community since other popular process -based models are based on similar modeling assumptions. Our findings demonstrate the benefits of improved forest evapotranspiration partitioning for simulating ecological processes with SWAT.

Agricultural nutrient runoff has been a major contributor to hypoxia in many downstream coastal ecosystems. Although programs have been designed to reduce nutrient loading in individual coastal waters, cross watershed interdependencies of nutrient runoff have not been quantified due to a lack of suitable modeling tools. Cross-watershed pollution leakage can occur when nutrient runoff moves from more to less regulated regions. We illustrate the use of an integrated assessment model IAM that combines economic and process-based biophysical tools to quantify Nitrogen loading leakage across three major US watersheds. We also assess losses in consumer and producer surplus from decreased commodity supply and higher prices when nutrient delivery to select coastal ecosystems is restricted. Reducing agricultural N loading in the Gulf of Mexico by 45% (a) increases loading in the Chesapeake Bay and Western Lake Erie by 4.2% and 5.5%, respectively, and (b) results in annual surplus losses of $7.1 and $6.95 billion with and without restrictions on leakage to the Chesapeake Bay and Lake Erie, respectively.

Vegetable production is commonly accompanied by high nitrogen fertilizer rates but low nitrogen use efficiency in China. Reduced fertilization has been frequently recommended in existing studies as an efficient measurement to avoid large amount of nutrient loss and subsequent nonpoint source pollution. However, the reported responses of vegetable yield and nitrogen losses to reduced fertilization rates varied in a large range, which has resulted into large uncertainties in the potential benefits of those recommended reduction rates. Thus, we constructed the relationship between responses of nitrogen losses and vegetable yield to reduced nitrogen fertilization rates to determine the optimal range of reduction rates for nitrogen fertilization in a proportional form based on data reported in literatures across China's mainland, and evaluated the roles of greenhouse, managing options, and vegetable species on the responses. The relationships were constructed separately for 4 subregions: Northern arid and semiarid, loess plateau regions (NSL), Temperate monsoon zone (TMZ), Southeast monsoon zone (SMZ), Southwest zone (SWZ). The optimal nitrogen fertilizer reduction range for the TMZ, SMZ and SWZ were 51 % to 67 %, 40 % to 66 % and 54 % to 80 %, respectively and no reduction for NSL. Vegetable yields were not be sacrificed when fertilizations were reduced within the optimal ranges. Greenhouse and managing options showed no significant effect on the responses of both vegetable yield and nitrogen losses by the optimal reduction range but vegetable species played a relatively important role on the responses of vegetable yield. This indicated that the optimal reduction rates can be effective on reducing nitrogen loss in both open-field and greenhouse conditions across China's mainland without extra managing options. Therefore, the optimal reduction rates can still serve as a good starting point for making regional plans of nitrogen reduction that help balancing the chasing of high vegetable yield and low nitrogen loss.

Remotely sensed products are often used in watershed modeling as additional constraints to improve model predictions and reduce model uncertainty. Remotely sensed products also enabled the spatial evaluation of model simulations due to their spatial and temporal coverage. However, their usability is not extensively explored in various regions. This study evaluates the effectiveness of incorporating remotely sensed evapotranspiration (RS-ET) and leaf area index (RS-LAI) products to enhance watershed modeling predictions. The objectives include reducing parameter uncertainty at the watershed scale and refining the model's capability to predict the spatial distribution of ET and LAI at sub-watershed scale. Using the Soil and Water Assessment Tool (SWAT) model, a systematic calibration procedure was applied. Initially, solely streamflow data was employed as a constraint, gradually incorporating RS-ET and RS-LAI thereafter. The results showed that while 14 parameter sets exhibit satisfactory performance for streamflow and RS-ET, this number diminishes to six with the inclusion of RS-LAI as an additional constraint. Furthermore, among these six sets, only three effectively captured the spatial patterns of ET and LAI at the sub-watershed level. Our findings showed that leveraging multiple remotely sensed products has the potential to diminish parameter uncertainty and increase the credibility of intra-watershed process simulations. These results contributed to broadening the applicability of remotely sensed products in watershed modeling, enhancing their usefulness in this field.

The complexity of irrigation systems and the need to adapt them to uncertainties requires developing approaches to synthesize their performances. This paper reviews performance assessment and indicators for agricultural water systems. It is aimed at finding various methods and indices used for irrigation performance assessment and standard classes for their characterization. The global application of the metrics was also documented. Adopting a systematic review approach, peer-reviewed journal articles published in the English language in the last two decades (1 March 2001 to 31 December 2020) were surveyed. Case studies were presented demonstrating the application of the indicators. The study revealed a lack of standardization and the use of a wide range of indicators among others, and recommended representing certain indicators as single. However, this review considered one indicator as the best. Suggestions for further studies were made. A systematic review of methods and indices for agricultural performance assessments was conducted.Performance indicators are applied globally in small and large agricultural systems under different spatial and temporal scales, scales of analysis, and management types.Some categories of PIs need further studies to come up with a single PI that will represent them.

The presence of antibiotics in environment is an emerging concern because of their ubiquitous occurrence, adverse eco-toxicological effects, and promotion of widespread antibiotic resistance. Urban soil, which plays a noticeable role in human health, may be a reservoir of antibiotics because of intensive human disturbance. However, little is understood about the vulnerability of soil to antibiotic contamination in urban areas and the spatial-temporal characteristics of anthropogenic and environmental pressures. In this study, we developed a framework for the dynamic assessment of soil vulnerability to antibiotic contamination in urban green spaces, combining antibiotic release, exposure, and consequence layers. According to the results, soil vulnerability risks shown obvious spatial-temporal variation in urban areas. Areas at a high risk of antibiotic contamination were usually found in urban centers with high population densities and in seasons with low temperature and vegetation coverage. Quinolones (e.g., ofloxacin and norfloxacin) were priority antibiotics that posed the highest vulnerability risks, followed by tetracyclines. We also confirmed the effectiveness of the vulnerability assessment by correlating soil vulnerability indexes and antibiotic residues in urban soils. Furthermore, urbanization-and land use-related parameters were shown to be critical in regulating soil vulnerability to antibiotic contamination based on sensitivity analysis. These findings have important implications for the prediction and mitigation of urban soil contamination with antibiotics and strategies to improve human health.

Streamflow reductions have been attributed to the impacts of soil nutrient availability on plant transpiration, connecting soil biogeochemical and hydrological processes. Here we conducted a plot-scale acid addition experiment and monitored long-term hydrology in a subtropical watershed to provide direct evidence for the underlying mechanisms of these connections. These results showed that acid deposition enhanced plant growth and thus increased plant transpiration in the early treatment period. It indicates that plants can increase their transport of water and nutrients to satisfy physiological demands under continuous acid deposition. Acid deposition mainly contributed to increased evapotranspiration and decreased streamflow at the watershed scale. These results provide complementary evidence of plants adjusting to acid deposition-induced changes in soil nutrient availability and these acclimations result in streamflow reductions at the watershed scale. Our results call for integrating forest biogeochemical feedback into watershed hydrology.

Harmful algal blooms (HABs) are a recurring problem in many temperate large lake and coastal marine ecosystems, caused mainly by anthropogenic eutrophication. Implementation of agricultural conservation practices (ACPs) offers a means to reduce non-point source nutrient runoff and mitigate HABs. However, the effectiveness of ACPs in a changing climate remains uncertain. We used an integrated biophysical modeling approach to predict how Lake Erie cyanobacterial HAB severity (bloom biomass) may change under several climate and ACP implementation scenarios, using western Lake Erie and its largely agricultural watershed as our study system. An ensemble of general circulation model projections was used to drive spatially explicit land use and hydrology models of the Maumee River watershed, the output of which informed a predictive model of Lake Erie HAB severity. Results show that, in the absence of changes in ACPs, the frequency of severe HABs is projected to increase during coming decades, owing to increased inputs of nutrients from the watershed. These anticipated increases are due to increased total precipitation and more frequent higher-magnitude rainfall events. While further implementation of ACPs appears capable of reducing severe HAB events, widespread implementation would be necessary to reduce HAB severity below current management targets. This study highlights how continued climate change will only exacerbate the need for land management practices that can reduce nutrient runoff in agriculturally dominated ecosystems, such as Lake Erie. It also shows how interdisciplinary, biophysical modeling approaches can help identify strategies to mitigate HABs in the face of anthropogenic stressors.

For watersheds with complex stream network or geographical heterogeneity, multisite calibration in the distributed mode is preferred for the SWAT model because parameters for sub-watersheds are calibrated independently. However, existing tools either can only perform calibration in the lump mode by calibrating parameters in identical ways across the whole watershed or perform sequential and cascading calibration. In this study, an open source toolbox named Distributed Model Parameter Optimization Toolbox for the SWAT model (DMPOTSWAT) is developed to perform multisite calibration in both the distributed and lump modes. Other functions including model evaluation with default parameter values, sensitivity analysis, selecting best parameter sets, uncertainty analysis, and visualization of calibration output are also provided. These functions are illustrated with a case study. The development of this new toolbox will facilitate calibrating the SWAT and potentially other hydrologic models in the distributed mode.

Hydrologic models are widely used to support evaluation and decision-making of nutrient and pesticide impacts on the environment including water quality and sensitive aquatic species, yet such tools are typically designed for research purposes that are time consuming to use and require certain expertise, data, and software. In this study, a web-based tool for the Agricultural Policy Environmental eXtender (APEX) model with nutrient and pesticide prediction functions was built incorporating GIS functionality, a spatial and online database, automated modeling system, and user-friendly interface. It provides a computation platform for conducting nutrient and pesticide evaluations at small watershed and field scales with commonly used scenarios and management options. Case studies were conducted, and results demonstrate the ability of the tool to identify spatial variability of runoff and sediment, nutrient, and pesticide losses. This tool can also provide rapid and site-specific guidance to decision makers for evaluating pesticide related assessments.

Abstract Antibiotics have been widely used to protect human health and improve food production, however, they persist in soil and undermine ecosystem sustainability. The human impacts on soil susceptibility to antibiotic pollution on various spatial scales are poorly understood. Here, we predicted the geographic characteristics of antibiotic pollution risk and explored the corresponding human impacts on multiple spatial scales in China, a representative of high level of human activity. Given that human activities increase antibiotic pollution risk, land systems play a more fundamental role in risk increase than do population and economic growth. With decreasing spatial scale, land use composition had pronounced contributions to antibiotic pollution risk instead of land management strategy (55.9 ± 13.4% vs . 7.0 ± 1.7%). Furthermore, we identified the regions of concern, and thresholds of the effects of land system intensification on risk increase based on their non-linear changes. The scale-dependent relationships elucidate the sustainability of human–environment systems across spatial scales, which is beneficial to the establishment of strategies and action plans worldwide.