Review of Watershed-Scale Water Quality and Nonpoint Source Pollution Models

A spatio-temporal analysis of environmental fate and transport processes of pesticides and their transformation products in agricultural landscapes dominated by subsurface drainage with SWAT

Pesticides are detected in surface water and groundwater, endangering the environment. In lowland regions with subsurface drainage systems, drained depressions become hotspots for transport of pesticides and their transformation products (TPs). This study focuses on detailed modelling of the degradation and transport of pesticides with different physico-chemical properties. The objective is to analyse complex hydrological transport processes, to understand the temporal and spatial dynamics of the degradation and transport of pesticides. The ecohydrological model SWAT+ simulates hydrological processes as well as agricultural management and pesticide degradation, and can therefore be used to develop pesticide loss reduction strategies. This study focuses on modelling of three pesticides (pendimethalin, diflufenican, and flufenacet), and two TPs, flufenacet-oxalic acid (FOA) and flufenacet sulfonic acid (FESA). The study area is a 100-hectare farmland in the northern German lowlands of Schleswig-Holstein that is characterized by an spacious drainage network of 6.3 km and managed according to common conventional agricultural practice. SWAT+ modelled streamflow with very good agreement between observed and simulated data during calibration and validation. Regarding pesticides, the model performance for highly mobile substances is better than for non-mobile pesticides. While the transport of the moderately to very mobile substances via tile drains played an important role in both wet and dry conditions, no transport via tile drains was modelled for the highly sorptive and non-mobile pendimethalin. In conclusion, the model can reliably represent the degradation of moderately to very mobile pesticides in small-scale tile drainage-dominated catchments, as well as surface runoff-induced peak loads. However, it has weaknesses in accounting for the subsurface transport of non-mobile substances, which can lead to an underestimation of the subsequent delivery after precipitation events and thus underestimates the total load.

Agricultural watershed conservation and optimization using a participatory hydrological approach

Maximizing the impact of agricultural wastewater conservation practices (CP) to achieve total maximum daily load (TMDL) scenarios in agricultural watersheds is a challenge for the practitioners. The complex modeling requirements of sophisticated hydrologic models make their use and interpretation difficult, preventing the inclusion of local watershed stakeholders' knowledge in the development of optimal TMDL scenarios. The present study develops a seamless modeling approach to transform the complex modeling outcomes of Hydrologic Simulation Program Fortran (HSPF) into a simplified participatory framework for developing optimized management scenarios. The study evaluates seven conservation practices in the Pomme de Terre watershed in Minnesota, USA, focusing on sediment and phosphorus pollutant load reductions incorporating farmers' opinions to guide practitioners toward implementing cost-effective CPs. Results show reduced tillage and filter strips are the most cost-effective practices for non-point source pollution reduction, followed by conservation cover perennials. The integration of SAM with HSPF is crucial for sustainable field-scale implementation of conservation practices through enhanced involvement of amateur-modeling stakeholders and farmers directly connected to fields.

The hidden threat of heavy metal leaching in urban runoff: Investigating the long-term consequences of land use changes on human health risk exposure

This study evaluated the potential effects of long-term land use and climate change on the quality of surface runoff and the health risks associated with it. The land use change projection 2030 was derived from the main changes in land use from 2009 to 2019, and rainfall data was obtained from the Long Ashton Research Station Weather Generator (LARS-WG) model. The Long-Term Hydrological Impact Assessment (L-THIA) model was then utilized to calculate the rate of runoff heavy metal (HM) pollutant loading from the urban catchment. It was found that areas with heavy development posed a significantly greater public health risk associated with runoff, with higher risks observed in high-development and traffic areas compared to industrial, residential, and commercial areas. Additionally, exposure to Lead (Pb), Mercury (Hg), and Arsenic (As) was found to contribute significantly to overall non-carcinogenic health risks for possible consumers of runoff. Carcinogenic risk values of As, Cadmium (Cd), and Pb were also observed to increase, particularly in high-development and traffic areas, by 2030. This investigation offers important insight into the health risks posed by metals present in surface runoff in urban catchment areas under different land use and climate change scenarios.

Modelling relationships between land use and water quality using statistical methods: A critical and applied review

Land use/land cover (LULC) can have significant impacts on water quality and the health of aquatic ecosystems. Consequently, understanding and quantifying the nature of these impacts is essential for the development of effective catchment management strategies. This article provides a critical review of the literature in which the use of statistical methods to model the impacts of LULC on water quality is demonstrated. A survey of these publications, which included hundreds of original research and review articles, revealed several common themes and findings. However, there are also several persistent knowledge gaps, areas of methodological uncertainty, and questions of application that require further study and clarification. These relate primarily to appropriate analytical scales, the significance of landscape configuration, the estimation and application of thresholds, as well as the potentially confounding influence of extraneous variables. Moreover, geographical bias in the published literature means that there is a need for further research in ecologically and climatically disparate regions, including in less developed countries of the Global South. The focus of this article is not to provide a technical review of statistical techniques themselves, but to examine important practical and methodological considerations in their application in modelling the impacts of LULC on water quality.

From field soil sampling to watershed model: Upscaling by integrating information entropy and interpolation method

Soil property data plays a crucial role in watershed hydrology and non-point source (H/NPS) modeling, but how to improve modeling accuracy with affordable soil samplings and the effects of sampling information on H/NPS modeling remains to be further explored. In this study, the number of sampling points and soil properties were optimized by the information entropy and the spatial interpolation method. Then the sampled properties were parameterized and the effects of different parameterization schemes on H/NPS modeling were tested using the Soil and Water Assessment Tool (SWAT). The results indicated that the required sampling points increased successively for soil bulk density (SOL_BD), soil saturated hydraulic conductivity (SOL_K) and soil available water capacity (SOL_AWC). Compared to the traditional database (Harmonized world soil database), the NSE and R2 performance by new scheme increased by 22.8% and 10.5%, respectively. The entropy-based optimization reduced the sampling points by 13.2%, indicating a more cost-effective scheme. Compared to hydrological simulation, sampled properties showed greater effects on NPS modeling, especially for nitrogen. This proposed method/framework can be generalized to other watersheds by upscaling field soil sampling information to the watershed scale, thus improving H/NPS simulation.

Modeling canopy water content in the assessment for rainfall induced surface and groundwater nitrate contamination: The Bilate cropland sub watershed

Nitrate contamination in surface and groundwater remains a widespread problem in agricultural watersheds is primarily associated to high levels of percolation or leakage from fertilized soil, which allows easy infiltration from soil into groundwater. This study was aimed to predict canopy water content to determine the nitrate contamination index resulting from nitrogen fertilizer loss in surface and groundwater. The study used Geographically Weighted Regression (GWR) model using MODIS 006 MOD13Q1-EVI Earth observation data, crop information and rainfall data. Satellite data collection was synchronized with regional crop calendars and calibrated to plant biomass. The average plant biomass during observed plant growth stages was between 0.19 kg/m2 at the minimum and 0.57 kg/m2 at the maximum. These values are based on the growth stages of crops and provide a solid basis for monitoring and validating crop water productivity data. The simulation results were validated with a high correlation coefficient (R2 = 0.996, P < 0.0005) for the observed rainfall in the growing zone compared to the predicted canopy water content. The nitrate contamination index assessment was conducted in 2004, 2008, 2009, 2010, 2011, 2013, 2014, 2015, 2018 and 2020. Canopy water content and root zone seasonal water content were measured in (%) per portion as indicators of the NO-3-N-nitrate contamination index in these years (0.391, 0.316, 0.298, 0.389, 0.380, 0.339, 0.242, 0.342 and 0.356).

Evaluating lake water quality with a GIS-based MCDA integrated approach: a case in Konya/Karapınar

Considering water quality is an essential requirement in terms of environmental planning and management. To protect and manage water resources effectively, it is necessary to develop an analytical decision-support system. In this study, a systematic approach was suggested to evaluate the lake water quality. The methodology includes the prediction of the values in different locations of the lakes from experimental data through inverse distance weighting (IDW) method, creation of maps by using Geographic Information System (GIS) integrated with analytic hierarchy process (AHP) from multi-criteria decision analysis (MCDA), reclassification into five class, combining the time-related spatial data into a single map to predict the whole lake water quality from the data of sampling points, and finally overlapping the final maps with topography/geology and land use. The proposed approach was verified and presented as case study for Meke and Acigol Lakes in Konya/Turkey which were affected by human and natural factors although they have ecological, hydromorphological, and socio-economic importance. In the proposed approach, categorizing water quality parameters as "hardness and minerals," "substrates and nutrients," "solids content," "metals," and "oil-grease" groups was helpful for AHP with the determined group weights of 0.484, 0.310, 0.029, and 0.046, respectively. Assigning weights within each group and then assigning weights between groups resulted in creating accurate final map. The proposed approach is flexible and applicable to any lake water quality data; even with a limited number of data, the whole lake water quality maps could be created for assessment.

Impact of climate change on water quality evolution in cold regions using a water-heat-nitrogen processes coupled model

Cold regions are particularly vulnerable to climate change. Thus, evaluating the response of water quality evolution to climate change in cold regions is vital for formulating adaptive countermeasures for pollution control under changing climatic conditions. Taking the Songhua River Basin (SRB) in Northeast China as the target area, we designed a water-heat-nitrogen coupled model based on the principle of water and energy transfer and nitrogen cycle processes model (WEP-N) in cold regions. The impact of climate change on pollution load and water quality was analyzed during the freezing, thawing, and non-freeze-thaw periods by taking the sudden change point (1998) of precipitation and runoff evolution in the SRB as the cut-off. The ammonia nitrogen load at Jiamusi station, the outlet control station in the SRB, was decreased by 1502.9 t in the change period (1999-2018) over the base period (1956-1998), with a - 9.2% decrease due to climate change. Compared to the ammonia nitrogen load during the base period, the ammonia nitrogen load decreased by - 171.3, - 506.9, and - 824.8 t during the freezing, thawing, and non-freeze-thaw periods, respectively, while the coefficient of variation showed an increasing trend during three periods, especially during the freezing and thawing periods. However, the water quality changes differed among periods owing to varying runoff during the year. Meanwhile, increasing runoff and decreasing ammonia nitrogen load improved water quality at Jiamusi station during the freezing period. During the thawing and non-freeze-thaw period, the water quality deteriorated due to the decrease in runoff more than the decrease in ammonia nitrogen load. Hence, the impact of climate change on water quality during thawing and non-freeze-thaw periods should be monitored to potentially offset the human influence on pollution control. The difference in the rate of change of the proportion of Class IV water between the two models with or without the soil freeze-thaw mechanism was 15.9%. The result shows that the application of a model that does not consider the freeze-thaw mechanism might slightly exaggerate the impact of climate change on water quality.

Effect of varying hydrologic regime on seasonal total maximum daily loads (TDML) in an agricultural watershed

Rising hypoxia due to the eutrophication of riverine ecosystems is primarily caused by the transport of nutrients. The majority of existing TMDL models cannot be efficienty applied to represent nutrient concentrations in riverine ecosystems having varying flow regimes due to seasonal differences. Accurate TMDL assessment requires nutrient loads and suspended matter estimation under varying flow regimes with minimal uncertainty. Though a large database can enhance accuracy, it can be resource intensive. This study presents the design of an innovative modeling strategy to optimize the use of existing datasets to effectively represent streamflow-load dynamics while minimizing uncertainty. The study developed an approach to assess TMDLs using six different flux models and kriging techniques (i) to enhance the accuracy of nutrient load estimation under different hydrologic regimes (flow stratifications) and (ii) to derive an optimal modeling strategy and sampling scheme for minimizing uncertainty. The flux models account for uncertainty in load prediction across varying flow strata, and the deployment of multiple load calculation procedures. Further, the proposed flux approach allows the determination of load exceedance under different TMDL scenarios aimed at minimizing uncertainty to achieve reliable load predictions. The study employed a 10-year dataset (2009-2018) consisting of daily flow data (m3/sec) and weekly data (mg/L) for nitrogen (N), phosphorus (P) and total suspended solids (TSS) concentrations in three distinct agricultural sites in+ the Minnesota River Watershed. The outcomes were analyzed geospatially in a Geographic Information System (GIS) environment using the kriging interpolation technique. The study recommends (i) triple stratification of flows to obtain accurate load estimates, and (ii) an optimal sampling scheme for nitrogen and phosphorous with 30.6 % and 49.8 % datapoints from high flow strata. The study outcomes are expected to contribute to the planning of economically and technically sound combinations of best management practices (BMPs) required for achieving total maximum daily loads (TMDL) in a watershed.

Meeting the Growing Need for Land-Water System Modelling to Assess Land Management Actions

Elevated contaminant levels and hydrological alterations resulting from land use are degrading aquatic ecosystems on a global scale. A range of land management actions may be used to reduce or prevent this degradation. To select among alternative management actions, decision makers require predictions of their effectiveness, their economic impacts, estimated uncertainty in the predictions, and estimated time lags between management actions and environmental responses. There are multiple methods for generating these predictions, but the most rigorous and transparent methods involve quantitative modelling. The challenge for modellers is two-fold. First, they must employ models that represent complex land-water systems, including the causal chains linking land use to contaminant loss and water use, catchment processes that alter contaminant loads and flow regimes, and ecological responses in aquatic environments. Second, they must ensure that these models meet the needs of endusers in terms of reliability, usefulness, feasibility and transparency. Integrated modelling using coupled models to represent the land-water system can meet both challenges and has advantages over alternative approaches. The need for integrated land-water system modelling is growing as the extent and intensity of human land use increases, and regulatory agencies seek more effective land management actions to counter the adverse effects. Here we present recommendations for modelling teams, to help them improve current practices and meet the growing need for land-water system models. The recommendations address several aspects of integrated modelling: (1) assembling modelling teams; (2) problem framing and conceptual modelling; (3) developing spatial frameworks; (4) integrating economic and biophysical models; (5) selecting and coupling models.

Investigating the Relationship between Surface Water Pollution and Onsite Wastewater Treatment Systems

Onsite wastewater treatment systems (OWTSs) are important nonpoint sources (NPSs) of pollution to consider in watershed management. However, limited OWTS data availability makes it challenging to account for them as an NPS of water pollution. In this study, we succeeded in obtaining OWTS permits and integrated them with environmental data to model the pollution potential from OWTSs at the watershed scale using GIS-based multicriteria decision analysis. Then, in situ water quality parameters─Escherichia coli (E. coli), total nitrogen, total phosphorus, temperature, and pH─were measured along the main tributary at base-flow conditions. Three general linear models were developed to relate E. coli to water quality parameters and OWTS pollution indicators. It was found that the model with the OWTS pollution potential had the lowest corrected Akaike information criterion (AICc) value (35.01) compared to the models that included classified OWTS pollution potential input criteria (AICc = 36.76) and land cover (AICc = 36.74). These results demonstrate that OWTSs are a significant contributor to surface water pollution, and future efforts should be made to improve access to OWTS data (i.e., location and age) to account for these systems as an NPS of water pollution.

A graph-based modeling framework for tracing hydrological pollutant transport in surface waters

Anthropogenic pollution of hydrological systems affects diverse communities and ecosystems around the world. Data analytics and modeling tools play a key role in fighting this challenge, as they can help identify key sources as well as trace transport and quantify impact within complex hydrological systems. Several tools exist for simulating and tracing pollutant transport throughout surface waters using detailed physical models; these tools are powerful, but can be computationally intensive, require significant amounts of data to be developed, and require expert knowledge for their use (ultimately limiting application scope). In this work, we present a graph modeling framework - which we call HydroGraphs - for understanding pollutant transport and fate across waterbodies, rivers, and watersheds. This framework uses a simplified representation of hydrological systems that can be constructed based purely on open-source data (National Hydrography Dataset and Watershed Boundary Dataset). The graph representation provides a flexible intuitive approach for capturing connectivity and for identifying upstream pollutant sources and for tracing downstream impacts within small and large hydrological systems. Moreover, the graph representation can facilitate the use of advanced algorithms and tools of graph theory, topology, optimization, and machine learning to aid data analytics and decision-making. We demonstrate the capabilities of our framework by using case studies in the State of Wisconsin; here, we aim to identify upstream nutrient pollutant sources that arise from agricultural practices and trace downstream impacts to waterbodies, rivers, and streams. Our tool ultimately seeks to help stakeholders design effective pollution prevention/mitigation practices and evaluate how surface waters respond to such practices.

Facies analysis, biostratigraphy, and provenance of the late Neogene Seulimeum Formation, Northwest Aceh basin, Sumatra (Indonesia)

A sedimentological, biostratigraphic, and petrographical investigation was conducted on exposed sedimentary rocks in the Seulimeum Formation in the Northwest Aceh Basin, Sumatra. Sedimentary facies analysis suggests a deep-marine depositional environment consists of an inner fan, middle fan, and outer fan to basin plain deposits. New foraminiferal data designated a late Miocene to early Pleistocene age for the studied rock unit, equivalent to N17 to N21 zone, with paleobathymetry in the bathyal environment. Petrographically, the sandstone of the Seulimeum Formation is included as subarkose, sublithic arenite, and lithic arenite, or classified as litho-quartzose, feldspatho-litho-quartzose, and litho-feldspatho-quartzose. Provenance analysis suggests that the origin of the sandstones is from the arc orogen sources. Furthermore, it is concluded that the development of the GSF zone in the late Neogene controls the formation of the deep-marine depositional setting. The west-south-west part of the fault is the footwall part (the Barisan Mountains), as the main high area of sedimentary source material consisting dominantly of the Woyla Group, with some contributions from Bentaro volcanic and Paleogene to early Neogene sediments. Our findings also suggest that the beginning of the Great Sumatran Fault, which corresponds with the uplift of the Barisan Mountains in the northern part of Sumatra, took place in the late Miocene, between 8.6 and 5.9 Ma.

Using SWMM for emergency response planning: A case study evaluating biological agent transport under various rainfall scenarios and urban surfaces

To assist in emergency preparedness for a biological agent terrorist attack or accidental pathogen release, potential contaminant levels and migration pathways of spores spread by urban stormwater were evaluated using a Storm Water Management Model (SWMM) of U.S. Coast Guard Base Elizabeth City, North Carolina. The high temporal-spatial resolution SWMM model was built using spore concentrations in stormwater runoff from asphalt, grass, and concrete collected from a point-scale field study. The subsequent modeled contamination scenarios included a notional plume release and point releases mimicking the field study under three rainfall conditions. The rainfall scenarios included a 6-hour natural rainfall event on Dec. 8, 2021 and two design storms (2-year and 100-year events). The observed spore concentrations from asphalt and concrete from the actual field experiment were applied to calibrate the washoff parameters in the SWMM model, using an exponential washoff function. The calibrated washoff coefficient (c₁) and exponent (c₂) were 0.01 and 1.00 for asphalt, 0.05 and 1.45 for grass, and 2.45 and 1.00 for concrete, respectively. The calibrated SWMM model simulated spore concentrations in runoff at times and magnitudes similar to the field study data. In the point release modeled scenario, the concrete surface generated 55.6% higher average spore concentrations than asphalt. Similarly, in the field experiment, a 175% (p < 0.05) higher average spore concentration in surface runoff was observed from concrete than from asphalt. This study demonstrates how SWMM may be used to evaluate spore washoff from urban surfaces under different precipitation amounts, intensities, and durations, and how visualized spatial migration pathways in stormwater runoff may be used for emergency planning and remediation.

Enhancing the Soil Health-Watershed Health Nexus: Introduction

Scientific concepts and measurements that relate soil and water resources are lacking in several areas, limiting our development of a framework or nexus to assess soil-watershed health. Current research designs rely on land management practices as a proxy for soil condition. Yet, conservation practices are often studied in isolation of each other, and adoption may be driven by state and federal farm programs that can incentivize a given management practice without accounting for current, novel farmer-driven adoption of conservation systems. Despite the value of conservation management, its ability to predict soil health is often limited if based solely on land management because chemical, physical, and biological processes vary across time, discipline, and terrain. Similarly, connections between soil health and water quality are constrained due to several "grand challenges" that include dissimilar scales and the number of metrics required to correlate soil and water systems. Equally important is soil sampling within the critical flow path(s) that determines sediment/contaminant loading. In some instances, most of the sediment/contaminant loading during a portion or entire year results from channel and bank erosion and not overland flow that may not be within conservation management hectares. Additional challenges include legacy effects of prior land management, climate variability, and varying turnover rates of soil and water systems. This special section aims to frame research issues that inspire new approaches and collaborations for tackling the challenge of leveraging soil health to strengthen water management across plot, field, and watershed scales, using models, statistics, and other novel methodologies.

Calculating the Environmental Impacts of Low-Impact Development Using Long-Term Hydrologic Impact Assessment: A Review of Model Applications

Low-impact development (LID) is a planning and design strategy that addresses water quality and quantity while providing co-benefits in the urban and suburban landscape. The Long-Term Hydrologic Impact Assessment (L-THIA) model estimates runoff and pollutant loadings using simple inputs of land use, soil type, and climatic data for the watershed-scale analysis of average annual runoff based on curve number analysis. Using Scopus, Web of Science, and Google Scholar, we screened 303 articles that included the search term "L-THIA", identifying 47 where L-THIA was used as the primary research method. After review, articles were categorized on the basis of the primary purpose of the use of L-THIA, including site screening, future scenarios and long-term impacts, site planning and design, economic impacts, model verification and calibration, and broader applications including policy development or flood mitigation. A growing body of research documents the use of L-THIA models across landscapes in applications such as the simulations of pollutant loadings for land use change scenarios and the evaluation of designs and cost-effectiveness. While the existing literature demonstrates that L-THIA models are a useful tool, future directions should include more innovative applications such as intentional community engagement and a focus on equity, climate change impacts, and the return on investment and performance of LID practices to address gaps in knowledge.

Analysis and estimation of nonpoint source pollution under different land use in Anjiagou watershed, Gansu, China

Agriculture-related nonpoint source pollution has been a worldwide issue in the past few decades. Estimating pollutant sources at the basin scale remains a challenge due to the complexity of pollutant transport mechanisms affected by land use, variable climatic condition, geological formation, and lack of long-term observation data. This study was based on the long-term plot observational data of surface runoff and water quality and used principal component analysis and other statistical analyses to analyze the variation of water quality affected by different land uses (cropland, tree forest, shrub, grassland, and wildland). The mean concentration method with the local calibrated/verified SWAT (soil and water assessment tool) model was used to quantify the load of nonpoint source (NPS) pollutants on slope areas under different land uses in the Anjiagou Watershed. Our research results determined permanganate index (COD_Mn), ammonia nitrogen (NH₃-N), total nitrogen (TN), fluoride (F^-), nitrite nitrogen (NO₂-N), total phosphorus (TP), and hexavalent chromium (Cr⁶⁺) as the significant pollutants while 5-day biochemical oxygen demand (BOD₅) was identified to be below the water quality standards of Grade V (water for agricultural and general landscape use) in the studied watershed. Pollutants were discharged through either hillslope at a total rate of 2.4 kg ha^-1, accounting for 67.6% of the total, or through waterway channels (32.4%). The pollutant concentrations were from 23.5 mg L^-1 to 37.4 mg L^-1, varying with pollutants and land uses. All examined water quality indicators exceeded the minimum safety standards defined by the regulations of the Gansu provincial government by averaging 3.5 times higher than the safety threshold. The pollutants from hillslopes exceeded water quality standards by a factor of 3.4-4.4 times compared with from the waterway channel by 1.9. Implementing soil and water conservation measures can mitigate pollutants to some extent, particularly during the process of highland runoff converging into waterways. At the watershed level, between 33 and 38% of the runoff and pollutants were discharged from croplands, between 59 and 66% from forest land, < 2% from grassland, and 1% from wildland. This study also demonstrates a simple but novel method to estimate NPS pollutants using long-term plot observations in conjunction with SWAT models, which can be used in other watersheds with similar conditions.

A coupling simulation and optimization method developed for environmental-economic management of Lake watershed

Many lake basins are facing the challenge of mitigating water shortage and water pollution while maintaining economic growth. Existing planning method for water pollution control often focus on how to alleviate water pollution effectively at the lowest cost, but rarely pay attention to the dynamic feedback and synergy effects between water pollution abatement, water conservation and social economy. This article proposes a method which consists of system dynamics model (SDM), Soil and Water Assessment Tool (SWAT) and objective programming model (OPM). It could be used to create insights on basin-wide water problems from a systematic perspective. The case study on Yilong Lake Watershed evaluates the proposed measures in existing local planning, calculates the optimal scheme, and discusses issues including the uncertainty of effectiveness, choice between recycling sewage and transferring outside, and the necessity of restricting food processing industry. This method could be improved on the simulation of social and industrial economy, the simulation of water cycle, and the spatial planning.

Impact of Intersectoral Dependencies in National Production on Wastewater Discharges: An Extended Input–Output Study of the Croatian Economy

The Croatian economy performs unfavorably in terms of the impact of production on wastewater discharges, which is particularly pronounced in the industrial sectors. Each unit of gross industrial value added produced in Croatia generates significantly more wastewater discharges than in most European countries with a similar level of economic development. Moreover, in 2020, only 26.9% of the total industrial wastewater discharges of the Croatian economy were treated, while 76.2% of the total industrial wastewater discharges were directly discharged into the environment. Since most of the industrial production in the Croatian economy is destined to meet the intermediate needs of other sectors, policy makers in Croatia must take into account that the level of industrial wastewater discharges is also influenced by the production level of sectors that depend on the intermediate products of wastewater-intensive industries. For this reason, we developed a wastewater extended input–output model of the Croatian economy to determine and analyze the impact of intersectoral linkages in Croatian production systems on the amount of untreated wastewater discharges. The results of the study show that wastewater flows in the Croatian economy are largely generated by the processes of production and consumption of intermediate products from the chemical and petroleum refining sectors, which also account for the largest share of the calculated wastewater footprint of total Croatian production. In light of the emerging empirical evidence, it can be concluded that targeting market-based and regulation-based measures at wastewater-intensive producers is not sufficient to reduce the relatively high level of untreated wastewater discharges in the Croatian economy. There is also a need for appropriate integrated policy measures in sectors that have a large wastewater footprint due to their established supply chains.

A review of non-point source water pollution modeling for the urban-rural transitional areas of China: Research status and prospect

China has experienced a rapid period of urbanization since the 1980s. Many traditional agricultural areas were transformed into the urban-rural transitional areas, in which both urban and rural characteristics exist. Non-point source pollution (NPSP) has become a major side effect of urbanization and agricultural production which caused wide public concerns. It is crucial to carry out research on identifying the spatiotemporal variation in NPSP in the urban-rural transitional area (especially in developing countries, e.g., in China), which is a prerequisite for improving water quality and guiding NPSP control efforts. Modeling approaches are great tools to provide quantitative information on NPSP and optimize the best management practices for NPSP control. We reviewed over twenty years of publications on NPSP modeling and applications in urban, rural and its transitional areas. The strengths and limitations of 20 commonly used NPSP models in China were concluded based on a brief introduction and the evolution history. Reporting the strengths and weaknesses of each NPSP model could enhance its utility in practice. In terms of the unique characteristics of urban-rural transitional areas, which are neither strictly urban nor rural, non-point source pollutants are often distinctly different between traditional pollutants from urban and agricultural areas since the great differences in the hydrological processes, and none of existing NPSP models are fully applicable to urban-rural transitional areas. Based on limited NPSP modeling studies in urban-rural transitional areas, the existing research insufficiency were technical and mechanism limitations of the model despite of numerous improvements in the past, concerns about simulation accuracy, limited investigations on new pollutants, and lack of monitoring data. Future development trend and concerns of NPSP models for urban-rural transitional areas were discussed, which could be of great help to the development of NPSP models and their applications in water quality management in the rapid urbanized China.

Response of Runoff Yield to Land Use Changes in the Small Watershed of Core Area for 2022 Winter Olympic Games in Zhangjiakou City Based on SWAT Model

Based on an improved high-precision land use map, the SWAT model of the small watershed of the core area for the 2022 Winter Olympic Games in Zhangjiakou City was established. The model was set up, calibrated, and validated with data from 2017 to 2019. In comparison with the measured flow discharge, all the coefficient of determination values of the simulated results at the upstream, midstream, and downstream (outlet) points were larger than 0.80 in both calibration and validation periods, and the relevant Nash–Sutcliffe efficiency coefficient values were above 0.62. With the model, the response of runoff yield in the small watershed to the land use change before (2015) and after (2019) Olympic construction was simulated and analyzed. The runoff yield change was only −5.1% from 2015 to 2019, which was not statistically significant (p = 0.87 > 0.05). Through simulation of two extreme scenarios, two runoff yield change coefficients were introduced to explore the effects of land use change on runoff yield. The results revealed that the neutralization effects of the land use change from grassland to forest (with strong water conservation capacity) and residential and bare land (with poor water conservation capacity) were the main reasons for the insignificant change of runoff yield. The results of this research may provide some inspiration to the application of SWAT model in small watershed and some guidance to the vegetation restoration practices for water conservation. These results can promote understanding on the response of runoff yield to the land use changes towards two extreme directions of forest land and residential and barren land, and provide some guidance for the vegetation restoration practices in the small watersheds hosting major events.

Estimating Point and Nonpoint Source Pollutant Flux by Integrating Various Models, a Case Study of the Lake Hawassa Watershed in Ethiopia’s Rift Valley Basin

Increasing pollutant emissions in the Lake Hawassa watershed (LHW) has led to a severe water quality deterioration. Allocation and quantification of responsible pollutant fluxes are suffering from scarce data. In this study, a combination of various models with monitoring data has been applied to determine the fluxes for Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD5), Total Dissolved Solid (TDS), Total Nitrogen (TN), Nitrate and Nitrite-nitrogen (NOx-N), Total Phosphorous (TP) and phosphate (PO4-P). Water, wastewater and stormwater samples were collected and analyzed at eight monitoring stations from rivers and point sources and six monitoring stations of stormwater samples. The flow simulated with soil and water assessment tool (SWAT) could be very well calibrated and validated with gauge data. This flow from SWAT model, measured flow during monitoring and pollutant concentrations were used in FLUX32 to estimate pollutant fluxes of main rivers and point sources in LHW. The formulas provided by Ethiopian Roads Authority and Gumbel’s theory of rainfall frequency analysis was employed to determine the 2-years return period rainfall depth for the City of Hawassa. The integration of HEC-GeoHMS and SCS-CN with the catchment area enabled to determine stormwater pollution load of Hawassa City. The estimated pollutant flux at each monitoring stations showed that the pollutant contribution from the point and nonpoint sources prevailing in the study area, where the maximum fluxes were observed at Tikur-Wuha sub-catchments. This station was located downstream of the two point sources and received flow from the upper streams where agricultural use is predominant. Furthermore, Hawassa city has been identified as a key pollutant load driver, owing to increased impacts from clearly identified point sources and stormwater pollutant flux from major outfalls. Agricultural activities, on the other hand, covers a large portion of the catchment and contributes significant amount to the overall load that reaches the lake. Thus, mitigation measures that are focused on pollutant flux reduction to the lake Hawassa have to target on the urban and agricultural activities.

Modeling distributed metal pollution transport in a mine impacted catchment: Short and long-term effects

A spatially distributed trace metal transport and transformation module was developed and implemented within the hydrological model TOPKAPI-ETH. The new module can be used to better understand, at high spatial and temporal resolution, the transport and reactions of trace metals as they move through a catchment from upland sources to downstream areas and water bodies. The newly developed module takes into consideration solid metal in multiple chemical phases with different reactivity and simulates their mutual transformation over time, which gives the possibility to analyze the fraction of different solid metal phases present in the river suspended sediment. The characteristics and potential of the model are demonstrated by simulating Zinc (Zn) and Cadmium (Cd) dynamics in a headwater catchment of the Xiang River in South China, which has been highly perturbed by mining activities. The developed module is shown to reasonably reproduce the observed dynamics of dissolved and total trace metals flux for 14 months at two monitoring stations. The distributed solute transport model was proved to be capable of explaining the reasons underlying the spatial variability of C-Q relationships that are driven by the combined effect of point and non-point pollution sources, as well as identifying the spatiotemporal hotspots of trace metal pollution. By means of synthetic numerical experiments, a limited impact of slow reactions on dissolved Cd transport from upland to river over short-temporal scales was demonstrated, while for longer scales, e.g. >5 years, this effect becomes more relevant, highlighting potential long-lasting sources of trace metal pollution and their impacts.

Scaling an Artificial Neural Network-Based Water Quality Index Model from Small to Large Catchments

Scaling models is one of the challenges for water resource planning and management, with the aim of bringing the developed models into practice by applying them to predict water quality and quantity for catchments that lack sufficient data. For this study, we evaluated artificial neural network (ANN) training algorithms to predict the water quality index in a source catchment. Then, multiple linear regression (MLR) models were developed, using the predicted water quality index of the ANN training algorithms and water quality variables, as dependent and independent variables, respectively. The most appropriate MLR model has been selected on the basis of the Akaike information criterion, sensitivity and uncertainty analyses. The performance of the MLR model was then evaluated by a variable aggregation and disaggregation approach, for upscaling and downscaling proposes, using the data from four very large- and three large-sized catchments and from eight medium-, three small- and seven very small-sized catchments, where they are located in the southern basin of the Caspian Sea. The performance of seven artificial neural network training algorithms, including Quick Propagation, Conjugate Gradient Descent, Quasi-Newton, Limited Memory Quasi-Newton, Levenberg–Marquardt, Online Back Propagation, and Batch Back Propagation, has been evaluated to predict the water quality index. The results show that the highest mean absolute error was observed in the WQI, as predicted by the ANN LM training algorithm; the lowest error values were for the ANN LMQN and CGD training algorithms. Our findings also indicate that for upscaling, the aggregated MLR model could provide reliable performance to predict the water quality index, since the r2 coefficient of the models varies from 0.73 ± 0.2 for large catchments, to 0.85 ± 0.15 for very large catchments, and for downscaling, the r2 coefficient of the disaggregated MLR model ranges from 0.93 ± 0.05 for very large catchments, to 0.97 ± 0.02 for medium catchments. Therefore, scaled models could be applied to catchments that lack sufficient data to perform a rapid assessment of the water quality index in the study area.

Evaluating the risks of spatial and temporal changes in nonpoint source pollution in a Chinese river basin

In watershed management, it is of great importance to evaluate the risks of nonpoint source (NPS) pollution. In this study, the Nonpoint Source Pollution Risk Index (NSPRI), a multi-factor NPS risk assessment model that was based on the source-sink landscape theory, was proposed and applied in Muzhuhe River Basin, Shandong, China to (1) highlight spatial and temporal variations in the risks from nitrogen and phosphorus losses, and (2) identify how the basin characteristics influenced the risk of nutrient loss. According to the analysis on land use change, the study area is featured with high proportions of forest and agricultural land uses; the area of urban and industrial land had increased considerably from 2000 and 2018. Based on the division of the calculated risk indices on subbasin scale, the area with extremely high risks has decreased from 56,442 ha to 43,922 ha. The average and coefficient of variation (CV) values of NSPRI in the river basin have dropped from 1.3 to 1.1, and from 78.2% to 48.9%, respectively. The distribution of NSPRI suggested an increase in spatial clustering and improvements in the ecological balance. Correlation analysis of the Soil and Water Assessment Tool (SWAT) model (R² > 0.68, ENS > 0.59) and NSPRI indicated the applicability of the method used (r > 0.84, p < 0.01). Analysis on the impact of metrics of land use composition, landscape, and environmental settings on NSPRI indicated that the water quality was more significantly correlated with land use composition, landscape pattern and vegetation cover than with flow path distance, soil erodibility, and rainfall erosivity. Moreover, results of redundancy analysis revealed that nutrient loss risk was better explained by land use compositions than by landscape configuration. The assessment method provided scientific support for NPS pollution control from the perspective of source-sink landscape theory.

Integrated watershed process model for evaluating mercury sources, transport, and future remediation scenarios in an industrially contaminated site

We used the Soil Water Assessment Tool (SWAT) as a framework to develop an empirical Hg flux model for Upper East Fork Poplar Creek (UEFPC), a Hg-contaminated watershed in Oak Ridge, Tennessee. By integrating long-term Hg monitoring data with simulated flow and suspended solid loads in a site-specific empirical Hg transport model, we (1) quantified the spatial, temporal, and flow regime controls on daily Hg flux (adjusted R² = 0.82) and (2) made predictions about Hg flux under future climate, land use, and management scenarios. We found that 62.79% of the average daily Hg flux in the watershed is currently driven by base flow, whereas variability in Hg flux is driven by storm and extreme flow. We estimate an average annual Hg flux of 28.82 g day^-1 leaving the watershed under baseline precipitation, with an estimated 43.73% reduction in daily Hg flux under drought conditions and a 296% increase in daily Hg flux in extreme precipitation scenarios. We estimated that a new mercury treatment facility would result in a 24.7% reduction in Hg flux under baseline conditions and a 33.4% reduction under extreme precipitation scenarios. The study demonstrated the merit of this approach, which can be replicated for sites where information on flow, suspended solids, and Hg concentrations is available.

Simplified Modelling of Coupled Surface-Groundwater Transport Using a Subcatchment Mass Balance Approach

Catchment models based on steady-state mass balances enable rapid assessment of contaminant fluxes and concentrations in rivers. However, such models often focus on surface drainage, without taking groundwater into account. This paper presents a novel steady-state mass-balance catchment model that includes groundwater. The model incorporates a conceptual reservoir under each surface subcatchment, with lateral subsurface exchanges between adjacent reservoirs and vertical exchanges between the reservoirs and the surface drainage network. This leads to an easily solved coupled algebraic system of equations. The approach is demonstrated for nitrogen in a meso-scale catchment in New Zealand. Exchange coefficients were extracted from a full groundwater model, while recharge sources were obtained from separate hydrological and leaching models. Other parameters such as decay coefficients were determined through calibration. Although the exchange coefficients are generated from a detailed groundwater model, alternatives such as simple groundwater models or phreatic contours could be used instead. The effective decay parameters were different from what was expected, which is partly due to the model structure (for example, the assumption of complete mixing in each reservoir), but may also be due to input uncertainty. The applications demonstrated the successful deployment of a novel, simple, fast-running and flexible coupled surface-groundwater model.

Ammonium Nitrogen Streamflow Transport Modelling and Spatial Analysis in Two Chinese Basins

Ammonium nitrogen (NH4+-N), which naturally arises from the decomposition of organic substances through ammonification, has a tremendous influence on local water quality. Therefore, it is vital for water quality protection to assess the amount, sources, and streamflow transport of NH4+-N. SPAtially Referenced Regressions on Watershed attributes (SPARROW), which is a hybrid empirical and mechanistic modeling technique based on a regression approach, can be used to conduct studies of different spatial scales on nutrient streamflow transport. In this paper, the load and delivery of NH4+-N in Poyang Lake Basin (PLB) and Haihe River Basin (HRB) were estimated using SPARROW. In PLB, NH4+-N load streamflow transport originating from point sources and farmland accounted for 41.83% and 32.84%, respectively. In HRB, NH4+-N load streamflow transport originating from residential land and farmland accounted for 40.16% and 36.75%, respectively. Hence, the following measures should be taken: In PLB, it is important to enhance the management of the point sources, such as municipal and industrial wastewater. In HRB, feasible measures include controlling the domestic pollution and reducing the usage of chemical fertilizers. In addition, increasing the vegetation coverage of both basins may be beneficial to their nutrient management. The SPARROW models built for PLB and HRB can serve as references for future uses for different basins with various conditions, extending this model’s scope and adaptability.

The projected impacts of smart decline on urban runoff contamination levels

There has been mounting interest about how the repurposing of vacant land (VL) through green infrastructure (the most common smart decline strategy) can reduce stormwater runoff and improve runoff quality, especially in legacy cities characterized by excessive industrial land uses and VL amounts. This research examines the long-term impacts of smart decline on both stormwater amounts and pollutants loads through integrating land use prediction models with green infrastructure performance models. Using the City of St. Louis, Missouri, USA as the study area, we simulate 2025 land use change using the Conversion of Land Use and its Effects (CLUE-S) and Markov Chain urban land use prediction models and assess these change's probable impacts on urban contamination levels under different smart decline scenarios using the Long-Term Hydrologic Impact Assessment (L-THIA) performance model. The four different scenarios are: (1) a baseline scenario, (2) a 10% vacant land re-greening (VLRG) scenario, (3) a 20% VLRG scenario, and (4) a 30% VLRG scenario. The results of this study illustrate that smart decline VLRG strategies can have both direct and indirect impacts on urban stormwater runoff and their inherent contamination levels. Direct impacts on urban contamination include the reduction of stormwater runoff and non-point source (NPS) pollutants. In the 30% VLRG scenario, the annual runoff volume decreases by 11%, both physical, chemical, and bacterial pollutants are reduced by an average of 19%, compared to the baseline scenario. Indirect impacts include reduction of the possibility of illegal dumping on VL through mitigation and prevention of future vacancies.

Development and Assessment of a New Framework for Agricultural Nonpoint Source Pollution Control

The transport of agricultural nonpoint source (NPS) pollutants in water pathways is affected by various factors such as precipitation, terrain, soil erosion, surface and subsurface flows, soil texture, land management, and vegetation coverage. In this study, based on the transmission mechanism of NPS pollutants, we constructed a five-factor model for predicting the path-through rate of NPS pollutants. The five indices of the hydrological processes, namely the precipitation index (α), terrain index (β), runoff index (TI), subsurface runoff index (LI), and buffer strip retention index (RI), are integrated with the pollution source data, including the rural living, livestock and farmland data, obtained from the national pollution source census. The proposed model was applied to the headwater of the Miyun Reservoir watershed for identifying the areas with high path-through rates of agricultural NPS pollutants. The results demonstrated the following. (1) The simulation accuracy of the model is acceptable in mesoscale watersheds. The total nitrogen (TN) and total phosphorus (TP) agriculture loads were determined as 705.11 t and 3.16 t in 2014, with the relative errors of the simulations being 19.62% and 24.45%, respectively. (2) From the spatial distribution of the agricultural NPS, the TN and TP resource loads were mainly distributed among the upstream of Dage and downstream of Taishitun, as well as the towns of Bakshiying and Gaoling. The major source of TN was found to be farmland, accounting for 47.6%, followed by livestock, accounting for 37.4%. However, the path-through rates of TP were different from those of TN; rural living was the main TP source (65%). (3) The path-through rates of agricultural NPS were the highest for the towns of Wudaoying, Dage, Tuchengzi, Anchungoumen, and Huodoushan, where the path-through rate of TN ranged from 0.17 to 0.26. As for TP, it was highest in Wudaoying, Kulongshan, Dage, and Tuchengzi, with values ranging from 0.012 to 0.019. (4) A comprehensive analysis of the distribution of the NPS pollution load and the path-through rate revealed the towns of Dage, Wudaoying, and Tuchengzi as the critical source areas of agricultural NPS pollutants. Therefore, these towns should be seriously considered for effective watershed management. In addition, compared with field monitoring, the export coefficient model, and the physical-based model, the proposed five-factor model, which is based on the path-through rate and the mechanism of agricultural NPS pollutant transfer, cannot only obtain the spatial distribution characteristics of the path-through rate on a field scale but also be applicable to large-scale watersheds for estimating the path-through rates of NPS pollutants.

Enhanced streamflow prediction with SWAT using support vector regression for spatial calibration: A case study in the Illinois River watershed, U.S

Accurate streamflow prediction plays a pivotal role in hydraulic project design, nonpoint source pollution estimation, and water resources planning and management. However, the highly non-linear relationship between rainfall and runoff makes prediction difficult with desirable accuracy. To improve the accuracy of monthly streamflow prediction, a seasonal Support Vector Regression (SVR) model coupled to the Soil and Water Assessment Tool (SWAT) model was developed for 13 subwatersheds in the Illinois River watershed (IRW), U.S. Terrain, precipitation, soil, land use and land cover, and monthly streamflow data were used to build the SWAT model. SWAT Streamflow output and the upstream drainage area were used as two input variables into SVR to build the hybrid SWAT-SVR model. The Calibration Uncertainty Procedure (SWAT-CUP) and Sequential Uncertainty Fitting-2 (SUFI-2) algorithms were applied to compare the model performance against SWAT-SVR. The spatial calibration and leave-one-out sampling methods were used to calibrate and validate the hybrid SWAT-SVR model. The results showed that the SWAT-SVR model had less deviation and better performance than SWAT-CUP simulations. SWAT-SVR predicted streamflow more accurately during the wet season than the dry season. The model worked well when it was applied to simulate medium flows with discharge between 5 m³ s^-1 and 30 m³ s^-1, and its applicable spatial scale fell between 500 to 3000 km². The overall performance of the model on yearly time series is “Satisfactory”. This new SWAT-SVR model has not only the ability to capture intrinsic non-linear behaviors between rainfall and runoff while considering the mechanism of runoff generation but also can serve as a reliable regional tool for an ungauged or limited data watershed that has similar hydrologic characteristics with the IRW.

Nonlinear Water Quality Response to Numerical Simulation of In Situ Phosphorus Control Approaches

The nonlinear and heterogeneous responses of nutrients to eutrophication control measures are a major challenge for in situ treatment engineering design, especially for large water bodies. Tackling the problem calls for a full understanding of potential water quality responses to various treatment schemes, which cannot be fulfilled by empirical-based methods or small-scale tests. This paper presents a methodology for Phoslock application based on the idea of object-oriented intelligent engineering design (OOID), which includes numerical simulation to explore the features of responses to numerous assumed schemes. A large plateau lake in Southwestern China was employed as a case study to illustrate the characteristics of the water quality response and demonstrate the applicability of this new approach. It was shown by the simulation and scenario analysis that the water quality response to Phoslock application always reflected nonlinearity and spatiotemporal heterogeneity, and always varied with objects, boundary conditions, and engineering design parameters. It was also found that some design parameters, like release position, had a significant impact on efficiency. Thus, a remarkable improvement could be obtained by cost-effective analysis based on scenarios using combinations of design parameters.

Assessing the Feasibility of a Cloud-Based, Spatially Distributed Modeling Approach for Tracking Green Stormwater Infrastructure Runoff Reductions

Use of green stormwater infrastructure (GSI) to mitigate urban runoff impacts has grown substantially in recent decades, but municipalities often lack an integrated approach to prioritize areas for implementation, demonstrate compelling evidence of catchment-scale improvements, and communicate stormwater program effectiveness. We present a method for quantifying runoff reduction benefits associated with distributed GSI that is designed to align with the spatial scale of information required by urban stormwater implementation. The model was driven by a probabilistic representation of rainfall events to estimate annual runoff and reductions associated with distributed GSI for various design storm levels. Raster-based calculations provide estimates on a 30-m grid, preserving unique combinations of drainage factors that drive runoff production, hydrologic storage, and infiltration benefits of GSI. The model showed strong correspondence with aggregated continuous runoff data from a set of urbanized catchments in Salinas, California, USA, over a three-year monitoring period and output sensitivity to the storm drain network inputs. Because the model runs through a web browser and the parameterization is based on readily available spatial data, it is suitable for nonmodeling experts to rapidly update GSI features, compare alternative implementation scenarios, track progress toward urban runoff reduction goals, and demonstrate regulatory compliance.

A comparative evaluation of the continuous and event-based modelling approaches for identifying critical source areas for sediment and phosphorus losses

Proper identification of critical source areas (CSAs) is important for economic viability of any best management practices (BMPs) aimed at reducing sediment and phosphorus loads to receiving water bodies. Both continuous and event-based hydrologic and water quality models are widely used to identify and assess CSAs, however, their comparative assessment is lacking. In this study, we have used continuous Soil and Water Assessment Tool (SWAT) and event-based Agriculture Non-Point Source (AGNPS) pollution models to identify CSAs for sediment and phosphorus in a watershed in Ontario, Canada. Along with their original version, both models were re-conceptualized to incorporate saturation excess mechanism of runoff generation, which is also refereed as variable source area (VSA) integration. The models were set-up using high resolution spatial, crop- and land-management, and meteorological dataset; and calibrated with reasonable accuracy against streamflow, sediment and phosphorus concentration data at multiple locations. Threshold value (t-value) approach was used to identify CSA areas in the watershed. Results showed that both models were in agreement (up to 96% of fields) that summer season did not constitute hot-moments (<6% of the watershed area as CSAs) for both sediment and phosphorus. SWAT models identified winter (~50% of watershed area as CSA) and AGNPS models identified early spring (~50% of watershed areas as CSAs) season as the hot-moment for both sediment and phosphorus. Contrasting result, as indicated by low (1%) matching in field CSA potential, was observed in autumn season. In the same season, VSA integrated SWAT and AGNPS models showed better matching (43% for sediment and 31% for phosphorus), highlighting the importance of VSA integration in the models. Qualitative validation of model-based CSA potential with oblique aerial-photograph-based CSA potential in two soil moisture conditions (wetter and drier) indicated slightly better performance of the SWAT models, and over-prediction of the AGNPS models. However, a more comprehensive analysis based on more detailed field observations is needed to further confirm the results.

Modelling spatio-temporal patterns of soil carbon and greenhouse gas emissions in grazing lands: Current status and prospects

The sustainability of grazing lands lies in the nexus of human consumption behavior, livestock productivity, and environmental footprint. Due to fast growing global food demands, many grazing lands have suffered from overgrazing, leading to soil degradation, air and water pollution, and biodiversity losses. Multidisciplinary efforts are required to understand how these lands can be better assessed and managed to attain predictable outcomes of optimal benefit to society. This paper synthesizes our understanding based on previous work done on modelling the influences of grazing of soil carbon (SC) and greenhouse gas emissions to identify current knowledge gaps and research priorities. We revisit three widely-used process-based models: DeNitrification DeComposition (DNDC), DayCent, and the Pasture Simulation model (PaSim) and two watershed models: The Soil & Water Assessment Tool (SWAT) and Variable Infiltration Capacity Model (VIC), which are widely used to simulate C, nutrient and water cycles. We review their structures and ability as process-based models in representing key feedbacks among grazing management, SOM decomposition and hydrological processes in grazing lands. Then we review some significant advances in the use of models combining biogeochemical and hydrological processes. Finally, we examine challenges of incorporating spatial heterogeneity and temporal variability into modelling C and nutrient cycling in grazing lands and discuss their weakness and strengths. We also highlight key research direction for improving the knowledge base and code structure in modelling C and nutrient cycling in grazing lands, which are essential to conserve grazing lands and maintain their ecosystem goods and services.