A SWAT-based optimization tool for obtaining cost-effective strategies for agricultural conservation practice implementation at watershed scales

An integrated tool for cost-effectively applying nutrient management practices for corn, soybeans, and wheat

Harmful algal blooms (HABs) problem in Lake Erie has become critical recently-primarily triggered by phosphorus losses from cropland in the Maumee River watershed (major crops of corn/soybeans/wheat). Implementing agricultural best management practices (BMPs) is crucial to reduce excess nutrient loadings. Nutrient management is the management of nutrient applications for crop production that maximizes nutrient use efficiencies and minimizes nutrient losses. However, an integrated watershed-scale tool is needed for cost-effectively applying nutrient management practices for corn/soybeans/wheat considering the 4Rs (Right nutrient source, Right rate, Right time, and Right place of nutrient applications). In this study, by combining an improved Soil and Water Assessment Tool (SWAT) for nutrient management (SWAT-NM) and an improved BMP Cost Evaluation Tool (BMP-COST) for economic evaluations of nutrient management (BMP-COST-NM) considering the 4Rs for corn/soybeans/wheat, an integrated tool SWAT-COST-NM was created. SWAT-COST-NM was demonstrated in the AXL watershed (a typical agricultural area in the Maumee River watershed). The impacts of single nutrient management practices (single-NM, which separately changed the rate, place, time, or nutrient source of fertilizer applications) and combined-NM practices (multiple single-NM practices combined as one nutrient management practice) for corn/soybeans/wheat were evaluated. Tradeoffs in yearly net costs, crop yields, and March-July/yearly nutrient losses (Total Phosphorus-TP, Dissolved Reactive Phosphorus-DRP, and Total Nitrogen-TN) existed. Nutrient management did not necessarily lead to sufficient increases in crop yields to generate extra revenues that match or exceed the additional costs of the activities (compared to existing practices). One of the combined-NM practices could simultaneously reduce March-July TP, DRP, and TN losses by 5.89%, 8.19%, and 8.23%, respectively, while increasing crop yields with additional income (0.50 $/ha/yr of cropped area). SWAT-COST-NM, which can quantify various factors and tradeoffs when evaluating the impacts of nutrient management practices for corn/soybeans/wheat, can assist decision-makers in cost-effectively applying nutrient management practices considering the 4Rs.

Enhancing the SWAT model for creating efficient rainwater harvesting and reuse strategies to improve water resources management

Rain barrels/cisterns are a type of green infrastructure (GI) practice that can help restore urban hydrology. Roof runoff captured and stored by rain barrels/cisterns can serve as a valuable resource for landscape irrigation, which would reduce municipal water usage and decrease runoff that other stormwater infrastructures need to treat. The expected benefits of rainwater harvesting and reuse with rain barrels/cisterns are comprehensive but neither systematically investigated nor well documented. A comprehensive tool is needed to help stakeholders develop efficient strategies to harvest rainwater for landscape irrigation with rain barrels/cisterns. This study further improved the Soil and Water Assessment Tool (SWAT) in simulating urban drainage networks by coupling the Storm Water Management Model (SWMM)'s closed pipe drainage network (CPDN) simulation methods with the SWAT model that was previously improved for simulating the impacts of rainwater harvesting for landscape irrigation with rain barrels/cisterns. The newly improved SWAT or SWAT-CPDN was applied to simulate the urban hydrology of the Brentwood watershed (Austin, TX) and evaluate the long-term effects of rainwater harvesting for landscape irrigation with rain barrels/cisterns at the field and watershed scales. The results indicated that the SWAT-CPDN could improve the prediction accuracy of urban hydrology with good performance in simulating discharges (15 min, daily, and monthly), evapotranspiration (monthly), and leaf area index (monthly). The impacts of different scenarios of rainwater harvesting and reuse strategies (rain barrel/cistern sizes, percentages of suitable areas with rain barrels/cisterns implemented, auto landscape irrigation rates, and landscape irrigation starting times) on each indicator (runoff depth, discharge volume, peak runoff, peak discharge, combined sewer overflow-CSO, freshwater demand, and plant growth) at the field or watershed scale varied, providing insights for the long-term multi-functional impacts (stormwater management and rainwater harvesting/reuse) of rainwater harvesting for landscape irrigation with rain barrels/cisterns. The varied rankings of scenarios found for achieving each goal at the field or watershed scale indicated that tradeoffs in rainwater harvesting and reuse strategies exist for various goals, and the strategies should be evaluated individually for different goals to optimize the strategies. Efficient rainwater harvesting and reuse strategies at the field or watershed scale can be created by stakeholders with the assist of the SWAT-CPDN to reduce runoff depth, discharge volume, peak runoff, peak discharge, CSO, and freshwater demand, as well as improve plant growth.

Unraveling spatial patterns and source attribution of nutrient transport: Towards optimal best management practices in complex river basin

A comprehensive understanding of nutrient transport patterns and clarification of pollutant sources' load contributions are critical prerequisites for developing scientific pollution control strategies in complex river basins. Here, we focused on the Minjiang River Basin (MRB) and employed the Soil and Water Assessment Tool (SWAT) model to systematically investigate the nitrogen (N) and phosphorus (P) loads from both point and non-point sources. Results revealed that the key source areas of N and P pollution in the MRB were predominantly located along the riverbanks, influenced by a combination of sediment, precipitation, agricultural activities such as fertilization. Our analysis indicated that soil nutrient loss, fertilization, and livestock farming were the major contributors to N and P inputs, accounting for over 70 % of the total input, followed by rural residential and urban point sources. Based on the identification of non-point source pollution as the primary load source, a multi-objective optimization was conducted using response surface methodology (RSM) coupled with the non-dominated sorting genetic algorithm-II (NSGA-II), resulting in the identification of optimal best management practices (BMPs) that achieve a reduction of 40.04 % in N load, 39.22 % in P load, and a net economic benefit of -1.13 billion yuan per year. Compared to the RSM and automated optimization results, the proposed management measures exhibited significant improvements in N and P load reduction and net benefits. Overall, the findings provide important insights for formulating agricultural management policies in the MRB and offering valuable implications for pollution management in other complex river basins.

Building a library of source samples for sediment fingerprinting - Potential and proof of concept

PURPOSE

Sediment fingerprinting of fluvial targets has proven useful to guide conservation management and prioritize sediment sources for Federal and State supported programs in the United States. However, the collection and analysis of source samples can make these studies unaffordable, especially when needed for multiple drainage basins. We investigate the potential use of source samples from a basin with similar physiography (using samples from one of a "pair" to evaluate samples from the other) or combined from multiple basins (a "library").

METHODS

Source samples from eight basins across six ecoregions were harvested from existing, published studies. Individual source samples were fingerprinted using a mixing model derived from source samples from other basins. The ability to identify source category was evaluated both as part of source verification and by classifying source samples as "targets."

RESULTS

Approximately half of cropland samples were identified as targets, both as pairs and with the multi-basin source dataset, indicating that cropland samples could be shared for basins in similar ecoregions and be combined for larger stream systems. Streambank samples were better identified with the multi-basin analysis relative to the pairs, and those from mixed land-use basins improved this differentiation except for samples from basins with a dominant land-use type. Inconsistent identification of pasture samples highlighted the need for local samples. Inconsistent identification of forest samples indicated that upland- and riparian-forest samples are distinct. Road samples were identified as both sources and targets, and other source types were rarely apportioned as road: these may have the best potential to supplement local source samples. This source-sample library was then used to improve the accuracy of sediment-source apportionment for a previously studied basin.

CONCLUSION

Ultimately, the source verification process already used in individual basin studies to evaluate the accuracy of sediment-fingerprinting apportionments was useful for determining how to supplement local source samples with those from other basins. This study shows that supplementing local source samples with those from basins with similar physiography has the potential to both improve fingerprinting accuracy and decrease the cost of this type of study.

Optimization and multi-uncertainty analysis of best management practices at the watershed scale: A reliability-level based bayesian network approach

Best management practices (BMPs) have been widely adopted to mitigate diffuse source pollutants, and the simulated processes of its pollutant reduction effectiveness suffer from manifold uncertainties, such as watershed model parameters and climate change. We presented a novel Bayesian modeling framework for BMPs planning, integrating process-based watershed modeling and Bayesian optimization algorithm to reveal the impact of multiple uncertainties. The proposed framework was applied to a BMPs planning case study in the Erhai watershed, the seventh-largest freshwater lake in China. Firstly, priority management areas (PMAs) were identified for BMPs siting using a simulation-optimization approach. Bayesian networks were subsequently embedded to reveal the multiple uncertainty sources in the optimal planning and the reliability level (RL) is introduced to represent the probability to meet the water quality target with BMPs implementation. The results suggest that E_NS of discharge and nutrients concentration simulation by LSPC are both greater than 0.5, which displays satisfactory performance. The identified PMAs account for 0.8% of the total watershed areas while contribute to more than 15% of nutrient loadings reduction. The analysis of multiple uncertainty sources reveals that precipitation is the most influential source of uncertainties in BMP effectiveness. The construction of hedgerows plays an important role in the nutrient reduction. With the improvement of the reliability levels, the cost increases sharply, indicating that the implementation of BMPs has a marginal utility. The study addressed the urgent need for effective and efficient BMPs planning by identifying PMAs and addressing multi-source uncertainties.

Seasonal variation, contribution and dynamics of trace elements in the drainage basin and estuary of the Serinhaém river, BA

In the present study a mass balance calculation was used to quantify trace elements (Al, Ba, Cd, Cr, Cu, Fe, Mn, Pb, Ti, V and Zn) fluxes exported from the Serinhaém River estuary to the Atlantic Ocean. The studied elements exportation in the particulate fraction showed higher fluxes in the first sampling campaign and a high export rate to the Atlantic Ocean during this period. The physical-chemical parameters showed the highest values in sampling campaign 1. These variations are probably the cause of the different trace elements behavior in fluvial and estuarine areas, where removal and addition processes between particulate and dissolved phases took place, affecting distribution coefficient and fluxes to the Atlantic Ocean. EPA ecosystems present values in accordance with Brazilian legislation for pristine areas, however, monitoring programs must be carried out in the region, to avoid future environmental problems.

Watershed-scale optimum livestock distribution and crop pattern planning constrained to the minimum nitrogen and phosphorus load in the runoff

Sustainable crop and livestock planning encounter serious challenges when tasked with reducing the associated nutrient pollution entering the watershed environment. To overcome these challenges, approaches for specifying optimal crop pattern and livestock distribution to limit the pollution in the catchment are advised. In this research, a simulation-optimization approach is used in which the Soil and Water Assessment Tool (SWAT) is employed for simulating the complex soil-water-plant quantity and quality relations, and the Harmony Search (HS) algorithm linked with SWAT is used to discover the optimal crop pattern and distribution of livestock in the Ilam Dam basin, Iran. In the developed HS-SWAT model, the cultivation area and the number of livestock in SWAT's hydrologic response units (HRU) are the decision variables for maximizing the net benefit obtained from the crop's and livestock's productions, while the nitrate and phosphate calculated in the outflow of the basin are restrained to meet the allowable rates. Results show that the scattered livestock in the basin have a great impact on the generated pollution where about 90% of the nitrate entering the downstream reservoir is the consequence of animal waste. In the optimum state, by reduction of the cultivation area and the number of livestock across the watershed, the concentration of N and P in the surface runoff is reduced significantly to meet the allowable level. According to the results, the HS-SWAT model performance indicates its capability for solving watershed crop pattern and livestock planning problems.

The Effects of Agricultural Conservation Practices on the Small Water Cycle: From the Farm- to the Management-Scale

Reinforcing the small water cycle is considered to be a holistic approach to both water resource and landscape management. In an agricultural landscape, this can be accomplished by incorporating agricultural conservation practices; their incorporation can reduce surface runoff, increase infiltration, and increase the water holding capacity of a soil. Some typical agricultural conservation practices include: conservation tillage, contour farming, residue incorporation, and reducing field sizes; these efforts aim to keep both water and soil in the landscape. The incorporation of such practices has been extensively studied over the last 40 years. The Soil and Water Assessment Tool (SWAT) was used to model two basins in the Czech Republic (one at the farm-scale and a second at the management-scale) to determine the effects of agriculture conservation practice adoption at each scale. We found that at the farm-scale, contour farming was the most effective practice at reinforcing the small water cycle, followed by residue incorporation. At the management-scale, we found that the widespread incorporation of agricultural conservation practices significantly reinforced the small water cycle, but the relative scale and spatial distribution of their incorporation were not reflected in the SWAT scenario analysis. Individual farmers should be incentivized to adopt agricultural conservation practices, as these practices can have great effects at the farm-scale. At the management-scale, the spatial distribution of agricultural conservation practice adoption was not significant in this study, implying that managers should incentivize any adoption of such practices and that the small water cycle would be reinforced regardless.

Improvement of simulating sub-daily hydrological impacts of rainwater harvesting for landscape irrigation with rain barrels/cisterns in the SWAT model

Rain barrels/cisterns, a popular type of low impact development (LID) practice, can restore urban hydrological processes and decrease municipal water use by harvesting roof runoff for later use, such as landscape irrigation. However, tools to assist decision makers in creating efficient rainwater harvesting and reuse strategies are limited. This study improved the Soil and Water Assessment Tool (SWAT) in simulating the subdaily hydrological impacts of rainwater harvesting for landscape irrigation with rain barrels/cisterns, including the simulation of rainwater harvesting with rain barrels/cisterns, rainwater reuse for auto landscape irrigation, evapotranspiration, initial abstraction, impervious area, soil profile, and lawn management operation. The improved SWAT was applied in the urbanized Brentwood watershed (Austin, TX) to evaluate its applicability and investigate the impacts of rainwater harvesting and reuse strategies on the reductions and reduction efficiencies (reductions per volume of rain barrels/cisterns implemented) of field scale runoff (peak and depth) and watershed scale streamflow (peak and volume) for two storm events. Scenarios explored included different sizes of rain barrels/cisterns, percentages of rooftop areas with rain barrels/cisterns implemented, auto landscape irrigation rates, and landscape irrigation starting times. The performance of rainwater harvesting and reuse strategies, which is determined by features of fields, watersheds, and storm events, varied for different reduction goals (streamflow or runoff, and peak or depth/volume). For instance, the scenario with rain barrel/cistern sizes of 7.5 mm (design runoff depth from treated roof area) and the scenario with 10% of suitable area implemented with rain barrels/cisterns provided the highest peak streamflow reduction efficiency and total streamflow volume reduction efficiency at the watershed scale, respectively for the smaller storm event. To achieve sustainable urban stormwater management, the improved SWAT model has enhanced capability to help stakeholders create efficient rainwater harvesting and reuse strategies to reduce field scale runoff and watershed scale streamflow.

Basin-Scale Pollution Loads Analyzed Based on Coupled Empirical Models and Numerical Models

Pollutant source apportionment is of great significance for water environmental protection. However, it is still challenging to accurately quantify pollutant loads at basin-scale. Refined analytical methods combined the pollution discharge coefficient method (PDCM), field observation, and numerical model (Soil & Water Assessment Tool, SWAT) to make quantitative source appointment in the Tuojiang River, a key tributary of the upper Yangtze River. The chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (N-NH4+) were analyzed. Results showed that the urban sewage treatment plant point source has the largest contribution to COD, TN, and N-NH4+, while TP is mostly from the agricultural sources throughout the year. The total inflowing loads of pollution sources are significantly affected by rainfall. The overall pollution characteristics showed that pollutant loads present in different seasons are as follows: wet season > normal season > dry season. The month with the highest levels of pollutants is July in the wet season. Among the nine cities, the city that contributes the most COD, TN and N-NH4+, is Neijiang, accounting for about 25%, and the city that contributes the most TP is Deyang, accounting for 23%. Among the sub-basins, the Fuxi River subbasin and Qiuxihe River subbasin contribute the most pollutant loads. The technical framework adopted in this paper can be used to accurately identify the types, administrative regions and sub-basins of the main pollution sources in the watershed, which is conducive to management and governance of the environment.

Prioritizing actions: spatial action maps for conservation

Spatial prioritization is a critical step in conservation planning, a process designed to ensure that limited resources are applied in ways that deliver the highest possible returns for biodiversity and human wellbeing. In practice, many spatial prioritizations fall short of their potential by focusing on places rather than actions, and by using data of snapshots of assets or threats rather than estimated impacts. We introduce spatial action mapping as an approach that overcomes these shortfalls. This approach produces a spatially explicit view of where and how much a given conservation action is likely to contribute to achieving stated conservation goals. Through seven case examples, we demonstrate simple to complex versions of how this method can be applied across local to global scales to inform decisions about a wide range of conservation actions and benefits. Spatial action mapping can support major improvements in efficient use of conservation resources and will reach its full potential as the quality of environmental, social, and economic datasets converge and conservation impact evaluations improve.

Source contribution analysis of nutrient pollution in a P-rich watershed: Implications for integrated water quality management

It is still a great challenge to address nutrient pollution issues caused by various point sources and non-point sources on the watershed scale. Source contribution analysis based on watershed modeling can help watershed managers identify major pollution sources, propose effective management plans and make smart decisions. This study demonstrated a technical procedure for addressing watershed-scale water pollution problems in an agriculture-dominated watershed, using the Dengsha River Watershed (DRW) in Dalian, China as an example. The SWAT model was improved by considering the constraints of soil nutrient concentration, i.e., nitrogen (N) and phosphorus (P), when modeling the nutrient uptake by a typical crop, corn. Then the modified SWAT model was used to quantify the contributions of all known pollution sources to the N and P pollution in the DRW. The results showed that crop production and trans-administrative wastewater discharge were the two dominant sources of nutrient pollution. This study further examined the responses of nutrient loss and crop yield to different fertilizer application schemes. The results showed that N fertilizer was the limiting factor for crop yield and that excessive levels of P were stored in the agricultural soils of the DRW. An N fertilizer application rate of approximately 40% of the current rate was suggested to balance water quality and environmental protection with crop production. The long-term impact of legacy P was investigated with a 100-year future simulation that showed the crop growth could maintain for 12 years even after P fertilization ceased. Our study highlights the need to consider source attribution, fertilizer application and legacy P impacts in agriculture-dominated watersheds. The analysis framework used in this study can provide a scientifically sound procedure for formulating adaptive and sustainable nutrient management strategies in other study areas.

Evaluation of thiobencarb runoff from rice farming practices in a California watershed using an integrated RiceWQ-AnnAGNPS system

The development of modeling technology to adequately simulate water and pesticide movement within the rice paddy environment faces several challenges. These include: (1) adequately representing ponded conditions; (2) the collection/implementation of temporal/spatial pesticide application data at field scales; (3) the integration of various mixed-landuses simulation schemes. Currently available models do not fully consider these challenges and results may not be sufficiently accurate to represent fate and transport of rice pesticides at watershed scales. Therefore, in this study, an integrated simulation system, "RiceWQ-AnnAGNPS", was developed to fully address these challenges and is illustrated in a California watershed with rice farming practices. The integrated system successfully extends field level simulations to watershed scales while considering the impact of mixed landuses on downstream loadings. Moreover, the system maintains the application information at fine spatial scales and handles varying treated paddy areas via the "split and adjust" approach. The new system was evaluated by investigating the fate and transport of thiobencarb residues in the Colusa Basin, California as a case study. Thiobencarb concentrations in both water and sediment phases were accurately captured by the calibrated RiceWQ model at the edge of field. After spatial upscaling, the integrated system successfully reflected both the seasonal pattern of surface runoff and the timing of monthly thiobencarb loadings. Incorporating future enhancements can further improve model performance by including more detailed water drainage schedules and management practices, improving the accuracy of summer runoff estimations, and incorporating a more sophisticated in-stream process module. This integrated system provides a framework for evaluating rice pesticide impacts as part of a basin level management approach to improve water quality, which can be extended to other rice agrochemicals, or other areas with fine-scale spatial information of pesticide applications.

The Role of Ponds in Pesticide Dissipation at the Agricultural Catchment Scale: A Critical Review

Ponds in agricultural areas are ubiquitous water retention systems acting as reactive biogeochemical hotspots controlling pesticide dissipation and transfer at the catchment scale. Several issues need to be addressed in order to understand, follow-up and predict the role of ponds in limiting pesticide transfer at the catchment scale. In this review, we present a critical overview of functional processes underpinning pesticide dissipation in ponds. We highlight the need to distinguish degradative and non-degradative processes and to understand the role of the sediment-water interface in pesticide dissipation. Yet it is not well-established how pesticide dissipation in ponds governs the pesticide transfer at the catchment scale under varying hydro-climatic conditions and agricultural operation practices. To illustrate the multi-scale and dynamic aspects of this issue, we sketch a modelling framework integrating the role of ponds at the catchment scale. Such an integrated framework can improve the spatial prediction of pesticide transfer and risk assessment across the catchment-ponds-river continuum to facilitate management rules and operations.

Using Multiobjective Optimization to Inform Green Infrastructure Decisions as Part of Robust Integrated Water Resources Management Plans

Uncertainty in the impacts of climate change and development on freshwater resources pose significant challenges for water resources management. Integrated and adaptive approaches to water resources management are a promising means of addressing uncertainty that afford flexibility in balancing multiple stakeholder objectives. However, guidance on designing such plans is lacking. In this study, we use multi-objective optimization to strategically incorporate green infrastructure (GI) into water resources management plans that maximize reductions in nutrient loads, minimize stormwater runoff, and minimize costs. Robust decision-making methods are applied to the resulting plan options to evaluate how optimized GI implementation varies under different possible future climates and to determine which solutions would be robust under a range of plausible future conditions. We demonstrate these coupled methods using a case study in southern Massachusetts, to address water quality issues related to point and nonpoint source nutrients in a rapidly developing watershed.

Source contribution to phosphorus loads from the Maumee River watershed to Lake Erie

Coastal eutrophication is a leading cause of degraded water quality around the world. Identifying the sources and their relative contributions to impaired downstream water quality is an important step in developing management plans to address water quality concerns. Recent mass-balance studies of Total Phosphorus (TP) loads of the Maumee River watershed highlight the considerable phosphorus contributions of non-point sources, including agricultural sources, degrading regional downstream water quality. This analysis builds upon these mass-balance studies by using the Soil and Water Assessment Tool to simulate the movement of phosphorus from manure, inorganic fertilizer, point sources, and soil sources, and respective loads of TP and Dissolved Reactive Phosphorus (DRP). This yields a more explicit estimation of source contribution from the watershed. Model simulations indicate that inorganic fertilizers contribute a greater proportion of TP (45% compared to 8%) and DRP (58% compared to 12%) discharged from the watershed than manure sources in the March-July period, the season driving harmful algal blooms. Although inorganic fertilizers contributed a greater mass of TP and DRP than manure sources, the two sources had similar average delivery fractions of TP (2.7% for inorganic fertilizers vs. 3.0% for manure sources) as well as DRP (0.7% for inorganic fertilizers vs. 1.2% for manure sources). Point sources contributed similar proportions of TP (5%) and DRP (12%) discharged in March-July as manure sources. Soil sources of phosphorus contributed over 40% of the March-July TP load and 20% of the March-July DRP load from the watershed to Lake Erie. Reductions of manures and inorganic fertilizers corresponded to a greater proportion of phosphorus delivered from soil sources of phosphorus, indicating that legacy phosphorus in soils may need to be a focus of management efforts to reach nutrient load reduction goals. In agricultural watersheds aground the world, including the Maumee River watershed, upstream nutrient management should not focus solely on an individual nutrient source; rather a comprehensive approach involving numerous sources should be undertaken.

Crop growth, hydrology, and water quality dynamics in agricultural fields across the Western Lake Erie Basin: Multi-site verification of the Nutrient Tracking Tool (NTT)

Agricultural field- and watershed-scale water quality models are used to assess the potential impact of management practices to reduce nutrient and sediment exports. However, observed data are often not available to calibrate and verify these models. Three years of data from the U.S. Department of Agriculture-Agricultural Research Service's 12 paired edge-of-field sites in northwest Ohio were used to calibrate and validate the Nutrient Tracking Tool. The goal of this study was to identify a single optimal parameter set for the Nutrient Tracking Tool in simulating annual crop yields, water balance, and nutrient loads across the Western Lake Erie Basin. A multi-site and multi-objective auto-calibration subroutine was developed in R to perform model calibration across the edge-of-field sites. The statistical metrics and evaluation criteria used in comparing the simulated results with the observed data were: Cohen's D Effect Size (Cohen's D < 0.20) and Percent bias (PBIAS ± 10% for crop yields, subsurface (tile) discharge, and surface runoff and ± 25% for dissolved reactive phosphorus (DRP) and nitrate‑nitrogen (nitrate-N) in tile discharge, and DRP, particulate phosphorus, and nitrate-N in surface runoff). In both calibration and validation, the Cohen's D and PBIAS for annual crop yields, tile discharge, surface runoff, DRP, particulate P, and nitrate-N showed that the average simulated results were similar to the average observed values for each variable. The calibrated model simulated well the annual averages of crop yields, flows, and nutrient losses across fields. The tile drainage and phosphorus transport subroutines in the Nutrient Tracking Tool should be further improved to better simulate the dynamics of discharge and phosphorus transport through subsurface drainage. Stakeholders can use the verified model to evaluate the effectiveness of conservation practices in improving the water quality across the Western Lake Erie Basin.