The Iowa Nutrient Reduction Strategy (INRS) establishes a goal of reducing nutrient discharge by 45% to Iowa streams and water bodies by 2035 (IDALS 2020), consistent with the nutrient reduction goal reported in the Gulf Hypoxia Action Plan (MRGMWN Task Force 2008). The INRS also embraced an interim Hypoxia Task Force goal to reduce nutrient losses 20% by 2025 (IDALS 2020). However, formidable challenges remain to attaining these goals as evidenced by pervasive elevated in-stream nitrogen (N) and phosphorus (P) levels in Iowa streams reported by Jones et al. (2018a; 2018b; 2019) and Schilling et al. (2020). Algal blooms have also been increasing in Iowa lakes and rivers, resulting in eutrophication, fish-kills, and harmful impacts on drinking water supplies, outdoor recreation, and tourism (IEC 2023; INRS 2023; Christianson et al. 2013). Mitigation of the seasonal hypoxic zone in the northern Gulf of Mexico, which is driven by nutrient export from the Mississippi River, has also proved elusive (Rabalais and Turner 2019).
We propose a methodology that integrates simulation models, pertinent data, and economic analysis to quantify the impacts of best management practices (BMPs) implementation on water quality and associated economic implications. Downing et al. (2021) state that economic studies of water quality regulations often report lower benefit estimates versus the costs, due in part to not understanding possible benefits of various ecosystem services. For example, new findings reveal that reducing N and P pollution in lakes and reservoirs not only reduces eutrophication, but also produces lower methane emissions that can impact the local and global climate (Downing et al. 2021).
Ecohydrological models can be used to test optimal management systems for cropland landscapes and to provide required inputs to economic models for both current and future nutrient reduction scenarios. In this study, we used the Soil and Water Assessment Tool (SWAT) model (https://swat.tamu.edu/), which has been applied worldwide for an extensive array of water resource problems (e.g., Akoko et al. 2021; Bressiani et al., 2015; Gassman et al. 2007, 2014; Tan et al. 2019; https://www.card.iastate.edu/swat_articles). SWAT’s simulation structure can represent spatially refined estimations of water quality, resulting in the model’s common use for simulation scenarios of changing land use, management conditions, and BMP implementation (Liu et al. 2019; Ricci et al. 2022; Secchi et al. 2007; Wang et al. 2019).
We developed an integrated assessment framework that features an interface between SWAT and an Economic Benefit Model (EBM) for the 31,892.4 km² Des Moines River Basin (DMRB) in central Iowa (figure 1a) to better understand the overall benefits of adopting different conservation practices. We chose the DMRB because: (a) it represents Iowa’s typical corn and soybean cropping system cropland; (b) it provides water quality insights relevant to the Des Moines metropolitan area; (c) an extensive collection of monitoring data is available for streamflow, nitrate, and P; and, (d) the study area contains a total of 31 lakes, which are essential for validating the proposed methodology (figure 1a).
Figure 1b describes the framework that links the SWAT model and the EBM. Brighenti et al. (2022; 2023) describes the development of the SWAT model. We divide the DMRB into subbasins representative of the HUC12 (USGS 2022; 2023) discretization (figure 1c). We use the SWAT model monthly nutrient outputs from 2001 to 2018 to assess the impact of field buffers and cover crops, and a combination of the two practices (stacked). We select target areas for BMP implementation—corn and soybean fields—and our simulated practices target 100% of this rotational land use. Furthermore, we incorporate N and P loads into the EBM to evaluate the BMP impacts in terms of economic benefits on water quality improvements, recreation impacts, and housing market impacts.
Mekonnen et al. (2015), Liu et al. (2019), Christianson et al. (2021), and Douglas-Mankin et al. (2021) establish the efficiency of field buffers and cover crops for reducing sediment and nutrient losses; and, modeling studies with SWAT, such as Kalcic et al. (2015), Merriman et al. (2018), and Motsinger et al. (2016), also show these practices effectively reduce pollutant losses. Iowa State University commonly recommends field buffers and cover crops for Iowa cropland landscapes (ISU 2022). Field buffers—strips of dense vegetation at the downslope boundary of a Hydrological Response Unit (HRU) in SWAT (for HRUs depicting a crop field)—intercept surface runoff. Field buffers remove contaminants by reducing overland flow and increasing infiltration area (Neitsch et al. 2011). We simulated field buffers in SWAT using White and Arnold’s (2009) filter strip routine. Cover crops can increase soil moisture capacity, and reduce sediment loss, nutrient runoff, and leaching. We implemented cover crops between corn and soybean crop rotations in SWAT, which we model as cereal rye.
We executed the models for four distinct scenarios: (a) a baseline, which represents current conditions and is used in the practices comparison; (b) cover crops, which are the same as the baseline with rye crop planted during fall; (c) field buffers, which are the same as the baseline with the implementation of vegetative strips for corn and soybean HRUs; and, (d) stacked, which is the implementation of cover crops and filter strips together. We applied the BMPs to 100% of the corn and soybean cropland HRUs in the DMRB.
We executed the SWAT model to simulate the impact of BMP implementation on water quality over an 18-year period to capture long-term effects. We analyzed model outputs such as nutrient loads to evaluate the effectiveness of the BMPs. Figure 2 and table 1 present the total nitrogen in surface runoff (TN), total phosphorus in surface runoff (TP), nitrate (NO3) and soluble phosphorus (SP) results for the three BMP scenarios (filter strips, cover crops, and stacked practices). We compare these results to the baseline scenario without BMP implementation.
|SWAT output||Cover crop||Field buffer||Stacked|
Overall, stacked practices—combining two strategies (i.e., cover crops and field buffers)—generate the most efficient nutrient reduction, showing a 36% reduction in NO3, a 55% reduction in TN, and a 70% reduction in TP. However, the reduction in SP was considerably less, showing only a 7% decrease in surface runoff. The field buffer was the second most effective scenario, showing a 2% reduction for NO3, 10% for SP, 37% for TN, and 65% for TP. The small reduction for NO3 is consistent with expectations (ISU 2022)—subsurface flow via tile drains transports the majority of NO3, thus surface vegetation does not capture it. The cover crop scenario results in 37% and 22% reductions in TN and TP respectively, and is the most effective practice when considering just NO3—a reduction of 34% (table 1 and figure 2). However, implementing cover crops results in a 6% increase of SP. This is consistent with a number of field studies as reported by Liu et al. (2019) and Nelson (2023), and underscores the need to consider all aspects of BMP effects when considering treatment approaches for a given watershed or region.
It is important to acknowledge that BMP efficiency in reducing N and P depends on several factors, including land use type, implementation scale, and the specific BMPs employed. Moreover, achieving significant nutrient reduction often requires a combination of BMPs, making it crucial to develop integrated approaches tailored to specific landscapes. For lake water quality analysis, the SWAT output of interest for the economic model is the TN and TP, which are used to compute the Secchi depth (table 1).
Our recreation models suggest Secchi depth is the only water quality measure with consistently significant coefficients; thus, we picked one random forest machine learning model to convert TN and TP output from the SWAT models to lake Secchi depth. In the first stage, we train our random forest model with Iowa Department of Natural Resources’ AQuIA database (https://programs.iowadnr.gov/aquia/) lake water quality data, such as TN, TP, and Secchi depth. In the second stage, we assume the TN and TP load changes in a HUC12 containing a lake as the changes from the baseline for each lake (based on the HUC12 in which the lake centroid is located). Table 1 also shows the model-predicted Secchi depth change in each scenario. The cover crop scenario produces the least improvement in Secchi depth, the field buffer scenario produces around 15% improvement, and, unsurprisingly, the stacked scenario implies the highest improvement (18%). The chosen random forest model shows the dominant effect of TP (i.e., lower TP equals better Secchi depth). Thus, the change in Secchi depth generally follows the ranking of TP reduction.
Table 2 shows recreation benefits in terms of compensating variation, a willingness-to-pay measure that indicates how much a person will pay for a given water quality change in our context.Our recreation model suggests that the highest benefit is associated with the stacked scenario since the Secchi depth improvement is the largest (table 1). The total benefit on average was around $17 million per year in the stacked scenario, followed by the field buffer scenario ($14 million) and the cover crop scenario ($1.3 million). Recreation benefits vary depending on which year of data we use. Ji et al. (2020) also find this temporal change, though our model here uses a slightly different model setting. Another observation is that though the improvement may happen in local lakes, the benefit spreads to other areas since local lakes attract households from other areas. CARD’s Iowa Lakes Project reports that the median travel distance from Iowa households to surveyed lakes (included in our study) is in the range of 30–60 miles as of 2019 (Wan, Ji, and Zhang 2019).1 In our specific case, the share of regional benefits is significant and larger than that of local benefits. On average, local benefits accounted for only one-third of the total benefit.
To estimate the housing impacts, we rely on two approaches: our own hedonic model built on Zillow Ztrax database and Iowa DNR water quality data, and the benefit transfer method built on Guignet et al. (2022).
Our primary study is based on a multivariate regression function that uses house sale price adjusted to 2020 prices, a water quality measure, Secchi depth, TN, TP, and a set of control variables such as whether the property is 100 meters or less (waterfront) or between 100 and 300 meters (nearby) from a water body,2 and includes typical house attributes such as building age, square footage, and number of bedrooms.3 We keep residential houses within 500 meters from a lake shore in our study. We adapt our preparation of Ztrax data from scripts provided by Zillow on the GitHub repository (https://github.com/zillow-research/ztrax).4 Once we have the estimated hedonic functions, we find the possible impacts of water quality change under different scenarios.
Guignet et al. (2022) provide the necessary unit elasticity information to conduct the benefit calculation and summarize these elasticities from their meta-regression model built on more than 20 individual hedonic studies on lake water quality. We choose the elasticities associated with TN, TP, and Secchi depth for our work.
Table 3 provides a summary of housing impacts under three SWAT scenarios. Several observations stand out. First, both our primary study and the benefit transfer approach detect the housing impacts under different scenarios, which, in almost all cases, agree with the impact direction. Second, the magnitude of the impacts differs between these two approaches. Third, within each approach, the impacts vary in response to which form of water quality measure we account for. Fourth, the majority of impacts come from the waterfront houses in most cases. With current estimates, it seems the specification with the linear Secchi depth in our primary study produces the closest estimates to the results from the benefit transfer approach with Secchi depth as the target water quality measure.
|Primary Study||Benefit Transfer|
|Linear WQ||logarithm WQ||Guignet et al. (2022)|
Using the annual average of recreation benefits and the housing benefit from the linear Secchi depth in our primary study, the stacked scenario produces the highest benefit with a total of about $19 million per year followed by the field buffer scenario ($15 million), then the cover crop scenario ($1.5 million).5
Rosen (1974) lays the theoretical foundation for the hedonic model to investigate the relationship between house prices and house attributes. However, the theory is quite silent on the specification and the form of house attributes included in the model. Thus, we expected the discrepancy shown in table 3. This creates a challenge for researchers in terms of how to choose the metric to quantify the benefits. An internal-meta analysis suggested in Klemick et al. (2018) would be useful here to know more about the uncertainties.6
Conclusion and future considerations
The methodology and preliminary results presented here demonstrate the possibility of implementing an integrated framework between SWAT and economic valuation models to study both environmental impacts and economic impacts of the adoption of BMPs at regional scale. More extensive calibration and validation of baseline SWAT sediment and nutrient loads is needed, along with accounting for a more complete set of BMPs. In addition, better economic evaluation models need to be developed to provide a more complete analysis of benefits associated with water quality. Overall, we believe this framework can contribute to water quality improvement programs and offer valuable information for researchers and stakeholders working in the field.
1. Interested readers can visit www.card.iastate.edu/lakes to learn more about our Iowa Lakes Project work and related concepts.
2. Our choice of cutoff distance follows Mamun et al (2023).
3. Formula available upon request.
4. Our hedonic analysis only has preliminary results at this stage. Interested readers can email email@example.com for the current model result and progress.
5. We use one-twentieth of the total housing benefits as the shortcut to annualize the total effect.
6. This is our second stage of work.
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