Elsevier

Environmental Modelling & Software

Volume 72, October 2015, Pages 327-340
Environmental Modelling & Software

Integrating statistical and hydrological models to identify implementation sites for agricultural conservation practices

https://doi.org/10.1016/j.envsoft.2015.01.018Get rights and content

Highlights

  • Novel techniques identify the best location for conservation practice installation.

  • Trellis plots help to determine the optimal distance to maximize pollution reduction.

  • Surface plots were created to visualize watershed response to pollution reduction.

  • This study helps decision makers and stakeholders in local and watershed-scale planning.

Abstract

Watershed models are scarcely used by watershed managers due to their complexity. This study facilitates information transfer by introducing simpler techniques related to easily obtained watershed characteristics, including distance to the watershed outlet and stream order. The Soil and Water Assessment Tool (SWAT) was calibrated for the Saginaw River Watershed, Michigan. Five agricultural best management practices (BMPs) were implemented in SWAT one at a time in each subbasin. Five statistical models were used to determine the pollution reduction at the watershed outlet using distance and BMP type, with results suggesting that a mixed effects model (model 5) was optimal. This model included subbasin as a random effect, while distance to watershed outlet and BMP type were fixed effects. Native grass and strip cropping were the most effective BMPs for reducing sediment and nutrient transport. Subbasins containing stream orders 1–3 were ideal for BMP implementation throughout the watershed.

Introduction

Maintaining sustainability of water resources while fulfilling the demand in global food and energy security is a challenging task, especially in light of increased global population and extreme climatic events (Jordan et al., 2012, Giri, 2013). Specifically, maintaining acceptable water quality is necessary to protect human, animal, and plant health. Preventing water quality degradation due to non-point source (NPS) pollution is difficult because of its diffuse and complex nature (Ouyang et al., 2009, Ding et al., 2010, Laia et al., 2011). According to the United States Environmental Protection Agency (USEPA), agriculture is the primary contributor of NPS pollution to rivers and lakes (USEPA, 2005). Agricultural NPS pollutants include sediment, nutrients, pathogens, organic materials, toxic chemicals and pesticides (USEPA, 1992). Excessive sediment loading in water bodies reduces dissolved oxygen levels and increases water temperature, negatively impacting aquatic organisms (Malone, 2009). An increase in nutrients such as nitrogen and phosphorus accelerates eutrophication, which is harmful to aquatic organisms. In addition, toxic chemicals and pesticides cause long-term contamination of surface and subsurface water resources and threaten both human and aquatic ecosystems (Love et al., 2011).

Best management practices (BMPs) are well known methods to reduce pollutants in surface runoff generated from agricultural lands (Arabi et al., 2007, Sommerlot et al., 2013a). The pollution reduction mechanism in BMPs follows three principles: 1) decreasing pollutant concentration through nutrient and pesticide management while reducing pollutant mass by erosion control practices; 2) reducing pollutant transport into waterbodies through various vegetative barriers; and 3) lowering pollutant mass through chemical and biochemical removal processes (Cunningham et al., 2003). However, the efficiency of BMPs varies within a watershed because it depends on various factors, including topography, soil characteristics, geological formations, climate, cropping systems, and cultural practices.

Efficiency of BMPs can be evaluated using monitoring or modeling, with monitoring being more accurate than modeling. However, monitoring water quality data throughout a watershed is time consuming, expensive, and often ineffective. In order to address these difficulties, modeling that utilizes currently available data is preferred because it is faster and less expensive than monitoring. Several studies have used physically-based watershed models to evaluate BMP effectiveness in improving water quality (Wang et al., 2009, Panagopoulos et al., 2012, Ciou et al., 2012, Amon-Armah et al., 2013, Lescot et al., 2013, Liu et al., 2013, Qiu, 2013, Sommerlot et al., 2013b). However, these models cannot be used effectively unless created with stakeholders' knowledge and used in development of a watershed management plan (Nejadhashemi et al., 2011, Sun, 2013). Involvement of stakeholders in each step of the watershed management plan is essential, as their social, economic, and environmental knowledge helps in achieving any proposed solution (Nejadhashemi et al., 2009). In addition, the likelihood of a successful watershed management plan is much higher if more effective stakeholder involvement takes place (Bosch et al., 2012). Effective stakeholder involvement occurs when they can employ scientific tools and data related to watershed processes and management (Maguire, 2003). However, most physically-based watershed models can be only used effectively by experts who have adequate training in agricultural science and significant knowledge in computer modeling (Saleh et al., 2011), making them less attractive to stakeholder groups.

In this study, the overall goal is to facilitate information transfer to stakeholders by introducing simpler techniques that predict pollution reduction at the watershed outlet using watershed characteristics. This would replace the use of complex models by watershed managers and stakeholders in the planning and decision making process, which can be used in agricultural watersheds around the world. We hypothesized that BMP effectiveness varies by BMP implementation site, which can be described either by its distance to the watershed outlet or the stream order concept. The specific objectives of the study were to 1) develop an empirical model to predict BMPs' pollution reduction using watershed characteristics such as distance to the watershed outlet and stream order, and 2) visualize the overall watershed response to pollution reduction and determine the optimal distance of BMP implementation from the watershed outlet.

Section snippets

Study area

This study was conducted in the Saginaw River Watershed located in east-central Michigan with a total area of 15,263 km2 (Fig. 1). The watershed drains northeast into Lake Huron. This watershed contains the largest contiguous freshwater coastal wetland of the nation and is one of the most diverse watersheds in Michigan (USEPA, 2009). The Saginaw River Watershed consists of 23% agricultural land, primarily corn and soybean. The remaining is 42% forest, 17% pasture, 11% wetlands, and 7% urban

Data distribution and correlation

Fig. 2 shows the Spearman's correlation matrix. In this figure the scatterplots are located in the lower panel, Spearman's rank correlation coefficients are presented in the upper panel, and histograms are presented on the diagonal. The red line in the scatterplots depicts the simple linear regression between a variable pair. The histograms represent the observed distribution of the data, while Spearman's rank correlation coefficients represent the correlation between two variables. All

Conclusion

Although physically-based models such as SWAT are accurate and reliable tools for watershed management, require expert knowledge of watershed processes to be used properly. Therefore, these types of models are not widely used beyond a planning stage and cannot be adjusted by stakeholders to examine new management scenarios without the help of experts. Our goal in this study was to develop user-friendly models that can facilitate information transfer of advanced models to non-expert users.

The

Acknowledgments

This work is supported by the USDA National Institute of Food and Agriculture, Hatch project MICL02212.

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