Exploring effective best management practices in the Miyun reservoir watershed, China

Ecological Engineering 123 (2018) 30–42

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Exploring effective best management practices in the Miyun reservoir watershed, China ⁎

Jiali Qiua,b, Zhenyao Shena, , Maoyi Huangc, Xuesong Zhangb,d,

T

⁎

a

State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, PR China Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD 20740, USA c Earth System Analysis and Modeling Group, Atmospheric Sciences & Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA d Earth System Sciences Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA b

A R T I C LE I N FO

A B S T R A C T

Keywords: Nonpoint source pollution Best management practices (BMPs) Multi-objective optimization Multi-stakeholders Water resources management

Miyun reservoir watershed is a major source of drinking water for China’s capital, Beijing, which has a population of 21.75 million. Recently, the capacity of the Miyun reservoir to supply clean drinking water has been threatened by increasing eutrophication (or algae bloom), mainly due to the discharge of wastewater and excessive fertilization application in the upstream watershed. Therefore, there is an urgent need to design effective best management practices (BMPs) to reduce upstream nutrient load and improve water quality in the Miyun reservoir. In this study, we built a watershed model (the Soil and Water Assessment Tool) for the Miyun Reservoir Watershed (MRW) and calibrated and validated it using long-term sediment, nitrogen (N) and phosphorus (P) data. Furthermore, we developed a Markov Chain based multi-objective optimization program to explore optimal BMPs with tradeoffs between economic costs and water quality responses. Using the watershed model and multi-objective optimization algorithms, we explored the potential effectiveness of BMPs under two scenarios that are currently being considered. Scenario 1 assumes that funding for BMP implementation comes from national grants and targets high water quality standards, whereas scenario 2 assumes funding is provided by farmers and targets water quality that meet the drinking water standards. We found substantial discrepancies between the two scenarios with respect to the types and spatial configurations of BMPs and associated economic costs, highlighting the need to reconcile concerns from different stakeholders in order to arrive at a BMP plan that all parties will agree upon. In addition, we found that cross-subwatershed coordination and targeting flood season instead of year-round water quality standards could pronouncedly reduce the economic costs of BMP implementations without substantially degrading water quality. The watershed scale optimization method developed here holds promise to serve as an effective tool to explore tradeoffs between economic costs, water quality improvements, and decision makers’ and stakeholders’ concerns in BMP design, thereby informing sustainable watershed scale water resources management and ecosystem maintenance.

1. Introduction The Miyun reservoir is the largest source of potable water and industrial water used to support rapid population growth and economic development in Beijing, China (Zheng et al., 2016). After the degradation of water quality in the Guanting Reservoir, which is the second largest water source for Beijing, the Miyun reservoir and its catchment area plays an even more important strategic role in ensuring the sustainable development of Beijing; therefore, the Miyun reservoir watershed (MRW) was zoned as a water protection area with the aims of improving its water quality and sustainably supplying clean water (Peisert and Sternfeld,

2005). Nonpoint source (NPS) pollution from the MRW’s upstream catchment, including excessive fertilizer application, sediment runoff and wastewater discharge, is the major cause of water quality problems, especially eutrophication, in the Miyun Reservoir and has attracted widespread attention (Li et al., 2016). The recent deterioration in water quality and shortages of water supply in the Miyun Reservoir have further threatened Beijing’s drinking water supply (Peisert and Sternfeld, 2005). Because there are no new alternative water sources, improving the water quality in the Miyun reservoir is urgently needed. Solutions to this challenge require the integrated management of terrestrial pollution sources in the reservoir’s upstream catchment.

⁎ Corresponding authors at: State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, PR China (Z. Shen). E-mail addresses: [email protected] (Z. Shen), [email protected] (X. Zhang).

https://doi.org/10.1016/j.ecoleng.2018.08.020 Received 23 October 2017; Received in revised form 18 July 2018; Accepted 23 August 2018 0925-8574/ © 2018 Elsevier B.V. All rights reserved.

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Fig. 1. Overview of the simulation-optimization framework for exploring BMPs in the MRW.

A wide range of BMPs can be implemented depending on site characteristics, economic cost, social acceptability and pollutant removal objectives (Pennington et al., 2017). A balance of the above critical factors should be carefully addressed for effective watershed management to select and place a series of BMPs in order to achieve water quality targets or achieve the greatest reduction in pollutant loads for a specific cost level. It is worth noting that the MRW involves multiple urban and rural water users that have competing needs. They often disagree regarding who should be responsible for the financial costs of BMPs and what are the practical targets of pollutant loads reductions (Peisert and Sternfeld, 2005). A successful watershed management plan requires public participation and shared responsibility (Bautista et al., 2017; Herringshaw et al., 2010), and it is therefore imperative to understand and quantify the impacts of multi-stakeholder concerns for effective BMP design. Multi-objective optimization algorithms have been widely used to explore watershed management practices consistent with water resource protection goals (Chen et al., 2015a; Herman et al., 2015; Sebti et al., 2015). Genetic algorithms are often used to optimize the selection and placement of BMPs at the watershed scale due to their global optimization ability to address nonconvex, constrained, multi-objective

Best management practices (BMPs) or conservation practices are recognized as effective and practicable measures to control the amount of pollutants generated by NPS (Noor et al., 2016). BMPs in general encompass structural practices that refer to the equipment or facilities installed or constructed on a site such as constructed wetlands, grassed swales and vegetative filter strips and non-structural practices that are implemented in terms of regulations or operating procedures, including land management practices or nutrient management practices for a whole field or region (EPA, 2016). Because field experiments are costly and cannot be used to exhaustively assess all BMP scenarios for large regions such as the MRW, process-based watershed models are often applied to assess and identify effective BMPs. The Soil and Water Assessment Tool (SWAT) model (Arnold et al., 1998) is a watershed model that depicts the connections between terrestrial and aquatic ecosystem processes and is widely used in watershed BMP selection and placement (Gassman et al., 2007). For example, SWAT is currently being used within the U.S. Department of Agriculture’s Conservation Effects Assessment Project (CEAP) (Johnson et al., 2015) and U.S. Environmental Protection Agency’s Hydrologic And Water Quality Modeling System (HAWQS) (EPA, 2017) to assist with BMP design to improve water quality. 31

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and high dimensional problems and find true Pareto-optimal solutions with a limited number of simulation runs (Deb et al., 2002; Zecchin et al., 2012). The non-dominated sorting genetic algorithms-II (NSGAII) is a popular method in such research efforts. The performance of NSGA-II depends on a set of control parameters that include crossover probability, mutation probability, number of generations and initial population size (Tolson et al., 2009; Zecchin et al., 2012). In addition, a specific well-accepted set of control parameters does not have universal applicability and cannot be generalized to other cases because they are obtained in a particular watershed with specific conditions and input data (Tolson et al., 2009). It is difficult to manually adjust these control parameters to achieve the maximum efficiency of NSGA-II because the parameters interact with each other to determine optimization performance (Perelman et al., 2013; Tolson et al., 2009). Here, we will use the NSGA-II expanded with the auto-adaptive adjustment of control parameters as suggested by Chen et al. (2015b) to automatically adjust these parameters during the optimization procession to enhance the overall performance of the algorithm rather than a trial-and-error procedure to manually determine the optimal control parameters of NSGA-II for the MRW BMP optimization. The objectives of the study are to (1) setup and calibrate a SWAT based watershed model in the MRW for simulating sediment and nutrient loads, (2) develop an NSGA-II based multi-objective optimization program to identify and select a desired set of Pareto-optimal BMP solutions and (3) apply the SWAT model and NSGA-II to explore BMP solutions as influenced by different optimization assumptions such as government vs. farmer preferences, individual vs. coordinated optimization of the two upstream watersheds draining into the MRW, and optimization to meet year-round vs. flood season only water quality standards. The outcomes resulting from these efforts are expected to yield a watershed scale BMP optimization tool that is useful for exploring tradeoffs between economic costs and water quality improvements in BMP design as influenced by different factors and concerns from different stakeholders, thereby informing sustainable watershed scale water resources management.

sub-basin, n is the number of sub-basins in the MRW, x is also referred to as a BMP configuration or as a chromosome within the NSGA-II algorithm (Deb et al., 2002), fr (x ) represents the load of pollutant r, r is the number of the pollutant types, and fc (x ) is the total cost of the BMPs in the entire watershed. The purpose of multi-objective optimization is to search the feasible solution space Ω and find those solutions that are Pareto optimal. A solution x ′ is dominated by another solution x if ∀ j ∈ {1, 2, …, r , r + 1}, f j (x ′) ⩾ f j (x ) ⋏ ∃ j ∈ {1, 2, …, r , r + 1}, f j (x ′) ⩾ f j (x )

(Zitzler and Thiele, 1999). If a solution x ∗ ∈ Ωis not dominated by all other solutions, then x ∗is Pareto optimal. All the Pareto-optimal solutions form the Pareto set (P ∗) . The objective function vectors corresponding to the Pareto optimal set comprise the Pareto front (PF ∗) . In the following sections, we will provide more details on the components of the multi-objective modeling framework and the BMPs exploration scenarios. 2.2. Study area description The MRW (40°19′–41°36′N and 115°27′–117°35′E) has an area of approximately 14,028 km2 and is located across both Beijing and Hebei Province, China (Fig. 2). The Miyun Reservoir has two inflow rivers: the Chao River and the Bai River. The land covers in the MRW are mainly forests (including artificial ecological and economic forests), agricultural land, rangeland, open waters and residential areas (Fig. S1). The topography of the MRW includes low hills, plains and mountains. The soil types are dominated by Cambisols and Regosols, which are the primary soils supporting agricultural activities in northern China due to their high fertility. Both agriculture and commercial forests are the main economic sources for farmers in this region. A large amount of N, P and other contaminants are released into the water environment due to the massive application of fertilizers and/or manure and the effluents of untreated sewage. These pollution sources have led to increased risks of water eutrophication and toxicity in the Miyun Reservoir. Therefore, it is imperative to implement a cost-effective combination of BMPs in this area to ensure the hygienic safety of drinking water for Beijing and the surrounding regions. Although it is commonly agreed that protecting and improving water quality in the Miyun Reservoir is important for sustaining essential ecosystem services, watershed management and water quality protection efforts are facing obstacles and challenges caused by conflicts between local government and residents regarding agricultural policy, land use management and economic activities.

2. Materials and methods 2.1. Overview of the BMP optimization framework Our simulation-optimization framework for identifying BMPs and understanding the influence of stakeholder preferences include the following four major components as shown in Fig. 1. First, a calibrated and validated SWAT model for the MRW and the Farm-level Economic model (FEM) are combined to evaluate economic costs and pollution reduction effects of candidate BMPs. Second, the NSGA-II expanded with an auto-adaptive control parameter adjustment algorithm is used to generate the initial Pareto-optimal spatial BMPs configurations on individual assessment point or combined multiple assessment points. A Pareto optimal BMPs configuration represents a set of spatial allocations of candidate BMPs such that no other set of spatial configurations could be better without decreasing performance elsewhere at least one desired criterion. Third, a reference point that reflects the desired objectives of a group of decision makers (or stakeholders) is used to find a smaller set of solutions from the Pareto optimal BMPs configurations. Finally, post-processing and visual inspection of the refined Paretooptimal BMPs solutions are conducted to understand the contrast between BMP configurations resulting from different stakeholders’ preferred criteria. The optimization objectives consist of pollution load reduction and economic cost dimensions. The fitness of a BMP solution is estimated by evaluating the following objective functions in vector form:

F (x ) = min (f1 (x ), f2 (x ), ⋯, fr (x ), fc (x ))

2.3. Environmental and economic characterization of BMPs The water quality control benefits of BMPs will be assessed using the SWAT model. SWAT is a continuous-time, long-term, distributed-parameter model (Arnold et al., 1998). SWAT subdivides a watershed into sub-basins connected by a stream network and further delineates Hydrologic Response Units (HRUs) consisting of unique combinations of land cover and soils in each sub-basin. SWAT is able to simulate surface and subsurface flows, sediment generation and deposition, and nutrient fate, transport and transformation through the watershed system. The water cycle processes considered within SWAT include precipitation, snowmelt, infiltration, evaporation, plant uptake, lateral flow, percolation, and baseflow. SWAT uses a modified version of the Soil Conservation Service Curve Number (SCS-CN) method to simulate surface runoff, a kinematic storage model to calculate lateral flow, and a shallow groundwater module to represent return flow (Neitsch et al., 2011). The Muskingum-Cunge method is used for channel flood routing (Kim and Lee, 2010). The SWAT model uses crop growth and soil biogeochemical algorithms from the Environmental Policy Integrated Climate (EPIC) model (Williams, 1995). Riverine water quality processes are simulated based on the revised QUAL2E method (Brown and Barnwell, 1987; Neitsch et al., 2011). The data used to set up the SWAT model and parameter sensitivity is

(1)

where x = (x1, x2, …, xn, s1, s2, …, sn), xi represents the non-structural BMPs in the ith sub-basin, si represents the structural BMPs in the ith 32

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Fig. 2. The location of the study area.

2.4. NSGA-II based multi-objective optimization

described in Table S1 in the supplementary document. In this study, the pollutant loads under the baseline condition (without BMP implementation, see the spatial distributions of pollutants in Fig. S2 in the supplementary document) and BMP scenario (one BMP for one scenario) at each sub-basin were simulated using the SWAT model (see the BMP efficiencies in Fig. S3 in the supplementary document). The water qualities at the assessment points with BMPs configurations in the entire watershed were calculated using the Markov chain method based on the watershed network information and the simulation results of the spatial distributions of pollutant loads and efficiencies of the BMPs for pollution reduction (Grimvall and Stalnacke, 1996; Munafò et al., 2005). The costs of the structural BMPs, which consists of constructed cost (Eq. (1)) and maintenance cost (Eq. (2)), were estimated using the Farm-level Economic Model (FEM) (Gassman et al., 2006; Osei et al., 2002) combined with empirical values from other studies (Andrews and Lampe, 2005; Lampe, 2004). The costs for the non-structural BMPs are based on local settings, including the construction cost, maintenance cost and their economic benefits (Table 1).

Cost = a + b (Length)c + d (Area)e + f (Volume ) g

NSGA-II is an elitist multi-objective GA developed by Deb et al. (2000). As used for multi-objective optimization, NSGA-II involves four major procedures: population size design to determine the number of solutions, fitness assignment for each solution in the population, environmental selection to select the fitter solutions into the next parent population and offspring reproduction, which creates new and promising solutions using different evolutionary operators (Deb et al., 2002; Zhang et al., 2010). In the following paragraphs, NSGA-II procedures are briefly introduced. NSGA-II requires users to initialize a population of solutions (Pt ) and an empty external archive (P¯t ), which are evolved using several operators that include fast non-domination sorting, crowded distance calculations, and mating selection and variation operations. Fast nondomination sorting (FNS) is an efficient operator that assigns ranks to the solutions in Pt and P¯t . The individuals who are not dominated by other individuals are put in the first front FT1 and are assigned a rank of 1. The individuals who are not dominated by other individuals except those in FT1 are put in the second front FT2 and assigned a rank of 2. All the individuals are similarly assigned to a specific front and rank number (Deb et al., 2002; Zhang et al., 2010). NSGA-II also uses crowding distance to discriminate individuals with the same front orders. For each objective dimension, a distance between the two individuals on either side of solution x ′ is calculated. The average distance along all objective dimensions is used as the crowding distance. Solution fitness increases with increasing crowding distance. Chromosomes with lower ranks and larger crowding distances are selected into Pt + 1, which will be evolved using the crossover and mutation operators in the Genetic algorithms (GA) (Goldberg et al., 1990a) to reproduce promising new candidates for the next generation P¯t + 1. The two best individuals with higher virtual fitness scores are selected from the Pt + 1 using a stochastic tournament selection method (Goldberg et al., 1990b; Julstrom, 1999). The crossover operator then exchanges important

(2)

where Length, Area and Volume are the BMP design parameters, and a, b, c, d, e, f, and g denote the empirical coefficients (Chen et al., 2015b).

Cannual = P ×

(1 + i)n−1 i (1 + i)n

(3)

where Cannual and P represents the annual maintenance cost and the current net maintenance cost (a corresponding proportion of the construction cost), respectively, and i and n denote the interest rate (0.05 in this study) and design lifetime (Table 1), respectively. For filter strip, n = 2, maintenance cost = 15% of construction cost, for detention basin and constructed wetland, n = 5, maintenance cost = 3% of construction cost, and for grassed waterway, n = 5, maintenance cost = 10% of construction cost. 33

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Table 1 Description of type and cost information of BMPs. BMP

BMP Type

Filter strips (FS) Detention basin (DB) Grassed waterway (GW) Constructed wetland (CW)

Width of 5 m, 10 m, 15 m Not present and Present Not present and Present Not present and Present

Conservation tillage (CT) Fertilizer reduction (FR)

Not present and Present Reduction rate of 30% and 20% For the slope of 15° and 25°

Converting farmland to forest (RFF) Alley cropping (AC) a

Not present and Present

Costa

Reference

¥26.7–62.2/m

2

¥22.2–44.5/m

2

FEM model Lampe (2004)

C = 3176.4V 0.71 Construction cost (¥1417.5) – Income (¥2520) = ¥-1102.5/ha Construction cost (¥240) - Income (¥1290) = ¥-1050/ha

Based on local situation, policies and regulations

Construction cost (¥6000) + Maintenance cost (¥900) – Income (¥37500) = ¥-30600/ha Construction cost (¥4155) – Income (¥6150) = ¥-1995/ha

Negative values indicate additional farmer incomes arising from BMP implementation. The units of V and C in the converted formulae are m3 and ¥.

building blocks of the two parent chromosomes to generate new “offspring” solutions. The “offspring” solutions are then randomly mutated to increase the diversity of the new population P¯t + 1. The selection, crossover and mutation procedures are repeated until P¯t + 1 is filled. The optimization efficiency of the NSGA-II is very sensitive to the control parameters, including the number of generations (i.e., how many times the population is evolved), initial population size, crossover and mutation probability (Deb et al., 2000; Tolson et al., 2009). This study incorporated an auto-adaptive pattern by automatically calibrating these NSGA-II control parameters, which included integer mutation probability (for BMP types), decimal mutation probability (for the designed schemes of each BMP) and introduction probability. These three control parameters were added as the inherent optimization variables of each chromosome.

government rather than farmers should pay the cost of BMP implementation; on the other hand, policymakers believed that farmers should at least share the cost. As such, the preferences of different stakeholders are expected to influence BMP acceptance levels and optimization results. To encourage the participation of different stakeholders when searching for the most effective and acceptable solutions for watershed management, stakeholder perspectives should be taken into consideration in the optimization process. We developed two the scenarios where the financial source was a national grant totally (scenario 1) and farmer payments (scenario 2), respectively. In scenario 1, the government paid all implementation costs of BMPs and subsidies based on environmental policy in China such as the Regulations on Restoring Farmland to Forest, the National Soil Testing and Fertilizer Program and other agricultural support policies consisting of direct payments for grain production in response to declining production, farm machinery purchase subsidies, and some non-structural environmental protection measures (Ni 2013; Smith and Siciliano 2015), whereas the extra profits resulting from BMP effects were attributed to farmers. In this sense, the cost of BMPs included subsidy expenditures, actual construction and maintenance costs that were not deducted by the economic benefits of those practices that belonged to farmers. In scenario 2, we assumed that farmers were responsible for watershed management and the implementation costs and economic benefits also belonged to them; additionally, they can get the subsidies supported by agricultural and environmental policies.

2.5. Refinement of the BMP solutions based on reference points A reference point represents the desired values or aspiration levels for the objectives that reflect a stakeholder’s perspective on the solutions. The process of refining the BMP solution based on reference points includes projecting the reference points onto the Pareto-front, calculating the distance between each solution and the reference point, ensuring a spread of refined solutions near the reference point and repeating the above steps until the decision maker obtains satisfactory solutions. Stakeholders will determine whether the refined Pareto-optimal solutions meet the watershed management targets or the desirable values of the objectives when the optimization is finished. If the refined BMP solutions do not meet the stakeholder objectives, the above steps will be repeated with a new reference point. During this process, stakeholders can change the location of the reference point and the projection direction when the aspiration levels associated with the tradeoff among all objectives are reset, including the water quality targets and BMP implementation budget. In this study, the reference point was set to the pollutant loads associated with the water quality target at the assessment points.

3. Results and discussions 3.1. SWAT model evaluation and major factors influencing BMP pollution removal efficiency The SWAT model was calibrated and validated using the measured flow and nutrient load data obtained at the two inlets of the Miyun Reservoir, which are located in the mouths of the Chao River watershed and the Bai River watershed. The sensitivity analyses of the parameters and the calibrated parameter values are described in Tables S2 and S3 in the supplementary document. We applied the SWAT-CUP SUFI-2 program (Abbaspour, 2007) to modify parameter value within reasonable limits that maximized model efficiency and minimized streamflow deviation. Taking into account the independence and the differences in surface characteristics and water quality between the two subwatersheds, this study calibrated the model in the Chao River subwatershed and the Bai River subwatershed separately. The change type of parameters was supported by other studies (Bai et al., 2016; Zhu et al., 2011). The sensitivity ranking and optimal value of parameter were different between these two subwatersheds. We adjusted the parameters such as CN2, ESCO, SOL_K and SOL_Z of the two watersheds differently because of the differences in the distribution of soil types and the sensitivity of parameters. These results were consistent with other

2.6. BMP optimization scenarios Public participation and perspectives on environmental protection and policy-making are increasingly demanded by environmental agencies and national and international organizations (Bautista et al., 2017). Previous studies have suggested that the perspectives of stakeholders on the environmental problems and potential environmental and economic benefits of BMPs (such as soil and water quality improvements, input savings and increased crop yields) influence their decisions on the adoption of BMPs (Qiu et al., 2014). In the previous literature, it was also concluded that financial source is a decisive factor influencing BMP adoption to control agricultural NPS pollution (Campbell et al., 2011; Floress et al., 2011; Qiu et al., 2014). For example, Qiu et al. (2014) found that almost all farmers think that 34

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practices and nutrient management practices, which can decrease cropland area, improve vegetation coverage and reduce inputs of nutrients, showed linear correlations with the spatial distributions of land cover (y = 0.1507x + 0.0323 for TN removal rate, and y = 0.1449x + 0.029 for TP removal rate, where x is the area proportion of croplands (unit: %)).

studies (Xu et al., 2009; Zhu et al., 2011). The GW_DELAY, SOL_AWC, CWQMN, CANMX, REVAPMN and ESCO values are smaller in the Bai River subwatershed, whereas the CN2, SOL_BD and SOL_K values are higher; these contribute to the higher streamflow in this subwatershed. The decreasing precipitation and the low available water capacity of soil layer led to low sensitivity of CN2 in this watershed. The low CN2 value could represent the local physical conditions where the soil infiltration capacity is high in the Miyun Reservoir Watershed, which has been supported by other studies, including Yang et al. (2007), Han (2003) and Zhan et al. (2013). According to the results of these studies, the high CN2 value would lead to the overestimation of flow in the Miyun Reservoir Watershed. The proportion of forestland in the Chao River subwatershed is higher than that in the Bai River, which resulted in higher infiltration rate given its smaller CN2 value. The CH_K2 in the Miyun Reservoir Watershed is high due to the clean gravel and large sand. In addition, the high CH_K2 value indicates that there is a strong interaction between the river channel and groundwater. This result was supported by other studies (Li et al., 2016; Yang et al., 2007). The water balance system in this watershed is characterized by large contributions of groundwater to streamflow in non-flood season due to the decreasing annual precipitation, which increases the sensitivity of parameters related to groundwater processes (GWQMN and REVAPMN) (Li et al., 2016). In our study watershed, 49.24% of the total area is forestland, and the remainder comprises agricultural areas, pastureland, waters and industrial and residential sites. High forest cover in the watershed implies significant impact of forest canopy interception on hydrological process, which was reflected by CANMX value in the SWAT (Wu and A Johnston, 2008). CANMX is related to the water loss process, and the higher value leads to the smaller streamflow in the watershed. This result was consistent with the results provided by Xiao et al. (2007), Bai et al. (2017) and Bärlund et al. (2007). Watershed characteristics and local factors determined the specific parameter sensitivity results that were inapplicable to other watersheds. The calibration and validation of the flows and nutrient loads were performed for 1990–1998 and 1999–2010, respectively, whereas the periods for sediment calibration and validation were 2006–2008 and 2009–2010, respectively, due to limitations of the data. Statistical metrics, including coefficient of determination (R2) (Legates and McCabe, 1999) and Nash-Sutcliffe efficiency (Ens) (Nash and Sutcliffe, 1970), were used to evaluate model performance. According to the monthly simulation results, as shown in Fig. 3, the simulated flows were in good agreement with the measured data (R2 ranged between 0.54 and 0.88, and Ens ranged between 0.49 and 0.84) at both hydrologic stations. SWAT also well reproduced the monthly nutrient loads (R2 ranged between 0.53 and 0.81, and Ens ranged between 0.44 and 0.78) and sediment loads (R2 ranged between 0.63 and 0.75, and Ens ranged between 0.39 and 0.74). In general, SWAT performance in the calibration period was better than in the validation period, mainly due to shifts in the hydrologic and management conditions between the two periods. Overall, the model assessment metrics indicate that the SWAT model achieved satisfactory performance when simulating water and nutrients in the MRW and can serve as a useful tool to explore BMP effects. Watershed characteristics, including land use, soil type, and topographical and meteorological conditions, influence pollutant transport and transformation processes across the watershed, therefore significantly affecting the NPS pollution loading (Karr and Schlosser, 1978; Memmah et al., 2015; Park et al., 2015). In this study, precipitation and proportion of land use (agricultural area) were the leading drivers that determined the BMP pollution removal efficiencies. Precipitation is a driving force for NPS pollution loading and is linearly correlated with runoff and pollutant loads (Qiu et al., 2018). Cropland area was the primary source for NPS pollution as a result of the excessive use of chemical fertilizers and manure, farmland tilling activities and low vegetation coverage (Qiu et al., 2018; Shen et al., 2014). The BMP pollutant removal rates (%), especially land management

3.2. Respective BMP Pareto-optimization results for the Chao River and Bai River subwatersheds To ensure drinking water quality, the concentrations of pollutants at the downstream part of the Chao River (point A) and Bai River (point B) subwatersheds should meet the standards designated for the Miyun water function areas. According to the water quality standards (GB3838, 2002), the concentrations of TN and TP at these two assessment points should not exceed 0.5 mg/L and 0.1 mg/L, respectively. Currently, the TP loads do not exceed the designated water quality standards at assessment points A and B. On the other hand, the TN loads need to be reduced by 85.91% and 68.53%, respectively, for the Chao River and Bai River subwatersheds. In our BMP optimization analysis, the TN and TP loads at assessment points A and B, in conjunction with the economic costs of implementing the BMPs, were considered as the optimization objectives. Although TP does not need to be reduced to meet the water quality standards, we included it as an optimization objective in order to ensure that it does not exceed the designated TP water quality standard during the optimization process. We executed the NSGA-II program to derive Pareto-optimal BMP solutions in the Chao River and Bai River subwatersheds. Fig. 4 (upper and middle panels) shows the Pareto optimal solutions of the BMPs and the associated tradeoffs with respect to the TN and TP loads and economic costs. We first analyzed the BMP results for the Chao River (assessment point A) under the two stakeholder perspectives. In general land use management practices (converting farmland to forestland), nutrient management, alley cropping and conservation tillage were primarily selected in the Pareto-optimal solutions on the upper-right part, with relatively low costs and low pollution removal efficiencies. In contrast, structural BMPs appeared on the lower-left part, where the solutions presented much higher costs to achieve higher pollution removal efficiencies. The selected points along the frontier that represented the TN removal rates of 40%, 60% and 80% show the tradeoff between the structural and non-structural BMPs in terms of pollution reduction and economic cost. (Fig. S4 in the supplementary document). The non-structural BMPs seemed unable to achieve high levels of reductions in TN and TP loads, and the structural BMPs needed to be added into the BMP schemes. These results were consistent with previous studies (Chen et al., 2015a; Ciou et al., 2012; Maringanti et al., 2011). Although the shapes of the Pareto-fronts under the different stakeholder perspectives were similar, the relationships among TN load, TP load and BMP cost varied under scenarios 1 and 2. In scenario 1, the BMP costs depended on the national grant, whereas the BMP benefits were vested in the farmers. These assumptions resulted in lower pollution removal efficiencies at the same costs when compared with those obtained in scenario 2. Second, the optimized BMP solutions for the Bai River are shown in Fig. 4 (Middle Panel). We also found that the shapes of the PFs in the middle panel of Fig. 4 were similar under scenarios 1 and 2. Similarly, we found that the structural BMPs appeared in the area with high cost, whereas the non-structural BMPs appeared in the area with low cost (Fig. S4 in the supplementary document). According to Fig. 4 and Fig. S4, the shapes of the PFs for the Bai River were similar to those for the Chao River, but the pollutant loads noticeably differed in the two subwatersheds, which can be explained by the pollutant load and efficiency differences between these two watersheds. As proposed in the previous study, the differences in parameter values for hydrological and water quality processes between these two watersheds accounted for the discrepancy in the effects of the same conservation practice, due to 35

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Fig. 3. Calibration and validation results.

cost for all the BMPs across the Chao River and Bai River subwatersheds (lower panel of Fig. 4 and Fig. S5 in the supplementary document). As shown in Table 2, under scenario 1, in order to ensure meeting the water quality standards in both the Chao and Bai River subwatersheds, we needed to reduce TN by 86.08% (10491.83 tons) in the Chao River and 68.56% (9694.77 tons) in the Bai River, leading to a total reduction in TN of 20186.60 tons. The cost of implementing the BMPs would be ¥43.34 million/year. By coordinating the BMPs across the Chao River and Bai River subwatersheds, we could achieve a similar amount of TN reduction (20178.11 tons) at a much lower cost of ¥39.82 million/year or a savings of ¥3.52 million/year (8%). Under scenario 2, for the same level of TN reduction, the coordinated optimization selected BMPs with an economic cost of ¥3.32 million/year, which is 22.6% less than that for the BMPs selected under the respective optimization scheme. Coordination across watersheds holds the potential to substantially reduce the economic costs of the selected BMPs under both scenarios 1 and 2 but at the cost of slightly lowering water quality at a specific subwatershed (the Chao River subwatershed). Therefore, the advantages and disadvantages of coordinated BMP selection across watersheds deserve careful assessment by balancing economic cost and water quality objectives.

the diversity in land covers, topographic characteristics, population, economic development, water consumption and the distribution of dams or reservoirs (Qiu et al., 2018). The larger number of dams or reservoirs and area of watershed in the Bai River subwatershed were the main reasons for which the flow volume in this subwatershed was larger than that in the Chao River subwatershed, which contributed to larger pollutant loads. The efficiencies of most structural BMPs in the Bai River subwatershed are slightly lower than those in the Chao River subwatershed. The higher CN2 (higher runoff) and the slightly lower concentration in the Bai River are the main reasons. The structural BMPs have significant impacts on the contamination in rivers or surface runoff instead of reducing runoff. However, the land-use management practices for controlling nutrient losses in the Bai River subwatershed were more effective than in the Chao River subwatershed because the proportion of agricultural land that was targeted for re-planning was higher in Bai River subwatershed, which contributed to greater changes of land covers and pollution sources. 3.3. Effects of cross-subwatersehd coordination on BMP optimization In the previous section, the BMP optimization results were obtained by treating the Chao River and Bai River subwatersheds separately. This assumption helped to ensure that the water quality goals at assessment points A and B were met, but the lack of coordination between the two subwatersheds may not have resulted in the most economic BMP solutions for achieving the same reductions in TN or TP loading into the MRW. We conducted a BMP optimization that simultaneously coordinated BMP types and placements across the two subwatersheds to minimize the following objectives: (1) combined TN reduction at A and B, (2) combined TP reduction at A and B, and (3) combined economic

3.4. Comparison of optimization results targeting year-round and flood season water quality standards TN loading during the flood season (June-October) accounted for 62.31% and 57.91% of the overall TN loads in the Chao River and Bai River subwatersheds, respectively. Because TN loading occurs during the flood season, assessing the environmental and economic 36

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Fig. 4. Tradeoffs between water quality and economic cost objectives of the optimized BMP solutions for Chao River (upper panel), Bai River (middle) and the joint optimization of the Chao and Bai Rivers. To plot the three dimension of the BMP solutions’ water quality and economic performance, we used the X- and Y-axis to represent TN and TP loading, respectively, and use a color ramp to denote the levels of the economic costs.

performances of BMPs selected to meet the flood season water quality standards alone is of interest. Table 3 shows a comparison between BMP removal efficiency and economic cost when targeting year-round

and flood season water quality standards. Compared with meeting yearround water quality standards, the BMPs that only met the flood season water quality standards resulted in slight decreases (less than 2%) in the 37

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Table 2 Multi-objective optimization of BMPs with respect to pollutant reduction and minimal financial cost. Assessment point

Scenario 1

Chao River Bai River Coordinated Chao and Bai River optimization *

Scenario 2

TN reduction

TP reduction

Cost

TN reduction

TP reduction

Cost

86.08% (10491.83) 68.56% (9694.77) 78.08% (20178.11)

87.39% (1155.08) 71.62% (1547.70) 77.74% (2730.34)

22.38 20.96 39.82

86.04% (10491.62) 68.72% (9717.39) 78.55% (20299.57)

89.36% (1189.87) 72.10% (1558.07) 79.02% (2794.28)

3.18 1.11 3.32

The values in the parentheses are the amounts of pollution load reduction in tons. Unit in millions ¥.

#

government but not deducted by the economic benefits of those practices (government perspectives). That is, to achieve the same water quality targets, scenario 1 selected fewer non-structural BMPs and included more structural BMPs, which contributed to the higher total cost of the selected BMPs.

TN removal efficiencies in the Chao River and Bai River subwatersheds but led to 20% or greater decreases in economic cost in those two subwatersheds under scenario 1. Under scenario 2, targeting only flood season water quality standards led to even higher percentages of economic savings (ca. 34% and 87% respectively for the Chao River and Bai River), without significantly degrading BMP removal efficiency. These results indicate that economically effective nutrient management in the MYW could focus on pollution control in the period of the flood season, which contributed almost 100% of the pollution loads that exceeded the water quality standards. The selected BMPs based on the flood season water quality standards only slightly increase pollution loads during the dry season (November-May) (3.62% and 5.34% in the Chao River and Bai River subwatersheds, respectively). The number and types of BMPs targeting floodwater quality standards are summarized in Table 4 and will be discussed in Section 3.5.

3.5.1. Multi-objective optimization results based on government payments (scenario 1) In scenario 1, the BMP costs are paid by the government through a national grant, whereas the resulting benefits are vested in the farmers. The numbers, placements and types of BMPs selected under the different assumptions varied substantially (Table 4 and Figs. 5 and 6). For example, more BMPs were selected to meet year-round water quality standards than from targeting the flood season alone. In particular, CT, AC, FS5m and CW were more often selected due to their greater nutrient removal efficiencies or increased nutrient use efficiencies. As shown in Table 4 and Fig. 5, non-structural BMPs were widely involved in half of the sub-basins, whereas costly practices, including detention basins, 5-m, 10-m and 15-m filter strips, and constructed wetlands, were suggested to be included in the sub-basins that were identified as the critical source areas or priority control areas to achieve the NPS pollution target. When optimizing the BMPs in the Chao River and Bai River separately, detention ponds were the most preferred structural BMP because they effectively intercept nutrients. 5-m and 10-m filter strips were the second most selected BMPs and were configured in several sub-basins due to their high effectiveness for retaining of nutrients, whereas 15-m filter strips, grassed waterways and constructed wetlands were the least selected BMPs due to their high costs (Chen et al., 2016). For the coordinated optimization of BMPs in the two subwatersheds, the numbers of FS, especially FS15m, were slightly increased to replace a large number of costly DBs. In addition, the total number of non-structural BMPs was decreased with the decrease in the costly DBs; this was another reason for the lower cost of the BMPs when coordinating BMP optimization across the two subwatersheds.

3.5. Optimized BMP types resulting from different stakeholder preferences As discussed in Sections 3.2, 3.3, and 3.4, different assumptions in the BMP optimization process resulted in substantial variations in the economic costs of the selected BMPs, without substantially changing the TN removal efficiencies. For example, the government needed to pay ¥43.34 million/year for the selected BMPs to ensure water quality in both the Chao River and Bai River to meet the national water quality standards, whereas the government only had to pay ¥39.82 million when the BMP optimization was coordinated across the two subwatersheds to achieve the same level of total TN reductions. Targeting flood season water quality standards can further reduce the economic costs of selected BMPs at the cost of modestly degraded water quality during the dry season. Not surprisingly, the optimized BMPs, including their types and placement within the two subwatersheds, varied greatly under the different assumptions during BMP optimization processes (Table 4 and Figs. 5 and 6). Table 2 shows the substantial differences between the economic costs of the BMPs at similar levels of TN reduction under scenarios 1 and 2. In general, scenario 2 achieved a much lower cost. A difference existed in the number of BMPs, which contributed to the different costs of BMPs set under the two scenarios. The economic benefits of the nonstructural BMPs that increased farmers’ income led to a higher fraction of non-structural BMPs under scenario 2 (farmer perspectives) than under scenario 1, where the cost of the non-structural BMPs included the actual construction costs and maintenance costs that are paid by

3.5.2. Multi-objective optimization results based on farmer payments (scenario 2) In scenario 2, farmers are responsible for the costs and economic benefits of BMP implementation. Therefore, the cost for the combined BMP was lower than that under scenario 1. The non-structural BMPs, including eco-friendly tillage practices such as AC and CT, land

Table 3 BMP removal efficiencies and economic costs when targeting year-round and flood season water quality standards (cost units: millions ¥). Assessment point

Scenario 1

Scenario 2

Periods to meet water quality standards

TN reduction

TP reduction

Cost

TN reduction

TP reduction

Cost

Chao River

Year-round Flood season

86.08% (10491.83) 85.40% (10408.01)

87.39% (1155.08) 86.78% (1144.27)

22.38 17.95

86.04% (10491.62) 85.40% (10407.89)

89.36% (1189.87) 88.74% (1178.95)

3.18 2.11

Bai River

Year-round Flood season

68.56% (9694.77) 66.85% (9452.97)

71.62% (1547.70) 70.13% (1515.50)

20.96 15.33

68.72% (9717.39) 67.53% (9549.12)

72.10% (1558.07) 70.66% (1526.95)

1.11 0.14

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Table 4 Optimized BMP types and number of subbasins with BMPs under different assumptions of cross-watershed coordination and targeted periods for meeting water quality standards.* Optimization results

Merging the results of the individual assessment point

Coordinating the two assessment points

Scenario 1

Scenario 1

Scenario 2

Scenario 2

BMP type

Year-round

Flood season

Year-round

Flood season

Year-round

Flood season

Year-round

Flood season

CFF15 CFF25 FR20% FR30% CT AC FS5m FS10m FS15m GW CW DB

26 15 0 23 7 23 6 5 1 1 6 18

27 11 0 23 4 19 5 3 0 1 5 19

46 2 1 47 2 46 6 3 0 1 7 17

47 1 0 48 1 47 5 4 0 1 4 20

10 16 9 0 5 21 12 5 2 0 1 6

8 17 10 0 5 20 8 4 3 0 1 6

42 6 16 31 0 48 5 5 0 1 0 2

45 2 19 27 0 48 10 1 3 0 0 1

* The acronyms are defined in Table 1.

Fig. 5. Types and placements of the BMPs by separately optimizing the BMPs in the Chao Rivera and Bai River subwatersheds.

effective option for reducing nutrient losses by undertaking tillage, planting and other farm operations along the contours of a slopping field to increase water infiltration and prevent soil erosion (Sklenicka et al., 2015). Converting farmland to forest has been successfully used to improve streamflow conditions in this region since 1999 (Tang et al., 2011), and our optimization results are consistent with the goals of that national water soil conservation program.

management practices such as CFF, and nutrient management practices (FR20% and FR30%) were configured within almost all the sub-basins due to their considerable economic benefits for farmers. Structural BMPs were selectively allocated in certain sub-basins that were identified as critical source areas of NPS pollution. These results were consistent with other studies (Chen et al., 2015a; Chen et al., 2015b). The use of CT reduces soil disturbances in fields where residues can reduce the flow of water on the site (Herman et al., 2015). AC is also an 39

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Fig. 6. Types and placements of BMPs by coordinating the optimization of the BMPs in the Chao Rivera and Bai River subwatersheds.

government pays BMP costs through national grants (scenario 1). Our results show that the BMPs optimized under scenario 2 were more cost effective for improving water quality. Although Figs. 5 and 6 clearly show the differences in the types and placements of BMPs under different assumptions of BMP optimization coordination and targeted periods of meeting water quality standards, scenario 2 consistently obtained more cost effective BMPs than scenario 1, indicating the importance of encouraging farmers to participate in future water resource management plans.

In scenario 2, the cost of the structural BMPs could be offset by the economic benefits from the non-structural BMPs that can effectively control the sources of nutrients. As a result, the total cost of the optimized BMPs was far lower than that of scenario 1. The coordinated BMP optimization across the Chao River and Bai River subwatersheds identified lower-cost BMPs for the reduction of nutrient loads than the respective uncoordinated optimizations of BMPs.

3.5.3. Visual examination of the optimization results based on the different stakeholders’ perspectives Figs. 5 and 6 demonstrate the difference in the spatial placements of the BMPs for the optimization results based on the two stakeholders’ perspectives. Differences existed in the numbers, types, and placements of the BMPs, which contributed to the different costs of the BMPs under the two scenarios. The allocations of the costs and benefits of the BMPs to the different stakeholders resulted in different BMP economic costs and subsequently critically influenced the optimization results under the two stakeholders’ perspectives. Because the benefits of the non-structural BMPs yielded surpluses to offset their costs, these practices are more likely to be accepted by farmers. For example, CFF, FR and AC, which were effective for controlling soil erosion, reducing the application of nutrients and the leaching intensity of soil nutrients, appeared more frequently under scenario 2 than under scenario 1 where the cost of non-structural BMPs included actual construction costs and maintenance costs that were not deducted by the economic benefits of those practices. In this sense, to achieve the same water quality targets, scenario 1, with a lower number and fewer types of non-structural BMPs, needed to be supplemented by more structural BMPs. According to China’s national policy,

4. Conclusions The water quality threats facing the MRW call for effective BMP design to mitigate excessive nutrient loading. A central question for BMP design in the MRW lies in which stakeholder (farmer vs. government) should be responsible for the economic costs of BMP implementation. In this study, we integrated the SWAT watershed model and the FEM model with the NSGA-II multi-objective optimization algorithm to explore tradeoffs associated with different BMP design scenarios. We conducted numerical experiments to understand the role of meeting different stakeholders’ (farmers vs. government) preferences on BMP optimization. The results showed substantial discrepancies between these two scenarios with respect to the types and placements of BMPs and associated economic costs, highlighting the need for reconciling concerns from different stakeholders in order to arrive at a BMP plan that all parties will agree upon. In addition, we found that cross-subwatershed coordination and targeting flood season instead of year-round water quality standards could pronouncedly reduce economic costs of BMP implementation while not substantially degrading 40

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water quality. We believe that the watershed scale optimization method developed here holds promise to serve as an effective tool for exploring tradeoffs between economic costs, water quality improvements, and decisions makers and stakeholders’ concerns regarding BMPs design, thereby informing sustainable watershed scale water resource management.

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