Evaluation of the Best Management Practices for Reducing Phosphorus Load in a Watershed in Terms of Cost and Greenhouse Gas Emissions

Round 1

Reviewer 1 Report

In this paper, the authors used the SWAT model and genetic algorithm to explore how BMP strategies decreased the phosphorus load and greenhouse gas emissions. The scenarios in the model are reasonable and practical for agricultural watershed management. However, it would be helpful if the following concerns were raised:.

1)   Fig.1 Please add the stream network and coordinates here, and let the reader know where it is.

2)  Give a specific reason for why you consider phosphorus and GHG together in the SWAT model in the introduction and emphasize the importance of your work.

3) There is no need to give such a long introduction to the SWAT model background, it is very common for readers, so simply this introduction.

4)  Merge figs. 5 to 7 into one.

5)     In the conclusion section, give more specific information about the implication of your work, and how can be valuable for stakeholders, decision-makers, and farms, not only for phosphorus, but also for nitrogen.

The paper needs a major revision before publication.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

The current manuscript provides a novel study focusing on the balance between greenhouse gas (GHG) emissions and total phosphorous (TP) reduction in best management practice (BMP) placement. The writing is generally good, and the reviewer has the following comments:

1) The introduction should put more effort into the current state of the art in BMP placement optimization considering TP. The linkage between TP reduction and GHG emission should also be discussed in more detail so that the involvement of GHG in the consideration is supported.

2) The map needs proper coordinate grids. Besides, please move the scale to a more appropriate location in Figure 1, and add another scale in the small insert. The names of rivers mentioned in the text should also be marked on the map if possible.

3) Where are the sources of GHG emissions from each land use? Besides, I am certain these values have ranges and are definitely not single values. Please consider the possible cost and GHG emission ranges in the optimization process.

4) Please explain why the scenarios are structured in the way delineated in lines 163-166?

5) Line 202: redundant dash (“-“).

6) The reviewer does not understand how Genetic Algorithm is used in this study. The number of scenarios is quite small, so all scenarios can be tried manually without any problem. I guess more explanation is needed for this part of the manuscript.

No specific issues.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Manuscript gives evaluation of the BMP for reducing TP load in a Yeongsan river watershed in terms of cost and GHG emissions. Manuscript is well written and structured but some improvements or explanations are needed.

In Section 2, please expand the part dealing with data. Did you use population data, farm animals data (livestock), population connected to WWTP, type of WWTP etc? SWAT is very demanding model regarding the use of input data...

In BMP scenarios, explain/describe is it possible to reduce 50 % of fertilizer usage and how? Scenarios must be practial and explained.

Why didnt you use reduction of TP in scenarious regarding of increasing degree of treatment on WWTP and better management on farm animals?

Please expand and add more discussion for section 3.2. Effects of application of BMP scenarios.

In table 3 please put reference for costs.

For future studies please mention, except TP, is it important to pay attention to total nitrogen (TN)?

Minor editing of English language required.

Author Response

Response to Reviewer 3 Comments

Manuscript gives evaluation of the BMP for reducing TP load in a Yeongsan river watershed in terms of cost and GHG emissions. Manuscript is well written and structured but some improvements or explanations are needed.

>> Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions/corrections highlighted/in track changes in the re-submitted files.

1) In Section 2, please expand the part dealing with data. Did you use population data, farm animals data (livestock), population connected to WWTP, type of WWTP etc? SWAT is very demanding model regarding the use of input data.

Response to Reviewer comment #1

>> In Section 2, concerning sewage treatment data, we configured four Wastewater Treatment Plants (WWTPs) to handle the domestic sewage generated by populations totaling 1.2 million, 37 thousand, 3 thousand, and 5 thousand individuals, respectively. Additionally, an industrial wastewater treatment plant was assigned to manage effluents originating from industrial complexes. Livestock manure is either composted or its liquid fraction is directed to sewage treatment facilities. Within our model, composted material is considered a source of fertilizer, while liquid waste is factored into the capacity of the sewage treatment plants.

2) In BMP scenarios, explain/describe is it possible to reduce 50 % of fertilizer usage and how? Scenarios must be practical and explained.

Response to Reviewer comment #2

>> The maximum feasible reduction rate of 50% in fertilizer usage within the BMP scenarios was determined based on prior research findings and practical considerations. In the context of the optimized BMP scenarios proposed in this study, this reduction scenario was particularly prominent in paddy fields but relatively less significant in soybean fields. Thus, the reduction of fertilizer usage by 50% represents only one aspect of the comprehensive optimal BMP scenario proposed in this study.

Reference 1. Liu et al., Cost-effectiveness and cost-benefit analysis of BMPs in controlling agricultural nonpoint source pollution in China based on the SWAT model, Environmental Monitoring and Assessment, 2014

Reference 2. Misaghi et al., Application of SWAT model to simulate nitrate and phosphate leaching from agricultural lands to the rivers, Advances in Environmental Technology, 2020

Reference 3. Zhang and Zhang, Modeling effectiveness of agricultural BMPs to reduce sediment load and organophosphate pesticide in surface runoff, Science of the Total Environment, 2011

3) Why didnt you use reduction of TP in scenarious regarding of increasing degree of treatment on WWTP and better management on farm animals?

Response to Reviewer comment #3

>> I appreciate your suggestion. The WWTPs employed in this study have already integrated advanced treatment processes, making further improvements in treatment levels practically challenging. Concerning livestock wastewater, the liquid fractions are currently managed alongside WWTP operations, while solid components undergo composting for subsequent use as fertilizers. It can be deduced that simulations covering both WWTP operations and fertilizer utilization have already been conducted. Additionally, although the study area encompasses urban regions, the predominant issue is non-point source pollution originating from agricultural activities. Consequently, our research prioritized and implemented methodologies most pertinent to agricultural contexts, with a particular focus on paddy and soybean fields, rather than emphasizing reductions in wastewater treatment plant discharges or enhancing farm animal management.

4) Please expand and add more discussion for section 3.2. Effects of application of BMP scenarios.

Response to Reviewer comment #4

>> We have revised the discussion section in 3.2 according to your suggestion.

Line 250 to 304, “A total of 18 BMP scenarios presented in Table 2 were applied to evaluate TP removal efficiency, costs, and GHG emissions. The TP removal efficiencies of the BMP scenarios are shown in Figure 5 (a) and Table 3. The costs associated with the implementation of BMPs depicted in Figure 5 (a) and Table 3 were computed by multiplying the area of the HRUs corresponding to paddy and soybean fields by the cost per unit area associated with each BMP. Jeon [29] reported that in the Yeongsan River Watershed, a 50% reduction in fertiliz-er and a 5 m RB are the most effective methodologies in terms of TP removal efficiency. Pyo et al. [16] indicated that in the Lake Erie watershed, RB and contour cropping were the most effective methodologies, whereas NT and nutrient management were relatively less effective. In this study, for paddy fields, BMP7 showed the highest efficiency at 26.87%, whereas BMP2 displayed the lowest efficiency at 0.53%. In soybean fields, BMP18 exhib-ited the highest reduction efficiency of 7.17%, whereas BMP9 showed the lowest efficiency of 0.04%. This indicates that to maximize TP removal efficiency in the Yeongsan River Watershed, a 50% reduction in fertilizer application for paddy fields and the establish-ment of a 5 m RB zone for soybean fields is the most effective strategy.

The costs incurred from the application of the BMP scenarios can be examined using Figure 5 (b) and Table 3. Pyo et al. [16] reported that CT, NT, and contour cropping meth-ods are the most cost-effective methodologies for the Lake Erie watershed. In this study, for the BMP scenarios applied to paddy fields, BMP2 required the highest cost of 1.09 million dollars, whereas BMP1 had no associated costs. In the case of BMP scenarios for soybean fields, BMP10 incurred the highest cost at 0.16 million dollars, and the BMP8 application did not necessitate any costs. The researchers in previous studies have indicated that the cost of implementing CT was considered as zero because farmers were already utilizing this method and it was deemed feasible to adopt without requiring additional subsidies. this rationale was also adopted for use in the current study [40].

The anticipated GHG emissions from the BMP scenarios are shown in Figure 5 (c) and Table 3. Similar to the cost calculations, the GHG emissions presented in Figures 5(c) and Table 3 were calculated by multiplying the GHG emissions per unit area associated with the BMPs by the area of HRUs corresponding to paddy and soybean field. For paddy fields, BMP1 showed the highest emissions at 60.49 kt CO2 eq., and BMP7 showed the lowest at 7.44 kt CO2 eq. For soybean fields, BMP10 showed the highest emissions at 9.13 kt CO2 eq., and BMP15 showed the lowest emissions at 1.13 kt CO2 eq. As presented in Ta-ble 2, the GHG emissions of the CT and DP were higher than those of the other BMPs. Given the larger area of paddy fields compared with soybean fields, it is expected that the application of BMP1 and BMP2 would result in higher GHG emissions. As mentioned previously, BMP1 is effective in terms of TP removal efficiency and cost; however, its ap-plication may require careful consideration because of the significant amount of GHG emissions recorded upon application.

We found that the optimal BMP scenarios differed in terms of TP removal efficiency, cost, and GHG emissions during application. To maximize the TP removal efficiency, BMP7 was optimal in paddy fields, and BMP18 was optimal in soybean fields. BMP1 and BMP8 were the most effective methods for reducing costs in both paddy and soybean fields. To minimize GHG emissions, BMP7 and BMP18 were the most effective in both paddy and soybean fields. However, implementing a 50% reduction in fertilizer usage may not be the most cost-effective BMP application. Drastically cutting fertilizer usage by such a margin may indeed improve water quality and GHG emissions, but it could also potentially decrease in agricultural production. Consequently, unilaterally reducing ferti-lizer usage could lead to conflicts among stakeholders. Pyo et al. [16] has shown that while contour cropping may excel in terms of cost-effectiveness, it may also result in a perception gap among stakeholders, indicating the presence of tradeoffs that need to be carefully considered. Therefore, instead of applying a single BMP with high efficiency throughout the entire watershed, this study determined that it is necessary to identify the optimal BMPs by comprehensively considering TP removal efficiency, cost, and GHG emissions. Through this approach, the research aimed to explore a combination of opti-mized BMP scenarios across the entire watershed.”

5) In table 3 please put reference for costs.

Response to Reviewer comment #5

>> We added to Table 2 the reference on which we based the calculations in Table 3.

Line 252 to 255, “The costs associated with the implementation of BMPs depicted in Figure 5 (a) and Table 3 were computed by multiplying the area of the HRUs corresponding to paddy and soybean fields by the cost per unit area associated with each BMP.”

6) For future studies please mention, except TP, is it important to pay attention to total nitrogen (TN)?

Response to Reviewer comment #6

>> In this study, the mention of the potential research on TN was intended to illustrate the feasibility of investigating TP removal efficiency and to propose that TN removal efficiency could be established as a target at key junctures where enhancing TN removal is deemed crucial based on researchers' requirements. To elucidate this point, we have made some adjustments to the relevant expressions.

Line 405 to 407, “While this study focused on enhancing TP removal efficiency, it provides a foundation for future research to assess improvements in water quality concerning total nitrogen load and sediment dynamics.”