the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Soil Organic Carbon Projections and Climate Adaptation Strategies across Pacific Rim Agro-ecosystems
Abstract. In Pacific Rim regions highly exposed to climate variability, accurate projections of soil organic carbon (SOC) are critical for furture effective land management and climate adaptation strategies. This study integrated digital soil mapping with CMIP6-based climate projections to estimate the spatiotemporal distribution of SOC stocks in subtropical (Zhuoshui River) and tropical (Laonong River) watersheds in Taiwan. We collected 1377 soil samples and data on 18 environmental covariates and modeled SOC stocks at a 20-m resolution through the Cubist and random forest algorithms, which were also combined with regression kriging. The Cubist-based kriging model was discovered to achieve the highest performance in SOC stock prediction. Forested areas were found to contain >80 % of SOC stocks, and tropical zones were discovered to store substantially less carbon than subtropical zones. Future emission scenarios revealed spatial heterogeneity in SOC stock dynamics. In scenario SSP1-2.6, a maximum SOC stock decline of approximately 20.9 % was predicted, particularly for uplands, because of erosion induced by extreme rainfall events (R95p and R99p), whereas in scenarios SSP2-4.5 and SSP5-8.5, increases of 7.9 % to 58 % were predicted, respectively; particularly corresponded to forested areas because of enhanced productivity caused by increased TNx and TXx (extremes of minimum and maximum temperature). Partial least squares path modeling revealed a climate–topography interaction in SOC stocks, dominated by topography and followed by prolonged dry spells. Examining the interactions between climatic extremes, landscape types, and SOC stocks is essential for enhancing soil resilience and ensuring stable SOC stocks in the future.
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Status: open (until 19 Nov 2025)
- RC1: 'Comment on egusphere-2025-4258', Anonymous Referee #1, 13 Oct 2025 reply
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                     RC2:  'Comment on egusphere-2025-4258', Anonymous Referee #2, 17 Oct 2025
            
                        
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                        This paper presents an important study integrating digital soil mapping (DSM) and CMIP6 climate projections to assess spatial–temporal SOC stock dynamics in two contrasting Taiwanese watersheds. The manuscript is generally well structured and provides a comprehensive analysis. However, some parts require improvement. Line 102: Please explain why the 0–30 cm soil depth was selected. Soil changes due to temperature and rainfall are generally most pronounced within the top 10 cm. Line 124: In Figure S1a, the legend for the colours is missing. Line 130: It would be helpful to indicate size in millimetres (mm). Line 134: Please provide the full name for the abbreviation TOC. Line 170: More information is needed on how the resolution was changed from 1 km to 20 m. Line 171: The land-cover class and soil order variables are categorical. Were these treated as factors or numeric data? Line 177: It would be clearer to move Section 2.4 ("Climate data in various emission scenarios and with extreme climate indices") to the end of the Methods section. Line 214: Please clarify why 20 committees were used for the Cubist model. Line 224: Please explain the rationale for using mtry = 7 and ntree = 500 in the Random Forest model. Line 230: The sentence “The distribution of the two data sets is depicted in Fig. 2.” should be moved to the Results section. Line 272: Replace “Coefficient of determination (R²)” with simply R². Lines 294–296: This section requires further explanation, as it is currently difficult to understand. Line 301: When creating the SOC map, did you use only the 70% training data or the entire dataset (100%)? Lines 335–345: This section should be moved to “3.7 Extreme climate index parameter estimates in three emission scenarios.” Line 481: It would strengthen the discussion to compare the SOC maps produced in this study with existing SOC maps from other publications. Figure 3: there are two “fig 3”, so remove one. Most samples appear concentrated in croplands, and future work could include a more balanced sampling across different land types (e.g., forest). Figure 6: Please specify which climate scenario (e.g., CWD) is displayed. Citation: https://doi.org/10.5194/egusphere-2025-4258-RC2 
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This is a review report for the manuscript, entitled: “Soil organic carbon projections and climate adaptation strategies across Pacific Rim Agro-ecosystems” by Syu et al.
Soil organic carbon (SOC) is highly relevant with C cycle and grain production. Thus, the responses of SOC to climate changes should be assessed for adaption management. This is a well-structed manuscript. The aims are clear and the methods are proper. This study applied a lot of associated methods (including machine learning and geostatistical approach) to evaluate the spatial-temporal SOC responses under different circumstances. So, the results are valuable for managers. Although the predictions can be estimated based on the environmental covariates under climate changes, I still hope the authors can consider or make an assumption regarding soil-forming time. Soil formation is a time-dependent process, and it appears that an inherent steady-state assumption has been made in this study, but not explicitly mentioned. Below are the specific comments.
Comments:
Line 28-33: In abstract, this sentence is clear, but too long. Please split it into two or more sentences.
Line 34: climate-topography interaction is an innovative finding in this ms. Try to clarify it in abs, not just mention the interaction.
Line 46: Using “many scales” as the subject sounds a bit awkward.
Line 62-83: This paragraph is quite wordy. The real point that the authors want to address is “meta-analytical evaluation” with a geostatistical approach. Please reorganize the paragraph and point out the points in this paragraph.
Line 293: Why were the slope, aspect, and flow accumulation not taken into consideration?
Materials and Methods: Consider making a figure to demonstrate all the data input and model processing.
In Fig. 5: The landscape region, in fact, is classified by elevation (<100m, 100m – 1000m, and >1000m). Therefore, “Forested region” is improper. It is quite confusing with the land cover. Besides, for the category of land cover in the x-axis, the “upland” should be upland farming.
Line 326: explain what is dry farming area? I can’t find it in fig. 5.
Line 383-384: The inference (citation) should not appear in the result section basically.
Line 434-436: The same as above. The two citations are suggested to move to the discussion section, where the authors can compare their studied area with others.
Line 522: The sentence is unclear to me.
Line 572-574: The authors should describe this limitation of the modeling work. Wildfires are important for soil formation and ecosystems.
Line 604: remote sensing parameters? Or “NDVI” is more specific.
Conclusion: So far, this conclusion is somewhat like a summary. Please try to draw some implications from this study in order to inspire the readers.