Byung-Mo Lee

Climate change poses serious threats to agricultural productivity and environmental sustainability, highlighting the need for scientific, evidence-based quantitative assessments. Process-based models provide scientific reliability in simulating crop growth, hydrological processes, and greenhouse gas emissions; however, their application to high resolution, national scale assessments across multiple climate scenarios demands substantial computational resources, thereby limiting the timeliness of policy analysis and decision making. To address this challenge, this study outlines a surrogate modeling framework that links process-based models with machine learning techniques. The concept and classification of surrogate models are reviewed, including data-driven models, meta-models, and hybrid and multi fidelity models. In addition, recent applications are examined to identify the purposes for which surrogate modeling techniques have been applied in combination with process-based models. Based on these insights, a national-scale assessment framework is proposed that integrates large scale input-output data generation, sensitivity analysis, dimensionality reduction, and uncertainty quantification techniques. The framework also utilizes outputs from multiple General Circulation Models (GCMs), thereby reducing assessment uncertainty and improving the reliability of results. This review suggests that combining the scientific reliability of process-based models with the efficiency of surrogate models provides a promising and scalable framework for future climate impact and vulnerability assessments, which could serve as a valuable tool for adaptation planning and effective national policy making in the agricultural sector.

This study investigates seasonal and interannual variations in agricultural groundwater quality in Chungcheongnam-do, South Korea, from 2021 to 2024 across 20 monitoring sites, including rice paddies (n = 5), upland fields (n = 5), and greenhouse cultivation areas (n = 10). Samples were collected in April and July each year and analyzed for pH, electrical conductivity (EC), NO 3-N, Cl-, major cations (Na + , K + , Ca 2+ , Mg 2+), and heavy metals (Cd, As, Pb). The mean pH values ranged from 6.4 (paddies) to 6.9 (uplands), generally within the acceptable range (6.0-8.5). EC was highest in paddies (0.45 dS m-1), followed by greenhouses (0.41 dS m-1) and uplands (0.28 dS m-1). NO 3-N concentrations were 10.2 mg L-1 in paddies, 9.1 mg L-1 in uplands, and 6.3 mg L-1 in greenhouses-exceeding the national average (5.30-6.68 mg L-1), with 10-20% of paddy and upland sites surpassing the regulatory threshold (20 mg L-1). Cl-concentrations were also elevated-2.3 mg L-1 (paddies), 28.2 mg L-1 (uplands), and 47.0 mg L-1 (greenhouses)-yet remained below the permissible limit. All samples complied with heavy metal standards. Seasonal differences in water quality were statistically insignificant, reflecting the relative stability of groundwater systems. However, declining EC and ionic concentrations in greenhouse areas appear to result from a shift toward hydroponic cultivation. These findings underscore growing concerns about nitrate contamination in paddy regions and highlight the need for targeted nutrient management and region-specific groundwater protection policies.

Pearson correlation matrix among CCME-WQI, key water quality parameters, farmland ratio, and cumulative precipitation across five provinces in Korea.

The objective of this study was to predict future phenological changes of Hydrochara affinis and Sternolophus rufipes in agro-ecosystem under climate change scenarios. Environmental shifts driven by climate change are affecting the phenology of species inhabiting agricultural ecosystems. A quantitative analysis of phenological change is necessary because these phenological changes can impact agricultural productivity. In this study, we monitored the adult flight phenology of Hydrochara affinis and Sternolophus rufipes, which were selected as climate change indicator species of agricultural ecosystem in South Korea, and simulated future phenological changes using SSP scenarios. The results indicate that the first flight of Hydrochara affinis and Sternolophus rufipes are expected to occur earlier in the future compared to past by 4-49 days and 4-50 days, respectively, under the SSP5-8.5 scenario. Phenological shifts were found to be more pronounced in SSP5-8.5 compared to SSP2-4.5. The analysis showed that, compared to the results of the RCP 8.5 scenario from previous studies, the flight timing of Hydrochara affinis and Sternolophus rufipes has been further advanced under the SSP5-8.5 scenario. Long-term monitoring is essential to analyze the impacts of climate change on agricultural ecosystems. The prediction methods and results of phenological changes in agricultural ecosystems presented in this study are expected to contribute to the development of climate adaptation strategies in agriculture.

Scientific, evidence-based vulnerability assessments are vital for predicting and mitigating the climate change risks poses to national systems. This study proposes a climate change vulnerability assessment method by combining a process-based model and machine learning, using nutrient runoff vulnerability in agricultural land as an example. A training dataset was generated using the process-based model APEX (Agricultural Policy and Environmental eXtender) to train a nonlinear regression machine learning model. The trained nonlinear regression model accurately replicated the APEX results, with an R² value of 0.53 for nitrogen in paddy, and 0.72 for nitrogen and 0.71 for phosphorus in upland. This model was then applied to quantify nitrogen and phosphorus discharge at the si (city) and gun (county) levels. The results for each indicator were normalized, and weights were assigned using the Analytic Hierarchy Process (AHP). Vulnerability assessments were then conducted for mid-term (2041 ~ 2070) and long-term (2071 ~ 2100) future scenarios, and compared with the baseline period (1981 ~ 2010). The results showed that the average nutrient discharge vulnerability score for si and gun units was 31.4 in the baseline period, with a minimum of 0.6 and a maximum of 70.0. Under the SSP5-8.5 scenario, the average score increased to 43.6 in the mid-term future and 54.7 in the long-term future. However, when scenarios of compliance with standard fertilization rates for paddy and a 20% reduction in fertilization rates for upland were applied, the average score dropped to 33.3 in the mid-term future and 44.5 in the long-term future. These results highlight potential policy measures to mitigate the increasing vulnerability due to climate change in the agricultural sector and provide a rapid reference for policymakers when designing effective adaptation strategies.