• Journal of Korean Society of Forest Science
• /
• v.109 no.3
• /
• pp.259-270
• /
• 2020

Extreme weather events, such as heat and drought, have occurred frequently over the past two decades. This has led to continuous reports of cases of forest damage due to physiological stress, not pest damage. In 2014, pine trees were collectively damaged in the forest genetic resources reserve of Sogwang-ri, Uljin, South Korea. An investigation was launched to determine the causes of the dieback, so that a forest management plan could be prepared to deal with the current dieback, and to prevent future damage. This study aimedto 1) understand the topographic and structural characteristics of the area which experienced pine tree dieback, 2) identify the main causes of the dieback, and 3) predict future risk areas through the use of machine-learning techniques. A model for identifying risk areas was developed using 14 explanatory variables, including location, elevation, slope, and age class. When three machine-learning techniques-Decision Tree, Random Forest (RF), and Support Vector Machine (SVM) were applied to the model, RF and SVM showed higher predictability scores, with accuracies over 93%. Our analysis of the variable set showed that the topographical areas most vulnerable to pine dieback were those with high altitudes, high daily solar radiation, and limited water availability. We also found that, when it came to forest stand characteristics, pine trees with high vertical stand densities (5-15 m high) and higher age classes experienced a higher risk of dieback. The RF and SVM models predicted that 9.5% or 115 ha of the Geumgang Pine Forest are at high risk for pine dieback. Our study suggests the need for further investigation into the vulnerable areas of the Geumgang Pine Forest, and also for climate change adaptive forest management steps to protect those areas which remain undamaged.

• Journal of Korea Water Resources Association
• /
• v.51 no.10
• /
• pp.905-916
• /
• 2018

The purpose of this study is to evaluate the climate change impact on watershed hydrology and flow duration in Geum River basin ($9,645.5km^2$) especially by extreme scenarios. The rainfall related extreme index, STARDEX (STAtistical and Regional dynamical Downscaling of EXtremes) was adopted to select the future extreme scenario from the 10 GCMs with RCP 8.5 scenarios by four projection periods (Historical: 1975~2005, 2020s: 2011~2040, 2050s: 2041~2070, 2080s: 2071~2100). As a result, the 5 scenarios of wet (CESM1-BGC and HadGEM2-ES), normal (MPI-ESM-MR), and dry (INM-CM4 and FGOALS-s2) were selected and applied to SWAT (Soil and Water Assessment Tool) hydrological model. The wet scenarios showed big differences comparing with the normal scenario in 2080s period. The 2080s evapotranspiration (ET) of wet scenarios varied from -3.2 to +3.1 mm, the 2080s total runoff (TR) varied from +5.5 to +128.4 mm. The dry scenarios showed big differences comparing with the normal scenario in 2020s period. The 2020s ET for dry scenarios varied from -16.8 to -13.3 mm and the TR varied from -264.0 to -132.3 mm respectively. For the flow duration change, the CFR (coefficient of flow regime, Q10/Q355) was altered from +4.2 to +10.5 for 2080s wet scenarios and from +1.7 to +2.6 for 2020s dry scenarios. As a result of the flow duration analysis according to the change of the hydrological factors of the Geum River basin applying the extreme climate change scenario, INM-CM4 showed suitable scenario to show extreme dry condition and FGOALS-s2 showed suitable scenario for the analysis of the drought condition with large flow duration variability. HadGEM2-ES was evaluated as a scenario that can be used for maximum flow analysis because the flow duration variability was small and CESM1-BGC was evaluated as a scenario that can be applied to the case of extreme flood analysis with large flow duration variability.

• Journal of Korea Water Resources Association
• /
• v.55 no.10
• /
• pp.761-774
• /
• 2022

Accurate hydrologic prediction is essential to analyze the effects of drought, flood, and climate change on flow rates, water quality, and ecosystems. Disentangling the uncertainty of the hydrological model is one of the important issues in hydrology and water resources research. Hydrologic data assimilation (DA), a technique that updates the status or parameters of a hydrological model to produce the most likely estimates of the initial conditions of the model, is one of the ways to minimize uncertainty in hydrological simulations and improve predictive accuracy. In this study, the two ensemble-based sequential DA techniques, ensemble Kalman filter, and particle filter are comparatively analyzed for the daily discharge simulation at the Yongdam catchment using airGRdatassim. The results showed that the values of Kling-Gupta efficiency (KGE) were improved from 0.799 in the open loop simulation to 0.826 in the ensemble Kalman filter and to 0.933 in the particle filter. In addition, we analyzed the effects of hyper-parameters related to the data assimilation methods such as precipitation and potential evaporation forcing error parameters and selection of perturbed and updated states. For the case of forcing error conditions, the particle filter was superior to the ensemble in terms of the KGE index. The size of the optimal forcing noise was relatively smaller in the particle filter compared to the ensemble Kalman filter. In addition, with more state variables included in the updating step, performance of data assimilation improved, implicating that adequate selection of updating states can be considered as a hyper-parameter. The simulation experiments in this study implied that DA hyper-parameters needed to be carefully optimized to exploit the potential of DA methods.

• Journal of Plant Biotechnology
• /
• v.49 no.3
• /
• pp.240-249
• /
• 2022

Measuring chlorophyll fluorescence (CF) is a useful tool for assessing a plant's ability to tolerate abiotic stresses such as drought, waterlogging and high temperature. Korean ginseng is highly sensitive to water stress in paddy fields. To evaluate the possibility of non-destructively diagnosing waterlogging stress using chlorophyll fluorescence (CF) imaging techniques, we screened 57 ginseng accessions for waterlogging tolerance. To evaluate waterlogging tolerance among the 2-year-old Korean ginseng accessions, we treated ginseng plants with water stress for 25 days. The physiological disorder rate was characterized through visual assessment (an assigned score of 0-5). The physiological disorder rates of Geumjin, Geumsun and GS00-58 were lower than that of other accessions. In contrast, lines GS97-62, GS97-69 and GS98-1-5 were deemed susceptible. Root traits, chlorophyll content and the reduction rates decreased in most ginseng accessions. Further, these metrics were significantly lower in susceptible genotypes compared to resistant ones. All CF parameters showed a positive or negative response to waterlogging stress, and this response continuously increased over the treatment time among the genotypes. The CF parameter Fv/Fm was used to screen the 57 accessions, and the results showed clear differences in Fv/Fm between the susceptible and resistant genotypes. Susceptible genotypes had an especially low Fv/Fm value of less than 0.8, reflecting damage to the reaction center of photosystem II. It is concluded that Fv/Fm can be used as a CF parameter index for screening waterlogging stress tolerance in ginseng genotypes.