• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.761-774
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• 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
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• v.49 no.3
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• pp.240-249
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• 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.