• Journal of Korea Water Resources Association
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• v.54 no.2
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• pp.105-119
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• 2021

An increase in the frequency and intensity of both floods and droughts has been recently observed due to an increase in climate variability. Especially, land-use change associated with industrial structure and urbanization has led to an imbalance between water supply and demand, acting as a constraint in water resource management. Accurate rainfall-runoff analysis plays a critical role in evaluating water availability in the water budget analysis. This study aimed to explore various continuous rainfall-runoff models over the Soyanggang dam watershed. Moreover, the ensemble modeling framework combining multiple models was introduced to present scenarios on streamflow considering uncertainties. In the ensemble modeling framework, rainfall-runoff models with fewer parameters are generally preferred for effective regionalization. In this study, more than 40 continuous rainfall-runoff models were applied to the Soyanggang dam watershed, and nine rainfall-runoff models were primarily selected using different goodness-of-fit measures. This study confirmed that the ensemble model showed better performance than the individual model over different flow regimes.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1273-1283
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• 2022

Recently, forest fires have frequently occurred due to climate change, leading to human and property damage every year. The forest fire monitoring technique using remote sensing can obtain quick and large-scale information of fire-damaged areas. In this study, the Gangneung and Donghae forest fires that occurred in March 2022 were analyzed using the spectral band of Sentinel-2, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI) to classify the affected areas of forest fires. The U-net based convolutional neural networks (CNNs) model was simulated for the fire-damaged areas. The accuracy of forest fire classification in Donghae and Gangneung classification was high at 97.3% (f₁=0.486, IoU=0.946). The same model used in Donghae and Gangneung was applied to Uljin and Samcheok areas to get rid of the possibility of overfitting often happen in machine learning. As a result, the portion of overlap with the forest fire damage area reported by the National Institute of Forest Science (NIFoS) was 74.4%, confirming a high level of accuracy even considering the uncertainty of the model. This study suggests that it is possible to quantitatively evaluate the classification of forest fire-damaged area using a spectral band and indices similar to that of the Compact Advanced Satellite 500 (CAS500-4) in the Sentinel-2.