• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.627-646
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• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.697-723
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• 2022

Data-driven models to predict groundwater levels 30 days in advance were developed for 12 groundwater monitoring stations in the middle-Jeju watershed, Jeju Island. Stacked long short-term memory (stacked-LSTM), a deep learning technique suitable for time series forecasting, was used for model development. Daily time series data from 2001 to 2022 for precipitation, groundwater usage amount, and groundwater level were considered. Various models were proposed that used different combinations of the input data types and varying lengths of previous time series data for each input variable. A general procedure for deep-learning-based model development is suggested based on consideration of the comparative validation results of the tested models. A model using precipitation, groundwater usage amount, and previous groundwater level data as input variables outperformed any model neglecting one or more of these data categories. Using extended sequences of these past data improved the predictions, possibly owing to the long delay time between precipitation and groundwater recharge, which results from the deep groundwater level in Jeju Island. However, limiting the range of considered groundwater usage data that significantly affected the groundwater level fluctuation (rather than using all the groundwater usage data) improved the performance of the predictive model. The developed models can predict the future groundwater level based on the current amount of precipitation and groundwater use. Therefore, the models provide information on the soundness of the aquifer system, which will help to prepare management plans to maintain appropriate groundwater quantities.

• Magazine of the Korean Society of Agricultural Engineers
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• v.11 no.1
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• pp.1561-1579
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• 1969

In Korea, the duty of water in paddy fields was measured at the Agricultural Experimental Station in Suwon about 60 years ago. After that time some testing has been made in several places, but the key points in its experiment were the water depth of evapo-transpiration. Improved breeds, progress in cultivation and management techniques as well as development of measuring apparatus in recent years have necessitated the review of the duty of water in paddy fields. The necessity of reviewing the conventional methods has become even more important, as no source of information has been made available through survey of water utilization on a soil use basis which requires data on peculiar features of the water depth of evapo-transpiration. For example, the duty of water in paddy field is largely affected by the water depth of evapo-transpiration in connection with the wetted paddy field, whereas in connection with the normal paddy fields without this characteristic the vertical percolation become the predominant factor in measuring the decreasing depth of water. Therefore, it becomes important. that not only the water depth of evapotranspiration but also the vertical percolation process should also be observed in order to arrive at a realistic conclusion. As the vertical percolation has aclose relationship to the height of the underground water, the change of the latter can be measured. As the conclusion of this experiment, the following subjects are indicated. 1. In order to determine the economic duty of water in paddy fields on a basis of varying soil features, the varying soil features in the benifited area should be investigated thoroughly. The water depths of evapo-transpiration(ET) ratio to evaporation in the evaporator(V) on a basis of the varying soil features are as follows: clay loam ET/V = 1.11, loam ET/V = 1.64, sandy loam ET.V = 1.63 2. The decreasing depth of water consists of the water depth of evapotranspiration, the vertical per colation and the percolation of foot path. Among these three, the percolation of foot path can be utilized again. 3. As the result of this experiment, it shows the decreasing depth of water as follows. clay loam 9.3 mm/day, loam 13.5mm/daty, sandy loam 15.3mm/day 4. On a basis of the varying soil features and the height of the underground water, the vertical percolation varies. 5. The change of the vertical percolation on a basis of the varying soil features shows as follows: clay loam $1{\sim}2$ mm/day, loam $2{\sim}3$mm/day, sandy loam $3{\sim}4$mm/day 6. The level of the underground water changes sensibly by priority of clay loam, loam, sandy loam. When it rains, the level of the underground water rises fast and falls down slowly. 7. The level of the underground water changes within the scope of 25cm 8. The transpiration ratio is given in table 8 and their value are as follows: clay loam 168.8, loam 255.6, sandy loam 272.5