• Magazine of the Korean Society of Agricultural Engineers
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• v.11 no.2
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• pp.1651-1660
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• 1969

The purpose of this study is to find out the reasonable amount of evapo-transpiration required for the paddy rice plant during the whole growing season. So. On the basis of the experimental data concerning the evapo-transpiration form 1966 to 1968, the author obtained the follow results. 1) The leaf area index in the densely planted plo is generally higher than that in the conventionally planted one during the first half of growing season So, the coefficient of transpiration in the former plot is somewaht higher than in the latter, and the coefficient of water surface evaporation under the plant cover has the inverse relation between both plots. 2) It is unreasonable that coefficient of evapo-transpiration is applied to the calculation of the evapo-transpiration requirements of each growing stage, because a degree of variation in meteorological factors and in the thickness of the plant growth is involved in it. 3) It is most reasonable that the rate of transpiration and of the water surface evapoation is applied to the calculation of the transpirated amount and evaporated one in each growing stage, because it shows almost constant value in spite of any meteorological conditions in so far as the variety of rice, planted density and control of applying fertilizer are sanme and the disease and blight are negligible. 4) The ratio of the amount of transpiration to the weight of the whole air dried yields has the tendency of decreasing as that of the yields increases having almost constant value despite the amount of pan evaporation; and the value is about 210 when the weight of root parts is included to that the yields. 5) Although the required amount of transpiration during the whole growing season can be calculated with the above ratio, Fig. 7 showing the relation between the amount of transpiration and the weight of the yields is more reasonable and will be convinient to find it. And the requirements of water surface evaporation during the same season can also be directly found witht theweight air dried straw refering to Fig. 8.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.655-667
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• 2023

Algal bloom outbreaks are frequently reported around the world, and serious water pollution problems arise every year in Korea. It is necessary to protect the aquatic ecosystem through continuous management and rapid response. Many studies using satellite images are being conducted to estimate the concentration of chlorophyll-a (Chl-a), an indicator of algal bloom occurrence. However, machine learning models have recently been used because it is difficult to accurately calculate Chl-a due to the spectral characteristics and atmospheric correction errors that change depending on the water system. It is necessary to consider the factors affecting algal bloom as well as the satellite spectral index. Therefore, this study constructed a dataset by considering water quality, hydrological and meteorological factors, and sentinel-2 images in combination. Representative ensemble models random forest and extreme gradient boosting (XGBoost) were used to predict the concentration of Chl-a in eight weirs located on the Nakdong river over the past five years. R-squared score (R²), root mean square errors (RMSE), and mean absolute errors (MAE) were used as model evaluation indicators, and it was confirmed that R² of XGBoost was 0.80, RMSE was 6.612, and MAE was 4.457. Shapley additive expansion analysis showed that water quality factors, suspended solids, biochemical oxygen demand, dissolved oxygen, and the band ratio using red edge bands were of high importance in both models. Various input data were confirmed to help improve model performance, and it seems that it can be applied to domestic and international algal bloom detection.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.2
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• pp.93-98
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• 2016

For accurate precipitation forecasts the choice of weather factors and prediction method is very important. Recently, machine learning has been widely used for forecasting precipitation, and artificial neural network, one of machine learning techniques, showed good performance. In this paper, we suggest a new method for forecasting precipitation using DBN, one of deep learning techniques. DBN has an advantage that initial weights are set by unsupervised learning, so this compensates for the defects of artificial neural networks. We used past precipitation, temperature, and the parameters of the sun and moon's motion as features for forecasting precipitation. The dataset consists of observation data which had been measured for 40 years from AWS in Seoul. Experiments were based on 8-fold cross validation. As a result of estimation, we got probabilities of test dataset, so threshold was used for the decision of precipitation. CSI and Bias were used for indicating the precision of precipitation. Our experimental results showed that DBN performed better than MLP.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.59 no.4
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• pp.477-482
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• 2014

Variation of anthocyanin contents were analyzed by different growing environments, three locations over three years with 3 black colored soybeans. Anthocyanin contents were different according to growing location, genotypes and planting time, so it can be concluded that anthocyanin content was effected by environmental and genetic variation. Planting date seemed to have a much greater influence on anthocyanin content than cultivar and location. Among different planting dates, anthocyanin contents increased in the seeds planted on June 15 rather than did May 30 and May 15. Compared with 3 cultivars and 3 locations, Ilpumgeomjungkong and Yeonchun had higher contents such as 11.58 mg/ and 9.85 mg/g, respectively. The correlations between color index and anthocyanin content were analyzed by Hunter'value. L (lightness) and b (yellowness) values were correlated negatively with D3G, C3G, Pt3G and total anthocyanin content while a (redness) value was correlated positively. The correlations between meteorological factors and anthocyanin content were analyzed. Anthocyanin content was correlated negatively with mean temperature and accumulated temperature whereas mean daily temperature difference showed positive correlation. We could conclude that the area in which mean temperature was low and daily temperature difference was high is good for attempts to improve black soybean seed quality by the increase of anthocyanin contents.

• Journal of Korea Water Resources Association
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• v.40 no.8
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• pp.617-628
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• 2007

The purpose of this paper is to prepare snowmelt parameters using RS and GIS and to assess the snowmelt impact in SLURP (Semi-distributed Land Use-based Runoff Process) model for Chungju-Dam watershed $(6,661.5km^2)$. Three sets of NOAA AVHRR images (1998-1999, 2000-2001, 2001-2002) were analyzed to prepare snow-related data of the model during winter period. Snow cover areas were extracted using 1, 3 and 4 channels, and the snow depth was spatially interpolated using snowfall data of ground meteorological stations. With the snowmelt parameters, DEM (Digital Elevation Model), land cover, NDVI (Normalized Difference Vegetation Index) and weather data, the model was calibrated for 3 years (1998, 2000, 2001), and verified for 1 year (1999) using the calibrated parameters. The average Nash-Sutcliffe efficiencies for 4 years (1998-2001) discharge comparison with and without snowmelt parameters were 0.76 and 0.73 for the full period, and 0.57 and 0.19 for the period of January to May. The results showed that the spatially prepared snow-related data reduced the calibration effort and enhanced the model results.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.2
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• pp.62-72
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• 2017

Spatio-temporal projection of evapotranspiration over croplands would be useful for assessment of climate change impact and development of adaptation strategies in agriculture. Potential evapotranspiration (PET) and dryness index (DI) during rice growing seasons were calculated using climate change scenario data provided by the National Institute of Meteorological Research (NIMR). A data processing tool for gridded climate data files, readGrADSWrapper, was used to calculate PET and DI during the current (1986-2005) and future (2006-2100) periods. Scripts were written to implement the formulas of PET and DI in R, which is an open source statistical data analysis tool. Evapotranspiration in rice fields ($PET_{Rice}$) was also determined using R scripts. The Spatio-temporal patterns of PET differed by regions in Korean Peninsula under current and future climate conditions. Overall, PET and $PET_{Rice}$ tended to increase throughout the $21^{st}$ century. Those results suggested that region-specific water resource managements would be needed to minimize the risk of water loss in the regions where considerable increases in PET would occur under the future climate conditions. For example, a number of provinces classified as a humid region were projected to become a sub-humid region in the future. The Spatio-temporal assessment of water resources based on PET and DI would help the development of climate change adaptation strategies for rice production in the 21st century. In addition, the studies on climate change impact would be facilitated using specialized data tools, e.g., readGrADSWrapper, for geospatial analysis of climate data.

• Korean Journal of Agricultural and Forest Meteorology
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• v.7 no.1
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• pp.45-50
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• 2005

This study was conducted to measure and understand the exchange of CO₂ and water in a rice canopy. Eddy covariance system was installed on a 10m tower along with other meteorological instruments. CO₂ flux and surface energy balance were measured throughout the whole growing season in 2003 over a typical paddy field in Icheon, Korea. During the early growth stage in May and June, most of net radiation was partitioned to latent heat flux with daytime Bowen ratio of 0.3 to 0.7. Evapotranspiration (i.e., daily integrated latent heat flux) typically ranged from 3 to 4 mm d/sup -1/, with even higher rates on sunny days. Daily integrated net ecosystem exchange (NEE) of CO₂ increased with increasing solar radiation and leaf area index (LAI). The NEE was especially high during the stages of young panicle formation and heading. On 1 June 2003, when the rice field was flooded, it was a weak sink of atmospheric CO₂ with an uptake rate of 9.1 gm/sup -2/d/sup -1/. Despite frequent rainy and cloudy conditions in summer, maximum NEE of 36.2 gm/sup -2/d/sup -1/ occurred on 31 July prior to heading stage. As rice crop senesced after early September, the NEE decreased.

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.2
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• pp.13-19
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• 2017

This paper analyzed MODIS data from 2006 to 2013 to determine relationship between meteorological changes and vegetation index. The experimental area was divided into the northern, central and southern regions according to the regional characteristics, and the smoothed MODIS LAI and NDVI were obtained using Timesat. In the case of precipitation, MODIS NDVI had correlation coefficients of 0.66, 0.44 and 0.35 in the northern, central and southern regions and the correlation was the highest in the northern region. In the case of temperature, MODIS LAI had correlation coefficients of 0.66, 0.64 and 0.68, and MODIS NDVI had 0.89, 0.89 and 0.80. The correlation of MODIS NDVI was higher and showed similar positive correlation regardless of region. In addition, The accuracy between Timesat plant seasonal start and actual plant seasonal start in MODIS NDVI was higher than MODIS LAI. The average error in MODIS LAI was 19 days in the central region and 20 days in the southern region. And the average error in MODIS NDVI was 6 days in the central region and 8 days in the southern region.

• Journal of Korea Water Resources Association
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• v.41 no.9
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• pp.873-884
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• 2008

In this study, rainfall quantile was estimated using scale invariance property of rainfall data with different durations and the applicability of such property was evaluated for the rainfall data of South Korea. For this purpose, maximum annual rainfall at 22 recording sites of Korea Meteorological Administration (KMA) having relatively long records were used to compare rainfall quantiles between at-site frequency analysis and scale invariance property. As the results, the absolute relative errors of rainfall quantiles between two methods show at most 10 % for hourly rainfall data. The estimated quantiles by scale invariance property can be generally applied in the 8 of 14 return periods used in this study. As an example of down-scaling method, rainfall quantiles of $10{\sim}50$ minutes duration were estimated by scale invariance property based on index duration of 1 hour. These results show less than 10 % of absolute relative errors except 10 minutes duration. It is found that scale invariance property can be applied to estimate rainfall quantile for unmeasured rainfall durations.

• Atmosphere
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• v.25 no.4
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• pp.639-657
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• 2015

There is an increasing need to improve the air quality over South Korea to protect public health from local and remote anthropogenic pollutant emissions that are in an increasing trend. Here, we evaluate the performance of the WRF-Chem (Weather Research and Forecasting-Chemistry) model in simulating near-surface air quality of major Korean cities, and investigate the impacts of time-varying chemical initial and lateral boundary conditions (IC/BCs) on the air quality simulation using a chemical downscaling technique. The model domain was configured over the East Asian region and anthropogenic MICS-Asia 2010 emissions and biogenic MEGAN-2 emissions were applied with RACM gaseous chemistry and MADE/SORGAM aerosol mechanism. Two simulations were conducted for a 30-days period on April 2010 with chemical IC/BCs from the WRF-Chem default chemical species profiles ('WRF experiment') and the MOZART-4 (Model for OZone And Related chemical Tracers version 4) ('WRF_MOZART experiment'), respectively. The WRF_MOZART experiment has showed a better performance to predict near-surface CO, $NO_2$, $SO_2$, and $O_3$ mixing ratios at 7 major Korean cities than the WRF experiment, showing lower mean bias error (MBE) and higher index of agreement (IOA). The quantitative impacts of the chemical IC/BCs have depended on atmospheric residence time of the pollutants as well as the relative difference of chemical mixing ratios between the WRF and WRF_MOZART experiments at the lateral boundaries. Specifically, the WRF_MOZART experiment has reduced MBE in CO and O3 mixing ratios by 60~80 ppb and 5~10 ppb over South Korea than those in the WRF-Chem default simulation, while it has a marginal impact on $NO_2$ and $SO_2$ mixing ratios. Without using MOZART-4 chemical IC, the WRF simulation has required approximately 6-days chemical spin-up time for the East Asian model domain. Overall, the results indicate that realistic chemical IC/BCs are prerequisite in the WRF-Chem simulation to improve a forecast skill of local air quality over South Korea, even in case the model domain is sufficiently large to represent anthropogenic emissions from China, Japan, and South Korea.