• Journal of Korea Water Resources Association
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• v.46 no.9
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• pp.933-946
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• 2013

The quantitative analysis of evapotranspiration (ET) is a key component in hydrological studies and the establishment of water resources planning. Generally, the quantitative analysis of ET is performed by the estimation method of potential or reference ET based on meteorological factors such as air temperature, wind speed, etc. Hargreaves equation is one of empirical methods for reference ET using air temperature data. In this study, in order to estimate more exact reference ET considering climatological characteristics in Korea, parameter regionalization of Hargreaves equation is carried out. Firstly, modified Hargreaves equation is presented after the analysis of the relationship between solar radiation and temperature. Secondly, parameter ($K_{ET}$) optimization of Hargreaves equation is performed using Penman-Monteith method and modified equation at 71 weather stations. Lastly, the equation for calculating $K_{ET}$ using temperature data is proposed and verified. As a result, reference ET from original Hargreaves equation is overestimated or underestimated compared with Penman-Monteith method. But modified equation in this study is more accurate in the climatic conditions of Korea. In addition, the applicability of the equation between $K_{ET}$ and temperature is confirmed.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.22 no.5
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• pp.295-305
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• 2010

Although the rubble mound breakwaters in Korea have been damaged by typhoons almost every year, quantification of erosion of armor block have seldomly been made. In this paper, the damage of armor units is standardized by the relative damage. In the case where the number of damaged units is reported, it is divided by the total number of units to calculate the relative damage. In the case where the rehabilitation cost is reported, the relative damage is calculated by using its relationship with the present value of the past rehabilitation cost. The relative damage is shown to have strong correlations with the typhoon parameters such as nearest central air pressure and maximum wind speed at each site. On the other hand, the existing numerical methods for calculating the cumulative damage are compared with hydraulic model tests. The method of Melby and Kobayashi (1998) is shown to give a reasonable result, and it is used to calculate the relative damage, which is compared with the measured damage. A good agreement is shown for the East Breakwater of Yeosu Harbor, while poor agreement is shown for other breakwaters. The poor agreement may be because waves of larger height than the design height occurred due to strong typhoons associated with climate change so that the relative damage increased during the last several decades.

• Journal of Korea Water Resources Association
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• v.49 no.3
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• pp.227-239
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• 2016

This study is to estimate Surface Energy Balance Algorithm for Land (SEBAL) daily spatial evapotranspiration (ET) comparing with eddy covariance flux tower ET in Seolmacheon mixed forest (SMK) and Cheongmicheon rice paddy (CFK). The SEBAL input data of Albedo, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) from Terra MODIS products and the meteorological data of wind speed, and solar radiation were prepared for 2 years (2012-2013). For the annual average flux tower ET of 302.8 mm in SMK and 482.0 mm in CFK, the SEBAL ETs were 183.3 mm and 371.5 mm respectively. The determination coefficients ($R^2$) of SEBAL ET versus flux tower ET for total periods were 0.54 in SMK and 0.79 in CFK respectively. The main reason of SEBAL ET underestimation for both sites was from the determination of hot pixel and cold pixel of the day and affected to the overestimation of sensible heat flux.

• Journal of Korea Water Resources Association
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• v.33 no.4
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• pp.471-482
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• 2000

Surface energy balance components were evaluated by Landsat TM data and GIS with meteorological data. Calibration and validation for the applicability of this methodology were made through the estimating of the large-scale evapotranspiration (ET). In addition, sensitivity and error analysis was conducted to see the effects of the surface energy balance components on ET and the accuracy of each components. Bochong-chon located on the upper part of Guem River basin was selected as the case study area. Spatial distribution map of ET were produced for five dates: Jan. 1, Apr. 3, May. 10, and Nov. 27, 1995. The study results showed tat ET was greatly varied with the aspect and theland use type on the surface. In the case of having northeast and southeast in the aspect, ET was linearly increased depending on growing net radiation. While surface temperature has a high value, NDVI(Normalized Difference Vegetation Index) has a low value in the vegetated area. Therefore, ground heat flux was increased but ET was relatively decreased. The results of sensitivity and error analysis showed that net radiation is most sensitive and effective, ranging from 12.5% to 23.6% of sensitivity. Furthermore, the surface temperature, air temperature, and wind speed have the significant effects on ET estimation using remotely sensed data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.573-581
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• 2020

Using CCTV images and weather parameters, a method for estimating PM (Particulate Matter) index was proposed, and an experiment was conducted. For CCTV images, we proposed a method of estimating the PM index by applying a deep learning technique based on a CNN (Convolutional Neural Network) with ROI(Region Of Interest) image including a specific spot and an full area image. In addition, after combining the predicted result values by deep learning with the two weather parameters of humidity and wind speed, a post-processing experiment was also conducted to calculate the modified PM index using the learned regression model. As a result of the experiment, the estimated value of the PM index from the CCTV image was R2(R-Squared) 0.58~0.89, and the result of learning the ROI image and the full area image with the measuring device was the best. The result of post-processing using weather parameters did not always show improvement in accuracy in all cases in the experimental area.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.53-62
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• 2024

In this study, we built a model to estimate frost occurrence in South Korea using single-input deep learning and multi-input deep learning. Meteorological factors used as learning data included minimum temperature, wind speed, relative humidity, cloud cover, and precipitation. As a result of statistical analysis for each factor on days when frost occurred and days when frost did not occur, significant differences were found. When evaluating the frost occurrence models based on single-input deep learning and multi-input deep learning model, the model using both GRU and MLP was highest accuracy at 0.8774 on average. As a result, it was found that frost occurrence model adopting multi-input deep learning improved performance more than using MLP, LSTM, GRU respectively.

• Korean Journal of Remote Sensing
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• v.34 no.3
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• pp.451-463
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• 2018

The empirical/statistical models to estimate the ground Particulate Matter ($PM_{2.5}$) concentration from Geostationary Ocean Color Imager (GOCI) Aerosol Optical Depth (AOD) product were developed and analyzed for the period of 2015 in Seoul, South Korea. In the model construction of AOD-$PM_{2.5}$, two vertical correction methods using the planetary boundary layer height and the vertical ratio of aerosol, and humidity correction method using the hygroscopic growth factor were applied to respective models. The vertical correction for AOD and humidity correction for $PM_{2.5}$ concentration played an important role in improving accuracy of overall estimation. The multiple linear regression (MLR) models with additional meteorological factors (wind speed, visibility, and air temperature) affecting AOD and $PM_{2.5}$ relationships were constructed for the whole year and each season. As a result, determination coefficients of MLR models were significantly increased, compared to those of empirical models. In this study, we analyzed the seasonal, monthly and diurnal characteristics of AOD-$PM_{2.5}$model. when the MLR model is seasonally constructed, underestimation tendency in high $PM_{2.5}$ cases for the whole year were improved. The monthly and diurnal patterns of observed $PM_{2.5}$ and estimated $PM_{2.5}$ were similar. The results of this study, which estimates surface $PM_{2.5}$ concentration using geostationary satellite AOD, are expected to be applicable to the future GK-2A and GK-2B.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.111-121
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• 2019

Evapotranspiration (ET) of vegetation is one of the major components of the hydrologic cycle, and its accurate estimation is important for hydrologic water balance, irrigation management, crop yield simulation, and water resources planning and management. For agricultural crops, ET is often calculated in terms of a short or tall crop reference, such as well-watered, clipped grass (reference crop evapotranspiration, $ET_o$). The Penman-Monteith equation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole $ET_o$ method. However, its accuracy is contingent on high quality measurements of four meteorological variables, and its use has been limited by incomplete and/or inaccurate input data. Therefore, this study evaluated the applicability of Backpropagation Neural Network (BPNN) model for estimating $ET_o$ from less meteorological data than required by the FAO 56-PM. A total of six meteorological inputs, minimum temperature, average temperature, maximum temperature, relative humidity, wind speed and solar radiation, were divided into a series of input groups (a combination of one, two, three, four, five and six variables) and each combination of different meteorological dataset was evaluated for its level of accuracy in estimating $ET_o$. The overall findings of this study indicated that $ET_o$ could be reasonably estimated using less than all six meteorological data using BPNN. In addition, it was shown that the proper choice of neural network architecture could not only minimize the computational error, but also maximize the relationship between dependent and independent variables. The findings of this study would be of use in instances where data availability and/or accuracy are limited.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.481-491
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• 2014

Blue-ice area is a glacial ice field in ice sheet, ice shelf and glaciers where snow ablation and sublimation is larger than snowfall. As the blue-ice area has large influences on the meteorite concentration mechanism and ice mass balance, it is required to quantify the concentration of blue-ice. We analyzed spectral reflectance characteristics of blue-ice, snow and cloud by using MODIS images obtained over blue-ice areas in McMurdo Dry Valleys, East Antarctica, from 2007 to 2012. We then developed Normalized Difference Blue-ice Index (NDBI) algorithm which quantifies the concentration of blue-ice. Snow and cloud have a high reflectance in visible and near-infrared (NIR) bands. Reflectance of blue-ice is high in blue band, while that lowers in the NIR band. NDBI is calculated by dividing the difference of reflectance in the blue and NIR bands by the sum of reflectances in the two bands so that NDBI = (Blue-NIR)/(Blue + NIR). NDBI calculated from the MODIS images showed that the blue-ice areas have values ranging from 0.2 to 0.5, depending on the exposure and concentration of blue-ice. It is obviously different from that of snow and cloud that has values less than 0.2 or rocks with negative values. The change of NDBI values in the blue-ice area has higher correlation with snow depth ($R^2=0.699$) than wind speed ($R^2=0.012$) or air temperature ($R^2=0.278$), all measured at a meteorological station installed in McMurdo Dry Valleys. As the snow depth increased, the NDBI value decreased, which suggests that snow depth can be estimated from NDBI values over blue-ice areas. The NDBI algorithm developed in this study will be useful for various polar research fields such as meteorite exploration, analysis of ice mass balance as well as the snow depth estimation.

• Korean Journal of Soil Science and Fertilizer
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• v.39 no.5
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• pp.274-279
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• 2006

The exact estimation of crop evapotranspiration containing reference or potential evapotranspiration is necessary for decision of crop water requirements. This study was carried out for the evaluation and application of various meteorological elements used for the calculation of reference evapotranspiration (RET) by FAO Penman-Monteith (PM) model. Meteorological elements including temperature, net radiation, soil heat flux, albedo, relative humidity, wind speed measured by meteorological instruments are required for RET calculation by FAO PM model. The average of albedo measured for crop growing period was 0.20, ranging from 0.12 to 0.23, and was slightly lower than 0.23. Determinant coefficients by measured albedo and green grass albedo were 0.97, 0.95 and standard errors were 0.74, 0.80 respectively. Usefulness of deductive regression models was admitted. To assess an influence of soil heat flux (G) on FAO PM, RET with G=0 was compared with RETs using G at 5cm soil depth ($G_{5cm}$) and G at surface ($G_{0cm}$). As the results, RET estimated by G=0 was well agreed with RET calculated by measured G. Therefore, estimated net radiation, G=0 and albedo of green grass could be used for RET calculation by FAO PM.