• 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.525-536
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• 2014

In this study, a method to assess and monitor hydrological drought using remote sensing was investigated for use in regions with limited observation data, and was applied to the Upper Namhangang basin in South Korea, which was seriously affected by the 2008-2009 drought. Drought information may be obtained more easily from meteorological data based on water balance than hydrological data that are hard to estimate. Air temperature data at 2 m above ground level (AGL) were estimated using remotely sensed data, evapotranspiration was estimated from the air temperature, and the correlations between precipitation minus evapotranspiration (P-PET) and streamflow percentiles were examined. Land Surface Temperature data with $1{\times}1km$ spatial resolution as well as Atmospheric Profile data with $5{\times}5km$ spatial resolution from MODIS sensor on board Aqua satellite were used to estimate monthly maximum and minimum air temperature in South Korea. Evapotranspiration was estimated from the maximum and minimum air temperature using the Hargreaves method and the estimates were compared to existing data of the University of Montana based on Penman-Monteith method showing smaller coefficient of determination values but smaller error values. Precipitation was obtained from TRMM monthly rainfall data, and the correlations of 1-, 3-, 6-, and 12-month P-PET percentiles with streamflow percentiles were analyzed for the Upper Namhan-gang basin in South Korea. The 1-month P-PET percentile during JJA (r = 0.89, tau = 0.71) and SON (r = 0.63, tau = 0.47) in the Upper Namhan-gang basin are highly correlated with the streamflow percentile with 95% confidence level. Since the effect of precipitation in the basin is especially high, the correlation between evapotranspiration percentile and streamflow percentile is positive. These results indicate that remote sensing-based P-PET estimates can be used for the assessment and monitoring of hydrological drought. The high spatial resolution estimates can be used in the decision-making process to minimize the adverse impacts of hydrological drought and to establish differentiated measures coping with drought.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.8
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• pp.866-871
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• 2006

The nutrient recovery in phosphate crystallization process was investigated by using laboratory scale uptlow reactors, adopting sequencing batch type configuration. The industrial waste lime was used as potential cation source with magnesium salt($MgCl_2$) as control. The research was focused on its successful application in a novel integrated sludge treatment process, which is comprised of a high performance fermenter followed by a crystallization reactor. In the struvite precipitation test using synthetic wastewater first, which has the similar characteristics with the real fermentation effluent, the considerable nutrient removal(about 60%) in both ammonia and phosphate was observed within $0.5{\sim}1$ hr of retention time. The results also revealed that a minor amount(<5%) of ammonia stripping naturally occurred due to the alkaline(pH 9) characteristic in feed substrate. Stripping of $CO_2$ by air did not increase the struvite precipitation rate but it led to increased ammonia removal. In the second experiment using the fermentation effluent, the optimal dosage of magnesium salt for struvite precipitation was 0.86 g Mg $g^{-1}$ P, similar to the mass ratio of the struvite. The optimal dosage of waste lime was 0.3 g $L^{-1}$, resulting in 80% of $NH_4-N$ and 41% of $PO_4-P$ removal, at about 3 hrs of retention time. In the microscopic analysis, amorphous crystals were mainly observed in the settled solids with waste lime but prism-like crystals were observed with magnesium salt. Based on mass balance analysis for an integrated sludge treatment process(fermenter followed by crystallization reactor) for full-scale application(treatment capacity Q=158,880 $m^3\;d^{-1}$), nutrient recycle loading from the crystallization reactor effluent to the main liquid stream would be significantly reduced(0.13 g N and 0.19 g P per $m^3$ of wastewater, respectively). The results of the experiment reveal therefore that the reuse of waste lime, already an industrial waste, in a nutrient recovery system has various advantages such as higher economical benefits and sustainable treatment of the industrial waste.

• Journal of Conservation Science
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• v.30 no.3
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• pp.263-275
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• 2014

It's shown how to proceed the study on Manufacturing techniques & Conservation to the Iron Pot from Cheonmachong Ancient Tomb(the 155th Tomb in Hwangnam-dong). In order to investigate manufacturing techniques of the Iron Pot, some parts of the relic were gathered. After mounting, polishing and etching on the relic, analyzing the metal microstructure was conducted. Also it's conducted a SEM-EDS analysis on the nonmetallic inclusion. White iron structure was observed in the metallurgical structure inspection, SEM-EDS analysis. It seems to be dried slowly at room temperature after casting, doesn't look as particular heat treatment to improve brittleness. It is estimated that it's as the handle seam side were verified about 3cm inch wide, 1.5 thick in center of body, so 2 separate half-completed products was cast with width-type mould. The manufacturing techniques Using white cast iron structure, width-type mould are observable to the Iron Pot excavated from Sikrichong Ancient Tomb & Hwangnamdaechong grand Ancient Tomb around those were constructed the same time. It's able to recognize that it's almost identical manufacturing techniques at that time. Conservation is generically following those are survey of pretreatment, foreign material removal, stabilization, restoration and color matching in the order. cleaning & drying were added to the process as occasion demands. The strengthening treatment were difficult with artifact's volume, low concentration Paraloid NAD-10 solution was spread two or three times with a brush, surface hardening also came up with 15wt% Paraloid NAD-10 solution after the conservation was complete. There were connection & restoration for the restoration to the damage after modeling forms that it's similar to damaged parts by using the Fiber Reinforced Plastic resins(POLYCOAT FH-245, mold laminated type). Throughout this research, capitalizing on accumulations of measurements about the production technique of Iron Pot in the time of the fifth and 6th centuries is no less important than the Iron artifact's conservation for a better study in the future.

• Korean Journal of Ecology and Environment
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• v.42 no.1
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• pp.1-8
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• 2009

This study used a mesocosm which presumes movement of the nutrient (especially $PO_{4^-}P$) in the wetland. After setting up the mesocosm inside the wetland and adding the $PO_{4^-}P$, observed the movement of the $PO_{4^-}P$ every hour. We analyzed the variables which had the possibility of affecting $PO_{4^-}P$ concentration in the wetland-flora, absorbing rate of algae, settling rate, release rate. Immediately after adding $PO_{4^-}P$, the concentration of the TP in water column at each mesocosm was 0.48, 12.4, 20.4, $23.6\;mg\;L^{-1}$, after 21 days they were 0.6, 1.92, 6.97 and $6.94\;mg\;L^{-1}$ respectively. The concentration of the TP in water column at the mesocosm decreased on average 73.7%. The concentration of the $PO_{4^-}P$ inside reed, algae and sediment in the mesocosm was increased from $0.73mg\;gDW^{-1}$, $3.81mg\;gDW^{-1}$, $466.1mg\;kg^{-1}$ to $0.83mg\;gDW^{-1}$, $4.57mg\;gDW^{-1}$ and $813.3mg\;kg^{-1}$ respectively. Algae is more sensitive than reeds in absorption of the nutrient. TP removal by settling was highest. Budgeting of TP indicated that P moved from particulates in the water column to sediment and algae. Immediately after adding $PO_{4^-}P$, water column (24.2%) and sediment (49.0%) dominated TP storage, with algae (10.3%) and reed (16.4%) holding smaller proportions of TP. After 21 days, Sediment (59.0%) and algae (17.9%) dominated TP storage, with water column (7.1%) and reed (15.8%) holding smaller proportions of TP. Estimation of phosphate movement using mesocosms is an appropriate method because wetlands have many controlling factors. Analysed data can be compared to background data for wetland construction and management.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.2
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• pp.85-96
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• 2019

Solar radiation is an important variable for estimation of energy balance and water cycle in natural and agricultural ecosystems. A deep neural network (DNN) model has been developed in order to estimate the daily global solar radiation. Temperature and precipitation, which would have wider availability from weather stations than other variables such as sunshine duration, were used as inputs to the DNN model. Five-fold cross-validation was applied to train and test the DNN models. Meteorological data at 15 weather stations were collected for a long term period, e.g., > 30 years in Korea. The DNN model obtained from the cross-validation had relatively small value of RMSE ($3.75MJ\;m^{-2}\;d^{-1}$) for estimates of the daily solar radiation at the weather station in Suwon. The DNN model explained about 68% of variation in observed solar radiation at the Suwon weather station. It was found that the measurements of solar radiation in 1985 and 1998 were considerably low for a small period of time compared with sunshine duration. This suggested that assessment of the quality for the observation data for solar radiation would be needed in further studies. When data for those years were excluded from the data analysis, the DNN model had slightly greater degree of agreement statistics. For example, the values of $R^2$ and RMSE were 0.72 and $3.55MJ\;m^{-2}\;d^{-1}$, respectively. Our results indicate that a DNN would be useful for the development a solar radiation estimation model using temperature and precipitation, which are usually available for downscaled scenario data for future climate conditions. Thus, such a DNN model would be useful for the impact assessment of climate change on crop production where solar radiation is used as a required input variable to a crop model.