• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.188-202
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• 2011

Due to emissions of greenhouse gases caused by increased use of fossil fuels, the climate change has been detected and this phenomenon would affect even larger changes in temperature and precipitation of South Korea. Especially, the increase of temperature by climate change can affect the amount and pattern of snowfall. Accordingly, we tried to predict future snowfall and the snowfall pattern changes by using the downscaled GCM (general circulation model) scenarios. Causes of snow varies greatly, but the information provided by GCM are maximum / minimum temperature, rainfall, solar radiation. In this study, the possibility of snow was focused on correlation between minimum temperatures and future precipitation. First, we collected the newest fresh snow depth offered by KMA (Korea meteorological administration), then we estimate the temperature of snow falling conditions. These estimated temperature conditions were distributed spatially and regionally by IDW (Inverse Distance Weight) interpolation. Finally, the distributed temperature conditions (or boundaries) were applied to GCM, and the future snowfall was predicted. The results showed a wide range of variation for each scenario. Our models predict that snowfall will decrease in the study region. This may be caused by global warming. Temperature rise caused by global warming highlights the effectiveness of these mechanisms that concerned with the temporal and spatial changes in snow, and would affect the spring water resources.

• Journal of Bio-Environment Control
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• v.20 no.2
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• pp.78-82
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• 2011

Ventilation rates, inside and outside weather data were measured in a arch-shape single-span plastic greenhouse growing tomatoes. On the roof of the experimental greenhouse, round windows which have a diameter of 0.6 m were installed at intervals of 8m. It showed that the number of air changes in this greenhouse were average 0.17 volumes per minute and in the range of 0.02 to 0.32 volumes per minute. These air changes are insufficient to meet the recommended ventilation rate for commercial greenhouses, and it is estimated that interval of 6 m is appropriate for spring or fall season. For summer season, it is necessary to narrow the space or to enlarge the open area of roof windows. Using the heat balance model, the evapotranspiration coefficients of greenhouse tomatoes were estimated from experimental ventilation data, overall heat transfer and solar radiation. It showed that the evapotranspiration coefficients were average 0.62 and in the 0.39 to 0.85 range. We suggest applying 0.6 as the evapotranspiration coefficient in design of ventilation for the single-span tomato greenhouses.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.4
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• pp.242-249
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• 2006

Potential impacts of the future climate change on crop production can be inferred by crop simulations at a landscape scale, if the climate data may be provided at appropriate spatial scales. Northern Gyunggi Province is one of the few prospective regions in South Korea for growing quality soybeans. Any geographical shift of production areas under the changing climate may influence the current land planning policy in this region. A soybean growth simulation was performed at 342 land units in northern Gyunggi province to test the potential geographical shift of the current production areas for quality soybeans in the near future (form 2011 to 2100). The land units for soybean cultivation were selected by the land use, the soil characteristics, and the minimum arable land area. Daily maximum and minimum temperature, precipitation, the number of rain days and solar radiation were extracted for each land unit from the future digital climate models (DCM, 2011-2040, 2041-2070, 2071-2100). Daily weather data for 30 years were randomly generated for each land unit for each normal year by using a well-known statistical method. They were used to run CROPGRO-Soybean model to simulate the growth, phonology, and yields of 3 cultivars representing different maturity groups grown at 342 land units. According to the model calculations, the warming trend in this region will accelerate the flowering and physiological maturity of all cultivars, resulting in a 7 to 9 days reduction in overall growing season and a 1 to 15% reduction in grain yield of early to medium maturity cultivars. There was a slight increase in grain yield of the late maturing cultivar under the projected climate by 2070, but a decreasing tend was dominant by the year 2100.

• The Journal of Society for e-Business Studies
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• v.24 no.4
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• pp.119-133
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• 2019

Recently, the importance prediction of photovoltaic power (PV) is considered as an essential function for scheduling adjustments, deciding on storage size, and overall planning for stable operation of PV facility systems. In particular, since most of PV power is generated in peak time, PV power prediction in a peak time is required for the PV system operators that enable to maximize revenue and sustainable electricity quantity. Moreover, Prediction of the PV power output in peak time without meteorological information such as solar radiation, cloudiness, the temperature is considered a challenging problem because it has limitations that the PV power was predicted by using predicted uncertain meteorological information in a wide range of areas in previous studies. Therefore, this paper proposes the LSTM (Long-Short Term Memory) based the PV power prediction model only using the meteorological, seasonal, and the before the obtained PV power before peak time. In this paper, the experiment results based on the proposed model using the real-world data shows the superior performance, which showed a positive impact on improving the PV power in a peak time forecast performance targeted in this study.

• 한국해양학회지
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• v.29 no.4
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• pp.325-337
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• 1994

The distributions of heat and momentum fluxes on the surface over the oceans around the Korean Peninsula are obtained based on the surface-layer flux model of Kim and Kang (1994), and their seasonal variations are examined in the present study. the input data of the model is the oceanatmosphere data with a grid interval of 2$^{\circ}$ in longitude and latitude. The atmosphere data, which are the pressure, temperature, and specific humidity on the 1000 mb level for 3 year period of 1985∼1987, are obtained from the European center for Medium Range Forecast. The sea surface temperature (SST) is obtained from National Meteorological Center (NMC). The solar insolation and longwave radiation on the ocean surface are obtained, respectively, from the NASA satellite data and based on an emprical formula. It is shown from the net heat flux that the oceans near Korea lose heat to the atmosphere in January and October with the rates of 200∼ 400 Wm/SUP -2/ and 100 Wm/SUP -2/, respectively. But the oceans are heated by the atmosphere in April and July with about the same rate of 100 Wm/SUP -2/. The annualmean net heat flux is negative over the entire domain except the northern part of the Yellow Sea. The largest annual-mean cooling rate of about 120 Wm/SUP -2/ is appeared off the southwest of Japan. In the East Sea, the annual-mean cooling rate is 60∼90 Wm/SUP -2/ in the southern and northern parts and about 30 Wm/SUP -2/ in the middle part. The magnitude of wind stress in january is 3∼ 5 times bigger than those of the other months. As a result, the spatial pattern of annual-mean wind stress is similar to that of January. It is also shown that the annual-mean wind stress curl is negative. in the East China Sea and the South Sea,but it is positive in the northern part of the Yellow Sea.In the East sea,the stress curl is positive in the southeast and northern parts and negative in the northwestern part.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.18-18
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• 2011

Climate change is impacting and will increasingly impact both the quantity and quality of the world's water resources in a variety of ways. In some areas warming climate results in increased rainfall, surface runoff, and groundwater recharge while in others there may be declines in all of these. Water quality is described by a number of variables. Some are directly impacted by climate change. Temperature is an obvious example. Notably, increased atmospheric concentrations of $CO_2$ triggering climate change increase the $CO_2$ dissolving into water. This has manifold consequences including decreased pH and increased alkalinity, with resultant increases in dissolved concentrations of the minerals in geologic materials contacted by such water. Climate change is also expected to increase the number and intensity of extreme climate events, with related hydrologic changes. A simple framework has been developed in New Zealand for assessing and predicting climate change impacts on water resources. Assessment is largely based on trend analysis of historic data using the non-parametric Mann-Kendall method. Trend analysis requires long-term, regular monitoring data for both climate and hydrologic variables. Data quality is of primary importance and data gaps must be avoided. Quantitative prediction of climate change impacts on the quantity of water resources can be accomplished by computer modelling. This requires the serial coupling of various models. For example, regional downscaling of results from a world-wide general circulation model (GCM) can be used to forecast temperatures and precipitation for various emissions scenarios in specific catchments. Mechanistic or artificial intelligence modelling can then be used with these inputs to simulate climate change impacts over time, such as changes in streamflow, groundwater-surface water interactions, and changes in groundwater levels. The Waimea Plains catchment in New Zealand was selected for a test application of these assessment and prediction methods. This catchment is predicted to undergo relatively minor impacts due to climate change. All available climate and hydrologic databases were obtained and analyzed. These included climate (temperature, precipitation, solar radiation and sunshine hours, evapotranspiration, humidity, and cloud cover) and hydrologic (streamflow and quality and groundwater levels and quality) records. Results varied but there were indications of atmospheric temperature increasing, rainfall decreasing, streamflow decreasing, and groundwater level decreasing trends. Artificial intelligence modelling was applied to predict water usage, rainfall recharge of groundwater, and upstream flow for two regionally downscaled climate change scenarios (A1B and A2). The AI methods used were multi-layer perceptron (MLP) with extended Kalman filtering (EKF), genetic programming (GP), and a dynamic neuro-fuzzy local modelling system (DNFLMS), respectively. These were then used as inputs to a mechanistic groundwater flow-surface water interaction model (MODFLOW). A DNFLMS was also used to simulate downstream flow and groundwater levels for comparison with MODFLOW outputs. MODFLOW and DNFLMS outputs were consistent. They indicated declines in streamflow on the order of 21 to 23% for MODFLOW and DNFLMS (A1B scenario), respectively, and 27% in both cases for the A2 scenario under severe drought conditions by 2058-2059, with little if any change in groundwater levels.

• Journal of Korean Society on Water Environment
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• v.29 no.1
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• pp.88-96
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• 2013

Much attention has been needed in water resource management at the watershed due to drought and flooding issues caused by climate change in recent years. Increase in air temperature and changes in precipitation patterns due to climate change are affecting hydrologic cycles, such as evaporation and soil moisture. Thus, these phenomena result in increased runoff at the watershed. The Soil and Water Assessment Tool (SWAT) model has been used to evaluate rainfall-runoff at the watershed reflecting effects on hydrology of various weather data such as rainfall, temperature, humidity, solar radiation, wind speed. For bias-correction of RCP data, at least 30 year data are needed. However, for most gaging stations, only precipitation data have been recorded and very little stations have recorded other weather data. In addition, the RCP scenario does not provide all weather data for the SWAT model. In this study, two scenarios were made to evaluate whether it would be possible to estimate streamflow using measured precipitation and long-term average values of other weather data required for running the SWAT. With measured long-term weather data (scenario 1) and with long-term average values of weather data except precipitation (scenario 2), the estimate streamflow values were almost the same with NSE value of 0.99. Increase/decrease by ${\pm}2%$, ${\pm}4%$ in temperature and humidity data did not affect streamflow. Thus, the RCP precipitation data for Hongcheon watershed were bias-corrected with measured long-term precipitation data to evaluate effects of climate change on streamflow. The results revealed that estimated streamflow for 2055s was the greatest among data for 2025s, 2055s, and 2085s. However, estimated streamflow for 2085s decreased by 9%. In addition, streamflow for Spring would be expected to increase compared with current data and streamflow for Summer will be decreased with RCP data. The results obtained in this study indicate that the streamflow could be estimated with long-term precipitation data only and effects of climate change could be evaluated using precipitation data as shown in this study.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.31 no.3
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• pp.256-263
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• 1995

This paper presents the evidence on the considerably strong stratification - destratification(SD) phenomena during spring - neap tidal cycle in summer of 1992 based on the observed temperature, salinity and density data. To find out the main factors causing SD in the bay, we computed the rate of potential energy balance of the surface heat flux, tidal and wind stirring proposed by Simpson and Hunter (1974) and Simpson and Bowders (1981) using observed data. It was found that the energy of the wind stirring was one - order smaller than those of the heat flux and the tidal stirring. It means that the variation of stratification phenomena in the bay mainly depend on tidal stirring and sea surface heating in summer if there was no exceptionally strong wind event like a typhoon. Finally, we tested the effects of typhoon on the mixing characteristics of the bay using the example of a empirical typhoon model. It was found that when wind speed is larger than 15m/sec in Deukryang Bay, the wind energy was always larger than the average heating energy based on empirical typhoon model test. Particularly, typhoon passed on the left side of the bay, strong wind energy happened, which is almost the same as tidal energy of spring tide.

• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.6
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• pp.190-197
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• 2012

The purpose of this study was to evaluate thermal environment and heat budget of light thin layer green roof through an experiment in order to quantify its heat budget. Two concrete model boxes($1.2m(W){\times}1.2m(D){\times}1.0m(H)$) were constructed: One experiment box with Zoysia japonica planted on substrate depth of 10cm and one control box without any plant. Between June 6th and 7th, 2012, outside climatic conditions(air temperature, relative humidity, wind direction, wind speed), evapotranspiration, surface and ceiling temperature, heat flux, and heat budget of the boxes were measured. Daily maximum temperature of those two days was $29.4^{\circ}C$ and $30^{\circ}C$, and daily evapotranspiration was $2,686.1g/m^2$ and $3,312.8g/m^2$, respectively. It was found that evapotranspiration increased as the quantity of solar radiation increased. A surface and ceiling temperature of those two boxes was compared when outside air temperature was the greatest. and control box showed a greater temperature in both cases. Thus it was found that green roof was effective in reducing temperature. As results of heat budget analysis, heat budget of a green roof showed a greater proportion of net radiation and latent heat while heat budget of the control box showed a greater proportion of sensible heat and conduction heat. The significance of this study was to analyze heat budget of green roof temperature reduction. As substrate depth and types, species and seasonal changes may have influences on temperature reduction of green roof, further study is necessary.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.42 no.5
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• pp.579-596
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• 1997

A simulation study was carried out to delineate potential effects of the lake-induced climate change on crop productivity around Lake Imha which was formed after a multi-purpose dam construction in Andong, Korea. Twenty seven cropping zones were identified within the 30 km by 25 km study area. Five automated weather stations were installed within the study area and operated for five years after the lake formation. A geostatistical method was used to calculate the monthly climatological normals of daily maximum and minimum temperature, solar radiation and precipitation for each cropping zone before and after the dam construction. Daily weather data sets for 30 years were generated for each cropping zone from the monthly normals data representing "No lake" and "After lake" climatic scenarios, respectively. They were fed into crop models (ORYZA1 for rice, SOYGRO for soybean, CERES-maize for corn) to simulate the yield potential of each cropping zone. Calculated daily maximum temperature was higher after the dam construction for the period of October through March and lower for the remaining months except June and July. Decrease in daily minimum temperature was predicted for the period of April through August. Monthly total radiation was predicted to decrease after the lake formation in all the months except February, June, and September and the largest drop was found in winter. But there was no consistent pattern in precipitation change. According to the model calculation, the number of cropping zones which showed a decreased yield potential was 2 for soybean and 6 for corn out of 27 zones with a 10 to 17% yield drop. Little change in yield potential was found at most cropping zones in the case of paddy rice, but interannual variation was predicted to increase after the lake formation. the lake formation.