• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.3
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• pp.119-123
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• 2012

The duration of low temperature in winter season is one of the important agrometeorological characteristics in crop growing fields. This study was conducted to develop a method to estimate the duration of low-temperature with monthly meteorological data. Using daily meteorological data from 61 observation sites from 1981 to 2010, we analyzed the relationships between the averages of monthly temperature minima and the durations of low-temperature ranging from -15 to $5^{\circ}C$, The monthly mean of the January minimum air temperature was appropriate for theestimation of the durations of lowtemperature below $0^{\circ}C$. We tested a simple second order equation to predict durations of low-temperature. To apply the equation to various temperature ranges, we suggested two different equations for the estimation of coefficients a and b, which are dependent on the base temperatures from -15 to $0^{\circ}C$. Thevalidation of the equations using other daily meteorological datasets from 1971 to 2000 showed that they were appropriate for the range from -10 to $0^{\circ}C$, but underestimated at $-15^{\circ}C$.

• The Korean Journal of Ecology
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• v.8 no.2
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• pp.89-98
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• 1985

Correlations between horizontal distributions of bamboos (Bambusaceae) in the Korean peninsula and environmental factors were studied using taxanomic and geographical literatures, both old and current. The vertical distributions of bamboos on Mt. Chiri were also studied, and environmental factors limiting horizontal and vertical distributions were compared. There are 18 species of bamboos (belonging to 5 genera) distributed in the Korean peninsula. The distributional range of each genus were distinct, although overlapped. Northern limit of bamboos of any species was marked by the line connecting Paikryung Island (124。40'E, 38。00'N), Mt. Changsoo, Mt. Myungji, Mt. Myohyung and Myungchum (129。40'E, 41。10'N). The optimum range of bamboos was concluded to be restricted to several southern province, with annual precipitation over 1,200 mm. The limiting factors on the distribution were inferred to be low temperature and duration of it. Mean daily minimum temperature of January and the number of days with daily mean temperatures below zero during January showed close associations with the distributional range, and an environmental factors favouring the distributrion of bamboos appeared to be vicinity of warm sea current, deep and extended snow acculation and southern exposure. The vertical distribution of bamboos on Mt. Chiri was limited by low temperature, unfavorable topographic and edaphic conditions caused by steep slope. Difference in the vertical limits between SE and NW slopes are caused by the differences in temperature and precipitation between the slopes. Bamboos were more abundant in valleys than on the ridge, apparently because the deeper snow in the valleys protected the plants from low temperature, heavy winter winds and desiccation.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.95-105
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• 2019

Evapotranpiration (ET) is one of the important factor in Hydrological cycle and irrigation planning. In this study, temperature-based artificial neural network (ANN) model for daily reference crop ET estimation was developed and compared with reference crop evapotranpiration ($ET_0$) from FAO-56 Penman-Monteith method (FAO-56 PM) and parameter regionalized Hargreaves method. The ANN model was trained and tested for 10 weather stations (5 inland stations and 5 costal stations) and two input climate factors, maximum temperature ($T_{max}$), minimum temperature ($T_{min}$), and extraterrestrial radiation (RA) were used for training and validation of temperature-based ANN model. Monthly reference ET by the ANN model also compared with parameter regionalized Hargreaves method for ANN model applicability evaluation. The ANN model evapotranspiration demonstrated more accordance to FAO-56 PM evapotranspiration than the $ET_0$ from parameter regionalized Hargreaves method(R-Hargreaves). The results of this study proposed that daily reference crop ET estimated by the ANN model could be used in the condition of no sufficient climate data.

• Journal of Environmental Science International
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• v.26 no.11
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• pp.1247-1253
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• 2017

The Fuji variety of apple, introduced in Japan, has excellent storage quality and good taste, such that it is the most commonly cultivated apple variety in Gunwi County, North Gyeongsang Province, Korean Peninsula. Accurate prediction of harvest maturity allows farmers to more efficiently manage their farm in important aspects such as working time, fruit storage, market shipment, and labor distribution. Temperature is one of the most important factors that determine plant growth, development, and yield. This paper reports on the beta distribution (function) model that can be used to simulate the the phenological response of plants to temperature. The beta function, commonly used as a skewed probability density in statistics, was introduced to estimate apple harvest maturity as a function of temperature in this study. The model parameters were daily maximum temperature, daily optimum temperature, and maximum growth rate. They were estimated from the input data of daily maximum and minimum temperature and apple harvest maturity. The difference in observed and predicted maturity day from 2009 to 2012, with optimal parameters, was from two days earlier to one day later.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.201-217
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• 2022

The United States is one of the largest producers of major crops such as wheat, maize, and soybeans, and is a major exporter of these crops. Therefore, it is important to estimate the crop production of the country in advance based on reliable long- term weather forecast information for stable crops supply and demand in Korea. The purpose of this study is to improve the seasonal predictability of the agro-climatic indices over the United States by using regional-scale daily temperature. For long-term numerical weather prediction, a dynamical downscaling is performed using Weather Research and Forecasting (WRF) model, a regional climate model. As the initial and lateral boundary conditions of WRF, the global hourly prediction data obtained from the Pusan National University Coupled General Circulation Model (PNU CGCM) are used. The integration of WRF is performed for 22 years (2000-2021) for period from June to December of each year. The empirical quantile mapping, one of the bias correction methods, is applied to the timeseries of downscaled daily mean, minimum, and maximum temperature to correct the model biases. The uncorrected and corrected datasets are referred WRF_UC and WRF_C, respectively in this study. The daily minimum (maximum) temperature obtained from WRF_UC presents warm (cold) biases over most of the United States, which can be attributed to the underestimated the low (high) temperature range. The results show that WRF_C simulates closer to the observed temperature than WRF_UC, which lead to improve the long- term predictability of the temperature- based agro-climatic indices.

• Water Engineering Research
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• v.4 no.3
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• pp.111-126
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• 2003

Meteorological data are often needed to evaluate the long-term effects of proposed hydrologic changes. The evaluation is frequently undertaken using deterministic mathematical models that require daily weather data as input including precipitation amount, maximum and minimum temperature, relative humidity, solar radiation and wind speed. Stochastic generation of the required weather data offers alternative to the use of observed weather records. The precipitation is modeled by a Markov Chain-exponential model. The other variables are generated by multivariate model with means and standard deviations of the variables conditioned on the wet or dry status of the day as determined by the precipitation model. Ultimately, the objective of this paper is to compare Richardson's model and the improved weather generation model in their ability to provide daily weather data for the crop model to study potential impacts of climate change on the irrigation needs and crop yield. However this paper does not refer to the improved weather generation model and the crop model. The new weather generation model improved will be introduced in the Journal of KWRA.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.2
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• pp.96-104
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• 2001

Usage of ecosystem models has been extended to landscape scales for understanding the effects of environmental factors on natural and agro-ecosystems and for serving as their management decision tools. Accurate prediction of spatial variation in daily temperature is required for most ecosystem models to be applied to landscape scales. There are relatively few empirical evaluations of landscape-scale temperature prediction techniques in mountainous terrain such as Korean Peninsula. We derived a periodic function of seasonal lapse rate fluctuation from analysis of elevation effects on daily temperatures. Observed daily maximum and minimum temperature data at 63 standard stations in 1999 were regressed to the latitude, longitude, distance from the nearest coastline and altitude of the stations, and the optimum models with $r^2$ of 0.65 and above were selected. Partial regression coefficients for the altitude variable were plotted against day of year, and a numerical formula was determined for simulating the seasonal trend of daily lapse rate, i.e., partial regression coefficients. The formula in conjunction with an inverse distance weighted interpolation scheme was applied to predict daily temperatures at 267 sites, where observation data are available, on randomly selected dates for winter, spring and summer in 2000. The estimation errors were smaller and more consistent than the inverse distance weighting plus mean annual lapse rate scheme. We conclude that this method is simple and accurate enough to be used as an operational temperature interpolation scheme at landscape scale in Korea and should be applicable to elsewhere.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.1
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• pp.1-9
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• 2006

Regardless of the recent observed warmer winters in Korea, more freeze injuries and associated economic losses are reported in fruit industry than ever before. Existing freeze-frost forecasting systems employ only daily minimum temperature for judging the potential damage on dormant flowering buds but cannot accommodate potential biological responses such as short-term acclimation of plants to severe weather episodes as well as annual variation in climate. We introduce 'dormancy depth', in addition to daily minimum temperature, as a complementary criterion for judging the potential damage of freezing temperatures on dormant flowering buds of grape vines. Dormancy depth can be estimated by a phonology model driven by daily maximum and minimum temperature and is expected to make a reasonable proxy for physiological tolerance of buds to low temperature. Dormancy depth at a selected site was estimated for a climatological normal year by this model, and we found a close similarity in time course change pattern between the estimated dormancy depth and the known cold tolerance of fruit trees. Inter-annual and spatial variation in dormancy depth were identified by this method, showing the feasibility of using dormancy depth as a proxy indicator for tolerance to low temperature during the winter season. The model was applied to 10 vineyards which were recently damaged by a cold spell, and a temperature-dormancy depth-freeze injury relationship was formulated into an exponential-saturation model which can be used for judging freeze risk under a given set of temperature and dormancy depth. Based on this model and the expected lowest temperature with a 10-year recurrence interval, a freeze risk probability map was produced for Hwaseong County, Korea. The results seemed to explain why the vineyards in the warmer part of Hwaseong County have been hit by more freeBe damage than those in the cooler part of the county. A dormancy depth-minimum temperature dual engine freeze warning system was designed for vineyards in major production counties in Korea by combining the site-specific dormancy depth and minimum temperature forecasts with the freeze risk model. In this system, daily accumulation of thermal time since last fall leads to the dormancy state (depth) for today. The regional minimum temperature forecast for tomorrow by the Korea Meteorological Administration is converted to the site specific forecast at a 30m resolution. These data are input to the freeze risk model and the percent damage probability is calculated for each grid cell and mapped for the entire county. Similar approaches may be used to develop freeze warning systems for other deciduous fruit trees.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.151-151
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• 2018

The present study is aimed to quantifying the uncertainty in the general circulation model (GCM) selection and its impacts on hydrology studies in the basins. For this reason, 13 GCMs was selected among the 26 GCM models of the Fifth Assessment Report (AR5) scenarios. Then, the climate data and hydrologic data with two Representative Concentration Pathways (RCPs) of the best model (INMCM4) and worst model (HadGEM2-AO) were compared to understand the uncertainty associated with GCM models. In order to project the runoff, the Precipitation-Runoff Modelling System (PRMS) was driven to simulate daily river discharge by using daily precipitation, maximum and minimum temperature as inputs of this model. For simulating the discharge, the model has been calibrated and validated for daily data. Root mean square error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were applied as evaluation criteria. Then parameters of the model were applied for the periods 2011-2040, and 2070-2099 to project the future discharge the five large basins of South Korea. Then, uncertainty caused by projected temperature, precipitation and runoff changes were compared in seasonal and annual time scale for two future periods and RCPs compared to the reference period (1976-2005). The findings of this study indicated that more caution will be needed for selecting the GCMs and using the results of the climate change analysis.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.6
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• pp.87-99
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• 2013

For analysis of climate change effects on agriculture, precise agricultural meteorological data are needed to target period and site. In this study, agricultural meteorological data under new climate change scenario (RCP 8.5) are constructed from 2011 to 2099 in 111 agriculture major station suggested by Rural Development Administration (RDA). For verifying constructed data, comparison with field survey data in Suwon shows same trend in maximum temperature, minimum temperature, average temperature, and precipitation in 2011. Also comparison with normals of daily data in 2025, 2055, and 2085 shows reliability of constructed data. In analysis of constructed data, we can calculate sum of days over temperature and under temperature. Results effectively show the change of average temperature in each region and odd days of precipitation which means flood and dry days in target region.