이 연구는 강원대학교 학술림 내의 산지계류를 대상으로 2년간(2017∼2018)의 현지 모니터링에 기초하여 수온과 강우, 유량 및 기온 등 환경인자간의 관계를 분석하고, 계절별 산지계류의 수온변화 예측기법에 대하여 검토하였다. 동절기를 제외한 봄, 여름 및 가을철로 구분하여 단계적 다중선형회귀분석을 실시하였으며, 계절별 산지계류의 수온변화에 미치는 환경인자의 영향을 분석하였다. 그 결과, 산지계류의 일평균 수온은 봄철 6.9∼17.7℃로 기온과 유의적 관계를 나타내었고, 여름철 12.2∼26.3℃로 기온, 유량과 유의적 관계를 나타냈으며, 가을철 3.6∼19.3℃로 기온 및 유량과 유의적 관계를 나타내는 등 계절별로 산지계류의 수온에 미치는 영향인자는 다르게 나타났다. 다중선형회귀식은 봄철 (0.553×기온)+(0.086×유량)+4.145(R2=0.505; p<0.01), 여름철 (0.756×기온)+(-0.072×유량)+2.670(R2=0.510; p<0.01), 가을철 (0.738×기온) +(0.028×강우)+2.660(R2=0.844; p<0.01)이었다. 도출된 모든 회귀식의 결정계수(R2)는 기온만으로 예측한 경우보다 높게 나타났고, 봄철에서 가을철로 갈수록 증가하였다. 향후 정밀도 높은 산지계류의 수온변화 예측을 위해서는 지속적인 현지 모니터링과 함께 시･공간적 데이터의 확보가 중요하다고 판단된다.

파프리카는 수분에 민감한 작물이므로 작물의 생산성 향상을 위하여 적정 관수조절은 매우 중요하다. 광환경 조건은 시설재배에서 여러 환경 변수 중 조절이 용이하지 못하며, 지역 별, 계절 별 분포가 다르기 때문에 광 환경 데이터를 이용한 증산과 관수의 추정이 필요하다. 본 연구에서는 파프리카의 정확한 증산 예측을 위하여 변형된 증산 추정식을 활용하였다. 또한 기상청의 광도 자료를 활용하여 지역 별 증산량과 관수량을 비교하였다. 우리나라의 경우 여름철 하루 중 광도의 편차가 심하고 장마기간이 있으므로 봄, 가을에 비하여 증산량이 오히려 낮았다. 그리고 광주기가 길어지는 봄에 증산량이 가장 많았으므로, 이 시기의 데이터를 이용하여 관수시설 용량을 지역별로 제시할 수 있었다. 이러한 결과는 시설 재배에서 관수설비 기준제시를 위한 자료 및 투입에너지 최적화에도 유용하게 활용될 것으로 판단된다.

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. It is necessary to get probabilistic forecasts to establish risk-based reservoir operation policies. Probabilistic forecasts may be useful for the users who assess and manage risks according to decision-making responding forecasting results. Probabilistic forecasting of seasonal inflow to Andong dam is performed and assessed using selected predictors from sea surface temperature and 500 hPa geopotential height data. Categorical probability forecast by Piechota's method and logistic regression analysis, and probability forecast by conditional probability density function are used to forecast seasonal inflow. Kernel density function is used in categorical probability forecast by Piechota's method and probability forecast by conditional probability density function. The results of categorical probability forecasts are assessed by Brier skill score. The assessment reveals that the categorical probability forecasts are better than the reference forecasts. The results of forecasts using conditional probability density function are assessed by qualitative approach and transformed categorical probability forecasts. The assessment of the forecasts which are transformed to categorical probability forecasts shows that the results of the forecasts by conditional probability density function are much better than those of the forecasts by Piechota's method and logistic regression analysis except for winter season data.

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. Forecasting of seasonal inflow to Andong dam is performed and assessed using statistical methods based on hydrometeorological data. Predictors which is used to forecast seasonal inflow to Andong dam are selected from southern oscillation index, sea surface temperature, and 500 hPa geopotential height data in northern hemisphere. Predictors are selected by the following procedure. Primary predictors sets are obtained, and then final predictors are determined from the sets. The primary predictor sets for each season are identified using cross correlation and mutual information. The final predictors are identified using partial cross correlation and partial mutual information. In each season, there are three selected predictors. The values are determined using bootstrapping technique considering a specific significance level for predictor selection. Seasonal inflow forecasting is performed by multiple linear regression analysis using the selected predictors for each season, and the results of forecast using cross validation are assessed. Multiple linear regression analysis is performed using SAS. The results of multiple linear regression analysis are assessed by mean squared error and mean absolute error. And contingency table is established and assessed by Heidke skill score. The assessment reveals that the forecasts by multiple linear regression analysis are better than the reference forecasts.

The traffic accidents in large cities such as Pusan metropolitan city have been increased every year due to increasing of vehicles numbers as well as the gravitation of the population. In addition to the carelessness of drivers, many meteorological factors have a great influence on the traffic accidents. Especially, the number of traffic accidents is governed by precipitation, visibility, humidity, cloud amounts and temperature, etc. In this study, we have analyzed various data of meteorological factors from 1992 to 1997 and determined the standardized values for contributing to each traffic accident. Using the relationship between meteorological factors(visibility, precipitation, relative humidity and cloud amounts) and the total automobile mishaps, an experimental prediction formula for their traffic accident rates was seasonally obtained at Pusan city in 1997. Therefore, these prediction formulas at each meteorological factor may be used to predict the seasonal traffic accident numbers and contributed to estimate the variation of its value according to the weather condition in Pusan city.