This study was carried out to investigate the consumptive use of water for red peppers and soy beans. The correlation between the soil moisture contents and the selected meteorological factors during the growing season was analyzed. Characteristics of the drought at Jinju, Yeosu, Gwangju, and Mokpo area were figured out in view of frequency analysis. The results obtained from this study could be used as a reasonable criteria for the estimation of the duty of water in the design of upland irrigation systems. Obtained results are summarized as follows: 1. Red peppers were grown at the three levels of soil moisture contents; 75 percent, 50 percent, and 25 percent, respectively. The red pepper grown at the 75 percent of soil moisture content showed the highest yield. The total evapotranspiration during the growing season from red peppers was 471. lmm, which was 86.6mm less than the pan evaporation. 2. The soy bean grown at 75 percent soil moisture content showed the highest yield, although there was no signicant difference in yields among treatments. The total evapotranspiration during the growing season from the soy bean was 342.8 mm, which was 119.2mm less than the pan evaporation. 3. Coefficients of consumptive use(k) and meteorological data are shown on Table-9. 4. The significant correlations between the evapotranspiration and the humidity and daily temperature range were observed. Results are shown on Table-11.. Evaporanspiration can be easily estimated from the humidity and daily temperature range by using the equation...... (1) Ept=4.808-0.041H+0.207T.......(1) where, Ept; evapotranspiration(mm/day) H ; humidity(%) T ; daily temperature range ($^{\circ}C$) 5. The variations of soil moisture content during the growing season at the soil depth of 5cm, 15cm, and 45cm are shown on Fig. 4~9. The results of the correlation analysis between the evapotranspiration from the crops and the soil moisture content are shown on Table-12. The evapotranspiration can be estimated from soil moisture content at the different depth of the soil by using the equation....... (2). Ept = 3.433 - 0. 364M1 +0. 359M$_2$- 0. 055M$_3$....... (2) where, Ept; evapotranspiration (mm/day) M1 soil moisture meter reading at 5cm depth M$_2$; " 15cm " M$_2$; " 40cm " 6. The estimated probab]e successive dry days in selected areas are shown on Table 13. Gumbel-Chow method was used to calculate the probable successive dry days. Further investigation are required to obtain the more detailed and reliable results.

This study was conducted to obtain the most effective distribution of grain size and the optimum lime content for lime-soil stabilization. To achieve the aim, the change of consistency, the characteristics of compaction and unconfined compressive strength were tested by adding of 0, 4, 6, 8, 10 and 12 percent lime by weight for all soils adjusted by given ratios of sand to clay. The results obtained were as follows; 1. There was a tendency that the plasticity index of lime-soil mixture was decreased by increasing the amount of lime, whereas the liquid limit was varied irregularly and the plastic limit was increased. 2. With the addition of more lime, the optimum moisture content of lime-soil mixture was increased, and the maximum dry density was decreased. 3. The optimum lime content of lime-soil mixture was varied from soil to soil, and the less amount of small grain size, the less value of optimum lime content. 4. The optimum distribution of grain size for lime-soil mixture was in the soil, having the ratio of about 60 percent of cohesive clay and about 40 percent of sand by weight. 5. In the soil having fine grain size, the effect of curing appeared for long periods of time, whereas the increasing rate of unconfined compressive strength was great on the soil of coarse grain size in the earlier stage of curing period.

Soil-cement mixtures involve problems in it's durability in grain size distribution and mineral composition of the used soils as well as in cement content, compaction energy, molding water content, and curing. As an attempt to solve the problems associated with durability of weathered granite soil with cement treated was investigated by conducting tests such as unconfined compression test, it's moisture, immers, wet-dry and freeze-thaw curing, mesurement of loss of weight with wet-dry and freeze-thaw by KS F criteria and CBR test with moisture curing on the five soil samples different in weathering and mineral composition. The experimental results are summarized as follows; The unconfined compressive strength was higher in moisture curing rather than in the immers and wet-dry, while it was lowest in freeze-thaw. Decreasing ratio of unconfined compressive strength in soil-cement mixtures were lowest in optimum moisture content or in the dry side rather than optimum moisture content with freeze-thaw. The highly significant ceofficient was obtained between the cement content and loss of weight with freeze-thaw and wet-dry. It was possible to obtain the durability of soil-cement mixtures, as the materials of base for roads, containing above 4 % of cement content, above 3Okg/cm$_2$ of unconfined compressive trength with seven days moisture curing or 12 cycle of freeze-thaw after it, above 100% of relative unconfined compressive strength, 80% of index of resistance, below 14% of loss of weight with 12 cycle of wet-dry and above 1. 80g/cm$_2$ of dry density.

Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.