• Magazine of the Korean Society of Agricultural Engineers
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• v.22 no.3
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• pp.75-87
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• 1980

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.

• Korean Journal of Soil Science and Fertilizer
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• v.46 no.1
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• pp.16-22
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• 2013

Water is the most important resource for the maximum water use efficiency and yield of maize. Water has to be applied moderately based on the water requirement of maize. Crop water requirement (WR) is a function of the potential evapo-transpiration (PET) and crop coefficient (Kc). PET can be estimated by the climate data measured at the weather station in the production region. Kc was measured by the NIAST (RDA) through lysimeter experiments. In this study, the growth stage of maize was divided into five ones (G-1: Apr. 25 ~ May 20, G-2: May 21 ~ Jun. 20, G-3: Jun. 21 ~ Jul. 20, G4: Jul. 11 ~ Jul. 25, G5: Jul. 26 ~ Aug. 20). The average PET during maize growing season of the 45 areas was 2.85 mm $day^{-1}$. The highest water requirement was at the G-3 stage among the maize growth stages. The mean water requirement (MWR) according to growth stage was 1.74 ~ 2.42 (average 2.02), 2.99 ~ 4.21 (average 3.41), 3.82 ~ 5.25 (average 4.41), 3.05 ~ 4.31 (average 3.48), and 2.62 ~ 3.49 (average 3.01) mm $day^{-1}$ in the G-1, G-2, G-3, G-4 and G-5 stage, respectively. The total water requirement (TWR) according to growth stage was 45.37 ~ 63.04 (average 52.56), 92.54 ~ 130.59 (average 105.77), 76.46 ~ 105.09 (average 88.14), 45.73 ~ 64.67 (average 52.20), and 68.25 ~ 90.75 (average 78.33) mm in the G-1, G-2, G-3, G-4 and G-5 stage, respectively.

• Journal of Korea Water Resources Association
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• v.54 no.9
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• pp.681-692
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• 2021

Vegetation processes have a significant impact on rainfall runoff processes through evapotranspiration control, but are rarely considered in the conceptual lumped hydrological model. This study evaluated the model performance of the Hapcheon Dam watershed by integrating the ecological module expressing the leaf area index data sensed remotely from the satellite into the hydrological partition module. The proposed eco-hydrological model has three main features to better represent the eco-hydrological process in humid regions. 1) The growth rate of vegetation is constrained by water shortage stress in the watershed. 2) The maximum growth of vegetation is limited by the energy of the watershed climate. 3) The interaction of vegetation and aquifers is reflected. The proposed model simultaneously simulates hydrologic components and vegetation dynamics of watershed scale. The following findings were found from the validation results using the model parameters estimated by the SCEM algorithm. 1) Estimating the parameters of the eco-hydrological model using the leaf area index and streamflow data can predict the streamflow with similar accuracy and robustness to the hydrological model without the ecological module. 2) Using the remotely sensed leaf area index without filtering as input data is not helpful in estimating streamflow. 3) The integrated eco-hydrological model can provide an excellent estimate of the seasonal variability of the leaf area index.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.3
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• pp.567-577
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• 2015

Climate and land use changes have impact on availability water resource by hydrologic cycle change. The purpose of this study is to evaluate the hydrologic behavior by the future potential climate and land use changes in Anseongcheon watershed ($371.1km^2$) using SWAT model. For climate change scenario, the HadGEM-RA (the Hadley Centre Global Environment Model version 3-Regional Atmosphere model) RCP (Representative Concentration Pathway) 4.5 and 8.5 emission scenarios from Korea Meteorological Administration (KMA) were used. The mean temperature increased up to $4.2^{\circ}C$ and the precipitation showed maximum 21.2% increase for 2080s RCP 8.5 scenario comparing with the baseline (1990-2010). For the land use change scenario, the Conservation of Land Use its Effects at Small regional extent (CLUE-s) model was applied for 3 scenarios (logarithmic, linear, exponential) according to urban growth. The 2100 urban area of the watershed was predicted by 9.4%, 20.7%, and 35% respectively for each scenario. As the climate change impact, the evapotranspiration (ET) and streamflow (ST) showed maximum change of 20.6% in 2080s RCP 8.5 and 25.7% in 2080s RCP 4.5 respectively. As the land use change impact, the ET and ST showed maximum change of 3.7% in 2080s logarithmic and 2.9% in 2080s linear urban growth respectively. By the both climate and land use change impacts, the ET and ST changed 19.2% in 2040s RCP 8.5 and exponential scenarios and 36.1% in 2080s RCP 4.5 and linear scenarios respectively. The results of the research are expected to understand the changing water resources of watershed quantitatively by hydrological environment condition change in the future.

• Journal of Korea Water Resources Association
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• v.46 no.12
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• pp.1235-1247
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• 2013

This study is to evaluate the climate change impact on future storage behavior of Chungju dam($2,750{\times}10^6m^3$) and the regulation dam($30{\times}10^6m^3$) using SWAT(Soil Water Assessment Tool) model. Using 9 years data (2002~2010), the SWAT was calibrated and validated for streamflow at three locations with 0.73 average Nash-Sutcliffe model Efficiency (NSE) and for two reservoir water levels with 0.86 NSE respectively. For future evaluation, the HadCM3 of GCMs (General Circulation Models) data by scenarios of SRES (Special Report on Emission Scenarios) A2 and B1 of the IPCC (Intergovernmental Panel on Climate Change) were adopted. The monthly temperature and precipitation data (2007~2099) were spatially corrected using 30 years (1977~2006, baseline period) of ground measured data through bias-correction, and temporally downscaled by Change Factor (CF) statistical method. For two periods; 2040s (2031~2050), 2080s (2071~2099), the future annual temperature were predicted to change $+0.9^{\circ}C$ in 2040s and $+4.0^{\circ}C$ in 2080s, and annual precipitation increased 9.6% in 2040s and 20.7% in 2080s respectively. The future watershed evapotranspiration increased up to 15.3% and the soil moisture decreased maximum 2.8% compared to baseline (2002~2010) condition. Under the future dam release condition of 9 years average (2002~2010) for each dam, the yearly dam inflow increased maximum 21.1% for most period except autumn. By the decrease of dam inflow in future autumn, the future dam storage could not recover to the full water level at the end of the year by the present dam release pattern. For the future flood and drought years, the temporal variation of dam storage became more unstable as it needs careful downward and upward management of dam storage respectively. Thus it is necessary to adjust the dam release pattern for climate change adaptation.

• Journal of Korea Water Resources Association
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• v.54 no.8
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• pp.577-587
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• 2021

Drought is generally considered to be a natural disaster caused by accumulated water shortages over a long period of time, taking months or years and slowly occurring. However, climate change has led to rapid changes in weather and environmental factors that directly affect agriculture, and extreme weather conditions have led to an increase in the frequency of rapidly developing droughts within weeks to months. This phenomenon is defined as 'Flash Drought', which is caused by an increase in surface temperature over a relatively short period of time and abnormally low and rapidly decreasing soil moisture. The detection and analysis of flash drought is essential because it has a significant impact on agriculture and natural ecosystems, and its impacts are associated with agricultural drought impacts. In South Korea, there is no clear definition of flash drought, so the purpose of this study is to identify and analyze its characteristics. In this study, flash drought detection condition was presented based on the satellite-derived drought index Evaporative Stress Index (ESI) from 2014 to 2018. ESI is used as an early warning indicator for rapidly-occurring flash drought a short period of time due to its similar relationship with reduced soil moisture content, lack of precipitation, increased evaporative demand due to low humidity, high temperature, and strong winds. The flash droughts were analyzed using hydrometeorological characteristics by comparing Standardized Precipitation Index (SPI), soil moisture, maximum temperature, relative humidity, wind speed, and precipitation. The correlation was analyzed based on the 8 weeks prior to the occurrence of the flash drought, and in most cases, a high correlation of 0.8(-0.8) or higher(lower) was expressed for ESI and SPI, soil moisture, and maximum temperature.

• Korean Journal of Environmental Agriculture
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• v.25 no.2
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• pp.164-169
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• 2006

This study was conducted for 10 days from 17 July to 26 July in 2005 to measure the amount of xylem sap flow under short-term variation of soil moisture regimes at -20 kPa, -50 kPa and -80 kPa in eight-year-old 'Fuji'/M.9 apple trees with different fruit loads. Fruit load was adjusted as three different treatments with standard (100%), 1/2 times (50%) and 2 times (200%) on the basis of optimum fruiting number per tree as the standard fruit load of Fuji cultivar. Trees with standard fruit load during the experimental period showed higher xylem sap flow at -50 kPa of soil moisture regimes than those of trees with 1/2 times and 2 times fruit load. Trees with 1/2 times and 2 times fruit load had similar patterns of the diurnal changes of xylem sap flow, vapor pressure deficit (VPD), and maximum evapotranspiration (ETm). However, trees with 2 times fruit load at -50 kPa and -80 kPa of soil moisture regimes produced lower amount of xylem sap flow than ETm. Trees with standard fruit load produced $1.06{\sim}3.93$ L/tree more amount of xylem sap flow than ETm at all soil moisture regimes. But xylem sap flow of tees with 2 times fruit load had 21% lower at -50 kPa and $31{\sim}36%$ lower at -20 kPa and -80 kPa of soil moisture regimes, respectively than that of trees with standard fruit load. Shoot growth and leaf area were significantly the highest in trees with standard fruit load while those of trees with 2 times fruit load recorded significantly lowest. Leaf water potential of trees with standard fruit load was lower than that of trees with 1/2 times and 2 times fruit load. It indicated that tees with standard fruit load had higher water use for transpiration than other treatments and tees with 2 times fruit load received more stress for the transpiration process under low soil moisture regimes. Consequently, 'Fuji'/M.9 apple trees, the fruit load and soil moisture should be maintained optimum to increase xylem sap flow and transpiration during higher growth period.

• Journal of Korea Water Resources Association
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• v.51 no.10
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• pp.905-916
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• 2018

The purpose of this study is to evaluate the climate change impact on watershed hydrology and flow duration in Geum River basin ($9,645.5km^2$) especially by extreme scenarios. The rainfall related extreme index, STARDEX (STAtistical and Regional dynamical Downscaling of EXtremes) was adopted to select the future extreme scenario from the 10 GCMs with RCP 8.5 scenarios by four projection periods (Historical: 1975~2005, 2020s: 2011~2040, 2050s: 2041~2070, 2080s: 2071~2100). As a result, the 5 scenarios of wet (CESM1-BGC and HadGEM2-ES), normal (MPI-ESM-MR), and dry (INM-CM4 and FGOALS-s2) were selected and applied to SWAT (Soil and Water Assessment Tool) hydrological model. The wet scenarios showed big differences comparing with the normal scenario in 2080s period. The 2080s evapotranspiration (ET) of wet scenarios varied from -3.2 to +3.1 mm, the 2080s total runoff (TR) varied from +5.5 to +128.4 mm. The dry scenarios showed big differences comparing with the normal scenario in 2020s period. The 2020s ET for dry scenarios varied from -16.8 to -13.3 mm and the TR varied from -264.0 to -132.3 mm respectively. For the flow duration change, the CFR (coefficient of flow regime, Q10/Q355) was altered from +4.2 to +10.5 for 2080s wet scenarios and from +1.7 to +2.6 for 2020s dry scenarios. As a result of the flow duration analysis according to the change of the hydrological factors of the Geum River basin applying the extreme climate change scenario, INM-CM4 showed suitable scenario to show extreme dry condition and FGOALS-s2 showed suitable scenario for the analysis of the drought condition with large flow duration variability. HadGEM2-ES was evaluated as a scenario that can be used for maximum flow analysis because the flow duration variability was small and CESM1-BGC was evaluated as a scenario that can be applied to the case of extreme flood analysis with large flow duration variability.