• Journal of Korea Water Resources Association
• /
• v.31 no.6
• /
• pp.795-806
• /
• 1998

Soil moisture is affected by regional climate, soil characteristics and land surface condition, etc,. Especially, the changes in land surface condition is more than other factors, which is mainly due to rapid urbanization and industrialization. This study is to evaluate how the change of land surface condition impacts on soil moisture field evolution using a simple model of soil moisture dynamics. For the quantification of soil moisture field, the first half of the paper is spared for the statistical characterization based on the first- and second-order statistics of Washita '92 and Monsoon '90 data. The second half is for evaluating the impact of land cover changes through simulation study using a model for soil moisture dynamics. The model parameters, the loss rate and the diffusion coefficient, have been estimated using the observed data statistics, where the changes of surface conditions are considered into the model by applying various parameter sets with different second-order statistics. This study is concentrated on evaluating the impact due to the changes of land surface condition variability. It is because we could easily quantify the impact of the changes of its areal mean based on the linear reservoir concept. As a result of the study, we found; (1)as the variability of land surface condition, increases, the soil moisture field dries up more easily, (2)as the variabilit y of the soil moisture field is the highest at the beginning of rainfall and decreases as time goes on to show the variability of land surface condition, (3)the diffusion effect due to surface runoff or water flow through the top soil layer is limited to a period of surface runoff and its overall impact is small compared to that of the loss rate field.

• Journal of Korea Water Resources Association
• /
• v.50 no.12
• /
• pp.837-847
• /
• 2017

Land Surface Model (LSM) was developed for the Soyang river basin located in Korean Peninsula to clarify the spatio-temporal variability of hydrological weather parameters. Variable Infiltration Capacity (VIC) model was used as a LSM. The spatial resolution of the model was 10 km and the time resolution was 1 day. Based on the daily flow data from 2007 to 2010, the 7 parameters of the model were calibrated using the Isolated Particle Swarm Optimization algorithm and the model was verified using the daily flow data from 2011 to 2014. The model showed a Nash-Sutcliffe Coefficient of 0.90 and a correlation coefficient of 0.95 for both calibration and validation periods. The hydrometeorological variables estimated for the Soyang river basin reflected well the seasonal characteristics of summer rainfall concentration, the change of short and shortwave radiation due to temperature change, the change of surface temperature, the evaporation and vegetation increase in the cover layer, and the corresponding change in total evapotranspiration. The model soil moisture data was compared with in-situ soil moisture data. The slope of the trend line relating the two data was 1.087 and correlation coefficient was 0.723 for the Spring, Summer and Fall season. The result of this study suggests that the LSM can be used as a powerful tool in developing precise and efficient water resources plans by providing accurate understanding on the spatio-temporal variation of hydrometeorological variables.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.34 no.6
• /
• pp.1765-1777
• /
• 2014

This study evaluated PMM (Probability Matching Method) for parameter estimation of the Z - R relation. As a first step, the sensitivity analysis was done to decide the threshold number of data pairs and the data interval for the development of a histogram. As a result, it was found that at least 1,000 number of data pairs are required to apply the PMM for the parameter estimation. This amount of data is similar to that collected for two hours. Also, the number of intervals for the histogram was found to be at least 100. Additionally, it was found that the matching the first-order moment is better than the cumulative probability, and that the data pairs comprising 30 to 100% are better for the PMM application. Finally, above findings were applied to a real rainfall event observed by the Bislsan radar and optimal parameters were estimated. The radar rain rate derived by applying these parameters was found to be well matched to the rain gauge rain rate.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.37 no.4
• /
• pp.669-680
• /
• 2017

This study evaluated the consistency of the standard flow to forecast low-flow based on various drought indices. The data used in this study were streamflow data at the Gurye2 station located in the Seomjin River and the Angang station located in the Hyeongsan River, as well as rainfall data of nearby weather stations (Namwon and Pohang). Using streamflow data, the streamflow accumulation drought index (SADI) was developed in this study to represent the hydrological drought condition. For SADI calculations, the threshold of drought was determined by a Change-Point analysis of the flow pattern and a reduction factor was estimated based on the kernel density function. Standardized runoff index (SRI) and standardized precipitation index (SPI) were also calculated to compared with the SADI. SRI and SPI were calculated for the 30-, 90-, 180-, and 270-day period and then an ROC curve analysis was performed to determine the appropriate time-period which has the highest consistency with the standard flow. The result of ROC curve analysis indicated that for the Seomjin River-Gurye2 station SADI_C3, SRI30, SADI_C1, SADI_C2, and SPI90 were confirmed in oder of having high consistency with standard flow under the attention stage and for the Hyeongsan River-Angang station, SADI_C3, SADI_C1, SPI270, SRI30, and SADI_C2 have order of high consistency with standard flow under the attention stage.

• Geotechnical Engineering
• /
• v.8 no.2
• /
• pp.5-20
• /
• 1992

The physical-based and lumped-parameter hydrologic groundwater flow model for predicting the rainfall-triggered rise of groundwater levels in hillside slopes is developed in this paper to assess the risk of landslides. The developed model consists of a vertical infiltration model for unsaturated zone linked to a linear storage reservoir model(LSRM) for saturated zone. The groundwater flow model has uncertain constants like soil depttL slope angle, saturated permeability, and potential evapotranspiration and four free model parameters like a, b, c, and K. The free model parameters could be estimated from known input-output records. The BARD algorithm is uses as the parameter estimation technique which is based on a linearization of the proposed model by Gauss -Newton method and Taylor series expansion. The application to examine the capacity of prediction shows that the developed model has a potential of use in forecast systems of predicting landslides and that the optimal estimate of potential 'a' in infiltration model is the most important in the global optimum analysis because small variation of it results in the large change of the objective function, the sum of squares of deviations of the observed and computed groundwater levels. 본 논문에서는 가파른 산사면에서 산사태의 발생을 예측하기 위한 수문학적 인 지하수 흐름 모델을 개발하였다. 이 모델은 물리적인 개념에 기본하였으며, Lumped-parameter를 이용하였다. 개발된 지하수 흐름 모델은 두 모델을 조합하여 구성되어 있으며, 비포화대 흐름을 위해서는 수정된 abcd 모델을, 포화대 흐름에 대해서는 시간 지체 효과를 고려할 수 있는 선형 저수지 모델을 이용하였다. 지하수 흐름 모델은 토층의 두께, 산사면의 경사각, 포화투수계수, 잠재 증발산 량과 같은 불확실한 상수들과 a, b, c, 그리고 K와 같은 자유모델변수들을 가진다. 자유모델변수들은 유입-유출 자료들로부터 평가할 수 있으며, 이를 위해서 본 논문에서는 Gauss-Newton 방법을 이용한 Bard 알고리즘을 사용하였다. 서울 구로구 시흥동 산사태 발생 지역의 산사면에 대하여 개발된 모델을 적용하여 예제 해석을 수행함으로써, 지하수 흐름 모델이 산사태 발생 예측을 위하여 이용할 수 있음을 입증하였다. 또한, 매개변수분석 연구를 통하여, 변수 a값은 작은 변화에 대하여 목적함수값에 큰 변화를 일으키므로 a의 값에 대한 최적값을 구하는 것이 가장 중요한 요소라는 결론을 얻었다.

• Journal of Korea Water Resources Association
• /
• v.41 no.5
• /
• pp.463-471
• /
• 2008

The purpose of this study is to analyze the features of groundwater use to utilize as basic information for water-cycle analysis system development and effective groundwater management in the Kap-cheon basin. The cumulative relationship between groundwater use and the number of wells was analyzed to estimate the representative total groundwater use and the number of wells for the Kap-cheon basin. Then, the spatial distribution of groundwater use in the basin were figured out using the detailed information on groundwater use in each well. Finally, the reasonability of groundwater resources management in Kap-cheon basin was evaluated by comparing groundwater recharge and groundwater use in sub-basins and major stream basins. The results of the analysis showed about 25% of the total wells could represent 90% of groundwater use ($37,923,516\;m^3$/year) in the Kap-cheon basin. A detailed analysis on the groundwater uses in the vicinity of down-town areas of Daejeon metropolitan city showed high groundwater uses ($1.4{\sim}11.1$ times) compared to the groundwater recharge previously estimated using the rainfall-runoff model. The ratio of groundwater use and groundwater recharge for the major river basins in Kap-cheon basin ranged from 1.9 to 2.3 indicating that more sustainable groundwater management should be exercised. The results of this study can be used as basic information in evaluating the change of groundwater flow, stream flow and water-cycle for various groundwater uses in the Kap-cheon basin.

• Journal of Korea Water Resources Association
• /
• v.42 no.3
• /
• pp.213-225
• /
• 2009

A stationary Markov chain model is a stochastic process with the Markov property. Having the Markov property means that, given the present state, future states are independent of the past states. The Markov chain model has been widely used for water resources design as a main tool. A main assumption of the stationary Markov model is that statistical properties remain the same for all times. Hence, the stationary Markov chain model basically can not consider the changes of mean or variance. In this regard, a primary objective of this study is to develop a model which is able to make use of exogenous variables. The regression based link functions are employed to dynamically update model parameters given the exogenous variables, and the model parameters are estimated by canonical correlation analysis. The proposed model is applied to daily rainfall series at Seoul station having 46 years data from 1961 to 2006. The model shows a capability to reproduce daily and seasonal characteristics simultaneously. Therefore, the proposed model can be used as a short or mid-term prediction tool if elaborate GCM forecasts are used as a predictor. Also, the nonstationary Markov chain model can be applied to climate change studies if GCM based climate change scenarios are provided as inputs.

• Journal of Korea Water Resources Association
• /
• v.51 no.9
• /
• pp.769-782
• /
• 2018

Estimation of design floods is typically required for hydrologic design purpose. Design floods are routinely estimated for water resources planning, safety and risk of the existing water-related structures. However, the hydrologic data, especially streamflow data for the design purposes in South Korea are still very limited, and additionally the length of streamflow data is relatively short compared to the rainfall data. Therefore, this study collected a large number design flood data and watershed characteristics (e.g. area, slope and altitude) from the national river database. We further explored to formulate a scaling approach for the estimation of design flood, which is a function of the watershed characteristics. Then, this study adopted a Hierarchical Bayesian model for evaluating both parameters and their uncertainties in the regionalization approach, which models the hydrologic response of ungauged basins using regression relationships between watershed structure and model. The proposed modeling framework was validated through ungauged watersheds. The proposed approach have better performance in terms of correlation coefficient than the existing approach which is solely based on area as a predictor. Moreover, the proposed approach can provide uncertainty associated with the model parameters to better characterize design floods at ungauged watersheds.

• Journal of the Korean earth science society
• /
• v.30 no.2
• /
• pp.185-195
• /
• 2009

We investigated the change of several meteorological variables due to deforestation. We established two sets of automatic weather observation system: one on a hill where forest was destructed by lumbering (Point 1) and the other in a neighboring district (Point 2) of fairly preserved forest. The observations were continued for one year (2006. 12-2007. 12). In this study, we analysed the data observed for one week from the nea day after summertime rainfall. The results showed that the air temperatures of Point 1 were about $1.5^{\circ}C$ higher than those of Point 2 during the daytime. But there were small gaps between the two poults during the nighttime. The relative humidities also differed greatly between the two during the daytime. It was as high as about 10% at Point 2. The surface and underground (15 cm in depth) soil temperatures were also fealty different between the two points during the daytime. They were $3-10^{\circ}C$ higher at Point 2 than those of Point 1. And the gaps reduced drastically during the nighttime. The averaged soil moistures were 7.1% at Point 1 and 19.5% at Point 2 during the observation period, respectively. The differences of wind direction were small, but the wind speeds differed between the two points. The observed wind speeds during the observation period were roughly estimated to be about 0.5m/s at Point 1 and 0.3m/s at Point 2. The heat budget analysis was also performed based on the observation data.

• The Journal of Engineering Geology
• /
• v.27 no.2
• /
• pp.153-164
• /
• 2017

The geological factors for causing ground subsidence are very diverse. It can be affected by any geological or extrinsic influences, and even within the same geological factor, the soil depression impact factor can be determined by different physical properties. As a result of reviewing a large number of papers and case histories, it can be seen that there are seven categories of ground subsidence factors. The depth and thickness of the overburden can affect the subsidence depending on the existence of the cavity, whereas the depth and orientation of the boundary between soil and rock are dominant factors in the ground composed of soil and rock. In case of soil layers, more various influencing factors exist such as type of soil, shear strength, relative density and degree of compaction, dry unit weight, water content, and liquid limit. The type of rock, distance from the main fracture and RQD can be influential factors in the bedrock. When approaching from the hydrogeological point of view, the rainfall intensity, the distance and the depth from the main channel, the coefficient of permeability and fluctuation of ground water level can influence to ground subsidence. It is also possible that the ground subsidence can be affected by external factors such as the depth of excavation and distance from the earth retaining wall, groundwater treatment methods at excavation work, and existence of artificial facilities such as sewer pipes. It is estimated that to evaluate the ground subsidence factor during the construction of underground structures in urban areas will be essential. It is expected that ground subsidence factors examined in this study will contribute for the reliable evaluation of the ground subsidence risk.