• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.601-610
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• 2023

The comprehension of water-level behavior in rivers is essential for effective flood and river environmental management. The objective of this study is to propose a methodology that can be used by field engineers engaged in actual practice, to readily identify the characteristics of water-level behavior during flood events. To this end, a total of 45 historical water-level records from 2010 to 2022 year, which provide flood information for the flood vulnerable districts of the Han River, were obtained. A Water-level Rise Behavior Curve (WRBC) was developed and suggested to quantify the amount of water-level rise per unit time during flood. As a result, the water-level rises by more than 80% of the total rise within the first 6.2 hours, followed by a gradual rise. The time required to achieve a particular equilibrium varied depending on the area and runoff characteristics of the upstream. Furthermore, the study revealed that the WRBC provides a statistical representation of the water-level rise trend during floods, and can be effectively utilized for flood mitigation measures in waterfront spaces and irrigation facilities.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6B
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• pp.503-512
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• 2009

Watershed models, which are a tool for water cycle mechanism, are classified as the distributed model and the lumped model. Currently, the distributed models have been more widely used than lumped model for many researches and applications. The lumped model estimates the parameters in the conceptual and empirical sense, on the other hand, in the case of distributed model the first-guess value is estimated from the grid-based watershed characteristics and rainfall data. Therefore, the distributed model needs more detailed parameter adjustment in its calibration and also one should precisely understand the model parameters' characteristics and sensitivity. This study uses Jungnang basin as a study area and $Vflo^{TM}$ model, which is a physics-based distributed hydrologic model, is used to analyze its parameters' sensitivity. To begin with, 100 years frequency-design rainfall is derived from Huff's method for rainfall duration of 6 hours, then the discharge is simulated using the calibrated parameters of $Vflo^{TM}$ model. As a result, hydraulic conductivity and overland's roughness have an effect on runoff depth and peak discharge, respectively, while channel's roughness have influence on travel time and peak discharge.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1B
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• pp.153-160
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• 2008

To estimate microbial contaminant loading discharged from diffuse sources, rainfall runoff of six rainfall events were monitored at three study watersheds of forestry and agricultural land use. Monitored indicator microorganism constituents were total coliform (TC), fecal coliform (FC), Escherichia coli (EC), and fecal streptococcus (FS). Soil loss during elevated flow rate caused higher suspended solid concentrations. Indicator microorganism concentrations were closely related with flow rate. TC event mean concentration (EMC) from unpolluted forestry was $5.3{\times}10^3CFU/100ml$, FC EMC was $1.4{\times}10^3CFU/100ml$, EC EMC was $1.1{\times}10^3CFU/100ml$, and FS EMC was $2.9{\times}10^2CFU/100ml$. From a watershed with agricultural-forestry land use, TC EMC was $1.7{\times}10^5CFU/100ml$, FC EMC was $8.5{\times}10^4CFU/100ml$, EC EMC was $8.9{\times}10^4CFU/100ml$, and FS EMC was $3.4{\times}10^4CFU/100ml$. Mixed land use of agricultural-forestry with bigger area, TC EMC was $1.9{\times}10^5CFU/100ml$, FC EMC was $9.6{\times}10^4CFU/100ml$, EC EMC was $7.0{\times}10^4CFU/100ml$, and FS EMC was $5.1{\times}10^4CFU/100ml$.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6B
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• pp.597-603
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• 2006

The purpose of this study is to improve the short term rainfall forecast skill using neural network model that can deal with the non-linear behavior between satellite data and ground observation, and minimize the flood damage. To overcome the geographical limitation of Korean peninsula and get the long forecast lead time of 3 to 6 hour, the developed rainfall forecast model took satellite imageries and wide range AWS data. The architecture of neural network model is a multi-layer neural network which consists of one input layer, one hidden layer, and one output layer. Neural network is trained using a momentum back propagation algorithm. Flood was estimated using rainfall forecasts. We developed a dynamic flood inundation model which is associated with 1-dimensional flood routing model. Therefore the model can forecast flood aspect in a protected lowland by levee failure of river. In the case of multiple levee breaks at main stream and tributaries, the developed flood inundation model can estimate flood level in a river and inundation level and area in a protected lowland simultaneously.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6B
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• pp.579-587
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• 2010

The mid-range streamflow forecast was performed using NWP(Numerical Weather Prediction) provided by KMA. The NWP consists of RDAPS for 48-hour forecast and GDAPS for 240-hour forecast. To enhance the accuracy of the NWP, QPM to downscale the original NWP and Quantile Mapping to adjust the systematic biases were applied to the original NWP output. The applicability of the suggested streamflow prediction system which was verified in Geum River basin. In the system, the streamflow simulation was computed through the long-term continuous SSARR model with the rainfall prediction input transform to the format required by SSARR. The RQPM of the 2-day rainfall prediction results for the period of Jan. 1~Jun. 20, 2006, showed reasonable predictability that the total RQPM precipitation amounts to 89.7% of the observed precipitation. The streamflow forecast associated with 2-day RQPM followed the observed hydrograph pattern with high accuracy even though there occurred missing forecast and false alarm in some rainfall events. However, predictability decrease in downstream station, e.g. Gyuam was found because of the difficulties in parameter calibration of rainfall-runoff model for controlled streamflow and reliability deduction of rating curve at gauge station with large cross section area. The 10-day precipitation prediction using GQPM shows significantly underestimation for the peak and total amounts, which affects streamflow prediction clearly. The improvement of GDAPS forecast using post-processing seems to have limitation and there needs efforts of stabilization or reform for the original NWP.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.15 no.4
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• pp.192-202
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• 2010

Submarine groundwater discharge (SGD) has been recognized as a provider for freshwater, nutrients, and dissolved constituents from continents to the oceans and paid more attention with regard to the mass balance of water or dissolved constituents on local and global scales. The submarine discharge of fresh groundwater (fresh SGD) through seepage or springs in coastal ocean may be especially important in aspects of water resource and marine environment managements in the future. Based on the worldwide compilations of observed fresh SGD, our review reveals that fresh SGD occurs in various marine environments along most shoreline of the world and the global estimates of fresh SGD were approximately 0.01-17% of surface runoff. In addition, the input of fresh SGD calculated and investigated in this study were about 50%, 57%, 89%, and 420% of total river discharge in Jeju Island, Yeongil Bay, Masan Bay, and Yeoja Bay, respectively. These inputs from fresh SGD along the shoreline of Korea Peninsula are much higher than those of the whole world, greatly vary with the region. However, since these estimates are based on the water balance method mainly used in coastal ocean, we have to perform continuous monitoring of various parameters, such as precipitation, tide, evapotanspiration and water residence time, which have an impact on the water balance in a lot of areas for evaluating the precise input of fresh SGD. In addition, since the method estimating the input of fresh SGD has brought up many problems, it is required to make an intercomparison between various methods such as hydrogeological assumption, numerical modeling, and seepage meter.

• Journal of the Korean Geographical Society
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• v.30 no.3
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• pp.242-254
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• 1995

The Hadano Basin is located at a distance of about 70kms and 60kms from Tokyo and Yokohama and lies in the south-west part of the Kanto region in Japan. The basin area, which correspoends to the catchment of the Kaname River, is about areal size of 60.7$\textrm{km}^2$ and extends about length of 8kms in E-W direction and about width of 5kms in N-S direction (Fig.1). The Hadano basin is filled with thick pile of the alluvum from deposits composed of volcanic materials, mostly came from the Hakone Volcano and overlain by Fuji Volcanic ashes. Fluvial deposits form the good aquifer, therefore water resources of Handano City has been largely depending upon the eroundwater. Urbanization and industrialization of the basin has been rapid in the last thirty years, after activation of "Factory Attraction Policy of Hadano City" in 1956. Growth in population and number of factory due to urbanization changed the land-use pattern of the basin rapidly and increased the water demands. Therefore, Hadano City exploited a new source of water supply, and have introduced the prefectureal waterworks since 1976. On the other hand, the rapid urbanization has brought about the pollution of streams in the basin by domestic sewage and industrial waste water. Diffusion rate of sewerage systems in Hadano City is 38% in 1993. In ordcr to examine the impact of anthropogenic factors on river environments, the author took up the change of land-use and diffusion area of sewerage as parameters, and performed field surveys on water discharge and quality. The survey has been made at upstream and downstream of the main stream regularly per month, to get informati ons about the variation of discharge and water quality aiong the stream and its diurnal fluctuation. Annual variation has been analyzed based the data from Hadano City Office. The results are summarized as follows. 1. Stream discharge has been increasing by urbanization (Fig.3). Water quality (C $l^{-10}$ , N $H^{+}$$_{ 4}$-N, BOD) has been improving gradually after the application of sewerage service, yet water pollution load at the lower station has increased than that at the upper one because of the larger anthropogenic discharge volumes (Fig.4). 2. Corrclation coefficient of discharges between upper and lower was 0.81-0.92. Pollutant loads of the R. Kamame after the confluence with R. Kuzuha grew up by 2.4-3.7 times as compared with its upper reaches, and it increased to 3.7-6.9 times after the confluence with the R. Muro (Fig.5). 3. The changes of water quality along the stream can be divided into two groups (Fig.6a). First: water quality of the R. Kaname and R. Shijuhachisse is becoming worse towards the lower reaches because the water from branches are polluted. Second: water quality are improved in the lower where spring and small branch streams supply clear water, for example R. Mizunashi, R. Muro and R. Kuzuha. 4. Measured discharge at the upper station in the R. Shijuhachisse is 0.153㎥/sec, and about 55% of this is recharged until it reaches to the lower point. The R. Mizunashi has a discharge of 1.155㎥/sec at the upper point, is recharged 0.24㎥/sec until the midstream and groundwater spring 0.2㎥/sec at the lower reaches. R. Kuzuha recharged all the mountain runoff (0.2㎥/sec) at the upper reaches. The R. Muro is supplied by many springs and the estimated discharge of spring was 0.47㎥/sec (Fig.6b). 5. Diurmal variations in discharge and water quality are influenced clearly by domestic and industrial waste waters (Fig.7, 8).ed clearly by domestic and industrial waste waters (Fig.7, 8).

• Journal of Korea Water Resources Association
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• v.50 no.7
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• pp.503-511
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• 2017

In order to estimate the demand for water resources planning and operation, methodology for determining the size of water supply facilities has been mainly applied to agricultural water, unlike living and industrial water, which reflects actual usage trends. This inevitably leads to an overestimation of agricultural water and can lead to an imbalance in the supply and demand of each use in terms of the total water resources plan. In this study, the difference of approaches of concept of net consumption was examined in comparison with the existing methodology and the characteristics of agricultural water demand were analyzed by applying it to whole Jeju Island. SWAT model was applied to estimate the amount of evapotranspiration, which is a key factor in estimating demand, and watershed modeling was performed to reflect geographical features, weather, runoff and water use characteristics of Jeju Island. For the past period (1992~2013), demand of Jeju Island as a whole was analyzed as 427 mm per year, and it showed a relatively high demand around the eastern and western coastal regions. Annual demand and seasonal variation characteristics of 10 river basins with watershed area of $30km^2$ or more were also analyzed. In addition, by applying the cultivated area of each crop in 2020 in the future, it is estimated that the demand corresponding to the 10-year frequency drought is 54% of the amount demanded in the previous research. This is due to the difference in approach depending on the purpose of the demand calculation. From the viewpoint of water resource management and operation, additional demand is expected as much as the net consumption. However, from the actual supply perspective, it can be judged that a facility plan that meets the existing demand amount is necessary. In order to utilize the methodologies and results presented in this study in practice, it is necessary to make a reasonable discussion in terms of policy and institutional as well as engineering verification.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.5 no.3
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• pp.224-232
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• 2000

Inorganic nutrient concentrations in relation to springtime physical parameters of the Yellow Sea were investigated during April 1996. Three major water masses, i.e., the Yellow Sea Warm Current Water (YSWC), Coastal Current Water (CCW) and Changjiang River Diluted Water (CRDW), prevailed in the study area. Water masses were vertically wel1 mixed throughout the study area, and nutrients were supplied adequately from bottom to surface layer. As result of ample nutrients supplied by vertical mixing together with progressed daylight condition, springtime phytoplankton blooms were observed, which was responsible for the depletion of inorganic nutrients in surface water column. Low nutrients concentration in bottom water of the central Yellow Sea (Stn. D9; nitrate: <2 ${\mu}$M, phosphate: <0.3 ${\mu}$) was associated with the entrance of YSWC which is characterized by high temperature and salinity. Influenced by runoff and vertical tidal mixing, CCW with high nutrient concentrations probably associated with China and Korea coastal waters with high nutrients concentration. For the local scale of inorganic nutrient distribution, nutrient transfers from coast to central areas were limited due to restriction imposed by tidal fronts (Stn. D6) and thus affected the horizontal nutrient profiles. Relatively high phytoplankton biomass was observed in the tidal front (Chl-${\alpha}$=12.38 ${\mu}$gL$^{-1}$) during the study period. Overall, the springtime nutrient distribution patterns in the Yellow Sea appeared to be affected by: (1) Large-scale influx of YSWC with low nutrient concentrations and CCW with high nutrient concentrations influenced by Korea and China coastal waters; (2) vertical mixing of water mass and phytoplankton distribution; and (3) local-scale tidal front as well as phytoplankton blooms alongthe tidal front.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.273-283
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• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.