• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5B
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• pp.459-469
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• 2010

This study is to develop a grid-based daily runoff model considering seasonal vegetation canopy condition. The model simulates the temporal and spatial variation of runoff components (surface, interflow, and baseflow), evapotranspiration (ET) and soil moisture contents of each grid element. The model is composed of three main modules of runoff, ET, and soil moisture. The total runoff was simulated by using soil water storage capacity of the day, and was allocated by introducing recession curves of each runoff component. The ET was calculated by Penman-Monteith method considering MODIS leaf area index (LAI). The daily soil moisture was routed by soil water balance equation. The model was evaluated for 930 $km^2$ Yongdam watershed. The model uses 1 km spatial data on landuse, soil, boundary, MODIS LAI. The daily weather data was built using IDW method (2000-2008). Model calibration was carried out to compare with the observed streamflow at the watershed outlet. The Nash-Sutcliffe model efficiency was 0.78~0.93. The watershed soil moisture was sensitive to precipitation and soil texture, consequently affected the streamflow, and the evapotranspiration responded to landuse type.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1B
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• pp.105-109
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• 2009

It is a well known fact that baseflow discharge of rainfall runoff impacts on water quality of surface water significantly. In this paper, impacts of nitrate discharged as base flow on stream water quality were studied by using a software, PULSE from USGS to calculate monthly ground water discharge from hydrograph. We used water quality and flow rate data for Ghapcehon2 site in Daejeon city for year 2005 as well as ground water quality data in the watershed acquired from government agencies. Agricultural and forestry land use are dominant for upstream of Ghapcheon2 in the watershed. Base flow contributes about 85~95% of stream flows during spring and fall while 25~38% of stream flow was induced by base flow during summer and winter. Monthly nitrate loading discharged as base flow for Ghapcheon2 was estimated by using averaged nitrate concentration of groundwater in the watershed. Nitrate loading induced by base flow at Ghapcheon2 was estimated as 5.4 ton of $NO_{3}{^-}-N/km^{2}$, which is about 60% of nitrate loading of surface water, 9.2 ton of $NO_{3}{^-}-N/km^{2}$. Seasonal variation of nitrate concentration of base flow was estimated by dividing monthly nitrate loading by monthly base flow discharge. Nitrate concentration of groundwater was increasing from rainy season. From this study, it can be understood that ground water quality monitoring is important for the proper manage of surface water quality.

• Korean Journal of Agricultural Science
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• v.10 no.2
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• pp.324-333
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• 1983

Modeling of longterm runoff is theoritically based on waterbalance analysis. Simplified equation of water balance with rainfall, evapotranspiration and soil moisture storage could be formulated into regression model with variables of rainfall, pan evaporation and previous-month streamflow. The hydrologic response of water shed could be represented lumpedly, qualitatively and deductively by regression coefficients of water-balance regression model. Characteristics of regression modeling of water-balance were summarized as follows; 1. Regression coefficient $b_1$ represents the rate of direct runoff component of precipitation. The bigger the drainage area, the less $b_1$ value. This means that there are more losses of interception, surface detension and transmission in the downstream watershed. 2. Regression coefficient $b_2$ represents the rate of baseflow due to changes of soil moisture storage. The bigger the drainage area and the milder the watershed slope, the bigger b, value. This means that there are more storage capacity of watershed in mild downstream watershed. 3. Regression coefficient $b_3$ represents the rate of watershed evaporation. This depends on the s oil type, soil coverage and soil moisture status. The bigger the drainage area, the bigger $b_3$ value. This means that there are more watershed evaporation loss since more storage of surface and subsurface water would be in down stream watershed. 4. It was possible to explain the seasonal variation of streamflow reasonably through regress ion coefficients. 5. Percentages of beta coefficients what is a relative measure of the importance of rainfall, evaporation and soil moisture storage to month streamflow are approximately 89%, 9% and 11% respectively.

• Journal of Soil and Groundwater Environment
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• v.12 no.5
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• pp.19-32
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• 2007

In Korea, there have been various methods of estimating groundwater recharge which generally can be subdivided into three types: baseflow separation method by means of groundwater recession curve, water budget analysis based on lumped conceptual model in watershed, and water table fluctuation method (WTF) by using the data from groundwater monitoring wells. However, groundwater recharge rate shows the spatial-temporal variability due to climatic condition, land use and hydrogeological heterogeneity, so these methods have various limits to deal with these characteristics. To overcome these limitations, we present a new method of estimating recharge based on water balance components from the SWAT-MODFLOW which is an integrated surface-ground water model. Groundwater levels in the interest area close to the stream have dynamics similar to stream flow, whereas levels further upslope respond to precipitation with a delay. As these behaviours are related to the physical process of recharge, it is needed to account for the time delay in aquifer recharge once the water exits the soil profile to represent these features. In SWAT, a single linear reservoir storage module with an exponential decay weighting function is used to compute the recharge from soil to aquifer on a given day. However, this module has some limitations expressing recharge variation when the delay time is too long and transient recharge trend does not match to the groundwater table time series, the multi-reservoir storage routing module which represents more realistic time delay through vadose zone is newly suggested in this study. In this module, the parameter related to the delay time should be optimized by checking the correlation between simulated recharge and observed groundwater levels. The final step of this procedure is to compare simulated groundwater table with observed one as well as to compare simulated watershed runoff with observed one. This method is applied to Mihocheon watershed in Korea for the purpose of testing the procedure of proper estimation of spatio-temporal groundwater recharge distribution. As the newly suggested method of estimating recharge has the advantages of effectiveness of watershed model as well as the accuracy of WTF method, the estimated daily recharge rate would be an advanced quantity reflecting the heterogeneity of hydrogeology, climatic condition, land use as well as physical behaviour of water in soil layers and aquifers.

• Korean Journal of Agricultural Science
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• v.7 no.2
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• pp.131-144
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• 1980

The Linear Regression Model to extend the monthly runoff data in the short-recorded river was proposed by the author in 1979. Here in this study generalization precedure is made to apply that model to any given river basin and to any given station. Lengthier monthly runoff data generated by this generalized model would be useful for water resources assessment and waterworks planning. The results are as follows. 1. This Linear Regression Model which is a transformed water-balance equation attempts to represent the physical properties of the parameters and the time and space varient system in catchment response lumpedly, qualitatively and deductively through the regression coefficients as component grey box, whereas deterministic model deals the foregoings distributedly, quantitatively and inductively through all the integrated processes in the catchment response. This Linear Regression Model would be termed "Statistically deterministic model". 2. Linear regression equations are obtained at four hydrostation in Geum-river basin. Significance test of equations is carried out according to the statistical criterion and shows "Highly" It is recognized th at the regression coefficients of each parameter vary regularly with catchment area increase. Those are: The larger the catchment area, the bigger the loss of precipitation due to interception and detention storage in crease. The larger the catchment area, the bigger the release of baseflow due to catchment slope decrease and storage capacity increase. The larger the catchment area, the bigger the loss of evapotranspiration due to more naked coverage and soil properties. These facts coincide well with hydrological commonsenses. 3. Generalized diagram of regression coefficients is made to follow those commonsenses. By this diagram, Linear Regression Model would be set up for a given river basin and for a given station (Fig.10).

• Journal of Korea Water Resources Association
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• v.51 no.10
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• pp.863-874
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• 2018

The purpose of this study is to evaluate the future climate change impact on stream aquatic ecology health of Han River watershed ($34,148km^2$) using SWAT (Soil and Water Assessment Tool) and random forest. The 8 years (2008~2015) spring (April to June) Aquatic ecology Health Indices (AHI) such as Trophic Diatom Index (TDI), Benthic Macroinvertebrate Index (BMI) and Fish Assessment Index (FAI) scored (0~100) and graded (A~E) by NIER (National Institute of Environmental Research) were used. The 8 years NIER indices with the water quality (T-N, $NH_4$, $NO_3$, T-P, $PO_4$) showed that the deviation of AHI score is large when the concentration of water quality is low, and AHI score had negative correlation when the concentration is high. By using random forest, one of the Machine Learning techniques for classification analysis, the classification results for the 3 indices grade showed that all of precision, recall, and f1-score were above 0.81. The future SWAT hydrology and water quality results under HadGEM3-RA RCP 4.5 and 8.5 scenarios of Korea Meteorological Administration (KMA) showed that the future nitrogen-related water quality in watershed average increased up to 43.2% by the baseflow increase effect and the phosphorus-related water quality decreased up to 18.9% by the surface runoff decrease effect. The future FAI and BMI showed a little better Index grade while the future TDI showed a little worse index grade. We can infer that the future TDI is more sensitive to nitrogen-related water quality and the future FAI and BMI are responded to phosphorus-related water quality.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.8-9
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• 2011

On 4 September 2010 an earthquake of magnitude 7.1 on the Richter scale occurred on the Canterbury Plains in the South Island of New Zealand. The Canterbury Plains are an area of extensive groundwater and spring fed surface water systems. Since the September earthquake there have been several thousand aftershocks (Fig. 1), the largest being a 6.3 magnitude quake which occurred close to the centre of Christchurch on 22February 2011. This second quake caused extensive damage to the city of Christchurch including the deaths of 189 people. Both of these quakes had marked hydrological impacts. Water is a vital natural resource for Canterburywith groundwater being extracted for potable supply and both ground and surface water being used extensively for agricultural and horticultural irrigation.The groundwater is of very high quality so that the city of Christchurch (population approx. 400,000) supplies untreated artesian water to the majority of households and businesses. Both earthquakes caused immediate hydrological effects, the most dramatic of which was the liquefaction of sediments and the release of shallow groundwater containing a fine grey silt-sand material. The liquefaction that occurred fitted within the empirical relationship between distance from epicentre and magnitude of quake described by Montgomery et al. (2003). . It appears that liquefaction resulted in development of discontinuities in confining layers. In some cases these appear to have been maintained by artesian pressure and continuing flow, and the springs are continuing to flow even now. In spring-fed streams there was an increase in flow that lasted for several days and in some cases flows remained high for several months afterwards although this could be linked to a very wet winter prior to the September earthquake. Analysis of the slope of baseflow recession for a spring-fed stream before and after the September earthquake shows no change, indicating no substantial change in the aquifer structure that feeds this stream.A complicating factor for consideration of river flows was that in some places the liquefaction of shallow sediments led to lateral spreading of river banks. The lateral spread lessened the channel cross section so water levels rose although the flow might not have risen accordingly. Groundwater level peaks moved both up and down, depending on the location of wells. Groundwater level changes for the two earthquakes were strongly related to the proximity to the epicentre. The February 2011 earthquake resulted in significantly larger groundwater level changes in eastern Christchurch than occurred in September 2010. In a well of similar distance from both epicentres the two events resulted in a similar sized increase in water level but the slightly slower rate of increase and the markedly slower recession recorded in the February event suggests that the well may have been partially blocked by sediment flowing into the well at depth. The effects of the February earthquake were more localised and in the area to the west of Christchurch it was the earlier earthquake that had greater impact. Many of the recorded responses have been compromised, or complicated, by damage or clogging and further inspections will need to be carried out to allow a more definitive interpretation. Nevertheless, it is reasonable to provisionally conclude that there is no clear evidence of significant change in aquifer pressures or properties. The different response of groundwater to earthquakes across the Canterbury Plains is the subject of a new research project about to start that uses the information to improve groundwater characterisation for the region. Montgomery D.R., Greenberg H.M., Smith D.T. (2003) Stream flow response to the Nisqually earthquake. Earth & Planetary Science Letters 209 19-28.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.2
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• pp.82-96
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• 2019

This study evaluated the status of watershed health in Geum River Basin by SWAT (Soil and Water Assessment Tool) hydrology and water quality. The watershed healthiness from watershed hydrology and stream water quality was calculated using multivariate normal distribution from 0(poor) to 1(good). Before evaluation of watershed healthiness, the SWAT calibration for 11 years(2005~2015) of streamflow(Q) at 5 locations with 0.50~0.77 average Nash-Sutcliffe model efficiency and suspended solid (SS), total nitrogen(T-N), and total phosphorus(T-P) at 3 locations with 0.67~0.94, 0.59~0.79, and 0.61~0.79 determination coefficient($R^2$) respectively. For 24 years (1985~2008) the spatiotemporal change of watershed healthiness was analyzed with calibarted SWAT and 5 land use data of 1985, 1990, 1995, 2000, and 2008. The 2008 SWAT results showed that the surface runoff increased by 40.6%, soil moisture and baseflow decreased by 6.8% and 3.0% respectively compared to 1985 reference year. The stream water quality of SS, T-N, and T-P increased by 29.2%, 9.3%, and 16.7% respectively by land development and agricultural activity. Based on the 1985 year land use condition. the 2008 watershed healthiness of hydrology and stream water quality decreased from 1 to 0.94 and 0.69 respectively. The results of this study be able to detect changes in watershed environment due to human activity compared to past natural conditions.

• Economic and Environmental Geology
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• v.56 no.2
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• pp.139-153
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• 2023

This study aimed to understand the spatiotemporal variations in nitrogen content in the Gyeongan stream along the main stream and at the discharge points of the sub-basins, and to identify the origin of the nitrogen. Field surveys and laboratory analyses, including chemical compositions and isotope ratios of nitrate and boron, were performed from November 2021 to November 2022. Based on the flow duration curve (FDC) derived for the Gyeongan stream, the dry season (mid-December 2021 to mid-June 2022) and wet season (mid-June to early November 2022) were established. In the dry season, most samples had the highest total nitrogen(T-N) concentrations, specifically in January and February, and the concentrations continued to decrease until May and June. However, after the flood season from July to September, the uppermost subbasin points (Group 1: MS-0, OS-0, GS-0) where T-N concentrations continually decreased were separated from the main stream and lower sub-basin points (Group 2: MS-1~8, OS-1, GS-1) where concentrations increased. Along the main stream, the T-N concentration showed an increasing trend from the upper to the lower reaches. However, it was affected by those of the Osan-cheon and Gonjiamcheon, the tributaries that flow into the main stream, resulting in respective increases or decreases in T-N concentration in the main stream. The nitrate and boron isotope ratios indicated that the nitrogen in all samples originated from manure. Mechanisms for nitrogen inflow from manure-related sources to the stream were suggested, including (1) manure from livestock wastes and rainfall runoff, (2) inflow through the discharge of wastewater treatment plants, and (3) inflow through the groundwater discharge (baseflow) of accumulated nitrogen during agricultural activities. Ultimately, water quality management of the Gyeongan stream basin requires pollution source management at the sub-basin level, including its tributaries, from a regional context. To manage the pollution load effectively, it is necessary to separate the hydrological components of the stream discharge and establish a monitoring system to track the flow and water quality of each component.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.50-63
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• 2018

The rapid urbanization had led to a distortion of natural hydrological cycle system. The change in hydrological cycle structure is causing streamflow depletion, changing the existing use tendency of water resources. To manage such phenomena, a streamflow depletion impact assessment technology to forecast depletion is required. For performing such technology, it is indispensable to build GIS-based spatial data as fundamental data, but there is a shortage of related research. Therefore, this study was conducted to use the use of GIS-based time series spatial data for streamflow depletion assessment. For this study, GIS data over decades of changes on a national scale were constructed, targeting 6 streamflow depletion impact factors (weather, soil depth, forest density, road network, groundwater usage and landuse) and the data were used as the basic data for the operation of continuous hydrologic model. Focusing on these impact factors, the causes for streamflow depletion were analyzed depending on time series. Then, using distributed continuous hydrologic model based DrySAT, annual runoff of each streamflow depletion impact factor was measured and depletion assessment was conducted. As a result, the default value of annual runoff was measured at 977.9mm under the given weather condition without considering other factors. When considering the decrease in soil depth, the increase in forest density, road development, and groundwater usage, along with the change in land use and development, and annual runoff were measured at 1,003.5mm, 942.1mm, 961.9mm, 915.5mm, and 1003.7mm, respectively. The results showed that the major causes of the streaflow depletion were lowered soil depth to decrease the infiltration volume and surface runoff thereby decreasing streamflow; the increased forest density to decrease surface runoff; the increased road network to decrease the sub-surface flow; the increased groundwater use from undiscriminated development to decrease the baseflow; increased impervious areas to increase surface runoff. Also, each standard watershed depending on the grade of depletion was indicated, based on the definition of streamflow depletion and the range of grade. Considering the weather, the decrease in soil depth, the increase in forest density, road development, and groundwater usage, and the change in land use and development, the grade of depletion were 2.1, 2.2, 2.5, 2.3, 2.8, 2.2, respectively. Among the five streamflow depletion impact factors except rainfall condition, the change in groundwater usage showed the biggest influence on depletion, followed by the change in forest density, road construction, land use, and soil depth. In conclusion, it is anticipated that a national streamflow depletion assessment system to be develop in the future would provide customized depletion management and prevention plans based on the system assessment results regarding future data changes of the six streamflow depletion impact factors and the prospect of depletion progress.