• Journal of Korean Society on Water Environment
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• v.36 no.3
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• pp.229-244
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• 2020

Hydrologic models, as a useful tool for understanding the hydrologic phenomena in the watershed, have become more complex with the increase of computer performance. The hydrologic model, with complex configurations and powerful performance, facilitates a broader understanding of the effects of climate and soil in hydrologic partitioning. However, the more complex the model is, the more effort and time is required to drive the model, and the more parameters it uses, the less accessible to the user and less applicable to the ungauged watershed. Rather, a parsimonious hydrologic model may be effective in hydrologic modeling of the ungauged watershed. Thus, a semi-distributed hydrologic partitioning model was developed with minimal composition and number of parameters to improve applicability. In this study, the validity and performance of the proposed model were confirmed by applying it to the Namgang Dam, Andong Dam, Hapcheon Dam, and Milyang Dam watersheds among the Nakdong River watersheds. From the results of the application, it was confirmed that despite the simple model structure, the hydrologic partitioning process of the watershed can be modeled relatively well through three vertical layers comprising the surface layer, the soil layer, and the aquifer. Additionally, discussions were conducted on antecedent soil moisture conditions widely applied to stormwater estimation using the soil moisture data simulated by the proposed model.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.1
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• pp.9-15
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• 2013

This study is to evaluate the parameter behavior of VfloTM distributed rainfall-runoff model by applying 3 kinds of rainfall interpolation methods viz. Inverse Distance Weighting (IDW), Kriging (KRI), and Thiessen network (THI). For the 1,544 $km^2$ Dongcheon watershed of Nakdong river, the model was calibrated using 4 storm events in 2007 and 2009, and validated using 2 storm events in 2010. The model was calibrated with Nash-Sutcliffe model efficiency of 0.97 for IDW, 0.94 for KRI, and 0.95 for THI respectively. For the sensitive parameters, the saturated hydraulic conductivity ($K_{sat}$) for IDW, KRI, and THI were 0.33, 0.31, and 0.43 cm/hr, and the soil suction head at the wetting front (${\Psi}_f$) were 4.10, 3.96, and 5.19 cm $H_2O$ respectively. These parameters affected the infiltration process by the spatial distribution of antecedent moisture condition before a storm.

• Journal of The Korean Society of Agricultural Engineers
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• v.50 no.6
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• pp.3-12
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• 2008

It is important to estimate accurate effective rainfall to analyse flood flow and long-term runoff for the rational planning, design, and management of water resource. The initial abstraction is also important to estimate effective rainfall. The Soil Conservation Service (SCS) has developed a procedure and it has been most commonly applied to estimate effective rainfall. But the SCS method still has weak points, because of unnatural assumptions such as antecedent moisture conditions and initial abstraction. The coefficient of initial abstraction(K) is depending on the soil moisture condition and antecedent rainfall. The maximum storage capacity of Umax which is calibrated by stream flow data in the proposed watershed was derived from the DAWAST(DAily WAtershed STreamflow) model. The values of K obtained from 69 storm events at the five watersheds are ranging from 0.133 to 0.365 and its mean value is 0.207. Effective rainfall could be estimated more reasonably by introducing new concept of initial abstraction. The equation of $K=0.076Sa^{0.255}$ was recommended instead of 0.2 and it could be applicable to the small-medium rural watersheds.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.1
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• pp.11-20
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• 2017

This study is to estimate the spatial soil moisture using multiple linear regression model (MLRM) and 15 minutes interval Land Surface Temperature (LST) data of Communication, Ocean and Meteorological Satellite (COMS). For the modeling, the input data of COMS LST, Terra MODIS Normalized Difference Vegetation Index (NDVI), daily rainfall and sunshine hour were considered and prepared. Using the observed soil moisture data at 9 stations of Automated Agriculture Observing System (AAOS) from January 2013 to May 2015, the MLRMs were developed by twelve scenarios of input components combination. The model results showed that the correlation between observed and modelled soil moisture increased when using antecedent rainfalls before the soil moisture simulation day. In addition, the correlation increased more when the model coefficients were evaluated by seasonal base. This was from the reverse correlation between MODIS NDVI and soil moisture in spring and autumn season.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1205-1214
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• 2021

Precipitation is one of a major causes of landslides by rising of pore water pressure, which leads to fluctuations of soil strength and stress. For this reason, precipitation is the most frequently used to determine the landslide thresholds. However, using only precipitation has limitations in predicting and estimating slope stability quantitatively for reducing false alarm events. On the other hand, Soil Moisture (SM) has been used for calculating slope stability in many studies since it is directly related to pore water pressure than precipitation. Therefore, this study attempted to evaluate the appropriateness of applying soil moisture in determining the landslide threshold. First, the reactivity of soil saturation level to precipitation was identified through time-series analysis. The precipitation threshold was calculated using daily precipitation (P_daily) and the Antecedent Precipitation Index (API), and the hydrological threshold was calculated using daily precipitation and soil saturation level. Using a contingency table, these two thresholds were assessed qualitatively. In results, compared to P_daily only threshold, Goesan showed an improvement of 75% (P_daily + API) and 42% (P_daily + SM) and Changsu showed an improvement of 33% (P_daily + API) and 44% (P_daily + SM), respectively. Both API and SM effectively enhanced the Critical Success Index (CSI) and reduced the False Alarm Rate (FAR). In the future, studies such as calculating rainfall intensity required to cause/trigger landslides through soil saturation level or estimating rainfall resistance according to the soil saturation level are expected to contribute to improving landslide prediction accuracy.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.147-147
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• 2022

In January 2021 heavy flood affected South Kalimantan with causing many casualties. The heavy rainfall is predicted to be generated due to the ENSO (El Nino-Southern Oscillation). The weak La-Nina mode appeared to generate more convective cloud above the warmed ocean and result in extreme rainfall with high anomaly compared to past historical rainfall event. Subsequently, the antecedent soil moisture distribution showed to have an important role in generating the flood response. Saturated flow and infiltration excess mainly contributed to the runoff generation due to the high moisture capacity. The hydro-meteorological processes in this event were deeply analyzed using the coupled atmospheric model of Weather Research and Forecasting (WRF) and the hydrological model extension (WRF-Hydro). The sensitivity analysis of the flood response to the SST anomaly and the soil moisture capacity also compared. Result showed that although SST and soil moisture are the main contributors, soil moisture have more significant contribution to the runoff generation despite of anomaly rainfall occurred. Model performance was validated using the Global Precipitation Measurement (GPM) and Soil Moisture Operational Products System (SMOPS) and performed reasonably well. The model was able to capture the hydro-meteorological process of atmosphere and hydrological feedbacks in the extreme weather event.

• Journal of Korea Water Resources Association
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• v.39 no.1 s.162
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• pp.47-57
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• 2006

This study derives an event-based tank model with a conceptual rainfall-infiltration process, modifying conventional tank models. The model comprises two serial tanks, one parallel tank and an infiltration regulating element. The infiltration process within the element is not represented as a function of only time, but as a function of soil moisture content for three possible cases owing to the relationship between rainfall intensity and infiltration capacity. This study considers the previous soil moisture condition of a watershed by using antecedent precipitation index. Six parameters of the model are identified by using the real coded genetic algorithm. The applicability and validity of the proposed model are assessed for the observed stormwater data from the research basin of the International Hydrological Program, the Pyeongchanggang River basin, Republic of Korea. The results computed streamflows show relatively good agreement with observed ones.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.1
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• pp.30-41
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• 2016

The temporal variation of soil water storage is important in hydrological modeling. In order to evaluate an antecedent wetness state, the antecedent precipitation index (API) has been used. The aim of this article is to compare observed soil water storage with APIs calculated by widely used four equations, to configure the relationship between soil water storage and API by a regression model for one-year(2009), and to predict the soil water storage for the next two years(2010~2011). The soil water storage was evaluated from the observed soil moisture dataset in soil depths of 10, 30, 60cm at 21 locations by TDR measurement system for 3 years. As a result, API with the exponential function among the four equations can describe the variation of the observed soil water storage. Monthly optimized parameters of the API's equations seemed to be roughly related with the (potential) evapotranspiration (PET). Using revised monthly optimized parameters of APIs considering the seasonal pattern of PET, we characterize the relationship between API and the observed soil water storage for one year, which looks better than those of other researches.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.18 no.3
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• pp.13-22
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• 2015

Unpaved forest roads are common accessways in mountain areas being used for forestry purposes. The presence of forest roads produces large volumes of surface runoff and sediment yield due to changes in soil properties and hillslope profile. Rainfall simulation experiments were conducted to estimate the impacts of above-ground vegetation and antecedent soil water condition on hydrology and sediment processes. A total of 9 small plots($1m{\times}0.5m$) were installed to represent different road surface conditions: no-vegetation(3 plots), vegetated surface(3 plots), and cleared vegetation surface(3 plots). Experiments were carried out on dry, wet, and very wet soil moisture conditions for each plot. Above ground parts of vegetation on road surface influenced significantly on surface runoff. Runoff from no-vegetation roads(39.24L) was greater than that from vegetated(25.05L), while cleared-vegetation condition is similar to no-vegetation roads(39.72L). Runoff rate responded in a similar way to runoff volume. Soil erosion was also controlled by land cover, but the magnitude is little than that of surface runoff. Even though slight differences among antecedent soil moisture conditions were found on both runoff and soil erosion, runoff rate and soil losses were increased in very wet condition, followed by wet condition. The experiments suggest that vegetation cover on forest road surface seems most effective way to reduce surface runoff and soil erosion during storm periods.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.12 no.1
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• pp.115-122
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• 1992

Stochastic rainfall-runoff process model which is mainly used in real-time streamflow forecasting is Transfer Function(TF) model that has a simple structure and can be easy to formulate state-space model. However, in order to forecast the streamflow accurately in real-time using the TF model, it is not only necessary to determine accurate structure of the model but also required to reduce forecasting error in early stage. In this study, after introducing 5-day Antecedent Precipitation Index (API5), which represents the initial soil moisture condition of the watershed, by using the threshold concept, the TF models in each API5 are identified by Box-Jenkins method and the results are compared with each other.