• 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.

• 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 Soil and Groundwater Environment
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• v.21 no.6
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• pp.36-45
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• 2016

Variability in precipitation due to climate change causes difficulties in securing stable surface water resource, which requires understanding of relation between precipitation and stream discharge. This study simulated stream discharge in a small mountainous forested catchment using antecedent precipitation index (API) models which represent variability of saturation conditions of soil layers depending on rainfall events. During 13 months from May 2015 to May 2016, stream discharge and rainfall were measured at the outlet and in the central part of the watershed, respectively. Several API models with average recession coefficients were applied to predict stream discharge using measured rainfall, which resulted in the best reflection time for API model was 1 day in terms of predictability of stream discharge. This indicates that soil water in riparian zones has fast response to rainfall events and its storage is relatively small. The model can be improved by employing seasonal recession coefficients which can consider seasonal fluctuation of hydrological parameters. These results showed API models can be useful to evaluate variability of streamflow in ungauged small forested watersheds in that stream discharge can be simulated using only rainfall data.

• 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.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.587-596
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• 2014

Soil moisture is one of the most important interests in hydrological response and the interaction between the land surface and atmosphere. Estimation of Antecedent Wetness Conditions (AWC) which is soil moisture condition prior to a rainfall in the basin should be considered for rainfall-runoff prediction. In this study, Soil Wetness Index (SWI), Antecedent Precipitation Index ($API_5$), remotely sensed Soil Moisture ($SM_{rs}$), and 5 days ground Soil Moisture ($SM_{g5}$) were selected to estimate the AWC at four study area in the Korean Peninsula. The remotely sensed soil moisture data were taken from the AMSR-E soil moisture archive. The maximum potential retention ($S_{obs}$) was obtained from direct runoff and rainfall using Soil Conservation Service-Curve Number (SCS-CN) method by rainfall data of 2011 for each study area. Results showed the great correlations between the maximum potential retention and SWI with a mean correlation coefficient which is equal to -0.73. The results of time length representing the time scale of soil moisture showed a gap from region to region. It was due to the differences of soil types and the characteristics of study area. Since the remotely sensed soil moisture has been proved as reasonable hydrological variables to predict a wetness in the basin, it should be continuously monitored.

• Journal of Korea Water Resources Association
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• v.35 no.3
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• pp.331-344
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• 2002

This study is aimed at the development of a deterministic runoff model which can be used for flood runoff. The model is formulated by the watershed runoff model. Based on the assumptions that runoff system is nonlinear, the proposed watershed runoff model is the conceptual model. In the model structure, the conceptual model divides the runoff system into a surface structure and a subsurface structure corresponding to the surface flow, and inter flow and ground water flow respectively. The lag time effect of surface can be represented by the sub-tank of surface structure in the conceptual model. The parameter calibration of inter flow and ground water flow in the subsurface structure of the conceptual model is performed by separating the components with numeric filter The runoff coefficient($\alpha$$_2$) is expressed as the function of antecedent precipitation index(API). The parameters with the surface flow can be calibrated with the runoff coefficient($\alpha$$_1$ and $\alpha$/$_{11}$) in the conceptual model. In the conceptual model, an algorithm is developed to calibrate the parameters automatically based on efficiency criteria. The comparative study shows that simulated value from the conceptual model well agreed to observed value.

• Korean Journal of Environment and Ecology
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• v.29 no.6
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• pp.917-922
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• 2015

This study was carried out to investigate solutes concentration change with respect to discharge change in coniferous forest experiment watershed in Gyeonggi-do, Korea. From 2005 to 2008. Precipitation, discharge, solutes has been analyzed from 23 precipitation events. The results showed that low API induced low discharge. $NH_4{^+}$, $K^+$, and $Ca^{2+}$ were indicated by clockwise and $Cl^-$ and $NO_3{^-}$ were represented by counterclockwise hysteresis loop. ${SO_4}^{2-}$, $Na^+$, $Mg^{2+}$ showed no hysteresis loop pattern. $Cl^-$, $Na^+$, $NH_4{^+}$ was relatively constant due to groundwater during precipitation, $NO_3{^-}$ was increased due to soil water compared to early precipitation. $Cl^-$, ${SO_4}^{2-}$, $Na^+$, $Mg^{2+}$, $Ca^{2+}$ was diluted with respect to increased discharge and $NO_3{^-}$ was diluted in early precipitation and then increased in the end. $NO_3{^-}$ and $Ca^{2+}$ eluviated in early precipitation. This characteristics was presumed by the effect of API, discharge and ground water.

• Journal of Korean Society on Water Environment
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• v.26 no.3
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• pp.507-517
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• 2010

Long-term monitoring was conducted to identify the runoff characteristics of non-point source according to the three forest types (deciduous forest, coniferous forest and mixed forest) in this study. Rainfall events of each deciduous forest, coniferous forest, and mixed forest were 10, 8, 12, respectively. Average runoff depth and coefficients of each forest type were founded to be coniferous forest and were followed by others in turns : deciduous forest, and mixed forest because various conditions (i.e., rainfall property, Antecedent Precipitation Index (API), soil property, slope, and forest management) could change runoff characteristics. In the analysis of the first flush phenomenon, it showed that SS and T-P were sensitive for the first flush phenomenon. The first flush phenomenon of them were showed differently by rainfall intensity, rainfall duration, and amount of rainfall. The research results indicated that range of the Event Mean Concentration (EMC) values in deciduous forest were 0.8~2.4 mg/L for $BOD_5$, 2.0~13.4 mg/L for $COD_{Mn}$, 1.3~2.9 mg/L for DOC, 1.150~3.913 mg/L for T-N, 0.010~0.350 mg/L for T-P and 3.1~291.8 mg/L for SS and in coniferous forest were 0.8~2.2 mg/L for $BOD_5$, 1.9~3.6 mg/L for $COD_{Mn}$, 1.0~2.0 mg/L for DOC, 1.025~2.957 mg/L for T-N, 0.002~0.084 mg/L for T-P and 0.8~5.4 mg/L for SS. Also, range of the EMC values in mixed forest were 1.3~2.3 mg/L for $BOD_5$, 2.4~4.8 mg/L for $COD_{Mn}$, 1.1~2.1 mg/L for DOC, 0.385~2.703 mg/L for T-N, 0.016~0.080 mg/L for T-P and 2.3~30.0 mg/L for SS.