• Journal of The Korean Society of Agricultural Engineers
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• v.53 no.5
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• pp.9-16
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• 2011

Curve Numbers (CN) for the combination of land use and hydrologic soil group were regionalized at Imha Watershed using Long-term Hydrologic Impact Assessment (L-THIA) coupled with SCE-UA. The L-THIA was calibrated during 1991-2000 and validated during 2001-2007 using monthly observed direct runoff data. The Nash-Sutcliffe (NS) coefficients for calibration and validation were 0.91 and 0.93, respectively, and showed high model efficiency. Based on the criteria of model calibration, both calibration and validation represented 'very good' fit with observe data. The spatial distribution of direct surface runoff by L-THIA represented runoff from Thiessen pologen at Subi and Sukbo rain gage station much higher than other area due to the combination of poor hydrologic condition (hydrologic soil C and D group) and locality heavy rainfall. As a results of hydrologic condition and treatment for land use type based on calibrated CNs, forest is recommended to be hydrologically modelled dived into deciduous, coniferous, and mixed forest due to the hydrological difference. The CNs for forest and upland showed the poor hydrologic condition. The steep slope of forest and alpine agricultural field make high runoff rate which is the poor hydrologic condition because CN method can not consider field slope. L-THIA linded with SCE-UA could generated a regionalized CNs for land use type with minimized time and effort, and maximized model's accuracy.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.149-153
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• 2006

Precipitation is the most important component and critical to the study of water and energy cycle. This study investigates the propagation of precipitation retrieval uncertainty in the simulation of hydrologic variables for varying spatial resolution on two different vegetation cover. We explore two remotely sensed rain retrievals (space-borne IR-only and radar rainfall) and three spatial grid resolutions. An offline Community Land Model (CLM) was forced with in situ meteorological data In turn, radar rainfall is replaced by the satellite rain estimates at coarser resolution $(0.25^{\circ},\;0.5^{\circ}\;and\;1^{\circ})$ to determine their probable impact on model predictions. Results show how uncertainty of precipitation measurement affects the spatial variability of model output in various modelling scales. The study provides some intuition on the uncertainty of hydrologic prediction via interaction between the land surface and near atmosphere fluxes in the modelling approach.

• Journal of Korea Water Resources Association
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• v.50 no.6
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• pp.387-395
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• 2017

Sea level rise, accompanied by climate change, is expected to exacerbate seawater intrusion in the coastal groundwater system. As the salinity of saturated groundwater increases, salinity can increase even in the unsaturated soil above the groundwater surface, which may cause crop damage in the agricultural land. The other adverse impact of sea level rise is reduced unsaturated soil thicknesses. In this study, a composite model to assess impacts of sea level rise in coastal agricultural land is proposed. The composite model is based on the combined applications of a three dimensional model for simulating saltwater intrusion into the groundwater and a vertical one dimensional model for simulating unsaturated zone flow and transport. The water level and salinity distribution of groundwater are calculated using the three dimensional seawater intrusion model. At some uppermost nodes, where salinity are higher than the reference value, of the 3D mesh one dimensional unsaturated zone modeling is conducted along the soil layer between the ground water surface and the ground surface. A particular location is judged salinized when the concentration at the root-zone depth exceeds the tolerable salinity for ordinary crops. The developed model is applied to a hypothetical agricultural reclamation land. IPCC RCP 4.5 and 8.5 scenarios were used as sea level rise data. Results are presented for 2050 and 2100. As a result of the study, it is predicted that by 2100 in the climate change scenario RCP 8.5, there will be 7.8% increase in groundwater saltwater-intruded area, 6.0% increase of salinized soil area, and 1.6% in increase in water-logging area.

• Journal of Digital Convergence
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• v.15 no.11
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• pp.245-250
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• 2017

Flash floods is defined as the flooding of intense rainfall over a relatively small area that flows through river and valley rapidly in short time with no advance warning. So that it can cause damage property and casuality. This study is to establish the flash-flood warning system using 38 accident data, reported from the National Disaster Information Center and Land Surface Model(TOPLATS) between 2009 and 2012. Three variables were used in the Land Surface Model: precipitation, soil moisture, and surface runoff. The three variables of 6 hours preceding flash flood were reduced to 3 factors through factor analysis. Decision tree, random forest, Naive Bayes, Support Vector Machine, and logistic regression model are considered as big data methods. The prediction performance was evaluated by comparison of Accuracy, Kappa, TP Rate, FP Rate and F-Measure. The best method was suggested based on reproducibility evaluation at the each points of flash flood occurrence and predicted count versus actual count using 4 years data.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1362-1364
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• 2003

As non-isothermal mixed pixel is widely existed, the pixel-mean temperature cannot adequately represent the actual thermal state of land surface. The row crop was chosen as target to discuss the problem of component temperature retrieval. At first, the matrix model was found to express the thermal radiant directionality of the target. Then correlation of multi-angle infrared radiance was analyzed. In order to increase the retrieving accuracy, we chose the retrievable parameters and established the iterative method combining with inverse matrix to retrieve component temperature. It was proved by field experiment that the method could improve the retrieving accuracy and stability remarkably.

• Korean Journal of Remote Sensing
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• v.25 no.2
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• pp.95-105
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• 2009

Irregular temporal sampling is a common feature of geophysical and biological time series in remote sensing. This study proposes an on-line system for reconstructing observation image series including bad or missing observation that result from mechanical problems or sensing environmental condition. The surface parameters associated with the land are usually dependent on the climate, and many physical processes that are displayed in the image sensed from the land then exhibit temporal variation with seasonal periodicity. An adaptive feedback system proposed in this study reconstructs a sequence of images remotely sensed from the land surface having the physical processes with seasonal periodicity. The harmonic model is used to track seasonal variation through time, and a Gibbs random field (GRF) is used to represent the spatial dependency of digital image processes. In this study, the Normalized Difference Vegetation Index (NDVI) image was computed for one week composites of the Advanced Very High Resolution Radiometer (AVHRR) imagery over the Korean peninsula, and the adaptive reconstruction of harmonic model was then applied to the NDVI time series from 1996 to 2000 for tracking changes on the ground vegetation. The results show that the adaptive approach is potentially very effective for continuously monitoring changes on near-real time.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.991-998
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• 2022

The purpose of this study is to develop GPU (Graphics Processing Unit) acceleration technique for 2-dimensional model and to assess the effectiveness for high resolution flood simulation in wide area In this study, GPU acceleration technique was implemented in the G2D (Grid based 2-Dimensional land surface flood model) model, using implicit scheme and uniform square grid, by using CUDA. The technique was applied to flood simulation in Jinju-si. The spatial resolution of the simulation domain is 10 m × 10 m, and the number of cells to calculate is 5,090,611. Flood period by typhoon Mitag, December 2019, was simulated. Rainfall radar data was applied to source term and measured discharge of Namgang-Dam (Ilryu-moon) and measured stream flow of Jinju-si (Oksan-gyo) were applied to boundary conditions. From this study, 2-dimensional flood model could be implemented to reproduce the measured water level in Nam-gang (Riv.). The results of GPU acceleration technique showed more faster flood simulation than the serial and parallel simulation using CPU (Central Processing Unit). This study can contribute to the study of developing GPU acceleration technique for 2-dimensional flood model using implicit scheme and simulating land surface flood in wide area.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.3B
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• pp.233-242
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• 2010

SEBAL (Surface Energy Balance Algorithm for Land) developed by Bastiaanssen (1995) is an image-processing model comprisedof twenty-five sub models that calculates spatial evapotranspiration (ET) and other energy exchanges at the surface. SEBAL uses image data from Landsat or other satellites measuring thermal infrared radiation, visible and near infrared. In this study, the model was applied to Gyeongancheon watershed, the main tributary of Han river Basin. ET was computed on apixel-by-pixel basis from an energy balance using 4 years (2001-2004) Landsat and MODIS images. The scale effect between Landsat (30 m) and MODIS (1 km) was evaluated. The results both from Landsat and MODIS were compared with FAO Penman-Monteith ET. The absolute errors between satellite ETs and Penman-Monteith ET were within 12%. The spatial and temporal characteristics of ET distribution within the watershed were also analyzed.

• Magazine of the Korean Society of Agricultural Engineers
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• v.30 no.3
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• pp.58-68
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• 1988

Most of the Korean watersheds are mountaineous and consist of various soil types and land uses And seldom watersheds are found to have long term hydrologic records. The SNUA, a hydrologic watershed model was developed to meet the unique characteristics of Korean watershed and simulate the storm hydrographs from a small mountaineous watershed. Also the applicability of the model was tested by comparing the simulated storm hydrographs and the observed from Dochuk watershed, Gwangjugun, Kyunggido The conclusions obtained in this study could be summarized as follows ; 1. The model includes the simulation of interception, evaporation and infiltration for land surface hydrologic cycle on the single storm basis and the flow routing features for both overland and channel systems. 2. Net rainfall is estimated from the continuous computation of water balance at the surface of interception storage accounting for the rainfall intensities and the evaporation losses at each time step. 3. Excess rainfall is calculated by the abstraction of infiltration loss estimated by the Green and Ainpt Model from the net rainfall. 4. A momentum equation in the form of kinematic wave representation is solved by the finite differential method to obtain the runoff rate at the exit of the watershed. 5. The developed SNUA Model is a type of distributed and event model that considers the spatial distribution of the watershed parameters and simulates the hydrograph on a single storm basis. 6. The results of verification test show that the simulated peak flows agree with the observed in the occurence time but have relative enors in the range of 5.4-40.6% in various flow rates and also show that the simulated total runoff have 6.9-32% of relative errors against the observed. 7. To improve the applicability of the model, it was thought that more studies like the application test to the other watersheds of various types or the addition of the other hydrologk components describing subsurface storages are needed.

• Korean Journal of Remote Sensing
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• v.31 no.6
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• pp.583-598
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• 2015

This paper presents derivation of background temperature from geostationary satellite and its validation based on ground measurements and Geographic Information System (GIS) for future use in weather and surface heat variability. This study only focuses on daily and monthly brightness temperature in 2012. From the analysis of COMS Meteorological Data Processing System (CMDPS) data, we have found an error in cloud distribution of model, which used as a background temperature field, and in examining the spatial homogeneity. Excessive cloudy pixels were reconstructed by statistical reanalysis based on consistency of temperature measurement. The derived Brightness temperature has correlation of 0.95, bias of 0.66 K and RMSE of 4.88 K with ground station measurements. The relation between brightness temperature and both elevation and vegetated land cover were highly anti-correlated during warm season and daytime, but marginally correlated during cold season and nighttime. This result suggests that time varying emissivity data is required to derive land surface temperature.