• Water Engineering Research
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• v.4 no.1
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• pp.31-43
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• 2003

The impacts of climate change on soil moisture in sub - Arctic watershed simulated by using the hydrologic model. A range of arbitrary changes in temperature and precipitation are applied to the runoff model to study the sensitivity of soil moisture due to potential changes in precipitation and temperature. The sensitivity analysis indicates that changes in precipitation are always amplified in soil moisture with the amplification factor for flow. The change in precipitation has effect on the soil moisture in the catchment. The percentage change in soil moisture levels can be greater than the percentage change in precipitation. Compared to precipitation, temperature increases or decreases alone have impacts on the soil moisture. These results show the potential for climate change to bring about soil moisture that may require a significant planning response. They are also indicative of the fact that hydrological impacts affecting water supply may be important in consider-ing the cost and benefits of potential climate change.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.205-205
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• 2021

Surface soil moisture, which governs the partitioning of precipitation into infiltration and runoff, plays an important role in the hydrological cycle. The assimilation of satellite soil moisture retrievals into a land surface model or hydrological model has been shown to improve the predictive skill of hydrological variables. This study aims to improve streamflow prediction with Weather Research and Forecasting model-Hydrological modeling system (WRF-Hydro) by assimilating Soil Moisture Active and Passive (SMAP) data at 3 km and analyze its impacts on hydrological components. We applied Cumulative Distribution Function (CDF) technique to remove the bias of SMAP data and assimilate SMAP data (April to July 2015-2019) into WRF-Hydro by using an Ensemble Kalman Filter (EnKF) with a total 12 ensembles. Daily inflow and soil moisture estimates of major dams (Soyanggang, Chungju, Sumjin dam) of South Korea were evaluated. We investigated how hydrologic variables such as runoff, evaporation and soil moisture were better simulated with the data assimilation than without the data assimilation. The result shows that the correlation coefficient of topsoil moisture can be improved, however a change of dam inflow was not outstanding. It may attribute to the fact that soil moisture memory and the respective memory of runoff play on different time scales. These findings demonstrate that the assimilation of satellite soil moisture retrievals can improve the predictive skill of hydrological variables for a better understanding of the water cycle.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.6
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• pp.173-183
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• 2015

The aim of this study was to develop a soil moisture simulation model equipped with meteorological data enhanced by WRF (Weather Research and Forecast) model, and this soil moisture model was applied for quantifying soil moisture content and irrigation requirement. The WRF model can provide grid based meteorological data at various resolutions. For applicability assessment, comparative analyses were conducted using WRF data and weather data obtained from weather station located close to test bed. Water balance of each upland grid was assessed for soils represented with four layers. The soil moisture contents simulated using the soil moisture model were compared with observed data to evaluate the capacity of the model qualitatively and quantitatively with performance statistics such as correlation coefficient (R), coefficient of determination (R2) and root mean squared error (RMSE). As a result, R is 0.76, $R^2$ is 0.58 and RMSE 5.45 mm in soil layer 1 and R 0.61, $R^2$ 0.37 and RMSE 6.73 mm in soil layer 2 and R 0.52, $R^2$ 0.27 and RMSE 8.64 mm in soil layer 3 and R 0.68, $R^2$ 0.45 and RMSE 5.29 mm in soil layer 4. The estimated soil moisture contents and irrigation requirements of each soil layer showed spatiotemporally varied distributions depending on weather and soil texture data incorporated. The estimated soil moisture contents using weather station data showed uniform distribution about all grids. However the estimated soil moisture contents from WRF data showed spatially varied distribution. Also, the estimated irrigation requirements applied WRF data showed spatial variabilities reflecting regional differences of weather conditions.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.401-408
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• 2013

Soil moisture is a very important variable in various area of hydrological processes. We predict the soil moisture using a support vector regression. The model is trained and tested using the soil moisture data observed in five sites in the Yongdam dam basin. With respect to soil moisture data of of four sites-Jucheon, Bugui, Sangieon and Ahncheon which are used to train the model, the correlation coefficient between the esimtates and the observed values is about 0.976. As the result of the application to Cheoncheon2 for validating the model, the correlation coefficient between the estimates and the observed values of soil moisture is about 0.835. We compare those results with those of artificial neural network models.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.79-89
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• 2022

Soil moisture is significantly related to crop growth and plays an important role in irrigation management. To predict soil moisture, various process-based model has been developed and used in the world. Various models (Land surface model) may have different performance depending on the model parameters and structures that causes the different model output for the same modeling condition. In this study, the three land surface models (Noah Land Surface Model, Soil Water Atmosphere Plant, Community Land Model) were used to compare the model performance (soil moisture prediction) and develop the multi-model simulation. At first, the genetic algorithm was used to estimate the optimal soil parameters for each model, and the parameters were used to predict soil moisture in the study area. Then, we used the multi-model approach based on Bayesian model averaging (BMA). The results derived from this approach showed a better match to the measurements than the results from the original single land surface model. In addition, identifying the strengths and weaknesses of the single model and utilizing multi-model methods can help to increase the accuracy of soil moisture prediction.

• Water Engineering Research
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• v.3 no.1
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• pp.31-44
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• 2002

The effect of diurnal cycle, intermittent visit of observation satellite, sensor installation, partial coverage of remote sensing, heterogeneity of soil properties and precipitation to the soil moisture estimation error were analyzed to present the global sampling strategy of soil moisture. Three models, the theoretical soil moisture model, WGR model proposed Waymire of at. (1984) to generate rainfall, and Turning Band Method to generate two dimensional soil porosity, active soil depth and loss coefficient field were used to construct sufficient two-dimensional soil moisture data based on different scenarios. The sampling error is dominated by sampling interval and design scheme. The effect of heterogeneity of soil properties and rainfall to sampling error is smaller than that of temporal gap and spatial gap. Selecting a small sampling interval can dramatically reduce the sampling error generated by other factors such as heterogeneity of rainfall, soil properties, topography, and climatic conditions. If the annual mean of coverage portion is about 90%, the effect of partial coverage to sampling error can be disregarded. The water retention capacity of fields is very important in the sampling error. The smaller the water retention capacity of the field (small soil porosity and thin active soil depth), the greater the sampling error. These results indicate that the sampling error is very sensitive to water retention capacity. Block random installation gets more accurate data than random installation of soil moisture gages. The Walnut Gulch soil moisture data show that the diurnal variation of soil moisture causes sampling error between 1 and 4 % in daily estimation.

• Water Engineering Research
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• v.1 no.3
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• pp.243-256
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• 2000

This physically-based hydrologic model is developed to calculate the soil-moisture balance on paddy fields. This model consists of three modules; the first is the unsaturated module, the second is the rice evapotranspiration module with SPAC(soil-plant-atmospheric-continuum), and the third is the groundwater and open channel flows based upon the interrehtionship module. The model simulates the hydrlogical processes of infiltration, soil water storage, deep perocolation or echarge to the shallow water table, transpiration and evaporation from the soil surface and also the interrelationship of the groundwater and river flow exchange. To verify the applicability of the developed model, it was applied to the Kimjae Plains, located in the center of the Dongjin river basin in Korea, during the most serious drought season of 1994. The result shows that the estimated water net requirement was 757mm and the water deficit was about 5.9% in this area in 1994. This model can easily evaluate the irrigated water quantity and visualize the common crop demands and soil moisture conditions.

• Korean Journal of Soil Science and Fertilizer
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• v.30 no.4
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• pp.314-327
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• 1997

A 2-dimensional soil water flow model was developed to describe the migration of soil moisture in drip-irrigated root zone employing cylindrical coordinate system. Several natural phenomena were incorporated into the model such as transpiration, various types of evaporation, and ponding due to the increase in irrigation rate. Model was solved numerically by finite difference method. The model was verified in several ways leading to the conclusion that it can describe the soil moisture migration in drip-irrigated root zone fairly well. From sensitivity analysis, vertical migration of soil moisture was found to move faster than the horizontal one, which indicates the vertical location just under the dripping point are adequate for measuring points of soil moisture. The pot shape of soil moisture in irrigated zone was proved to be caused by evaporation at the soil surface. Also, it was found that the hydraulic conductivity has greatly influential to the soil moisture migration, and that the soil moisture continues to migrate vertically after irrigation stops.

• 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.

• Water Engineering Research
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• v.5 no.1
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• pp.1-16
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• 2004

The purpose of this study is to understand the characteristics of hourly PET(Potential Evapo Transpiration) variation estimated using Penman ET model. The estimated PET using Penman model was compared with measured ET. For this study, two subwatersheds were selected, and fluxes, meteorological data and soil moisture data were measured during the summer and winter days. During the winter days, the aerodynamic term of Penman ET is much greater than that of energy term of Penman ET for dry soil condition. The opposite phenomena appeared fer wet soil condition. During the summer days, energy term is much more important factor for ET estimation compared with aerodynamic term regardless of soil moisture condition. Penman ET, measured ET, and energy term show the similar hourly variation pattern mainly because the influence of net radiation on the estimation of Penman ET is much more significant compared with other variables. Even though there are much more soil moisture in the soil during the wet days, the estimated hourly ET from Penman model and measured hourly ET have smaller values compared with those of dry days, indicating the effect of cloudy weather condition.