• 한국해양학회지
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• v.27 no.4
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• pp.290-302
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• 1992

Seasonal variations of salinity in the upper 500 m of the tropical Atlantic Ocean are examined, based on both climatological seasonal salinity observations and numerical simulations with hydrological forcing. The seasonal cycle of sea surface salinity has strong seasonal variations caused by shifts of the freshwater surplus zone (i.e. the intertropical convergence zone) and the river outflow. The climatological seasonal salinity in this analysis concurs with other independent observations described by Default (1981) and Levitus (1982), but provides more consistent patterns with temperature structure. The effect of salinity on density below 100 m depth in the tropical Atlantic is negligible compared to tat of temperature, which in the mixed layer salinity affects density significantly. The systematic difference between observed and simulated salinity is found to be the fact that the simulated salinity is higher in the subtropics than the observed salinity, and possible sources about the difference are also discussed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.2
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• pp.469-478
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• 2014

The annual maximum series (AMS) is usually used to estimate hydrological quantiles in practice because it is simple to construct and straightforward to probabilistic interpretation. However, it is limited to use the AMS in Korea due to the lack of reliable observed data which leads to the overestimation of design rainfall and/or flood. Using the 40-year observations of rainfall provided by the Korea Meteorological Administration, this study constructed the AMS and non-annual exceedance series (NAES) after identifying the independent storm event, analyzed the correlation between design rainfalls estimated from the AMS and NAES, and proposed a new method of point frequency analysis to estimate design rainfalls from the small number of observations.

• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.993-1001
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• 2017

Drought is a worldwide natural disaster with extensively adverse impacts on natural ecosystems, agricultural products, social communities and regional economy. Various global satellite observations, including SMAP soil moisture, GRACE terrestrial water storage, Terra and Aqua vegetation productivity, evapotranspiration, and satellite precipitation measures are currently used to characterize seasonal timing and inter-annual variations of regional water supply pattern, vegetation growth, drought events, and its associated influence ecosystems and human society. We suggest the satellite monitoring system development to quantify meteorological, eco-hydrological, and socio-ecological factors related to drought events, and characterize spatial and temporal drought patterns in Korea. The combination of these complementary remote sensing observations(visible to microwave bands) provide an effective means for evaluating regional variations in the timing, frequency, and duration of drought, and availability of water supply influencing vegetation and crop growth. This integrated drought monitoring could help national capacity to deal with natural disasters.

• Journal of Soil and Groundwater Environment
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• v.16 no.5
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• pp.82-89
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• 2011

Understanding snowmelt movement to the watershed is crucial for both climate change and hydrological studies because the snowmelt is a significant component of groundwater and surface runoff in temperature area. In this work, a new energy balance budget algorithm has been developed for melting snow from a snowpack at the Central Sierra Snow Laboratory (CSSL) in California, US. Using two sets of experiments, artificial rain-on-snow experiments and observations of diel variations, carried out in the winter of 2002 and 2003, we investigate how to calculate the amount of snowmelt from the snowpack using radiation energy and air temperature. To address the effect of air temperature, we calculate the integrated daily solar radiation energy input, and the integrated discharge of snowmelt under the snowpack and the energy required to generate such an amount of meltwater. The difference between the two is the excess (or deficit) energy input and we compare this energy to the average daily temperature. The resulting empirical relationship is used to calculate the instantaneous snowmelt rate in the model used by Lee et al. (2008a; 2010), in addition to the net-short radiation. If for a given 10 minute interval, the energy obtained by the melt calculation is negative, then no melt is generated. The input energy from the sun is considered to be used to increase the temperature of the snowpack. Positive energy is used for melting snow for the 10-minute interval. Using this energy budget algorithm, we optimize the intrinsic permeability of the snowpack for the two sets of experiments using one-dimensional water percolation model, which are $52.5{\times}10^{-10}m^2$ and $75{\times}10^{-10}m^2$ for the artificial rain-on-snow experiments and observations of diel variation, respectively.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.47-60
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• 2017

The permafrost active layer plays an important role in permafrost dynamics. Ecological patterns, processes, and water and ice contents in the active layer are spatially and temporally complex depending on landscape heterogeneity and local-scale variations in hydrological processes. Although there has been emerging interest in the application of optical remote sensing techniques to permafrost environments, optical sensors are significantly limited in accessing information on near surface geo-cryological conditions. The primary objective of this study was to investigate capability of L-band SAR data for monitoring spatio-temporal variability of permafrost ecosystems and underlying soil conditions. This study exploits information from different polarimetric SAR observables in relation to permafrost environmental conditions. Experimental results show that each polarimetric radar observable conveys different information on permafrost environments. In the case of the dual-pol mode, the radar observables consist of two backscattering powers and one correlation coefficient between polarimetric channels. Among them, the dual-pol scattering powers are highly sensitive to freeze/thaw transition and can discriminate grasslands or ponds in thermokarst area from other permafrost ecosystems. However, it is difficult to identify the ground conditions with dual-pol observables. Additional backscattering powers and correlation coefficients obtained from quad-pol mode help understanding seasonal variations ofradar scattering and assessing geo-cryological information on soil layers. In particular, co-pol coherences atHV-basis and circular-basis were found to be very usefultools for mapping and monitoring near surface soil properties.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.156-156
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• 2018

The subbasin spatial scale can affect a hydrological simulation result. The objective of this study was to investigate an appropriate subbasin spatial scale for reproducing the different flow phases with the Soil and Water Assessment Tool (SWAT). Moreover, this study addressed the total hydrologic model uncertainty using the Generalized Likelihood Uncertainty Estimation (GLUE) method. The hydrologic modelling uncertainty analysis revealed that the courser subbasin spatial scale provided a relatively better coverage of most of the observations by the 95PPU. On the other hand, the finer subbasin spatial scale produced the best single simulation output closer to the observation. Moreover, most of the observed high flows were enveloped by the 95PPU while this did not happen for the low flows. The overall average performance improvement through an appropriate subbasin spatial scale for reproducing the different flow phases in the Yongdam and Gilgelabay watersheds were found to be 36% and 53%, respectively. It is, therefore, a worth that to put more effort in reproducing the different flow phases by investigating an appropriate subbasin spatial scale to improve our understanding about the frequency and magnitude of the different flow phases.

• Membrane and Water Treatment
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• v.13 no.5
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• pp.219-225
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• 2022

5th Assessment Report of the Intergovernmental Panel on Climate Change Weather (AR5) predicts that recent severe hydrological events will affect the quality of water and increase water pollution. To analyze changes in water quality due to future climate change, input data (precipitation, average temperature, relative humidity, average wind speed, and solar radiation) were compiled into a representative concentration curve (RC), defined using 8.5. AR5 and future use are calculated based on land use. Semi-distributed emission model Calculate emissions for each target period. Meteorological factors affecting water quality (precipitation, temperature, and flow) were input into a multiple linear regression (MLR) model and an artificial neural network (ANN) to analyze the data. Extensive experimental studies of flow properties have been carried out. In addition, an Acoustic Doppler Velocity (ADV) device was used to monitor the flow of a large open channel connection in a wastewater treatment plant in Ho Chi Minh City. Observations were made along different streams at different locations and at different depths. Analysis of measurement data shows average speed profile, aspect ratio, vertical position Measure, and ratio the vertical to bottom distance for maximum speed and water depth. This result indicates that the transport effect of the compound was considered when preparing the hazard analysis.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.101-101
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• 2020

Traditional stochastic simulation of hydroclimatological variables often underestimates the variability and correlation structure of larger timescale due to the difficulty in preserving long-term memory. However, the Long Short-Term Memory (LSTM) model illustrates a remarkable long-term memory from the recursive hidden and cell states. The current study, therefore, employed the LSTM model in stochastic generation of hydrologic and climate variables to examine how much the LSTM model can preserve the long-term memory and overcome the drawbacks of conventional time series models such as autoregressive (AR). A trigonometric function and the Rössler system as well as real case studies for hydrological and climatological variables were tested. Results presented that the LSTM model reproduced the variability and correlation structure of the larger timescale as well as the key statistics of the original time domain better than the AR and other traditional models. The hidden and cell states of the LSTM containing the long-memory and oscillation structure following the observations allows better performance compared to the other tested conventional models. This good representation of the long-term variability can be important in water manager since future water resources planning and management is highly related with this long-term variability.

• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.947-960
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• 2017

Soil moisture plays an important role to affect the Earth's radiative energy balance and water cycle. In general, satellite observations are useful for estimating the soil moisture content. Passive microwave satellites have an advantage of direct sensitivity on surface soil moisture. However, their coarse spatial resolutions (10-36 km) are not suitable for regional-scale hydrological applications. Meanwhile, in-situ ground observations of point-based soil moisture content have the disadvantage of spatially discontinuous information. This paper presents an experimental soil moisture retrieval using Sentinel-1 SAR (Synthetic Aperture Radar) with 10m spatial resolution for cropland in South Korea. We developed a soil moisture retrieval algorithm based on the technique of linear regression and SVR (support vector regression) using the ground observations at five in-situ sites and Sentinel-1 SAR data from April to October in 2015-2017 period. Our results showed the polarization dependency on the different soil sensitivities at backscattered signals, but no polarization dependence on the accuracies. No particular seasonal characteristics of the soil moisture retrieval imply that soil moisture is generally more affected by hydro-meteorology and land surface characteristics than by phenological factors. At the narrower range of incidence angles, the relationship between the backscattered signal and soil moisture content was more distinct because the decreasing surface interference increased the retrieval accuracies under the condition of evenly distributed soil moisture (during the raining period or on the paddy field). We had an overall error estimate of RMSE (root mean square error) of approximately 6.5%. Our soil moisture retrieval algorithm will be improved if the effects of surface roughness, geomorphology, and soil properties would be considered in the future works.

• Journal of Wetlands Research
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• v.22 no.1
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• pp.15-23
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• 2020

The purpose of this study is to investigate how the degree of distribution influences the calibration of snow and runoff in distributed hydrological models using a multi-criteria calibration method. The Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) developed by NOAA-National Weather Service (NWS) is employed to estimate optimized parameter sets. We have 3 scenarios depended on the model complexity for estimating best parameter sets: Lumped, Semi-Distributed, and Fully-Distributed. For the case study, the Durango River Basin, Colorado is selected as a study basin to consider both snow and water balance components. This study basin is in the mountainous western U.S. area and consists of 108 Hydrologic Rainfall Analysis Project (HRAP) grid cells. 5 and 13 parameters of snow and water balance models are calibrated with the Multi-Objective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm. Model calibration and validation are conducted on 4km HRAP grids with 5 years (2001-2005) meteorological data and observations. Through case study, we show that snow and streamflow simulations are improved with multiple criteria calibrations without considering model complexity. In particular, we confirm that semi- and fully distributed models are better performances than those of lumped model. In case of lumped model, the Root Mean Square Error (RMSE) values improve by 35% on snow average and 42% on runoff from a priori parameter set through multi-criteria calibrations. On the other hand, the RMSE values are improved by 40% and 43% for snow and runoff on semi- and fully-distributed models.