• Journal of Korea Water Resources Association
• /
• v.54 no.spc1
• /
• pp.1095-1105
• /
• 2021

The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.

• Journal of Korea Water Resources Association
• /
• v.54 no.spc1
• /
• pp.1107-1118
• /
• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Journal of Korea Water Resources Association
• /
• v.53 no.2
• /
• pp.107-119
• /
• 2020

This study analyzed past drought characteristics based on the observed rainfall data and performed a long-term outlook for future extreme droughts using Representative Concentration Pathways 8.5 (RCP 8.5) climate change scenarios. Standardized Precipitation Index (SPI) used duration of 1, 3, 6, 9 and 12 months, a meteorological drought index, was applied for quantitative drought analysis. A single long-term time series was constructed by combining daily rainfall observation data and RCP scenario. The constructed data was used as SPI input factors for each different duration. For the analysis of meteorological drought observed relatively long-term since 1954 in Korea, 12 rainfall stations were selected and applied 10 general circulation models (GCM) at the same point. In order to analyze drought characteristics according to climate change, trend analysis and clustering were performed. For non-stationary frequency analysis using sampling technique, we adopted the technique DEMC that combines Bayesian-based differential evolution ("DE") and Markov chain Monte Carlo ("MCMC"). A non-stationary drought frequency analysis was used to derive Severity-Duration-Frequency (SDF) curves for the 12 locations. A quantitative outlook for future droughts was carried out by deriving SDF curves with long-term hydrologic data assuming non-stationarity, and by quantitatively identifying potential drought risks. As a result of performing cluster analysis to identify the spatial characteristics, it was analyzed that there is a high risk of drought in the future in Jeonju, Gwangju, Yeosun, Mokpo, and Chupyeongryeong except Jeju corresponding to Zone 1-2, 2, and 3-2. They could be efficiently utilized in future drought management policies.

• Journal of Soil and Groundwater Environment
• /
• v.12 no.5
• /
• pp.19-32
• /
• 2007

In Korea, there have been various methods of estimating groundwater recharge which generally can be subdivided into three types: baseflow separation method by means of groundwater recession curve, water budget analysis based on lumped conceptual model in watershed, and water table fluctuation method (WTF) by using the data from groundwater monitoring wells. However, groundwater recharge rate shows the spatial-temporal variability due to climatic condition, land use and hydrogeological heterogeneity, so these methods have various limits to deal with these characteristics. To overcome these limitations, we present a new method of estimating recharge based on water balance components from the SWAT-MODFLOW which is an integrated surface-ground water model. Groundwater levels in the interest area close to the stream have dynamics similar to stream flow, whereas levels further upslope respond to precipitation with a delay. As these behaviours are related to the physical process of recharge, it is needed to account for the time delay in aquifer recharge once the water exits the soil profile to represent these features. In SWAT, a single linear reservoir storage module with an exponential decay weighting function is used to compute the recharge from soil to aquifer on a given day. However, this module has some limitations expressing recharge variation when the delay time is too long and transient recharge trend does not match to the groundwater table time series, the multi-reservoir storage routing module which represents more realistic time delay through vadose zone is newly suggested in this study. In this module, the parameter related to the delay time should be optimized by checking the correlation between simulated recharge and observed groundwater levels. The final step of this procedure is to compare simulated groundwater table with observed one as well as to compare simulated watershed runoff with observed one. This method is applied to Mihocheon watershed in Korea for the purpose of testing the procedure of proper estimation of spatio-temporal groundwater recharge distribution. As the newly suggested method of estimating recharge has the advantages of effectiveness of watershed model as well as the accuracy of WTF method, the estimated daily recharge rate would be an advanced quantity reflecting the heterogeneity of hydrogeology, climatic condition, land use as well as physical behaviour of water in soil layers and aquifers.

• Journal of Korea Water Resources Association
• /
• v.46 no.8
• /
• pp.833-842
• /
• 2013

The quantile mapping is utilized to reproduce reliable GCM(Global Climate Model) data by correct systematic biases included in the original data set. This scheme, in general, projects the Cumulative Distribution Function (CDF) of the underlying data set into the target CDF assuming that parameters of target distribution function is stationary. Therefore, the application of stationary quantile mapping for nonstationary long-term time series data of future precipitation scenario computed by GCM can show biased projection. In this research the Nonstationary Quantile Mapping (NSQM) scheme was suggested for bias correction of nonstationary long-term time series data. The proposed scheme uses the statistical parameters with nonstationary long-term trends. The Gamma distribution was assumed for the object and target probability distribution. As the climate change scenario, the 20C3M(baseline scenario) and SRES A2 scenario (projection scenario) of CGCM3.1/T63 model from CCCma (Canadian Centre for Climate modeling and analysis) were utilized. The precipitation data were collected from 10 rain gauge stations in the Han-river basin. In order to consider seasonal characteristics, the study was performed separately for the flood (June~October) and nonflood (November~May) seasons. The periods for baseline and projection scenario were set as 1973~2000 and 2011~2100, respectively. This study evaluated the performance of NSQM by experimenting various ways of setting parameters of target distribution. The projection scenarios were shown for 3 different periods of FF scenario (Foreseeable Future Scenario, 2011~2040 yr), MF scenario (Mid-term Future Scenario, 2041~2070 yr), LF scenario (Long-term Future Scenario, 2071~2100 yr). The trend test for the annual precipitation projection using NSQM shows 330.1 mm (25.2%), 564.5 mm (43.1%), and 634.3 mm (48.5%) increase for FF, MF, and LF scenarios, respectively. The application of stationary scheme shows overestimated projection for FF scenario and underestimated projection for LF scenario. This problem could be improved by applying nonstationary quantile mapping.

• Journal of Wetlands Research
• /
• v.20 no.4
• /
• pp.295-303
• /
• 2018

Inland wetlands in the Republic of Korea provide key breeding and wintering habitats, while coastal wetlands provide nutrient-rich habitats for stopover sites for East Asia/Australasia Flyway(EAAF) migrants. However, since the 1960's, Korea has reclaimed these coastal wetlands gradually for agriculture and urban expansion. The habitat loss has rippled across global populations of migrant shorebirds in EAAF. To protect a similar loss, the United States, specifically Missouri, developed the moist-soil management technique. Wetland impoundments are constructed from levees with water-flow control gates with specific soils, topography, available water sources, and target goals. The impoundments are subjected to a combination of carefully timed and regulated flooding and drawdown regimes with occasional soil disturbance. This serves a dual purpose of removing undesirable vegetation, while maximizing habitat and forage for wildlife. Flooding and drawdown schedules must be dynamic with constantly shifting climate conditions. Korea's latitude ($N33^{\circ}25^{\prime}{\sim}N38^{\circ}37^{\prime}$) is comparable to Missouri ($N36^{\circ}69^{\prime}{\sim}N40^{\circ}41^{\prime}$); as such, moist-soil management could prove to be an effective wetland restoration technique for Korea. In order to meet specific conservation goals (i.e. shorebird staging site restoration), it is necessary to test the proposed methodology on a site that can meet the required specifications for moist-soil management. Moist-soil management has the potential to not only create key habitat for endangered wildlife, but also provide valuable ecosystem services, including water filtration.

• Korean Journal of Remote Sensing
• /
• v.34 no.2_1
• /
• pp.295-300
• /
• 2018

Net radiation is the total amount of radiation energy used as a heat flux for the Earth's energy cycle, and net radiation from the surface is an important factor in areas such as hydrology, climate, meteorological studies and agriculture. It is very important to monitoring the net radiation through remote sensing to be able to understand the trend of heat island and urbanization phenomenon. However, net radiation estimation using only remote sensing data is generally causes difference in accuracy depending on cloud. Therefore, in this paper, we retrieved and monitored high resolution surface net radiation at 1 hour interval in Eunpyeong New Town where urbanization using Communication, Ocean and Meteorological Satellite (COMS), Landsat-8 satellite and Computational Fluid Dynamics (CFD) model data reflecting the difference in building height. We compared the observed and estimated net radiation at the flux tower. As a result, estimated net radiation was similar trend to the observed net radiation as a whole and it had the accuracy of RMSE $54.29Wm^{-2}$ and Bias $27.42Wm^{-2}$. In addition, the calculated net radiation showed well the meteorological conditions such as precipitation, and showed the characteristics of net radiation for the vegetation and artificial area in the spatial distribution.

• Spatial Information Research
• /
• v.22 no.1
• /
• pp.55-63
• /
• 2014

Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.

• Journal of Wetlands Research
• /
• v.16 no.4
• /
• pp.303-318
• /
• 2014

Baseflow gives a significant contribution to stream function in the regions where climatic characteristics are seasonally distinct. In this regard, variable baseflow can make it difficult to maintain a stable water supply, as well as causing disruption to the stream ecosystem. Changes in land use can affect both the direct flow and baseflow of a stream, and consequently, most other components of the hydrologic cycle. Baseflow estimation depends on the observed streamflow in gauge watersheds, but accurate predictions of streamflow through modeling can be useful in determining baseflow data for ungauged watersheds. Accordingly, the objectives of this study are to 1) improve predictions of SWAT by applying the alpha factor estimated using RECESS for calibration; 2) estimate baseflow in an ungauged watershed using the WHAT system; and 3) evaluate the effects of changes in land use on baseflow characteristics. These objectives were implemented in the Gapcheon watershed, as an ungauged watershed in South Korea. The results show that the alpha factor estimated using RECESS in SWAT calibration improves the prediction for streamflow, and, in particular, recessions in the baseflow. Also, the changes in land use in the Gapcheon watershed leads to no significant difference in annual baseflow between comparable periods, regardless of precipitation, but does lead to differences in the seasonal characteristics observed for the temporal distribution of baseflow. Therefore, the Guem River, into which the stream from the Gapcheon watershed flows, requires strategic seasonal variability predictions of baseflow due to changes in land use within the region.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.12 no.4
• /
• pp.307-316
• /
• 2010

Complex terrain refers to irregular surface properties of the earth that influence gradients in climate, lateral transfer of materials, landscape distribution in soils properties, habitat selection of organisms, and via human preferences, the patterning in development of land use. Complex terrain of mountainous areas represents ca. 20% of the Earth's terrestrial surface; and such regions provide fresh water to at least half of humankind. Most major river systems originate in such terrain, and their resources are often associated with socio-economic competition and political disputes. The goals of the TERRECO-IRTG focus on building a bridge between ecosystem understanding in complex terrain and spatial assessments of ecosystem performance with respect to derived ecosystem services. More specifically, a coordinated assessment framework will be developed from landscape to regional scale applications to quantify trade-offs and will be applied to determine how shifts in climate and land use in complex terrain influence naturally derived ecosystem services. Within the scope of TERRECO, the abiotic and biotic studies of water yield and quality, production and biodiversity, soil processing of materials and trace gas emissions in complex terrain are merged. There is a need to quantitatively understand 1) the ecosystem services derived in regions of complex terrain, 2) the process regulation occurred to maintain those services, and 3) the sensitivities defining thresholds critical in stability of these systems. The TERRECO-IRTG is dedicated to joint study of ecosystems in complex terrain from landscape to regional scales. Our objectives are to reveal the spatial patterns in driving variables of essential ecosystem processes involved in ecosystem services of complex terrain region and hence, to evaluate the resulting ecosystem services, and further to provide new tools for understanding and managing such areas.