• Journal of the Korean GEO-environmental Society
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• v.18 no.7
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• pp.21-35
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• 2017

The 3S Basin is described as an important contributor in terms of many aspects in the Mekong River Basin in Southeast Asia. However, the 3S Basin has been suffering adverse consequences of changing discharge and sediment, which are derived from farming, deforestation, hydropower dam construction, climate change, and soil erosion. Consequently, a large population and ecology system that live along the 3S Basin are seriously affected. Accordingly, the calculating and simulating discharge and sediment become ever more urgent. There are many methods to simulate discharge and sediment. However, most of them are designed only during a single rainfall event and they require many kinds of data. Therefore, this study applied a Catchment-scale Soil Erosion model (C-SEM) to simulate discharge and sediment in the 3S Basin. The simulated results were judged with others references's data and the observed discharge of Strung Treng station, which is located in the mainstream and near the outlet of the 3S Basin. The results revealed that the 3S Basin distributes 31% of the Mekong River Basin's total discharge. In addition, the simulated sediment results at the 3S Basin's outlet also substantiated the importance of the 3S Basin to the Mekong River Basin. Furthermore, the results are also useful for the sustainable management practices in the 3S Basin, where the sediment data is unavailable.

• Korean Journal of Agricultural Science
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• v.47 no.3
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• pp.471-483
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• 2020

Flood prediction is an important issue to prevent damages by flood inundation caused by increasing high-intensity rainfall with climate change. In recent years, machine learning algorithms have been receiving attention in many scientific fields including hydrology, water resources, natural hazards, etc. The performance of a machine learning algorithm was investigated to predict the water elevation of a river in this study. The aim of this study was to develop a new method for securing a large enough lead time for flood defenses by predicting river water elevation using the a long- short-term memory (LSTM) technique. The water elevation data at the Oisong gauging station were selected to evaluate its applicability. The test data were the water elevation data measured by K-water from 15 February 2013 to 26 August 2018, approximately 5 years 6 months, at 1 hour intervals. To investigate the predictability of the data in terms of the data characteristics and the lead time of the prediction data, the data were divided into the same interval data (group-A) and time average data (group-B) set. Next, the predictability was evaluated by constructing a total of 36 cases. Based on the results, group-A had a more stable water elevation prediction skill compared to group-B with a lead time from 1 to 6 h. Thus, the LSTM technique using only measured water elevation data can be used for securing the appropriate lead time for flood defense in a river.

• Journal of Korea Water Resources Association
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• v.45 no.7
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• pp.697-711
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• 2012

This study applied the extended Kalman Filter, a data assimilation method, for the real-time quality improvement of runoff measurements. The state-space model of the extended Kalman Filter was composed of a rainfall-runoff model and the runoff measurement. This study divided the purpose of quality improvement of runoff measurements into two; one is to suppress the abnormally high variation of dam inflow data, and the other to amend the missing or erroneous measurements. For each case, a proper model of extended Kalman Filter was proposed, and the main difference between two models is whether only the variation is considered or both the bias and variation are considered in the estimation of covariance function. This study was applied to the Chungju Dam Basin to confirm the proposed models were effectively worked to improve the quality of both the dam inflow data and the runoff measurements with some missing and erroneous part.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.4
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• pp.55-63
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• 2013

This study investigates the trends and uncertainty of the impacts of climate change on paddy rice production in the Geumho river basin. The Long Ashton Research Station stochastic Weather Generator (LARS-WG) was used to derive future climate data for the Geumho river basin from 15 General Circulation models (GCMs) for 3 Special Report on Emissions Scenarios (SRES) (A2, A1B and B1) included in the Intergovernmental Panel on Climate Change (IPCC) 4th assessment report. The Food and Agricultural Organization (FAO) AquaCrop, a water-driven crop model, was statistically calibrated for the 1982 to 2010 climate. The index of agreement (IoA), prediction efficiency ($R^2$), percent bias (PBIAS), root mean square error (RMSE) and a visual technique were used to evaluate the adjusted AquaCrop simulated yield values. The adjusted simulated yields showed RMSE, NSE, IoA and PBIAS of 0.40, 0.26, 0.76 and 0.59 respectively. The 5, 9 and 15 year central moving averages showed $R^2$ of 0.78, 0.90 and 0.96 respectively after adjustment. AquaCrop was run for the 2020s (2011-2030), 2050s (2046-2065) and 2090s (2080-2099). Climate change projections for Geumho river basin generally indicate a hotter and wetter future climate with maximum increase in the annual temperature of $4.5^{\circ}C$ in the 2090s A1B, as well as maximum increase in the rainfall of 45 % in the 2090s A2. The means (and ranges) of paddy rice yields are projected to increase by 21 % (17-25 %), 34 % (27-42 %) and 43 % (31-54 %) for the 2020s, 2050s and 2090s, respectively. The A1B shows the largest rice yield uncertainty in all time slices with standard deviation of 0.148, 0.189 and $0.173t{\cdot}ha^{-1}$ for the 2020s, 2050s and 2090s, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.1-7
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• 2013

The interference among the rain radars and interference in the adjacent wireless station due to the spurious signals from the rain radar were analyzed in this paper. The rain radar measures the rain intensity using S-band signal. The measured data are utilized in forecasting the rainfall. The interference among the rain radars or in the adjacent wireless stations may be caused by the operation with low elevation angle and the high output power. Based on the propagation analysis of S band signal and the deduced interference protection ratio of rain radar, the interference due to the rain radar are analyzed. Also, the radiation spectrum characteristics of a rain radar are deduced from the caused interference effects by the spurious signals of the rain radar. To minimize the interference effects for adjacent wireless stations, it is required to get the rejection characteristics of spurious signals above 105 dB. In viewpoints of interference for rain radars, it is necessary to operate the rain radar with a different PRF and operation time opposite to adjacent rain radars.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.485-493
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• 2006

This study is to combine precipitation data with different spatial-temporal characteristics using an optimal weighting method. This optimal weighting method is designed for combination of AWS rain gage data and S-band RADAR-estimated rain data with weighting function in inverse proportion to own mean square error for the previous time step. To decide the optimal weight coefficient for optimized precipitation according to different training time, the method has been performed on Changma case with a long spell of rainy hour for the training time from 1 hour to 10 hours. Horizontal field of optimized precipitation tends to be smoothed after 2 hours training time, and then optimized precipitation has a good agreement with synoptic station rainfall assumed as true value. This result suggests that this optimal weighting method can be used for production of high-resolution quantitative precipitation rate using various data sets.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.158-158
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• 2018

Drought is a recurrent natural hazard in Bangladesh. It has significant impacts on agriculture, environment, and society. Well-timed information on the onset, extent, intensity, duration, and impacts of drought can mitigate the potential drought-related losses. Thus, drought characteristics need to be explained in terms of frequency, severity, and duration. This paper aims to characterize the spatial and temporal pattern of meteorological drought using EDI and illustrated drought severity over Bangladesh. Twenty-seven (27) station-based daily rainfall data for the study period of 1981-2015 were used to calculate the EDI values over Bangladesh. The evaluation of EDI is conducted for 4 sub-regions over the country to confirm the historical drought record-developed at the regional scale. The finding shows that on average, the frequency of severe to extreme drought is approximately 0.7 events per year. As a result of the regional analysis, most of the recorded historical drought events were successfully detected during the study period. Additionally, the seasonal analysis showed that the extreme droughts were frequently hit in northwestern, middle portion of the eastern and small portion of central parts of Bangladesh during the Kharif(wet) and Rabi(dry) seasons. The severe drought was affected recurrently in the central and northern regions of the country during all cropping seasons. The study also points out that the northern, south-western and central regions in Bangladesh are comparatively vulnerable to both extreme and severe drought event. The study showed that EDI would be a useful tool to identify the drought-prone area and time and potentially applicable to the climate change-induced drought evolution monitoring at regional to the national level in Bangladesh. The outcome of the present study can be used in taking anticipatory strategies to mitigate the drought damages on agricultural production as well as human sufferings in drought-prone areas of Bangladesh.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.154-154
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• 2018

To interpret the climate projections for the future as well as present, recognition of the consequences of the climate internal variability and quantification its uncertainty play a vital role. The Korean Peninsula belongs to the Far East Asian Monsoon region and its rainfall characteristics are very complex from time and space perspective. Its internal variability is expected to be large, but this variability has not been completely investigated to date especially using models of high temporal resolutions. Due to coarse spatial and temporal resolutions of General Circulation Models (GCM) projections, several studies adopted dynamic and statistical downscaling approaches to infer meterological forcing from climate change projections at local spatial scales and fine temporal resolutions. In this study, stochastic downscaling methodology was adopted to downscale daily GCM resolutions to hourly time scale using an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). After extracting factors of change from the GCM realizations, these were applied to the climatic statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series which can be considered to be representative of future climate conditions. Further, 30 ensemble members of hourly precipitation were generated for each selected station to quantify uncertainty. Spatial map was generated to visualize as separated zones formed through K-means cluster algorithm which region is more inconsistent as compared to the climatological norm or in which region the probability of occurrence of the extremes event is high. The results showed that the stations located near the coastal regions are more uncertain as compared to inland regions. Such information will be ultimately helpful for planning future adaptation and mitigation measures against extreme events.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.5
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• pp.1-11
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• 2021

Soil moisture plays a critical role in hydrological processes, land-atmosphere interactions and climate variability. It can limit vegetation growth as well as infiltration of rainfall and therefore very important for agriculture sector and food protection. Recently, due to the increased damage from drought caused by climate change, there is a frequent occurrence of shortage of agricultural water, making it difficult to supply and manage stable agricultural water. Efficient water management is necessary to reduce drought damage, and soil moisture management is important in case of upland crops. In this study, soil moisture was calculated based on the water balance model, and the suitability of soil moisture data was verified through the application. The regional soil moisture was calculated based on the meteorological data collected by the meteorological station, and applied the Runs theory. We analyzed the spatiotemporal variability of soil moisture and drought impacts, and analyzed the correlation between actual drought impacts and drought damage through correlation analysis of Standardized Precipitation Index (SPI). The soil moisture steadily decreased and increased until the rainy season, while the drought size steadily increased and decreased until the rainy season. The regional magnitude of the drought was large in Gyeonggi-do and Gyeongsang-do, and in winter, severe drought occurred in areas of Gangwon-do. As a result of comparative analysis with actual drought events, it was confirmed that there is a high correlation with SPI by each time scale drought events with a correlation coefficient.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.287-294
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• 2021

A Python-based LSTM model was constructed using a Tensorflow backend to estimate the amount of outflow during floods in the Gokgyo-cheon basin flowing into the Sapgyo Lake. To understand the effects of the length of input data used for learning, i.e., the sequence length, on the performance of the model, the model was implemented by increasing the sequence length to three, five, and seven hours. Consequently, when the sequence length was three hours, the prediction performance was excellent over the entire period. As a result of predicting three extreme rainfall events in the model verification, it was confirmed that an average NSE of 0.96 or higher was obtained for one hour in the leading time, and the accuracy decreased gradually for more than two hours in the leading time. In conclusion, the flood level at the Gangcheong station of Gokgyo-cheon can be predicted with high accuracy if the prediction is performed for one hour of leading time with a sequence length of three hours.