• The Journal of Engineering Geology
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• v.26 no.2
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• pp.235-250
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• 2016

In Korea, 70% of the annual rainfall falls in summer, and the number of days of extreme rainfall (over 200 mm) is increasing over time. Because rainfall is the most important trigger of landslides, it is necessary to decide a rainfall threshold for landslide warning and to develop a landslide warning model. This study selected 12 study areas that contained landslides with exactly known triggering times and locations, and also rainfall data. The feasibility of applying a Rainfall Triggering Index (RTI) to Korea is analyzed, and three RTI models that consider different time units for rainfall intensity are compared. The analyses show that the 60-minute RTI model failed to predict landslides in three of the study areas, while both the 30- and 10-minute RTI models gave successful predictions for all of the study areas. Each RTI model showed different mean response times to landslide warning: 4.04 hours in the 60-minute RTI model, 6.08 hours in the 30-minute RTI model, and 9.15 hours in the 10-minute RTI model. Longer response times to landslides were possible using models that considered rainfall intensity for shorter periods of time. Considering the large variations in rainfall intensity that may occur within short periods in Korea, it is possible to increase the accuracy of prediction, and thereby improve the early warning of landslides, using a RTI model that considers rainfall intensity for periods of less than 1 hour.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.11-17
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• 2012

The Waikato River is New Zealand's longest River, though relatively small on international scales. It drains the central North Island and has New Zealand's largest lake (Lake Taupo) at its headwaters. The upper reaches have sustained flows fed by large aquifers which are recharged by rainfall events providing relatively constant river flows, whereas the lower reaches respond more directly to rainfall events having more peaky flows after rainfall and extreme low flows during dry periods. Consumptive allocation from the river is relatively low with only about 3% of the mean annual flow being allocated. However, more than seven times the river's flow is allocated for non-consumptive purposes before discharging to the Tasman Sea. The majority of this non-consumptive allocation is for hydro power generation and as cooling water at both thermal and geothermal power stations which produce up to 25% of New Zealand's electricity. The upper half of the river has been heavily modified with the construction of eight dams for power generation. This has resulted in a succession of cascading dams replacing the previously uncontrolled river. The Waikato River also provides drinking water for Auckland City (NZ's largest city) and Hamilton City (NZ's 4th largest city). In recent years there has also been considerable growth in water requirements for pasture irrigation to support the intensification of dairy farming in the catchment. Operators of the power stations are concerned that any further consumptive allocation will further reduce their ability to generate electricity. The Waikato Regional Council, who is charged with managing the river and allocation of water, has recently set new rules for managing the conflicting allocation demands on the Waikato River. This has resulted in an end to further allocation of water where it results in a loss of water for electricity generation from renewable resources (fresh water and geothermal water). The exception to this is the prioritisation of water for municipal supplies ahead of other consumptive uses such as industries and irrigators.

• New & Renewable Energy
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• v.5 no.4
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• pp.39-43
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• 2009

The variation of inflow at stream and hydrologic performance for small scale hydro power(SSHP) plants due to climate change have been studied. The model, which can predict flow duration characteristic of stream, was developed to analyze the variation of inflow caused from rainfall condition. And another model to predict hydrologic performance for SSHP plants is established. Monthly inflow data measured at Andong dam for 32 years were analyzed. The existing SSHP plant located in upstream of Andong dam was selected and analyzed hydrologic performance characteristics. The predicted results from the developed models show that the data were in good agreement with measured results of long term inflow at Andong dam and the existing SSHP plant. Inflow and ideal hydro power potential had increased greatly in recent years, however, these did not lead annual energy production increment of existing SSHP plant. As a results, it was found that the models represented in this study can be used to predict the primary design specifications and inflow of SSHP plants effectively.

• Solar Energy
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• v.9 no.3
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• pp.81-91
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• 1989

This study represents the method to predict the flow duration curve and primary design specifications of small hydropower plant at hydropower site through analyzing the monthly rainfall data. Weibull distribution was selected to characterize the rainfall data and Thiessen method was used to calculate monthly average flowrate at site. Application of these results, primary design specifications such as design flowrate, annual average load factor and utility factor, annual average hydropower density and annual electric energy production were estimated and discussed for surveyed site located in Daigi-ri, Kangwon province. And performance characteristic model of small hydro-power plant was applied to estimate these specifications.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.371-378
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• 2020

Rainfall is one of the most active forces that cause soil erosion. The action of rain on the soil exerts an erosive power caused by the impact of the drops, which fall with variable speed and kinetic energy, depending directly on the diameter of the drop. The objective of this study is to determine algorithms capable of estimating rainfall erosivity for the region of Maceió-AL. For this purpose, erosion rains were collected between 2003 and 2006 using a RD-69 disdrometer, which continuously and automatically measures rainfall distribution in a range of 1 min. The determination of algorithms in the form of power equation to estimate was adjusted with one and two independent variables (amount of rainfall, duration and maximum intensity).

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.4
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• pp.743-754
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• 2019

The erosive potential of precipitation can be evaluated by the kinetic energy transferred to the soil by the impact of the rain drop. A kinetic energy rate of the rain drops was estimated by the disdrometer classifying impact signals. This equation in the form of power presented an adjustment measure between the rain rate and rainfall quantity of 97% and 95% for continental and maritime rains, respectively. The exponent of the power equation, initially, shows no dependence on the type of rainfall. However, the multiplicative factor presented variation, which can be adjusted according to rainfall events. This equation was validated by the coefficient of determination, the average absolute error and the confidence error. The kinetic energy of precipitation, associated to certain types of soil, will allow the determination of the potential of the erosion caused by the rains.

• Journal of Soil and Groundwater Environment
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• v.19 no.4
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• pp.12-22
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• 2014

This study evaluated the pattern of groundwater fluctuation in cyrstalline rock using time series and factor analyses. From the results, groundwater level for the 18 wells was classified into 4 types reflecting the hydrogeological properties and rainfall event. Type 1 (DB1-5, DB1-6, DB2-2, KB-10, KB-13) was significantly influenced by groundwater flow through water-conducting features, whereas type 2 (DB1-3, DB1-7, KB-1~KB-3, KB-7, KB-11, KB-14, KB-15) was affected by minor fracture network as well as rainfall event. Type 3 (DB1-1, DB1-2) was mainly influenced by surface infiltration of rainfall event. Type 4 (DB1-8, KB-9) was reflected by the irregular variation of groundwater level caused by anisotropy and heterogeneity of crystalline rock.

• Journal of Korea Water Resources Association
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• v.54 no.5
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• pp.301-309
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• 2021

In this study, we tried to improve the performance of the existing U-net-based deep learning rainfall prediction model, which can weaken the meaning of time series order. For this, ConvLSTM2D U-Net structure model considering temporal consistency of data was applied, and we evaluated accuracy of the ConvLSTM2D U-Net model using a RainNet model and an extrapolation-based advection model. In addition, we tried to improve the uncertainty in the model training process by performing learning not only with a single model but also with 10 ensemble models. The trained neural network rainfall prediction model was optimized to generate 10-minute advance prediction data using four consecutive data of the past 30 minutes from the present. The results of deep learning rainfall prediction models are difficult to identify schematically distinct differences, but with ConvLSTM2D U-Net, the magnitude of the prediction error is the smallest and the location of rainfall is relatively accurate. In particular, the ensemble ConvLSTM2D U-Net showed high CSI, low MAE, and a narrow error range, and predicted rainfall more accurately and stable prediction performance than other models. However, the prediction performance for a specific point was very low compared to the prediction performance for the entire area, and the deep learning rainfall prediction model also had limitations. Through this study, it was confirmed that the ConvLSTM2D U-Net neural network structure to account for the change of time could increase the prediction accuracy, but there is still a limitation of the convolution deep neural network model due to spatial smoothing in the strong rainfall region or detailed rainfall prediction.

• Journal of Hydrogen and New Energy
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• v.14 no.1
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• pp.61-68
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• 2003

This paper presents the methodology to analyze flow duration characteristics and performance prediction technique for small hydro power(SHP) Plants and its application. The flow duration curve can be decided by using monthly rainfall data at the most of the SHP sites with no useful hydrological data. It was proved that the monthly rainfall data can be characterized by using the cumulative density function of Weibull distribution and Thiessen method were adopted to decide flow duration curve at SHP plants. And, the performance prediction technique has been studied and development. One SHP plant was selected and performance characteristics was analyzed by using the developed technique, Primary design specfications such as design flowrate, plant capacity, operational rate and annual electricity production for the SHP plant were estimated, It was found that the methodology developed in this study can be a useful tool to predict the performance of SHP plants and candidate sites in Korea.

• Journal of Korea Water Resources Association
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• v.43 no.11
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• pp.995-1009
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• 2010

Accelerated soil erosion due to extreme climate change, such as increased rainfall intensity, and human-induced environmental changes, is a widely recognized problem. Existing soil erosion models are generally based on the gross erosion concept to compute annual upland soil loss in tons per acre per year. However, such models are not suitable for event-based simulations of erosion and deposition in time and space. Recent advances in computer geographic information system (GIS) technologies have allowed hydrologists to develop physically based models, and the trend in erosion prediction is towards process-based models, instead of conceptually lumped models. This study aims to propose an effective and robust distributed rainfall-sediment yield-runoff model consisting of basic element modules: a rainfall-runoff module based on the kinematic wave method for subsurface and surface flow, and a runoff-sediment yield-runoff model based on the unit stream power method. The model was tested on the Cheoncheon catchment, upstream of the Yongdam dam using hydrological data for three extreme flood events due to typhoons. The model provided acceptable simulation results with respect to both discharge and sediment discharge even though the simulated sedigraphs were underestimated, compared to observations. The spatial distribution of erosion and deposition demonstrated that eroded sediment loads were deposited in the cells along the channel network, which have a short overland flow length and a gentle local slope while the erosion rate increased as rainfall became larger. Additionally, spatially heterogeneous rainfall intensity, dependant on Thiessen polygons, led to spatially-distinct erosion and deposition patterns.