• Atmosphere
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• v.21 no.4
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• pp.481-495
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• 2011

This study is performed to consider the threshold values of heavy rain warning in Korea using 98 surface meteorological station data and 590 Automatic Weather System stations (AWSs), damage data of National Emergency Management Agency for the period of 2005 to 2009. It is in need to arrange new criteria for heavy rain considering concept of rainfall intensity and rainfall damage to reflect the changed characteristics of rainfall according to the climate change. Rainfall values from the most frequent rainfall damage are at 30 mm/1 hr, 60 mm/3 hr, 70 mm/6 hr, and 110 mm/12 hr, respectively. The cumulative probability of damage occurrences of one in two due to heavy rain shows up at 20 mm/1 hr, 50 mm/3 hr, 80 mm/6 hr, and 110 mm/12 hr, respectively. When the relationship between threshold values of heavy rain warning and the possibility of rainfall damage is investigated, rainfall values for high connectivity between heavy rain warning criteria and the possibility of rainfall damage appear at 30 mm/1 hr, 50 mm/3 hr, 80 mm/6 hr, and 100 m/12 hr, respectively. It is proper to adopt the daily maximum precipitation intensity of 6 and 12 hours, because 6 hours rainfall might be include the concept of rainfall intensity for very-short-term and short-term unexpectedly happened rainfall and 12 hours rainfall could maintain the connectivity of the previous heavy rain warning system and represent long-term continuously happened rainfall. The optimum combinations of criteria for heavy rain warning of 6 and 12 hours are 80 mm/6 hr or 100 mm/12 hr, and 70 mm/6 hr or 110 mm/12 hr.

• Journal of Korea Water Resources Association
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• v.56 no.11
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• pp.751-764
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• 2023

Gangwon State is centered on the Taebaek Mountains with very different climate characteristics depending on the region, and localized heavy rainfall is a frequent occurrence. Heavy rain disasters have a short duration and high spatial and temporal variability, causing many casualties and property damage. In the last 10 years (2012~2021), the number of heavy rain disasters in Gangwon State was 28, with an average cost of 45.6 billion won. To reduce heavy rain disasters, it is necessary to establish a disaster management plan at the local level. In particular, the current criteria for heavy rain warnings are uniform and do not consider local characteristics. Therefore, this study aims to propose a heavy rainfall warning criteria that considers the threshold rainfall for the advisory areas located in Gangwon State. As a result of analyzing the representative value of threshold rainfall by advisory area, the Mean value was similar to the criteria for issuing a heavy rain warning, and it was selected as the criteria for a heavy rain warning in this study. The rainfall events of Typhoon Mitag in 2019, Typhoons Maysak and Haishen in 2020, and Typhoon Khanun in 2023 were applied as rainfall events to review the criteria for heavy rainfall warnings, as a result of Hit Rate accuracy verification, this study reflects the actual warning well with 72% in Gangneung Plain and 98% in Wonju. The criteria for heavy rain warnings in this study are the same as the crisis warning stages (Attention, Caution, Alert, and Danger), which are considered to be possible for preemptive rain disaster response. The results of this study are expected to complement the uniform decision-making system for responding to heavy rain disasters in the future and can be used as a basis for heavy rain warnings that consider disaster risk by region.

• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.37-51
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• 2017

Despite the improvement in accuracy of heavy rain forecasting, socioeconomic costs due to heavy rain hazards continue to increase. This is due to a lack of understanding of the effects of weather. In this study, the risk of heavy rain hazard was analyzed using the concepts of hazard, vulnerability, and exposure, which are key concepts of impact forecast presented by WMO. The potential impacts were constructed by the exposure and vulnerability variables, and the hazard index was calculated by selecting three variables according to the criteria of heavy rain warning. Weights of the potential impact index were calculated by using PCA and hazard index was calculated by applying the same weight. Correlation analysis between the potential impact index and damages showed a high correlation and it was confirmed that the potential impact index appropriately reflects the actual damage pattern. The heavy rain hazard risk was estimated by using the risk matrix consisting of the heavy rain potential impact index and the hazard index. This study provides a basis for the impacts analysis study for weather warning with spatial/temporal variation and it can be used as a useful data to establish the local heavy rain hazard prevention measures.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.334-334
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• 2022

Improvement of old-fashioned rain gauge systems for automatic, timely, continuous, and accurate precipitation observation is highly essential for weather/climate prediction and natural hazards early warning, since the occurrence frequency and intensity of heavy and extreme precipitation events (especially floods) are recently getting more increase and severe worldwide due to climate change. Although rain gauge accuracy of 0.1 mm is recommended by the World Meteorological Organization (WMO), the traditional rain gauges in both weighting and tipping bucket types are often unable to meet that demand due to several existing technical limitations together with higher production and maintenance costs. Therefore, we aim to introduce a newly developed and cost-effective hybrid rain gauge system at 0.1 mm accuracy that combines advantages of weighting and tipping bucket types for continuous, automatic, and accurate precipitation observation, where the errors from long-term load cells and external environmental sources (e.g., winds) can be removed via an automatic drainage system and artificial intelligence-based data quality control procedure. Our rain gauge system consists of an instrument unit for measuring precipitation, a communication unit for transmitting and receiving measured precipitation signals, and a database unit for storing, processing, and analyzing precipitation data. This newly developed rain gauge was designed according to the weather instrument criteria, where precipitation amounts filled into the tipping bucket are measured considering the receiver's diameter, the maximum measurement of precipitation, drainage time, and the conductivity marking. Moreover, it is also designed to transmit the measured precipitation data stored in the PCB through RS232, RS485, and TCP/IP, together with connecting to the data logger to enable data collection and analysis based on user needs. Preliminary results from a comparison with an existing 1.0-mm tipping bucket rain gauge indicated that our developed rain gauge has an excellent performance in continuous precipitation observation with higher measurement accuracy, more correct precipitation days observed (120 days), and a lower error of roughly 27 mm occurred during the measurement period.

• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.311-323
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• 2023

In order to reduce disaster damage by localized heavy rains, floods, and urban inundation, it is important to know in advance whether natural disasters occur. Currently, heavy rain watch and heavy rain warning by the criteria of the Korea Meteorological Administration are being issued in Korea. However, since this one criterion is applied to the whole country, we can not clearly recognize heavy rain damage for a specific region in advance. Therefore, in this paper, we tried to reset the current criteria for a special weather report which considers the regional characteristics and to predict the damage caused by rainfall after 1 hour. The study area was selected as Gyeonggi-province, where has more frequent heavy rain damage than other regions. Then, the rainfall inducing disaster or hazard-triggering rainfall was set by utilizing hourly rainfall and heavy rain damage data, considering the local characteristics. The heavy rain damage prediction model was developed by a decision tree model and a random forest model, which are machine learning technique and by rainfall inducing disaster and rainfall data. In addition, long short-term memory and deep neural network models were used for predicting rainfall after 1 hour. The predicted rainfall by a developed prediction model was applied to the trained classification model and we predicted whether the rain damage after 1 hour will be occurred or not and we called this as 1ST-Model. The 1ST-Model can be used for preventing and preparing heavy rain disaster and it is judged to be of great contribution in reducing damage caused by heavy rain.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.389-392
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• 2004

A recent unusual change in the weather is formed as a localized heavy rain in a short time. This phenomenon has caused a flash flood, and flash floods extensively have damaged human lives many times. In large river's case, the extent of loss of lives and properties has been decreased through the flood warning system by flood control stations of each stream. However, the extent of damage in other small rivers has increased reversely. Therefore, it is necessary to establish a new flood warning system against flash floods instead of the existing flood warning system. It is a specific character that the damage from flash floods in mountain streams brings much more loss of lives than large river's flood. The purpose of this study is calculating the characteristic of flash floods in streams, analyzing topographical characteristics of water basin through applying GIS techniques with the calculation as mentioned above and researching what topographical conditions have influence on hydrological flash floods in water basin. The flash flood prediction model we used is made by GIUH (geomorphoclimatic instantaneous unit hydrograph) with hydrologic-topographical technology. As applying the flash flood prediction model, this is a procedure for calculating topographical information in basin: we made a topological data up out of database with utilizing GIS, and we also produced a DEM (digital elevation model) and used it as a topographical data for determining amount of flash floods.

• Journal of Korea Water Resources Association
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• v.51 no.1
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• pp.1-10
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• 2018

Recently, localized heavy rainfall has led to increasing flood damage in urban areas such as Gangnam, Seoul ('12), Busan ('13), Ulsan ('16) Incheon and Busan ('17) etc. Urban flooding occurs relatively rapidly compared to flood damage in river basin, and property damage including damage to houses, cars and shopping centers is more serious than facility damage to structures such as levees and small bridges. In Korea, heavy rain warnings are currently announced using the criteria set by KMA (Korea Meteorological Administration). However, these criteria do not reflect regional characteristics and are not suitable to urban flood. So in this study, estimated the flooding limit rainfall amount based on the damage records for Seoul and Ulsan. And for regions that can not estimate the flooding limit rainfall since there is no damage records, we estimated the flooding limit rainfall using a Neuro-Fuzzy model with runoff characteristics. Based on the estimated flooding limit rainfall, the urban flood warning criteria was set. and applied to the actual flood event. As a result of comparing the estimated flooding limit rainfall with the actual flooding limit rainfall, the error of 1.8~20.4% occurred. And evacuation time was analyzed from a minimum of 28 minutes to a maximum of 70 minutes. Therefore, it can be used as a warning criteria in the urban flood.

• Journal of Korean Society of Forest Science
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• v.101 no.1
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• pp.28-35
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• 2012

Rainfall is widely recognized as a major landslide-triggering factor. Most of the latest landslides that occurred in South Korea were caused by short-duration heavy rainfall. However, the relationship between rainfall characteristics and landslide occurrence is poorly understood. To examine the effect of rainfall on landslide occurrence, cumulative rainfall(mm) and rainfall intensity(mm/hr) of serial rain and antecedent rainfall(mm) were analyzed for 18 landslide events that occurred in the southern and central regions of South Korea in June and July 2011. It was found that all of these landslides occurred by heavy rainfall for one or three days, with the rainfall intensity exceeding 30 mm/hr or with a cumulative rainfall of 200 mm. These plotted data are beyond the landslide warning criteria of Korea Forest Service and the critical line of landslide occurrence for Gyeongnam Province. It was also found that the time to landslide occurrence after rainfall start(T) was shortened with the increasing average rainfall intensity(ARI), showing an exponential-decay curve, and this relation can be expressed as "T = $94.569{\cdot}exp$($-0.068{\cdot}ARI$)($R^2$=0.64, p<0.001)". The findings in this study may provide important evidences for the landslide forecasting guidance service of Korea Forest Service as well as essential data for the establishment of non-structural measures such as a warning and evacuation system in the face of sediment disasters.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.485-496
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• 2020

The characteristics of volumetric water content changes in soil slopes were studied here in an effort to identify the signs of heavy rain causing shallow slope failure. Volumetric water contents in cases with and without shallow failure were measured in flume and test-bed experiments. Measurement data from 282 experiments of both types revealed that the volumetric water content gradient in shallow failure events ranged from 0.072 to 0.309. In non-failure cases, the range was 0.01~0.32. Therefore, this one specific value cannot predict shallow slope failure. However, as the volumetric water content gradient increased, there was a clear tendency to shallow failure. By using this trend, criteria for four warning levels are suggested.

• Journal of Korea Water Resources Association
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• v.56 no.4
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• pp.261-272
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• 2023

In recent years, natural disasters such as heavy rainfall and typhoons have occurred more frequently, and their severity has increased due to climate change. The Korea Meteorological Administration (KMA) currently uses the same criteria for all regions in Korea for watch and warning based on the maximum cumulative rainfall with durations of 3-hour and 12-hour to reduce damage. However, KMA's criteria do not consider the regional characteristics of damages caused by heavy rainfall and typhoon events. In this regard, it is necessary to develop new criteria considering regional characteristics of damage and cumulative rainfalls in durations, establishing four stages: blue, yellow, orange, and red. A classification model, called DSCM (Disaster Severity Classification Model), for the four-stage disaster severity was developed using four machine learning models (Decision Tree, Support Vector Machine, Random Forest, and XGBoost). This study applied DSCM to local governments of Seoul, Incheon, and Gyeonggi Province province. To develop DSCM, we used data on rainfall, cumulative rainfall, maximum rainfalls for durations of 3-hour and 12-hour, and antecedent rainfall as independent variables, and a 4-class damage scale for heavy rain damage and typhoon damage for each local government as dependent variables. As a result, the Decision Tree model had the highest accuracy with an F1-Score of 0.56. We believe that this developed DSCM can help identify disaster risk at each stage and contribute to reducing damage through efficient disaster management for local governments based on specific events.