Preparing proper disaster prevention measures is important for decreasing the casualties and property losses resulting from floods. One of the most efficient measures in this regard is real-time flood forecasting using quantitative precipitation forecasts (QPFs) based on either short-term radar-based extrapolation or longer-term numerical weather prediction. As both methods have individual advantages and limitations, in this study we developed a new real-time blending technique to improve the accuracy of rainfall forecasts for hydrological applications. We tested the hydrological applicability of six QPFs used for urban flood forecasting in Seoul, South Korea: the McGill Algorithm for Prediction Nowcasting by Lagrangian Extrapolation (MAPLE), KOrea NOwcasting System (KONOS), Spatial-scale Decomposition method (SCDM), Unified Model Local Data Assimilation and Prediction System (UM LDAPS), and Advanced Storm-scale Analysis and Prediction System (ASAPS), as well as our proposed blended approach based on the assumption that the error of the previously predicted rainfall is similar to that of current predicted rainfall. We used the harmony search algorithm to optimize real-time weights that would minimize the root mean square error between predicted and observed rainfall for a 1 h lead time at 10 min intervals. We tested these models using the Storm Water Management Model (SWMM) and Grid-based Inundation Analysis Model (GIAM) to estimate urban flood discharge and inundation using rainfall from the QPFs as input. Although the blended QPF did not always have the highest correlation coefficient, its accuracy varied less than that of the other QPFs. In addition, its simulated water depth in pipe and spatial extent were most similar to observed inundated areas, demonstrating the value of this new approach for short-term flood forecasting.

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Flood prediction is difficult in urban areas because only sparse gauge data and radar data of low accuracy are usually used to analyze flooding and inundation. Sub-basins of urban areas are extremely small, so rainfall data of high spatial resolution are required for analyzing complex drainage systems with high spatial variability. This study aimed to produce three types of quantitative precipitation estimation (QPE) products using rainfall data that was derived from 190 gauges, including the new high-density rain-gauge network operated by the SK Planet company, and the automated weather stations of the Korea Meteorological Administration, along with weather radar data. This study also simulated urban runoff for the Gangnam District of Seoul, South Korea, using the obtained QPE products to evaluate hydraulic and hydrologic impacts according to three rainfall fields. The accuracy of this approach was assessed in terms of the amount and spatial distribution of rainfall in an urban area. The QPE products provided highly accurate results and simulations of peak runoff and overflow phenomena. They also accurately described the spatial variability of the rainfall fields. Overall, the integration of high-density gauge data with radar data proved beneficial for quantitative rainfall estimation.

One of the major limitations of existing unmanned aerial vehicles is limited flight endurance. In this study, we designed an innovative uninterrupted electromagnetic propulsion device for high-endurance missions of a quadcopter drone for the lucrative exploration of earth and other planets with atmospheres. As an airborne platform, this device could achieve scientific objectives better than state-of-the-art revolving spacecraft and walking robots, without any terrain limitation. We developed a mixed reality simulation based on a quadcopter drone and an X-Plane flight simulator. A computer with the X-Plane flight simulator represented the virtual part, and a real quadcopter operating within an airfield represented the real part. In the first phase of our study, we developed a connection interface between the X-Plane flight simulator and the quadcopter ground control station in MATLAB. The experimental results generated from the Earth’s atmosphere show that the flight data from the real and the virtual quadcopters are precise and very close to the prescribed target. The proof-of-concept of the mixed reality simulation of the quadcopter at the Earth atmosphere was verified and validated through several experimental flights of the F450 spider quadcopter with a Pixhawk flight controller with the restricted endurance at the airfield location of Hangang Drone Park in Seoul, South Korea. We concluded that the new generation drones integrated with lightweight electromagnetic propulsion devices are a viable option for achieving unrestricted flight endurance with improved payload capability for Earth and other planetary explorations with the aid of mixed reality simulation to meet the mission flight path demands. This study provides insight into mixed reality simulation aiming for Mars explorations and high-endurance missions in the Earth’s atmosphere with credibility using quadcopter drones regulated by dual-head electromagnetic propulsion devices.

The common statement that a rain gauge network usually provides better observation at specific points while weather radar provides more accurate observation of the spatial distribution of rain field over a large area has never been subjected to quantitative evaluation. The aim of this paper is to evaluate the statement by using some statistical criteria. The Monte Carlo simulation experiment, inverse distance weighting (IDW) interpolation method, and cross-validation technique are used to investigate the relation between the accuracy of the interpolated rainfall and the rain gauge density. The radar reflectivity–rainfall intensity (Z–R) relationship is constructed by the least squares fitting method from observation data of radar and rain gauges. The variation in this relationship and the accuracy of the radar rainfall with rain gauge density are evaluated by using the Monte Carlo simulation experiment. Three storm events are selected as the case studies. The obtained results show that the accuracy of interpolated and radar rainfall increases nonlinearly with increasing gauge density. The higher correlation coefficient (γ) value of radar-rainfall estimation, compared to gauge interpolation, especially in the convective storm, proves that radar observation provides a more accurate spatial structure of the rain field than gauge observation does.

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More than 70% of South Korea has mountainous terrain, which leads to significant spatiotemporal variability of rainfall. The country is exposed to the risk of flash floods owing to orographic rainfall. Rainfall observations are important in mountainous regions because flood control measures depend strongly on rainfall data. In particular, radar rainfall data are useful in these regions because of the limitations of rain gauges. However, radar rainfall data include errors despite the development of improved estimation techniques for their calculation. Further, the radar does not provide accurate data during heavy rainfall in mountainous areas. This study presents a radar rainfall adjustment method that considers the elevation in mountainous regions. Gauge rainfall and radar rainfall field data are modified by using standardized ordinary cokriging considering the elevation, and the conditional merging technique is used for combining the two types of data. For evaluating the proposed technique, the Han River basin was selected; a high correlation between rainfall and elevation can be seen in this basin. Further, the proposed technique was compared with the mean field bias and original conditional merging techniques. Comparison with kriged rainfall showed that the proposed method has a lesser tendency to oversmooth the rainfall distribution when compared with the other methods, and the optimal mean areal rainfall is very similar to the value obtained using gauges. It reveals that the proposed method can be applied to an area with significantly varying elevation, such as the Han River basin, to obtain radar rainfall data of high accuracy.

The objectives of this study are to develop the flow nomograph for real-time flood forecasting and to assess its applicability in restored Cheonggye stream. The Cheonggye stream basin has the high impermeability and short concentration time and complicated hydrological characteristics. Therefore, the flood prediction method using runoff model is ineffective due to the limit of forecast. Flow nomograph which is able to forecast flood only with rainfall information. To set the forecast criteria of flow nomograph at selected flood forecast points and calculated criterion flood water level for each point, and in order to reflect various flood events set up simulated rainfall scenario and calculated rainfall intensity and rainfall duration time for each condition of rainfall. Besides, using a rating curve, determined scope of flood discharge following criterion flood water level and using SWMM model calculated flood discharge for each forecasting point. Using rainfall information following rainfall scenario calculated above and flood discharge following criterion flood water level developed flow nomograph and evaluated it by applying it to real flood event. As a result of performing this study, the applicability of flow nomograph to the basin of Cheonggye stream appeared to be high. In the future, it is reckoned to have high applicability as a method of prediction of flood of urban stream basin like Cheonggye stream.