Consideration of rainfall intermittency and log-normality on the merging of radar and the rain gauge rain rate

Ro, Yonghun; Yoo, Chulsang

doi:10.1016/j.jhydrol.2020.125178

Cited by 8 publications

(3 citation statements)

References 66 publications

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“…The Dorim stream basin is primarily observed by the Gwanaksan Meteorological Radar. This radar is an S‐band single‐biased Doppler radar with an antenna altitude of approximately 640 m, antenna diameter of 8.5 m, observation radius of approximately 240 km, and effective radius of approximately 120 km, enabling ultra‐short‐term rainfall prediction in the Dorim stream basin (Ha et al, 2015; Ro & Yoo, 2020).…”

Section: Discussionmentioning

confidence: 99%

Flooding time nomograph for urban river flood prediction: Case study of Dorim stream basin, Seoul

Yoon

Lee

Choi

et al. 2023

J Flood Risk Management

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Global climate change is intensifying flood damage in urban rivers. Notably, most small and medium-sized urban rivers have a brief concentration period and are highly vulnerable to sudden heavy rains that lead to a rapid increase in water levels. Therefore, rapid flood forecasting must be performed through accurate flood occurrence and timing prediction. In this study, a flooding time nomograph (FTN) was proposed to predict the flood occurrence time according to rainfall conditions, such as intensity, time distribution, and duration. In addition, rainfall-runoff simulations were performed by establishing different virtual rainfall scenarios using Huff's quartile rainfall time distribution. The simulation results were used to formulate the relationship between the rainfall intensity and flood occurrence time to generate the FTN. The applicability of this tool was verified through a comparison with the observed flood occurrence time for an actual rainfall event, which was highly accurate in the target watershed, with a correlation coefficient >0.8 and Nash-Sutcliffe efficiency >0.6. Therefore, the proposed FTN can be used to reasonably predict the occurrence of floods and the time of flood occurrence using only predicted rainfall information. K E Y W O R D S flood, flood forecasting and warning, flooding time nomograph, rainfall-runoff model, urban stream 1 | INTRODUCTION Extreme weather events caused by global climate change are frequent worldwide. According to the 6th evaluation report published by the Intergovernmental Panel on Climate Change (IPCC), the surface temperature has sharply increased since the fifth evaluation report, with the highest temperatures observed since 1850, and extreme climate change, unprecedented in modern human history, is occurring (IPCC, 2021). To examine the heavy rainfall characteristics of major urban areas in Korea, Yoon et al. (2016) performed quantile regression analyses by applying various classification criteria for extreme precipitation to eight major cities in Korea.According to the Mann-Kendall test and Sen test results, the annual top 10th maximum precipitation has increased by 3.1%-15.0% on average over the past 30 years . At present, the temperature is

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Section: Discussionmentioning

confidence: 99%

Flooding time nomograph for urban river flood prediction: Case study of Dorim stream basin, Seoul

Yoon

Lee

Choi

et al. 2023

J Flood Risk Management

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“…In rain gauge-derived rainfall, the variance was the largest in the SB of Event 3 among all cases. Tese results showed that radar-derived rainfall has a smaller spatial deviation than rain gauge-derived rainfall [45]; however, the amount of rainfall is small [46,47] because the radar observes raindrops in the atmosphere, but as droplets fall, observation loss may occur in the rain gauge depending on weather conditions. Te amount of ER calculated using the diference in CAR between the two boxes was similar to that of the radar and rain gauge.…”

Section: Determination Of the Taermentioning

confidence: 98%

Estimation of the Total Amount of Enhanced Rainfall for a Cloud Seeding Experiment: Case Studies of Preventing Forest Fire, Drought, and Dust

Chang

Chae

et al. 2023

Advances in Meteorology

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In this study, a method for verifying the effect of cloud seeding in the case of a mixture of natural and artificial rainfall bands was proposed, and its applicability to each experimental case was evaluated. Water resources that could be secured through cloud seeding were also quantified for the experiments on forest fire prevention, drought mitigation, and dust reduction in 2020. Data on numerical simulations, radar-derived rainfall, rain gauge-derived rainfall, and weather conditions were applied. Areas with seeding and nonseeding effects were classified according to the numerical simulation results and wind system, and enhanced rainfall was determined by comparing the changes in rainfall between the two areas. The amount of water resources was determined by considering the area of the seeding effect and rainfall density. As a result, 1.74 mm (4.75 million tons) of rainfall increased from the experiment on forest fire prevention, 0.84 mm (1.30 million tons) on drought mitigation, and 2.78 mm (24.44 million tons) on dust reduction. Thus, an average rainfall of 1.0 mm could be achieved through the experiment. These results helped verify the pure seeding effect and achieve the experimental purpose.

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“…Despite the introduction of remote observation instruments, such as radar and satellites, generating quantitative rainfall data remains challenging because of uncertainty arising from the di culties in observations over mountains and oceans (Tesfagiorgis et Studies have attempted to merge radar and ground observations or use machine learning to generate quantitative rainfall data (Tang et al 2018;Shin et al 2019;Ro and Yoo 2020). These research showed that how important to secure accurate ground observation data for precise analysis of water resources and water cycle.…”

Section: )mentioning

confidence: 99%

Comparative study of rainfall measurement by optical disdrometer, tipping-bucket rain gauge, and weighing precipitation gauge

Chang

Hwang

et al. 2022

Preprint

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In this study, the rainfall measurement characteristics of an optical Particle Size Velocity (Parsivel) disdrometer, tipping-bucket rain gauge (TBG), and Pluvio weighing precipitation gauge (WPG) were analyzed and compared. Correlation analysis was performed between the 10-min and 1-h rainfall data observed with the Parsivel, TBG, and Pluvio from 2010 to 2019 at the Cloud Physics Observation Site which is located in north-east area of South Korea (N37.6869, E128.7586). At higher rainfall intensities, the Parsivel observed more rainfall; however, the TBG lost more rainfall during observation. The correlation between the Pluvio and Parsivel data was higher than that between the TBG and Parsivel data. Additionally, the Pluvio showed reduced loss in the rainfall observation than that by the TBG. The correlation between the Pluvio and TBG data was the highest, and the coefficient of determination increased by a maximum of 42.08% for 1-h rainfall compared to that for 10-min rainfall. Therefore, the Pluvio can generate relatively accurate rainfall data for water resource utilization.

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Consideration of rainfall intermittency and log-normality on the merging of radar and the rain gauge rain rate

Cited by 8 publications

References 66 publications

Flooding time nomograph for urban river flood prediction: Case study of Dorim stream basin, Seoul

Flooding time nomograph for urban river flood prediction: Case study of Dorim stream basin, Seoul

Estimation of the Total Amount of Enhanced Rainfall for a Cloud Seeding Experiment: Case Studies of Preventing Forest Fire, Drought, and Dust

Comparative study of rainfall measurement by optical disdrometer, tipping-bucket rain gauge, and weighing precipitation gauge

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