Subsurface bubble layers generated by extremely heavy rainfall

1996 ◽  
Vol 100 (4) ◽  
pp. 2807-2807
Author(s):  
Jeffrey A. Nystuen
Keyword(s):  
Heavy Rainfall ◽  
Bubble Layers

Enteric viruses in drinking water supplies

2002 ◽  
Vol 2 (3) ◽  
pp. 17-22
Author(s):  
A.P. Wyn-Jones ◽  
J. Watkins ◽  
C. Francis ◽  
M. Laverick ◽  
J. Sellwood
Keyword(s):  
Drinking Water ◽  
Heavy Rainfall ◽  
Liquid Culture ◽  
Plaque Assay ◽  
Enteric Viruses ◽  
Enteric Virus ◽  
The Other ◽  
Raw Water ◽  
Rt Pcr ◽  
Water Supplies

Two rural spring drinking water supplies were studied for their enteric virus levels. In one, serving about 30 dwellings, the water was chlorinated before distribution; in the other, which served a dairy and six dwellings the water was not treated. Samples of treated (40 l) and untreated (20 l) water were taken under normal and heavy rainfall conditions over a six weeks period and concentrated by adsorption/elution and organic flocculation. Infectious enterovirus in concentrates was detected in liquid culture and enumerated by plaque assay, both in BGM cells, and concentrates were also analysed by RT-PCR. Viruses were found in both raw water supplies. Rural supplies need to be analysed for viruses as well as bacterial and protozoan pathogens if the full microbial hazard is to be determined.

Study on Manhole Internal Pressure Countermeasures during Heavy Rainfall

2017 ◽  
Vol 2017 (4) ◽  
pp. 5684-5698
Author(s):  
Yuki Kuwahara ◽  
Yoshiharu Itaya ◽  
Yuji Itou
Keyword(s):  
Internal Pressure ◽  
Heavy Rainfall

scholarly journals The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

scholarly journals Extreme Floods in the Eastern Part of Europe: Large-Scale Drivers and Associated Impacts

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1122
Author(s):  
Monica Ionita ◽  
Viorica Nagavciuc
Keyword(s):  
Water Vapor ◽  
Heavy Rainfall ◽  
Large Scale ◽  
Vapor Transport ◽  
Water Vapor Transport ◽  
Flood Events ◽  
Rainfall Events ◽  
Catchment Areas ◽  
Large Scale Atmospheric Circulation ◽  
Heavy Rainfall Events

The role of the large-scale atmospheric circulation in producing heavy rainfall events and floods in the eastern part of Europe, with a special focus on the Siret and Prut catchment areas (Romania), is analyzed in this study. Moreover, a detailed analysis of the socio-economic impacts of the most extreme flood events (e.g., July 2008, June–July 2010, and June 2020) is given. Analysis of the largest flood events indicates that the flood peaks have been preceded up to 6 days in advance by intrusions of high Potential Vorticity (PV) anomalies toward the southeastern part of Europe, persistent cut-off lows over the analyzed region, and increased water vapor transport over the catchment areas of Siret and Prut Rivers. The vertically integrated water vapor transport prior to the flood peak exceeds 300 kg m−1 s−1, leading to heavy rainfall events. We also show that the implementation of the Flood Management Plan in Romania had positive results during the 2020 flood event compared with the other flood events, when the authorities took several precaution measurements that mitigated in a better way the socio-economic impact and risks of the flood event. The results presented in this study offer new insights regarding the importance of large-scale atmospheric circulation and water vapor transport as drivers of extreme flooding in the eastern part of Europe and could lead to a better flood forecast and flood risk management.

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