Detecting and forecasting extreme rainfall

2011 ◽  
pp. 1-16
Author(s):  
Brian Golding
Keyword(s):  
Extreme Rainfall

The Influence of Geographical Factors on Extreme Rainfalls in Lampung Province

2019 ◽  
Vol 1 (1) ◽  
pp. 33
Author(s):  
M Welly
Keyword(s):  
Geographical Location ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Design Flood ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Ungauged Basin ◽  
Flood Discharge ◽  
Design Rainfall ◽  
Geographical Factors

Many people in Indonesia calculate design rainfall before calculating the design flooddischarge. The design rainfall with a certain return period will eventually be convertedinto a design flood discharge by combining it with the characteristics of the watershed.However, the lack of a network of rainfall recording stations makes many areas that arenot hydrologically measured (ungauged basin), so it is quite difficult to know thecharacteristics of rain in the area concerned. This study aims to analyze thecharacteristics of design rainfall in Lampung Province. The focus of the analysis is toinvestigate whether geographical factors influence the design rainfall that occurs in theparticular area. The data used in this study is daily rainfall data from 15 rainfallrecording stations spread in Lampung Province. The method of frequency analysis usedin this study is the Gumbel method. The research shows that the geographical location ofan area does not have significant effect on extreme rainfall events. The effect of risingearth temperatures due to natural exploitation by humans tends to be stronger as a causeof extreme events such as extreme rainfall.Keywords: Influence, geographical, factors, extreme, rainfall.

Trend of Extreme Precipitations Indices in West-central Côte d’Ivoire: Case of the Lobo Watershed

2020 ◽  
Vol 5 (10) ◽  
pp. 1281-1287
Author(s):  
F. B. Allechy ◽  
M. Youan Ta ◽  
V. H. N’Guessan Bi ◽  
F. A. Yapi ◽  
A. B. Koné ◽  
...  
Keyword(s):  
Cote D'ivoire ◽  
Côte D’Ivoire ◽  
Crop Yields ◽  
Extreme Rainfall ◽  
Cote D’Ivoire ◽  
West Central ◽  
Côte D'ivoire ◽  
Northern Half ◽  
Agricultural Potential ◽  
Annual Total

The Lobo watershed located in the west-central part of Côte d'Ivoire is an area with high agricultural potential, influenced by climate variations and changes that reduce crop yields. The objective of this study is to analyse trends in ETCCDI extreme rainfall indices from rainfall data from 1984 to 2013 using ClimPACT2 software. This study shows that the trend of the indices: number of consecutive wet days (CWD), number of rainy days (R1mm) and the cumulative annual total rainfall (PRCPTOT) is decreasing. On the other hand, the number of consecutive dry days (CDD) is on the rise. In general, the whole basin has experienced a decrease in rainfall as well as wet sequences and an increase in dry sequences. These different trends observed in this study are more pronounced in the northern half of the watershed.

Economic Impact of Sugarcane Variety CO-86032 on Farmers Economy in Maharashtra

2020 ◽  
pp. 354-359
Keyword(s):  
Economic Impact ◽  
Production Technology ◽  
Economic Benefit ◽  
Extreme Rainfall ◽  
Sugar Recovery ◽  
Sugarcane Variety ◽  
Public Research ◽  
Productivity Improvement ◽  
Farming Community ◽  
The Government

The area under sugarcane in Maharashtra state was found to be more stable and consistent rather than production and productivity. It may be due to the F & RP of sugarcane. In the year 1996, MPKV, Rahuri released a promising variety of sugarcane viz., Co-86032 which is very famous in farming community due to its hardiness, sugar recovery (percent) and resistance to the extreme rainfall as well as deficit rainfall. The total economic worthiness of university released sugarcane variety Co-86032(production technology) over other competing varieties of sugarcane in the Maharashtra was `51449.14per ha. The sugarcane growers in Maharashtra state earned net economic benefit of `11059.40 crores from improved sugarcane variety Co-86032. Therefore, it is suggested that the Government should allocate substantial funds to public research in sugarcane for productivity improvement.

Effects of extreme rainfall and drought events on grassland ecosystems

2014 ◽  
Vol 38 (9) ◽  
pp. 1008-1018 ◽  
Author(s):  
ZHANG Bin ◽  
◽  
ZHU Jian-Jun ◽  
LIU Hua-Min ◽  
and PAN Qing-Min
Keyword(s):  
Extreme Rainfall ◽  
Grassland Ecosystems

CLIMATOLOGY OF EXTREME RAINFALL EVENTS IN EASTERN AND NORTHERN SANTA CATARINA STATE, BRAZIL: PRESENT AND FUTURE CLIMATE

2013 ◽  
Vol 31 (3) ◽  
pp. 413 ◽  
Author(s):  
André Becker Nunes ◽  
Gilson Carlos Da Silva
Keyword(s):  
Climate Model ◽  
Climate Modeling ◽  
Linear Trend ◽  
Extreme Rainfall ◽  
Eastern Region ◽  
Santa Catarina ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
History Of ◽  
Santa Catarina State

ABSTRACT. The eastern region of Santa Catarina State (Brazil) has an important history of natural disasters due to extreme rainfall events. Floods and landslides are enhancedby local features such as orography and urbanization: the replacement of natural surface coverage causing more surface runoff and, hence, flooding. Thus, studies of this type of events – which directly influence life in the towns – take on increasing importance. This work makes a quantitative analysis of occurrences of extreme rainfall events in the eastern and northern regions of Santa Catarina State in the last 60 years, through individual analysis, considering the history of floods ineach selected town, as well as an estimate through to the end of century following regional climate modeling. A positive linear trend, in most of the towns studied, was observed in the results, indicating greater frequency of these events in recent decades, and the HadRM3P climate model shows a heterogeneous increase of events for all towns in the period from 2071 to 2100.Keywords: floods, climate modeling, linear trend. RESUMO. A região leste do Estado de Santa Catarina tem um importante histórico de desastres naturais ocasionados por eventos extremos de precipitação. Inundações e deslizamentos de terra são potencializados pelo relevo acidentado e pela urbanização das cidades da região: a vegetação nativa vem sendo removida acarretando um maior escoamento superficial e, consequentemente, em inundações. Desta forma, torna-se de suma importância os estudos acerca deste tipo de evento que influencia diretamente a sociedade em geral. Neste trabalho é realizada uma análise quantitativa do número de eventos severos de precipitação ocorridos nas regiões leste e norte de Santa Catarina dos últimos 60 anos, por meio de uma análise pontual, considerandoo histórico de inundações de cada cidade selecionada, além de uma projeção para o fim do século de acordo com modelagem climática regional. Na análise dos resultados observou-se uma tendência linear positiva na maioria das cidades, indicando uma maior frequência deste tipo de evento nas últimas décadas, e o modelo climático HadRM3P mostra um aumento heterogêneo no número de eventos para todas as cidades no período de 2071 a 2100.Palavras-chave: inundações, modelagem climática, tendência linear.

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.

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