scholarly journals 1 to 10 days extreme rainfall studies for Kerala state

MAUSAM ◽  
2022 ◽  
Vol 46 (2) ◽  
pp. 175-180
Author(s):  
S. A. SASEENDRAN ◽  
K. K. SINGH ◽  
J. BAHADUR ◽  
O. N. DHAR
Keyword(s):  
Extreme Precipitation ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Statistical Technique ◽  
Rainfall Data ◽  
Hydraulic Structures ◽  
Annual Maximum ◽  
Probable Maximum Precipitation ◽  
Maximum Precipitation ◽  
Kerala State

 The daily rainfall data for 80 years from 98 stations in Kerala region have been analysed to arrive at the Probable Maximum Precipitation (PMP) estimates for rainfall durations or 1 to 10 days. Hershfield's statistical technique has been adopted for the estimation of PMP from annual maximum data. The study will be useful in the estimation of extreme precipitation for computation of design floods, required for design of spillways of dams and other major hydraulic structures in the Kerala state.    

scholarly journals A New Look at Storm Separation Technique in Estimation of Probable Maximum Precipitation in Mountainous Areas

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1177
Author(s):  
Yifan Liao ◽  
Bingzhang Lin ◽  
Xiaoyang Chen ◽  
Hui Ding
Keyword(s):  
Extreme Rainfall ◽  
Separation Technique ◽  
Annual Maximum ◽  
Return Periods ◽  
Mountainous Areas ◽  
Maximum Rainfall ◽  
Probable Maximum Precipitation ◽  
Maximum Precipitation ◽  
L Moments ◽  
Annual Maximum Rainfall

Storm separation is a key step when carrying out storm transposition analysis for Probable Maximum Precipitation (PMP) estimation in mountainous areas. The World Meteorological Organization (WMO) has recommended the step-duration-orographic-intensification-factor (SDOIF) method since 2009 as an effective storm separation technique to identify the amounts of precipitation caused by topography from those caused by atmospheric dynamics. The orographic intensification factors (OIFs) are usually developed based on annual maximum rainfall series under such assumption that the mechanism of annual maximum rainfalls is close to that of the PMP-level rainfall. In this paper, an alternative storm separation technique using rainfall quantiles, instead of annual maximum rainfalls, with rare return periods estimated via Regional L-moments Analysis (RLMA) to calculate the OIFs is proposed. Based on Taiwan’s historical 4- and 24-h precipitation data, comparisons of the OIFs obtained from annual maximum rainfalls with that from extreme rainfall quantiles at different return periods, as well as the PMP estimates of Hong Kong from transposing the different corresponding separated nonorographic rainfalls, were conducted. The results show that the OIFs obtained from rainfall quantiles with certain rare probabilities are more stable and reasonable in terms of stability and spatial distribution pattern.

scholarly journals Estimation of probable maximum precipitation for tropical catchment

2018 ◽  
Vol 162 ◽  
pp. 03012 ◽  
Author(s):  
Saad Sammen ◽  
Thamer Mohamed ◽  
Abd Alhalim Ghazali ◽  
Lariyah Sideq ◽  
Azlan Abdul Aziz
Keyword(s):  
Return Period ◽  
Statistical Approach ◽  
Extreme Rainfall ◽  
Physical Method ◽  
Hydraulic Structures ◽  
Probable Maximum Precipitation ◽  
Maximum Precipitation ◽  
Storm Duration ◽  
Probable Maximum Flood

Probable Maximum Precipitation (PMP) is the maximum precipitation depth for specific region or station within a certain time. The main purpose of PMP estimation is calculate the Probable Maximum Flood (PMF). The PMF is considered necessary for design and manage the hydraulic structures. PMP can be estimate using two methods, either using a physical method or by using statistical method. In this study, statistical approach was used to estimate the PMP for Temengor catchment in Perak state, Malaysia. Extreme value type-1 distribution (EV1) is adopted to estimate the extreme rainfall and Hershfeid method was used to estimate PMP value. Also, intensity duration curve (IDC) was derived for 1, 2 and 3 days storm duration with return period 5, 10, 50, 100, 500 years. The results showed that the values of PMP for 1000 return period are 222.361mm, 311.847mm and 348.307mm for 1, 2 and 3 days respectively.

scholarly journals An assessment of temporal effect on extreme rainfall estimates

2018 ◽  
Vol 379 ◽  
pp. 145-150
Author(s):  
Samiran Das ◽  
Dehua Zhu ◽  
Cheng Chi-Han
Keyword(s):  
Risk Analysis ◽  
Yangtze River ◽  
Decadal Variability ◽  
Yangtze River Basin ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Annual Maximum ◽  
Temporal Behaviour ◽  
Annual Maximum Daily Rainfall ◽  
Maximum Daily Rainfall

Abstract. This study assesses the temporal behaviour in terms of inter-decadal variability of extreme daily rainfall of stated return period relevant for hydrologic risk analysis using a novel regional parametric approach. The assessment is carried out based on annual maximum daily rainfall series of 180 meteorological stations of Yangtze River Basin over a 50-year period (1961–2010). The outcomes of the analysis reveal that while there were effects present indicating higher quantile values when estimated from data of the 1990s, it is found not to be noteworthy to exclude the data of any decade from the extreme rainfall estimation process for hydrologic risk analysis.

scholarly journals Changing Lateral Boundary Conditions for Probable Maximum Precipitation Studies: A Physically Consistent Approach

2021 ◽  
Vol 22 (1) ◽  
pp. 113-123
Author(s):  
Karianne Ødemark ◽  
Malte Müller ◽  
Ole Einar Tveito
Keyword(s):  
Boundary Conditions ◽  
Extreme Precipitation ◽  
Moisture Transport ◽  
Extreme Event ◽  
New Method ◽  
Precipitation Event ◽  
Lateral Boundary ◽  
Probable Maximum Precipitation ◽  
Maximum Precipitation ◽  
Lateral Boundary Conditions

AbstractThis article presents a conceptual study toward establishing a new method for altering lateral boundary conditions in numerical model based estimates for probable maximum precipitation (PMP). We altered an extreme event in a physically and dynamically consistent way in a regional convective-scale weather prediction model (AROME-MetCoOp) by applying fields from a global ensemble climate model approach based on EC-EARTH. Ten ensemble members are downscaled with the regional model, which results in 10 different realizations of an extreme precipitation event for the west coast of Norway. We show how the position and orientation of the moisture flow is different between the individual ensemble members, which leads to relatively large changes in precipitation values for a selected catchment. For example, the modification of the moisture transport on scales of several hundred kilometers impacts the extreme precipitation amount by about 75% among the model members. Compared with historical rainfall records, precipitation changes of 62% and 71% are found for two selected catchments. Although the present study is restricted to one particular extreme event that is modified 10 times with the ensemble approach, there is a considerable spread of the moisture transport compared to the spread of the moisture transport of extreme precipitation events of the past 40 years. We conclude that the described approach is a step toward a new method to derive PMP values for a given catchment; however, a larger amount of events and larger ensembles would have to be considered to estimate PMP values.

scholarly journals Evaluasi Kesesuaian Data Tropical Rainfall Measuring Mission (TRMM) dengan Data Pos Hujan Pada Das Temef di Kabupaten Timor Tengah Selatan

2020 ◽  
Vol 16 (1) ◽  
pp. 51-62
Author(s):  
Denik Sri Krisnayanti ◽  
Davianto Frangky B. Welkis ◽  
Fery Moun Hepy ◽  
Djoko Legono
Keyword(s):  
Correlation Coefficient ◽  
Correction Factor ◽  
Daily Rainfall ◽  
Tropical Rainfall Measuring Mission ◽  
Rainfall Data ◽  
Annual Maximum ◽  
Monthly Basis ◽  
Discharge Data ◽  
Tropical Rainfall ◽  
South Central

The construction of the Temef Dam in Oenino Village, Oenino District, and Konbaki Village, Polen District, South Central Timor Regency requires long and reliable rainfall data. To overcome the minimum data or the unavailability of automatic rainfall (ARR) and discharge data in the past decades, the use of Tropical Rainfall Measuring Mission (TRMM) satellite data is foreseen. The accuracy of TRMM data is obtained when the parameters of suitability and compatibility of TRMM are in a good agreement with the ARR. For the Temef watershed, there are six rainfall stations that were reviewed, namely Fatumnasi, Oeoh, Noelnoni, Polen, Nifukani, and Batinifukoko rainfall stations. Direct comparisons of rainfall data were conducted for 20 years (1998-2018) with temporal resolution on a monthly and daily basis. The results of the study show that the rainfall patterns in the TRMM data product (version 3B42V7) tend to be consistent with 3 rainfall stations in the Temef watershed namely Noelnoni, Fatumnasi, and Batinifukoko. A correlation coefficient of 0.505 – 0.813 was obtained from TRMM data calibration at monthly basis while a correction factor level of 0.0056 - 0.0129 was obtained for daily.  The calibration on the annual maximum daily rainfall data resulted in a correction factor of 0.0298 - 0.2516. Monthly and daily TRMM data fit well with the data of 3 rainfall stations. However, the Noelnoni rainfall station showed poor results on the annual maximum daily rainfall.Keywords: TRMM data, ARR data, correction factor, correlation coefficient

scholarly journals Trend and spatial variability of 1-day extreme rainfall from 1980 - 2015: study at the adminisitrative area of UPT PSDA Pasuruan

2019 ◽  
Vol 8 (1) ◽  
pp. 29
Author(s):  
Indarto Indarto
Keyword(s):  
Spatial Variability ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Statistical Test ◽  
Rainfall Event ◽  
Rainfall Data ◽  
Rainfall Events ◽  
Significance Level ◽  
Main Input ◽  
Crossing Test

This study aims to analyze trends,  shift and spatial variability of extreme-rainfall in the area of UPT PSDA Pasuruan. The daily rainfall data from 64 stations from 1980 until 2015 were used as main input. The 1-day extreem rainfall data is determined as the maximum annual of 24-hour rainfall events.  The statistical  analysis using Mann-Kendall, Rank-Sum, and Median Crossing Test using significance level α = 0,05. The spatial variability of extrem rainfall data is described using average annual 24-hour rainfall during the periods of record. Each station is represented by one value. The values are then interpolated using IDW interpolation methods to maps the spatial variability of extreem rainfall event.  The results show the value of statistical test for each stations that show the existing  trend, shift, or randomness of data. The result also produce thematic maps show the spatial variability of extreme rainfall and the value of each trend.

scholarly journals Estimation of Extreme Precipitation in Norway

1989 ◽  
Vol 20 (4-5) ◽  
pp. 257-276 ◽  
Author(s):  
E.J. Førland ◽  
D. Kristoffersen
Keyword(s):  
Extreme Precipitation ◽  
Probable Maximum Precipitation ◽  
Maximum Precipitation ◽  
Probable Maximum Flood

Probable Maximum Precipitation (PMP) is an important parameter for estimation of Probable Maximum Flood. This paper describes results of PMP estimation by different methods, both meteorological and statistical. A survey of the highest recorded rainfall values in Norway is also presented.

scholarly journals Simulation of extreme precipitation in the Rhine basin by nearest-neighbour resampling

1998 ◽  
Vol 2 (2/3) ◽  
pp. 195-209 ◽  
Author(s):  
T. Brandsma ◽  
T. A. Buishand
Keyword(s):  
Time Series ◽  
Atmospheric Circulation ◽  
Extreme Precipitation ◽  
Daily Rainfall ◽  
Snow Accumulation ◽  
Nearest Neighbour ◽  
Annual Maximum ◽  
Rhine Basin ◽  
Resampling Method ◽  
German Part

Abstract. The use of the nonparametric nearest-neighbour resampling technique is studied for generating time series of daily rainfall and temperature for seven stations in the German part of the Rhine basin. The emphasis is on the reproduction of extreme N-day precipitation amounts. The daily temperatures are used to determine snow accumulation and melt in winter. Two versions of the resampling method, conditional on the atmospheric circulation and unconditional, show comparable results. For precipitation, the autocorrelation properties are well reproduced, whereas for temperature the autocorrelation coefficients are systematically underpredicted. The distributions of the N-day annual maximum precipitation amounts are adequately preserved. Despite the systematic underprediction of the temperature autocorrelation, the distributions of N-day maximum snowmelt are well reproduced. A 1000-year simulation for the seven stations shows that unprecedented rainfall situations can be generated.

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