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 Probable Maximum Precipitation Comparison using Hershfield’s Statistical Method and Hydro-Meteorological Method for Sungai Perak Hydroelectric Scheme

2018 ◽  
Vol 7 (4.35) ◽  
pp. 603 ◽  
Author(s):  
Razali J ◽  
Sidek L.M ◽  
Rashid M.A ◽  
Hussein A ◽  
M. Marufuzzaman
Keyword(s):  
Extreme Precipitation ◽  
Physical Method ◽  
Loss Of Life ◽  
Probable Maximum Precipitation ◽  
Maximum Precipitation ◽  
Extreme Flood ◽  
Probable Maximum Flood ◽  
Potential Risks ◽  
Dam Overtopping ◽  
Downstream Area

One of the potential risks attributed to the occurrence of dam overtopping and dam wall failure due to the inadequacy of the spillway capacities is the loss of life and property damages in the downstream area. The current practices in most countries in minimizing these risks are by analyzing the extreme precipitation that leads to extreme flood. Extreme precipitation is best known as Probable Maximum Precipitation (PMP) and this estimation is useful in determining Probable Maximum Flood (PMF) in reviewing the spillway adequacy of dam structures. This paper presented PMP estimations using two approaches; physical method (Hydro-meteorological Method) and statistical approach (Hershfield’s Method) at the Sungai Perak Hydroelectric Scheme that consists of four cascading dams namely Temengor dam, Bersia dam, Kenering dam and Chenderoh dam. The highest PMP estimates from these two methods will be chosen as the rainfall input to establish PMF hydrographs. Estimations using Hydro-meteorological generalized map produces 40-50% higher estimates compared to Hersfield’s method with the PMP values of 550mm (1hours), 600mm (3hours), 800mm (6hours), 820mm (12hours), 1300mm (24hour) and 1600mm (72 hours). Accepting the Hydro-meteorological Method to determine PMF values for this hydroelectric scheme may be the best course since the estimations of the extreme precipitations using this method are the highest.

scholarly journals Probable Maximum Precipitation (PMP) Analysis for Bakun Dam Area

2011 ◽  
Vol 2 (2) ◽  
pp. 53-59
Author(s):  
Yiau S.S. ◽  
F.J. Putuhena
Keyword(s):  
Catchment Area ◽  
Flood Control ◽  
Design Criterion ◽  
Control Measures ◽  
Dam Construction ◽  
Research Publication ◽  
Probable Maximum Precipitation ◽  
Maximum Precipitation ◽  
Probable Maximum Flood ◽  
The One

Probable Maximum Precipitation is defined as the greatest depth of precipitation which is possible for a given time and duration over a given size storm area under known meteorological conditions. This concept has been used as design criterion of major flood control measures such as spillways of large dams worldwide. It is essential for the generation of Probable Maximum Flood. This paper represents the results of PMP analysis for Bakun Dam Area which has a catchment area of 14,750 km2. Three sets of results were produced, i.e. by statistical method (with frequency factors from World Meteorological Organization manual and National Hydraulic Research Institute of Malaysia in Technical Research Publication No. 1 (TRP 1)) for duration of 1 hour, 8 hours, 24 hours and daily and by experimental method for production of daily PMP. The results were compared with each other and the one made by Sarawak Electricity Supply Corporation on Bakun Dam construction. The set of PMP values results from substitution of Km developed by NAHRIM was concluded to be the most reliable results as daily PMP (276mm) was consistent with the one (280mm) produced by SESCO. However, 6 days PMP value (950mm) done by SESCO was recommended as the Bakun Dam Area cover huge catchment area which higher duration of PMP value should be applied .

scholarly journals Estimation of Probable Maximum Precipitation (PMP) and Probable Maximum Flood (PMF) Using GSSHA Model (Case Study Area Upper Citarum Watershed)

2021 ◽  
Vol 893 (1) ◽  
pp. 012023
Author(s):  
Puji R A Sibuea ◽  
Dewi R Agriamah ◽  
Edi Riawan ◽  
Rusmawan Suwarman ◽  
Atika Lubis
Keyword(s):  
Model Simulation ◽  
Hydrologic Model ◽  
Model Case ◽  
Probable Maximum Precipitation ◽  
Maximum Precipitation ◽  
Hydrologic Analysis ◽  
Probable Maximum Flood ◽  
The Mean ◽  
The Impact

Abstract Probable Maximum Flood (PMF) used in the design of hydrological structures reliabilities and safety which its value is obtained from the Probable Maximum Precipitation (PMP). The objectives of this study are to estimate PMP and PMF value in Upper Citarum Watershed and understand the impact from different PMP value to PMF value with two scenarios those are Scenario A and B. Scenario A will calculate the PMP value from each Global Satellite Mapping of Precipitation (GSMaP) rainfall data grid and Scenario B calculate the PMP value from the mean area rainfall. PMP value will be obtained by the statistical Hershfield method, and the PMF will be obtained by employed the PMP value as the input data in Gridded Surface Subsurface Hydrologic Analysis (GSSHA) hydrologic model. Model simulation results for PMF hydrographs from both scenarios show that spatial distribution of rainfall in the Upper Citarum watershed will affect the calculated discharge and whether Scenario A or B can be applied in the study area for PMP duration equal or higher than 72 hours. PMF peak discharge for Scenario A is averagely 13,12% larger than Scenario B.

Évaluation des méthodes utilisées pour l'estimation de la crue maximale probable en régions nordiques

1999 ◽  
Vol 26 (3) ◽  
pp. 355-367 ◽  
Author(s):  
I Debs ◽  
D Sparks ◽  
J Rousselle ◽  
S Birikundavyi
Keyword(s):  
Return Period ◽  
Extreme Event ◽  
Snow Accumulation ◽  
Model Estimation ◽  
Hydrologic Response ◽  
Return Periods ◽  
Design Flood ◽  
Probable Maximum Precipitation ◽  
Maximum Precipitation ◽  
Probable Maximum Flood

Among all existing methods for estimating extreme floods, the probable maximum flood method is the safest, since it is a flood with a probability of excedance that is theoretically zero. In the early 1970s, this flood was calculated as the combination of the probable maximum precipitation (PMP) and the probable maximum snow accumulation (PMSA). In the 1990s, this combination has been considered to be highly improbable. Experts advise against combining two maximized events and, instead, recommend combining one maximized event with a relatively typical extreme event. This article presents a sensitivity analysis on the return period to be used for the typical extreme event to be combined with the maximized event to obtain a "more realistic" PMF. To achieve this, all the steps of a PMF study were reviewed and applied to the Sainte-Marguerite watershed, i.e., calibration and (or) validation of SSARR model, estimation of the PMP, the PMSA, and the temperature sequence. Different flood scenarios have been simulated including accumulated snowfall corresponding to return periods of 50, 100, and 500 years, followed by PMP and PMSA, followed by precipitation corresponding to return periods of 50, 100, and 500 years. It has been noticed that the use of a return period of 50, 100, or 500 years, to represent the unmaximized extreme event, has little effect on the hydrologic response of the basin. Based on the results of this work the use of a return period of 100 years is recommended.Key words: probable maximum flood, probable maximum precipitation, probable maximum snow accumulation, design flood, SSARR model.

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 Effects of variability in probable maximum precipitation patterns on flood losses

2018 ◽  
Author(s):  
Andreas Paul Zischg ◽  
Guido Felder ◽  
Rolf Weingartner ◽  
Niall Quinn ◽  
Gemma Coxon ◽  
...  
Keyword(s):  
River Basin ◽  
Critical Infrastructure ◽  
Flood Hazard ◽  
Infrastructure Management ◽  
Probable Maximum Precipitation ◽  
Maximum Precipitation ◽  
Precipitation Patterns ◽  
Probable Maximum Flood ◽  
Flood Losses ◽  
Model Chain

Abstract. The assessment of the impacts of extreme floods is important for dealing with residual risk, particularly for critical infrastructure management and for insurance purposes. Thus, modelling of the probable maximum flood (PMF) from probable maximum precipitation (PMP) by coupling hydrologic and hydraulic models has gained interest in recent years. Herein, we examine whether variability in precipitation patterns exceeds or is below other uncertainties in flood loss estimation and if the flood losses within a river basin are related to the probable maximum discharge at the basin outlet. We developed a model experiment with an ensemble of probable maximum precipitation scenarios created by Monte-Carlo simulations. For each rainfall pattern, we computed the flood losses with a model chain and benchmarked the effects of variability in rainfall distribution with other model uncertainties. The results show that flood losses vary considerably within the river basin and depend on the timing and superimposition of the flood peaks from the basin's sub-catchments. In addition to the flood hazard component, the other components of flood risk, exposure and vulnerability, contribute remarkably to the overall variability. This leads to the conclusion that the estimation of the probable maximum expectable flood losses in a river basin should not be based exclusively on the PMF. Consequently, the basin-specific sensitivities to different precipitation patterns and the spatial organisation of the settlements within the river basin need to be considered in the analyses of probable maximum flood losses.

scholarly journals Understanding Model-Based Probable Maximum Precipitation Estimation as a Function of Location and Season from Atmospheric Reanalysis

2018 ◽  
Vol 19 (2) ◽  
pp. 459-475 ◽  
Author(s):  
Xiaodong Chen ◽  
Faisal Hossain
Keyword(s):  
Vertical Velocity ◽  
Extreme Precipitation ◽  
Numerical Models ◽  
Engineering Practice ◽  
Probable Maximum Precipitation ◽  
Maximum Precipitation ◽  
Extreme Precipitation Events ◽  
Atmospheric Instability ◽  
Moisture Availability ◽  
Precipitation Events

Abstract Extreme precipitation events bring huge societal and economic loss around the world every year, and they have undergone spatially heterogeneous changes in the past half-century. They are fundamental to probable maximum precipitation (PMP) estimation in engineering practice, making it important to understand how extreme storm magnitudes are related to key meteorological conditions. However, there is currently a lack of information that can potentially inform the engineering profession on the controlling factors for PMP estimation. In this study, the authors present a statistical analysis of the relationship between extreme 3-day precipitation and atmospheric instability, moisture availability, and large-scale convergence over the continental United States (CONUS). The analysis is conducted using the North America Regional Reanalysis (NARR) and ECMWF ERA-Interim reanalysis data and a high-resolution regional climate simulation. While extreme 3-day precipitation events across the CONUS are mostly related to vertical velocity and moisture availability, those in the southwestern U.S. mountain regions are also controlled by atmospheric instability. Vertical velocity and relative humidity have domainwide impacts, while no significant relationship is found between extreme precipitation and air temperature. Such patterns are stable over different seasons and extreme precipitation events of various durations between 1 and 3 days. These analyses can directly help in configuring the numerical models for PMP estimation at a given location for a given storm.

scholarly journals Effects of variability in probable maximum precipitation patterns on flood losses

2018 ◽  
Vol 22 (5) ◽  
pp. 2759-2773 ◽  
Author(s):  
Andreas Paul Zischg ◽  
Guido Felder ◽  
Rolf Weingartner ◽  
Niall Quinn ◽  
Gemma Coxon ◽  
...  
Keyword(s):  
River Basin ◽  
Spatial Organization ◽  
Critical Infrastructure ◽  
Flood Hazard ◽  
Infrastructure Management ◽  
Probable Maximum Precipitation ◽  
Maximum Precipitation ◽  
Precipitation Patterns ◽  
Probable Maximum Flood ◽  
Flood Losses

Abstract. The assessment of the impacts of extreme floods is important for dealing with residual risk, particularly for critical infrastructure management and for insurance purposes. Thus, modelling of the probable maximum flood (PMF) from probable maximum precipitation (PMP) by coupling hydrological and hydraulic models has gained interest in recent years. Herein, we examine whether variability in precipitation patterns exceeds or is below selected uncertainty factors in flood loss estimation and if the flood losses within a river basin are related to the probable maximum discharge at the basin outlet. We developed a model experiment with an ensemble of probable maximum precipitation scenarios created by Monte Carlo simulations. For each rainfall pattern, we computed the flood losses with a model chain and benchmarked the effects of variability in rainfall distribution with other model uncertainties. The results show that flood losses vary considerably within the river basin and depend on the timing and superimposition of the flood peaks from the basin's sub-catchments. In addition to the flood hazard component, the other components of flood risk, exposure, and vulnerability contribute remarkably to the overall variability. This leads to the conclusion that the estimation of the probable maximum expectable flood losses in a river basin should not be based exclusively on the PMF. Consequently, the basin-specific sensitivities to different precipitation patterns and the spatial organization of the settlements within the river basin need to be considered in the analyses of probable maximum flood losses.

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