scholarly journals Estimating Probable Maximum Precipitation for Linau River Basin in Sarawak

2014 ◽  
Vol 5 (3) ◽  
pp. 1-5
Author(s):  
M. Hussain ◽  
S. Nadya ◽  
F.J. Chia
Keyword(s):  
Statistical Method ◽  
River Basin ◽  
Public Safety ◽  
Geographical Location ◽  
Hydraulic Modeling ◽  
Rain Gauge ◽  
Data Set ◽  
Probable Maximum Precipitation ◽  
Maximum Precipitation ◽  
The Impact

 The probable maximum precipitation (PMP) is the greatest depth of precipitation for a given duration that is physically possible over a given size storm area at a particular geographical location at a certain time of the year. PMP is very important to be considered for the design of river regulating structures i.e Dams and Barrages to overcome any possible chance of overtopping failure as well as for public safety and hazards downstream of any of these structures. Especially if these structures located in the upstream of the of the populated town or city than the failure could damage severely such areas. As such the PMP convention is always a requirement as primary design dam/reservoir criteria when public safety is of concern. The PMP is used to derive Probable Maximum Flood (PMF), which further used in hydraulic modeling to check the impact assessments for such occasions. This paper focuses on estimation of PMP for Linau River Basin in Sarawak using statistical method proposed by World Meteorological Organization (WMO), which is described in its operational manual. Long Lidam and Long Laku are located in Linau River Basin but Long Laku has long discontinuity in the data set thus the rainfall series at Long Lidam is further used for PMP estimation. The missing data was in-filled using Belaga rain gauge station as Long Lidam rainfall has good correlation with Belaga rainfall data. Hershfield statistical method has been adopted to estimate the 24-hour duration PMP. The Probable Maximum Precipitation for 24-hour duration storm is estimated as 691 mm for the Linau River Basin.

scholarly journals Development of a daily gridded precipitation data set for the Middle East

2008 ◽  
Vol 12 ◽  
pp. 165-170 ◽  
Author(s):  
A. Yatagai ◽  
P. Xie ◽  
P. Alpert
Keyword(s):  
Middle East ◽  
Climate Models ◽  
Daily Precipitation ◽  
Rain Gauge ◽  
Monthly Precipitation ◽  
Data Set ◽  
Key Points ◽  
Fertile Crescent ◽  
Precipitation Analysis ◽  
The Impact

Abstract. We show an algorithm to construct a rain-gauge-based analysis of daily precipitation for the Middle East. One of the key points of our algorithm is to construct an accurate distribution of climatology. One possible advantage of this product is to validate high-resolution climate models and/or to diagnose the impact of climate changes on local hydrological resources. Many users are familiar with a monthly precipitation dataset (New et al., 1999) and a satellite-based daily precipitation dataset (Huffman et al., 2001), yet our data set, unlike theirs, clearly shows the effect of orography on daily precipitation and other extreme events, especially over the Fertile Crescent region. Currently the Middle-East precipitation analysis product is consisting of a 25-year data set for 1979–2003 based on more than 1300 stations.

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.

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.

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