Flood-frequency analyses, Manual of Hydrology: Part 3

Dalrymple, Tate

doi:10.3133/wsp1543a

Cited by 23 publications

(9 citation statements)

References 10 publications

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“…Flood marks on buildings and paleoflood information are two types of temporal information expansion data. Spatial information expansion involves trading space for time by using flood information from neighboring systems, viz., a regional flood frequency analysis methodology such as the index flood method (Dalrymple 1960), to improve upon the flood frequency analysis at the site of interest. Introducing hydrologic understanding of local flood production factors is the goal of causal information expansion.…”

Section: Frameworkmentioning

confidence: 99%

Probabilistic flood hazard assessment framework development : extreme rainfall analysis

Skahill¹,

Kanney²

2019

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This report introduces a framework for probabilistic flood hazard assessment (PFHA) whose basis leverages recent advances in the science of spatial extremes. The framework basis includes a latent variable model (LVM) or a max-stable process application wherein for either case model inference is likelihood based. The framework is flexible in that it can leverage robust approaches to quantify model uncertainty while also supporting the capacity to readily combine additional relevant data types; for example, historical and/or paleoflood data for flood frequency analyses. This report profiles applications of Bayesian inference for flood hazard curve development for at-site and spatial LVM analyses. Pointwise spatial model development using an LVM or a max-stable process requires the parameters of the model characterizing the pointwise extremes to vary spatially as a function of gridded covariate data relevant to the hydrometeorological extreme under consideration. Recent advances in mathematical regularization facilitate spatial pointwise model reduction. The PFHA framework accommodates the multiple model parameterizations encapsulated within a given LVM or max-stable process deployment by generalizing model choice using information criteria. DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents.

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Section: Frameworkmentioning

confidence: 99%

Probabilistic flood hazard assessment framework development : extreme rainfall analysis

Skahill¹,

Kanney²

2019

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“…with Θ = (Λ * , θ * , Λ 1 , θ 1 ), or, in an equivalent way on the basis on the index approach [28], as:…”

Section: The Tcev Distributionmentioning

confidence: 99%

Stationary and Non-Stationary Frameworks for Extreme Rainfall Time Series in Southern Italy

Luca

Galasso

2018

Water

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This study tests stationary and non-stationary approaches for modelling data series of hydro-meteorological variables. Specifically, the authors considered annual maximum rainfall accumulations observed in the Calabria region (southern Italy), and attention was focused on time series characterized by heavy rainfall events which occurred from 1 January 2000 in the study area. This choice is justified by the need to check if the recent rainfall events in the new century can be considered as very different or not from the events occurred in the past. In detail, the whole data set of each considered time series (characterized by a sample size N > 40 data) was analyzed, in order to compare recent and past rainfall accumulations, which occurred in a specific site. All the proposed models were based on the Two-Component Extreme Value (TCEV) probability distribution, which is frequently applied for annual maximum time series in Calabria. The authors discussed the possible sources of uncertainty related to each framework and remarked on the crucial role played by ergodicity. In fact, if the process is assumed to be non-stationary, then ergodicity cannot hold, and thus possible trends should be derived from external sources, different from the time series of interest: in this work, Regional Climate Models’ (RCMs) outputs were considered in order to assess possible trends of TCEV parameters. From the obtained results, it does not seem essential to adopt non-stationary models, as significant trends do not appear from the observed data, due to a relevant number of heavy events which also occurred in the central part of the last century.

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“…The annual series interval is the average interval in which an event of a given size is likely to reoccur as an annual maximum. The recurrence interval of the partial duration series is the average interval between events of a given size regardless of their relation to the year or any other time period [3]. For an event having a recurrence interval of T, the probability that it will be equaled or exceeded in any one year is obtained from equation 2.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Hydrologic Data for Irrigation System Design: A Case Study

2023

Dearma J Cosmetic Laser Therapy

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The scope and the efficient operation of an irrigation system depends on systematic collection, collation, and analysis of the hydrological/meteorological data of the project area. Relying on existing rainfall records, the peak rainfall, mean rainfall, rainfall frequency and effective monthly rainfall were established. Atayi stream discharge characteristic curve was used to determine the stream stage-discharge relationship while the instantaneous rate of discharge of the stream was determined using float. The generated data was plotted on a hydrograph to obtain 2.64m3/s. The high-level volume discharge and the low-level flow per annum were found to be 29.08m3 and 18.33m3 respectively. For the crop evapotranspiration, the Penman open water evaporation was deployed using climatic data for Igwu scheme as collected by Umudike Research Institute, Umuahia to obtain 1871 mm per annum compared to 2200mm by Piche or Pan evaporation. The site hydrologic data collected and analyzed supports the adequacy of basic hydrologic factors for irrigated paddy rice cultivation in the study area.

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Flood-frequency analyses, Manual of Hydrology: Part 3

Abstract: Single-station analysis________________ Regional flood frequency. Basic frequency curve 27 Homogeneity test_ 27 Mean annual flood_ 29 Physiographic factors._____________

Cited by 23 publications

References 10 publications

Probabilistic flood hazard assessment framework development : extreme rainfall analysis

Probabilistic flood hazard assessment framework development : extreme rainfall analysis

Stationary and Non-Stationary Frameworks for Extreme Rainfall Time Series in Southern Italy

Evaluation of Hydrologic Data for Irrigation System Design: A Case Study

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