scholarly journals Uncertainty of quantile estimators using the population index flood method

2003 ◽  
Vol 39 (8) ◽  
Author(s):  
O. G. B. Sveinsson ◽  
J. D. Salas ◽  
D. C. Boes
Keyword(s):  
Population Index ◽  
Index Flood ◽  
Quantile Estimators

Appraisal of regional and index flood quantile estimators

1995 ◽  
Vol 9 (1) ◽  
pp. 49-75 ◽  
Author(s):  
J. R. Stedinger ◽  
L. -H. Lu
Keyword(s):  
Index Flood ◽  
Quantile Estimators

scholarly journals Population index flood method for regional frequency analysis

2001 ◽  
Vol 37 (11) ◽  
pp. 2733-2748 ◽  
Author(s):  
Oli G. B. Sveinsson ◽  
Duane C. Boes ◽  
Jose D. Salas
Keyword(s):  
Frequency Analysis ◽  
Regional Frequency Analysis ◽  
Population Index ◽  
Index Flood ◽  
Regional Frequency

Evaluation of a Fox Population Index

1960 ◽  
Vol 24 (4) ◽  
pp. 406
Author(s):  
Charles W. Lemke ◽  
Donald R. Thompson
Keyword(s):  
Population Index

scholarly journals Delineation of homogenous regions using hydrological variables predicted by projection pursuit regression

2016 ◽  
Vol 20 (12) ◽  
pp. 4717-4729 ◽  
Author(s):  
Martin Durocher ◽  
Fateh Chebana ◽  
Taha B. M. J. Ouarda
Keyword(s):  
Target Location ◽  
Projection Pursuit ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Projection Pursuit Regression ◽  
Index Flood ◽  
Regional Flood Frequency Analysis ◽  
Quantile Estimates ◽  
Target Locations ◽  
Hydrological Variables

Abstract. This study investigates the utilization of hydrological information in regional flood frequency analysis (RFFA) to enforce desired properties for a group of gauged stations. Neighbourhoods are particular types of regions that are centred on target locations. A challenge for using neighbourhoods in RFFA is that hydrological information is not available at target locations and cannot be completely replaced by the available physiographical information. Instead of using the available physiographic characteristics to define the centre of a target location, this study proposes to introduce estimates of reference hydrological variables to ensure a better homogeneity. These reference variables represent nonlinear relations with the site characteristics obtained by projection pursuit regression, a nonparametric regression method. The resulting neighbourhoods are investigated in combination with commonly used regional models: the index-flood model and regression-based models. The complete approach is illustrated in a real-world case study with gauged sites from the southern part of the province of Québec, Canada, and is compared with the traditional approaches such as region of influence and canonical correlation analysis. The evaluation focuses on the neighbourhood properties as well as prediction performances, with special attention devoted to problematic stations. Results show clear improvements in neighbourhood definitions and quantile estimates.

scholarly journals Heterogeneity measures in hydrological frequency analysis: review and new developments

2017 ◽  
Vol 21 (3) ◽  
pp. 1651-1668 ◽  
Author(s):  
Ana I. Requena ◽  
Fateh Chebana ◽  
Taha B. M. J. Ouarda
Keyword(s):  
Basic Step ◽  
New Developments ◽  
Ungauged Sites ◽  
Index Flood ◽  
Step Procedure ◽  
Homogeneous Regions ◽  
The Impact ◽  
Region Size ◽  
Degree Of Heterogeneity ◽  
Illustrative Application

Abstract. Some regional procedures to estimate hydrological quantiles at ungauged sites, such as the index-flood method, require the delineation of homogeneous regions as a basic step for their application. The homogeneity of these delineated regions is usually tested providing a yes/no decision. However, complementary measures that are able to quantify the degree of heterogeneity of a region are needed to compare regions, evaluate the impact of particular sites, and rank the performance of different delineating methods. Well-known existing heterogeneity measures are not well-defined for ranking regions, as they entail drawbacks such as assuming a given probability distribution, providing negative values and being affected by the region size. Therefore, a framework for defining and assessing desirable properties of a heterogeneity measure in the regional hydrological context is needed. In the present study, such a framework is proposed through a four-step procedure based on Monte Carlo simulations. Several heterogeneity measures, some of which commonly known and others which are derived from recent approaches or adapted from other fields, are presented and developed to be assessed. The assumption-free Gini index applied on the at-site L-variation coefficient (L-CV) over a region led to the best results. The measure of the percentage of sites for which the regional L-CV is outside the confidence interval of the at-site L-CV is also found to be relevant, as it leads to more stable results regardless of the regional L-CV value. An illustrative application is also presented for didactical purposes, through which the subjectivity of commonly used criteria to assess the performance of different delineation methods is underlined.

scholarly journals SERVICE CENTRES IN RURAL DEVELOPMENT : A CASE STUDY OF JAUNPUR DISTRICT, UTTAR PRADESH

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Dinesh Kumar ◽  
Dinesh Kumar ◽  
Dinesh Kumar
Keyword(s):  
Uttar Pradesh ◽  
Secondary Data ◽  
Spatial Arrangement ◽  
Population Index ◽  
Service Centre ◽  
Block Level ◽  
Centrality Score ◽  
Eastern Uttar Pradesh ◽  
Fifth Order

This paper attempts to deal with the identifying the service centers and calculation of the spatial arrangement with complementary area of service centres in Jaunpur district Jaunpur district of Uttar Pradesh. The study area is situated in Eastern Uttar Pradesh of the Middle Ganga Plain. The study is exclusively based on secondary data collected at block level from different offices. The centrality score has been calculated on the basis of three type of indices like functional centrality index, working population index and tertiary population index. There are 31 function or services selected judicially from five sectors (administrative, agricultural and financial, educational, health and transport and communication) to measure the centrality of service centre. The thissen polygon and berry breaking point method has been used for measure the complementary area. Total 88 service centres have been identified as first, second, third, fourth and fifth order service centre. The number of I, II, III, IV, and V order centres accounts for 43, 24, 16, 4, and 1 respectively.

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