Title
Regional maximum rainfall analysis using L-moments at the Titicaca Lake drainage, Peru
Date Issued
01 August 2017
Access level
metadata only access
Resource Type
journal article
Publisher(s)
Springer-Verlag Wien
Abstract
The present study investigates the application of the index flood L-moments-based regional frequency analysis procedure (RFA-LM) to the annual maximum 24-h rainfall (AM) of 33 rainfall gauge stations (RGs) to estimate rainfall quantiles at the Titicaca Lake drainage (TL). The study region was chosen because it is characterised by common floods that affect agricultural production and infrastructure. First, detailed quality analyses and verification of the RFA-LM assumptions were conducted. For this purpose, different tests for outlier verification, homogeneity, stationarity, and serial independence were employed. Then, the application of RFA-LM procedure allowed us to consider the TL as a single, hydrologically homogeneous region, in terms of its maximum rainfall frequency. That is, this region can be modelled by a generalised normal (GNO) distribution, chosen according to the Z test for goodness-of-fit, L-moments (LM) ratio diagram, and an additional evaluation of the precision of the regional growth curve. Due to the low density of RG in the TL, it was important to produce maps of the AM design quantiles estimated using RFA-LM. Therefore, the ordinary Kriging interpolation (OK) technique was used. These maps will be a useful tool for determining the different AM quantiles at any point of interest for hydrologists in the region.
Start page
1295
End page
1307
Volume
129
Issue
April 3
Language
English
OCDE Knowledge area
Oceanografía, Hidrología, Recursos hídricos
Scopus EID
2-s2.0-84976491332
Source
Theoretical and Applied Climatology
ISSN of the container
0177798X
Sponsor(s)
This study was possible because of the availability (to the scientific community) of various free software packages of statistical software R, particularly, the lmomRFA package for RFA-LM and the gstat package for spatial geo-statistical modelling. Moreover, we thanks the SNF project DECADE by supporting this publication.
Sources of information: Directorio de Producción Científica Scopus