Loading…

A stochastic model for the analysis of the temporal change of dry spells

In the present paper a stochastic approach which considers the arrival of rainfall events as a Poisson process is proposed to analyse the sequences of no rainy days. Particularly, among the different Poisson models, a non-homogeneous Poisson model was selected and then applied to the daily rainfall...

Full description

Saved in:
Bibliographic Details
Published in:Stochastic environmental research and risk assessment 2015-01, Vol.29 (1), p.143-155
Main Authors: Sirangelo, B., Caloiero, T., Coscarelli, R., Ferrari, E.
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c505t-3ee1458f4b408e46b3afc6b08715f104d2559804ef92b91f813fb981edd0b1dc3
cites cdi_FETCH-LOGICAL-c505t-3ee1458f4b408e46b3afc6b08715f104d2559804ef92b91f813fb981edd0b1dc3
container_end_page 155
container_issue 1
container_start_page 143
container_title Stochastic environmental research and risk assessment
container_volume 29
creator Sirangelo, B.
Caloiero, T.
Coscarelli, R.
Ferrari, E.
description In the present paper a stochastic approach which considers the arrival of rainfall events as a Poisson process is proposed to analyse the sequences of no rainy days. Particularly, among the different Poisson models, a non-homogeneous Poisson model was selected and then applied to the daily rainfall series registered at the Cosenza rain gauge (Calabria, southern Italy), as test series. The aim was to evaluate the different behaviour of the dry spells observed in two different 30-year periods, i.e. 1951–1980 and 1981–2010. The analyses performed through Monte Carlo simulations assessed the statistical significance of the variation of the mean expected values of dry spells observed at annual scale in the second period with respect to those observed in the first. The model has then been verified by comparing the results of the test series with the ones obtained from other three rain gauges of the same region. Moreover, greater occurrence probabilities for long dry spells in 1981–2010 than in 1951–1980 were detected for the test series. Analogously, the return periods evaluated for fixed long dry spells through the synthetic data of the period 1981–2010 resulted less than half of the corresponding ones evaluated with the data generated for the previous 30-year period.
doi_str_mv 10.1007/s00477-014-0904-5
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1651391343</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1642626682</sourcerecordid><originalsourceid>FETCH-LOGICAL-c505t-3ee1458f4b408e46b3afc6b08715f104d2559804ef92b91f813fb981edd0b1dc3</originalsourceid><addsrcrecordid>eNqNkU1LAzEQhoMoWGp_gLeAFy-rM_nYTY6lqBUKXvQc9iOxK7vNmmwP_fdmrYgIgqf54HmHmXkJuUS4QYDiNgKIosgARQYaRCZPyAwFzzPOpD79zgWck0WMbZU0kmuNMCPrJY2jr7dlHNua9r6xHXU-0HFrabkru0NsI_Xusx5tP_hQdjThu1c7tZtwoHGwXRcvyJkru2gXX3FOXu7vnlfrbPP08LhabrJaghwzbi0KqZyoBCgr8oqXrs4rUAVKhyAaJqVWIKzTrNLoFHJXaYW2aaDCpuZzcn2cOwT_vrdxNH0b67RBubN-Hw3mErlGLvg_UMFylueKJfTqF_rm9yHdP1FMKpSKq0ThkaqDjzFYZ4bQ9mU4GAQzOWGOTpjkhJmcMDJp2FETE5u-Fn5M_lP0AdCYiRU</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1625815838</pqid></control><display><type>article</type><title>A stochastic model for the analysis of the temporal change of dry spells</title><source>Springer Link</source><creator>Sirangelo, B. ; Caloiero, T. ; Coscarelli, R. ; Ferrari, E.</creator><creatorcontrib>Sirangelo, B. ; Caloiero, T. ; Coscarelli, R. ; Ferrari, E.</creatorcontrib><description>In the present paper a stochastic approach which considers the arrival of rainfall events as a Poisson process is proposed to analyse the sequences of no rainy days. Particularly, among the different Poisson models, a non-homogeneous Poisson model was selected and then applied to the daily rainfall series registered at the Cosenza rain gauge (Calabria, southern Italy), as test series. The aim was to evaluate the different behaviour of the dry spells observed in two different 30-year periods, i.e. 1951–1980 and 1981–2010. The analyses performed through Monte Carlo simulations assessed the statistical significance of the variation of the mean expected values of dry spells observed at annual scale in the second period with respect to those observed in the first. The model has then been verified by comparing the results of the test series with the ones obtained from other three rain gauges of the same region. Moreover, greater occurrence probabilities for long dry spells in 1981–2010 than in 1951–1980 were detected for the test series. Analogously, the return periods evaluated for fixed long dry spells through the synthetic data of the period 1981–2010 resulted less than half of the corresponding ones evaluated with the data generated for the previous 30-year period.</description><identifier>ISSN: 1436-3240</identifier><identifier>EISSN: 1436-3259</identifier><identifier>DOI: 10.1007/s00477-014-0904-5</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Aquatic Pollution ; Chemistry and Earth Sciences ; Computational Intelligence ; Computer Science ; Computer simulation ; Drying ; Earth and Environmental Science ; Earth Sciences ; Ecological risk assessment ; Environment ; Gages ; Gauges ; Math. Appl. in Environmental Science ; Monte Carlo simulation ; Original Paper ; Physics ; Poisson distribution ; Probability Theory and Stochastic Processes ; Rain ; Rain gauges ; Rainfall ; Statistics for Engineering ; Stochastic models ; Stochasticity ; Waste Water Technology ; Water Management ; Water Pollution Control</subject><ispartof>Stochastic environmental research and risk assessment, 2015-01, Vol.29 (1), p.143-155</ispartof><rights>Springer-Verlag Berlin Heidelberg 2014</rights><rights>Springer-Verlag Berlin Heidelberg 2015</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c505t-3ee1458f4b408e46b3afc6b08715f104d2559804ef92b91f813fb981edd0b1dc3</citedby><cites>FETCH-LOGICAL-c505t-3ee1458f4b408e46b3afc6b08715f104d2559804ef92b91f813fb981edd0b1dc3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Sirangelo, B.</creatorcontrib><creatorcontrib>Caloiero, T.</creatorcontrib><creatorcontrib>Coscarelli, R.</creatorcontrib><creatorcontrib>Ferrari, E.</creatorcontrib><title>A stochastic model for the analysis of the temporal change of dry spells</title><title>Stochastic environmental research and risk assessment</title><addtitle>Stoch Environ Res Risk Assess</addtitle><description>In the present paper a stochastic approach which considers the arrival of rainfall events as a Poisson process is proposed to analyse the sequences of no rainy days. Particularly, among the different Poisson models, a non-homogeneous Poisson model was selected and then applied to the daily rainfall series registered at the Cosenza rain gauge (Calabria, southern Italy), as test series. The aim was to evaluate the different behaviour of the dry spells observed in two different 30-year periods, i.e. 1951–1980 and 1981–2010. The analyses performed through Monte Carlo simulations assessed the statistical significance of the variation of the mean expected values of dry spells observed at annual scale in the second period with respect to those observed in the first. The model has then been verified by comparing the results of the test series with the ones obtained from other three rain gauges of the same region. Moreover, greater occurrence probabilities for long dry spells in 1981–2010 than in 1951–1980 were detected for the test series. Analogously, the return periods evaluated for fixed long dry spells through the synthetic data of the period 1981–2010 resulted less than half of the corresponding ones evaluated with the data generated for the previous 30-year period.</description><subject>Aquatic Pollution</subject><subject>Chemistry and Earth Sciences</subject><subject>Computational Intelligence</subject><subject>Computer Science</subject><subject>Computer simulation</subject><subject>Drying</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Ecological risk assessment</subject><subject>Environment</subject><subject>Gages</subject><subject>Gauges</subject><subject>Math. Appl. in Environmental Science</subject><subject>Monte Carlo simulation</subject><subject>Original Paper</subject><subject>Physics</subject><subject>Poisson distribution</subject><subject>Probability Theory and Stochastic Processes</subject><subject>Rain</subject><subject>Rain gauges</subject><subject>Rainfall</subject><subject>Statistics for Engineering</subject><subject>Stochastic models</subject><subject>Stochasticity</subject><subject>Waste Water Technology</subject><subject>Water Management</subject><subject>Water Pollution Control</subject><issn>1436-3240</issn><issn>1436-3259</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNqNkU1LAzEQhoMoWGp_gLeAFy-rM_nYTY6lqBUKXvQc9iOxK7vNmmwP_fdmrYgIgqf54HmHmXkJuUS4QYDiNgKIosgARQYaRCZPyAwFzzPOpD79zgWck0WMbZU0kmuNMCPrJY2jr7dlHNua9r6xHXU-0HFrabkru0NsI_Xusx5tP_hQdjThu1c7tZtwoHGwXRcvyJkru2gXX3FOXu7vnlfrbPP08LhabrJaghwzbi0KqZyoBCgr8oqXrs4rUAVKhyAaJqVWIKzTrNLoFHJXaYW2aaDCpuZzcn2cOwT_vrdxNH0b67RBubN-Hw3mErlGLvg_UMFylueKJfTqF_rm9yHdP1FMKpSKq0ThkaqDjzFYZ4bQ9mU4GAQzOWGOTpjkhJmcMDJp2FETE5u-Fn5M_lP0AdCYiRU</recordid><startdate>20150101</startdate><enddate>20150101</enddate><creator>Sirangelo, B.</creator><creator>Caloiero, T.</creator><creator>Coscarelli, R.</creator><creator>Ferrari, E.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7XB</scope><scope>88I</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>KR7</scope><scope>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>S0W</scope><scope>SOI</scope><scope>7U1</scope><scope>7U2</scope><scope>7SU</scope><scope>7TA</scope><scope>JG9</scope></search><sort><creationdate>20150101</creationdate><title>A stochastic model for the analysis of the temporal change of dry spells</title><author>Sirangelo, B. ; Caloiero, T. ; Coscarelli, R. ; Ferrari, E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c505t-3ee1458f4b408e46b3afc6b08715f104d2559804ef92b91f813fb981edd0b1dc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Aquatic Pollution</topic><topic>Chemistry and Earth Sciences</topic><topic>Computational Intelligence</topic><topic>Computer Science</topic><topic>Computer simulation</topic><topic>Drying</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Ecological risk assessment</topic><topic>Environment</topic><topic>Gages</topic><topic>Gauges</topic><topic>Math. Appl. in Environmental Science</topic><topic>Monte Carlo simulation</topic><topic>Original Paper</topic><topic>Physics</topic><topic>Poisson distribution</topic><topic>Probability Theory and Stochastic Processes</topic><topic>Rain</topic><topic>Rain gauges</topic><topic>Rainfall</topic><topic>Statistics for Engineering</topic><topic>Stochastic models</topic><topic>Stochasticity</topic><topic>Waste Water Technology</topic><topic>Water Management</topic><topic>Water Pollution Control</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sirangelo, B.</creatorcontrib><creatorcontrib>Caloiero, T.</creatorcontrib><creatorcontrib>Coscarelli, R.</creatorcontrib><creatorcontrib>Ferrari, E.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural &amp; Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Science Journals</collection><collection>ProQuest Engineering Database</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>DELNET Engineering &amp; Technology Collection</collection><collection>Environment Abstracts</collection><collection>Risk Abstracts</collection><collection>Safety Science and Risk</collection><collection>Environmental Engineering Abstracts</collection><collection>Materials Business File</collection><collection>Materials Research Database</collection><jtitle>Stochastic environmental research and risk assessment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sirangelo, B.</au><au>Caloiero, T.</au><au>Coscarelli, R.</au><au>Ferrari, E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A stochastic model for the analysis of the temporal change of dry spells</atitle><jtitle>Stochastic environmental research and risk assessment</jtitle><stitle>Stoch Environ Res Risk Assess</stitle><date>2015-01-01</date><risdate>2015</risdate><volume>29</volume><issue>1</issue><spage>143</spage><epage>155</epage><pages>143-155</pages><issn>1436-3240</issn><eissn>1436-3259</eissn><abstract>In the present paper a stochastic approach which considers the arrival of rainfall events as a Poisson process is proposed to analyse the sequences of no rainy days. Particularly, among the different Poisson models, a non-homogeneous Poisson model was selected and then applied to the daily rainfall series registered at the Cosenza rain gauge (Calabria, southern Italy), as test series. The aim was to evaluate the different behaviour of the dry spells observed in two different 30-year periods, i.e. 1951–1980 and 1981–2010. The analyses performed through Monte Carlo simulations assessed the statistical significance of the variation of the mean expected values of dry spells observed at annual scale in the second period with respect to those observed in the first. The model has then been verified by comparing the results of the test series with the ones obtained from other three rain gauges of the same region. Moreover, greater occurrence probabilities for long dry spells in 1981–2010 than in 1951–1980 were detected for the test series. Analogously, the return periods evaluated for fixed long dry spells through the synthetic data of the period 1981–2010 resulted less than half of the corresponding ones evaluated with the data generated for the previous 30-year period.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00477-014-0904-5</doi><tpages>13</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1436-3240
ispartof Stochastic environmental research and risk assessment, 2015-01, Vol.29 (1), p.143-155
issn 1436-3240
1436-3259
language eng
recordid cdi_proquest_miscellaneous_1651391343
source Springer Link
subjects Aquatic Pollution
Chemistry and Earth Sciences
Computational Intelligence
Computer Science
Computer simulation
Drying
Earth and Environmental Science
Earth Sciences
Ecological risk assessment
Environment
Gages
Gauges
Math. Appl. in Environmental Science
Monte Carlo simulation
Original Paper
Physics
Poisson distribution
Probability Theory and Stochastic Processes
Rain
Rain gauges
Rainfall
Statistics for Engineering
Stochastic models
Stochasticity
Waste Water Technology
Water Management
Water Pollution Control
title A stochastic model for the analysis of the temporal change of dry spells
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T21%3A31%3A09IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20stochastic%20model%20for%20the%20analysis%20of%20the%20temporal%20change%20of%20dry%20spells&rft.jtitle=Stochastic%20environmental%20research%20and%20risk%20assessment&rft.au=Sirangelo,%20B.&rft.date=2015-01-01&rft.volume=29&rft.issue=1&rft.spage=143&rft.epage=155&rft.pages=143-155&rft.issn=1436-3240&rft.eissn=1436-3259&rft_id=info:doi/10.1007/s00477-014-0904-5&rft_dat=%3Cproquest_cross%3E1642626682%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c505t-3ee1458f4b408e46b3afc6b08715f104d2559804ef92b91f813fb981edd0b1dc3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1625815838&rft_id=info:pmid/&rfr_iscdi=true