Loading…

Drought forecasting: A review of modelling approaches 2007–2017

Droughts are prolonged precipitation-deficient periods, resulting in inadequate water availability and adverse repercussions to crops, animals and humans. Drought forecasting is vital to water resources planning and management in minimizing the negative consequences. Many models have been developed...

Full description

Saved in:
Bibliographic Details
Published in:Journal of water and climate change 2020-09, Vol.11 (3), p.771-799
Main Authors: Fung, K. F., Huang, Y. F., Koo, C. H., Soh, Y. W.
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-c263t-51dfa3a50e3acd62ad993f4ce4c4b94167ea77271e03d9c28348a748ebe993613
cites cdi_FETCH-LOGICAL-c263t-51dfa3a50e3acd62ad993f4ce4c4b94167ea77271e03d9c28348a748ebe993613
container_end_page 799
container_issue 3
container_start_page 771
container_title Journal of water and climate change
container_volume 11
creator Fung, K. F.
Huang, Y. F.
Koo, C. H.
Soh, Y. W.
description Droughts are prolonged precipitation-deficient periods, resulting in inadequate water availability and adverse repercussions to crops, animals and humans. Drought forecasting is vital to water resources planning and management in minimizing the negative consequences. Many models have been developed for this purpose and, indeed, it would be a long process for researchers to select the best suited model for their research. A timely, thorough and informative overview of the models' concepts and historical applications would be helpful in preventing researchers from overlooking the potential selection of models and saving them considerable amounts of time on the problem. Thus, this paper aims to review drought forecasting approaches including their input requirements and performance measures, for 2007–2017. The models are categorized according to their respective mechanism: regression analysis, stochastic, probabilistic, artificial intelligence based, hybrids and dynamic modelling. Details of the selected papers, including modelling approaches, authors, year of publication, methods, input variables, evaluation criteria, time scale and type of drought are tabulated for ease of reference. The basic concepts of each approach with key parameters are explained, along with the historical applications, benefits and limitations of the models. Finally, future outlooks and potential modelling techniques are furnished for continuing drought research.
doi_str_mv 10.2166/wcc.2019.236
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2483168989</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2483168989</sourcerecordid><originalsourceid>FETCH-LOGICAL-c263t-51dfa3a50e3acd62ad993f4ce4c4b94167ea77271e03d9c28348a748ebe993613</originalsourceid><addsrcrecordid>eNotkMtKQzEQhoMoWGp3PkDArafmdnJxV6pVoeBG1yHNmfTCaXNMTi3ufAff0Ccxpc5mhuFj_uFD6JqSMaNS3h28HzNCzZhxeYYGTBBdGV6L8zITQSrGhLhEo5w3pFRdG070AE0eUtwvVz0OMYF3uV_vlvd4ghN8ruGAY8Db2EDbljV2XZei8yvImBGifr9_Sp66QhfBtRlG_32I3mePb9Pnav769DKdzCvPJO-rmjbBcVcT4M43krnGGB6EB-HFwggqFTilmKJAeGM801xop4SGBRRQUj5EN6e75YmPPeTebuI-7UqkZUJzKrXRplC3J8qnmHOCYLu03rr0ZSmxR0-2eLJHT7Z44n9HOlmJ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2483168989</pqid></control><display><type>article</type><title>Drought forecasting: A review of modelling approaches 2007–2017</title><source>Alma/SFX Local Collection</source><creator>Fung, K. F. ; Huang, Y. F. ; Koo, C. H. ; Soh, Y. W.</creator><creatorcontrib>Fung, K. F. ; Huang, Y. F. ; Koo, C. H. ; Soh, Y. W.</creatorcontrib><description>Droughts are prolonged precipitation-deficient periods, resulting in inadequate water availability and adverse repercussions to crops, animals and humans. Drought forecasting is vital to water resources planning and management in minimizing the negative consequences. Many models have been developed for this purpose and, indeed, it would be a long process for researchers to select the best suited model for their research. A timely, thorough and informative overview of the models' concepts and historical applications would be helpful in preventing researchers from overlooking the potential selection of models and saving them considerable amounts of time on the problem. Thus, this paper aims to review drought forecasting approaches including their input requirements and performance measures, for 2007–2017. The models are categorized according to their respective mechanism: regression analysis, stochastic, probabilistic, artificial intelligence based, hybrids and dynamic modelling. Details of the selected papers, including modelling approaches, authors, year of publication, methods, input variables, evaluation criteria, time scale and type of drought are tabulated for ease of reference. The basic concepts of each approach with key parameters are explained, along with the historical applications, benefits and limitations of the models. Finally, future outlooks and potential modelling techniques are furnished for continuing drought research.</description><identifier>ISSN: 2040-2244</identifier><identifier>EISSN: 2408-9354</identifier><identifier>DOI: 10.2166/wcc.2019.236</identifier><language>eng</language><publisher>London: IWA Publishing</publisher><subject>Artificial intelligence ; Climate ; Drought ; Drought forecasting ; Dynamic models ; Forecasting ; Generalized linear models ; Hybrids ; Hydrology ; Markov analysis ; Mean square errors ; Modelling ; Neural networks ; Precipitation ; Regression analysis ; Resource management ; Statistical analysis ; Stochasticity ; Stream flow ; Variables ; Vegetation ; Water availability ; Water resources ; Water resources management ; Water resources planning ; Water shortages ; Weather ; Wind</subject><ispartof>Journal of water and climate change, 2020-09, Vol.11 (3), p.771-799</ispartof><rights>Copyright IWA Publishing Sep 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c263t-51dfa3a50e3acd62ad993f4ce4c4b94167ea77271e03d9c28348a748ebe993613</citedby><cites>FETCH-LOGICAL-c263t-51dfa3a50e3acd62ad993f4ce4c4b94167ea77271e03d9c28348a748ebe993613</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>Fung, K. F.</creatorcontrib><creatorcontrib>Huang, Y. F.</creatorcontrib><creatorcontrib>Koo, C. H.</creatorcontrib><creatorcontrib>Soh, Y. W.</creatorcontrib><title>Drought forecasting: A review of modelling approaches 2007–2017</title><title>Journal of water and climate change</title><description>Droughts are prolonged precipitation-deficient periods, resulting in inadequate water availability and adverse repercussions to crops, animals and humans. Drought forecasting is vital to water resources planning and management in minimizing the negative consequences. Many models have been developed for this purpose and, indeed, it would be a long process for researchers to select the best suited model for their research. A timely, thorough and informative overview of the models' concepts and historical applications would be helpful in preventing researchers from overlooking the potential selection of models and saving them considerable amounts of time on the problem. Thus, this paper aims to review drought forecasting approaches including their input requirements and performance measures, for 2007–2017. The models are categorized according to their respective mechanism: regression analysis, stochastic, probabilistic, artificial intelligence based, hybrids and dynamic modelling. Details of the selected papers, including modelling approaches, authors, year of publication, methods, input variables, evaluation criteria, time scale and type of drought are tabulated for ease of reference. The basic concepts of each approach with key parameters are explained, along with the historical applications, benefits and limitations of the models. Finally, future outlooks and potential modelling techniques are furnished for continuing drought research.</description><subject>Artificial intelligence</subject><subject>Climate</subject><subject>Drought</subject><subject>Drought forecasting</subject><subject>Dynamic models</subject><subject>Forecasting</subject><subject>Generalized linear models</subject><subject>Hybrids</subject><subject>Hydrology</subject><subject>Markov analysis</subject><subject>Mean square errors</subject><subject>Modelling</subject><subject>Neural networks</subject><subject>Precipitation</subject><subject>Regression analysis</subject><subject>Resource management</subject><subject>Statistical analysis</subject><subject>Stochasticity</subject><subject>Stream flow</subject><subject>Variables</subject><subject>Vegetation</subject><subject>Water availability</subject><subject>Water resources</subject><subject>Water resources management</subject><subject>Water resources planning</subject><subject>Water shortages</subject><subject>Weather</subject><subject>Wind</subject><issn>2040-2244</issn><issn>2408-9354</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNotkMtKQzEQhoMoWGp3PkDArafmdnJxV6pVoeBG1yHNmfTCaXNMTi3ufAff0Ccxpc5mhuFj_uFD6JqSMaNS3h28HzNCzZhxeYYGTBBdGV6L8zITQSrGhLhEo5w3pFRdG070AE0eUtwvVz0OMYF3uV_vlvd4ghN8ruGAY8Db2EDbljV2XZei8yvImBGifr9_Sp66QhfBtRlG_32I3mePb9Pnav769DKdzCvPJO-rmjbBcVcT4M43krnGGB6EB-HFwggqFTilmKJAeGM801xop4SGBRRQUj5EN6e75YmPPeTebuI-7UqkZUJzKrXRplC3J8qnmHOCYLu03rr0ZSmxR0-2eLJHT7Z44n9HOlmJ</recordid><startdate>20200901</startdate><enddate>20200901</enddate><creator>Fung, K. F.</creator><creator>Huang, Y. F.</creator><creator>Koo, C. H.</creator><creator>Soh, Y. W.</creator><general>IWA Publishing</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7TG</scope><scope>7UA</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H97</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope></search><sort><creationdate>20200901</creationdate><title>Drought forecasting: A review of modelling approaches 2007–2017</title><author>Fung, K. F. ; Huang, Y. F. ; Koo, C. H. ; Soh, Y. W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c263t-51dfa3a50e3acd62ad993f4ce4c4b94167ea77271e03d9c28348a748ebe993613</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Artificial intelligence</topic><topic>Climate</topic><topic>Drought</topic><topic>Drought forecasting</topic><topic>Dynamic models</topic><topic>Forecasting</topic><topic>Generalized linear models</topic><topic>Hybrids</topic><topic>Hydrology</topic><topic>Markov analysis</topic><topic>Mean square errors</topic><topic>Modelling</topic><topic>Neural networks</topic><topic>Precipitation</topic><topic>Regression analysis</topic><topic>Resource management</topic><topic>Statistical analysis</topic><topic>Stochasticity</topic><topic>Stream flow</topic><topic>Variables</topic><topic>Vegetation</topic><topic>Water availability</topic><topic>Water resources</topic><topic>Water resources management</topic><topic>Water resources planning</topic><topic>Water shortages</topic><topic>Weather</topic><topic>Wind</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fung, K. F.</creatorcontrib><creatorcontrib>Huang, Y. F.</creatorcontrib><creatorcontrib>Koo, C. H.</creatorcontrib><creatorcontrib>Soh, Y. W.</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Meteorological &amp; Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central</collection><collection>Agricultural &amp; Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>Earth, Atmospheric &amp; Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) 3: Aquatic Pollution &amp; Environmental Quality</collection><collection>SciTech Premium Collection</collection><collection>Meteorological &amp; Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) Professional</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric &amp; Aquatic 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>Environmental Science Collection</collection><jtitle>Journal of water and climate change</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fung, K. F.</au><au>Huang, Y. F.</au><au>Koo, C. H.</au><au>Soh, Y. W.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Drought forecasting: A review of modelling approaches 2007–2017</atitle><jtitle>Journal of water and climate change</jtitle><date>2020-09-01</date><risdate>2020</risdate><volume>11</volume><issue>3</issue><spage>771</spage><epage>799</epage><pages>771-799</pages><issn>2040-2244</issn><eissn>2408-9354</eissn><abstract>Droughts are prolonged precipitation-deficient periods, resulting in inadequate water availability and adverse repercussions to crops, animals and humans. Drought forecasting is vital to water resources planning and management in minimizing the negative consequences. Many models have been developed for this purpose and, indeed, it would be a long process for researchers to select the best suited model for their research. A timely, thorough and informative overview of the models' concepts and historical applications would be helpful in preventing researchers from overlooking the potential selection of models and saving them considerable amounts of time on the problem. Thus, this paper aims to review drought forecasting approaches including their input requirements and performance measures, for 2007–2017. The models are categorized according to their respective mechanism: regression analysis, stochastic, probabilistic, artificial intelligence based, hybrids and dynamic modelling. Details of the selected papers, including modelling approaches, authors, year of publication, methods, input variables, evaluation criteria, time scale and type of drought are tabulated for ease of reference. The basic concepts of each approach with key parameters are explained, along with the historical applications, benefits and limitations of the models. Finally, future outlooks and potential modelling techniques are furnished for continuing drought research.</abstract><cop>London</cop><pub>IWA Publishing</pub><doi>10.2166/wcc.2019.236</doi><tpages>29</tpages></addata></record>
fulltext fulltext
identifier ISSN: 2040-2244
ispartof Journal of water and climate change, 2020-09, Vol.11 (3), p.771-799
issn 2040-2244
2408-9354
language eng
recordid cdi_proquest_journals_2483168989
source Alma/SFX Local Collection
subjects Artificial intelligence
Climate
Drought
Drought forecasting
Dynamic models
Forecasting
Generalized linear models
Hybrids
Hydrology
Markov analysis
Mean square errors
Modelling
Neural networks
Precipitation
Regression analysis
Resource management
Statistical analysis
Stochasticity
Stream flow
Variables
Vegetation
Water availability
Water resources
Water resources management
Water resources planning
Water shortages
Weather
Wind
title Drought forecasting: A review of modelling approaches 2007–2017
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T15%3A11%3A45IST&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=Drought%20forecasting:%20A%20review%20of%20modelling%20approaches%202007%E2%80%932017&rft.jtitle=Journal%20of%20water%20and%20climate%20change&rft.au=Fung,%20K.%20F.&rft.date=2020-09-01&rft.volume=11&rft.issue=3&rft.spage=771&rft.epage=799&rft.pages=771-799&rft.issn=2040-2244&rft.eissn=2408-9354&rft_id=info:doi/10.2166/wcc.2019.236&rft_dat=%3Cproquest_cross%3E2483168989%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c263t-51dfa3a50e3acd62ad993f4ce4c4b94167ea77271e03d9c28348a748ebe993613%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2483168989&rft_id=info:pmid/&rfr_iscdi=true