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Generation of Monthly Precipitation Under Climate Change for the Upper Blue Nile River Basin, Ethiopia
This work develops a methodology to project the future precipitation in large river basins under limited data and climate change while preserving the historical temporal and spatial characteristics. The computationally simple and reliable conditional generation method (CGM) is presented and applied...
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Published in: | Journal of the American Water Resources Association 2008-10, Vol.44 (5), p.1231-1247 |
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container_title | Journal of the American Water Resources Association |
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creator | Kim, Ungtae Kaluarachchi, Jagath J Smakhtin, Vladimir U |
description | This work develops a methodology to project the future precipitation in large river basins under limited data and climate change while preserving the historical temporal and spatial characteristics. The computationally simple and reliable conditional generation method (CGM) is presented and applied to generate reliable monthly precipitation data in the upper Blue Nile River Basin of Ethiopia where rain-fed agriculture is prevalent. The results showed that the temporal analysis with the CGM performs better to reproduce the historical long-term characteristics than other methods, and the spatial analysis with the CGM reproduced the historical spatial structure accurately. A 100-year time series analysis using the outcomes of the six general circulation models showed that precipitation changes by the 2050s (2040 through 2069) can be -7 to 28% with a mean increase of about 11%. The seasonal results showed increasing wet conditions in all seasons with changes of mean precipitation of 5, 47, and 6% for wet, dry, and mild seasons, respectively. |
doi_str_mv | 10.1111/j.1752-1688.2008.00220.x |
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The computationally simple and reliable conditional generation method (CGM) is presented and applied to generate reliable monthly precipitation data in the upper Blue Nile River Basin of Ethiopia where rain-fed agriculture is prevalent. The results showed that the temporal analysis with the CGM performs better to reproduce the historical long-term characteristics than other methods, and the spatial analysis with the CGM reproduced the historical spatial structure accurately. A 100-year time series analysis using the outcomes of the six general circulation models showed that precipitation changes by the 2050s (2040 through 2069) can be -7 to 28% with a mean increase of about 11%. 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The computationally simple and reliable conditional generation method (CGM) is presented and applied to generate reliable monthly precipitation data in the upper Blue Nile River Basin of Ethiopia where rain-fed agriculture is prevalent. The results showed that the temporal analysis with the CGM performs better to reproduce the historical long-term characteristics than other methods, and the spatial analysis with the CGM reproduced the historical spatial structure accurately. A 100-year time series analysis using the outcomes of the six general circulation models showed that precipitation changes by the 2050s (2040 through 2069) can be -7 to 28% with a mean increase of about 11%. The seasonal results showed increasing wet conditions in all seasons with changes of mean precipitation of 5, 47, and 6% for wet, dry, and mild seasons, respectively.</description><subject>basins</subject><subject>Blue Nile River Basin</subject><subject>climate change</subject><subject>climate models</subject><subject>conditional generation method</subject><subject>Earth sciences</subject><subject>Earth, ocean, space</subject><subject>Exact sciences and technology</subject><subject>Freshwater</subject><subject>general circulation models</subject><subject>hydrologic models</subject><subject>Hydrology. Hydrogeology</subject><subject>precipitation</subject><subject>rain</subject><subject>rivers</subject><subject>spatial data</subject><subject>temporal variation</subject><subject>time series analysis</subject><subject>Water resources</subject><issn>1093-474X</issn><issn>1752-1688</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><recordid>eNqFkcFO3DAQhqOqlUopz4Av7alJx3biOMeyhaUIKAK27S2aOGPWNCSp7a3g7TFaxJW5zIz-T_-MZrKMcSh4iq-3Ba8rkXOldSEAdAEgBBT3b7KdF-FtqqGReVmXf95nH0K4BeAV13Ins0sayWN008gmy86mMa6HB3bhybjZxa2wGnvybDG4O4zEFmscb4jZybO4Jraa5yQeDBti524gdun-P_UY3PiFHca1m2aHH7N3FodAe895N1sdHV4vjvPTn8sfi2-nuZXQxJz3pTFWATUgjel6I0ELUVWNwU6khTtlpNWy74TVpex0TxqpRNSVLUUtjdzNPm99Zz_921CI7Z0LhoYBR5o2oRVcalVLeB0EoSrRNAn89AxiMDhYj6NxoZ19OoZ_SJzmpZAqcfmWcyHS_YuO_m-bBtZV-_t82V6fHJQKvv9qZeL3t7zFqcUbnzxXVwK4TJ9RoLiSj5hQjmk</recordid><startdate>20081001</startdate><enddate>20081001</enddate><creator>Kim, Ungtae</creator><creator>Kaluarachchi, Jagath J</creator><creator>Smakhtin, Vladimir U</creator><general>Blackwell Publishing Ltd</general><general>American Water Resources Association</general><scope>FBQ</scope><scope>BSCLL</scope><scope>IQODW</scope><scope>7QH</scope><scope>7ST</scope><scope>7TG</scope><scope>7U6</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>H97</scope><scope>KL.</scope><scope>L.G</scope><scope>SOI</scope></search><sort><creationdate>20081001</creationdate><title>Generation of Monthly Precipitation Under Climate Change for the Upper Blue Nile River Basin, Ethiopia</title><author>Kim, Ungtae ; Kaluarachchi, Jagath J ; Smakhtin, Vladimir U</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-f309t-1d4ccf60e903ccbdc30822559cab2518b6c3f83db2f843b8de8ae4aa85f4273c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>basins</topic><topic>Blue Nile River Basin</topic><topic>climate change</topic><topic>climate models</topic><topic>conditional generation method</topic><topic>Earth sciences</topic><topic>Earth, ocean, space</topic><topic>Exact sciences and technology</topic><topic>Freshwater</topic><topic>general circulation models</topic><topic>hydrologic models</topic><topic>Hydrology. Hydrogeology</topic><topic>precipitation</topic><topic>rain</topic><topic>rivers</topic><topic>spatial data</topic><topic>temporal variation</topic><topic>time series analysis</topic><topic>Water resources</topic><toplevel>online_resources</toplevel><creatorcontrib>Kim, Ungtae</creatorcontrib><creatorcontrib>Kaluarachchi, Jagath J</creatorcontrib><creatorcontrib>Smakhtin, Vladimir U</creatorcontrib><collection>AGRIS</collection><collection>Istex</collection><collection>Pascal-Francis</collection><collection>Aqualine</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environment Abstracts</collection><jtitle>Journal of the American Water Resources Association</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, Ungtae</au><au>Kaluarachchi, Jagath J</au><au>Smakhtin, Vladimir U</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Generation of Monthly Precipitation Under Climate Change for the Upper Blue Nile River Basin, Ethiopia</atitle><jtitle>Journal of the American Water Resources Association</jtitle><date>2008-10-01</date><risdate>2008</risdate><volume>44</volume><issue>5</issue><spage>1231</spage><epage>1247</epage><pages>1231-1247</pages><issn>1093-474X</issn><eissn>1752-1688</eissn><coden>JWRAF5</coden><abstract>This work develops a methodology to project the future precipitation in large river basins under limited data and climate change while preserving the historical temporal and spatial characteristics. The computationally simple and reliable conditional generation method (CGM) is presented and applied to generate reliable monthly precipitation data in the upper Blue Nile River Basin of Ethiopia where rain-fed agriculture is prevalent. The results showed that the temporal analysis with the CGM performs better to reproduce the historical long-term characteristics than other methods, and the spatial analysis with the CGM reproduced the historical spatial structure accurately. A 100-year time series analysis using the outcomes of the six general circulation models showed that precipitation changes by the 2050s (2040 through 2069) can be -7 to 28% with a mean increase of about 11%. The seasonal results showed increasing wet conditions in all seasons with changes of mean precipitation of 5, 47, and 6% for wet, dry, and mild seasons, respectively.</abstract><cop>Oxford, UK</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1111/j.1752-1688.2008.00220.x</doi><tpages>17</tpages></addata></record> |
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subjects | basins Blue Nile River Basin climate change climate models conditional generation method Earth sciences Earth, ocean, space Exact sciences and technology Freshwater general circulation models hydrologic models Hydrology. Hydrogeology precipitation rain rivers spatial data temporal variation time series analysis Water resources |
title | Generation of Monthly Precipitation Under Climate Change for the Upper Blue Nile River Basin, Ethiopia |
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