Integration of multiple drought indices using a triple collocation approach
Three drought indices (the Standardized Precipitation Index [SPI], Evaporative Stress Index [ESI], and Soil Moisture Anomaly Index [SMAI]) were integrated using triple collocation (TC) to produce the merged drought index (MDI). The new index was then compared with the Gravity recovery and climate ex...
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description | Three drought indices (the Standardized Precipitation Index [SPI], Evaporative Stress Index [ESI], and Soil Moisture Anomaly Index [SMAI]) were integrated using triple collocation (TC) to produce the merged drought index (MDI). The new index was then compared with the Gravity recovery and climate experiment (GRACE)–Drought severity index (DSI), a comprehensive drought index reflecting storage variation in surface, sub-surface, and groundwater levels across East Asia and Australia, from 2003 to 2014. Before merging the three drought indices, their performance was analyzed. The mean correlation between the three drought indices and the GRACE–DSI indicated that the performance of the ESI was superior to the SMAI and SPI over the study areas. In terms of average weight results using the merging approach, the ESI was associated with larger weights (0.372 and 0.359) and contributions (43% and 38%), followed by the SMAI and SPI for East Asia and Australia, respectively. The SMAI achieved a similar weight (0.360) and contribution (39%) as the ESI across Australia. To determine the robustness of the MDI as estimated by TC weights, we evaluated the MDI and the reference GRACE-DSI with respect to documented drought records in the study areas. The MDI produced trends similar to those of the GRACE-DSI in Australia, while MDI and GRACE-DSI trends were not similar in East Asia. The correlation between the MDI and GRACE-DSI in Australia (0.41–0.62) was also higher than in East Asia (0.24–0.32) during the study periods. This discrepancy was due to the conceptual difference in that MDI reflects the near-surface water storage variation while GRACE-DSI reflects the variation of deeper water. Nevertheless, our results showed that the MDI out-performed single drought indices and was able to capture documented drought events across the study regions. This suggests that merging different drought indices into a single tool can better represent droughts, and may be a valuable approach for water resource management. |
doi_str_mv | 10.1007/s00477-021-02044-7 |
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The new index was then compared with the Gravity recovery and climate experiment (GRACE)–Drought severity index (DSI), a comprehensive drought index reflecting storage variation in surface, sub-surface, and groundwater levels across East Asia and Australia, from 2003 to 2014. Before merging the three drought indices, their performance was analyzed. The mean correlation between the three drought indices and the GRACE–DSI indicated that the performance of the ESI was superior to the SMAI and SPI over the study areas. In terms of average weight results using the merging approach, the ESI was associated with larger weights (0.372 and 0.359) and contributions (43% and 38%), followed by the SMAI and SPI for East Asia and Australia, respectively. The SMAI achieved a similar weight (0.360) and contribution (39%) as the ESI across Australia. To determine the robustness of the MDI as estimated by TC weights, we evaluated the MDI and the reference GRACE-DSI with respect to documented drought records in the study areas. The MDI produced trends similar to those of the GRACE-DSI in Australia, while MDI and GRACE-DSI trends were not similar in East Asia. The correlation between the MDI and GRACE-DSI in Australia (0.41–0.62) was also higher than in East Asia (0.24–0.32) during the study periods. This discrepancy was due to the conceptual difference in that MDI reflects the near-surface water storage variation while GRACE-DSI reflects the variation of deeper water. Nevertheless, our results showed that the MDI out-performed single drought indices and was able to capture documented drought events across the study regions. This suggests that merging different drought indices into a single tool can better represent droughts, and may be a valuable approach for water resource management.</description><identifier>ISSN: 1436-3240</identifier><identifier>EISSN: 1436-3259</identifier><identifier>DOI: 10.1007/s00477-021-02044-7</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Aquatic Pollution ; Chemistry and Earth Sciences ; Collocation methods ; Computational Intelligence ; Computer Science ; Drought ; Drought index ; Earth and Environmental Science ; Earth Sciences ; Environment ; GRACE (experiment) ; Groundwater ; Groundwater levels ; Math. Appl. in Environmental Science ; Moisture index ; Physics ; Probability Theory and Stochastic Processes ; Resource management ; Soil moisture ; Soil stresses ; Standardized precipitation index ; Statistics for Engineering ; Surface water ; Trends ; Variation ; Waste Water Technology ; Water Management ; Water Pollution Control ; Water resources management ; Water storage ; Weight</subject><ispartof>Stochastic environmental research and risk assessment, 2022-04, Vol.36 (4), p.1177-1195</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-827f9ad14865ac5d897f94bfd256cc62fd9d9ec4a6168b31ebd925d801fe6d483</citedby><cites>FETCH-LOGICAL-c319t-827f9ad14865ac5d897f94bfd256cc62fd9d9ec4a6168b31ebd925d801fe6d483</cites><orcidid>0000-0002-1262-9290</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00477-021-02044-7$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00477-021-02044-7$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Baik, Jongjin</creatorcontrib><creatorcontrib>Park, Jongmin</creatorcontrib><creatorcontrib>Hao, Yuefeng</creatorcontrib><creatorcontrib>Choi, Minha</creatorcontrib><title>Integration of multiple drought indices using a triple collocation approach</title><title>Stochastic environmental research and risk assessment</title><addtitle>Stoch Environ Res Risk Assess</addtitle><description>Three drought indices (the Standardized Precipitation Index [SPI], Evaporative Stress Index [ESI], and Soil Moisture Anomaly Index [SMAI]) were integrated using triple collocation (TC) to produce the merged drought index (MDI). The new index was then compared with the Gravity recovery and climate experiment (GRACE)–Drought severity index (DSI), a comprehensive drought index reflecting storage variation in surface, sub-surface, and groundwater levels across East Asia and Australia, from 2003 to 2014. Before merging the three drought indices, their performance was analyzed. The mean correlation between the three drought indices and the GRACE–DSI indicated that the performance of the ESI was superior to the SMAI and SPI over the study areas. In terms of average weight results using the merging approach, the ESI was associated with larger weights (0.372 and 0.359) and contributions (43% and 38%), followed by the SMAI and SPI for East Asia and Australia, respectively. The SMAI achieved a similar weight (0.360) and contribution (39%) as the ESI across Australia. To determine the robustness of the MDI as estimated by TC weights, we evaluated the MDI and the reference GRACE-DSI with respect to documented drought records in the study areas. The MDI produced trends similar to those of the GRACE-DSI in Australia, while MDI and GRACE-DSI trends were not similar in East Asia. The correlation between the MDI and GRACE-DSI in Australia (0.41–0.62) was also higher than in East Asia (0.24–0.32) during the study periods. This discrepancy was due to the conceptual difference in that MDI reflects the near-surface water storage variation while GRACE-DSI reflects the variation of deeper water. Nevertheless, our results showed that the MDI out-performed single drought indices and was able to capture documented drought events across the study regions. This suggests that merging different drought indices into a single tool can better represent droughts, and may be a valuable approach for water resource management.</description><subject>Aquatic Pollution</subject><subject>Chemistry and Earth Sciences</subject><subject>Collocation methods</subject><subject>Computational Intelligence</subject><subject>Computer Science</subject><subject>Drought</subject><subject>Drought index</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Environment</subject><subject>GRACE (experiment)</subject><subject>Groundwater</subject><subject>Groundwater levels</subject><subject>Math. Appl. in Environmental Science</subject><subject>Moisture index</subject><subject>Physics</subject><subject>Probability Theory and Stochastic Processes</subject><subject>Resource management</subject><subject>Soil moisture</subject><subject>Soil stresses</subject><subject>Standardized precipitation index</subject><subject>Statistics for Engineering</subject><subject>Surface water</subject><subject>Trends</subject><subject>Variation</subject><subject>Waste Water Technology</subject><subject>Water Management</subject><subject>Water Pollution Control</subject><subject>Water resources management</subject><subject>Water storage</subject><subject>Weight</subject><issn>1436-3240</issn><issn>1436-3259</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kEtLxDAUhYMoOIzzB1wVXFfzatIsZfAxOOBG1yHNoxPpNDVJF_57oxXdubjce7nnOxcOAJcIXiMI-U2CkHJeQ4xKQUprfgJWiBJWE9yI09-ZwnOwScl3BWqIEAiuwNNuzLaPKvswVsFVx3nIfhpsZWKY-0Ou_Gi8tqmakx_7SlU5fp91GIagF0xNUwxKHy7AmVNDspufvgav93cv28d6__yw297ua02QyHWLuRPKINqyRunGtKLstHMGN0xrhp0RRlhNFUOs7QiynRG4yCBylhnakjW4WnzL2_fZpizfwhzH8lJiRlrEcctZUeFFpWNIKVonp-iPKn5IBOVXbnLJTZbc5HdukheILFAq4rG38c_6H-oTmlBw7g</recordid><startdate>20220401</startdate><enddate>20220401</enddate><creator>Baik, Jongjin</creator><creator>Park, Jongmin</creator><creator>Hao, Yuefeng</creator><creator>Choi, Minha</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><orcidid>https://orcid.org/0000-0002-1262-9290</orcidid></search><sort><creationdate>20220401</creationdate><title>Integration of multiple drought indices using a triple collocation approach</title><author>Baik, Jongjin ; Park, Jongmin ; Hao, Yuefeng ; Choi, Minha</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-827f9ad14865ac5d897f94bfd256cc62fd9d9ec4a6168b31ebd925d801fe6d483</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Aquatic Pollution</topic><topic>Chemistry and Earth Sciences</topic><topic>Collocation methods</topic><topic>Computational Intelligence</topic><topic>Computer Science</topic><topic>Drought</topic><topic>Drought index</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Environment</topic><topic>GRACE (experiment)</topic><topic>Groundwater</topic><topic>Groundwater levels</topic><topic>Math. Appl. in Environmental Science</topic><topic>Moisture index</topic><topic>Physics</topic><topic>Probability Theory and Stochastic Processes</topic><topic>Resource management</topic><topic>Soil moisture</topic><topic>Soil stresses</topic><topic>Standardized precipitation index</topic><topic>Statistics for Engineering</topic><topic>Surface water</topic><topic>Trends</topic><topic>Variation</topic><topic>Waste Water Technology</topic><topic>Water Management</topic><topic>Water Pollution Control</topic><topic>Water resources management</topic><topic>Water storage</topic><topic>Weight</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Baik, Jongjin</creatorcontrib><creatorcontrib>Park, Jongmin</creatorcontrib><creatorcontrib>Hao, Yuefeng</creatorcontrib><creatorcontrib>Choi, Minha</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 & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Science Database</collection><collection>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 & Technology Collection</collection><collection>Environment Abstracts</collection><jtitle>Stochastic environmental research and risk assessment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Baik, Jongjin</au><au>Park, Jongmin</au><au>Hao, Yuefeng</au><au>Choi, Minha</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Integration of multiple drought indices using a triple collocation approach</atitle><jtitle>Stochastic environmental research and risk assessment</jtitle><stitle>Stoch Environ Res Risk Assess</stitle><date>2022-04-01</date><risdate>2022</risdate><volume>36</volume><issue>4</issue><spage>1177</spage><epage>1195</epage><pages>1177-1195</pages><issn>1436-3240</issn><eissn>1436-3259</eissn><abstract>Three drought indices (the Standardized Precipitation Index [SPI], Evaporative Stress Index [ESI], and Soil Moisture Anomaly Index [SMAI]) were integrated using triple collocation (TC) to produce the merged drought index (MDI). The new index was then compared with the Gravity recovery and climate experiment (GRACE)–Drought severity index (DSI), a comprehensive drought index reflecting storage variation in surface, sub-surface, and groundwater levels across East Asia and Australia, from 2003 to 2014. Before merging the three drought indices, their performance was analyzed. The mean correlation between the three drought indices and the GRACE–DSI indicated that the performance of the ESI was superior to the SMAI and SPI over the study areas. In terms of average weight results using the merging approach, the ESI was associated with larger weights (0.372 and 0.359) and contributions (43% and 38%), followed by the SMAI and SPI for East Asia and Australia, respectively. The SMAI achieved a similar weight (0.360) and contribution (39%) as the ESI across Australia. To determine the robustness of the MDI as estimated by TC weights, we evaluated the MDI and the reference GRACE-DSI with respect to documented drought records in the study areas. The MDI produced trends similar to those of the GRACE-DSI in Australia, while MDI and GRACE-DSI trends were not similar in East Asia. The correlation between the MDI and GRACE-DSI in Australia (0.41–0.62) was also higher than in East Asia (0.24–0.32) during the study periods. This discrepancy was due to the conceptual difference in that MDI reflects the near-surface water storage variation while GRACE-DSI reflects the variation of deeper water. Nevertheless, our results showed that the MDI out-performed single drought indices and was able to capture documented drought events across the study regions. This suggests that merging different drought indices into a single tool can better represent droughts, and may be a valuable approach for water resource management.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00477-021-02044-7</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0002-1262-9290</orcidid></addata></record> |
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subjects | Aquatic Pollution Chemistry and Earth Sciences Collocation methods Computational Intelligence Computer Science Drought Drought index Earth and Environmental Science Earth Sciences Environment GRACE (experiment) Groundwater Groundwater levels Math. Appl. in Environmental Science Moisture index Physics Probability Theory and Stochastic Processes Resource management Soil moisture Soil stresses Standardized precipitation index Statistics for Engineering Surface water Trends Variation Waste Water Technology Water Management Water Pollution Control Water resources management Water storage Weight |
title | Integration of multiple drought indices using a triple collocation approach |
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