Integrated Drought Index (IDI) for Drought Monitoring and Assessment in India
Drought monitoring and declaration in India are challenging due to the requirement of multiple drought indices representing meteorological, hydrological, and agricultural droughts that are often not available in near real‐time. In addition, the current drought monitoring efforts do not consider grou...
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description | Drought monitoring and declaration in India are challenging due to the requirement of multiple drought indices representing meteorological, hydrological, and agricultural droughts that are often not available in near real‐time. In addition, the current drought monitoring efforts do not consider groundwater storage variability. To overcome this, we develop an Integrated Drought Index (IDI) that combines the response of meteorological, hydrological, and agricultural droughts and accounts for groundwater storage. We use the Gaussian copula to integrate the 12‐month Standardized Precipitation Index (SPI), 4‐month Standardized Runoff Index (SRI), 1‐month Standardized Soil moisture Index (SSI), and 1‐month Standardized Groundwater Index (SGI) to develop IDI. Hydrologic variables (total runoff, soil moisture, and groundwater) required in IDI were simulated using the Variable Infiltration Capacity (VIC) with SIMple Groundwater Model (VIC‐SIMGM). We evaluated IDI against the Drought Severity Index (DSI), terrestrial and groundwater storage anomalies from the Gravity Recovery and Climate Experiment (GRACE) satellites, groundwater well, and streamflow anomalies. Moreover, we identify the three major droughts with the highest severity (based on IDI) that occurred in 1965, 1987, and 2002 in the Sabarmati river basin. The three most severe droughts occurred in 1966, 1979, and 2010 in the Brahmani basin. Notwithstanding the large intermodel uncertainty, which arises primarily from precipitation projections, the drought frequency based on IDI is projected to decline in Sabarmati while it increases in Brahmani basin under the warming climate. Our results show that IDI can be effectively used for drought monitoring and assessment under retrospective and future climate in India.
Key Points
Developed an Integrated Drought Index based on meteorological, hydrological, and agricultural droughts
IDI uses SPI, SRI, SSI, and SGI by incorporating the response of precipitation, runoff, soil moisture, and groundwater
IDI performs well against the Drought Severity Index (DSI) and groundwater well and streamflow observations |
doi_str_mv | 10.1029/2019WR026284 |
format | Article |
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Key Points
Developed an Integrated Drought Index based on meteorological, hydrological, and agricultural droughts
IDI uses SPI, SRI, SSI, and SGI by incorporating the response of precipitation, runoff, soil moisture, and groundwater
IDI performs well against the Drought Severity Index (DSI) and groundwater well and streamflow observations</description><identifier>ISSN: 0043-1397</identifier><identifier>EISSN: 1944-7973</identifier><identifier>DOI: 10.1029/2019WR026284</identifier><language>eng</language><publisher>Washington: John Wiley & Sons, Inc</publisher><subject>Agricultural drought ; Anomalies ; Climate ; Climate change ; Computer simulation ; Drought ; Drought index ; Drought monitoring ; Environmental monitoring ; Future climates ; Global warming ; GRACE (experiment) ; Gravity ; Groundwater ; Groundwater runoff ; Groundwater storage ; Hydrologic drought ; Hydrological drought ; Hydrology ; Infiltration capacity ; Integrated Drought Index ; Meteorological drought ; Moisture index ; Precipitation ; River basins ; Rivers ; Runoff ; Soil ; Soil moisture ; Soils ; Standardized precipitation index ; Stream discharge ; Stream flow ; VIC ; Water wells</subject><ispartof>Water resources research, 2020-02, Vol.56 (2), p.n/a</ispartof><rights>2019. American Geophysical Union. All Rights Reserved.</rights><rights>2020. American Geophysical Union. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a3730-31f964faaeee53623122b6df26ae65b85cb14dd833ab5dd09865058d419f08903</citedby><cites>FETCH-LOGICAL-a3730-31f964faaeee53623122b6df26ae65b85cb14dd833ab5dd09865058d419f08903</cites><orcidid>0000-0001-8461-4162 ; 0000-0002-3046-6296</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1029%2F2019WR026284$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2019WR026284$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,11493,27901,27902,45550,45551,46443,46867</link.rule.ids></links><search><creatorcontrib>Shah, Deep</creatorcontrib><creatorcontrib>Mishra, Vimal</creatorcontrib><title>Integrated Drought Index (IDI) for Drought Monitoring and Assessment in India</title><title>Water resources research</title><description>Drought monitoring and declaration in India are challenging due to the requirement of multiple drought indices representing meteorological, hydrological, and agricultural droughts that are often not available in near real‐time. In addition, the current drought monitoring efforts do not consider groundwater storage variability. To overcome this, we develop an Integrated Drought Index (IDI) that combines the response of meteorological, hydrological, and agricultural droughts and accounts for groundwater storage. We use the Gaussian copula to integrate the 12‐month Standardized Precipitation Index (SPI), 4‐month Standardized Runoff Index (SRI), 1‐month Standardized Soil moisture Index (SSI), and 1‐month Standardized Groundwater Index (SGI) to develop IDI. Hydrologic variables (total runoff, soil moisture, and groundwater) required in IDI were simulated using the Variable Infiltration Capacity (VIC) with SIMple Groundwater Model (VIC‐SIMGM). We evaluated IDI against the Drought Severity Index (DSI), terrestrial and groundwater storage anomalies from the Gravity Recovery and Climate Experiment (GRACE) satellites, groundwater well, and streamflow anomalies. Moreover, we identify the three major droughts with the highest severity (based on IDI) that occurred in 1965, 1987, and 2002 in the Sabarmati river basin. The three most severe droughts occurred in 1966, 1979, and 2010 in the Brahmani basin. Notwithstanding the large intermodel uncertainty, which arises primarily from precipitation projections, the drought frequency based on IDI is projected to decline in Sabarmati while it increases in Brahmani basin under the warming climate. Our results show that IDI can be effectively used for drought monitoring and assessment under retrospective and future climate in India.
Key Points
Developed an Integrated Drought Index based on meteorological, hydrological, and agricultural droughts
IDI uses SPI, SRI, SSI, and SGI by incorporating the response of precipitation, runoff, soil moisture, and groundwater
IDI performs well against the Drought Severity Index (DSI) and groundwater well and streamflow observations</description><subject>Agricultural drought</subject><subject>Anomalies</subject><subject>Climate</subject><subject>Climate change</subject><subject>Computer simulation</subject><subject>Drought</subject><subject>Drought index</subject><subject>Drought monitoring</subject><subject>Environmental monitoring</subject><subject>Future climates</subject><subject>Global warming</subject><subject>GRACE (experiment)</subject><subject>Gravity</subject><subject>Groundwater</subject><subject>Groundwater runoff</subject><subject>Groundwater storage</subject><subject>Hydrologic drought</subject><subject>Hydrological drought</subject><subject>Hydrology</subject><subject>Infiltration capacity</subject><subject>Integrated Drought Index</subject><subject>Meteorological drought</subject><subject>Moisture index</subject><subject>Precipitation</subject><subject>River basins</subject><subject>Rivers</subject><subject>Runoff</subject><subject>Soil</subject><subject>Soil moisture</subject><subject>Soils</subject><subject>Standardized precipitation index</subject><subject>Stream discharge</subject><subject>Stream flow</subject><subject>VIC</subject><subject>Water wells</subject><issn>0043-1397</issn><issn>1944-7973</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp90E1LAzEQBuAgCtbqzR-w4EXB1UkmyW6OpfVjoUUoSo8h22TrljZbk120_96WinjyNDA87wy8hFxSuKPA1D0DqmZTYJLl_Ij0qOI8zVSGx6QHwDGlqLJTchbjEoByIbMemRS-dYtgWmeTUWi6xXubFN66r-S6GBU3SdWE3_2k8XXbhNovEuNtMojRxbh2vk1qvw_V5pycVGYV3cXP7JO3x4fX4XM6fnkqhoNxajBDSJFWSvLKGOecQMmQMlZKWzFpnBRlLuYl5dbmiKYU1oLKpQCRW05VBbkC7JOrw91NaD46F1u9bLrgdy81QyUQM67oTt0e1Dw0MQZX6U2o1yZsNQW9L0z_LWzH8cA_65Xb_mv1bDqcMo4Z4DeAuWqe</recordid><startdate>202002</startdate><enddate>202002</enddate><creator>Shah, Deep</creator><creator>Mishra, Vimal</creator><general>John Wiley & Sons, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7QL</scope><scope>7T7</scope><scope>7TG</scope><scope>7U9</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H94</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>M7N</scope><scope>P64</scope><orcidid>https://orcid.org/0000-0001-8461-4162</orcidid><orcidid>https://orcid.org/0000-0002-3046-6296</orcidid></search><sort><creationdate>202002</creationdate><title>Integrated Drought Index (IDI) for Drought Monitoring and Assessment in India</title><author>Shah, Deep ; Mishra, Vimal</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a3730-31f964faaeee53623122b6df26ae65b85cb14dd833ab5dd09865058d419f08903</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Agricultural drought</topic><topic>Anomalies</topic><topic>Climate</topic><topic>Climate change</topic><topic>Computer simulation</topic><topic>Drought</topic><topic>Drought index</topic><topic>Drought monitoring</topic><topic>Environmental monitoring</topic><topic>Future climates</topic><topic>Global warming</topic><topic>GRACE (experiment)</topic><topic>Gravity</topic><topic>Groundwater</topic><topic>Groundwater runoff</topic><topic>Groundwater storage</topic><topic>Hydrologic drought</topic><topic>Hydrological drought</topic><topic>Hydrology</topic><topic>Infiltration capacity</topic><topic>Integrated Drought Index</topic><topic>Meteorological drought</topic><topic>Moisture index</topic><topic>Precipitation</topic><topic>River basins</topic><topic>Rivers</topic><topic>Runoff</topic><topic>Soil</topic><topic>Soil moisture</topic><topic>Soils</topic><topic>Standardized precipitation index</topic><topic>Stream discharge</topic><topic>Stream flow</topic><topic>VIC</topic><topic>Water wells</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shah, Deep</creatorcontrib><creatorcontrib>Mishra, Vimal</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Water resources research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shah, Deep</au><au>Mishra, Vimal</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Integrated Drought Index (IDI) for Drought Monitoring and Assessment in India</atitle><jtitle>Water resources research</jtitle><date>2020-02</date><risdate>2020</risdate><volume>56</volume><issue>2</issue><epage>n/a</epage><issn>0043-1397</issn><eissn>1944-7973</eissn><abstract>Drought monitoring and declaration in India are challenging due to the requirement of multiple drought indices representing meteorological, hydrological, and agricultural droughts that are often not available in near real‐time. In addition, the current drought monitoring efforts do not consider groundwater storage variability. To overcome this, we develop an Integrated Drought Index (IDI) that combines the response of meteorological, hydrological, and agricultural droughts and accounts for groundwater storage. We use the Gaussian copula to integrate the 12‐month Standardized Precipitation Index (SPI), 4‐month Standardized Runoff Index (SRI), 1‐month Standardized Soil moisture Index (SSI), and 1‐month Standardized Groundwater Index (SGI) to develop IDI. Hydrologic variables (total runoff, soil moisture, and groundwater) required in IDI were simulated using the Variable Infiltration Capacity (VIC) with SIMple Groundwater Model (VIC‐SIMGM). We evaluated IDI against the Drought Severity Index (DSI), terrestrial and groundwater storage anomalies from the Gravity Recovery and Climate Experiment (GRACE) satellites, groundwater well, and streamflow anomalies. Moreover, we identify the three major droughts with the highest severity (based on IDI) that occurred in 1965, 1987, and 2002 in the Sabarmati river basin. The three most severe droughts occurred in 1966, 1979, and 2010 in the Brahmani basin. Notwithstanding the large intermodel uncertainty, which arises primarily from precipitation projections, the drought frequency based on IDI is projected to decline in Sabarmati while it increases in Brahmani basin under the warming climate. Our results show that IDI can be effectively used for drought monitoring and assessment under retrospective and future climate in India.
Key Points
Developed an Integrated Drought Index based on meteorological, hydrological, and agricultural droughts
IDI uses SPI, SRI, SSI, and SGI by incorporating the response of precipitation, runoff, soil moisture, and groundwater
IDI performs well against the Drought Severity Index (DSI) and groundwater well and streamflow observations</abstract><cop>Washington</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1029/2019WR026284</doi><tpages>22</tpages><orcidid>https://orcid.org/0000-0001-8461-4162</orcidid><orcidid>https://orcid.org/0000-0002-3046-6296</orcidid></addata></record> |
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subjects | Agricultural drought Anomalies Climate Climate change Computer simulation Drought Drought index Drought monitoring Environmental monitoring Future climates Global warming GRACE (experiment) Gravity Groundwater Groundwater runoff Groundwater storage Hydrologic drought Hydrological drought Hydrology Infiltration capacity Integrated Drought Index Meteorological drought Moisture index Precipitation River basins Rivers Runoff Soil Soil moisture Soils Standardized precipitation index Stream discharge Stream flow VIC Water wells |
title | Integrated Drought Index (IDI) for Drought Monitoring and Assessment in India |
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