Loading…
Copula-based drought risk analysis on rainfed agriculture under stationary and non-stationary settings
Assessing the risk to the agricultural system is important for agricultural sustainability. The present study analyses agricultural drought risk with respect to different drought severities. Different drought indices - namely, Standardized Precipitation Evapotranspiration Index (SPEI), Standardized...
Saved in:
Published in: | Hydrological sciences journal 2022-08, Vol.67 (11), p.1683-1701 |
---|---|
Main Authors: | , , |
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-c338t-715c1fd68a403a4e9db63e140f592ba2b025de921c23fe79dc334dca1a7ab9923 |
---|---|
cites | cdi_FETCH-LOGICAL-c338t-715c1fd68a403a4e9db63e140f592ba2b025de921c23fe79dc334dca1a7ab9923 |
container_end_page | 1701 |
container_issue | 11 |
container_start_page | 1683 |
container_title | Hydrological sciences journal |
container_volume | 67 |
creator | Das, Subhadarsini Das, Jew Umamahesh, N. V. |
description | Assessing the risk to the agricultural system is important for agricultural sustainability. The present study analyses agricultural drought risk with respect to different drought severities. Different drought indices - namely, Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Soil moisture Index (SSI), Vegetation Condition Index (VCI), and Temperature Condition Index (TCI) - are used to evaluate the conditional probability. Non-stationary analysis is carried out for SPEI and SSI to incorporate the impact of large-scale oscillations and regional hydrological variability. Copula analysis is performed between drought conditions and various crop yield anomalies over Maharashtra, India, during 1998-2015. The outcomes suggest that SPEI is a significant drought indicator over the maximum number of districts in all the crops. Sea Surface Temperature (SST) and Indian Summer Monsoon Index (ISMI) are selected as suitable covariates to model the non-stationarity in the SPEI time series. The drought risk is estimated to increase with drought severity for all of the selected crops. It is observed that the exclusion of non-stationarity will underestimate the agricultural risk. |
doi_str_mv | 10.1080/02626667.2022.2079416 |
format | article |
fullrecord | <record><control><sourceid>proquest_infor</sourceid><recordid>TN_cdi_proquest_journals_2719620306</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2719620306</sourcerecordid><originalsourceid>FETCH-LOGICAL-c338t-715c1fd68a403a4e9db63e140f592ba2b025de921c23fe79dc334dca1a7ab9923</originalsourceid><addsrcrecordid>eNp9kE1LAzEQhoMoWKs_QQh43pqP3WxzU4pfUPCi5zC7SWrqNqlJFum_N6UVPHmZgeF5h5kHoWtKZpTMyS1hggkh2hkjjJXSypqKEzRhtCEVr3lziiZ7ptpD5-gipTUhvJaCT5BdhO04QNVBMhrrGMbVR8bRpU8MHoZdcgkHjyM4bwsAq-j6cchjNHj02kScMmQXPMRdCWjsg6_-jJLJ2flVukRnFoZkro59it4fH94Wz9Xy9ellcb-ses7nuWpp01OrxRxqwqE2UneCG1oT20jWAesIa7SRjPaMW9NKXWK17oFCC52UjE_RzWHvNoav0aSs1mGM5ZGkWEulYIQTUajmQPUxpBSNVdvoNuVeRYnaK1W_StVeqToqLbm7Q67ICHED3yEOWmXYDSHaCL53SfH_V_wAASV_ag</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2719620306</pqid></control><display><type>article</type><title>Copula-based drought risk analysis on rainfed agriculture under stationary and non-stationary settings</title><source>Taylor and Francis Science and Technology Collection</source><creator>Das, Subhadarsini ; Das, Jew ; Umamahesh, N. V.</creator><creatorcontrib>Das, Subhadarsini ; Das, Jew ; Umamahesh, N. V.</creatorcontrib><description>Assessing the risk to the agricultural system is important for agricultural sustainability. The present study analyses agricultural drought risk with respect to different drought severities. Different drought indices - namely, Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Soil moisture Index (SSI), Vegetation Condition Index (VCI), and Temperature Condition Index (TCI) - are used to evaluate the conditional probability. Non-stationary analysis is carried out for SPEI and SSI to incorporate the impact of large-scale oscillations and regional hydrological variability. Copula analysis is performed between drought conditions and various crop yield anomalies over Maharashtra, India, during 1998-2015. The outcomes suggest that SPEI is a significant drought indicator over the maximum number of districts in all the crops. Sea Surface Temperature (SST) and Indian Summer Monsoon Index (ISMI) are selected as suitable covariates to model the non-stationarity in the SPEI time series. The drought risk is estimated to increase with drought severity for all of the selected crops. It is observed that the exclusion of non-stationarity will underestimate the agricultural risk.</description><identifier>ISSN: 0262-6667</identifier><identifier>EISSN: 2150-3435</identifier><identifier>DOI: 10.1080/02626667.2022.2079416</identifier><language>eng</language><publisher>Abingdon: Taylor & Francis</publisher><subject>Agricultural drought ; agricultural risk ; Analysis ; Anomalies ; Conditional probability ; copula ; Crop yield ; Crops ; Drought ; Drought conditions ; Drought index ; Environmental risk ; Evapotranspiration ; Evapotranspiration-precipitation relationships ; Farming systems ; Hydrologic analysis ; Hydrology ; India ; Moisture effects ; Moisture index ; Oscillations ; Probability theory ; rainfed crops ; Rainfed farming ; Risk analysis ; Risk assessment ; Sea surface ; Sea surface temperature ; Soil moisture ; Soil temperature ; Summer monsoon ; Surface temperature ; Sustainability ; Sustainable agriculture ; Vegetation</subject><ispartof>Hydrological sciences journal, 2022-08, Vol.67 (11), p.1683-1701</ispartof><rights>2022 IAHS 2022</rights><rights>2022 IAHS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c338t-715c1fd68a403a4e9db63e140f592ba2b025de921c23fe79dc334dca1a7ab9923</citedby><cites>FETCH-LOGICAL-c338t-715c1fd68a403a4e9db63e140f592ba2b025de921c23fe79dc334dca1a7ab9923</cites><orcidid>0000-0003-0460-8956</orcidid></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>Das, Subhadarsini</creatorcontrib><creatorcontrib>Das, Jew</creatorcontrib><creatorcontrib>Umamahesh, N. V.</creatorcontrib><title>Copula-based drought risk analysis on rainfed agriculture under stationary and non-stationary settings</title><title>Hydrological sciences journal</title><description>Assessing the risk to the agricultural system is important for agricultural sustainability. The present study analyses agricultural drought risk with respect to different drought severities. Different drought indices - namely, Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Soil moisture Index (SSI), Vegetation Condition Index (VCI), and Temperature Condition Index (TCI) - are used to evaluate the conditional probability. Non-stationary analysis is carried out for SPEI and SSI to incorporate the impact of large-scale oscillations and regional hydrological variability. Copula analysis is performed between drought conditions and various crop yield anomalies over Maharashtra, India, during 1998-2015. The outcomes suggest that SPEI is a significant drought indicator over the maximum number of districts in all the crops. Sea Surface Temperature (SST) and Indian Summer Monsoon Index (ISMI) are selected as suitable covariates to model the non-stationarity in the SPEI time series. The drought risk is estimated to increase with drought severity for all of the selected crops. It is observed that the exclusion of non-stationarity will underestimate the agricultural risk.</description><subject>Agricultural drought</subject><subject>agricultural risk</subject><subject>Analysis</subject><subject>Anomalies</subject><subject>Conditional probability</subject><subject>copula</subject><subject>Crop yield</subject><subject>Crops</subject><subject>Drought</subject><subject>Drought conditions</subject><subject>Drought index</subject><subject>Environmental risk</subject><subject>Evapotranspiration</subject><subject>Evapotranspiration-precipitation relationships</subject><subject>Farming systems</subject><subject>Hydrologic analysis</subject><subject>Hydrology</subject><subject>India</subject><subject>Moisture effects</subject><subject>Moisture index</subject><subject>Oscillations</subject><subject>Probability theory</subject><subject>rainfed crops</subject><subject>Rainfed farming</subject><subject>Risk analysis</subject><subject>Risk assessment</subject><subject>Sea surface</subject><subject>Sea surface temperature</subject><subject>Soil moisture</subject><subject>Soil temperature</subject><subject>Summer monsoon</subject><subject>Surface temperature</subject><subject>Sustainability</subject><subject>Sustainable agriculture</subject><subject>Vegetation</subject><issn>0262-6667</issn><issn>2150-3435</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEQhoMoWKs_QQh43pqP3WxzU4pfUPCi5zC7SWrqNqlJFum_N6UVPHmZgeF5h5kHoWtKZpTMyS1hggkh2hkjjJXSypqKEzRhtCEVr3lziiZ7ptpD5-gipTUhvJaCT5BdhO04QNVBMhrrGMbVR8bRpU8MHoZdcgkHjyM4bwsAq-j6cchjNHj02kScMmQXPMRdCWjsg6_-jJLJ2flVukRnFoZkro59it4fH94Wz9Xy9ellcb-ses7nuWpp01OrxRxqwqE2UneCG1oT20jWAesIa7SRjPaMW9NKXWK17oFCC52UjE_RzWHvNoav0aSs1mGM5ZGkWEulYIQTUajmQPUxpBSNVdvoNuVeRYnaK1W_StVeqToqLbm7Q67ICHED3yEOWmXYDSHaCL53SfH_V_wAASV_ag</recordid><startdate>20220818</startdate><enddate>20220818</enddate><creator>Das, Subhadarsini</creator><creator>Das, Jew</creator><creator>Umamahesh, N. V.</creator><general>Taylor & Francis</general><general>Taylor & Francis Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7ST</scope><scope>7TG</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>KL.</scope><scope>L.G</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0003-0460-8956</orcidid></search><sort><creationdate>20220818</creationdate><title>Copula-based drought risk analysis on rainfed agriculture under stationary and non-stationary settings</title><author>Das, Subhadarsini ; Das, Jew ; Umamahesh, N. V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c338t-715c1fd68a403a4e9db63e140f592ba2b025de921c23fe79dc334dca1a7ab9923</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Agricultural drought</topic><topic>agricultural risk</topic><topic>Analysis</topic><topic>Anomalies</topic><topic>Conditional probability</topic><topic>copula</topic><topic>Crop yield</topic><topic>Crops</topic><topic>Drought</topic><topic>Drought conditions</topic><topic>Drought index</topic><topic>Environmental risk</topic><topic>Evapotranspiration</topic><topic>Evapotranspiration-precipitation relationships</topic><topic>Farming systems</topic><topic>Hydrologic analysis</topic><topic>Hydrology</topic><topic>India</topic><topic>Moisture effects</topic><topic>Moisture index</topic><topic>Oscillations</topic><topic>Probability theory</topic><topic>rainfed crops</topic><topic>Rainfed farming</topic><topic>Risk analysis</topic><topic>Risk assessment</topic><topic>Sea surface</topic><topic>Sea surface temperature</topic><topic>Soil moisture</topic><topic>Soil temperature</topic><topic>Summer monsoon</topic><topic>Surface temperature</topic><topic>Sustainability</topic><topic>Sustainable agriculture</topic><topic>Vegetation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Das, Subhadarsini</creatorcontrib><creatorcontrib>Das, Jew</creatorcontrib><creatorcontrib>Umamahesh, N. V.</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical 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>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environment Abstracts</collection><jtitle>Hydrological sciences journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Das, Subhadarsini</au><au>Das, Jew</au><au>Umamahesh, N. V.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Copula-based drought risk analysis on rainfed agriculture under stationary and non-stationary settings</atitle><jtitle>Hydrological sciences journal</jtitle><date>2022-08-18</date><risdate>2022</risdate><volume>67</volume><issue>11</issue><spage>1683</spage><epage>1701</epage><pages>1683-1701</pages><issn>0262-6667</issn><eissn>2150-3435</eissn><abstract>Assessing the risk to the agricultural system is important for agricultural sustainability. The present study analyses agricultural drought risk with respect to different drought severities. Different drought indices - namely, Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Soil moisture Index (SSI), Vegetation Condition Index (VCI), and Temperature Condition Index (TCI) - are used to evaluate the conditional probability. Non-stationary analysis is carried out for SPEI and SSI to incorporate the impact of large-scale oscillations and regional hydrological variability. Copula analysis is performed between drought conditions and various crop yield anomalies over Maharashtra, India, during 1998-2015. The outcomes suggest that SPEI is a significant drought indicator over the maximum number of districts in all the crops. Sea Surface Temperature (SST) and Indian Summer Monsoon Index (ISMI) are selected as suitable covariates to model the non-stationarity in the SPEI time series. The drought risk is estimated to increase with drought severity for all of the selected crops. It is observed that the exclusion of non-stationarity will underestimate the agricultural risk.</abstract><cop>Abingdon</cop><pub>Taylor & Francis</pub><doi>10.1080/02626667.2022.2079416</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0003-0460-8956</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0262-6667 |
ispartof | Hydrological sciences journal, 2022-08, Vol.67 (11), p.1683-1701 |
issn | 0262-6667 2150-3435 |
language | eng |
recordid | cdi_proquest_journals_2719620306 |
source | Taylor and Francis Science and Technology Collection |
subjects | Agricultural drought agricultural risk Analysis Anomalies Conditional probability copula Crop yield Crops Drought Drought conditions Drought index Environmental risk Evapotranspiration Evapotranspiration-precipitation relationships Farming systems Hydrologic analysis Hydrology India Moisture effects Moisture index Oscillations Probability theory rainfed crops Rainfed farming Risk analysis Risk assessment Sea surface Sea surface temperature Soil moisture Soil temperature Summer monsoon Surface temperature Sustainability Sustainable agriculture Vegetation |
title | Copula-based drought risk analysis on rainfed agriculture under stationary and non-stationary settings |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T17%3A41%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_infor&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Copula-based%20drought%20risk%20analysis%20on%20rainfed%20agriculture%20under%20stationary%20and%20non-stationary%20settings&rft.jtitle=Hydrological%20sciences%20journal&rft.au=Das,%20Subhadarsini&rft.date=2022-08-18&rft.volume=67&rft.issue=11&rft.spage=1683&rft.epage=1701&rft.pages=1683-1701&rft.issn=0262-6667&rft.eissn=2150-3435&rft_id=info:doi/10.1080/02626667.2022.2079416&rft_dat=%3Cproquest_infor%3E2719620306%3C/proquest_infor%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c338t-715c1fd68a403a4e9db63e140f592ba2b025de921c23fe79dc334dca1a7ab9923%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2719620306&rft_id=info:pmid/&rfr_iscdi=true |