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...

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Bibliographic Details
Published in:Hydrological sciences journal 2022-08, Vol.67 (11), p.1683-1701
Main Authors: Das, Subhadarsini, Das, Jew, Umamahesh, N. V.
Format: Article
Language:English
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
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Summary: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.
ISSN:0262-6667
2150-3435
DOI:10.1080/02626667.2022.2079416