Investigating of the performance of univariate and integrated multivariate indices in agricultural drought monitoring (Case study: Karkheh basin)
Drought is one of the major climate hazards that has significantly impacted the environmental and socio-economic conditions of Iran in recent years. As such, investigating and monitoring drought is essential for understanding its occurrence and reducing vulnerability. This study evaluates the effici...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | fas |
| Published: |
Iranian Scientific Association of Desert Management and Control (ISADMC)
2024-11-01
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| Series: | مدیریت بیابان |
| Subjects: | |
| Online Access: | https://www.jdmal.ir/article_715763_693b4dd75f1159ecb94e7f1ac8915b31.pdf |
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| Summary: | Drought is one of the major climate hazards that has significantly impacted the environmental and socio-economic conditions of Iran in recent years. As such, investigating and monitoring drought is essential for understanding its occurrence and reducing vulnerability. This study evaluates the efficiency of integrated multivariate indices compared to univariate indices for monitoring agricultural drought based on satellite imagery in the Karkheh basin. Multivariate remote sensing indices, including the Vegetation Health Index (VHI) and the Normalized Vegetation Soil Water Index (NVSWI), alongside the univariate Normalized Land Surface Water Index (NLSWI), were calculated to monitor drought conditions across the entire Karkheh watershed during May from 2001 to 2022. The results revealed frequent drought occurrences with varying intensities (low to severe) during the statistical periods of 2001–2004, 2006–2010, 2012, and 2013–2015. The performance of multivariate and univariate indices was validated against the Standardized Precipitation Index (SPI) at one-, three-, and six-month scales, as well as the Streamflow Drought Index (SDI). The highest correlation was observed between the NVSWI and SPI-1 (r = 0.71) and SDI (r = 0.64). Similarly, the VHI demonstrated a relatively strong correlation with SPI-1 (r = 0.66) and SDI (r = 0.56). In contrast, the univariate NLSWI showed the weakest correlations with SPI-1 (r = 0.42), SPI-3 (r = 0.21), and SDI (r = 0.40). The findings indicate that integrated multivariate indices, such as NVSWI and VHI, outperform the univariate NLSWI index in agricultural drought monitoring. This superiority is attributed to the inclusion of multiple factors, such as vegetation health and land surface temperature, in multivariate indices. As a result, NVSWI and VHI provide more comprehensive and accurate monitoring of drought conditions compared to univariate indices. |
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| ISSN: | 2476-3985 2476-3721 |