Desert Management

Desert Management

Investigating of the performance of univariate and integrated multivariate indices in agricultural drought monitoring (Case study: Karkheh basin)

Document Type : Original Article

Authors
Mahshid Karimi 1
Navid Dehghani 2
Nastaran Jalilian 3
Kaka Shahedi 4
1 Researcher, Soil Conservation and Watershed Management Research Department, Kermanshah Agricultural and Natural Resources Research and Education Center, (AREEO), Kermanshah, Iran.
2 Assistant Prof., Watershed Management Department, Soil Conservation and Watershed Research Institute, Tehran, Iran.
3 Associate Prof., Forests and Rangelands Research Division, Kermanshah Agricultural and Natural Resources Research and Education Center, (AREEO), Kermanshah, Iran.
4 Prof., Watershed Management Department, Sari Agricultural Science and Natural Resources University, Iran.
10.22034/jdmal.2024.2036111.1473
Abstract
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.
Keywords
Agricultural drought
Remote sensing
Univariate indices
Multivariate indices

Subjects

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Desert Management
Volume 12, Issue 3 - Serial Number 31
6 Article
Autumn 2024
Pages 99-122

  • Receive Date 20 July 2024
  • Revise Date 13 September 2024
  • Accept Date 14 September 2024