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<p>Drought, a severe natural disaster, leads to significant water shortages in several parts of the globe. Therefore, analyzing accurate spatio-temporal characteristics of drought are essential for effective drought mitigation policies. In geo-statistics, variogram models are useful tools to evaluate the spatial correlation for interpolation. Due to complex structure of drought, the selection of optimal variogram model for spatial interpolation of drought characteristics is challenging. This research infers the ability various variogram models for the spatial interpolation of drought duration under various time scales of Multivariate Modified Water Drought Severity Index (MMWDSI) in three future scenario using Ordinary Kriging (OK) and Universal Kriging (UK). In our application, we used spatial data on drought duration characterized by the MMWDSI across three future scenarios at 94 locations in Pakistan. In this research, seventeen variogram models are evaluated for analyzing spatial patterns of drought duration. The optimal selection of the most appropriate variogram models is assessed by comparing error metrics (i.e., Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and mean bias) under Leave-One-Out Cross-Validation (LOOCV) and K-fold cross-validation methods. From the results of this research, we found that the Exponentially Correlated Cauchy (Exc) variogram model is optimal due to its minimal error metrics except in one month time scales at SSP3-8.5 future scenario. Furthermore, the Hole-effect (Hol) model is deemed suitable for handling complex data at the most of the remaining time scales. This research reveals that as time scales and scenarios change, they influence the effectiveness of the variogram. Overall, the study suggests that no single model is universally superior for the accurate spatial interpolation of drought duration. These findings emphasize the importance of selecting the appropriate variogram models during the interpolation stage to achieve accurate and precise predictions of drought duration.</p>

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Spatial predictive analysis of drought duration in relation to climate change using interpolation techniques

  • 期刊论文

  • 发表日期：

    2024年12月31日

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  • Sufian Ahmad,
  • Aamina Batool,
  • Zulfiqar Ali

摘要

Drought, a severe natural disaster, leads to significant water shortages in several parts of the globe. Therefore, analyzing accurate spatio-temporal characteristics of drought are essential for effective drought mitigation policies. In geo-statistics, variogram models are useful tools to evaluate the spatial correlation for interpolation. Due to complex structure of drought, the selection of optimal variogram model for spatial interpolation of drought characteristics is challenging. This research infers the ability various variogram models for the spatial interpolation of drought duration under various time scales of Multivariate Modified Water Drought Severity Index (MMWDSI) in three future scenario using Ordinary Kriging (OK) and Universal Kriging (UK). In our application, we used spatial data on drought duration characterized by the MMWDSI across three future scenarios at 94 locations in Pakistan. In this research, seventeen variogram models are evaluated for analyzing spatial patterns of drought duration. The optimal selection of the most appropriate variogram models is assessed by comparing error metrics (i.e., Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and mean bias) under Leave-One-Out Cross-Validation (LOOCV) and K-fold cross-validation methods. From the results of this research, we found that the Exponentially Correlated Cauchy (Exc) variogram model is optimal due to its minimal error metrics except in one month time scales at SSP3-8.5 future scenario. Furthermore, the Hole-effect (Hol) model is deemed suitable for handling complex data at the most of the remaining time scales. This research reveals that as time scales and scenarios change, they influence the effectiveness of the variogram. Overall, the study suggests that no single model is universally superior for the accurate spatial interpolation of drought duration. These findings emphasize the importance of selecting the appropriate variogram models during the interpolation stage to achieve accurate and precise predictions of drought duration.