Mazhar, N., Ghalub, A.K., Malki, I., Noreena and Arshad, S. 2025. Enhancing aridity index assessment in Pakistan's dryland ecosystems: A machine learning approach integrating remote sensing and seasonal lag effects. Physics and Chemistry of the Earth, Parts A/B/C. 141 (Part 1) 104135. https://doi.org/10.1016/j.pce.2025.104135
| Title | Enhancing aridity index assessment in Pakistan's dryland ecosystems: A machine learning approach integrating remote sensing and seasonal lag effects |
|---|---|
| Type | Journal article |
| Authors | Mazhar, N., Ghalub, A.K., Malki, I., Noreena and Arshad, S. |
| Abstract | Dryland ecosystems are highly vulnerable to increased aridity, thus exacerbating the drought stress. From this perspective, our study aimed to evaluate the aridity index (AI) and Standardized Precipitation Index at a three-month scale (SPI-3) across three arid stations of Pakistan from 1990 to 2023. Seven remote sensing indices were used as covariates with SPI-3 and mean temperature for enhanced prediction. Four well-optimized machine learning models were employed on seasonally decomposed time series. Mann-Kendall and Sen’s slope analysis revealed a significant (p < 0.001) increasing trend of AI and SPI-3 values, indicating a comparatively lower aridity in recent years. It was consistent with the increasing trend of NDVI with Sen’s slope range from 0.0002 to 0.003. Cross correlation showed a seasonal effect of biophysical indicators on AI with a positive correlation of r = 0.4 with NDVI and r = 0.6 with NDWI at lag 0, indicating a late lag effect. Furthermore, machine learning prediction of AI with a three-month lag size revealed an outperformance of Gradient Boosting Regression with R² = 0.806 and RMSE = 0.076, followed by Random Forest with R² = 0.732 and RMSE = 0.089. The Dry Barren Soil Index (DBSI), NDWI, and SPI-3 gained high feature importance in the highly performed model. Our study highlights the significance of temporal and seasonal relationships of aridity and biophysical indicators in dryland ecosystems, informing region-specific land and water resource management policies to mitigate hydroclimatic extremes. |
| Keywords | Standardized Precipitation Index |
| Potential Evapotranspiration | |
| Cross Correlation | |
| Gradient Boosting Regression | |
| 3-month seasonal lag | |
| Dry Barren Soil Index | |
| Article number | 104135 |
| Journal | Physics and Chemistry of the Earth, Parts A/B/C |
| Journal citation | 141 (Part 1) |
| ISSN | 1474-7065 |
| Year | 2025 |
| Publisher | Elsevier |
| Accepted author manuscript | License CC BY-NC-ND 4.0 File Access Level Open (open metadata and files) |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.pce.2025.104135 |
| Web address (URL) | https://doi.org/10.1016/j.pce.2025.104135 |
| Publication dates | |
| Published online | 01 Oct 2025 |
| Published in print | Nov 2025 |