Year: 2026
Type: article
Abstract: Agricultural drought monitoring requires integrated indicators that capture the coupled variability of soil moisture and TerraClimate Actual Evapotranspiration (AET) while accounting for uncertainty in multi-source datasets. However, using existing drought indices and model-based indicators alone can be constrained by scale differences, heterogeneous errors, and incomplete representation of drought-related processes, particularly in climatically heterogeneous agricultural regions. To address this gap, we develop the Soil Moisture and Evapotranspiration Revealed Drought Index (SERDI) by integrating ERA5 and GLDAS soil moisture with TerraClimate AET, using Triple Collocation(TC) as the primary uncertainty-weighted formulation and Scaled Triple Collocation (STC) as a domain-based sensitivity analysis of spatial scaling effects. This framework is evaluated at seven selected locations—Bahawalpur, Faisalabad, Gujranwala, Lahore, Multan, Sargodha, and Sialkot—spanning contrasting hydroclimatic conditions along the south-to-northeast agroclimatic gradient of Punjab, Pakistan, using monthly data from 2000 to 2023. Internal consistency and TC-based diagnostics showed that ERA5-SM had the lowest estimated error variance and consequently received the largest merging weights, while SERDI_TC retained strong correlation \(\left(r=0.93-0.99\right)\) with its standardized inputs and coherent spatiotemporal drought patterns, including comparatively stronger drought signals at the sampled southern locations. The domain-based STC sensitivity analysis produced a drought signal highly consistent with SERDI_TC \(\left(r=0.9927;RMSE=0.1205;MAE=0.0615\right)\) , indicating that spatially varying scaling did not materially alter the integrated signal under the sampled conditions. External benchmarking against six literature-based drought indicators showed the strongest correspondence with SMDI \(\left(lag-0r=0.89-0.95;ROC-AUC=0.973\right)\) , followed by ESI \(\left(r=0.66-0.78;ROC-AUC=0.854\right)\) , while the meteorological and vegetation-based benchmarks showed moderate to weaker and more spatially variable relationships.
Source: Scientific Reports
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7222875497
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