Environmental Research Letters (Jan 2024)
Future drought overestimations due to no constraints of CO2 physiological effect and land-atmosphere coupling on potential evapotranspiration
Abstract
Various offline drought indices have been widely used to project dryness/wetness and drought changes. However, the results derived from these indices often differ from or even contradict observations and direct projections made by coupled climate models. Therefore, it is crucial to investigate this scientific debate thoroughly and identify the potential causes. This study adopts a water demand-side perspective, focusing on potential evapotranspiration (PET), to address such controversy. Employing the Standardized Precipitation-Evapotranspiration Index (SPEI), three PET models including the Food and Agriculture Organization of the United Nations’ report 56 (FAO-56) Penman–Monteith (PM) model, a corrected FAO-56 PM model incorporating CO _2 physiological effect (PM _CO2 ), and a land-atmosphere coupled PET model (PET-LAC) are further compared. Despite projected increases in PET across most land areas, the PM shows the most pronounced increases among these PET models. Compared to PM _CO2 and PET-LAC, the PM model predicts the most significant drying, with the 3-month SPEI decreasing by 0.50 ± 0.23 yr ^−1 . Additionally, it projects the most substantial drought intensification, with increases in areas, intensity, and duration of 28 ± 6.9%, 0.70 ± 0.20 yr ^−1 , and 2.90 ± 0.83 month yr ^−1 , respectively. Meanwhile, these projections correspond to the most extensive area percentages, with 78.5 ± 10% for drying, 94.8 ± 7.2% for drought intensity, and 93.6 ± 7.9% for drought duration. These findings imply that the commonly used PM model overestimates future drought conditions. Differences and contradictions between the drought projections from PM-based offline indices and direct climate model outputs can be partly attributed to the omission of CO _2 physiological effect and land-atmosphere coupling constraints in the PM model. This study highlights the importance of improving PET models by incorporating these constraints, thereby providing valuable insights for enhancing the accuracy of future drought assessments.
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