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A Sensitivity Study of Applying a Two‐Source Potential Evapotranspiration Model in the Standardized Precipitation Evapotranspiration Index for Drought Monitoring

Author:
Zhang, Baoqing, Wang, Zikui, Chen, Guan
Source:
Land degradation & development 2017 v.28 no.2 pp. 783-793
ISSN:
1085-3278
Subject:
canopy, drought, equations, evapotranspiration, global warming, models, monitoring, soil, stream flow, vegetation cover, vegetation index, China
Abstract:
The Standardized Precipitation Evapotranspiration Index (SPEI) based on three different potential evapotranspiration (PET) estimation methods, including the Thornthwaite (TW) equation, the Penman–Monteith (PM) equation, and the Two‐Source (2S) PET model were compared and evaluated by observed evidences over the Loess Plateau, which includes large area of sparse vegetation cover. The results show that the Loess Plateau has experienced substantial climate warming over the past four decades, which intensively raised the atmospheric water demand (PET), thus the drought condition. The upward trends of PETTW were the most extensive, while the upward trends of PET₂S and PETPM were similar to each other. The differences between SPEIPM and SPEITW were larger than the differences between SPEIPM and SPEI₂S. The variability of SPEI₂S and SPEIPM were more consistent with the observed streamflow and the normal difference vegetation index than that of SPEITW, and the SPEI₂S even showed higher agreements than the SPEIPM. The 2S PET model considers radiation balances at the canopy level and soil surface separately, which helps this model more accurately estimate the PET in regions with sparse vegetation. The PM equation is a more physically based PET estimation method than the TW equation, which takes the other variables that effect atmospheric water demand into account. Therefore, the 2S PET model (first) and the PM equation (second) are recommended in calculating the SPEI over regions with large area of sparse vegetation cover, as a result of better physical mechanism and higher correlations with different types of observations. Copyright © 2016 John Wiley & Sons, Ltd.
Agid:
5877423