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

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.

An Evaluation of the Performance and the Contribution of Different Modified Water Demand Estimates in Drought Modeling Over Water‐stressed Regions

“…The annual average T is 9·1°C with a maximum annual temperature reaching to 20·9°C in summer and a minimum mean temperature of À4·6°C in winter. The long-term annual P ranges from 120 mm in the northwest to 830 mm in the southeast, approximately 60-70% of which falls from June to September in the form of high-intensity rainstorms, causing extreme soil erosion and droughts frequently occur (Liu et al, 2016;Zhang et al, 2016). The annual PET is much higher than the P, ranging from 800 to 1200 mm (Zhang et al, 2016).…”

“…The long-term annual P ranges from 120 mm in the northwest to 830 mm in the southeast, approximately 60-70% of which falls from June to September in the form of high-intensity rainstorms, causing extreme soil erosion and droughts frequently occur (Liu et al, 2016;Zhang et al, 2016). The annual PET is much higher than the P, ranging from 800 to 1200 mm (Zhang et al, 2016). The Loess Plateau is well known for severe water scarcity and soil erosion in the world, caused by steep slopes, loose loess soils, periodic high-intensity rainstorms, and sparse vegetation coverage Gao et al, 2015).…”

“…Although the SPEI was revised by Beguería et al (2014), several potential issues with the method used to estimate the water demand have been identified after widely use of the SPEI (Xu et al, 2012;Yuan & Quiring, 2014;Ma et al, 2015;Zhang & He, 2015b;Zhang et al, 2016). These issues are mainly focused on two aspects.…”

“…In response to the first aspect, the dual-source (2S) PET model (Shuttleworth & Wallace, 1985;Mo et al, 2004), which can be used to examine the mass fluxes and energy balance of sparse canopy, has been recommended to estimate the water demand in multiscalar drought identification over non-humid regions by Zhang et al (2016). This model assumes the sparse plant canopy as a dual-component system and calculates the transpiration from the canopy level and the evaporation from the substrate soil, respectively, based on aerodynamic principles and energy balance.…”

“…The computation ofP considers precipitation, evapotranspiration, runoff, and soil water storage, all of which are major factors influencing the demand for water on land surface, and thus drought or wet conditions. Based on the aforementioned analysis, it is known that the lack of accurate and reliable estimation of water demand in multiscalar drought modeling has been one of the most important obstacles in efforts for examining and characterizing the variability of water consumption and its effects on drought monitoring and prediction, particularly in waterstressed regions (Sheffield et al, 2012;Trenberth et al, 2014;Zhang & He, 2015b;Zhang et al, 2016). Although there are many different modified water demand estimation methods, it is still unclear that the physical mechanism of which one is most suitable for drought identification over water-stressed regions.…”