Long-term monthly 0.05° terrestrial evapotranspiration dataset (1982–2018) for the Tibetan Plateau

Abstract

Evapotranspiration (ET) plays a crucial role in the water balance of the Tibetan Plateau (TP), often referred to as the “Asian water tower” region. However, accurately monitoring and comprehending the spatial and temporal variations of ET components (including soil evaporation Es, canopy transpiration Ec, and intercepted water evaporation Ew) in this remote area remains a significant challenge due to the limited availability of observational data. This study generates a 37-year dataset (1982–2018) of monthly ET components for the TP using the MOD16-STM (MOD16 soil texture model). This model utilizes up-to-date soil properties, meteorological data, and remote sensing datasets. The estimated ET results strongly correlate with measurements from nine flux towers, demonstrating a low root mean square error of 13.48 mm per month, a mean bias of 2.85 mm per month, a coefficient of determination of 0.83, and an index of agreement of 0.92. The annual average ET for the entire TP, defined as elevations higher than 2500 m, is approximately 0.93(± 0.037) × 103 Gt yr−1. The predominant contributor to ET on the TP is Es, accounting for 84 % of the total ET. Our findings reveal a noteworthy upward trend in ET in most central and eastern parts of the TP, with a rate of approximately 1–4 mm yr−1 (p<0.05) and a significant downward trend with rates between −3 and 1 mm yr−1 in the northwestern part of TP during the period from 1982 to 2018. The average annual increase in ET for the entire TP over the past 37 years is approximately 0.96 mm yr−1. This upward trend can be attributed to the TP's warming and wetting climate conditions. The MOD16-STM ET dataset demonstrates a reliable performance across the TP compared to previous research outcomes. This dataset is valuable for research on water resource management, drought monitoring, and ecological studies. The entire dataset is freely accessible through the Science Data Bank (https://doi.org/10.11922/sciencedb.00020, Ma et al., 2021) and the National Tibetan Plateau Data Center (TPDC) (https://doi.org/10.11888/Terre.tpdc.271913, Yuan, 2021).