Global Evapotranspiration Datasets Assessment Using Water Balance in South America

Remote Sensing

Adalberto Meller¹  Alexandre Abdalla Araújo¹  Alexandre de Amorim Teixeira¹  Marcus André Fuckner¹  Trent Biggs²  Rafael Barbedo³  Rodrigo Cauduro Dias de Paiva³  Gabriele Leão Cyganski³  Adriana Aparecida Moreira³  Gabriel Matte Rios Fernandez³  João Paulo Lyra Fialho Brêda³  Vinícius Alencar Siqueira³  Bruno Comini de Andrade³  Anderson Ruhoff³  Leonardo Laipelt³  Ayan Santos Fleischmann⁴ 

[1] Agência Nacional de Águas e Saneamento Básico (ANA), Brasilia 70610-200, DF, Brazil;Department of Geography, San Diego State University, San Diego, CA 92182, USA;Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, RS, Brazil;Mamirauá Institute for Sustainable Development, Tefe 69553-225, AM, Brazil;

关键词: global evapotranspiration;    basin water balance;    BESS;    ERA5;    GLDAS;    GLEAM;   

DOI  :  10.3390/rs14112526

来源: DOAJ

【 摘 要 】

Evapotranspiration ($ET$) connects the land to the atmosphere, linking water, energy, and carbon cycles. $ET$ is an essential climate variable with a fundamental importance, and accurate assessments of the spatiotemporal trends and variability in $ET$ are needed from regional to continental scales. This study compared eight global actual $ET$ datasets ($E{T}_{gl}$) and the average actual $ET$ ensemble ($E{T}_{ens}$) based on remote sensing, climate reanalysis, land-surface, and biophysical models to $ET$ computed from basin-scale water balance ($E{T}_{wb}$) in South America on monthly time scale. The 50 small-to-large basins covered major rivers and different biomes and climate types. We also examined the magnitude, seasonality, and interannual variability of $ET$, comparing $E{T}_{gl}$ and $E{T}_{ens}$ with $E{T}_{wb}$. Global $ET$ datasets were evaluated between 2003 and 2014 from the following datasets: Breathing Earth System Simulator (BESS), ECMWF Reanalysis 5 (ERA5), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), MOD16, Penman–Monteith–Leuning (PML), Operational Simplified Surface Energy Balance (SSEBop) and Terra Climate. By using $E{T}_{wb}$ as a basis for comparison, correlation coefficients ranged from 0.45 (SSEBop) to 0.60 ($E{T}_{ens}$), and RMSE ranged from 35.6 ($E{T}_{ens}$) to 40.5 mm·month⁻¹ (MOD16). Overall, $E{T}_{gl}$ estimates ranged from 0 to 150 mm·month⁻¹ in most basins in South America, while $E{T}_{wb}$ estimates showed maximum rates up to 250 mm·month⁻¹. $E{T}_{gl}$ varied by hydroclimatic regions: (i) basins located in humid climates with low seasonality in precipitation, including the Amazon, Uruguay, and South Atlantic basins, yielded weak correlation coefficients between monthly $E{T}_{gl}$ and $E{T}_{wb}$, and (ii) tropical and semiarid basins (areas where precipitation demonstrates a strong seasonality, as in the São Francisco, Northeast Atlantic, Paraná/Paraguay, and Tocantins basins) yielded moderate-to-strong correlation coefficients. An assessment of the interannual variability demonstrated a disagreement between $E{T}_{gl}$ and $E{T}_{wb}$ in the humid tropics (in the Amazon), with $E{T}_{gl}$ showing a wide range of interannual variability. However, in tropical, subtropical, and semiarid climates, including the Tocantins, São Francisco, Paraná, Paraguay, Uruguay, and Atlantic basins (Northeast, East, and South), we found a stronger agreement between $E{T}_{gl}$ and $E{T}_{wb}$ for interannual variability. Assessing $ET$ datasets enables the understanding of land–atmosphere exchanges in South America, to improvement of $ET$ estimation and monitoring for water management.

【 授权许可】

Unknown