Global Evapotranspiration Datasets Assessment Using Water Balance in South America
Evapotranspiration (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula>) connects the land to the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/11/2526 |
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| author | Anderson Ruhoff Bruno Comini de Andrade Leonardo Laipelt Ayan Santos Fleischmann Vinícius Alencar Siqueira Adriana Aparecida Moreira Rafael Barbedo Gabriele Leão Cyganski Gabriel Matte Rios Fernandez João Paulo Lyra Fialho Brêda Rodrigo Cauduro Dias de Paiva Adalberto Meller Alexandre de Amorim Teixeira Alexandre Abdalla Araújo Marcus André Fuckner Trent Biggs |
| author_facet | Anderson Ruhoff Bruno Comini de Andrade Leonardo Laipelt Ayan Santos Fleischmann Vinícius Alencar Siqueira Adriana Aparecida Moreira Rafael Barbedo Gabriele Leão Cyganski Gabriel Matte Rios Fernandez João Paulo Lyra Fialho Brêda Rodrigo Cauduro Dias de Paiva Adalberto Meller Alexandre de Amorim Teixeira Alexandre Abdalla Araújo Marcus André Fuckner Trent Biggs |
| author_sort | Anderson Ruhoff |
| collection | DOAJ |
| description | Evapotranspiration (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula>) connects the land to the atmosphere, linking water, energy, and carbon cycles. <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula> is an essential climate variable with a fundamental importance, and accurate assessments of the spatiotemporal trends and variability in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula> are needed from regional to continental scales. This study compared eight global actual <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula> datasets (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>g</mi><mi>l</mi></mrow></msub></mrow></semantics></math></inline-formula>) and the average actual <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula> ensemble (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>e</mi><mi>n</mi><mi>s</mi></mrow></msub></mrow></semantics></math></inline-formula>) based on remote sensing, climate reanalysis, land-surface, and biophysical models to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula> computed from basin-scale water balance (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>w</mi><mi>b</mi></mrow></msub></mrow></semantics></math></inline-formula>) 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula>, comparing <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>g</mi><mi>l</mi></mrow></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>e</mi><mi>n</mi><mi>s</mi></mrow></msub></mrow></semantics></math></inline-formula> with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>w</mi><mi>b</mi></mrow></msub></mrow></semantics></math></inline-formula>. Global <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>w</mi><mi>b</mi></mrow></msub></mrow></semantics></math></inline-formula> as a basis for comparison, correlation coefficients ranged from 0.45 (SSEBop) to 0.60 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>e</mi><mi>n</mi><mi>s</mi></mrow></msub></mrow></semantics></math></inline-formula>), and RMSE ranged from 35.6 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>e</mi><mi>n</mi><mi>s</mi></mrow></msub></mrow></semantics></math></inline-formula>) to 40.5 mm·month<sup>−1</sup> (MOD16). Overall, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>g</mi><mi>l</mi></mrow></msub></mrow></semantics></math></inline-formula> estimates ranged from 0 to 150 mm·month<sup>−1</sup> in most basins in South America, while <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>w</mi><mi>b</mi></mrow></msub></mrow></semantics></math></inline-formula> estimates showed maximum rates up to 250 mm·month<sup>−1</sup>. <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>g</mi><mi>l</mi></mrow></msub></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>g</mi><mi>l</mi></mrow></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>w</mi><mi>b</mi></mrow></msub></mrow></semantics></math></inline-formula>, 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>g</mi><mi>l</mi></mrow></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>w</mi><mi>b</mi></mrow></msub></mrow></semantics></math></inline-formula> in the humid tropics (in the Amazon), with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>g</mi><mi>l</mi></mrow></msub></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>g</mi><mi>l</mi></mrow></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>w</mi><mi>b</mi></mrow></msub></mrow></semantics></math></inline-formula> for interannual variability. Assessing <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula> datasets enables the understanding of land–atmosphere exchanges in South America, to improvement of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula> estimation and monitoring for water management. |
| first_indexed | 2024-03-10T00:56:03Z |
| format | Article |
| id | doaj.art-b64a54c475f84f94aa766982880cea15 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T00:56:03Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b64a54c475f84f94aa766982880cea152023-11-23T14:43:11ZengMDPI AGRemote Sensing2072-42922022-05-011411252610.3390/rs14112526Global Evapotranspiration Datasets Assessment Using Water Balance in South AmericaAnderson Ruhoff0Bruno Comini de Andrade1Leonardo Laipelt2Ayan Santos Fleischmann3Vinícius Alencar Siqueira4Adriana Aparecida Moreira5Rafael Barbedo6Gabriele Leão Cyganski7Gabriel Matte Rios Fernandez8João Paulo Lyra Fialho Brêda9Rodrigo Cauduro Dias de Paiva10Adalberto Meller11Alexandre de Amorim Teixeira12Alexandre Abdalla Araújo13Marcus André Fuckner14Trent Biggs15Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, RS, BrazilInstituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, RS, BrazilInstituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, RS, BrazilMamirauá Institute for Sustainable Development, Tefe 69553-225, AM, BrazilInstituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, RS, BrazilInstituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, RS, BrazilInstituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, RS, BrazilInstituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, RS, BrazilInstituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, RS, BrazilInstituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, RS, BrazilInstituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, RS, BrazilAgência Nacional de Águas e Saneamento Básico (ANA), Brasilia 70610-200, DF, BrazilAgência Nacional de Águas e Saneamento Básico (ANA), Brasilia 70610-200, DF, BrazilAgência Nacional de Águas e Saneamento Básico (ANA), Brasilia 70610-200, DF, BrazilAgência Nacional de Águas e Saneamento Básico (ANA), Brasilia 70610-200, DF, BrazilDepartment of Geography, San Diego State University, San Diego, CA 92182, USAEvapotranspiration (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula>) connects the land to the atmosphere, linking water, energy, and carbon cycles. <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula> is an essential climate variable with a fundamental importance, and accurate assessments of the spatiotemporal trends and variability in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula> are needed from regional to continental scales. This study compared eight global actual <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula> datasets (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>g</mi><mi>l</mi></mrow></msub></mrow></semantics></math></inline-formula>) and the average actual <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula> ensemble (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>e</mi><mi>n</mi><mi>s</mi></mrow></msub></mrow></semantics></math></inline-formula>) based on remote sensing, climate reanalysis, land-surface, and biophysical models to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula> computed from basin-scale water balance (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>w</mi><mi>b</mi></mrow></msub></mrow></semantics></math></inline-formula>) 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula>, comparing <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>g</mi><mi>l</mi></mrow></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>e</mi><mi>n</mi><mi>s</mi></mrow></msub></mrow></semantics></math></inline-formula> with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>w</mi><mi>b</mi></mrow></msub></mrow></semantics></math></inline-formula>. Global <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>w</mi><mi>b</mi></mrow></msub></mrow></semantics></math></inline-formula> as a basis for comparison, correlation coefficients ranged from 0.45 (SSEBop) to 0.60 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>e</mi><mi>n</mi><mi>s</mi></mrow></msub></mrow></semantics></math></inline-formula>), and RMSE ranged from 35.6 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>e</mi><mi>n</mi><mi>s</mi></mrow></msub></mrow></semantics></math></inline-formula>) to 40.5 mm·month<sup>−1</sup> (MOD16). Overall, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>g</mi><mi>l</mi></mrow></msub></mrow></semantics></math></inline-formula> estimates ranged from 0 to 150 mm·month<sup>−1</sup> in most basins in South America, while <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>w</mi><mi>b</mi></mrow></msub></mrow></semantics></math></inline-formula> estimates showed maximum rates up to 250 mm·month<sup>−1</sup>. <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>g</mi><mi>l</mi></mrow></msub></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>g</mi><mi>l</mi></mrow></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>w</mi><mi>b</mi></mrow></msub></mrow></semantics></math></inline-formula>, 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>g</mi><mi>l</mi></mrow></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>w</mi><mi>b</mi></mrow></msub></mrow></semantics></math></inline-formula> in the humid tropics (in the Amazon), with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>g</mi><mi>l</mi></mrow></msub></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>g</mi><mi>l</mi></mrow></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><msub><mi>T</mi><mrow><mi>w</mi><mi>b</mi></mrow></msub></mrow></semantics></math></inline-formula> for interannual variability. Assessing <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula> datasets enables the understanding of land–atmosphere exchanges in South America, to improvement of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>T</mi></mrow></semantics></math></inline-formula> estimation and monitoring for water management.https://www.mdpi.com/2072-4292/14/11/2526global evapotranspirationbasin water balanceBESSERA5GLDASGLEAM |
| spellingShingle | Anderson Ruhoff Bruno Comini de Andrade Leonardo Laipelt Ayan Santos Fleischmann Vinícius Alencar Siqueira Adriana Aparecida Moreira Rafael Barbedo Gabriele Leão Cyganski Gabriel Matte Rios Fernandez João Paulo Lyra Fialho Brêda Rodrigo Cauduro Dias de Paiva Adalberto Meller Alexandre de Amorim Teixeira Alexandre Abdalla Araújo Marcus André Fuckner Trent Biggs Global Evapotranspiration Datasets Assessment Using Water Balance in South America Remote Sensing global evapotranspiration basin water balance BESS ERA5 GLDAS GLEAM |
| title | Global Evapotranspiration Datasets Assessment Using Water Balance in South America |
| title_full | Global Evapotranspiration Datasets Assessment Using Water Balance in South America |
| title_fullStr | Global Evapotranspiration Datasets Assessment Using Water Balance in South America |
| title_full_unstemmed | Global Evapotranspiration Datasets Assessment Using Water Balance in South America |
| title_short | Global Evapotranspiration Datasets Assessment Using Water Balance in South America |
| title_sort | global evapotranspiration datasets assessment using water balance in south america |
| topic | global evapotranspiration basin water balance BESS ERA5 GLDAS GLEAM |
| url | https://www.mdpi.com/2072-4292/14/11/2526 |
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