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Registro Completo |
Biblioteca(s): |
Embrapa Arroz e Feijão. |
Data corrente: |
12/01/2007 |
Data da última atualização: |
12/01/2007 |
Autoria: |
LOBATO, E. J. V.; CABRAL, D. M.; ANDRADE, R. S. de. |
Título: |
Utilização da distribuição Gama em séries pluviométricas do município de Goiânia (GO). |
Ano de publicação: |
2002 |
Fonte/Imprenta: |
Goiânia: Edição do autor, 2002. |
Páginas: |
69 p. |
Idioma: |
Português |
Conteúdo: |
Introdução; A distribuição Gama: um breve histórico; Metodologia; Probabilidade de ocorrência de precipitação pluvial e sua interpretação; Considerações finais. |
Palavras-Chave: |
Distribuição Gama; Goiânia; Goiás; Meteorologia agrícola; Precipitação; Previsão. |
Thesagro: |
Cerrado. |
Categoria do assunto: |
-- |
Marc: |
LEADER 00795nam a2200229 a 4500 001 1214578 005 2007-01-12 008 2002 bl uuuu 00u1 u #d 100 1 $aLOBATO, E. J. V. 245 $aUtilização da distribuição Gama em séries pluviométricas do município de Goiânia (GO). 260 $aGoiânia: Edição do autor$c2002 300 $a69 p. 520 $aIntrodução; A distribuição Gama: um breve histórico; Metodologia; Probabilidade de ocorrência de precipitação pluvial e sua interpretação; Considerações finais. 650 $aCerrado 653 $aDistribuição Gama 653 $aGoiânia 653 $aGoiás 653 $aMeteorologia agrícola 653 $aPrecipitação 653 $aPrevisão 700 1 $aCABRAL, D. M. 700 1 $aANDRADE, R. S. de
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Embrapa Arroz e Feijão (CNPAF) |
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![](/consulta/web/img/deny.png) | Acesso ao texto completo restrito à biblioteca da Embrapa Semiárido. Para informações adicionais entre em contato com cpatsa.biblioteca@embrapa.br. |
Registro Completo
Biblioteca(s): |
Embrapa Semiárido. |
Data corrente: |
05/07/2023 |
Data da última atualização: |
23/01/2024 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
BEZERRA, U. A.; CUNHA, J.; VALENTE, F.; NÓBREGA, R. L. B.; ANDRADEM J. M.; MOURA, M. S. B. de; VERHOEF, A.; PEREZ-MARIN, A. M.; GALVÃO, C. O. |
Afiliação: |
ULISSES A. BEZERRA, UFCG; JOHN CUNHA, UFCG; FERNANDA VALENTE, Forest Research Centre, University of Lisboa, Lisboa, Portugal; RODOLFO L. B. NÓBREGA, University of Bristol, School of Geographical Sciences, United Kingdom; JOÃO M. ANDRADE, UFPE; MAGNA SOELMA BESERRA DE MOURA, CPATSA; ANNE VERHOEF, The University of Reading, Department of Geography and Environmental Science, United Kingdom; ALDRIN M. PEREZ-MARIN, INSA; CARLOS O. GALVÃO, UFCG. |
Título: |
STEEP: a remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests. |
Ano de publicação: |
2023 |
Fonte/Imprenta: |
Agricultural and Forest Meteorology, v. 333, 109408, 2023. |
ISSN: |
0168-1923 |
DOI: |
https://doi.org/10.1016/j.agrformet.2023.109408 |
Idioma: |
Inglês |
Conteúdo: |
Improvement of evapotranspiration (ET) estimates using remote sensing (RS) products based on multispectral and thermal sensors has been a breakthrough in hydrological research. In large-scale applications, methods that use the approach of RS-based surface energy balance (SEB) models often rely on oversimplifications. The use of these models for Seasonally Dry Tropical Forests (SDTF) has been challenging due to incompatibilities between the assumptions underlying those models and the specificities of this environment, such as the highly contrasting phenological phases or ET being mainly controlled by soil–water availability. We developed a RS-based SEB model from a one-source bulk transfer equation, called Seasonal Tropical Ecosystem Energy Partitioning (STEEP). Our model uses the plant area index to represent the woody structure of the plants in calculating the moment roughness length. We included the parameter kB−1 and its correction using RS soil moisture in the calculation of the aerodynamic resistance for heat transfer. Besides, λET caused by remaining water availability in endmembers pixels was quantified using the Priestley-Taylor equation. We implemented the algorithm on Google Earth Engine, using freely available data. To evaluate our model, we used eddy covariance data from four sites in the Caatinga, the largest SDTF in South America, in the Brazilian semiarid region. Our results show that STEEP increased the accuracy of ET estimates without requiring any additional climatological information. This improvement is more pronounced during the dry season, which, in general, ET for these SDTF is overestimated by traditional SEB models, such as the Surface Energy Balance Algorithms for Land (SEBAL). The STEEP model had similar or superior behavior and performance statistics relative to global ET products (MOD16 and PMLv2). This work contributes to an improved understanding of the drivers and modulators of the energy and water balances at local and regional scales in SDTF. MenosImprovement of evapotranspiration (ET) estimates using remote sensing (RS) products based on multispectral and thermal sensors has been a breakthrough in hydrological research. In large-scale applications, methods that use the approach of RS-based surface energy balance (SEB) models often rely on oversimplifications. The use of these models for Seasonally Dry Tropical Forests (SDTF) has been challenging due to incompatibilities between the assumptions underlying those models and the specificities of this environment, such as the highly contrasting phenological phases or ET being mainly controlled by soil–water availability. We developed a RS-based SEB model from a one-source bulk transfer equation, called Seasonal Tropical Ecosystem Energy Partitioning (STEEP). Our model uses the plant area index to represent the woody structure of the plants in calculating the moment roughness length. We included the parameter kB−1 and its correction using RS soil moisture in the calculation of the aerodynamic resistance for heat transfer. Besides, λET caused by remaining water availability in endmembers pixels was quantified using the Priestley-Taylor equation. We implemented the algorithm on Google Earth Engine, using freely available data. To evaluate our model, we used eddy covariance data from four sites in the Caatinga, the largest SDTF in South America, in the Brazilian semiarid region. Our results show that STEEP increased the accuracy of ET estimates without requiring any additiona... Mostrar Tudo |
Palavras-Chave: |
Balanço de energia de superfície; Google Earth Engine; Resistência aerodinâmica para transferência de calor. |
Thesagro: |
Balanço de Energia; Caatinga; Evapotranspiração; Floresta Tropical; Sensoriamento Remoto. |
Thesaurus NAL: |
Energy balance; Evapotranspiration; Remote sensing; Tropical forests. |
Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
Marc: |
LEADER 03237naa a2200385 a 4500 001 2154801 005 2024-01-23 008 2023 bl uuuu u00u1 u #d 022 $a0168-1923 024 7 $ahttps://doi.org/10.1016/j.agrformet.2023.109408$2DOI 100 1 $aBEZERRA, U. A. 245 $aSTEEP$ba remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests.$h[electronic resource] 260 $c2023 520 $aImprovement of evapotranspiration (ET) estimates using remote sensing (RS) products based on multispectral and thermal sensors has been a breakthrough in hydrological research. In large-scale applications, methods that use the approach of RS-based surface energy balance (SEB) models often rely on oversimplifications. The use of these models for Seasonally Dry Tropical Forests (SDTF) has been challenging due to incompatibilities between the assumptions underlying those models and the specificities of this environment, such as the highly contrasting phenological phases or ET being mainly controlled by soil–water availability. We developed a RS-based SEB model from a one-source bulk transfer equation, called Seasonal Tropical Ecosystem Energy Partitioning (STEEP). Our model uses the plant area index to represent the woody structure of the plants in calculating the moment roughness length. We included the parameter kB−1 and its correction using RS soil moisture in the calculation of the aerodynamic resistance for heat transfer. Besides, λET caused by remaining water availability in endmembers pixels was quantified using the Priestley-Taylor equation. We implemented the algorithm on Google Earth Engine, using freely available data. To evaluate our model, we used eddy covariance data from four sites in the Caatinga, the largest SDTF in South America, in the Brazilian semiarid region. Our results show that STEEP increased the accuracy of ET estimates without requiring any additional climatological information. This improvement is more pronounced during the dry season, which, in general, ET for these SDTF is overestimated by traditional SEB models, such as the Surface Energy Balance Algorithms for Land (SEBAL). The STEEP model had similar or superior behavior and performance statistics relative to global ET products (MOD16 and PMLv2). This work contributes to an improved understanding of the drivers and modulators of the energy and water balances at local and regional scales in SDTF. 650 $aEnergy balance 650 $aEvapotranspiration 650 $aRemote sensing 650 $aTropical forests 650 $aBalanço de Energia 650 $aCaatinga 650 $aEvapotranspiração 650 $aFloresta Tropical 650 $aSensoriamento Remoto 653 $aBalanço de energia de superfície 653 $aGoogle Earth Engine 653 $aResistência aerodinâmica para transferência de calor 700 1 $aCUNHA, J. 700 1 $aVALENTE, F. 700 1 $aNÓBREGA, R. L. B. 700 1 $aANDRADEM J. M. 700 1 $aMOURA, M. S. B. de 700 1 $aVERHOEF, A. 700 1 $aPEREZ-MARIN, A. M. 700 1 $aGALVÃO, C. O. 773 $tAgricultural and Forest Meteorology$gv. 333, 109408, 2023.
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