Gonçalo Rodrigues

Prof. Dr. Gonçalo Rodrigues

LEAF - Linking Landscape, Environment, Agriculture and Food, Instituto Superior de Agronomia, Universidade de Lisboa
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orcid

Research Focus & Metrics

Academic Career Length

18Years (2009-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 January 2012 in Agricultural Water Management
Verified
Journal Article
Closed access
RRRicardo D. Rosa
Ricardo D. Rosa
Paula ParedesPaula Paredes
Paula Paredes
Gonçalo RodriguesGonçalo Rodrigues
Gonçalo Rodrigues
IAIsabel Alves
Isabel Alves
RFRui M. Fernando
Rui M. Fernando
RARichard G. Allen
Richard G. Allen

1 August 2009 in Biosystems Engineering
Verified
Journal Article
Closed access
Gonçalo RodriguesGonçalo Rodrigues
Gonçalo Rodrigues
Luis Santos PereiraLuis Santos Pereira
Luis Santos Pereira

1 March 2014 in Agricultural Water Management
Verified
Journal Article
Closed access
Paula ParedesPaula Paredes
Paula Paredes
Gonçalo RodriguesGonçalo Rodrigues
Gonçalo Rodrigues
IAIsabel Alves
Isabel Alves
Luis Santos PereiraLuis Santos Pereira
Luis Santos Pereira

1 January 2012 in Agricultural Water Management
Verified
Journal Article
Closed access
RRRicardo D. Rosa
Ricardo D. Rosa
Paula ParedesPaula Paredes
Paula Paredes
Gonçalo RodriguesGonçalo Rodrigues
Gonçalo Rodrigues
RFRui M. Fernando
Rui M. Fernando
IAIsabel Alves
Isabel Alves
RARichard G. Allen
Richard G. Allen

1 September 2015 in Agricultural Water Management
Verified
Journal Article
Closed access
Luis Santos PereiraLuis Santos Pereira
Luis Santos Pereira
Paula ParedesPaula Paredes
Paula Paredes
Gonçalo RodriguesGonçalo Rodrigues
Gonçalo Rodrigues
MNManuela Neves
Manuela Neves

14 June 2021 in Agronomy
Verified
Journal Article
Open access
PDF file
Gonçalo RodriguesGonçalo Rodrigues
Gonçalo Rodrigues
Ricardo BragaRicardo Braga
Ricardo Braga
This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.