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Theoretical and Applied Climatology

, Volume 124, Issue 1–2, pp 461–473 | Cite as

Seasonal evaluation of evapotranspiration fluxes from MODIS satellite and mesoscale model downscaled global reanalysis datasets

  • Prashant K. SrivastavaEmail author
  • Dawei Han
  • Tanvir Islam
  • George P. Petropoulos
  • Manika Gupta
  • Qiang Dai
Original Paper

Abstract

Reference evapotranspiration (ETo) is an important variable in hydrological modeling, which is not always available, especially for ungauged catchments. Satellite data, such as those available from the MODerate Resolution Imaging Spectroradiometer (MODIS), and global datasets via the European Centre for Medium Range Weather Forecasts (ECMWF) reanalysis (ERA) interim and National Centers for Environmental Prediction (NCEP) reanalysis are important sources of information for ETo. This study explored the seasonal performances of MODIS (MOD16) and Weather Research and Forecasting (WRF) model downscaled global reanalysis datasets, such as ERA interim and NCEP-derived ETo, against ground-based datasets. Overall, on the basis of the statistical metrics computed, ETo derived from ERA interim and MODIS were more accurate in comparison to the estimates from NCEP for all the seasons. The pooled datasets also revealed a similar performance to the seasonal assessment with higher agreement for the ERA interim (r = 0.96, RMSE = 2.76 mm/8 days; bias = 0.24 mm/8 days), followed by MODIS (r = 0.95, RMSE = 7.66 mm/8 days; bias = −7.17 mm/8 days) and NCEP (r = 0.76, RMSE = 11.81 mm/8 days; bias = −10.20 mm/8 days). The only limitation with downscaling ERA interim reanalysis datasets using WRF is that it is time-consuming in contrast to the readily available MODIS operational product for use in mesoscale studies and practical applications.

Keywords

Pool Dataset Ungauged Basin British Atmospheric Data Centre Brue Catchment Global Reanalysis Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors would like to thank the Commonwealth Scholarship Commission, British Council, UK and the Ministry of Human Resource Development, Government of India for providing the necessary support and funding for this research. The authors would like to acknowledge the British Atmospheric Data Centre, UK for providing the ground datasets. The author also acknowledges the Advanced Computing Research Centre at University of Bristol for providing the access to supercomputer facility (The Blue Crystal) for some of the analysis. Dr. Petropoulos’s contribution was supported by the European Commission Marie Curie Re-Integration Grant “TRANSFORM-EO” project. Authors would also like to thank Gareth Ireland for the language proof reading of the manuscript. The views expressed here are those of the authors solely and do not constitute a statement of policy, decision, or position on behalf of NOAA/NASA or the authors’ affiliated institutions. 

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Copyright information

© Springer-Verlag Wien 2015

Authors and Affiliations

  • Prashant K. Srivastava
    • 1
    • 2
    • 3
    Email author
  • Dawei Han
    • 3
  • Tanvir Islam
    • 3
    • 4
    • 5
  • George P. Petropoulos
    • 6
  • Manika Gupta
    • 7
  • Qiang Dai
    • 3
  1. 1.Hydrological SciencesNASA Goddard Space Flight CenterGreenbeltUSA
  2. 2.Earth System Science Interdisciplinary CenterUniversity of MarylandCollege ParkUSA
  3. 3.Department of Civil EngineeringUniversity of BristolBristolUK
  4. 4.NOAA/NESDIS Center for Satellite Applications and ResearchCollege ParkUSA
  5. 5.Cooperative Institute for Research in the AtmosphereColorado State UniversityFort CollinsUSA
  6. 6.Department of Geography and Earth SciencesUniversity of AberystwythAberystwythUK
  7. 7.Water Resources, Department of Civil EngineeringIIT DelhiDelhiIndia

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