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WRF Dynamical Downscaling and Bias Correction Schemes for NCEP Estimated Hydro-Meteorological Variables

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  • Prashant Srivastava
  • Tanvir Islam
  • Manika Gupta
  • George Petropoulos
  • Qiang Dai

Abstract

Rainfall and Reference Evapotranspiration (ETo) are the most fundamental and significant variables in hydrological modelling. However, these variables are generally not available over ungauged catchments. ETo estimation usually needs measurements of weather variables such as wind speed, air temperature, solar radiation and dew point. After the development of reanalysis global datasets such as the National Centre for Environmental Prediction (NCEP) and high performance modelling framework Weather Research and Forecasting (WRF) model, it is now possible to estimate the rainfall and ETo for any coordinates. In this study, the WRF modelling system was employed to downscale the global NCEP reanalysis datasets over the Brue catchment, England, U.K. After downscaling, two statistical bias correction schemes were used, the first was based on sophisticated computing algorithms i.e., Relevance Vector Machine (RVM), while the second was based on the more simple Generalized Linear Model (GLM). The statistical performance indices for bias correction such as %Bias, index of agreement (d), Root Mean Square Error (RMSE), and Correlation (r) indicated that the RVM model, on the whole, displayed a more accomplished bias correction of the variability of rainfall and ETo in comparison to the GLM. The study provides important information on the performance of WRF derived hydro-meteorological variables using NCEP global reanalysis datasets and statistical bias correction schemes which can be used in numerous hydro-meteorological applications. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Prashant Srivastava & Tanvir Islam & Manika Gupta & George Petropoulos & Qiang Dai, 2015. "WRF Dynamical Downscaling and Bias Correction Schemes for NCEP Estimated Hydro-Meteorological Variables," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(7), pages 2267-2284, May.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:7:p:2267-2284
    DOI: 10.1007/s11269-015-0940-z
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    References listed on IDEAS

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    1. Asnor Ishak & Renji Remesan & Prashant Srivastava & Tanvir Islam & Dawei Han, 2013. "Error Correction Modelling of Wind Speed Through Hydro-Meteorological Parameters and Mesoscale Model: A Hybrid Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(1), pages 1-23, January.
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    1. Paredes, Paula & Trigo, Isabel & de Bruin, Henk & Simões, Nuno & Pereira, Luis S., 2021. "Daily grass reference evapotranspiration with Meteosat Second Generation shortwave radiation and reference ET products," Agricultural Water Management, Elsevier, vol. 248(C).
    2. Paredes, P. & Pereira, L.S. & Almorox, J. & Darouich, H., 2020. "Reference grass evapotranspiration with reduced data sets: Parameterization of the FAO Penman-Monteith temperature approach and the Hargeaves-Samani equation using local climatic variables," Agricultural Water Management, Elsevier, vol. 240(C).
    3. Prashant K. Srivastava & Prem C. Pandey & George P. Petropoulos & Nektarios N. Kourgialas & Varsha Pandey & Ujjwal Singh, 2019. "GIS and Remote Sensing Aided Information for Soil Moisture Estimation: A Comparative Study of Interpolation Techniques," Resources, MDPI, vol. 8(2), pages 1-17, April.
    4. Shailendra Pratap & Prashant K. Srivastava & Ashish Routray & Tanvir Islam & Rajesh Kumar Mall, 2020. "Appraisal of hydro-meteorological factors during extreme precipitation event: case study of Kedarnath cloudburst, Uttarakhand, India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 100(2), pages 635-654, January.
    5. Prashant K. Srivastava & Dawei Han & Aradhana Yaduvanshi & George P. Petropoulos & Sudhir Kumar Singh & Rajesh Kumar Mall & Rajendra Prasad, 2017. "Reference Evapotranspiration Retrievals from a Mesoscale Model Based Weather Variables for Soil Moisture Deficit Estimation," Sustainability, MDPI, vol. 9(11), pages 1-17, October.
    6. Paredes, Paula & Martins, Diogo S. & Pereira, Luis Santos & Cadima, Jorge & Pires, Carlos, 2018. "Accuracy of daily estimation of grass reference evapotranspiration using ERA-Interim reanalysis products with assessment of alternative bias correction schemes," Agricultural Water Management, Elsevier, vol. 210(C), pages 340-353.
    7. Pelosi, A. & Chirico, G.B., 2021. "Regional assessment of daily reference evapotranspiration: Can ground observations be replaced by blending ERA5-Land meteorological reanalysis and CM-SAF satellite-based radiation data?," Agricultural Water Management, Elsevier, vol. 258(C).
    8. Swati Maurya & Prashant K. Srivastava & Manika Gupta & Tanvir Islam & Dawei Han, 2016. "Integrating Soil Hydraulic Parameter and Microwave Precipitation with Morphometric Analysis for Watershed Prioritization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5385-5405, November.

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