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Probabilistic forecasting of reference evapotranspiration with a limited area ensemble prediction system

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  • Pelosi, A.
  • Medina, H.
  • Villani, P.
  • D’Urso, G.
  • Chirico, G.B.

Abstract

The increasing availability of operational limited area ensemble prediction systems (LEPS) opens up new opportunities for the application of weather forecasts in agriculture and water resource management. This study aims to evaluate the performances of probabilistic daily reference crop evapotranspiration (ET0) forecasts with lead times up to 5days and a spatial resolution of 7km, computed by using COSMO-LEPS outputs (provided by the European Consortium for small–scale modelling, COSMO), in a region of southern Italy known for its complex topography in proximity to the Mediterranean coastline. ET0 was estimated by means of three different estimation methods, i.e. the Hargreaves-Samani (HS), Priestley-Taylor (PT) and FAO Penman-Monteith (PM) equations, in order to assess the size of the weather forecast errors with models of different accuracies. Forecasts were verified with ground-based data from 18 automatic weather stations, and for two irrigation seasons. Performances were assessed with both deterministic indices, including BIAS, RMSE, correlation coefficients and coefficients of variation of the 16-member ensemble forecasts, and probabilistic metrics, such as the Brier skill score, reliability diagrams and relative operating characteristic. ET0 forecasts with PM equation were robust and reliable, with slight sensitivity to the forecast lead time. High performances were also achieved with HS and PT equations, except for locations close to the coastline, where large systematic errors affect the numerical weather forecasts.

Suggested Citation

  • Pelosi, A. & Medina, H. & Villani, P. & D’Urso, G. & Chirico, G.B., 2016. "Probabilistic forecasting of reference evapotranspiration with a limited area ensemble prediction system," Agricultural Water Management, Elsevier, vol. 178(C), pages 106-118.
    • Handle: RePEc:eee:agiwat:v:178:y:2016:i:c:p:106-118
    DOI: 10.1016/j.agwat.2016.09.015
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    References listed on IDEAS

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    1. Vuolo, Francesco & D’Urso, Guido & De Michele, Carlo & Bianchi, Biagio & Cutting, Michael, 2015. "Satellite-based irrigation advisory services: A common tool for different experiences from Europe to Australia," Agricultural Water Management, Elsevier, vol. 147(C), pages 82-95.
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    Cited by:

    1. Yin, Juan & Deng, Zhen & Ines, Amor V.M. & Wu, Junbin & Rasu, Eeswaran, 2020. "Forecast of short-term daily reference evapotranspiration under limited meteorological variables using a hybrid bi-directional long short-term memory model (Bi-LSTM)," Agricultural Water Management, Elsevier, vol. 242(C).
    2. Yang, Yang & Cui, Yuanlai & Bai, Kaihua & Luo, Tongyuan & Dai, Junfeng & Wang, Weiguang & Luo, Yufeng, 2019. "Short-term forecasting of daily reference evapotranspiration using the reduced-set Penman-Monteith model and public weather forecasts," Agricultural Water Management, Elsevier, vol. 211(C), pages 70-80.
    3. Nasta, Paolo & Bonanomi, Giuliano & Šimůnek, Jirka & Romano, Nunzio, 2021. "Assessing the nitrate vulnerability of shallow aquifers under Mediterranean climate conditions," Agricultural Water Management, Elsevier, vol. 258(C).
    4. Ruiming, Fang & Shijie, Song, 2020. "Daily reference evapotranspiration prediction of Tieguanyin tea plants based on mathematical morphology clustering and improved generalized regression neural network," Agricultural Water Management, Elsevier, vol. 236(C).
    5. Longo-Minnolo, G. & Vanella, D. & Consoli, S. & Intrigliolo, D.S. & Ramírez-Cuesta, J.M., 2020. "Integrating forecast meteorological data into the ArcDualKc model for estimating spatially distributed evapotranspiration rates of a citrus orchard," Agricultural Water Management, Elsevier, vol. 231(C).
    6. 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).
    7. Corbari, Chiara & Salerno, Raffaele & Ceppi, Alessandro & Telesca, Vito & Mancini, Marco, 2019. "Smart irrigation forecast using satellite LANDSAT data and meteo-hydrological modeling," Agricultural Water Management, Elsevier, vol. 212(C), pages 283-294.
    8. Amarnath, Giriraj & Simons, G. W. H. & Alahacoon, Niranga & Smakhtin, V. & Sharma, Bharat & Gismalla, Y. & Mohammed, Y. & Andrie, M. C. M., 2018. "Using smart ICT to provide weather and water information to smallholders in Africa: the case of the Gash River Basin, Sudan," Papers published in Journals (Open Access), International Water Management Institute, pages 22:52-66.
    9. Pelosi, A., 2023. "Performance of the Copernicus European Regional Reanalysis (CERRA) dataset as proxy of ground-based agrometeorological data," Agricultural Water Management, Elsevier, vol. 289(C).

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