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| Titel |
Forecasting irrigation demand by assimilating satellite images and numerical weather predictions |
| VerfasserIn |
Anna Pelosi, Hanoi Medina, Paolo Villani, Salvatore Falanga Bolognesi, Guido D'Urso, Giovanni Battista Chirico |
| Konferenz |
EGU General Assembly 2016
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250129873
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| Publikation (Nr.) |
 EGU/EGU2016-10042.pdf |
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| Zusammenfassung |
| Forecasting irrigation water demand, with small predictive uncertainty in the short-medium
term, is fundamental for an efficient planning of water resource allocation among multiple
users and for decreasing water and energy consumptions.
In this study we present an innovative system for forecasting irrigation water demand,
applicable at different spatial scales: from the farm level to the irrigation district
level.
The forecast system is centred on a crop growth model assimilating data from satellite
images and numerical weather forecasts, according to a stochastic ensemble-based approach.
Different sources of uncertainty affecting model predictions are represented by an ensemble
of model trajectories, each generated by a possible realization of the model components
(model parameters, input weather data and model state variables). The crop growth model is
based on a set of simplified analytical relations, with the aim to assess biomass,
leaf area index (LAI) growth and evapotranspiration rate with a daily time step.
Within the crop growth model, LAI dynamics is let be governed by temperature and
leaf dry matter supply, according to the development stage of the crop. The model
assimilates LAI data retrieved from VIS-NIR high-resolution multispectral satellite
images. Numerical weather model outputs are those from the European limited
area ensemble prediction system (COSMO-LEPS), which provides forecasts up
to five days with a spatial resolution of seven kilometres. Weather forecasts are
sequentially bias corrected based on data from ground weather stations. The forecasting
system is evaluated in experimental areas of southern Italy during three irrigation
seasons. The performance analysis shows very accurate irrigation water demand
forecasts, which make the proposed system a valuable support for water planning
and saving at farm level as well as for water management at larger spatial scales. |
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