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Improving flow forecasting by error correction modelling in altered catchment conditions

pubblicazioni - Articolo

Improving flow forecasting by error correction modelling in altered catchment conditions

E’ stato sviluppato un sistema di correzione di tipo statistico in grado di migliorare la capacità predittiva dei modelli idrologici concettuali nei bacini idrografici dove il regime naturale è largamente modificato dalle attività antropiche.

Despite human is an increasingly significant component of the hydrologic cycle in many river basins, most hydrologic models are still developed to accurately reproduce the natural processes and ignore the effect of human activities on the watershed response. This results in non-stationary model forecast errors and poor predicting performance every time these models are used in non-pristine watersheds. In the last decade, the representation of human activities in hydrological models has been extensively studied. However, mathematical models integrating the human and the natural dimension are not very common in hydrological applications and nearly unknown in the day-to-day practice. In this paper we propose a new simple data-driven flow forecast correction method that can be used to simultaneously tackle forecast errors from structural, parameter and input uncertainty, and errors that arise from neglecting human-induced alterations in conceptual rainfall-runoff models. The correction system is composed of two layers: (i) a classification system that, based on the current flow condition, detects whether the source of error is natural or human-induced; and (ii) a set of error correction models that are alternatively activated, each tailored to the specific source of errors. As a case study we consider the highly anthropized Aniene river basin in Italy, where a flow forecasting system is being established to support the operation of a hydropower dam. Results show that, even by using very basic methods, namely if-then classification rules and linear correction models, the proposed methodology considerably improves the forecasting capability of the original hydrological model.

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