Floods and flash floods are complex events, depending on weather dynamics, basin physiographical characteristics, land use cover and water management. For this reason, the prediction of such events usually deals with very accurate model tuning and validation, which is usually site-specific and based on climatological data, such as discharge time series or flood databases. In this work, we developed and tested two hydrological-stress indices for flood detection in the Italian Central Apennine District: a heterogeneous geographical area, characterized by complex topography and medium-to-small catchment extension. The proposed indices are threshold-based and developed considering operational requirements of National Civil Protection Department end-users. They are calibrated and tested through the application of signal theory, in order to overcome data scarcity over ungauged areas, as well as incomplete discharge time series. The validation has been carried out on a case study basis, using flood reports from various sources of information, as well as hydrometric-level time series, which represent the actual hydrological quantity monitored by Civil Protection operators. Obtained results show that the overall accuracy of flood prediction is greater than 0.8, with false alarm rates below 0.5 and the probability of detection ranging from 0.51 to 0.80. Moreover, the different nature of the proposed indices suggests their application in a complementary way, as the index based on drained precipitation appears to be more sensitive to rapid flood propagation in small tributaries, while the discharge-based index is particularly responsive to main-channel dynamics.
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