{'fraction_non_alphanumeric': 0.014367816091954023, 'fraction_numerical': 0.0, 'mean_word_length': 5.514018691588785, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
STOCHASTIC GENERATION AND HYBRID DOWNSCALING FOR IMPROVING THE ESTIMATION OF RETURN PERIODS OF FLOODING

In this study, a statistical methodology to model river flooding is proposed. The methodology makes use of synthetic generation of river discharges, from fitted observed values, to augment the observational record with likely events that have not been observed. It also relies on computing return periods on the final variable of interest, flooding water depth, from analyzing a complete time series of flooding. Such a series is obtained through hybrid downscaling, which combines dynamic and statistical downscaling to reduce the total computational effort required to generate the series.