In situ reconstruction of long-term extreme flooding magnitudes and frequencies based on geological archives

Human disturbances dominated the unprecedentedly high frequency of Yellow River flood over the last millennium

A warming climate may increase flood hazard through boosting the global hydrological cycle. However, human impact through modifications to the river and its catchment is not well quantified. Here, we show a 12,000-year-long record of Yellow River flood events by synthesizing sedimentary and documentary data of levee overtops and breaches. Our result reveals that flood events in the Yellow River basin became almost an order of magnitude more frequent during the last millennium than the middle Holocene and 81 ± 6% of the increased flood frequency can be ascribed to anthropogenic disturbances. Our findings not only shed light on the long-term dynamics of flood hazards in this world's most sediment-laden river but also inform policy of sustainable management of large rivers under anthropogenic stress elsewhere.

Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method

Flash-floods are increasingly recognized as a frequent natural hazard worldwide. Iran has been among the mostdevastated regions affected by the major floods. While the temporal flash-flood forecasting models are mainly developed for warning systems, the models for assessing hazardous areas can greatly contribute to adaptation and mitigation policy-making and disaster risk reduction. Former researches in the flash-flood hazard mapping have heightened the urge for the advancement of more accurate models. Thus, the current research proposes the state-of-the-art ensemble models of boosted generalized linear model (GLMBoost) and random forest (RF), and Bayesian generalized linear model (BayesGLM) methods for higher performance modeling. Furthermore, a pre-processing method, namely simulated annealing (SA), is used to eliminate redundant variables from the modeling process. Results of the modeling based on the hit and miss analysis indicates high performance for both models (accuracy = 90-92%, Kappa = 79-84%, Success ratio = 94-96%, Threat score = 80-84%, and Heidke skill score = 79-84%). The variables of distance from the stream, vegetation, drainage density, land use, and elevation have shown more contribution among others for modeling the flash-flood. The results of this study can significantly facilitate mapping the hazardous areas and further assist watershed managers to control and remediate induced damages of flood in the data-scarce regions.