As flood impacts are increasing in large parts of the world, understanding the primary drivers of changes in risk is essential for effective adaptation. To gain more knowledge on the basis of empirical case studies, we analyze eight paired floods, that is, consecutive flood events that occurred in the same region, with the second flood causing significantly lower damage. These success stories of risk reduction were selected across different socioeconomic and hydro‐climatic contexts. The potential of societies to adapt is uncovered by describing triggered societal changes, as well as formal measures and spontaneous processes that reduced flood risk. This novel approach has the potential to build the basis for an international data collection and analysis effort to better understand and attribute changes in risk due to hydrological extremes in the framework of the IAHSs Panta Rhei initiative. Across all case studies, we find that lower damage caused by the second event was mainly due to significant reductions in vulnerability, for example, via raised risk awareness, preparedness, and improvements of organizational emergency management. Thus, vulnerability reduction plays an essential role for successful adaptation. Our work shows that there is a high potential to adapt, but there remains the challenge to stimulate measures that reduce vulnerability and risk in periods in which extreme events do not occur.

As one of nature’s most destructive calamities, floods cause fatalities, property destruction, and infrastructure damage, affecting millions of people worldwide. Due to its ability to accurately anticipate and successfully mitigate the effects of floods, flood modeling is an important approach in flood control. This study provides a thorough summary of flood modeling’s current condition, problems, and probable future directions. The study of flood modeling includes models based on hydrologic, hydraulic, numerical, rainfall–runoff, remote sensing and GIS, artificial intelligence and machine learning, and multiple-criteria decision analysis. Additionally, it covers the heuristic and metaheuristic techniques employed in flood control. The evaluation examines the advantages and disadvantages of various models, and evaluates how well they are able to predict the course and impacts of floods. The constraints of the data, the unpredictable nature of the model, and the complexity of the model are some of the difficulties that flood modeling must overcome. In the study’s conclusion, prospects for development and advancement in the field of flood modeling are discussed, including the use of advanced technologies and integrated models. To improve flood risk management and lessen the effects of floods on society, the report emphasizes the necessity for ongoing research in flood modeling.

The management of water resources depends heavily on hydrological prediction, and advances in machine learning (ML) present prospects for improving predictive modelling capabilities. This study investigates the use of a variety of widely used machine learning algorithms, such as CatBoost, ElasticNet, k-Nearest Neighbors (KNN), Lasso, Light Gradient Boosting Machine Regressor (LGBM), Linear Regression (LR), Multilayer Perceptron (MLP), Random Forest (RF), Ridge, Stochastic Gradient Descent (SGD), and the Extreme Gradient Boosting Regression Model (XGBoost), to predict the river inflow of the Garudeshwar watershed, a key element in planning for flood control and water supply. The substantial engineering feature used in the study, which incorporates temporal lag and contextual data based on Indian seasons, leads it distinctiveness. The study concludes that the CatBoost method demonstrated remarkable performance across various metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R²) values, for both training and testing datasets. This was accomplished by an in-depth investigation and model comparison. In contrast to CatBoost, XGBoost and LGBM demonstrated a higher percentage of data points with prediction errors exceeding 35% for moderate inflow numbers above 10,000. CatBoost established itself as a reliable method for hydrological time-series modelling, easily managing both categorical and continuous variables, and thereby greatly enhancing prediction accuracy. The results of this study highlight the value and promise of widely used machine learning algorithms in hydrology and offer valuable insights for academics and industry professionals.

In this study, drought events over a large area of the Northern Hemisphere, including continental Europe, Ireland, the United Kingdom, and the Mediterranean basin, were analyzed using the Standardized Precipitation Index (SPI) at various times scales (3, 6, 12, and 24 months). To this purpose, the Global Precipitation Climatology Centre (GPCC) Full Data Monthly Product Version 2018 data set, with spatial resolutions of 0.5° longitude/latitude and for the period 1951–2016, has been used. First, the temporal evolution of the percentage of grid points, falling within the severe and extreme drought categories, has been evaluated. Then, a trend analysis has been performed at a seasonal scale, considering the autumn-winter and the spring-summer periods, and at an annual scale. The results of this paper highlight that the Mediterranean basin and North Africa are the most consistently vulnerable areas showing a general reduction in SPI values especially for the long time scale.

In order to investigate the behaviour of climatic and hydrological variables, several statistical and stochastic techniques are currently applied to time series. In the present study a statistical analysis of annual and seasonal precipitation has been performed over 109 cumulated rainfall series with more than 50 years of data observed in a region of Southern Italy (Calabria). Trend analyses have been made by using both nonparametric (Mann–Kendall test) and parametric (linear regression analysis) procedures. The long historical series of monthly rainfall data employed in this work have been previously processed through a pre‐whitening (PW) technique in order to reduce the autocorrelation of rainfall series and its effects on outcomes of trend detection. The application of the above mentioned procedures has shown a decreasing trend for annual and winter–autumn rainfall and an increasing trend for summer precipitation. Moreover the Mann–Whitney test has been used to evidence the possible change points in the data. The higher percentages of rainfall series show possible year changes during decade 1960–1970 for almost all of the temporal aggregation rainfall. Copyright © 2009 Royal Meteorological Society