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Allgemeiner Test des SARIMAX Auto-Arima Modells zur Vorhersage von univariaten Zeitreihen aus der Wasserbedarfserfassung.

Intense rainfall events with high intensities over short durations frequently result in substantial runoff and increased potential for flooding in affected catchments. The accurate assessment of flood hazards remains challenging due to the high variability of rainfall dynamics and their spatial distributions. Rain gauge stations provide precise point measurements; however, they lack information on the spatial distribution of rainfall. Conversely, weather radar offers high-resolution spatial and temporal rainfall data but is subject to biases and uncertainties that require correction. Previous studies have predominantly focused on pointwise comparisons of rainfall data products. In contrast, this study utilizes data from 109 catchments in Lower Saxony, Germany, to evaluate the ability of station-based and radar-derived rainfall data (using the corrected Radklim product from the German Weather Service) to explain and classify observed runoff events. These events are categorized as Flash Floods, Short-Rain Floods or Long-Rain Floods and the quality of the rainfall data is analyzed in relation to these classifications. Furthermore, the study investigates whether significant runoff events can be exclusively explained by one rainfall data source. By comparing catchment-averaged rainfall from stations and radar, this research highlights the strengths and limitations of both data types in representing rainfall-runoff relationships. The findings will contribute to improved flood hazard assessment and emphasize the importance of selecting appropriate rainfall datasets for hydrological analyses and early warning systems.

The Mahananda River basin, located in Eastern India, faces escalating flood risks due to its complex hydrology and geomorphology, threatening socioeconomic and environmental stability. This study presents a novel approach to flood susceptibility (FS) mapping and updates the region's flood inventory. Multitemporal Sentinel-1 (S1) SAR images (2020–2022) were processed using a U-Net transfer learning model to generate a water body frequency map, which was integrated with the Global Flood Dataset (2000–2018) and refined through grid-based classification to create an updated flood inventory. Eleven geospatial layers, including elevation, slope, soil moisture, precipitation, soil type, NDVI, Land Use Land Cover (LULC), geomorphology, wind speed, drainage density, and runoff, were used as flood conditioning factors (FCFs) to develop a hybrid FS mapping approach. This approach integrates the Fuzzy Analytic Hierarchy Process (FuzzyAHP) with six machine learning (ML) algorithms to create hybrid models FuzzyAHP-RF, FuzzyAHP-XGB, FuzzyAHP-GBM, FuzzyAHP-avNNet, FuzzyAHP-AdaBoost, and FuzzyAHP-PLS. Future flood trends (1990–2030) were projected using CMIP6 data under SSP2-4.5 and SSP5-8.5 scenarios with MIROC6 and EC-Earth3 ensembles. The SHAP algorithm identified LULC, NDVI, and soil type as the most influential FCFs, contributing over 60 % to flood susceptibility. Results show that 31.10 % of the basin is highly susceptible to flooding, with the western regions at greatest risk due to low elevation and high drainage density. Future projections indicate that 30.69 % of the area will remain highly vulnerable, with a slight increase under SSP5-8.5. Among the models, FuzzyAHP-XGB achieved the highest accuracy (AUC = 0.970), outperforming FuzzyAHP-GBM (AUC = 0.968) and FuzzyAHP-RF (AUC = 0.965). The experimental results showed that the proposed approach can provide a spatially well-distributed flood inventory derived from freely available remote sensing (RS) datasets and a robust framework for long-term flood risk assessment using hybrid ML techniques. These findings offer critical insights for improving flood risk management and mitigation strategies in the Mahananda River basin.