Drought is one of the most complex natural hazards, characterized by its slow onset, persistent nature, diverse sectoral impacts (e.g., agriculture, water resources, ecosystems), and dependence on meteorological, hydrological, and socioeconomic factors. Over the years, significant scientific effort has been devoted to developing methodologies that address its multifaceted nature, reflecting the interdisciplinary challenges of drought analysis. However, previous reviews have typically focused on individual methods, while this study presents a unified, multidisciplinary framework that integrates multiple drought analysis methods and links them to key factors guiding method selection. To address this gap, five widely used methods—index-based, remote sensing, threshold-level methods (TLM), impact-based methods, and the storyline approach—are critically evaluated from a multidisciplinary perspective. In addition, the study examines spatial and temporal trends in scientific publications, illustrating how the application of these methods has evolved over time and across regions. The primary objective of this review is twofold: (1) to provide a holistic, state-of-the-art synthesis of these methods, their applications, and their limitations; and (2) to evaluate and prioritize the critical decision-making factors, including drought type, data type/availability, study scale, and management objectives that influence method selection. By bridging this gap, the paper offers a conceptual decision-support framework for selecting context-appropriate drought analysis methods. However, challenges remain, including the vast diversity of methods beyond the scope of this review and the limited consideration of less influential factors such as user expertise, computational resources, and policy context. The paper concludes with insights and recommendations for optimizing method selection under varying circumstances, aiming to support both drought research and effective policy implementation.

Surface bypasses are downstream migration structures that can help reduce hydropower-induced damage to migrating fish. However, no comprehensive design concept that facilitates good surface bypass performance for a wide range of sites and species is available. This is why fish-passage efficiencies at recently built bypass structures vary widely between 0% and up to 97%. We reviewed 50 surface bypass performance studies and existing guidelines for salmonids, eels and potamodromous species to identify crucial design criteria for surface bypasses employed in North America, Europe and Australia. Two-tailed Pearson correlation of bypass efficiency and bypass design criteria shows that bypass entrance area (r = 0.3300, P = 0.0036) and proportion of inflow to the bypass (r = 0.3741, P = 0.0032) are the most influential parameters on bypass efficiency. However, other parameters such as guiding structures (P = 0.2181, ordinary Student’s t-test) and trash-rack spacing (r = –0.1483, P = 0.3951, Spearman correlation), although not statistically significant, have been shown to have an effect on efficiency in some studies. The use of different performance criteria and efficiency definitions for bypass evaluation hampers direct comparison of studies and, therefore, deduction of design criteria. To enable meta-analyses and improve bypass design considerations, we suggest a list of standardised performance parameters for bypasses that should be considered in future bypass-performance studies.

This paper presents a dataset documenting 71 floating debris accumulations at bridges following an extreme hydrological event that hit Belgium and Germany in July 2021. Data were collected from various sources including public authorities’ documents, public online databases, post event pictures and field visits. The dataset covers bridges geometry, flood conditions and debris accumulation. In particular, it systematically details accumulations dimensions and quantifies accumulations components, which contain a significant portion of anthropogenic objects, in addition to driftwood. This dataset constitutes a unique set of invaluable data to better understand debris accumulation at bridges but also to analyze their impact on both the affected structures and flood conditions.

Extreme heavy rainfall in Western Europe on 13–15 July 2021 caused severe flooding, notably in Germany's Rhineland‐Palatinate and North Rhine‐Westphalia. This study examines Flood Forecasting, Early Warning, and Disaster Response weaknesses during this event, focusing on the city of Stolberg. An interdisciplinary mixed‐methods approach integrated meteorological, hydrological, and social science research. Data included river gages, precipitation measurements, warnings, and 300 documents, with 30 expert interviews. Weaknesses included imprecise meteorological forecasts due to dynamic weather, leading to general warnings without specific impact guidance. Limited flood forecasting hindered local preparation and response, exacerbated by an emergency response system unprepared for the event's scale. The top‐down approach of Flood Forecasting and Early Warning conflicted with the bottom‐up processes of Disaster Response, hampering effective crisis management. The study reveals critical weaknesses and calls for improved forecasting, integrated response plans, communication protocols, and crisis channels to enhance flood resilience. Future research should explore these issues in other extreme flood events and compare international Flood Forecasting, Early Warning, and Disaster Response systems.