We investigate the potential of causal inference methods (CIMs) to reveal hydrological connections from time series. Four CIMs are selected from two criteria, linear or nonlinear and bivariate or multivariate. A priori, multivariate, and nonlinear CIMs are best suited for revealing hydrological connections because they fit nonlinear processes and deal with confounding factors such as rainfall, evapotranspiration, or seasonality. The four methods are applied to a synthetic case and a real karstic case study. The synthetic experiment confirms our expectation: unlike the other methods, the multivariate nonlinear framework has a low false-positive rate and allows for ruling out a connection between two disconnected reservoirs forced with similar effective precipitation. However, for the real case study, the multivariate nonlinear method was unstable because of the uneven distribution of missing values affecting the final sample size for the multivariate analyses, forcing us to cope with the results' robustness. Nevertheless, if we recommend a nonlinear multivariate framework to reveal actual hydrological connections, all CIMs bring valuable insights into the system's dynamics, making them a cost-effective and recommendable comparative tool for exploring data. Still, causal inference remains attached to subjective choices, operational constraints, and hypotheses challenging to test. As a result, the robustness of the conclusions that the CIMs can draw always deserves caution, especially with real, imperfect, and limited data. Therefore, alongside research perspectives, we encourage a flexible, informed, and limit-aware use of CIMs without omitting any other approach that aims at the causal understanding of a system.

The Emergency Events Database (EM-DAT) compiles global disaster data resulting from technological and natural hazards. EM-DAT details the human and economic impacts from 1900 to present, with systematic recording since 1988. Serving the humanitarian, disaster risk reduction, and academic sectors, EM-DAT's transition to open access and climate change concerns have expanded its reach and visibility. The dataset, freely available for non-commercial use, is downloadable in a tabular format. It is categorized by hazard type and standardized by individual disaster events for each country. With over 26,000 unique entries, the database is populated through the monitoring of textual documents and their manual processing. The data collection and validation processes involve systematic daily checks from predefined sources, searches for additional sources, and periodic thematic updates. The evolution of EM-DAT's data content mirrors societal and technological advancements in disaster reporting. Besides these progresses, known inconsistencies and biases in data quality have been reported. The article acknowledges these issues, highlighting their potential implications for research and decision-making.

Flood disasters stem from complex interac8ons of environmental and human factors. In Spain, as in much of Europe, floods o^en coincide with other hazards like storms, landslides, or infrastructure failures, causing significant economic and human loss. Preven8ng future disasters requires understanding the interplay of exposure, vulnerability, and capacity, alongside effec8ve risk communica8on throughout all stages of disaster management, from emergency response to public awareness. Advancements over recent decades, such as the United Na8ons' 2015-2030 Sendai Framework for Disaster Risk Reduc8on 5 , the European Union's 2007 Flood Direc8ve 6 , and tools like the European Flood Awareness System 7 have improved preparedness and early warning capabili8es.However, flood risks con8nue to grow, necessita8ng stronger evidence-based prac8ces. Databases such as the Emergency Events Database 3,8 and HANZE 9 , are invaluable for analysing past events and iden8fying links between human, economic and environmental factors.The 2024 Spain Flood is stark reminder of the need to nurture a culture of proac8ve risk management, a commitment already established 35 years ago during the 1990 Interna8onal Decade for Natural

For more than a century, the study of streamflow recession has been dominated by seemingly physically based parametric methods that make assumptions on the nonlinear nature of the hydrograph recession. In practice, several studies have shown that various degrees of nonlinearity occur in the same time series and that parametric methods can underfit nonlinear recession patterns. As a result, these methods are often applied empirically to each recession segment. We propose a parsimonious data‐driven model, EDM‐Simplex, with two objectives: forecasting recession and characterizing its nonlinear behavior. We evaluate the new model through a global sensitivity analysis applied to three distinctive hydrograph series from a heterogeneous karstic catchment. The results show excellent 1‐day‐ahead forecasting performance (median Nash and Sutcliffe efficiency > 0.99) for all time series with four recession extraction methods. The sensitivity analysis also showed that empirical nonlinearity, that is, sensitivity to initial conditions, is best estimated through the absolute forecast performance and its decline over time. This indicator leads to different interpretations of nonlinearity compared to previous methods but is just as sensitive to the choice of recession extraction method. In particular, when forecasts were made for recession segments containing early stages of recession or flow anomalies, the upstream recession was significantly more linear than the downstream recession hydrographs affected by the karst. Consequently, our results support future research to interpret observed nonlinearities as a function of the catchment hydrological states for better integration of empirical, physical‐based, and operational approaches to recession analysis.