Keith Beven

Process‐behavioural modelling aims to achieve accurate representation of hydrological processes in hydrological models. Evaluation of model performance on discharge alone cannot achieve this; more informative observables are needed. This poses the question of which types of observables might be most informative in discriminating between different potential models. As a way of testing the value of different observables to this end, a method was developed that uses a virtual reality model of a catchment. Based on this virtual reality, ‘observations’ were then simulated by perturbing the ‘real’ values. These observations were used to evaluate the outputs of 1800 simulations with alternative parameter sets, but with the same model structure. The simulated results for the hydrological variables discharge, discharge peaks, discharge components, incremental discharge, cosmic ray neutron sensing soil moisture (CRNS), and total water storage were then normalised with the uncertainties derived for each variable from the virtual reality model. This allowed the individual variables to be compared in terms of their ability to identify non‐process‐behavioural simulations. For the assumed uncertainties, it was shown that the discharge component separation by the tracer model discriminates between models particularly strongly, while CRNS serves as a complementary variable for evaluation. For all the models tried, however, significant relaxations of the limits of acceptability were required to accept any models as process‐behavioural. This appeared to be the result primarily of the perturbations applied to the inputs which resulted in significant model residuals during certain periods. Since in this case there is no significant model structural error, this suggests that hydrological modellers should, as far as possible, assess the uncertainty in the model forcing data as part of the model evaluation process.

1 More than 100 years ago, the problem of how to estimate runoff from catchment areas given precipitation observations was already exercising hydrologists. At that time, of course, computers were p...

This paper makes the argument for treating the response of small catchments as an exercise in the identification of hysteretic functions as a way of overcoming the impossibility of knowing all the small‐scale detail of time variable and spatial heterogeneous catchment processes. An initial form of analysis is proposed based on the Data‐Based Mechanistic (DBM) transfer function methodology to define functions for classes of events based on rainfall input volumes and antecedent flow as an index of catchment wetness. From the resulting transfer functions, event‐scale storage‐discharge plots can be derived if within‐event evapotranspiration is neglected as small relative to event inputs. The great hysteresis challenge is to find a way of identifying a more continuous mechanistic function that allows for the change in input–output gains and transfer function state variables directly from the observed data without invoking a particular conceptual storage model structure (or structures) or a difficult‐to‐interpret machine‐learning framework.

This commentary considers the recent explosion of models based on Deep Learning/Machine Learning methods in hydrological journals and how their application might better contribute to hydrological understanding in the face of epistemic uncertainties in an inexact science.

The International Association of Hydrological Sciences (IAHS) Working Group on the ‘History of Hydrology’ (https://iahs.info/Initiatives/Working-Groups/History-of-Hydrology/) was established under the leadership of Keith Beven in December 2022. In 2025, Okke Batelaan succeeded Keith Beven as chair. Before the establishment, several scientific activities demonstrated broad interest in the history of hydrology, thereby underscoring the opportunity and need for this Working Group (Beven et al. 2025). In 2018-2019, well-attended EGU sessions on the ‘History of Hydrology’ were organised. In 2019, a special issue on the ‘History of Hydrology’ in the ‘Hydrology and Earth System Sciences’ resulted in 13 published papers.The aims of the Working Group are:1: To provide a central repository for information on the History of Hydrology with liaison, links and metadata on the existing initiatives and copies or links to important historical papers from multiple countries.2: To encourage more international contributions from countries that are not currently well represented in the existing resources, including the identification of important historical papers from those countries.3: To encourage the recording of the contributions of female hydrologists.4: To encourage the recording of the histories of experimental catchments where important advances in understanding of hydrological processes have been made.5: To encourage the recording of the histories of hydrological models and the people who worked with them.6: To provide a mechanism for the recording of the history of projects representing good practice in sustainable hydrology for societies under change, building on the Case Studies in Panta Rhei.Since its establishment, the Working Group has been active in further sessions on the History of Hydrology at the IAHS-IUGG General Assembly, Berlin, in 2023, at EGU in 2025-2026, while a special workshop ‘From the History of Hydrology to the Future of Education’ was organised at Eawag, Switzerland, in 2025. A new Special Collection on ‘History of Hydrology’ in the Hydrological Sciences Journal has been very successful with so far 22 papers. Since 2018, more than 20 ‘History of Hydrology Interviews’ have been recorded with hydrologists (https://www.youtube.com/@historyofhydrologyintervie846). In these interviews, hydrologists share their personal stories about their careers, inspirations, successes, failures, collaborations, friendships, influences, and thoughts about the future. These recordings are inspirational for all, especially students, early-career researchers, and senior researchers. The often personal and historical accounts of scientific directions and developments, which are rarely found in journal papers, are a valuable source of information for hydrological education. The ‘History of Hydrology Wiki’ (http://www.history-of-hydrology.net/mediawiki/index.php?title=Main_Page) is another high-value educational resource, as it provides biographies of hydrologists, histories of experimental and research catchments, histories of institutions, hydrological textbooks, and an annotated bibliography.Alltogether, the Working Group on the ‘History of Hydrology’ provides a gold mine of information that can be infused into hydrological teaching and education and inspire the next generation of hydrologists. Beven et al., 2025, On the value of a history of hydrology and the establishment of a History of Hydrology Working Group. Hydrological Sciences Journal 70(5):717-729. https://doi.org/10.1080/02626667.2025.2452357.

A modern update of the different definitions of the Ages of Hydrology as provided by Chow in 1964 and Dooge in 1999.

The Limits of Acceptability approach has been demonstrated to be an effective conditioning tool due to its capacity to consider epistemic uncertainty. However, its application faces two challenges—the low efficiency when random sampling is used and the difficulty in setting limits prior to calibration. Here an algorithm DREAM_(LoAX) was developed and added to GLUE framework. As an extension of DREAM_(LoA) of Vrugt and Beven (2018), https://doi.org/10.1016/j.jhydrol.2018.02.026, it evaluates model performance based on limit boundaries, thus inherits the merits of the GLUE framework (explicit consideration of epistemic errors). Moreover, the importance of initial choice of limits is strongly reduced by allowing iterative evolution of limits based on historical model performance. By testing a series of examples (including a high‐dimensional numeric example, a single‐objective hydrological example, and a multi‐objective hydrological example) with or without error‐free assumption using synthetic or real observations, the search capacity of DREAM_(LoAX) to locate acceptable models is demonstrated. The algorithm also shows comparable efficiency to DREAM and DREAM_(LoA). More importantly, it provides real‐time diagnostic information regarding when (at which timestep), where (for which objective), and how (to which direction and to which extent) the model fails when uncertainty is pronounced, allowing potential uncertainty sources in the data or flaws in the model structure to be identified. In this context, DREAM_(LoAX) is not only a useful conditioning tool, but also a diagnostic and learning tool for development of improved modeling.