What we see now: event-persistence and the predictability of hydro-eco-geomorphological systems

What we see now in the landscape is the result of a long history of events with varying degrees of persistence. We have only limited access to much of that history and we know that many current events have only a minimal impact on what we see. Even rather extreme events may have impacts that are not very long-lasting but can have the effect of changing the antecedent states for future events. That means that sampling of sequences of events might be important in understanding the evolution of the catchments. In some cases, however, extreme events can have an impact on the system that persists over hundreds or thousands of years. Any evolution of the landscape is then constrained by those past events, however much it might be also constrained by self-organisational principles. It might be difficult to verify those principles given the epistemic uncertainties about past histories and system properties that are generic to the studies that are possible within a research project or career. These arguments are investigated in a simple slab model of landslip failures in a hillslope hollow subject to stochastic forcing over long periods of time. The complementarity of an event-persistence approach to hydro-eco-geomorphological systems is captured in suggestions for future research questions

How to make advances in hydrological modelling

After some background about what I have learned from a career in hydrological modelling, I present some opinions about how we might make progress in improving hydrological models in future, including how to decide whether a model is fit for purpose; how to improve process representations in hydrological models; and how to take advantage of Models of Everywhere. Underlying all those issues, however, is the fundamental problem of improving the hydrological data available for both forcing and evaluating hydrological models. It would be a major advance if the hydrological community could come together to prioritise and commission the new observational methods that are required to make real progress

Facets of uncertainty:epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication

This paper presents a discussion of some of the issues associated with the multiple sources of uncertainty and non-stationarity in the analysis and modelling of hydrological systems. Different forms of aleatory, epistemic, semantic, and ontological uncertainty are defined. The potential for epistemic uncertainties to induce disinformation in calibration data and arbitrary non-stationarities in model error characteristics, and surprises in predicting the future, are discussed in the context of other forms of non-stationarity. It is suggested that a condition tree is used to be explicit about the assumptions that underlie any assessment of uncertainty. This also provides an audit trail for providing evidence to decision makers

A guide to good practice in modeling semantics for authors and referees

This opinion piece makes some suggestions about guidelines for modeling semantics that can be referred to by authors and referees. We discuss descriptions of model structures, different forms of simulation and prediction, descriptions of different sources of uncertainty in modeling practice, the language of model validation, and concepts of predictability and fitness-for-purpose. While not expecting universal agreement on these suggestions, given the loose usage of words in the literature, we hope that the discussion of the issues involved will at least give pause for thought and encourage good practice in model development and applications. Key Points Semantics of hydrological modelling lack clarity Clarifications for simulation and forecasting and treatment of uncertainty Clarifications for model evaluation and falsificatio

Macropores and water flow in soils revisited

The original review of macropores and water flow in soils by Beven and Germann is now 30 years old and has become one of the most highly cited papers in hydrology. This paper attempts to review the progress in observations and theoretical reasoning about preferential soil water flows over the intervening period. It is suggested that the topic has still not received the attention that its importance deserves, in part because of the ready availability of software packages rooted firmly in the Richards domain, albeit that there is convincing evidence that this may be predicated on the wrong experimental method for natural conditions. There is still not an adequate physical theory linking all types of flow, and there are still not adequate observational techniques to support the scale dependent parameterizations that will be required at practical field and hillslope scales of application. Some thoughts on future needs to develop a more comprehensive representation of soil water flows are offered

Exploratory studies into the prospects for seasonal forecasting of lake levels and outflows

Some of the largest lakes in the world are in Africa and seasonal forecasts of levels and outflows can potentially help with water supply, irrigation and hydropower operations; in particular regarding the risks from floods or droughts. Some factors which increase the prospects for real-time forecasting include the significant time delays between rainfall and outflows resulting from the huge volumes of water stored, and that many studies have shown possible links between regional rainfall and climate indices for the Indian Ocean and elsewhere. On the other hand, on account of the huge areas covered, catchments can span several climate zones and rainfall and flow monitoring networks are often sparse. Exploratory studies into some of these issues are described based on case studies for two large lakes, including some preliminary findings regarding data assimilation and the complexity of models required. The studies were performed using a range of stochastic signal identification tools and are compared with the findings from an ensemble streamflow prediction approach. Preliminary conclusions are then drawn regarding the relevance of these results to the development of operational forecasting models

Models of everywhere revisited: a technological perspective

The concept ‘models of everywhere’ was first introduced in the mid 2000s as a means of reasoning about the environmental science of a place, changing the nature of the underlying modelling process, from one in which general model structures are used to one in which modelling becomes a learning process about specific places, in particular capturing the idiosyncrasies of that place. At one level, this is a straightforward concept, but at another it is a rich multi-dimensional conceptual framework involving the following key dimensions: models of everywhere, models of everything and models at all times, being constantly re-evaluated against the most current evidence. This is a compelling approach with the potential to deal with epistemic uncertainties and nonlinearities. However, the approach has, as yet, not been fully utilised or explored. This paper examines the concept of models of everywhere in the light of recent advances in technology. The paper argues that, when first proposed, technology was a limiting factor but now, with advances in areas such as Internet of Things, cloud computing and data analytics, many of the barriers have been alleviated. Consequently, it is timely to look again at the concept of models of everywhere in practical conditions as part of a trans-disciplinary effort to tackle the remaining research questions. The paper concludes by identifying the key elements of a research agenda that should underpin such experimentation and deployment

Exploratory studies into the prospects for seasonal forecasting of lake levels and outflows

Some of the largest lakes in the world are in Africa and seasonal forecasts of levels and outflows can potentially help with water supply, irrigation and hydropower operations; in particular regarding the risks from floods or droughts. Some factors which increase the prospects for real-time forecasting include the significant time delays between rainfall and outflows resulting from the huge volumes of water stored, and that many studies have shown possible links between regional rainfall and climate indices for the Indian Ocean and elsewhere. On the other hand, on account of the huge areas covered, catchments can span several climate zones and rainfall and flow monitoring networks are often sparse. Exploratory studies into some of these issues are described based on case studies for two large lakes, including some preliminary findings regarding data assimilation and the complexity of models required. The studies were performed using a range of stochastic signal identification tools and are compared with the findings from an ensemble streamflow prediction approach. Preliminary conclusions are then drawn regarding the relevance of these results to the development of operational forecasting models

The seventh facet of uncertainty:wrong assumptions, unknowns and surprises in the dynamics of human–water systems

The scientific literature has focused on uncertainty as randomness, while limited credit has been given to what we call here the “seventh facet of uncertainty”, i.e. lack of knowledge. This paper identifies three types of lack of understanding: (i) known unknowns, which are things we know we don’t know; (ii) unknown unknowns, which are things we don’t know we don’t know; and (iii) wrong assumptions, things we think we know, but we actually don’t know. Here we discuss each of these with reference to the study of the dynamics of human–water systems, which is one of the main topics of Panta Rhei, the current scientific decade of the International Association of Hydrological Sciences (IAHS), focusing on changes in hydrology and society. In the paper, we argue that interdisciplinary studies of socio-hydrological dynamics leading to a better understanding of human–water interactions can help in coping with wrong assumptions and known unknowns. Also, being aware of the existence of unknown unknowns, and their potential capability to generate surprises or black swans, suggests the need to complement top-down approaches, based on quantitative predictions of water-related hazards, with bottom-up approaches, based on societal vulnerabilities and possibilities of failure

On hypothesis testing in hydrology:why falsification of models is still a really good idea

This opinion piece argues that in respect of testing models as hypotheses about how catchments function, there is no existing methodology that adequately deals with the potential for epistemic uncertainties about data and hydrological processes in the modelling processes. A rejectionist framework is suggested as a way ahead, wherein assessments of uncertainties in the input and evaluation data are used to define limits of acceptability prior to any model simulations being made. The limits of acceptability might also depend on the purpose of the modelling so that we can be more rigorous about whether a model is actually fit-for-purpose. Different model structures and parameter sets can be evaluated in this framework, albeit that subjective elements necessarily remain, given the epistemic nature of the uncertainties in the modelling process. One of the most effective ways of reducing the impacts of epistemic uncertainties, and allow more rigorous hypothesis testing, would be to commission better observational methods. Model rejection is a good thing in that it requires us to be better, resulting in advancement of the science