Hydrological and Hydrodynamic Processes and Modelling

Understanding and modelling the component processes of the hydrological cycle, and how they interact at the catchment scale, is at the heart of hydrological research. From the early 1960s, when digital computing became a reality, modelling has evolved from earlier spatially lumped approaches to fully distributed models where the equations of mass, energy, and momentum conservation are solved on increasingly refined grids. This has been enabled by huge developments in computing power, remote sensing imagery, and big data, but it is an open question as to whether we have seen a proportional increase in predictive power. It has been argued that in the case of physically based distributed models, the constituent equations, particularly Richard’s equation, are not representative of what is happening in the landscape, where the responses occur as a function of natural hydrological response units. However, alternative physical/mathematical theories that can be parameterised from the available distributed data sets on soil properties, land use, etc., have remained elusive and remain a major challenge in representing the heterogeneity of hydrological responses across the catchment landscape. Meanwhile, the existing formulations will continue to see improvements in process representation and parameterisation, as well as underpin many areas of research where spatial process representation is a prerequisite, particularly in representing land surface–atmosphere interactions.

Hydrology is an applied science, and some applications urgently require models, whatever their imperfections, to be put to the test amongst the perils of the real world, as opposed to more comfortable research domains. The increasing vulnerability of populations worldwide to flooding has seen the increasing use of distributed hydrological and hydrodynamic models, not just for the design of flood protection works, but for real-time flood forecasting and warning. In this regard, significant progress has been made in quantifying predictive uncertainty, which should be used in support of decision making. If a model is to be used operationally, quantifying predictive uncertainty correctly should be mandatory.

New interdisciplinary frontiers have opened up which require model formulations that can represent the interactions with these disciplines. Notable amongst these are the interactions with climate, weather, ecology, human systems, and the water–food–energy nexus.  Many challenges remain to engage productively with these disciplines, and to ensure that appropriate hydrological modelling is undertaken.

Aim

Against the above background, the aim of this section is to encourage papers that address the many challenges that remain in both theory and practice. We invite papers that propose new theoretical and experimental developments that can advance understanding and predictability in land surface, soil, and atmosphere interactions, in process representation, and in distributed modelling. Papers reporting new developments in supporting technologies, such as instrumentation, computing, and the use of artificial intelligence, are also encouraged, as are papers that engage with the interdisciplinary challenges mentioned above.

There is huge diversity and heterogeneity in hydrological response across the five continents, so papers are encouraged that enhance the modelling of catchments everywhere; this is particularly important for improving global hydrological models. Papers on new advances in the operational use of models are encouraged, particularly those that demonstrate the reliability of real-time flood warning systems.

Special Issue proposals are sought that can report on the current state of the art in theory or practice, topics that are creating excitement or controversy, or the operational use of models and the quantification of predictive uncertainty for operational use.

Scope

• New process and theoretical developments
• New advances in distributed modelling
• Improved representation of land surface, soil, and atmosphere interactions
• Use of distributed models to track information within the catchment landscape
• Enhancing hydrological predictability
• Coupled numerical weather prediction and real-time flood forecasting models
• Hydrological modelling within the water–food–energy nexus
• Hydro-ecological modelling
• Coupled human and hydrological system modelling
• Hydrological and hydrodynamic modelling for design flood estimation
• Real time flood forecasting systems: predictive uncertainty and warning reliability
• Stochastic modelling of hydrological processes in space and time
• Modelling in data scarce regions; use of global data sets.
• Modelling and attribution of hydrological change
• Modelling of ungauged basins