Physically based (process) models based on mathematical descriptions of water motion are widely used in river basin management. During the last decade the so-called data-driven models are becoming more and more common. These models rely upon the methods of computational intelligence and machine learning, and thus assume the presence of a considerable amount of data describing the modelled system9s physics (i.e. hydraulic and/or hydrologic phenomena). This paper is a preface to the special issue on Data Driven Modelling and Evolutionary Optimization for River Basin Management, and presents a brief overview of the most popular techniques and some of the experiences of the authors in data-driven modelling relevant to river basin management. It also identifies the current trends and common pitfalls, provides some examples of successful applications and mentions the research challenges.

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Data-driven modelling: some past experiences and new approaches

Linear Theory of Hydrologic Systems

In spite of trying to understand processes in the same spatial domain, the catchment hydrology and water quality scientific communities are relatively disconnected and so are their respective models. This is emphasized by an inadequate representation of transport processes, in both catchment-scale hydrological and water quality models. While many hydrological models at the catchment scale only account for pressure propagation and not for mass transfer, catchment scale water quality models are typically limited by overly simplistic representations of flow processes. With the objective of raising awareness for this issue and outlining potential ways forward we provide a nontechnical overview of (1) the importance of hydrology-controlled transport through catchment systems as the link between hydrology and water quality; (2) the limitations of current generation catchment-scale hydrological and water quality models; (3) the concept of transit times as tools to quantify transport; and (4) the benefits of transit time based formulations of solute transport for catchment-scale hydrological and water quality models. There is emerging evidence that an explicit formulation of transport processes, based on the concept of transit times has the potential to improve the understanding of the integrated system dynamics of catchments and to provide a stronger link between catchment-scale hydrological and water quality models. WIREs Water 2016, 3:629-657. doi: 10.1002/wat2.1155 For further resources related to this article, please visit the .

https://onlinelibrary.wiley.com/doi/epdf/10.1002/wat2.1155

Transit times – the link between hydrology and water quality at the catchment scale

Rainfall runoff relations for karstic springs. Part I: convolution and spectral analyses

In this paper, a rainfall-runoff modeling system is developed based on a nonlinear Volterra functional series and a hydrological conceptual modeling approach. Two models, i.e. the time-variant gain model (TVGM) and the distributed time-variant gain model (DTVGM) that are built on the platform of Digital Elevation Model (DEM), Remote Sensing (RS) and Unit Hydro-logical Process were proposed. The developed DTVGM model was applied to two cases in the Heihe River Basin that is located in the arid and semiarid region of northwestern China and the Chaobai River basin located in the semihumid region of northern China. The results indicate that, in addition to the classic dynamic differential approach to describe nonlinear processes in hy-drological systems, it is possible to study such complex processes through the proposed sys-tematic approach to identify prominent hydrological relations. The DTVGM, coupling the advan-tages of both nonlinear and distributed hydrological models, can simulate variant hydrological processes under different environment conditions. Satisfactory results were obtained in fore-casting the time-space variations of hydrological processes and the relationships between land use/cover change and surface runoff variation.

Development of distributed time-variant gain model for nonlinear hydrological systems

Determination of an optimal IUH for linear, time invariant systems from multi-storm records

Identification of a Volterra series conceptual model based on a cascade of nonlinear reservoirs

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Determination of an optimal IUH for linear, time invariant systems from multi-storm records

Identification of a Volterra series conceptual model based on a cascade of nonlinear reservoirs