Developing observational methods to drive future hydrological science: Can we make a start as a community?

Abstract

Hydrology is still, and for good reasons, an inexact science (for a recent discussion, see Beven, 2019a), even if evolving hydrological understanding has provided a basis for improved water management for at least the last three millennia. The limitations of that understanding have, however, become much more apparent and important in the last century as the pressures of increasing populations, and the anthropogenic impacts on catchment forcing and responses, have intensified (see Abbott et al., 2019; Montanari et al., 2013; Sivapalan, Savenije, & Blöschl, 2012; Srinivasan et al., 2017; Wagener et al., 2010; Wilby, 2019). At the same time, the sophistication of hydrological analyses and models has been developing rapidly, often driven more by the availability of computational power and geographical data sets than any real increases in understanding of hydrologicalprocesses. This sophistication has created an illusion of real progress, but a case can be made that we are still rather muddling along, limited by the significant uncertainties in hydrological observations, knowledge of catchment characteristics, and related gaps in conceptual understanding, particularly of the subsurface. These knowledge gaps are illustrated by the fact that for many catchments, we cannot close the water balance without significant uncertainty (e.g.,

Moving beyond the catchment scale: Value and opportunities in large‐scale hydrology to understand our changing world

“…It is self‐evident (but cannot be overstated) that for a large‐scale perspective to hydrology, large‐scale data are needed for climate, land and hydrology variables, at a satisfactory resolution and extent and with comprehensive metadata. However, challenges remain in terms of the maintenance of hydrological data networks (Beven et al, ; Hannah et al, ; Ruhi, Messager, & Olden, ). A particular problem for hydrological variables is the “spatial footprint” of the area represented by an individual river gauge (in comparison to a point temperature or precipitation measurement) in determining time series homogeneity.…”

Citizens AND HYdrology (CANDHY): conceptualizing a transdisciplinary framework for citizen science addressing hydrological challenges

“…However, hydrology remains a data-scarce science, with many important variables (such as river flow, water quality, sediments, rainfall/snow depths, and groundwater levels) being severely under-sampled. This has decisive implications for our ability to assess and manage water resources, deal with challenges and forecast events (Beven et al, 2020, Cudennec et al, 2020, Pecora and Lins, 2020. Also, variables that are less used in practical applications can be crucial for the scientific understanding of complex processes and systems.…”

A dense network of cosmic-ray neutron sensors for soil moisture observation in a highly instrumented pre-Alpine headwater catchment in Germany

“…But while soil moisture is a much desired quantity in research and applications, its observation remains a challenge (Beven et al, 2020;Ochsner et al, 2013). Numerous techniques exist for measuring soil moisture at specific points in space, including vertical profiles, such as time domain reflectometry (TDR), frequency domain reflectometry (FDR), and soil sampling.…”

“…The relation between sensor counts and permittivity was individually calibrated for each device prior to installation according to Bogena et al 2017and Qu et al (2013). We used the dielectric mixing model of Birchak et al (1974) to link with the volumetric water content (θ )…”