Hilary McMillan

TL;DR: The Panta Rhei Everything Flows project as mentioned in this paper is dedicated to research activities on change in hydrology and society, which aims to reach an improved interpretation of the processes governing the water cycle by focusing on their changing dynamics in connection with rapidly changing human systems.

Abstract: This review and commentary sets out the need for authoritative and concise information on the expected error distributions and magnitudes in observational data We discuss the necessary components of a benchmark of dominant data uncertainties and the recent developments in hydrology which increase the need for such guidance We initiate the creation of a catalogue of accessible information on characteristics of data uncertainty for the key hydrological variables of rainfall, river discharge and water quality (suspended solids, phosphorus and nitrogen) This includes demonstration of how uncertainties can be quantified, summarizing current knowledge and the standard quantitative results available In particular, synthesis of results from multiple studies allows conclusions to be drawn on factors which control the magnitude of data uncertainty and hence improves provision of prior guidance on those uncertainties Rainfall uncertainties were found to be driven by spatial scale, whereas river discharge uncertainty was dominated by flow condition and gauging method Water quality variables presented a more complex picture with many component errors For all variables, it was easy to find examples where relative error magnitudes exceeded 40% We consider how data uncertainties impact on the interpretation of catchment dynamics, model regionalization and model evaluation In closing the review, we make recommendations for future research priorities in quantifying data uncertainty and highlight the need for an improved ‘culture of engagement’ with observational uncertainties Copyright © 2012 John Wiley & Sons, Ltd

TL;DR: In this article, a methodology is presented to systematically assess and quantify the uncertainty in discharge measurements due to several combined sources, including errors in stage and velocity measurements during individual gaugings, assumptions regarding a particular form of stage-discharge relationship, extrapolation of the stage discharge relationship beyond the maximum gauging, and cross-section change due to vegetation growth and/or bed movement.

TL;DR: In this article, the authors used data from a dense gauge/radar network in the Mahurangi catchment (New Zealand) to directly evaluate the form of basic statistical rainfall error models.

TL;DR: In this paper, the Dynamic TOPMODEL is applied to the Maimai M8 catchment (3.8 ha), New Zealand using rainfall-runoff and water table information in model calibration.