Benchmarking observational uncertainties for hydrology: rainfall, river discharge and water quality

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.

Sensitivity Analysis of Environmental Models: A Systematic Review with Practical Workflow

“…Approaches to quantify data uncertainty vary depending on the type of variable under study and are gaining increasing consideration in the environmental modelling community. For an example of meteorological and water quality and quantity variables and their uncertainties see for instance McMillan et al (2012). When suitable data are either unavailable or sparse, ranges or probability distributions can be elicited from experts.…”

Hydraulic–hydrologic model for water resources management of the Zambezi basin

“…Uncertainty of river discharge simulations comes probably from errors in the rating curve estimations (Di Baldassarre & Montanari 2009), individual measurements of discharge, which have uncertainties in the range of 2-19% using velocity-area methods (McMillan et al 2012) and data reporting and handling. In the case of the Zambezi River, the large flow range and the variable channel geometry in the floodplains results in low reliability of the discharge measurements.…”

Quantile hydrologic model selection and model structure deficiency assessment: 1. Theory

“…As is shown later in this section, even a complex likelihood function is unable to replicate it. No noise is added to the data for two reasons: (1) the added noise can only represent measurement errors since the structure error has already been represented and the quantile model selection does not distinguish between structure and measurement errors (though it can done based on a priori specification of measurement errors based on benchmarking studies (such as McMillan et al [2012]) and (2) the induced error structure is complex enough, additional complexity by adding noise to it is a relative distraction.…”