Benchmarking observational uncertainties for hydrology: rainfall,river discharge and water quality |
| |
| Authors: | Hilary McMillan Tobias Krueger Jim Freer |
| |
| Institution: | 1. National Institute of Water and Atmospheric Research, , Christchurch, New Zealand;2. School of Environmental Sciences, University of East Anglia, , Norwich, UK;3. School of Geographical Sciences, University of Bristol, , Bristol, UK |
| |
| 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. |
| |
| Keywords: | observational uncertainty data uncertainty hydrology error distributions hydrometric data water quality data |
|
|
