Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/928908
- There are no hydrological monsters, just models and observations with large uncertainties!
- The University of Newcastle. Faculty of Engineering & Built Environment, School of Engineering
- Catchments that do not behave in the way the hydrologist expects, expose the frailties of hydrological science, particularly its unduly simplistic treatment of input and model uncertainty. A conceptual rainfall–runoff model represents a highly simplified hypothesis of the transformation of rainfall into runoff. Sub-grid variability and mis-specification of processes introduce an irreducible model error, about which little is currently known. In addition, hydrological observation systems are far from perfect, with the principal catchment forcing (rainfall) often subject to large sampling errors. When ignored or treated simplistically, these errors develop into monsters that destroy our ability to model certain catchments. In this paper, these monsters are tackled using Bayesian Total Error Analysis, a framework that accounts for user-specified sources of error and yields quantitative insights into how prior knowledge of these uncertainties affects our ability to infer models and use them for predictive purposes. A case study involving a catchment with an apparent water balance anomaly (a hydrological monstrosity!) illustrates these concepts. It is found that, in the absence of additional information, the rainfall–runoff record is insufficient to explain this anomaly – it could be due to a large export of groundwater, systematic overestimation of catchment rainfall of the order of 40%, or a conspiracy of these factors. There is “no free lunch” in hydrology. The rainfall–runoff record on its own is insufficient to decompose the different sources of uncertainty affecting calibration, testing and prediction, and hydrological monstrosities will persist until additional independent knowledge of uncertainties is obtained.
- Hydrological Sciences Journal Vol. 55, Issue 6, p. 980-991
- Publisher Link
- Taylor & Francis
Bayesian total error analysis;
model structural error;
- Resource Type
- journal article