A terminology and typology of uncertainty is presented together with a framework for the modelling process, its interaction with the broader water management process and the role of uncertainty at different stages in the modelling processes. Brief reviews have been made of 14 different (partly complementary) methods commonly used in uncertainty assessment and characterisation: data uncertainty engine (DUE), error propagation equations, expert elicitation, extended peer review, inverse modelling (parameter estimation), inverse modelling (predictive uncertainty), Monte Carlo analysis, multiple model simulation, NUSAP, quality assurance, scenario analysis, sensitivity analysis, stakeholder involvement and uncertainty matrix. The applicability of these methods has been mapped according to purpose of application, stage of the modelling process and source and type of uncertainty addressed. It is concluded that uncertainty assessment is not just something to be added after the completion of the modelling work. Instead uncertainty should be seen as a red thread throughout the modelling study starting from the very beginning, where the identification and characterisation of all uncertainty sources should be performed jointly by the modeller, the water manager and the stakeholders.

No abstract

No abstract

Uncertainties in model structures have been recognised often to be the main source of uncertainty in predictive model simulations. Despite this knowledge, uncertainty studies are traditionally limited to a single deterministic model and the uncertainty addressed by a parameter uncertainty study. The extent to which a parameter uncertainty study may encompass model structure errors in a groundwater model is studied in a case study. Three groundwater models were constructed on the basis of three different hydrogeological interpretations. Each of the models was calibrated inversely against groundwater heads and streamflows. A parameter uncertainty analysis was carried out for each of the three conceptual models by Monte Carlo simulations. A comparison of the predictive uncertainties for the three conceptual models showed large differences between the uncertainty intervals. Most discrepancies were observed for data types not used in the model calibration. Thus uncertainties in the conceptual models become of increasing importance when predictive simulations consider data types that are extrapolates from the data types used for calibration.

A physically based, coupled and distributed hydrologic model has been set up for the Ringkøbing Fjord catchment, Denmark. This transient model, built with the MIKE SHE/ MIKE 11 code, comprises all major components of the terrestrial water cycle, including a three-dimensional fi nite-diff erence groundwater model. The dynamic coupling of the hydrologic processes secures physically sound feedback and makes the model an ideal tool for evalua ng the overall water balance and quan fying poten al water balance issues. Historically, failure to obtain water balance closure has been a persistent and much debated problem in Denmark, presumably arising mainly from uncertain es in precipita on, actual evapotranspira on, and groundwater fl ow to the sea. In this study, the water balance issues were addressed within the modeling framework through analysis of diff erent rain gauge catch correc ons and poten al evapotranspira on input. The analysis focused on the eff ect of diff erent rain gauge catch correc ons on the model performance, the op mized parameter sets, and state variables not included in the model calibra on. The results suggest that water balance problems can be reduced by using a dynamic rain gauge catch correc on based on daily wind speed and temperature fi elds. The model op miza on and performance evalua on revealed, however, that several parameter sets gave similar performance compared with the observed groundwater head and river discharge data but s ll resulted in signifi cant diff erences with respect to internal water fl uxes such as evapotranspira on, groundwater recharge, and stream fl ow components. New observa onal data are needed to constrain the model further and thus reduce the water balance uncertain es convincingly.Abbrevia ons: ET, evapotranspira on; HOBE, Hydrological Observatory and Exploratorium; LAI, leaf area index; SVAT, soil-vegeta on-atmosphere transfer.A correct quan fi ca on of the water balance and its individual components (precipitation, evapotranspiration, river runoff , subsurface runoff , and storage change) is vital for water resources management (Wisser et al., 2010). Quantifi cation of these components relies on the availability of accurate hydrologic data and on the application of reliable hydrologic models. Accurate precipitation measurements are of particular importance, especially in the northern latitudes, where uncertainties are large due to undercatch of solid precipitation (Adam and Lettenmaier, 2003;Tian et al., 2007;Yang et al., 2005). Recent studies have documented the large uncertainty in precipitation estimated at global (Fekete et al., 2004;Legates, 1995) and continental scales (Tian et al., 2007;Walsh et al., 1998). Th ere are, however, few detailed modeling studies of the eff ect of precipitation accuracy and catch correction at local to regional scales.Problems are oft en encountered when modeling the water balance at the catchment scale (Arnold et al., 2000;Henriksen et al., 2003). Hydrologic models are typically calibrated against existing fi eld data, of wh...