IntroductionThe time water spends travelling subsurface through a catchment to the stream network (i.e. the catchment water transit time) fundamentally describes the storage, flow pathway heterogeneity and sources of water in a catchment. The distribution of transit times reflects how catchments retain and release water and solutes that in turn set biogeochemical conditions and affect contamination release or persistence. Thus, quantifying the transit time distribution provides an important constraint on biogeochemical processes and catchment sensitivity to anthropogenic inputs, contamination and land-use change. Although the assumptions and limitations of past and present transit time modelling approaches have been recently reviewed (McGuire and McDonnell, 2006), there remain many fundamental research challenges for understanding how transit time can be used to quantify catchment flow processes and aid in the development and testing of rainfall-runoff models. In this Commentary study, we summarize what we think are the open research questions in transit time research. These thoughts come from a 3-day workshop in January 2009 at the International Atomic Energy Agency in Vienna. We attempt to lay out a roadmap for this work for the hydrological community over the next 10 years. We do this by first defining what we mean (qualitatively and quantitatively) by transit time and then organize our vision around needs in transit time theory, needs in field studies of transit time and needs in rainfall-runoff modelling. Our goal in presenting this material is to encourage widespread use of transit time information in process studies to provide new insights to catchment function and to inform the structural development and testing of hydrologic models. What is transit time?The terminology on time concepts associated with water movement through catchments can be confusing and a barrier to its use. Water transit time through the system can be defined as:where t w is the elapsed time from the input of water through a system input boundary at time t in to the output of that water through a system output boundary at time t out . In a catchment, the land surface and the catchment outlet may be considered as the main input and output boundaries for most of the water flow through the catchment (Figure 1). However, the land surface constitutes both a water input boundary and an output boundary for water that experiences evapotranspiration (ET). Considering also the subsurface depth dimension of a catchment, groundwater flow into and out of the catchment system is determined by prevailing groundwater divides and hydraulic gradients, which may vary in time and space and differ from the topographically determined catchment boundaries. For general transient flow conditions, water may thus flow into and out from the catchment system through different boundaries that are not all fixed in time and space. By analogy to the water transit time definition and quantification in Equation (1), one can similarly define and quantify the mean age o...

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Although not matching the formal definition of the predictive probability distribution, meteorological and hydrological ensembles have been frequently interpreted and directly used to assess flood-forecasting predictive uncertainty. With the objective of correctly assessing the predictive probability of floods, this paper introduces ways of taking into account the measures of uncertainty provided in the form of ensemble forecasts by modifying a number of well-established uncertainty postprocessors, such as Bayesian Model Averaging and Model Conditional Processor. The uncertainty postprocessors were developed on the assumption that the future unknown quantity (predictand) is uncertain while model forecasts (predictors) are given, which imply that they are perfectly known. With this in mind, we propose to relax this assumption by considering ensemble predictions, in analogy to measurement errors, as expressions of errors in model predictions to be integrated in the postprocessors coefficients estimation process. The analyses of the methodologies proposed in this work are conducted on a real case study based on meteorological ensemble predictions for the Po River at Pontelagoscuro in Italy. After showing how improper can be the direct use of ensemble predictions to describe the predictive probability distribution, results from the modified postprocessors are compared and discussed. BIONDI AND TODINI 9860Water Resources Research

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