No abstract

No abstract

No abstract

No abstract

[1] An integrated hydrological modeling approach based on the discretization of a watershed into spatial units called representative elementary watersheds (REWs) has been introduced in earlier publications. Global balance laws were formulated at the spatial scale of a REW by integrating the point-scale conservation equations over particular control volumes. The choice of the control volumes is subject to the specific flow behavior to be described and is dependent on the hydrological characteristics of the spatial regions. These include the unsaturated subsurface flow, groundwater flow, and overland and channel flow. The REW-scale balance laws constitute generally valid governing equations for environmental flows encountered in hydrological systems and are applicable, in contrast to point-scale equations, independently from the chosen spatial and temporal scale of representation. This paper presents a first application of the REW approach to a complex hydrological system and shows how a theory that has so far only been used for synthetic cases is applicable to real-world situations. In this context the most challenging research effort remains the formulation of appropriate closure schemes for mass and momentum (and energy) fluxes at the REW scale. It is recognized that the schemes proposed for the closure of the fluxes in this paper are subject to limitations but are sufficient to expose the philosophy and the essential working principles. The advantages of the particular spatial discretization and the current limitations of the closure schemes are highlighted. In this context it is pointed out which way future research should go to consolidate the REW approach as a more general and scale-independent modeling philosophy for hydrological systems.

Recent advances in meteorological forecast skill now enable significantly improved estimates of precipitation quantity, timing and spatial distribution to be made up to 10 days ahead for model scales of 40 km in forecast mode. Here we outline a prototype methodology to downscale these precipitation estimates using regional Numerical Weather Prediction models to spatial scales appropriate to hydrological forecasting and then use these to drive high-resolution scale (1 or 5 km grid scale) water balance and rainfall-runoff models. The aim is to develop a European Flood Forecasting System (EFFS) and determine what flood forecast skill can be achieved for given basins, meteorological events and prediction products. The output from the system is a probabilistic assessment of n-day ahead discharge exceedence risk (where n < 10) for the whole of Europe at 5 km resolution which may then be updated as the forecast lead time reduces. At each stage the discharge estimates can be used to drive detailed (25-100 m resolution) hydraulic models to estimate the flood inundation which may potentially occur. Initial results are presented from a prototype version of the system used to perform a hindcast of the January 1995 flooding events in NW-Europe (Rhine, Meuse).

[1] Ensemble streamflow forecasts obtained by using hydrological models with ensemble weather products are becoming more frequent in operational flow forecasting. The uncertainty of the ensemble forecast needs to be assessed for these products to become useful in forecasting operations. A comprehensive framework for Bayesian revision has been recently developed and applied to operational flood forecasting with deterministic weather forecasts. The Bayesian revision yields a posterior density, conditional on all information available to the forecaster at the onset of a forecast run. This conditional density objectively quantifies the uncertainty. Here the Bayesian approach is generalized for use with ensemble weather predictions. An end-to-end application of a Bayesian postprocessor for ensemble streamflow forecasts in the river Rhine forecasting system is presented. A verification of the postprocessor shows good performance when compared in terms of the ranked probability skill score to non-Bayesian uncertainty assessment, such as ranking threshold exceedance probabilities for members of a streamflow ensemble prediction. In this context it is also addressed how the proposed Bayesian processor can serve in supporting rational decision making for flood warning under conditions of uncertainty.

In previous work by the authors a rigorous procedure for the derivation of global watershed-scale balance laws for mass, momentum, energy and entropy has been pursued. To complement these, a set of constitutive relations for the closure of the mass and momentum balance equations has also been derived, based on the exploitation of the second law of thermodynamics. In this paper these governing equations, including the constitutive relations, are rederived for the simpler case of the stream channel network of a natural watershed. The derived constitutive relationships for mass and force exchanges amongst channel reaches are physically consistent and thermodynamically admissible insofar as they respect physical constraints and keep the total entropy production of the system always positive. Next, the resulting system of coupled nonlinear ordinary di¬erential equations are simultaneously solved for a natural watershed under realistic conditions. The numerical model presented permits the estimation of space-time elds of average velocity, storage and discharge within all reaches of the network tree during run-o¬ events. The network response, as well as space-time elds of velocity and discharge, are computed for a number of rainfall events of di¬erent magnitude and di¬erent levels of network discretization. The nonlinearity of the response and the e¬ects of di¬erent discretizations of the network are analysed in terms of computational experiments.