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Abstract. Advances in mesoscale numerical weather predication make it possible to provide rainfall forecasts along with many other data fields at increasingly higher spatial resolutions. It is currently possible to incorporate highresolution NWPs directly into flood forecasting systems in order to obtain an extended lead time. It is recognised, however, that direct application of rainfall outputs from the NWP model can contribute considerable uncertainty to the final river flow forecasts as the uncertainties inherent in the NWP are propagated into hydrological domains and can also be magnified by the scaling process. As the ensemble weather forecast has become operationally available, it is of particular interest to the hydrologist to investigate both the potential and implication of ensemble rainfall inputs to the hydrological modelling systems in terms of uncertainty propagation. In this paper, we employ a distributed hydrological model to analyse the performance of the ensemble flow forecasts based on the ensemble rainfall inputs from a shortrange high-resolution mesoscale weather model. The results show that: (1) The hydrological model driven by QPF can produce forecasts comparable with those from a raingaugedriven one; (2) The ensemble hydrological forecast is able to disseminate abundant information with regard to the nature of the weather system and the confidence of the forecast itself; and (3) the uncertainties as well as systematic biases are sometimes significant and, as such, extra effort needs to be made to improve the quality of such a system.

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The potential role of rural land use in mitigating flood risk and protecting water supplies continues to be of great interest to regulators and planners. The ability of hydrologists to quantify the impact of rural land use change on the water cycle is however limited and we are not able to provide consistently reliable evidence to support planning and policy decisions. This shortcoming stems mainly from lack of data, but also from lack of modelling methods and tools. Numerous research projects over the last few years have been attempting to address the underlying challenges. This paper describes these challenges, significant areas of progress and modelling innovations, and proposes priorities for further research. The paper is organised into five inter-related subtopics: (1) evidence-based modelling; (2) upscaling to maximise the use of process knowledge and physicsbased models; (3) representing hydrological connectivity in models; (4) uncertainty analysis; and (5) integrated catchment modelling for ecosystem service management. It is concluded that there is room for further advances in hydrological data analysis, sensitivity and uncertainty analysis methods and modelling frameworks, but progress will also depend on continuing and strengthened commitment to long-term monitoring and inter-disciplinarity in defining and delivering land use impacts research.

A very high-resolution X-band vertically pointing weather radar was deployed in the island of Jersey, UK, from February to May 2004, to study the variation of the vertical reflectivity of precipitation (VPR) in this region. Intercomparison studies were carried out with a C-band scanning weather radar operated by Jersey Met. Department. C-band radar rainfall estimations and raingauge measurements were well correlated at very short ranges (<10 km), but there are clear difficulties in the estimation of precipitation due to ground clutter, and anomalous propagation echoes as well as the variation of VPR. Average VPRs were obtained from the X-band radar that helped reduce the bright band enhancement in C-band scanning weather radar measurements. This paper presents the overall results obtained during this radar experiment. The experiment was one of a series designed to understand the VPR and design removal algorithms for bright band contamination of quantitative radar measurements of precipitation.

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Abstract. This study attempts to characterise the manner with which inherent error in radar rainfall estimates input influence the character of the stream flow simulation uncertainty in validated hydrological modelling. An artificial statistical error model described by Gaussian distribution was developed to generate realisations of possible combinations of normalised errors and normalised bias to reflect the identified radar error and temporal dependence. These realisations were embedded in the 5 km/15 min UK Nimrod radar rainfall data and used to generate ensembles of stream flow simulations using three different hydrological models with varying degrees of complexity, which consists of a fully distributed physically-based model MIKE SHE, a semidistributed, lumped model TOPMODEL and the unit hydrograph model PRTF. These models were built for this purpose and applied to the Upper Medway Catchment (220 km 2 ) in South-East England. The results show that the normalised bias of the radar rainfall estimates was enhanced in the simulated stream flow and also the dominate factor that had a significant impact on stream flow simulations. This preliminary radar-error-generation model could be developed more rigorously and comprehensively for the error characteristics of weather radars for quantitative measurement of rainfall.