Risk-based approaches have been increasingly accepted and operationalized in flood risk management during recent decades. For instance, commercial flood risk models are used by the insurance industry to assess potential losses, establish the pricing of policies and determine reinsurance needs. Despite considerable progress in the development of loss estimation tools since the 1980s, loss estimates still reflect high uncertainties and disparities that often lead to questioning their quality. This requires an assessment of the validity and robustness of loss models as it affects prioritization and investment decision in flood risk management as well as regulatory requirements and business decisions in the insurance industry. Hence, more effort is needed to quantify uncertainties and undertake validations. Due to a lack of detailed and reliable flood loss data, first order validations are difficult to accomplish, so that model comparisons in terms of benchmarking are essential. It is checked if the models are informed by existing data and knowledge and if the assumptions made in the models are aligned with the existing knowledge. When this alignment is confirmed through validation or benchmarking exercises, the user gains confidence in the models. Before these benchmarking exercises are feasible, however, a cohesive survey of existing knowledge needs to be undertaken. With that aim, this work presents a review of flood loss–or flood vulnerability–relationships collected from the public domain and some professional sources. Our survey analyses 61 sources consisting of publications or software packages, of which 47 are reviewed in detail. This exercise results in probably the most complete review of flood loss models to date containing nearly a thousand vulnerability functions. These functions are highly heterogeneous and only about half of the loss models are found to be accompanied by explicit validation at the time of their proposal. This paper exemplarily presents an approach for a quantitative comparison of disparate models via the reduction to the joint input variables of all models. Harmonization of models for benchmarking and comparison requires profound insight into the model structures, mechanisms and underlying assumptions. Possibilities and challenges are discussed that exist in model harmonization and the application of the inventory in a benchmarking framework.

A survey of the regional snow accumulation variability on Spitsbergen, Svalbard, was canied out during three field campaigns in May 1997. The survey was carried out along three trnnsects from west-to-east approximately at the following latitudes: 77"30', 78" and 78"50' degrees north. The altitudes span fi-om sea level to 1000 metres elevation. Snow depth was measured with two different ground-penetrating radar systems, PulsEKKO (450 MHz) and GSSI SIR System-2 (500 MHz), pulled behind sncw machines. Snow characteristics such as snow temperature, snow density and stratigraphy were measured in snow pits in nine areas, three along each transect. Our data suggest the following: (1) the accumulation-elevation gradients vary from 3 m d 1 0 0 m in the northeast to 237 mm/IOO minthe central-south with anaveragevalue of 104mm/100 m for all measurements: (2) snow accumulation was 38 to 49% higher at the eastern coast than at the western coast: (3) a clear minimum in accumulation (or continental climate) is seen for the central (inland) locations in the middle and noahern transects while no such minimum exists along the southern transect; (4) a south-to-north gradient produces 55% and 40% less snow accumulation at the northern locations compared to the southern locations at the western and eastern coasts, respectively. These drops in winter snow accumulation occur over a distance of less than 200 h.

This paper summarizes the most signifi cant snow-related research that has been conducted in Svalbard. Most of the research has been performed during the 1990s and includes investigations of snow distribution, snowmelt, snow pack characteristics, remote sensing of snow and biological studies where snow conditions play an important role. For example, studies have shown regional trends with about 50 % higher amounts of snow accumulation at the east coast of Spitsbergen compared to the west coast. Further, the accumulation rates are about twice as high in the south compared to the north. On average, the increase in accumulation with elevation is 97 mm water equivalents per 100 m increase in elevation. Several researchers reported melt rates, which are primarily driven by incoming shortwave radiation, in the range of 10-20 mm/day during spring. Maximum melt rates close to 70 mm/day have been measured. In addition to presenting an overview of research activities, we discuss new, unpublished results in areas where considerable progress is being made. These are i) modelling of snow distribution, ii) modelling of snowmelt runoff and iii) monitoring of snow coverage by satellite imagery. We also identify some weaknesses in current research activities. They are lacks of i) integration between various studies, ii) comparative studies with other Arctic regions, iii) applying local fi eld studies in models that can be used to study larger areas of Svalbard and, fi nally, iv) using satellite remote sensing data for operational monitoring purposes.

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End-of-winter snow accumulation has been measured over large areas on Spitsbergen, Svalbard, using Ground Penetrating Radar (GPR) in the years of 1997, 1998, and 1999. Measuring transects following different latitudes reveal west-to-east and south-to-north gradients of snow accumulation. Generally, the east coast receives over 40% more snow (in water equivalent) than the west coast. A continental effect with lower accumulation rates can be seen in central parts of Spitsbergen at middle and northern latitudes. In the southern part of Spitsbergen accumulation rates are approximately twice as high as in the north. Elevation gradients of snow accumulation vary considerably, from –9 mm per 100 metre increase of elevation in the north-east to 258 mm/100 m in the central south. In average, the accumulation increases with 97 mm/100 m. Finally, accumulation rates close to the summit of Austfonna ice cap range from about 200 mm to 800 mm, i.e., with a factor of 4, over a few tens of kilometres. The average snow accumulation for all glacier localities measured on Spitsbergen (i.e., Austfonna excluded) for all three years is 590 mm.