Abstract

• Precipitation data from an X-band radar in northern Scotland are assessed. • Assessment against river flow using a distributed hydrological model brings fresh insights. • Lowest usable beam elevation is the main limit on performance in this mountainous area. • Use of dual-polarisation estimators improves performance during summer months. • The X-band radar is suited for filling gaps in the existing C-band radar network. A set of Quantitative Precipitation Estimates (QPEs) from a dual-polarisation X-band radar observation campaign in a mountainous area of Northern Scotland is assessed with reference to observed river flows as well as being compared to estimates from the UK C-band radar and raingauge networks. Employing estimation methods of varying complexity, the X-band QPEs are trialled as alternative inputs to Grid-to-Grid (G2G), a distributed hydrological model, to produce simulated river flows for comparison with observations. This hydrological assessment complements and extends a previous meteorological assessment that used point raingauge data only. Precipitation estimates for two periods over the observation campaign in 2016 (March to April and June to August) are assessed. During the second period, increased incorporation of dual-polarisation variables into the radar processing chain is found to be of considerable benefit, whereas during the first period the low height of the melting layer often restricts their use. As a result of the complex topography in Northern Scotland, the Lowest Usable Elevation (LUE) of the X-band radar observations is found to be a stronger indicator of the hydrological model performance than range from the radar. For catchments with an LUE of less than 3 km, the best X-band QPE typically performs better for modelling river flow than using an estimate from the UK C-band radar network. The hydrological assessment framework used here brings fresh insights into the performance of the different QPEs, as well as providing a stimulus for targeted improvements to dual-polarisation radar-based QPEs that have wider relevance beyond the case study situation.

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