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.

参考观测到的河流流量以及与英国 C 波段雷达和雨量计网络。采用不同复杂度的估计方法，X 波段 QPE 被试用作为分布式水文模型 Grid-to-Grid (G2G) 的替代输入，以生成模拟河流流量以与观测结果进行比较。该水文评估补充并扩展了之前仅使用点雨量计数据的气象评估。评估了 2016 年观测活动的两个时期（3 月至 4 月和 6 月至 8 月）的降水估计值。在第二个时期，在雷达处理链中增加双极化变量的结合被发现具有相当大的好处，而在第一阶段，熔化层的低高度通常限制了它们的使用。由于苏格兰北部地形复杂，发现 X 波段雷达观测的最低可用高程 (LUE) 比雷达测距更能反映水文模型的性能。对于 LUE 小于 3 公里的集水区，最好的 X 波段 QPE 在模拟河流流量方面通常比使用英国 C 波段雷达网络的估计值更好。这里使用的水文评估框架为不同 QPE 的表现带来了新的见解，