Using sub-daily precipitation for grid-based hydrological modelling across Great Britain: Assessing model performance and comparing flood impacts under climate change
Using sub-daily precipitation for grid-based hydrological modelling across Great Britain: Assessing model performance and comparing flood impacts under climate change
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使用次日降水量进行英国基于网格的水文建模：评估模型性能并比较气候变化下的洪水影响
DOI：
10.1016/j.ejrh.2023.101588
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发表时间：
2023
期刊：
影响因子：
4.6
通讯作者：
Kay A
中科院分区：
文献类型：
--
作者：
Kay A
国家尺度的基于网格的水文模型通常以精细的空间和时间分辨率运行，但驱动数据往往无法在所需的分辨率。在这里，最近的观测为基础的每小时1公里网格化降水数据集应用1公里的水文模型，以模拟每日平均河流流量。性能进行比较，使用同等分解和配置文件分解的日常数据，大量的集水区。然后使用高分辨率允许对流气候模式（CPM）的逐时和逐日降水量驱动水文模型进行基线计算（1980-2000年）和未来（2060-2080）时期，以调查潜在峰值流量变化的差异。新的水文见解平均而言，基于观测的每小时数据的使用对于高流量和峰值流量偏差提供了比相等分解的每日数据明显的改进，对于平均流量和平均流量偏差提供了小的改进，但对于低流量几乎没有差别。在响应较快的集水区，性能通常会提高更多;在某些集水区，性能会下降。使用剖面分解的每日数据提供了小的平均流量偏差改善和一些峰值流量偏差改善，但其他因素降低。平均而言，每小时CPM降水量的峰值流量的未来变化仅略大于同等分解的每日数据。未来的工作将着眼于每小时平均流量的模拟。
Study regionGreat Britain.Study focusNational-scale grid-based hydrological models are usually run at fine spatial and temporal resolutions, but driving data are often not available at the required resolutions. Here, a recent observation-based hourly 1 km gridded precipitation dataset is applied with a 1 km hydrological model to simulate daily mean river flows. Performance is compared to use of equally-disaggregated and profile-disaggregated daily data, for a large number of catchments. Hourly and daily precipitation from a high-resolution convection-permitting climate model (CPM) are then used to drive the hydrological model for baseline (1980–2000) and future (2060–2080) periods, to investigate differences in potential peak flow changes.New hydrological insightsOn average, use of observation-based hourly data provides a clear improvement over equally-disaggregated daily data for high flows and peak flow bias, a small improvement for average flows and mean flow bias, but little difference for low flows. Performance in faster-responding catchments typically improves more; performance in some catchments degrades. Use of profile-disaggregated daily data provides the small mean flow bias improvement and some peak flow bias improvement, but other factors degrade. On average, future changes in peak flows from hourly CPM precipitation are only slightly larger than from equally-disaggregated daily data. Future work will look at simulation of hourly mean flows.
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