WRF-Hydro模式在水文模拟与预报应用中的研究进展

介绍了WRF-Hydro模式的应用进展,探讨和总结了WRF-Hydro在水文模拟与预报中的应用概况、主要参数的敏感性及在应用中WRF/WRF-Hydro双耦模式与WRF独立运行模式的异同。结果表明,WRFHydro能够对水循环关键要素如降水、土壤湿度、产流等实现在耦合、非耦合情况下的模拟和预报,是具有物理机制的数值预报工具,能够进一步改进水文气象要素的模拟和预报能力;在应用和分析其水文性能前,应对WRF-Hydro的主要参数进行敏感性试验和率定。通常,入渗系数被认为是对产流量影响最敏感的参数,曼宁糙率是对流量过程线影响最大的参数;与WRF模式相比,WRF/WRF-Hydro双耦模式能够在不同空间网格上实现对大气、陆面和水文过程的多尺度模拟,能够更好地模拟降雨和陆面水循环过程;WRF-Hydro与大气模式的耦合将成为未来大气水文耦合研究的发展趋势,但现在双向耦合的研究尚未完全成熟,还需在更多不同气候区的不同类型流域、针对不同降雨产流过程进行进一步的验证,同时开展参数优化技术研究,以提高水文气象要素的模拟预报能力。

Advances of WRF-Hydro and Its Application in Hydrological Simulation and Forecasting

This study introduces the advances and the application of the WRF-Hydro modeling system. The application progress of WRF-Hydro in the area of hydrological simulation and forecasting is discussed together with the sensitivity of the main parameters, and the differences between the fully coupled WRF/WRF-Hydro and the WRF model. Related studies show that the WRF-Hydro can be used for coupled and uncoupled simulation and prediction of the major water cycle components, such as precipitation, soil moisture and runoff generation. The WRF-Hydro development can improve the prediction skill of hydrometeorological forecasts using science-based numerical prediction tools. Sensitivity tests and calibration of the key parameters should be carried out before applying and analyzing the performance of WRF-Hydro. Generally, the infiltration factor (REFKDT) is the most sensitive parameter to the total water volume, and Manning roughness (MannN) is the most crucial parameter to the shape of the hydrograph. Compared with the WRF model, the fully coupled WRF-Hydro/WRF system permits multi-scale functionality to the modeling of atmospheric, land surface and hydrological processes on different spatial grids, which is more conducive to improving rainfall and better simulating the land-surface water cycle. The fully coupled WRF-Hydro with a numerical weather prediction model will become the trend of the atmospheric-hydrologic coupling studies in the future. The conclusions need to be further validated in more watersheds with typical climatic conditions and various rainfall-runoff processes. At the same time, the optimization of the main sensitive parameters should be further studied in order to improve the accuracy of the model.