Evaluation of Flood Prediction Capability of the WRF-Hydro Model Based on Multiple Forcing Scenarios

The Weather Research and Forecasting (WRF)-Hydro model as a physical-based, fully-distributed, multi-parameterization modeling system easy to couple with numerical weather prediction model, has potential for operational flood forecasting in the small and medium catchments (SMCs). However, this model requires many input forcings, which makes it difficult to use it for the SMCs without adequate observed forcings. The Global Land Data Assimilation System (GLDAS), the WRF outputs and the ideal forcings generated by the WRF-Hydro model can provide all forcings required in the model for these SMCs. In this study, seven forcing scenarios were designed based on the products of GLDAS, WRF and ideal forcings, as well as the observed and merged rainfalls to assess the performance of the WRF-Hydro model for flood simulation. The model was applied to the Chenhe catchment, a typical SMC located in the Midwestern China. The flood prediction capability of the WRF-Hydro model was also compared to that of widely used Xinanjiang model. The results show that the three forcing scenarios, including the GLDAS forcings with observed rainfall, the WRF forcings with observed rainfall and GLDAS forcings with GLDAS-merged rainfall, are optimal input forcings for the WRF-Hydro model. Their mean root mean square errors (RMSE) are 0.18, 0.18 and 0.17 mm/h, respectively. The performance of the WRF-Hydro model driven by these three scenarios is generally comparable to that of the Xinanjiang model (RMSE = 0.17 mm/h).

Rainfall–Runoff Processes and Modelling in Regions Characterized by Deficiency in Soil Water Storage

The partial runoff is complicated in semi-arid and some semi-humid zones in terms of what the runoff generates in partial vertical positions. The partial runoff is highlighted by horizontal soil heterogeneity as well. How to identify the partial runoff and develop a variable threshold for runoff generation is a great difficulty and challenge. In this work, the partial runoff is identified by using a variable active runoff layer structure, and a variable soil water storage capacity is proposed to act as a threshold for runoff generation. A variable layer-based runoff model (VLRM) for simulating the complex partial runoff was therefore developed, using dual distribution curves for variable soil water storage capacity over basin. The VLRM is distinct in that the threshold for runoff generation is denoted by variable soil water storage capacity instead of infiltration capacity or constant soil water storage capacity. A series of flood events in two typical basins of North China are simulated by the model, and also by the Xinanjiang model. Results demonstrate that the new threshold performs well and the new model outperforms the Xinanjiang model. The approach improves current hydrological modelling for complex runoff in regions with large deficiencies in soil water storage.

An Improved Grid-Xinanjiang Model and Its Application in the Jinshajiang Basin, China

A modified form of the distributed Grid-Xinanjiang model (GXAJ) characterizing the infiltration excess and saturation excess runoff mechanisms coupled to a two-source potential evapotranspiration model (TSPE) was proposed to simulate the hydrological process and study the spatiotemporal pattern of the precipitation, evapotranspiration, and soil moisture in the Jinshajiang River basin. In the flow routing module, the flow is routed by the physically nonlinear Muskingum–Cunge method. The TSPE model can calculate the spatiotemporal variation of the potential canopy transpiration (CT), interception evaporation (IE), and potential soil evaporation (SE). Subsequently, the calculated potential evapotranspiration (PE) is coupled to the GXAJ model to calculate the water budget in each grid. An a priori parameter estimation was developed to obtain the spatially varied parameters from geographical data, including digital elevation model (DEM) data, soil data, vegetation data, and routing data. Hydrometeorological data were interpolated to 4750 grids with cell sizes of 10 × 10 km by the Thiessen Polygon method. The DEM data was used to extract the flow direction, river length, hillslope, and channel slopes and to adjust the altitude-related meteorological variables. The reprocessed Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) from the Beijing Normal University (BNU) dataset, which has a spatial resolution of 1 km × 1 km, was used to obtain the spatiotemporal variation in the LAI. The developed GXAJ model was applied to three sub-basins in the Jinshajiang River basin and was compared to the traditional GXAJ model. The developed GXAJ model satisfactorily reproduced the streamflow at each catchment outlet and matched the peak discharges better than the traditional GXAJ model for both the dry and wet seasons. The uneven distribution of the simulated mean annual evapotranspiration in the whole watershed was closely related to the vegetation types, ranging from 189.81 to 585.45 mm. Forest and woodland, shrubland, grassland, and cropland were shown to have mean annual evapotranspiration values of 485.6, 289.4, 275.9, and 392.3 mm, respectively. The ratios of the annual evapotranspiration to precipitation (E/P) of the forest, woodland, shrubland, grassland, and cropland were 54, 83, 53, and 48%, respectively.