Improved Streamflow Forecast in a Small-Medium Sized River Basin with Coupled WRF and WRF-Hydro: Effects of Radar Data Assimilation

Abstract

Accurate and long leading time flood forecasting is very important for flood disaster mitigation. It is an effective method to couple the Quantitative Precipitation Forecast (QPF) products provided by Numerical Weather Prediction (NWP) models to a distributed hydrological model with the goal of extending the leading time for flood forecasting. However, the QPF products contain a certain degree of uncertainty and would affect the accuracy of flood forecasting, especially in the mountainous regions. Radar data assimilation plays an important role in improving the quality of QPF and further improves flood forecasting. In this paper, radar data assimilation was applied in order to construct a high-resolution atmospheric-hydrological coupling model based on the WRF and WRF-Hydro models. Four experiments with conventional observational and radar data assimilation were conducted to evaluate the flood forecasting capability of this coupled model in a small-medium sized basin based on eight typical flood events. The results show that the flood forecast skills are highly QPF-dependent. The QPF from the WRF model is improved by assimilating radar data and further increasing the accuracy of flood forecasting, although both precipitation and flood are slightly over-forecasted. However, the improvements by assimilating conventional observational data are not obvious. In general, radar data assimilation can improve flood forecasting effectively in a small-medium sized basin based on the atmospheric-hydrological coupling model.

Assessment of the Coupled WRF-Hydro Model on Streamflow Simulations over the Source Region of the Yellow River

“…Hydrological model parameters serve as the reflections of the underlying surface characteristics, and variations in default parameters' applicability across different basins are noteworthy. In terms of the WRF-Hydro model, prior studies have categorized the sensitivity parameters governing streamflow processes into those controlling streamflow distribution and water volume and those regulating flood peaks and flood hydrographs [18]. A stepwise manual approach is adopted in calibrating the sensitivity parameters, following previous WRF-Hydro studies [38].…”

“…This model can operate as a standalone land surface hydrological model or can be coupled with an atmospheric model (such as WRF) to achieve a two-way feedback process between the atmosphere and land surface. Distinguishing itself from traditional land surface hydrological models, the WRF-Hydro model is explicitly designed to furnish continuous spatially gridded information on soil temperature and moisture, evapotranspiration, water and heat exchange fluxes, and runoff [17,18]. Notably, the WRF-Hydro model has demonstrated success in numerous coupled atmosphere-hydrology studies [19,20].…”

Meteorological - Hydrological Coupling Flood Forecast and Error Propagation Characteristics Based on Radar Data Assimilation in Small- to Medium Sized River Basin：A Case Study of Zhanghe River Basin in China

“…Combined with the rapid confluence of such watersheds, this lack of data means that forecast lead times are short and prediction accuracy is low. One approach to generating longer-term flood forecasts, and thereby improving flood control and disaster mitigation in small-and medium-sized rivers basins, is by introducing precipitation forecasts during the lead time and using high-precision meteorological and hydrological information to carry out flood forecasting and early warning work [2][3][4][5][6][7][8][9]. Therefore, increasing attention has been paid to flood forecasting based on the coupling of numerical prediction models with hydrological models.…”

“…However, it is still challenging to forecast rainfall and runoff using a coupled NWP and hydrological model for small-and medium-sized river basins. The primary reasons are as follows: (1) Due to the nonlinearity and complexity of the atmosphere, numerical weather forecasting is less precise for small to medium-sized river basins compared to larger basins [4,[25][26][27]. (2) With the increase in resolution, it is more difficult to match meteorological and hydrological coupling in scale [6][7][8].…”

“…They found that the assimilation of radar reflectivity data generated the most accurate results. Gu et al [4] conducted several case studies in the Qingjiang Basin in Hubei Province to examine the effects of conventional observation data and radar data assimilation on QPF and flood forecasting. Their results showed that radar data assimilation made the time distribution of the rainfall more accurate and the peak error smaller.…”