Radar-rain gauge merging methods have been widely used to produce high-quality precipitation with fine spatial resolution by combing the advantages of the rain gauge observation and the radar quantitative precipitation estimation (QPE). Different merging methods imply a specific choice on the treatment of radar and rain gauge data. In order to improve their applicability, significant studies have focused on evaluating the performances of the merging methods. In this study, a categorization of the radar-rain gauge merging methods was proposed as: (1) Radar bias adjustment category, (2) radar-rain gauge integration category, and (3) rain gauge interpolation category for a total of six commonly used merging methods, i.e., mean field bias (MFB), regression inverse distance weighting (RIDW), collocated co-kriging (CCok), fast Bayesian regression kriging (FBRK), regression kriging (RK), and kriging with external drift (KED). Eight different storm events were chosen from semi-humid and semi-arid areas of Northern China to test the performance of the six methods. Based on the leave-one-out cross validation (LOOCV), conclusions were obtained that the integration category always performs the best, the bias adjustment category performs the worst, and the interpolation category ranks between them. The quality of the merging products can be a function of the merging method that is affected by both the quality of radar QPE and the ability of the rain gauge to capture small-scale rainfall features. In order to further evaluate the applicability of the merging products, they were then used as the input to a rainfall-runoff model, the Hybrid-Hebei model, for flood forecasting. It is revealed that a higher quality of the merging products indicates a better agreement between the observed and the simulated runoff.

With the aim of improving the understanding of water exchanges in medium-scale catchments of northern China, the spatiotemporal characteristics of rainfall and several key water cycle elements e.g., soil moisture, evapotranspiration and generated runoff, were investigated using a fully coupled atmospheric-hydrologic modeling system by integrating the Weather Research and Forecasting model (WRF) and its terrestrial hydrologic component WRF-Hydro (referred to as the fully coupled WRF/WRF-Hydro). The stand-alone WRF model (referred to as WRF-only) is also used as a comparison with the fully coupled system, which was expected to produce more realistic simulations, especially rainfall, by allowing the redistribution of surface and subsurface water across the land surface. Six storm events were sorted by different spatial and temporal distribution types, and categorical and continuous indices were used to distinguish the applicability in space and time between WRF-only and the fully coupled WRF/WRF-Hydro. The temporal indices showed that the coupled WRF-Hydro could improve the time homogeneous precipitation, but for the time inhomogeneous precipitation, it might produce a larger false alarm than WRF-only, especially for the flash storm that occurred in July, 2012. The spatial indices showed a lower mean bias error in the coupled system, and presented an enhanced simulation of both space homogeneous and inhomogeneous storm events than WRF-only. In comparison with WRF-only, the fully coupled WRF/WRF-Hydro had a closer to the observations particularly in and around the storm centers. The redistributions fluctuation of spatial precipitation in the fully coupled system was highly correlated with soil moisture, and a low initial soil moisture could lead to a large spatial fluctuated range. Generally, the fully coupled system produced slightly less runoff than WRF-only, but more frequent infiltration and larger soil moisture. While terrestrial hydrologic elements differed with relatively small amounts in the average of the two catchments between WRF-only and the fully coupled WRF/WRF-Hydro, the spatial distribution of elements in the water cycle before and after coupling with WRF-Hydro was not consistent. The soil moisture, runoff and precipitation in the fully coupled system had a similar spatial trend, but evapotranspiration did not always display the same.

An attempt was made to evaluate the impact of assimilating Doppler Weather Radar (DWR) reflectivity together with Global Telecommunication System (GTS) data in the three-dimensional variational data assimilation (3DVAR) system of the Weather Research Forecast (WRF) model on rain storm prediction in Daqinghe basin of northern China. The aim of this study was to explore the potential effects of data assimilation frequency and to evaluate the outputs from different domain resolutions in improving the meso-scale NWP rainfall products. In this study, four numerical experiments (no assimilation, 1 and 6 h assimilation time interval with DWR and GTS at 1 km horizontal resolution, 6 h assimilation time interval with radar reflectivity, and GTS data at 3 km horizontal resolution) are carried out to evaluate the impact of data assimilation on prediction of convective rain storms. The results show that the assimilation of radar reflectivity and GTS data collectively enhanced the performance of the WRF-3DVAR system over the Beijing-Tianjin-Hebei region of northern China. It is indicated by the experimental results that the rapid update assimilation has a positive impact on the prediction of the location, tendency, and development of rain storms associated with the study area. In order to explore the influence of data assimilation in the outer domain on the output of the inner domain, the rainfall outputs of 3 and 1 km resolution are compared. The results show that the data assimilation in the outer domain has a positive effect on the output of the inner domain. Since the 3DVAR system is able to analyze certain small-scale and convective-scale features through the incorporation of radar observations, hourly assimilation time interval does not always significantly improve precipitation forecasts because of the inaccurate radar reflectivity observations. Therefore, before data assimilation, the validity of assimilation data should be judged as far as possible in advance, which can not only improve the prediction accuracy, but also improve the assimilation efficiency.

The data assimilation technique is an effective method for reducing initial condition errors in numerical weather prediction (NWP) models. This paper evaluated the potential of the weather research and forecasting (WRF) model and its three-dimensional data assimilation (3DVar) module in improving the accuracy of rainfall-runoff prediction through coupled atmospheric-hydrologic systems. The WRF model with the assimilation of radar reflectivity and conventional surface and upper-air observations provided the improved initial and boundary conditions for the hydrological process; subsequently, three atmospheric-hydrological systems with variable complexity were established by coupling WRF with a lumped, a grid-based Hebei model, and the WRF-Hydro modeling system. Four storm events with different spatial and temporal rainfall distribution from mountainous catchments of northern China were chosen as the study objects. The assimilation results showed a general improvement in the accuracy of rainfall accumulation, with low root mean square error and high correlation coefficients compared to the results without assimilation. The coupled atmospheric-hydrologic systems also provide more accurate flood forecasts, which depend upon the complexity of the coupled hydrological models. The grid-based Hebei system provided the most stable forecasts regardless of whether homogeneous or inhomogeneous rainfall was considered. Flood peaks before assimilation were underestimated more in the lumped Hebei model relative to the other coupling systems considered, and the model seems more applicable for homogeneous temporal and spatial events. WRF-Hydro did not exhibit desirable predictions of rapid flood process recession. This may reflect increasing infiltration due to the interaction of atmospheric and land surface hydrology at each integration, resulting in mismatched solutions for local runoff generation and confluence.

The influence of climate change and human activities on hydrological elements has increased along with increasing dependence on water resources. Therefore, quantitative attribution of hydrological elements has received wide attention. In this study, the double mass curve (DMC) is used to assess the abrupt change point of the hydrological data series, based on which the periods with/without large-scale human activities causing runoff attenuation are separated. The land use transition matrix is then employed to analyze the land use types at different historical stages, and the sensitivities of the runoff attenuation to different land use/cover change (LUCC) categories are quantified. A soil and water assessment tool (SWAT) model that considers the underlying surface is constructed with six designed scenarios of different climate and LUCC conditions. Taking three typical mountainous basins in North China as the study area, the quantitative contributions of climate change and human activities to the water resources are identified. The results of the study have brought enlightenment to the water resource sustainable utilization and management in North China, and the methodologies can be transferred to runoff attribution analysis in water shortage areas.