Hai He

Droughts can lead to drought disasters, which have become one of the main natural disasters affecting the development of social economies and ecological environments around the world. Timely and effective drought process simulation and prediction based on atmospheric–hydrological coupling is crucial for drought prevention and resistance. The initial condition (IC) is one source causing uncertainty in drought process simulation and prediction, and the impacts are different with drought duration, basin size and region. Therefore, a quantitative method that measures the uncertainty caused by ICs on the drought process simulation in different climate zones is proposed in this study. In this study, the VIC (Variable Infiltration Capacity) model at a resolution of 0.05°, which is proven as an ideal model to reflect drought processes, was used as the hydrological model to obtain soil moisture. By analyzing the Soil Moisture Anomaly Percentage Index (SMAPI) error characteristics that were simulated based on different ICs, an uncertainty index for drought process simulation was constructed in different climate zones. It was found that with the development of a drought process, the uncertainty converges, and it decreases to within 10% after a drought occurs for 5 to 6 months, while it is less than 5% in the particular basin in a humid region. In climate transition zones, both the uncertainty and its decrease rate are greater than those in humid regions. Climate characteristics, as well as soil types and vegetation types, are fundamental factors that cause differences in drought process simulation and uncertainty changes. The precipitation and temperature distribution more obviously vary spatially and temporally, a greater uncertainty is caused by ICs. This quantitative method reveals the impact of ICs on drought process simulation in different climate regions and provides a basis for the further improvement of drought simulation and prediction based on atmospheric–hydrological coupling.

Accurate and timely precipitation forecasts are a key factor for improving hydrological forecasts. Therefore, it is fundamental to evaluate the skill of Numerical Weather Prediction (NWP) for precipitation forecasting. In this study, the Global Environmental Multi-scale (GEM) model, which is widely used around Canada, was chosen as the high-resolution medium-term prediction model. Based on the forecast precipitation with the resolution of 0.24° and taking regional differences into consideration, the study explored the forecasting skill of GEM in nine drought sub-regions around China. Spatially, GEM performs better in East and South China than in the inland areas. Temporally, the model is able to produce more precise precipitation during flood periods (summer and autumn) compared with the non-flood season (winter and spring). The forecasting skill variability differs with regions, lead time and season. For different precipitation categories, GEM for trace rainfall and little rainfall performs much better than moderate rainfall and above. Overall, compared with other prediction systems, GEM is applicable for the 0–96 h forecast, especially for the East and South China in flood season, but improvement for the prediction of heavy and storm rainfall and for the inland areas should be focused on as well.

Based on Coupled Model Intercomparison Project Phase 5 (CMIP5) dataset and a variable infiltration capacity (VIC) hydrological model, this study assesses the possible influence of climate change in the upstream region of the Yangtze River on droughts in the future 30 years. Long-term daily soil moisture content were simulated by VIC model at a 50 km × 50 km resolution from 1951 to 2013. Regional historical drought events were then recognized based on soil moisture anomaly percentage index and validated with field data. Five relatively independent representative global circulation models were selected and the outputs of them were downscaled temporally and spatially as the inputs of VIC model for daily soil moisture content simulations both in the period of 1971–2000 for the present-day climate and in the period of 2021–2050 for the future. The results show that the projected annual mean temperature is likely to increase from 1.4 °C to 1.8 °C. The projected change in mean annual precipitation could be increased slightly by 0.6% to 1.3%, but the trend of precipitation change in summer and autumn might be opposite of that. Comparing the drought characteristics values recognized in 1971–2000, seven to eight additional regional drought events are likely to happen in 2021–2050. Drought duration and drought intensity are also likely to extend for 18 d to 25 d and increase by 1.2% to 6.2%, respectively. But, drought area could decrease slightly by 1.3% to 2.7% on average. These changes in drought characteristics values suggest that regional drought could become more severely prolonged and frequent in future.

In recent years, the Weather Research and Forecast (WRF) model has been utilized to generate quantitative precipitation forecasts with higher spatial and temporal resolutions. However, factors including horizontal resolution, domain size, and the physical parameterization scheme have a strong impact on the dynamic downscaling ability of the WRF model. In this study, the influence of these factors has been analyzed in precipitation forecasting for the Xijiang Basin, southern China—a region with complex topography. The results indicate that higher horizontal resolutions always result in higher Critical Success Indexes (CSI), but higher biases as well. Meanwhile, the precipitation forecast skills are also influenced by the combination of microphysics parameterization scheme and cumulus convective parameterization scheme. On the basis of these results, an optimized configuration of the WRF model is built in which the horizontal resolution is 10 km, the microphysics parameterization is the Lin scheme, and the cumulus convective parameterization is the Betts–Miller–Janjic scheme. This configuration is then evaluated by simulating the daily weather during the 2013–2014 flood season. The high Critical Success Index scores and low biases at various thresholds and lead times confirm the high accuracy of the optimized WRF model configuration for Xijiang Basin. However, the performance of the WRF model varies from different sub-basins due to the complexity of the mesoscale convective system (MCS) over this region.