Drought risk analysis is essential for regional water resource management. In this study, the probabilistic relationship between precipitation and meteorological drought in Beijing, China, was calculated under three different precipitation conditions (precipitation equal to, greater than, or less than a threshold) based on copulas. The Standardized Precipitation Evapotranspiration Index (SPEI) was calculated based on monthly total precipitation and monthly mean temperature data. The trends and variations in the SPEI were analysed using Hilbert-Huang Transform (HHT) and Mann-Kendall (MK) trend tests with a running approach. The results of the HHT and MK test indicated a significant decreasing trend in the SPEI. The copula-based conditional probability indicated that the probability of meteorological drought decreased as monthly precipitation increased and that 10 mm can be regarded as the threshold for triggering extreme drought. From a quantitative perspective, when R≤10 mm, the probabilities of moderate drought, severe drought, and extreme drought were 22.1%, 18%, and 13.6%, respectively. This conditional probability distribution not only revealed the occurrence of meteorological drought in Beijing but also provided a quantitative way to analyse the probability of drought under different precipitation conditions. Thus, the results provide a useful reference for future drought prediction.
A cruise survey was conducted in the Pearl River Estuary (PRE) in May 2014. In situ observations during this cruise reveal that at the surface in the central channel of the PRE there was a strong convergence of lateral velocity during an ebb tide and a divergence during a flood tide. The ebb tide convergence was also observed in satellite synthetic aperture radar imagery. The Finite-Volume Coastal Ocean Model was executed in the PRE domain to accurately simulate the convergence and divergence of the cross-estuary velocity during ebb and flood tides, respectively. Numerical experiments in an idealized estuary domain are implemented with three distinct forcing scenarios: tide, river discharge, and a combination of both. Model results show that the cross-estuary momentum balance plays a significant role in the dynamics of the velocity convergence and divergence in the PRE. Additionally, the interaction between tide and river discharge enhances the surface convergence on ebb tide and generates the surface divergence on flood tide. The model results reveal that the along-estuary variation of channel depth and width is responsible for the tide-induced lateral velocity convergence and divergence in the channel, supported by the fact that when the channel depth and width are set to constant values in the model, the channel-trapped ebb convergence substantially weakens, and the flood divergence disappears. This study provides an indication for other wide estuaries.
To improve the forecasting performance in dynamically active coastal waters forced by winds, tides, and river discharges in a coupled estuary-shelf model off Hong Kong, a multivariable data assimilation (DA) system using the ensemble optimal interpolation (EnOI) method has been developed and implemented. The system assimilates the Conductivity-Temperature-Depth (CTD) profilers, time-series buoy measurement, and remote sensing sea surface temperature (SST) data into a high-resolution estuary-shelf ocean model around Hong Kong. We found that the time window selection associated with the local dynamics and the number of observation samples are two key factors in improving assimilation in the unique estuary-shelf system. DA with a varied assimilation time window based on the intra-tidal variation in the local dynamics can reduce the errors in the estimation of the innovation vector caused by the model-observation mismatch at the analysis time, and improve greatly simulation in both the estuary and coastal regions. Statistically, the overall root-mean-square error (RMSE) between the DA forecasts and not-yet-assimilated observations for temperature and salinity have been reduced by 33.0% and 31.9% in the experiment period, respectively. By assimilating higher resolution remote sensing SST data instead of lower resolution satellite SST, the RMSE of SST is improved by ~18%. Besides, by assimilating real-time buoy mooring data, the model bias can be continuously corrected both around the buoy location and beyond. The assimilation of the combined buoy, CTD, and SST data can provide an overall improvement of the simulated three-dimensional solution. A dynamics-oriented assimilation scheme is essential for the improvement of model forecasting in the estuary-shelf system under multiple forcings.
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