Using a regional numerical weather prediction model for GNSS positioning over Brazil

The global navigation satellite system (GNSS) can provide centimeter positioning accuracy at low costs. However, in order to obtain the desired high accuracy, it is necessary to use high-quality atmospheric models. We focus on the troposphere, which is an important topic of research in Brazil where the tropospheric characteristics are unique, both spatially and temporally. There are dry regions, which lie mainly in the central part of the country. However, the most interesting area for the investigation of tropospheric models is the wet region which is located in the Amazon forest. This region substantially affects the variability of humidity over other regions of Brazil. It provides a large quantity of water vapor through the humidity convergence zone, especially for the southeast region. The interconnection and large fluxes of water vapor can generate serious deficiencies in tropospheric modeling. The CPTEC/INPE (Center for Weather Forecasting and Climate Studies/Brazilian Institute for Space Research) has been providing since July 2012 a numerical weather prediction (NWP) model for South America, known as Eta. It has yield excellent results in weather prediction but has not been used in GNSS positioning. This NWP model was evaluated in precise point positioning (PPP) and network-based positioning. Concerning PPP, the best positioning results were obtained for the station SAGA, located in Amazon region. Using the NWP model, the 3D RMS are less than 10 cm for all 24 h of data, whereas the values reach approximately 60 cm for the Hopfield model. For network-based positioning, the best results were obtained mainly when the tropospheric characteristics are critical, in which case an improvement of up to 7.2 % was obtained in 3D RMS using NWP models.     

Modeling disruptions causing domino effects in urban guided transport systems faced by flood hazards

Coastal cities are more vulnerable to floods due to the joint impact of rainfall and tide level. Quantitative risk assessment of disaster-causing factors is critical to urban flood management. This paper presents an integrated method to quantify the hazard degree of disaster-causing factors, rainfall and tide level, and to investigate the optimal management of flooding risk in different disaster-causing factor areas. First, an urban flood inundation model is used to simulate inundated extents in different drainage districts. Then, formulas are put forward to calculate the hazard degree of rainfall and tide level based on inundated extents in different combinations of rainfall and tide level. According to the hazard degree, the main disaster-causing factor could be identified in each drainage district. Finally, the optimal management of flooding risk in different disaster-causing factor areas is selected by disaster reduction analysis and cost–benefit analysis. Furthermore, the coastal city, Haikou of China, is taken as a case study. The results indicate that the hazard degree increases with the increasing distance between the drainage district and the Qiongzhou Strait or the Nandu River in the eastern of Haikou. Heavy rain is the main disaster-causing factor in inland areas, while high tide level is the main disaster-causing factor in island areas. For the area whose main disaster-causing factor is heavy rain, water storage projects could effectively reduce flooding. Meanwhile, pumps are economical choices for the area where tide level is the main disaster-causing factor. The results can provide reference for drainage planning in other coastal areas.