Explore the Accuracy of the Pedestrian Level Temperature Estimated by the Combination of LCZ with WRF Urban Canopy Model through the Microclimate Measurement Network

Due to the urban heat island effect becoming more evident in the cities in Taiwan, the urban climate has become an essential factor in urban development. Taiwan is located on the border of tropical and subtropical climate zones, the climate condition is hot and humid, and the city shows high-density development. The dense urban development has increased the heat storage capacity of the ground and buildings. However, if only the climate stations set by the Central Meteorological Bureau to observe the climate data are applied, the predicted results differ from the actual urban climate conditions due to the small number of these stations and the too far distance between them. Therefore, this study employs the local climate zone (LCZ), which can classify the land features by considering both land use and land cover, and can be freely generated from satellite images. The LCZ classification method can view the type of the city through the height and density of obstacles. This study also combines the urban canopy model (UCM) of the mesoscale climate prediction model and weather research and forecasts (WRF). This approach can calculate vertical and horizontal planes of the city, such as building volume, road width, the influence of streets and roofs, roof heat capacity, building wall heat capacity, etc., to predict the climatic conditions in different lands in the study area. Simultaneously, to understand the actual distribution of urban climate more accurately, this study used the microclimate measurement network built in the research area to produce pedestrian-level temperature distribution and compared the estimated results with the actual measured values for urban climate assessment. This study can understand the cause of urban heat islands and assist urban planners more appropriately formulate heat island mitigation strategies in different regions.

Near-real-time flood forecasting for an urban coastal catchment: An approach in combination of numerical weather and 3-way coupled hydrodynamic flood modelling

<p>The rapid increase in heavy precipitation flooding events highlights the need for efficient flood forecasting techniques to facilitate flood hydrological research and effective flood management by civic bodies. The current study aims to develop a near-real-time flood forecasting framework by integrating a 3-way coupled hydrodynamic flood model framework with numerical weather modelling based rainfall forecasts. The proposed framework has been demonstrated over Mumbai city in India, which is subjected to flooding every year during the monsoon months. A fine-resolution atmospheric simulation with the Weather Research and Forecasting (WRF) model has been performed for rainfall forecasts, which serve as an input to the flood model. To access the impact of urbanization on rainfall extremes, three scenarios are considered in the WRF simulations, i.e., WRF model: (1) without Urban canopy model (WRF-NoUCM), (2) coupled with a single-layer Urban canopy model (WRF-SUCM), and (3) coupled with a multi-layer Urban canopy model (WRF-MUCM). Further, a three-way coupled flood model has been developed where the MIKE 11 model (streamflow) with the drainage network (stormwater drains) and the MIKE 21 model (overland flow) have been considered for flood inundation and subsequently hazard mapping. In addition, the tidal elevation is provided along the coastline in the model setup. The flood maps developed by three WRF forecasted rainfall scenarios have been compared with that of the maps developed with observed rainfall. The extent to which the scenarios have been able to imitate the pattern and extent of flooding generated by observed rainfall has been investigated to decide the best scenario to be adapted in the comprehensive flood forecasting network. This state-of-art flood forecasting approach may be implemented in other flood-prone coastal regions as a major non-structural flood management strategy to reduce flood risk and vulnerabilities for the people dwelling in those regions.</p>