Impact of a Detailed Urban Parameterization on Modeling the Urban Heat Island in Beijing Using TEB-RAMS

The Town Energy Budget (TEB) model coupled with the Regional Atmospheric Modeling System (RAMS) is applied to simulate the Urban Heat Island (UHI) phenomenon in the metropolitan area of Beijing. This new model with complex and detailed surface conditions, called TEB-RAMS, is from Colorado State University (CSU) and the ASTER division of Mission Research Corporation. The spatial-temporal distributions of daily mean 2 m air temperature are simulated by TEB-RAMS during the period from 0000 UTC 01 to 0000 UTC 02 July 2003 over the area of 116°E~116.8°E, 39.6°N~40.2°N in Beijing. The TEB-RAMS was run with four levels of two-way nested grids, and the finest grid is at 1 km grid increment. An Anthropogenic Heat (AH) source is introduced into TEB-RAMS. A comparison between the Land Ecosystem-Atmosphere Feedback model (LEAF) and the detailed TEB parameterization scheme is presented. The daily variations and spatial distribution of the 2 m air temperature agree well with the observations of the Beijing area. The daily mean 2 m air temperature simulated by TEB-RAMS with the AH source is 0.6 K higher than that without specifying TEB and AH over the metropolitan area of Beijing. The presence of urban underlying surfaces plays an important role in the UHI formation. The geometric morphology of an urban area characterized by road, roof, and wall also seems to have notable effects on the UHI intensity. Furthermore, the land-use dataset from USGS is replaced in the model by a new land-use map for the year 2010 which is produced by the Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS). The simulated regional mean 2 m air temperature is 0.68 K higher from 01 to 02 July 2003 with the new land cover map

Landsat-Based Land Cover Change in the Beijing-Tianjin-Tangshan Urban Agglomeration in 1990, 2000 and 2010

Rapid urbanization dramatically changes the local environment. A hybrid classification method is designed and applied to multi-temporal Landsat images and ancillary data to obtain land cover change datasets. A support vector machine (SVM) classifier is used to classify multi-temporal Landsat Enhanced Thematic Mapper Plus (ETM+) images that were collected in 2000 at the pixel level. These images are also segmented with the mean shift method. The impervious surface is refined based on a combination of the segmented objects and the SVM classification results. The changed areas in 1990 and 2010 are determined by comparing the Thematic Mapper (TM) and ETM+ images via the re-weighted multivariate alteration detection transformation method. The TM images that were masked as changed areas in 1990 and 2000 are input into the SVM classifier. Land cover maps for 1990 and 2010 are produced by combining the unchanged area in 2000 with the new classes of the changed areas in 1990 and 2010. Land cover change has continuously accelerated since 1990. Remarkably, arable land decreased, while the impervious surface area significantly increased

High-Frequency Glacial Lake Mapping Using Time Series of Sentinel-1A/1B SAR Imagery: An Assessment for the Southeastern Tibetan Plateau

Glacial lakes are an important component of the cryosphere in the Tibetan Plateau. In response to climate warming, they threaten the downstream lives, ecological environment, and public infrastructures through outburst floods within a short time. Although most of the efforts have been made toward extracting glacial lake outlines and detect their changes with remotely sensed images, the temporal frequency and spatial resolution of glacial lake datasets are generally not fine enough to reflect the detailed processes of glacial lake dynamics, especially for potentially dangerous glacial lakes with high-frequency variability. By using full time-series Sentinel-1A/1B imagery over a year, this study presents a new systematic method to extract the glacial lake outlines that have a fast variability in the southeastern Tibetan Plateau with a time interval of six days. Our approach was based on a level-set segmentation, combined with a median pixel composition of synthetic aperture radar (SAR) backscattering coefficients stacked as a regularization term, to robustly estimate the lake extent across the observed time range. The mapping results were validated against manually digitized lake outlines derived from Gaofen-2 panchromatic multi-spectral (GF-2 PMS) imagery, with an overall accuracy and kappa coefficient of 96.54% and 0.95, respectively. In comparison with results from classical supervised support vector machine (SVM) and unsupervised Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) methods, the proposed method proved to be much more robust and effective at detecting glacial lakes with irregular boundaries that have similar backscattering as the surroundings. This study also demonstrated the feasibility of time-series Sentinel-1A/1B SAR data in the continuous monitoring of glacial lake outline dynamics

Characterization of Kyagar Glacier and Lake Outburst Floods in 2018 Based on Time-Series Sentinel-1A Data

Early recognition of glacial lake outburst floods (GLOFs) is required for timely and cost-effective remedial efforts to be implemented. Although the formation of ice-dammed lakes is known to begin as a pond or river that was blocked by ice from the glacier terminus, the relationship between glacier dynamics and lake development is not well understood. Using a time-series of Sentinel-1A synthetic aperture radar (SAR) data acquired just before and after the lake outburst event in 2018, information is presented on the dynamic characteristics of Kyagar Glacier and its ice-dammed lake. Glacier velocity data derived from interferometry show that the glacier tongue experienced an accelerated advance (maximum velocity of 20 cm/day) just one month before the lake outburst, and a decreased velocity (maximum of 13 cm/day) afterward. Interferometric and backscattering properties of this region provide valuable insight into the diverse glaciated environment. Furthermore, daily temperature and total precipitation data derived from the ECMWF re-analysis (ERA)Interim highlight the importance of the sustained high-temperature driving force, supporting empirical observations from previous studies. The spatial and temporal resolution offered by the Sentinel-1A data allows variations in the glacier surface motion and lake evolution to be detected, meaning that the interaction mechanism between the glacial lake and the associated glacier can be explored. Although the glacier surge provided the boundary conditions favorable for lake formation, the short-term high temperatures and precipitation caused the melting of ice dams and also a rapid increase in the amount of water stored, which accelerated the potential for a lake outburst

Influences of fluid physical properties, solid particles, and operating conditions on the hydrodynamics in slurry reactors

Slurry reactors are popular in many industrial processes, involved with numerous chemical and biological mixtures, solid particles with different concentrations and properties, and a wide range of operating conditions. These factors can significantly affect the hydrodynamic in the slurry reactors, having remarkable effects on the design, scale-up, and operation of the slurry reactors. This article reviews the influences of fluid physical properties, solid particles, and operating conditions on the hydrodynamics in slurry reactors. Firstly, the influence of fluid properties, including the density and viscosity of the individual liquid and gas phases and the interfacial tension, has been reviewed. Secondly, the solid particle properties (i.e., concentration, density, size, wettability, and shape) on the hydrodynamics have been discussed in detail, and some vital but often ignored features, especially the influences of particle wettability and shape, as well as the variation of surface tension because of solid concentration alteration, are highlighted in this work. Thirdly, the variations of physical properties of fluids, hydrodynamics, and bubble behavior resulted from the temperature and pressure variations are also summarized, and the indirect influences of pressure on viscosity and surface tension are addressed systematically. Finally, conclusions and perspectives of these notable influences on the design and scale-up of industrial slurry reactors are presented. (c) 2021 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd. All rights reserved

Spatiotemporal Variations and Risk Analysis of Chinese Typhoon Disasters

Typhoons are a product of air-sea interaction, which are often accompanied by high winds, heavy rains, and storm surges. It is significant to master the characteristics and pattern of typhoon activity for typhoon warning and disaster prevention and mitigation. We used the Kernel Density Estimation (KDE) index as the hazard index; the probability of exceeding, or reaching, return period or exceeding a certain threshold was used to describe the probability of hazard occurrence. The results show that the overall spatial distribution of typhoon hazards conforms to a northeast-southwest zonal distribution, decreasing from the southeast coast to the northwest. Across the six typical provinces of China assessed here, data show that Hainan possesses the highest hazard risk. Hazard index is relatively high, mainly distributed between 0.005 and 0.015, while the probability of exceeding a hazard index greater than 0.015 is 0.15. In light of the four risk levels assessed here, the hazard index that accounts for the largest component of the study area is mainly distributed up to 0.0010, all mild hazard levels. Guangdong, Guangxi, Hainan, Fujian, Zhejiang, and Jiangsu as well as six other provinces and autonomous regions are all areas with high hazard risks. The research results can provide important scientific evidence for the sustainable development of China’s coastal provinces and cities. The outcomes of this study may also provide the scientific basis for the future prevention and mitigation of marine disasters as well as the rationalization of related insurance

Feature Comparison and Optimization for 30-M Winter Wheat Mapping Based on Landsat-8 and Sentinel-2 Data Using Random Forest Algorithm

Winter wheat cropland is one of the most important agricultural land-cover types affected by the global climate and human activity. Mapping 30-m winter wheat cropland can provide beneficial reference information that is necessary for understanding food security. To date, machine learning algorithms have become an effective tool for the rapid identification of winter wheat at regional scales. Algorithm implementation is based on constructing and selecting many features, which makes feature set optimization an important issue worthy of discussion. In this study, the accurate mapping of winter wheat at 30-m resolution was realized using Landsat-8 Operational Land Imager (OLI), Sentinel-2 Multispectral Imager (MSI) data, and a random forest algorithm. This paper also discusses the optimal combination of features suitable for cropland extraction. The results revealed that: (1) the random forest algorithm provided robust performance using multi-features (MFs), multi-feature subsets (MFSs), and multi-patterns (MPs) as input parameters. Moreover, the highest accuracy (94%) for winter wheat extraction occurred in three zones, including: pure farmland, urban mixed areas, and forest areas. (2) Spectral reflectance and the crop growth period were the most essential features for crop extraction. The MFSs combined with the three to four feature types enabled the high-precision extraction of 30-m winter wheat plots. (3) The extraction accuracy of winter wheat in three zones with multiple geographical environments was affected by certain dominant features, including spectral bands (B), spectral indices (S), and time-phase characteristics (D). Therefore, we can improve the winter wheat mapping accuracy of the three regional types by improving the spectral resolution, constructing effective spectral indices, and enriching vegetation information. The results of this paper can help effectively construct feature sets using the random forest algorithm, thus simplifying the feature construction workload and ensuring high-precision extraction results in future winter wheat mapping research

Bubble formation in continuous liquid phase under industrial jetting conditions

Accurate prediction of bubble diameter is crucial for the proper design, optimization, and scale-up of gas-liquid apparatuses. Bubble formation at submerged multiple orifices in a gas-liquid apparatus under industrial conditions is systematically investigated in this work. It is found that the bubble diameter firstly increases and then approaches to a relatively constant value in the low viscous liquid when increasing the orifice superficial gas velocity. Parametric studies demonstrate that large orifice diameter and high liquid viscosity lead to larger bubble diameter, and the inflection point in the curve of bubble diameter versus orifice superficial gas velocity is also affected. With surfactant added in solutions, the bubble diameter decreases markedly. Based on the experimental data, a semi-empirical correlation for predicting the bubble diameter is proposed using nonlinear least square optimization. The new correlation, containing the influence of orifice diameter, orifice superficial gas velocity and liquid properties on the bubble diameter, is further validated by comparing prediction results with experimental data over a wide range of operating conditions and working systems from the literature. Therefore, it is thought useful for the industrial design of gas-liquid apparatus. (C) 2019 Elsevier Ltd. All rights reserved