ALBERT

Description: Cellular automata (CA) is a bottom-up self-organizing modeling tool for simulating contagion-like phenomena such as complex land-use change and urban growth. It is not known how CA modeling responds to changes in spatial observation scale when a larger-scale study area is partitioned into subregions, each with its own CA model. We examined the impact of changing observation scale on a model of urban growth at UA-Shanghai (a region within a one-hour high-speed rail distance from Shanghai) using particle swarm optimization-based CA (PSO-CA) modeling. Our models were calibrated with data from 1995 to 2005 and validated with data from 2005 to 2015 on spatial scales: (1) Regional-scale: UA-Shanghai was considered as a single study area; (2) meso-scale: UA-Shanghai was partitioned into three terrain-based subregions; and (3) city-scale: UA-Shanghai was partitioned into six cities based on administrative boundaries. All three scales yielded simulations averaging about 87% accuracy with an average Figure-of-Merit (FOM) of about 32%. Overall accuracy was reduced from calibration and validation. The regional-scale model yielded less accurate simulations as compared with the meso- and city-scales for both calibration and validation. Simulation success in different subregions is independent at the city-scale, when compared with regional- and meso-scale. Our observations indicate that observation scale is important in CA modeling and that smaller scales probably lead to more accurate simulations. We suggest smaller partitions, smaller observation scales and the construction of one CA model for each subregion to better reflect spatial variability and to produce more reliable simulations. This approach should be especially useful for large-scale areas such as huge urban agglomerations and entire nations.

Description: Regional environmental risk (RER) denotes potential threats to the natural environment, human health and socioeconomic development caused by specific risks. It is valuable to assess long-term RER in coastal areas with the increasing effects of global change. We proposed a new approach to assess coastal RER considering spatial factors using principal component analysis (PCA) and used a future land use simulation (FLUS) model to project future RER scenarios considering the impact of sea level rise (SLR). In our study, the RER status was classified in five levels as highest, high, medium, low and lowest. We evaluated the 30 m × 30 m gridded spatial pattern of the long-term RER at Ningbo of China by assessing its 1975–2015 history and projecting this to 2020–2050. Our results show that RER at Ningbo has increased substantially over the past 40 years and will slowly increase over the next 35 years. Ningbo’s city center and district centers are exposed to medium-to-highest RER, while the suburban areas are exposed to lowest-to-medium lower RER. Storm surges will lead to strong RER increases along the Ningbo coast, with the low-lying northern coast being more affected than the mountainous southern coast. RER at Ningbo is affected principally by the combined effects of increased human activity, rapid population growth, rapid industrialization, and unprecedented urbanization. This study provides early warnings to support practical regulation for disaster mitigation and environmental protection.

Description: Cellular automata (CA) is a spatially explicit modeling tool that has been shown to be effective in simulating urban growth dynamics and in projecting future scenarios across scales. At the core of urban CA models are transition rules that define land transformation from non-urban to urban. Our objective is to compare the urban growth simulation and prediction abilities of different metaheuristics included in the R package optimx. We applied five metaheuristics in optimx to near-optimally parameterize CA transition rules and construct CA models for urban simulation. One advantage of metaheuristics is their ability to optimize complexly constrained computational problems, yielding objective parameterization with strong predictive power. From these five models, we selected conjugate gradient-based CA (CG-CA) and spectral projected gradient-based CA (SPG-CA) to simulate the 2005–2015 urban growth and to project future scenarios to 2035 with four strategies for Su-Xi-Chang Agglomeration in China. The two CA models produced about 86% overall accuracy with standard Kappa coefficient above 69%, indicating their good ability to capture urban growth dynamics. Four alternative scenarios out to the year 2035 were constructed considering the overall effect of all candidate influencing factors and the enhanced effects of county centers, road networks and population density. These scenarios can provide insight into future urban patterns resulting from today’s urban planning and infrastructure, and can inform future development strategies for sustainable cities. Our proposed metaheuristic CA models are also applicable in modeling land-use and urban growth in other rapidly developing areas.

Description: Land surface temperature (LST) is a fundamental Earth parameter, on both regional and global scales. We used seven Landsat images to derive LST at Suzhou City, in spring and summer 1996, 2004, and 2016, and examined the spatial factors that influence the LST patterns. Candidate spatial factors include (1) land coverage indices, such as the normalized difference built-up index (NDBI), the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), (2) proximity factors such as the distances to the city center, town centers, and major roads, and (3) the LST location. Our results showed that the intensity of the surface urban heat island (SUHI) has continuously increased, over time, and the spatial distribution of SUHI was different between the two seasons. The SUHIs in Suzhou were mainly distributed in the city center, in 1996, but expanded to near suburban, in 2004 and 2016, with a substantial expansion at the highest level of SUHIs. Our buffer-zone-based gradient analysis showed that the LST decays logarithmically, or decreases linearly, with the distance to the Suzhou city center. As inferred by the generalized additive models (GAMs), strong relationships exist between the LST and the candidate factors, where the dominant factor was NDBI, followed by NDWI and NDVI. While the land coverage indices were the LST dominant factors, the spatial proximity and location also substantially influenced the LST and the SUHIs. This work improved our understanding of the SUHIs and their impacts in Suzhou, and should be helpful for policymakers to formulate counter-measures for mitigating SUHI effects.

Description: Scientific and reasonable battery thermal management systems contribute to improve the performance of a power battery, prolong its life of service, and improve its safety. Based on TAFEL-LAE895 type 100Ah ternary lithium ion power battery, this paper is conducted on charging and discharging experiments at different rates to study the rise of temperature and the uniformity of the battery. Paraffin can be used to reduce the surface temperature of the battery, while expanded graphite (EG) is added to improve the thermal conductivity and viscosity of the composite phase change material (CPCM), and to reduce the fluidity after melting. With the increase of graphite content, the heat storage capacity of phase change material (PCM) decreases, which affects the thermal management effect directly. Therefore, this paper combines heat pipe and semiconductor refrigeration technology to transform heat from the inner CPCM to the thermoelectric cooling sheet for heat dissipation. The results show that the surface temperature of the battery can be kept within a reasonable range when discharging at high rate. The temperature uniformity of the battery is improved and the energy of the battery is saved.

Description: In recent decades, both observation and simulation data have demonstrated an obvious decrease in runoff and soil moisture, with increasing evapotranspiration, over the Loess Plateau. In this study, we employed a Variable Infiltration Capacity model coupled with scenario simulation to explore the impact of change in climate and land cover on four hydrological variables (HVs) over the Loess Plateau, i.e., evapotranspiration (ET), runoff (Runoff), shallow soil moisture (SM1), and deep soil moisture (SM2). Results showed precipitation, rather than temperature, had the closest relationship with the four HVs, with r ranging from 0.76 to 0.97 (p 〈 0.01), and this was therefore presumed to be the dominant climate-based driving factor in the variation of hydrological regimes. Vegetation conversion, from cropland and grassland to woodland, significantly reduced runoff and increased soil moisture consumption, to sustain an increased ET, and, assuming that the reduction of SM2 is entirely evaporated, we can attribute 71.28% ± 18.64%, 65.89% ± 24.14% of the ET increase to the water loss of SM2 in the two conversion modes, respectively. The variation in HVs, induced by land cover change, were higher than the expected climate change with respect to SM1, while different factors were selected to determine HVs variation in six catchments, due to differences in the mode and intensity of vegetation conversion, and the degree of climate change. Our findings are critical for understanding and quantifying the impact of climate change and vegetation conversions, and provide a further basis for the design of water resources and land-use management strategies with respect to climate change, especially in the water-limited Loess Plateau.