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This paper investigates the climate change impact on cooling energy consumption of office buildings in four major architectural climate zones in China. The results show that there are apparent differences in the responses of monthly or yearly cooling loads to climatic change in different climates. Dominant factors affecting monthly cooling loads change from dry bulb temperature (DBT) to wet bulb temperature (WBT) with the climate zones changing from severe cold to hot. The yearly cooling load was mainly afftectd by DBT in severe cold climate, whereas the WBT had the dominant effect in the other climates. With the continuous warming climate, the yearly cooling load did not rise significantly for any climate zone except for severe cold climate zone where cooling load had a significant increase (p

Meteorological parameters are the basis for building energy-saving design and operation of Heating, Ventilation and Air Conditioning systems (HVAC). In this study, changes in meteorological parameters of Harbin, Tianjin, Kunming, Shanghai, and Guangzhou, representing the five major climate zones of China, were analyzed to determine the effects of climate change on meteorological parameters. The results demonstrated that outdoor design temperatures for heating or air-conditioning significantly increased in all five climate zones. The increasing rate of design temperatures for both heating and air-conditioning in winter were in the range of 0.2 °C–0.7 °C/decade, and the rate of design temperature for air-conditioning in summer was in the range of 0.1 °C–0.4 °C/decade. The design load was calculated by outdoor design temperature. Compared with the design load during 1961–1990, the heating design load decreased 2.8% and 4.4% in Harbin and Tianjin during 1981–2010, respectively. The design load for air-conditioning in winter decreased 1.7%, 1.4%, 2.4% and 3.2% in Harbin, Tianjin, Shanghai and Guangzhou, respectively. By contrast, design load for air-conditioning in summer during 1981–2010 increased 0.8%, 1.1%, 2.9% and 2.2% in Harbin, Tianjin, Shanghai and Guangzhou, respectively. On average, the effect of climate change on design loads is more intense in winter than summer, which may be beneficial for building energy-saving design. The results in this study suggest that climate change has evident effects on design meteorological parameters and design load. It is necessary to note that responses of design meteorological parameters to climate change are apparently different in five climate zones. Hence, different design strategies for building energy-saving should be considered appropriately according to different climate change characteristics in different climate zones.

Climate plays an important role in heating energy consumption owing to the direct relationship between space heating and changes in meteorological conditions. To quantify the impact, the Transient System Simulation Program software was used to simulate the heating loads of office buildings in Harbin, Tianjin, and Shanghai, representing three major climate zones (i.e., severe cold, cold, and hot summer and cold winter climate zones) in China during 1961–2010. Stepwise multiple linear regression was performed to determine the key climatic parameters influencing heating energy consumption. The results showed that dry bulb temperature (DBT) is the dominant climatic parameter affecting building heating loads in all three climate zones across China during the heating period at daily, monthly, and yearly scales (R 2 ≥ 0.86). With the continuous warming climate in winter over the past 50 years, heating loads decreased by 14.2, 7.2, and 7.1 W/m2 in Harbin, Tianjin, and Shanghai, respectively, indicating that the decreasing rate is more apparent in severe cold climate zone. When the DBT increases by 1 °C, the heating loads decrease by 253.1 W/m2 in Harbin, 177.2 W/m2 in Tianjin, and 126.4 W/m2 in Shanghai. These results suggest that the heating energy consumption can be well predicted by the regression models at different temporal scales in different climate conditions owing to the high determination coefficients. In addition, a greater decrease in heating energy consumption in northern severe cold and cold climate zones may efficiently promote the energy saving in these areas with high energy consumption for heating. Particularly, the likely future increase in temperatures should be considered in improving building energy efficiency.

The Haihe River basin of North China is characterized by extremely low per capita water resources and a consistently long-term decreasing trend of precipitation and runoff over the last few decades. This study analyzes the climatological features of rainy season (May&ndash, September) precipitation in the Haihe River basin and its branch systems based on a high-density hourly observational dataset during 2007&ndash, 2017. We show that there are two high-rainfall zones in the basin, with one along the south of the Yanshan Mountains to Taihang Mountains and another along the Tuma River in the south. Rainstorm centers exist amidst the two zones. July generally sees the highest precipitation, followed by August, and May has the lowest precipitation. The major flood season is reached between the third pentad of July and the fourth pentad of August. The precipitation is high at night but low in the daytime. In the pre-flood season before early July, rainfalls mostly arrive at 16:00&ndash, 21:00 h. After entering the major flood season, the diurnal precipitation has two peaks, one at 17:00&ndash, 22:00 h and the other at 0:00&ndash, 7:00 h. In the post-flood season after mid-August, the most rain occurs at night, with the peak appearing at 0:00&ndash, 8:00 h. The short-duration precipitation is mainly distributed in the mountainous areas, and the long-duration precipitation that contributes most to seasonal rainfalls appears in the plain areas, and the continuous precipitation mostly occurs in the windward slopes of the Taihang Mountains and the Yanshan Mountains. In addition, urbanization process around large city stations may have affected the rainy season precipitation to a certain extent in the Haihe River basin, with large and medium city stations experiencing around 10% higher precipitation than small city stations. However, this issue needs to be investigated exclusively.

This paper aims at developing a mathematical model of hazard assessment of debris flow (DF) by applying hydrodynamics mechanism (HM) in the judging of DF starting. On the basis of HM, finite-volume method has been used and the flow passages have been classified into ground channel, river channel, and overflow channel; hence, the simulating result of depth distribution could be adopted to judge DF starting by comparing with DF critical depth. Meanwhile, rainfall, terrain, geology, vegetation, and population have been selected as the impact factors, and normalization method has been utilized to calculate the five factors’ weights based on the synthesis of analytic hierarchy process and fuzzy-weighting method. Combining the impact factors’ weights with its digitized results, respectively, thus, DF hazard assessment could be achieved with the programming calculation of Fortran. Indeed, the assessment results have been validated by comparing with the statistical result of geological hazards distribution in Guangxi and the Quanzhou County Debris Flow Event in 2011. The validation results show that the mathematical model is reliable, and it is feasible in the hazard assessment of DF.

The present paper concerns the impact of climate on building heating energy consumption for different types of energy in Tianjin, a large city in northern China. The results show that heating energy consumption is dominantly related to dry bulb temperature (DBT), but the impacts vary with energy types and time scales (day, month or year). DBT can explain 97.7% of the coal consumption at different time scales and 83.0–89.7% of the natural gas consumption. DBT can also account for 25% of the daily and monthly heating electricity consumption and 89% of the annual heating electricity consumption. These results suggest that different measures for building energy conservation should be taken because of the differing climate impacts for different energy types. Especially, likely increase in temperature in the future should be considered to improve the energy efficiency of buildings. In addition, different models should be used to predict building energy consumption at different time scales. This would be helpful for energy policy makers and energy providers for adjusting energy use strategies.

Recently, representation-based classifiers have gained increasing interest in hyperspectral image (HSI) clas sification. In this letter, based on our previously developed joint collaborative representation (JCR) classifier, an improved version, which is called weighted JCR (WJCR) classifier, is proposed. JCR adopts the same weights when extracting spatial and spectral features from surrounding pixels. Differing from JCR, WJCR attempts to utilize more appropriate weights by considering the similarity between the center pixel and its surroundings. Experimental results using two real HSIs demon strate that the proposed WJCR outperforms the original JCR and some other traditional classifiers, such as the support vector machine (SVM), the SVM with a composite kernel, and simultaneous orthogonal matching pursuit.

In this letter, kernel collaborative representation with Tikhonov regularization (KCRT) is proposed for hyperspectral image classification. The original data is projected into a high-dimensional kernel space by using a nonlinear mapping function to improve the class separability. Moreover, spatial information at neighboring locations is incorporated in the kernel space. Experimental results on two hyperspectral data prove that our proposed technique outperforms the traditional support vector machines with composite kernels and other state-of-the-art classifiers, such as kernel sparse representation classifier and kernel collaborative representation classifier.

This paper concerns the impacts of future climate change under two forcing scenarios on energy demand of commercial building and residential buildings with different energy-saving levels in Tianjin. Heating load of commercial building will decrease under the two scenarios in the next 90 years but increase of cooling load is found. All residential buildings will decrease heating load in the future 90 years. In particular, the decreasing rate of energy demand during 2011–2100 by the residential building slows down from the first- to the third-stage energy-saving levels. Additionally, the difference in energy demand between the two scenarios becomes smaller as the energy-saving level increases. These suggest that higher energy-saving levels are beneficial for decreasing not only energy consumption but also its sensitivity to climate change. Practical application: Climate change in the future causes the large and significant increase in cooling energy demand but decrease in heating energy demand. This would be helpful for the adjustment of energy use strategy by government. Also, the possible changes in future energy demands for heating and cooling will be of interest to energy providers. The responses of heating energy demand to the future climate change show large difference among the residential buildings with different energy-saving levels. This will provide useful information for policy makers and building industry managers on how to make appropriate measures keep occupants comfort and reduce energy use.

The climate change and urban heat island (UHI) effects on outdoor meteorological parameters were determined by analysing design temperatures for heating and air-conditioning in urban centre, suburban and outer suburban areas in a large city in northern China. The results showed that design temperatures increased apparently over the past 20 years and decreased considerably from the urban centre to suburban and outer suburban areas. The climate change and UHI impacts would lead to 1%–5% changes in design loads, decreasing the heating design load and increasing the cooling design load as the buildings moves from outer suburban, suburban to urban areas and from the past to the present. The decrease in the heating load exceeds the increase in the cooling load, being beneficial to improve building energy efficiency. Climate change and UHI impacts should be fully considered to promote energy-saving of building design.

Exploring changes of building energy consumption and its relationships with climate can provide basis for energy-saving and carbon emission reduction. Heating and cooling energy consumption of different types of buildings during 1981-2010 in Tianjin city, was simulated by using TRNSYS software. Daily or hourly extreme energy consumption was determined by percentile methods, and the climate impact on extreme energy consumption was analyzed. The results showed that days of extreme heating consumption showed apparent decrease during the recent 30 years for residential and large venue buildings, whereas days of extreme cooling consumption increased in large venue building. No significant variations were found for the days of extreme energy consumption for commercial building, although a decreasing trend in extreme heating energy consumption. Daily extreme energy consumption for large venue building had no relationship with climate parameters, whereas extreme energy consumption for commercial and residential buildings was related to various climate parameters. Further multiple regression analysis suggested heating energy consumption for commercial building was affected by maximum temperature, dry bulb temperature, solar radiation and minimum temperature, which together can explain 71.5 % of the variation of the daily extreme heating energy consumption. The daily extreme cooling energy consumption for commercial building was only related to the wet bulb temperature (R2= 0.382). The daily extreme heating energy consumption for residential building was affected by 4 climate parameters, but the dry bulb temperature had the main impact. The impacts of climate on hourly extreme heating energy consumption has a 1-3 hour delay in all three types of buildings, but no delay was found in the impacts of climate on hourly extreme cooling energy consumption for the selected buildings.

Recently, representation-based classifications have gained increasing interest in hyperspectral imagery, such as the newly proposed sparse-representation classification and nearest-regularized subspace (NRS). These classifiers provide excellent performance that is comparable to or even better than the classic support vector machine. However, all these representation-based methods were originally designed to be pixel-wise classifiers which only consider the spectral signature while ignoring the spatial-contextual information. A Markov random field (MRF), providing a basis for modeling contextual constraints, has currently been successfully applied for hyperspectral image analysis. We mainly investigate the benefits of combining these representation-based classifications with an MRF model in order to acquire better classification results. Two real hyperspectral images are used to validate the proposed classification scheme. Experimental results demonstrated that the proposed method significantly outperforms other state-of-the-art approaches. For example, NRS-MRF performed with an accuracy of 94.92% for the Reflective Optics System Imaging Spectrometer data with 60 training samples per class, while the original NRS obtained an accuracy of 81.95%, an improvement of approximately 13%.