Search

A reliable projection of future risk of decadal megadrought event is crucial for adaption and mitigation over eastern China to future climate changes. However, large uncertainties are induced by emission scenarios, model structures, and internal variability in current model simulation. Using a 50-member ensemble of simulations from the Community Earth System Model Version 2 Large Ensemble (CESM2-LE), it is found that, under the same emission scenario, internal variability contributes a large part to the total uncertainties of decadal precipitation variability over eastern China, and the interdecadal Pacific oscillation (IPO), could contribute about 30% to internal uncertainty during future period (2021–2080). Then, an emergent constraint based on IPO is applied to the model simulated precipitation. Finally, constrained precipitation changes show that northern China will have a high megadrought risk in the 2050s to 2060s, and Yangtze River Valley will have a high megadrought risk in the 2030s to 2040s. These will have great benefit to specific strategies of social infrastructure in the future.

Solar energy captured by solar photovoltaic (PV) systems has great potential to meet the high demand for renewable energy sources in urban areas. A photovoltaic noise barrier (PVNB) system, which integrates a PV system with a noise barrier, is a promising source for harvesting solar energy to overcome the problem of having limited land available for solar panel installations. When estimating the solar PV potential at the city scale, it is difficult to identify sites for installing solar panels. A computational framework is proposed for estimating the solar PV potential of PVNB systems based on both existing and planned noise barrier sites. The proposed computational framework can identify suitable sites for installing photovoltaic panels. A deep learning-based method is used to detect existing noise barrier sites from massive street-view images. The planned noise barrier sites are identified with urban policies. Based on the existing and planned sites of noise barriers in Nanjing, the annual solar PV potentials in 2019 are 29,137 MW h and 113,052 MW h, respectively. The estimation results show that the potential PVNB systems based on the existing and planned noise barrier in 2019 have the potential installed capacity of 14.26 MW and 57.24 MW, with corresponding potential annual power generation of 4662 MW h and 18,088 MW h, respectively.

An indoor navigation network, simplified from complex building structures, is of primary importance for indoor navigation. Methods for generating such networks are becoming increasingly imp...

Existing Delaunay triangulation algorithms for LiDAR data can only guarantee the efficiency of a certain reconstruction step, but cannot guarantee the overall efficiency. This paper presents a Delaunay triangulation algorithm which integrates two existing approaches to improve the overall efficiency of LiDAR data triangulation. The proposed algorithm consists of four steps: (1) dividing a point cloud into grid cells, (2) sorting a point cloud using a KD-tree, (3) triangulating the point cloud and exporting inactive triangles in main memory, and (4) scheduling the above steps. The proposed algorithm was tested using three LiDAR data sets. The LiDAR data was used for comparing the proposed algorithm with the Streaming Delaunay algorithm with respect to both time efficiency and memory usage. Results from the experiments suggest that the proposed algorithm is three to four times faster than Streaming Delaunay while using nearly the same memory space.

The mapping of spatiotemporal point features plays an important role in geovisualization. However, such mapping suffers from low efficiency due to computational redundancy when similar symbols are ...

Creating appropriate colour schemes is challenging for both novice and experienced cartographers. This paper introduces an artificial bee colony (ABC) algorithm to automatically create various colo...

Regionality, comprehensiveness, and complexity are regarded as the basic characteristics of geography. The exploration of their core connotations is an essential way to achieve breakthroughs in geography in the new era. This paper focuses on the important method in geographic research: Geographic modeling and simulation. First, we clarify the research requirements of the said three characteristics of geography and its potential to address geo-problems in the new era. Then, the supporting capabilities of the existing geographic modeling and simulation systems for geographic research are summarized from three perspectives: Model resources, modeling processes, and operational architecture. Finally, we discern avenues for future research of geographic modeling and simulation systems for the study of regional, comprehensive and complex characteristics of geography. Based on these analyses, we propose implementation architecture of geographic modeling and simulation systems and discuss the module composition and functional realization, which could provide theoretical and technical support for geographic modeling and simulation systems to better serve the development of geography in the new era.

The Ming Dynasty Mega-drought (MDMD) (1637-1643) occurred at the end of Ming Dynasty and is the severest drought event in China in the last millennium. This unprecedented drought contributed signif...

Soil erosion can cause various ecological problems, such as land degradation, soil fertility loss, and river siltation. Rainfall is the primary water-driven force for soil erosion, and its potential effect on soil erosion is reflected by rainfall erosivity that relates to the raindrop kinetic energy. As it is difficult to observe large-scale dynamic characteristics of raindrops, all the current rainfall erosivity models use the function based on rainfall amount to represent the raindrops' kinetic energy. With the development of global atmospheric re-analysis data, numerical weather prediction techniques become a promising way to estimate rainfall kinetic energy directly at regional and global scales with high spatial and temporal resolutions. This study proposed a novel method for large-scale and long-term rainfall erosivity investigations based on the Weather Research and Forecasting (WRF) model, avoiding errors caused by inappropriate rainfall–energy relationships and large-scale interpolation. We adopted three microphysical parameterizations schemes (Morrison, WDM6, and Thompson aerosol-aware) to obtain raindrop size distributions, rainfall kinetic energy, and rainfall erosivity, with validation by two disdrometers and 304 rain gauges around the United Kingdom. Among the three WRF schemes, Thompson aerosol-aware had the best performance compared with the disdrometers at a monthly scale. The results revealed that high rainfall erosivity occurred in the west coast area at the whole country scale during 2013–2017. The proposed methodology makes a significant contribution to improving large-scale soil erosion estimation and for better understanding microphysical rainfall–soil interactions to support the rational formulation of soil and water conservation planning.

The Secchi disk depth (ZSD) plays a critical role in describing water clarity. Several studies have shown linkages between Three Gorges Dam (TGD) and the downstream lacustrine ecosystem in the middle and lower Yangtze River basin. However, the potential influence on the ZSD fluctuation in the entire anthropogenic reservoirs of Three Gorges (ER) and Dongting Lake (DTL) has not been reported, possibly due to technical obstacles in obtaining statistically significant spatial and temporal results. We addressed this challenge by using remote sensing technology: the Landsat 8 Operational Land Imager (OLI). We proposed a new, robust remote-sensing algorithm to estimate ZSD from OLI imagery using red and green band-ratio, leading to MAPE of 21.68% and RMSE of 0.076m for ZSD ranging from 0.1m to 1.05m. After satisfactory image-based validation, the algorithm was implemented on OLI data to derive ZSD patterns over ER and DTL from 2013 to 2017. Several crucial findings can be drawn: 1) Spatial-temporal patterns of ZSD exhibited notable fluctuations over both ER and DTL, and they also demonstrated a significant correlation with each other because of the opposite temporal cycle of ZSD fluctuations between ER and DTL; 2) Temporally, monthly fluctuations of ZSD between ER and DTL had opposite temporal cycles, which was mainly attributed to the surface runoff and sediment discharge driven by the outbound runoff variations of TGD. Spatially, the heterogeneity of the ZSD pattern in ER might have resulted from the different geographical regions being divided by large anthropologic hydrological facilities, such as TGD; 3) The relationship between ZSD and total suspended matter (TSM) showed a significant negative correlation, as did the relationship between ZSD and Kd(490). These findings demonstrate that TSM often plays a principal role in light attenuation of extremely turbid inland waters; 4) An inversed phenomenon of water clarity was observed at the intersection of DTL and the Yangtze River around Chenglingji site (YRAC), which was due to the opposite temporal cycle of ZSD fluctuations between DTL and ER after the impoundment of TGD; and 5) Owing to the analysis of noise-equivalent ZSD, OLI data can be used to derive ZSD, since the imagery uncertainty is 0.07m by means of our band-ratio algorithm, which demonstrates similar results to MODIS. The proposed ZSD-derived algorithm in this study could be suitable for other turbid lakes or reservoirs to formulate related strategies of water quality management in the middle and lower Yangtze River basin, and the unveiled findings here improve our understanding of ZSD spatiotemporal fluctuations in large river-connected lakes, such as Poyang Lake.

Grid codes are an important part of a grid system and provide a unique identifier for the cells in the spherical triangular discrete global grid, where the transformation between geographic coordinates and grid codes is the basis of global spatial data integration and various analytical applications. Owing to the particularity of the topological properties, the spherical space cannot be continuously paved with the same grid cells as those in the plane space. Thus, the recursive approximation method is typically used to establish the transformation algorithm between the geographic coordinates and the grid codes. The efficiency of those algorithms that were based on recursive approximation obviously decreased with the increased grid subdivision level; thus, it is not conducive to the integration of massive high-resolution spatial data. Whereas the geometric properties of the grid cells in the spherical triangular discrete global grid differ during the initial subdivision levels, as the level increases, these geometric properties tend to be the same, along with the properties of the local regions similar to the plane. Considering this characteristic, this paper proposes a hybrid bidirectional transformation algorithm. In the proposed algorithm, the recursive approximation method is used at the lower level where there is a large difference in the geometrical properties of the grid cells, and the direct mapping method using a similar plane grid is used at the higher level where the geometrical properties of the grid cells are virtually the same. In experiments conducted using the proposed algorithm with geographic coordinates at different scales and grid codes at different levels, the running time remained stable with no significant change throughout increases in the hierarchical level. Further, it was proven to satisfy the requirements of the absolute accuracy evaluation method. Compared with the traditional recursive approximation algorithm, the proposed algorithm has obvious advantages in the transformation between grid codes and geographic coordinates, as it can better support spatial data integration and various analytical applications in the spherical triangular discrete global grid system.

There is increasing interest in the global CO2 emissions transfer caused by international trade. However, the reduction potential of technology spillover is rarely considered. More importantly, the effect of technology spillover on climate change and the social cost of carbon (SCC) should be further investigated to inform climate policy-makers. In this study, we investigate the climate economic impact of technology spillover through unidirectional coupling of the emissions embodied in bilateral trade (EEBT) method and the dynamic integrated model of climate and economy (DICE). The results indicate that technological progress in the electricity sector could contribute to climate change mitigation and SCC lowering. The trade-related CO2 emissions of key CO2 exporters could be substantially reduced, such as China, India, Russia, and resource- and labor-intensive countries. High-tech countries outsourced large amounts of CO2 emissions to labor- and resource-intensive countries. However, a large income gap remains between high-tech and labor/resource-intensive countries. These results indicate the great importance of technological progress and sustainable management throughout the global supply chain.

Creating a satisfying qualitative color scheme from scratch may be difficult for novice mapmakers and experts. A probability-based method is proposed to identify knowledge regarding qualitative color selection from readily available color schemes and formalize the discovered knowledge to assist in color creation. An unsupervised method to extract the general trends of color selection is presented, and the issue of qualitative color selection is translated into a multi-constraint optimization problem. A feasible solution for achieving the global optimum is then provided. A probability-based method is also proposed to match abstract color values with specific mapping features. This proposed approach is evaluated in a case study. The results of the case study suggest that the proposed method allows users to create qualitative color schemes more efficiently and confidently.

Indoor airflow is an important factor affecting indoor thermal comfort and human health; it is also an important consideration for architectural design, ideal living environments, and green building fields. Currently, most building models required for indoor airflow simulation have been manually created by researchers, which is inefficient and error-prone in modeling. To overcome this problem, this study examines the similarities and differences between the general standard data model Industry Foundation Class (IFC) in the BIM (Building Information Model) and the spatial grid required for indoor airflow simulation and proposes a conversion method from an IFC model to spatial grid. This method uses the rich semantic, geometric, and relational information in the IFC model to automatically extract building interior space, divide numerical calculation grid, and map semantic attribute information to the spatial grid. On this basis, an integrated simulation framework is constructed with OpenFOAM to simulate the airflow movement of the indoor space of the building. The simulation results show that the method used in this study can automatically construct the spatial grid using existing IFC models without re-modeling, which provides important support for indoor airflow modeling. In addition, the integrated simulation framework connects model construction, simulation analysis, and other steps through tool chain. This process simplifies the simulation operation, provides convenience for indoor airflow simulation, is conducive to the wide use of indoor airflow simulation in architectural design and architectural design optimization, and also provides reference for related numerical simulation research and digital city modeling.

Apart from regions, image edge lines are an important information source, and they deserve more attention in object-based image analysis (OBIA) than they currently receive. In the region-line primitive association framework (RLPAF), we promote straight-edge lines as line primitives to achieve powerful OBIAs. Along with regions, straight lines become basic units for subsequent extraction and analysis of OBIA features. This study develops a new software system called remote-sensing knowledge finder (RSFinder) to implement RLPAF for engineering application purposes. This paper introduces the extended technical framework, a comprehensively designed feature set, key technology, and software implementation. To our knowledge, RSFinder is the world’s first OBIA system based on two types of primitives, namely, regions and lines. It is fundamentally different from other well-known region-only-based OBIA systems, such as eCogntion and ENVI feature extraction module. This paper has important reference values for the development of similarly structured OBIA systems and line-involved extraction algorithms of remote sensing information.

In this paper, we first propose several novel concepts for object-based image analysis, which include line-based shape regularity, line density, and scale-based best feature value (SBV), based on the region-line primitive association framework (RLPAF). We then propose a raft cultivation area (RCA) extraction method for high spatial resolution (HSR) remote sensing imagery based on multi-scale feature fusion and spatial rule induction. The proposed method includes the following steps: (1) Multi-scale region primitives (segments) are obtained by image segmentation method HBC-SEG, and line primitives (straight lines) are obtained by phase-based line detection method. (2) Association relationships between regions and lines are built based on RLPAF, and then multi-scale RLPAF features are extracted and SBVs are selected. (3) Several spatial rules are designed to extract RCAs within sea waters after land and water separation. Experiments show that the proposed method can successfully extract different-shaped RCAs from HR images with good performance.

With the integration of the global economy and increased international trade, CO

CO2 emissions embodied in international trade have drawn wide attention in the academia and national climate negotiations. In this study, we estimate the embodied CO2 emissions in the trade between China and Australia using long time series disaggregated sectoral data based on missions embodied in bilateral trade (EEBT) method, and analyse the major driving forces for these emissions changes based on structural decomposition analysis (SDA) method. The results show that export volume contributed to these embodied CO2 emissions increase, while emission intensity could offset these CO2 emissions increase. We further predict embodied CO2 emission in Sino-Australia trade under different scenarios and the results indicate that technology spillover can significantly reduce embodied CO2 emissions in trade. Electricity, transportation and cement industries have significant potential to reduce carbon emissions. In addition, we evaluate future carbon reduction potential till 2030. The results in this study can inform decision-makers for Australia and China to formulate energy conservation and emission reduction policies.

With the integration of the global economy and increased international trade, CO2 emissions embodied in trade have attracted broad attention. In this study, we examined Taiwan's CO2 emissions embodied in its imports and exports with its 167 trade partners from 1970 to 2015 using the emissions embodied in bilateral trade (EEBT) method. The results show that since the mid-1990s, Taiwan has shifted to being a net CO2 emissions exporter. The share of Taiwan's CO2 emissions exported to developed countries has decreased. However, the share exported to Mainland China, Hong Kong special administrative region and many developing countries in Southeast Asia has increased. The results of a structural decomposition analysis show that emissions intensity changes were the main driving force for Taiwan's exported CO2 emissions during 1970-1996, while increased export volumes were the largest driving force for Taiwan's exported CO2 emissions during 1996-2015. Emissions intensity changes and population growth are the main driving factors for Taiwan's CO2 emissions produced and consumed by itself increases. Based on the SDA results, environmentally friendly policies, such as energy efficiency and industrial structure optimization, are suggested to help Taiwan shift towards a sustainable economy.

Concomitant with the acceleration of urbanization, the indoor scene of buildings becoming increasingly complex in recent years. In order to meet the needs of diverse indoor application analysis, the multi-level of detail (LOD) model of indoor scene has been paid increasing attention. However, there are two kinds of LODs in the fields of geographic information and building information, which cannot be effectively integrated. From the perspective of geographic scenario, this paper proposes a new concept of LOD to realize the construction of indoor scene model under different details. According to the effects on indoor space, the indoor components are divided into three semantic levels: enclosing component, connecting component and decorating component. Finally, several examples of indoor models combining semantic LOD, geometric LOD and process LOD are introduced.

Cities play an important role in carbon emissions reduction and climate change mitigation. In this study, we examine the CO2 emissions embodied in the imports and exports of the Hong Kong special administrative region (SAR) from 1990 to 2015 using the emissions embodied in bilateral trade (EEBT) method. The results show that Hong Kong SAR has been a net CO2 importer and Mainland China plays a dominating role in the embodied CO2 emissions of Hong Kong SAR. The sectors with high imported CO2 emissions are mainly the energy sectors, such as electricity, heat, gas and water production and supply. Population density, GDP per capita, and trade openness have significantly positive effects on net CO2 emissions increases in Hong Kong SAR, with population density taking the leading role. Population density increases by 1%, total net CO2 emissions increase by 4%. Population slowdown scenario simulation results show that net CO2 emissions of Hong Kong SAR could be reduced by 5.5%. Whereas, net CO2 emissions of Hong Kong SAR could increase by 8.7% in the accelerated economic development scenario. Therefore, to reduce CO2 emissions, Hong Kong SAR need control its population, develop the circular economy, and promote green lifestyle and consumption patterns.