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Zhang G.,University of Wisconsin - Madison | Huang Q.,University of Wisconsin - Madison | Zhu A.-X.,Nanjing Normal University | Zhu A.-X.,State Key Laboratory Cultivation Base of Geographical Environment Evolution | And 3 more authors.
International Journal of Geographical Information Science | Year: 2016

Performing point pattern analysis using Ripley’s K function on point events of large size is computationally intensive as it involves massive point-wise comparisons, time-consuming edge effect correction weights calculation, and a large number of simulations. This article presented two strategies to optimize the algorithm for point pattern analysis using Ripley’s K function and utilized cloud computing to further accelerate the optimized algorithm. The first optimization sorted the points on their x and y coordinates and thus narrowed the scope of searching for neighboring points down to a rectangular area around each point in estimating K function. Using the actual study area in computing edge effect correction weights is essential to estimate an unbiased K function, but is very computationally intensive if the study area is of complex shape. The second optimization reused the previously computed weights to avoid repeating expensive weights calculation. The optimized algorithm was then parallelized using Open Multi-Processing (OpenMP) and hybrid Message Passing Interface (MPI)/OpenMP on the cloud computing platform. Performance testing showed that the optimizations effectively accelerated point pattern analysis using K function by a factor of 8 using both the sequential version and the OpenMP-parallel version of the optimized algorithm. While the OpenMP-based parallelization achieved good scalability with respect to the number of CPU cores utilized and the problem size, the hybrid MPI/OpenMP-based parallelization significantly shortened the time for estimating K function and performing simulations by utilizing computing resources on multiple computing nodes. Computational challenge imposed by point pattern analysis tasks on point events of large size involving a large number of simulations can be addressed by utilizing elastic, distributed cloud resources. © 2016 Informa UK Limited, trading as Taylor & Francis Group

Zhang C.,China University of Geosciences | Zhang C.,Chinese University of Hong Kong | Chen M.,Jiangsu Center For Collaborative Innovation In Geographical Information Resource Dev And Applied | Chen M.,Nanjing Normal University | And 4 more authors.
Applied Geography | Year: 2015

The spatial decision support system (SDSS) is widely used in environmental problem management. In this paper, focusing on the air quality problem in the Pearl River Delta, China, we present a virtual geographic environment (VGE) system to integrate multiscale meteorological and air quality models for policy making. It is a comprehensive modeling tool to aid decision makers and various stakeholders to participate in air quality management by providing geographic visualizations and friendly interfaces. With nested multiscale models, a synthetic understanding of cross-boundary air quality processes can be captured to understand both regional and local effects. With the help of Linux-Apache-MySQL-Perl (LAMP) architecture, users can manage and retrieve modeling data and model parameterizations to reach a consensus on the simulation results and share modeling knowledge. Aided by a high-resolved emission inventory, such a multiscale system enables practical applications for various scenarios. As a case study, the system was applied to simulate and analyze the SO2 concentration process and local contribution in the Hong Kong Special Administrative Region (HKSAR) based on hourly simulation results with spatial resolutions of 0.5 and 3 km from multiscale models. The results from the multiscale modeling and the limited local contribution suggest that Hong Kong and the surrounding region should closely cooperate to develop a better environment. © 2015 Elsevier Ltd.

Zhang C.,Beijing Normal University | Zhang C.,Chinese University of Hong Kong | Chen M.,Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application | Chen M.,Nanjing Normal University | And 4 more authors.
Ecological Modelling | Year: 2015

Geography investigates changes in physical structures and distributions of objects in spatiotemporal world, which are shaped by geographic process (geo-process). With extensive simulation models used to study geo-process, this paper examines the status of geo-process modeling (namely model-based simulation) for multidisciplinary geo-processes across scales in virtual geographic environments (VGEs). The conceptual framework of integrated modeling in VGEs is proposed with a review of specific issues, including model sharing and management, collaborative modeling and uncertainty analysis. The contribution of a model base in model reusability and modeling management, concerning input data, parameterization, and simulation output, is detailed. Finally, this paper concludes with a discussion of future research directions for holistic geo-process modeling. © 2015 Elsevier B.V.

Zhang J.,Nanjing Normal University | Zhang J.,Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application | Zhang J.,State Key Laboratory Cultivation Base of Geographical Environment Evolution | Tian P.,Northeast Forestry University | And 6 more authors.
Journal of Geophysical Research G: Biogeosciences | Year: 2016

It is important to clarify the quantity and composition of hydrologic N export from terrestrial ecosystem and its primary controlling factors, because it affected N availability, productivity, and C storage in natural ecosystems. The most previous investigations were focused on the effects of N deposition and human disturbance on the composition of hydrologic N export. However, few studies were aware of whether there were significant differences in the concentrations and composition of hydrologic N export from natural ecosystems in different climate zones and what is the primary controlling factor. In the present study, three natural forest ecosystems and one natural grassland ecosystem that were located in different climate zones and with different soil pH range were selected. The concentrations of total dissolved N, dissolved organic nitrogen (DON), NH4 +, NO3 − in soil solution and stream water, soil properties, and soil gross N transformation rates were measured to answer above questions. Our results showed that NO3 − concentrations and the composition pattern of hydrologic N export from natural ecosystems varied greatly in the different climate zones. The NO3 − concentrations in stream water varied largely, ranging from 0.1 mg N L−1 to 1.6 mg N L−1, while DON concentration in stream water, ranging from 0.1 to 0.9 mg N L−1, did not differ significantly, and the concentrations of NH4 + were uniformly low (average 0.1 mg N L−1) in all studied sites. There was a trade-off relationship between the proportions of NO3 − and DON to total dissolved N in stream water. In subtropical strongly acidic forests soil site, DON was the dominance in total dissolved N in stream water, while NO3 −-N became dominance in temperate acidic forests soil site, subtropical alkaline forests soil region, and the alpine meadow sites on the Tibetan Plateau. The proportions of NO3 − to total dissolved N in both soil solution and stream water significantly increased with the increasing of the gross autotrophic nitrification rates (p < 0.01). Our results indicated that the characteristics of soil N transformations were the most primary factor regulating the composition of hydrologic N losses from ecosystems. The nitrification was the central soil N transformation processes regulating N composition in soil solution and hydrologic N losses. These results provided important information on understanding easily the composition of hydrologic N export from terrestrial ecosystem. ©2016. American Geophysical Union. All Rights Reserved.

Zhang L.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute | Zhang L.,University of Chinese Academy of Sciences | Nan Z.,Nanjing Normal University | Nan Z.,Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application | And 3 more authors.
PLoS ONE | Year: 2016

Land use change and climate variability are two key factors impacting watershed hydrology, which is strongly related to the availability of water resources and the sustainability of local ecosystems. This study assessed separate and combined hydrological impacts of land use change and climate variability in the headwater region of a typical arid inland river basin, known as the Heihe River Basin, northwest China, in the recent past (1995-2014) and near future (2015-2024), by combining two land use models (i.e., Markov chain model and Dyna-CLUE) with a hydrological model (i.e., SWAT). The potential impacts in the near future were explored using projected land use patterns and hypothetical climate scenarios established on the basis of analyzing long-term climatic observations. Land use changes in the recent past are dominated by the expansion of grassland and a decrease in farmland; meanwhile the climate develops with a wetting and warming trend. Land use changes in this period induce slight reductions in surface runoff, groundwater discharge and streamflow whereas climate changes produce pronounced increases in them. The joint hydrological impacts are similar to those solely induced by climate changes. Spatially, both the effects of land use change and climate variability vary with the sub-basin. The influences of land use changes are more identifiable in some sub-basins, compared with the basin-wide impacts. In the near future, climate changes tend to affect the hydrological regimes much more prominently than land use changes, leading to significant increases in all hydrological components. Nevertheless, the role of land use change should not be overlooked, especially if the climate becomes drier in the future, as in this case it may magnify the hydrological responses. © 2016 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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