Time series of hydrogen and oxygen stable isotope ratios (δ2H and δ18O) in rivers can be used to quantify groundwater contributions to streamflow, and timescales of catchment storage. However, these isotope hydrology techniques rely on distinct spatial or temporal patterns of δ2H and δ18O within the hydrologic cycle. In New Zealand, lack of understanding of spatial and temporal patterns of δ2H and δ18O of river water hinders development of regional and national-scale hydrological models. We measured δ2H and δ18O monthly, together with river flow rates at 58 locations across New Zealand over a two-year period. Results show: (a) general patterns of decreasing δ2H and δ18O with increasing latitude were altered by New Zealand's major mountain ranges; δ2H and δ18O were distinctly lower in rivers fed from higher elevation catchments, and in eastern rain-shadow areas of both islands; (b) river water δ2H and δ18O values were partly controlled by local catchment characteristics (catchment slope, PET, catchment elevation, and upstream lake area) that influence evaporation processes; (c) regional differences in evaporation caused the slope of the river water line (i.e., the relationship between δ2H and δ18O in river water) for the (warmer) North Island to be lower than that of the (cooler, mountain-dominated) South Island; (d) δ2H seasonal offsets (i.e., the difference between seasonal peak and mean values) for individual sites ranged from 0.50‰ to 5.07‰. Peak values of δ18O and δ2H were in late summer, but values peaked 1 month later at the South Island sites, likely due to greater snow-melt contributions to streamflow. Strong spatial differences in river water δ2H and δ18O caused by orographic rainfall effects and evaporation may inform studies of water mixing across landscapes. Generally distinct seasonal isotope cycles, despite the large catchment sizes of rivers studied, are encouraging for transit time analysis applications. 相似文献
Based on statistical data and population flow data for 2016,and using entropy weight TOPSIS and the obstacle degree model,the centrality of cities in the Yangtze River Economic Belt(YREB)together with the factors influencing centrality were measured.In addition,data for the population flow were used to analyze the relationships between cities and to verify centrality.The results showed that:(1)The pattern of centrality conforms closely to the pole-axis theory and the central geography theory.Two axes,corresponding to the Yangtze River and the Shanghai-Kunming railway line,interconnect cities of different classes.On the whole,the downstream cities have higher centrality,well-defined gradients and better development of city infrastructure compared with cities in the middle and upper reaches.(2)The economic scale and size of the population play a fundamental role in the centrality of cities,and other factors reflect differences due to different city classes.For most of the coastal cities or the capital cities in the central and western regions,factors that require long-term development such as industrial facilities,consumption,research and education provide the main competitive advantages.For cities that are lagging behind in development,transportation facilities,construction of infrastructure and fixed asset investment have become the main methods to achieve development and enhance competitiveness.(3)The mobility of city populations has a significant correlation with the centrality score,the correlation coefficients for the relationships between population mobility and centrality are all greater than 0.86(P<0.01).The population flow is mainly between high-class cities,or high-class and low-class cities,reflecting the high centrality and huge radiating effects of high-class cities.Furthermore,the cities in the YREB are closely linked to Guangdong and Beijing,reflecting the dominant economic status of Guangdong with its geographical proximity to the YREB and Beijing's enormous influence as the national political and cultural center,respectively. 相似文献
ABSTRACTEffective public transit planning needs to address realistic travel demands, which can be illustrated by corridors across major residential areas and activity centers. It is vital to identify public transit corridors that contain the most significant transit travel demand patterns. We propose a two-stage approach to discover primary public transit corridors at high spatio-temporal resolutions using massive real-world smart card and bus trajectory data, which manifest rich transit demand patterns over space and time. The first stage was to reconstruct chained trips for individual passengers using multi-source massive public transit data. In the second stage, a shared-flow clustering algorithm was developed to identify public transit corridors based on reconstructed individual transit trips. The proposed approach was evaluated using transit data collected in Shenzhen, China. Experimental results demonstrated that the proposed approach is a practical tool for extracting time-varying corridors for many potential applications, such as transit planning and management. 相似文献
ABSTRACT High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning. 相似文献
Methane content in coal seam is an essential parameter for the assessment of coalbed gas reserves and is a threat to underground coal mining activities. Compared with the adsorption-isotherm-based indirect method, the direct method by sampling methane-bearing coal seams is apparently more accurate for predicting coalbed methane content. However, the traditional sampling method by using an opened sample tube or collecting drill cuttings with air drilling operation would lead to serious loss of coalbed methane in the sampling process. The pressurized sampling method by employing mechanical-valve-based pressure corer is expected to reduce the loss of coalbed methane, whereas it usually results in failure due to the wear of the mechanical valve. Sampling of methane-bearing coal seams by freezing was proposed in this study, and the coalbed gas desorption characteristics under freezing temperature were studied to verify the feasibility of this method. Results show that low temperature does not only improve the adsorption velocity of the coalbed gas, but also extend the adsorption process and increase the total adsorbed gas. The total adsorbed methane gas increased linearly with decreasing temperature, which was considered to be attributed to the decreased Gibbs free energy and molecular average free path of the coalbed gas molecular caused by low temperature. In contrast, the desorption velocity and total desorbed gas are significantly deceased under lower temperatures. The process of desorption can be divided into three phases. Desorption velocity decreases linearly at the first phase, and then, it shows a slow decreases at the second phase. Finally, the velocity of desorption levels off to a constant value at the third phase. The desorbed coalbed gas shows a parabolic relation to temperature at each phase, and it increases with increasing temperature at the first phase, and then, it poses a declining trend with increasing temperature at the rest phases. The experimental results show that decreasing the system temperature can restrain desorption of coalbed methane effectively, and it is proven to be a feasible way of sampling methane-bearing coal seams.
Cushion is a layer of granular materials between the raft and the ground. The shear behavior of the interface between the cushion and the raft may influence the seismic performance of the superstructure. In order to quantify such influences, horizontal shear tests on the interfaces between different cushion materials and concrete raft under monotonic and cyclic loading were carried out. The vertical pressure P_v, material type and cushion thickness h_c were taken as variables. Conclusions include: 1) under monotonic loading, P_v is the most significant factor; the shear resistance P_(hmax) increases as P_v increases, but the normalized factor of resistance μ_n has an opposite tendency; 2) for the materials used in this study, μ_n varies from 0.40 to 0.70, the interface friction angle δ_s varies from 20° to 35°, while u_(max) varies from 3 mm to 15 mm; 3) under cyclic loading, the interface behavior can be abstracted as a "three-segment" back-bone curve, the main parameters include μ_n, the displacement u_1 and stiffness K_1 of the elastic stage, the displacement u_2 and stiffness K_2 of the plastic stage; 4) by observation and statistical analysis, the significance of different factors, together with values of K_1, K_2 and μ_n have been obtained. 相似文献
Rock brittleness directly affects reservoir fracturing and its evaluation is essential for establishing fracturing conditions prior to reservoir reforming. Dynamic and static brittleness data were collected from siltstones of the Qingshankou Formation in Songliao Basin. The brittle–plastic transition was investigated based on the stress–strain relation. The results suggest that the brittleness indices calculated by static elastic parameters are negatively correlated with the stress drop coefficient and the brittleness index B2, defined as the average of the normalized Young’s modulus and Poisson’s ratio, is strongly correlated with the stress drop. The brittleness index B2, Young’s modulus, and Poisson’s ratio correlate with the brittle minerals content; that is, quartz, carbonates, and pyrite. We also investigated the correlation between pore fluid and porosity and dynamic brittle characteristic based on index B2. Pore fluid increases the plasticity of rock and reduces brittleness; moreover, with increasing porosity, rock brittleness decreases. The gas-saturated siltstone brittleness index is higher than that in oil- or water-saturated siltstone; the difference in the brittleness indices of oil- and water-saturated siltstone is very small. By comparing the rock mechanics and ultrasonic experiments, we find that the brittleness index obtained from the rock mechanics experiments is smaller than that obtained from the ultrasonic experiments; nevertheless, both decrease with increasing porosity as well as their differences. Ultrasonic waves propagate through the rock specimens without affecting them, whereas rock mechanics experiments are destructive and induce microcracking and porosity increases; consequently, the brittleness of low-porosity rocks is affected by the formation of internal microcrack systems. 相似文献