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1.
Base flows are important for tropical regions with pronounced dry seasons, which are facing increasing water demands. Base flow generation, however, is one of the most challenging hydrological processes to characterize in the tropics. In many years during the May–December wet season in the Panama Canal Watershed (PCW), base flows in rivers abruptly increase. This increase persists until the start of the December–April dry season. Understanding this unusual base flow jump (BFJ) behaviour is critical to improve water provisioning in the seasonal tropics, especially during droughts and extended dry seasons. This study developed an integrated approach combining piecewise regression on cumulative average base flow and sensitivity analysis to calculate the timing and magnitude of BFJ. Rainfall, forest cover, mean land surface slope, catchment area, and estimated subsurface storage were tested as predictors for the occurrence and magnitude of the BFJs in seven subcatchments of the PCW. Sensitivity analysis on correlated predictors allowed ranking of predictor contributions due to isolated and cross-correlation effects. Correlations between observed BFJs and BFJs predicted by watershed and rainfall-related predictors were 0.92 and 0.65 for BFJ timing and magnitude, respectively. Forest cover was the second most significant predictor after cumulative rainfall for jump magnitude, owing to larger subsurface storage and groundwater recharge in forests than pastures. Catchments in the mountainous eastern PCW always generated larger jumps due to their higher rainfall and greater forest cover than the western PCW catchments. The cross-correlations between predictors contributed to more than 50% of the jump variances. The results demonstrate the importance of rainfall gradient and catchment characteristics in affecting the sudden and sustained BFJs, which can help inform land management decisions intended to enhance water supplies in the tropics. This study underscores the need for more research to further understand the hydrological processes involved in the BFJ phenomenon, including better BFJ models and field characterizations, to help improve tropical ecosystem services under a changing environment.  相似文献   
2.
基于16S rRNA高通量基因测序技术,对毛乌素沙地小叶锦鸡儿(Caragana microphylla)、柠条锦鸡儿(Caragana korshinskii)根系微域(即根系、根际土、根区土、灌丛间空白土)间的细菌群落多样性和结构差异性进行表征。本研究对各根系微域间细菌群落的Alpha多样性指数进行了单因素方差分析以及基于OTU水平的PCA分析,探究其在根系微域间Alpha和Beta多样性的层级变化,证实了有关植物根系微域生态位分化的报道,并发现锦鸡儿属植物根系微域间细菌群落的多样性和结构组成随着4个微域类型由外及内呈现出显著的层级差异性(P<0.05)。通过对优势细菌群组的结构组成分析,发现锦鸡儿属植物对特定细菌群组具有显著的向根系内筛选富集的作用(P<0.05)。这种植物通过根系微域对特定细菌群组的逐级筛选富集作用,是导致锦鸡儿属植物灌丛下不同生态位间细菌群落结构和组成发生层级变异的主要原因。  相似文献   
3.
朱芳  苏勤  陶云  沈惊宏 《热带地理》2020,40(4):636-648
日常生活环境和旅游目的地环境都可以为个体提供恢复的机会,不同环境的属性特征导致不同程度的恢复。为了探究环境类型与恢复性效应之间的关系及停留时间的调节作用,文章采用问卷调查方式,利用配对样本t检验和混合设计方差分析相结合的方法,探讨游客在不同环境中的恢复性效应,并对这一现象提供心理上的解释。结果表明:1)恢复体验的4个维度(放松体验、心理脱离、掌握体验、控制体验)在旅游目的地环境中的得分均高于日常生活环境。2)掌握和控制体验维度在城市旅游目的地中的变化程度高于乡村旅游目的地,两种类型目的地的放松和心理脱离维度差异较小。3)随着在目的地停留时间的延长,乡村旅游目的地放松体验和心理脱离维度变化程度高于城市旅游目的地,两种类型目的地环境恢复性效应会相互靠近。因此,游客的环境恢复性效应不仅受到环境和目的地类型的影响,同时还与游客的适应程度有关,且受到其停留时间的调节作用。  相似文献   
4.
以FeCl3?6H2O、NiCl2?6H2O、MgCl2?6H2O为原料,以NaOH为沉淀剂采用水热法合成了镁镍铁水滑石 (MgNiFe-LDH),通过X射线衍射 (XRD)、傅里叶变换红外光谱 (FTIR) 和扫描电镜 (SEM) 分析了MgNiFe-LDH结构及形貌,探讨了不同陈化时间和水热温度对MgNiFe-LDH合成的影响。将制得的MgNiFe-LDH添加至聚丙烯 (PP) 中,制备MgNiFe-LDH/PP复合材料,通过对材料的加速老化测其力学性能,研究了添加MgNiFe-LDH前后材料力学性能的差异。研究结果表明,当Mg2+:Ni2+:Fe3+=2:1:1,反应温度为140℃、陈化时间为24 h时,合成的MgNiFe-LDH为片状,形貌较为规整,颗粒粒径约为2μm;经加速老化后,填充MgNiFe-LDH 复合材料的抗老化性能优于纯PP,当MgNiFe-LDH的填充量为6 wt% 复合材料拉伸强度不变,可明显提高PP的抗老化性能。  相似文献   
5.
摘要:目的 探讨菌株Salinivibrio sp.YH4分泌的丝氨酸蛋白酶EYHS的耐盐性及结构特征。方法 明胶底物酶谱法分析EYHS的耐盐性。应用生物信息学手段对EYHS及6种耐盐的S8家族丝氨酸蛋白酶结构特征进行分析。结果 EYHS在4 mol/L的NaCl溶液中仍具有活性,属于耐盐蛋白酶。EYHS及6种S8家族丝氨酸蛋白酶分子表面的loop区等无规则卷曲所占比例较高,α-螺旋与β-片层则主要位于酶分子内部。EYHS分子表面酸性氨基酸含量较高,且具有弱疏水内核。多序列比对发现蛋白酶的催化三联体两侧存在高度保守的基序和保守的极性氨基酸及芳香族氨基酸,并存在多个保守的Gly与Ala。同源模建和表面电荷分布显示,α螺旋和β片层围成了蛋白酶的催化腔,EYHS活性中心包含由Asp32、His65与Ser215组成的催化三联体,且催化位点区域表面静电势为负。结论 上述结构特征可能有助于耐盐丝氨酸蛋白酶EYHS在高盐环境下维持其稳定性和适度柔性,并有助于其催化功能的发挥,为深入研究耐盐丝氨酸蛋白酶的高盐环境适应性提供了一定的理论依据。  相似文献   
6.
深入探讨了柴达木盆地盐湖区土壤黑碳分布及土气交换特征。研究结果显示,研究区土壤总黑碳含量范围为0.50~6.79 g/kg,平均值达到1.64 g/kg;土壤烟炱组分黑碳含量范围为0.50~4.75 g/kg,平均值达到1.144 g/kg;气溶胶黑碳含量范围为0.34~4.92μg/m~3,平均值达到0.98μg/m~3。柴达木盆地盐湖区盐湖资源的大量开采和工业化生产等可能造成该地区黑碳大量排放。盐湖区土壤黑碳稳定碳同位素值(δ~(13)C_(BC))在-25.33‰~-23.46‰之间变化,平均值为-24.21‰,表明该地区土壤黑碳来源可能受C_3植物和化石燃料来源的共同影响。首次运用逃逸系数探讨了柴达木盆地盐湖区黑碳土气交换行为,结果表明研究区黑碳土气交换行为确实存在,但存在一定程度的模拟不确定性。  相似文献   
7.
沉积物硼(B)同位素组成可以反映其地质成因及经历的地质过程,因此在许多领域的研究中都有较为广泛的应用。通过对位于柴达木盆地碱山背斜顶部的SG-1b钻孔沉积物(7.3~1.6 Ma)水溶组分的B同位素研究,发现钻孔沉积物B含量在38.55~172.3μg/g之间,平均含量为87.6μg/g;δ~(11)B值的变化范围在3.61‰~16.26‰之间,平均值为10.65‰,B含量与δ~(11)B值具有一定的正相关关系。进一步分析表明,受到碱山背斜构造隆升以及晚新生代以来气候干旱化的影响,柴西古湖逐渐咸化萎缩,沉积环境以及碳酸盐含量、粘土矿物含量及其矿物组合等也在发生变化,B含量和δ~(11)B值自钻孔底部向上的逐步增加以及后期的急剧增加,与水溶离子含量以及矿物和粒度等的变化一致,这说明柴达木盆地晚中新世以来湖泊沉积物的B含量和δ~(11)B值可以很好地反映研究区气候和湖水的演化过程,共同指示了研究区自7.3 Ma以来气候的持续干旱化和湖水盐度的逐步增加,以及3.3Ma以来干旱化和湖水浓缩过程的加剧。  相似文献   
8.
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   
9.
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   
10.
Hu  Shengyong  Han  Dandan  Feng  Guorui  Zhang  Ao  Hao  Guocai  Hu  Lanqing  Zhu  Liping  Li  Bo 《Natural Resources Research》2020,29(2):1361-1373

The transfer and evolution of stress among rock blocks directly change the void ratios of crushed rock masses and affect the flow of methane in coal mine gobs. In this study, a Lagrange framework and a discrete element method, along with the soft-sphere model and EDEM numerical software, were used. The compaction processes of rock blocks with diameters of 0.6, 0.8, and 1.0 m were simulated with the degrees of compression set at 0%, 5%, 10%, 15%, 20%, and 25%. This study examines the influence of stress on void ratios of compacted crushed rock masses in coal mine gobs. The results showed that stress was mainly transmitted downward through strong force chains. As the degree of compression increased, the strong force chains extended downward, which resulted in the stress at the upper rock mass to become significantly higher than that at the lower rock mass. It was determined that under different degrees of compression, the rock mass of coal mine gobs could be divided, from the bottom to the top, into a lower insufficient compression zone (ICZ) and an upper sufficient compression zone (SCZ). From bottom to top, the void ratios in the ICZ sharply decreased and those in the SCZ slowly decreased. Void ratios in the ICZ were 1.2–1.7 times higher than those in the SCZ.

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