陆面过程中稳定水同位素逐日变化的数学模拟——引入稳定同位素效应的CLM实例分析

将稳定同位素效应引入CLM(Community Land Model),并对巴西马瑙斯站在平衡年的稳定水同位素的逐日变化进行模拟和分析.结果表明: 降水、水汽和地表径流中δ18O存在明显的季节变化,并与相应的水量存在显著的负相关关系,但凝结物中δ18O与地面凝结量存在显著的正相关关系,蒸发水汽中δ18O与蒸发量之间无显著的相关关系.受土壤贮水削峰功能的影响,表层土壤和根区水中δ18O的季节变化全无.植被层蒸发水汽中稳定同位素的丰度与大气的干湿程度存在密切联系: 当降水量少时,大气干燥,植被层的蒸发较少,植被蒸发中δ18O较高;当降水量较大时,空气湿润,植被层的蒸发量较大,蒸发中δ18O则较低.植被蒸腾中δ18O的变化与源区水体中δ18O的变化保持一致,尤其是与根区水中的δ18O.由于地下径流直接源自根区水的补充,因此,地下径流中δ18O等于根区水中的δ18O.模拟结果还显示,降水MWL (大气水线)的梯度项和常数项均比全球平均MWL略偏小.尽管主要来自降水的贡献,但地表径流和植被层水体的MWLs与降水MWL存在较大的差异,这一方面与两类水体在蒸发过程中的稳定同位素的富集作用有关,另一方面与CLM模拟的水量有关.大气水汽线与降水的MWL的梯度值相近,说明大气水汽与降水近似处于稳定同位素平衡状态.另外,模拟的地面的凝结线与植被层的凝结线均与全球大气水线相近,且具有非常高相关程度,说明CLM的模拟是合理的.     

Assessing the effects of land cover and future climate conditions on the provision of hydrological services in a medium‐sized watershed of Portugal

The separated and combined effects of land‐cover scenarios and future climate on the provision of hydrological services were evaluated in Vez watershed, northern Portugal. Soil and Water Assessment Tool was calibrated against daily discharge, sediments and nitrates, with good agreements between model predictions and field observations. Four hypothetical land‐cover scenarios were applied under current climate conditions (eucalyptus/pine, oak, agriculture/vine and low vegetation). A statistical downscaling of four General Circulation Models, bias‐corrected with ground observations, was carried out for 2021–2040 and 2041–2060, using representative concentration pathway 4.5 scenario. Also, the combined effects of future climate conditions were evaluated under eucalyptus/pine and agriculture/vine scenario. Results for land cover revealed that eucalyptus/pine scenario reduced by 7% the annual water quantity and up to 17% in the summer period. Although climate change has only a modest effect on the reduction of the total annual discharge (?7%), the effect on the water levels during summer was more pronounced, between ?15% and ?38%. This study shows that climate change can affect the provision of hydrological services by reducing dry season flows and by increasing flood risks during the wet months. Regarding the combined effects, future climate may reduce the low flows, which can be aggravated with eucalyptus/pine scenario. In turn, peak flows and soil erosion can be offset. Future climate may increase soil erosion and nitrate concentration, which can be aggravated with agriculture scenario. Results moreover emphasize the need to consider both climate and land‐cover impacts in adaptation and land management options at the watershed scale. Copyright © 2015 John Wiley & Sons, Ltd.     

Incorporation of a dynamic root distribution into CLM4.5: Evaluation of carbon and water fluxes over the Amazon

Roots are responsible for the uptake of water and nutrients by plants and have the plasticity to dynamically respond to different environmental conditions. However, most land surface models currently prescribe rooting profiles as a function only of vegetation type, with no consideration of the surroundings. In this study, a dynamic rooting scheme, which describes root growth as a compromise between water and nitrogen availability, was incorporated into CLM4.5 with carbon–nitrogen(CN) interactions(CLM4.5-CN) to investigate the effects of a dynamic root distribution on eco-hydrological modeling. Two paired numerical simulations were conducted for the Tapajos National Forest km83(BRSa3) site and the Amazon, one using CLM4.5-CN without the dynamic rooting scheme and the other including the proposed scheme. Simulations for the BRSa3 site showed that inclusion of the dynamic rooting scheme increased the amplitudes and peak values of diurnal gross primary production(GPP) and latent heat flux(LE) for the dry season, and improved the carbon(C) and water cycle modeling by reducing the RMSE of GPP by 0.4 g C m~(-2)d~(-1), net ecosystem exchange by 1.96 g C m~(-2)d~(-1), LE by 5.0 W m~(-2), and soil moisture by 0.03 m~3m~(-3), at the seasonal scale, compared with eddy flux measurements, while having little impact during the wet season. For the Amazon, regional analysis also revealed that vegetation responses(including GPP and LE) to seasonal drought and the severe drought of 2005 were better captured with the dynamic rooting scheme incorporated.     

Grid pattern recognition in road networks using the C4.5 algorithm

Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks.     

1958-2017年太湖蒸发量年际变化趋势及主控因子

湖泊蒸发对气候变化非常敏感,是水文循环响应气候变化的指示因子,因此研究湖泊蒸发的控制因素,对于理解区域水文循环有重要意义.本文利用太湖中尺度涡度通量网避风港站观测数据校正JRA-55再分析资料,驱动CLM4.0-LISSS模型,并利用2012-2017年涡度相关通量数据和湖表面温度数据检验模型模拟蒸发结果,验证了该模型在太湖的适用性;估算了1958-2017年间太湖的湖面蒸发量,并利用Manner-Kendall趋势检验分析了湖面蒸发的变化趋势,寻找太湖实际蒸发的年际变化的主控因子.结果如下:校正后的JRA-55再分析资料模拟的太湖蒸发与观测值之间存在季节偏差,但是季节偏差在年尺度上相互抵消,再分析资料可用于年际尺度太湖蒸发变化的模拟;1958-2017年间太湖蒸发量以1977年为界,先下降(-3.6 mm/a),后增加(2.3 mm/a);多元逐步回归结果表明,向下的短波辐射是太湖1958-2017年间太湖蒸发变化的主控因子,向下的长波辐射、气温、比湿也对湖泊蒸发年际变化有一定影响,但是风速对蒸发量的年际变化影响不大.     

基于C4.5决策树算法的道路结冰预报模型构建及应用

道路结冰是气象上规定的主要灾害性天气之一,准确认识道路结冰发生规律并科学有效地预报是防灾减灾的关键问题。以影响道路结冰的日积雪深度、日最低气温、日降水量等气象要素为主要因子,将是否结冰抽象成二元分类问题,借助数据挖掘中C4.5决策树算法进行分类分析,得到便于预报员使用的预报道路结冰规则集。在实验部分,选取2004—2013年连云港和嘉定为例,用前8 a的连云港(嘉定)地区道路结冰数据样本进行分类规则的学习,从而得到适合连云港(嘉定)地区的4(3)条预报道路结冰规则集,然后用剩下2 a的数据进行检验,准确率高达96.82%(94.87%)。实验表明采用C4.5决策树算法构建的道路结冰预报模型不仅预报准确率高、易解释、方便预报员直接使用,而且预报规则与道路结冰现象的天气过程及理论一致。     

CLM4.0模式对中国区域土壤湿度的数值模拟及评估研究

本文利用普林斯顿大学全球大气强迫场资料,驱动公用陆面过程模式（Community Land Model version 4.0,CLM4.0）模拟了中国区域1961～2010年土壤湿度的时空变化。将模拟结果与观测结果、美国国家环境预报中心再分析数据（National Centers for Environmental Prediction Reanalysis,NCEP）和高级微波扫描辐射计（Advanced Microwave Scanning Radiometer-EOS,AMSR-E）反演的土壤湿度进行了对比分析,结果表明CLM4.0模拟结果可以反映出中国区域观测土壤湿度的空间分布和时空变化特征,但东北、江淮和河套三个地区模拟值相对于观测值在各层次均系统性偏大。模拟与NCEP再分析土壤湿度的空间分布基本一致,与AMSR-E的反演值在35°N以北的分布也基本一致;从1961～2010年土壤湿度模拟结果分析得出,各层土壤湿度空间分布从西北向东南增加。低值区主要分布在新疆、青海、甘肃和内蒙古西部地区。东北平原、江淮地区和长江流域为高值区。土壤湿度数值总体上从浅层向深层增加。不同深度土壤湿度变化趋势基本相同。除新疆西部和东北部分地区外,土壤湿度在35°N以北以减少趋势为主,30°N以南的长江流域、华南及西南地区以增加为主。在全球气候变暖的背景下,CLM4.0模拟的夏季土壤湿度在不同程度上响应了降水的变化。中国典型干旱区和半干旱区土壤湿度减小,湿润区增加。其中湿润区土壤湿度对降水的响应最为显著,其次是半干旱区和干旱区。     

Results of a CLM4 Land Surface Simulation over China Using a Multisource Integrated Land Cover Dataset

In this study, the high-accuracy multisource integrated Chinese land cover （MICLCover） dataset was used in version 4 of the Community Land Model （CLM4） to assess how the new land cover information affected land surface simulation over China. Compared to the default land cover dataset in CLM4, the MICL data indicated lower values for bare soil （14.6% reduction）, nee- dleleaf tree （3.6%）, and broadleaf tree （1.9%）; higher values for shrub cover （1.8% increase）, grassland （9.9%）, cropland （5.0%）, glaciers （0.5%）, lakes （1.6%）, and wetland （1.1%）; and unchanged for urban areas. Two comparative CLM4 simulations were conducted for the 33-yr period from 1972 to 2004, one using the MICL dataset and the other using the default dataset. The results revealed that the MICL data produced a 0.3% lower mean annual surface albedo over China than the original data. The largest contributor to the reduced value was semiarid regions （2.1% reduction）. The MICL-data albedo value agreed more closely with observations （MODIS broad- band black-sky albedo products） over arid and semiarid regions than for the original data to some extent. The simulated average sensible heat flux over China increased by only 0.1 W m 2 owing to the reduced values in arid and semiarid regions, as opposed to increases in humid and semihumid regions, while an increased latent heat flux of I W m-2 was reflected in almost identical changes over the whole region. In addition, the mean annual runoff simulated by CLM4 using MICL data decreased by 6.8 mm yr-1, primarily due to large simulated decreases in humid regions.     

Comparisons of Simulations of Soil Moisture Variations in the Yellow River Basin Driven by Various Atmospheric Forcing Data Sets

Based on station observations, The European Centre for Medium-Range Weather Forecasts reanalysis (ERA40), the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and Princeton University's global meteorological forcing data set (Princeton), four atmospheric forcing fields were constructed for use in driving the Community Land Model version 3.5 (CLM3.5). Simulated soil moisture content throughout the period 1951-2000 in the Yellow River basin was vali...     

基于MODIS数据的决策树分类方法研究与应用

介绍了目前国际上流行的两种决策树算法———CART算法与C4·5算法,并引入了两种机器学习领域里的分类新技术———boosting和bagging技术,为探究这些决策树分类算法与新技术在遥感影像分类方面的潜力,以中国华北地区MODIS250m分辨率影像进行了土地覆盖决策树分类试验与分析。研究结果表明决策树在满足充分训练样本的条件下,相对于传统方法如最大似然法(MLC)能明显提高分类精度,而在样本量不足下决策树分类表现差于MLC;并发现在单一决策树生成中,分类回归树CART算法表现较C4·5算法具有分类精度和树结构优势,分类精度的提高取决于树结构的合理构建与剪枝处理;另外在决策树CART中引入boosting技术,能明显提高那些较难识别类别的分类准确率18·5%到25·6%。