青藏高原雪盖对亚洲季风影响研究进展

资料分析和数值模拟均表明,青藏高原作为一个特殊的抬高了的下垫面,其上雪盖面积和积雪日数等的异常均与亚洲夏季风的强弱及起讫有关．雪盖面积大、积雪日数多的年份,亚洲季风弱且爆发迟,推进慢;雪盖面积小、积雪日数少的年份,亚洲季风强且爆发早,推进快．但也存在不同的看法     

The economic impact of climate change on Kenyan crop agriculture: A Ricardian approach

This paper measures the economic impact of climate on crops in Kenya. We use cross-sectional data on climate, hydrological, soil and household level data for a sample of 816 households. We estimate a seasonal Ricardian model to assess the impact of climate on net crop revenue per acre. The results show that climate affects crop productivity. There is a non-linear relationship between temperature and revenue on one hand and between precipitation and revenue on the other. Estimated marginal impacts suggest that global warming is harmful for crop productivity. Predictions from global circulation models confirm that global warming will have a substantial impact on net crop revenue in Kenya. The results also show that the temperature component of global warming is much more important than precipitation. Findings call for monitoring of climate change and dissemination of information to farmers to encourage adaptations to climate change. Improved management and conservation of available water resources, water harvesting and recycling of wastewater could generate water for irrigation purposes especially in the arid and semi-arid areas.     

Detecting Arctic snow and ice cover with FY-1D global data

Chinese meteorological satellite FY-1D can obtain global data from four spectral channels which include visible channel(0.58-0.68 μm) and infrared channels(0.84-0.89 μm,10.3-11.3 μm,11.5-12.5 μm).2366 snow and ice samples,2024 cloud samples,1602 land samples and 1648 water samples were selected randomly from Arctic imageries.Land and water can be detected by spectral features.Snow-ice and cloud can be classified by textural features.The classifier is Bayes classifier.By synthesizing five d ays classifying result of Arctic snow and ice cover area,complete Arctic snow and ice cover area can be obtained.The result agrees with NOAA/NESDIS IMS products up to 70%.     

Estimation of seasonal crop water consumption in a vineyard using Bowen ratio‐energy balance method

Information about seasonal crop water consumption is useful to develop the appropriate irrigation scheme. Measurements of energy balance components using the Bowen ratio method were made for a complete growing season at a vineyard in the arid region of northwest China. Vine in the experiment was furrow‐irrigated using a trellis system. The measured evapotranspiration was compared with estimates using the soil water balance method. It is shown that the Bowen ratio method provided accurate estimates of evapotranspiration from the vineyard and this requires that the Bowen ratio system is appropriately installed. The energy balance components showed typical diurnal pattern with peaks that occurred around the midday, except for the ground heat flux which delayed its peak by 2–3 h. The sensible heat flux was greater than the latent heat flux and followed the net radiation closely. The ratio of the latent heat flux to net radiation was low in the early growing season and increased over time. Under the limited irrigation experienced in the vineyard, the latent heat flux was controlled by available soil moisture and the total evapotranspiration in the growing season was 253 mm. The seasonal progression of the crop coefficient is similar to that reported in the literature, with the maximum occurring during the month of September. The crop coefficient can be estimated as a non‐linear function of day of year (DOY) and used to estimate evapotranspiration from vineyards in the region. Copyright © 2007 John Wiley & Sons, Ltd.     

Estimation of potential evapotranspiration over the Yellow River basin: reference crop evaporation or Shuttleworth–Wallace?

Potential evapotranspiration (PET) is a key input to hydrological models. Its estimation has often been via the Penman–Monteith (P–M) equation, most recently in the form of an estimate of reference evapotranspiration (RET) as recommended by FAO‐56. In this paper the Shuttleworth–Wallace (S–W) model is implemented to estimate PET directly in a form that recognizes vegetation diversity and temporal change without reference to experimental measurements and without calibration. The threshold values of vegetation parameters are drawn from the literature based on the International Geosphere–Biosphere Programme land cover classification. The spatial and temporal variation of the LAI of vegetation is derived from the composite NOAA‐AVHRR normalized difference vegetation index (NDVI) using a method based on the SiB2 model, and the Climate Research Unit database is used to provide the required meteorological data. All these data inputs are publicly and globally available. Consequently, the implementation of the S–W model developed in this study is applicable at the global scale, an essential requirement if it is to be applied in data‐poor or ungauged large basins. A comparison is made between the FAO‐56 method and the S–W model when applied to the Yellow River basin for the whole of the last century. The resulting estimates of RET and PET and their association with vegetation types and leaf area index (LAI) are examined over the whole basin both annual and monthly and at six specific points. The effect of NDVI on the PET estimate is further evaluated by replacing the monthly NDVI product with the 10‐day product. Multiple regression relationships between monthly PET, RET, LAI, and climatic variables are explored for categories of vegetation types. The estimated RET is a good climatic index that adequately reflects the temporal change and spatial distribution of climate over the basin, but the PET estimated using the S–W model not only reflects the changes in climate, but also the vegetation distribution and the development of vegetation in response to climate. Although good statistical relationships can be established between PET, RET and/or climatic variables, applying these relationships likely will result in large errors because of the strong non‐linearity and scatter between the PET and the LAI of vegetation. It is concluded that use of the implementation of the S–W model described in this study results in a physically sound estimate of PET that accounts for changing land surface conditions. Copyright © 2006 John Wiley & Sons, Ltd.     

Influence of Land Use/Cover Change on Storm Runoff— A Case Study of Xitiaoxi River Basin in Upstream of Taihu Lake Watershed

Land use/cover change (LUCC) is one of the main boundary conditions which influence many hydrologic processes. In view of the importance of Taihu Lake Watershed in China and the urgency of discovering the impacts of LUCC on storm runoff, two flood events under five land cover scenarios in the Xitiaoxi River Basin of the upstream of Taihu Lake watershed were simulated by distributed hydrologic modeling system HEC-HMS. The influences of each land cover on storm runoff were discussed. It was concluded that under the same rainstorm the ascending order of runoff coefficient and peak flow produced by the 5 different land covers were woodland, shrub, grassland, arable land, and built-up land; the descending order of swelling time were woodland, shrub, grassland, arable land, and built-up land. Scenario of built-up land was the first to reach peak flow, then arable land, grassland, shrub, and woodland. There were close relationships between the runoff coefficients produced by the 5 different land covers. The degrees of impacts on runoff coefficient of land cover change modes were sorted by descending: woodland to built-up land, shrub to built-up land, grassland to built-up land, arable land to built-up land, woodland to arable land, shrub to arable land, arable land to grassland, shrub to grassland, grassland to arable land, and woodland to shrub. Urbanization will contribute to flood disaster, while forestation will mitigate flood disaster.     

基于HJ-1A高光谱的黄河口碱蓬和柽柳盖度反演模型研究

碱蓬和柽柳是黄河口湿地典型的盐生植物类型,是多种保护珍禽的主要栖息地,具有景观尺度较小、分布广且多混生的特点。应用覆盖黄河口北部潮滩的HJ-1A高光谱遥感影像,基于现场测量的端元光谱和从遥感影像中使用顺序最大角凸锥法(SAMCC)自动提取的端元光谱,应用线性光谱分解法(LSU)、正交子空间投影法(OSP)、匹配滤波法(MF)、最小能量约束法(CEM)和自适应一致估计法(ACE)5种不同光谱解混方法进行混合像元光谱解混,对比两种方法得到的端元光谱分别对碱蓬和柽柳盖度的反演能力,并给出相应的反演模型。结果显示:(1)现场测量端元光谱取得了较好的碱蓬和柽柳盖度反演结果,其中应用LSU方法的光谱解混结果与现场测量盖度的决定系数对于碱蓬和柽柳分别达到了0.88和0.95;(2)两种端元获取方式的光谱解混结果中,LSU和OSP方法均获得了较高的相关性,ACE解混方法的相关性都最低;(3)SAMCC方法提取端元光谱对柽柳的分解结果与现场测量盖度的相关性远高于碱蓬。     

基于GEE的地表覆盖与气候状态关联研究

为探究地表覆盖与气候状态间的关联性,本文选取2019年的Landsat影像数据,结合温度、降水量、PM_2.5浓度3种气候指标,利用GEE平台,结合NDVI、MNDWI、NDBI,采用SVM、RF、CART方法进行地表覆盖分类,探究气候指标与地表覆盖类型分布的关联性;提出了使用3种气候指标构建分类特征进行地表覆盖分类的方法,并通过消融试验分析了气候指标对地表覆盖分类精度的影响。结果表明:①RF有较好的分类结果,总体精度为96.0%;②3种气候指标均能提高地表覆盖分类精度,其中PM_2.5浓度效果最好;③温度与植被、水体关联性较大,PM_2.5浓度与城区、植被关联性较大,降水量与耕地关联性较大。     

大光斑波形数据在土地覆盖分类的适用性分析

针对GLAS地学激光测高系统是冰、云和陆地高程卫星(ICESat)的唯一监测工具,能够记录地表光斑内的地物信息,是否能应用于黄土高原土地覆盖分类的问题进行了研究。利用粒子群和最小二乘法相结合的方法对GLAS波形数据进行高斯分解,获取高斯波个数、波形总能量、波形信号起始和信号结束位置4个波形参数;基于波形自动分类方法对黄土高原水体、森林、城市用地、其他地类(裸地、低矮植被等)进行分类。通过基于覆盖相同研究区域的30 m地表覆盖数据(Globe Land30),验证分类的准确性。结果表明,GLAS大光斑波形数据对黄土高原的4种地类能够很好地进行区分,总分类精度高达87.68%,Kappa系数为65.79%。研究表明,GLAS波形数据可以作为获取土地覆盖信息的有效数据源,为研究黄土高原土地覆盖变化提供更丰富的数据支持。