桃汛试验对万家寨水库冲淤影响分析

通过对桃汛试验前后两个时段对比分析,认为桃汛试验没有增加万家寨库区淤积;库区淤积形态有所调整,淤积三角洲向坝前推进,库尾段平均河底高程没有抬升.     

1950—2010年黄河径流情势变化特点

对1950—2010年黄河径流地区分布不均匀、断流和连续枯水段历时较长等特点进行了分析,阐述了天气变化、植被变化、水利工程对径流的调节、工农业及生活用水等影响黄河径流变化的因素,提出了相关建议:①长期推行节水和水土保持建设;②建立流域机构统一管理与省(区)水行政主管部门分级管理相结合的黄河水资源管理体系;③积极推进补水工程建设;④建立污染物入河总量控制、排污入河许可等水资源保护制度,提升对污染物输移扩散的预测预报水平与能力。     

2010年长江流域暴雨洪水初步分析

分析2010年长江流域暴雨洪水,有利于更全面认识长江流域暴雨洪水特性,可为今后更好开展长江流域防汛水情保障工程。从主汛期长江流域暴雨及气候特征、干支流主要洪水过程及洪水特性等方面,进行了初步的总结和分析。2010年长江主汛期洪水发生范围广、持续时间长且局部地区洪水量级大,通过与三峡水库蓄水前后几次大的历史洪水分析对比,初步认为,2010年长江流域主汛期出现的洪水为长江上中游区域性较大洪水。     

广东韩江松源河省界界面水质变化及环境管理策略

根据2007、2008年7次调查的实测资料,分析了松源河及多宝水库的水质变化过程,并讨论了影响水质恶化的原因;根据水质变化特点,提出了水质保洁及科学管理的策略,为梅州的绿色崛起、“以人为本”的和谐社会服务.     

Evaluation of AMSR-E soil moisture results using the in-situ data over the Little River Experimental Watershed, Georgia

An operational global soil moisture data product is currently generated from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua satellite using the retrieval procedure described in Njoku and Chan [Njoku, E.G. and Chan, S.K., 2006. Vegetation and surface roughness effects on AMSR-E land observations, remote sensing environment, 100(2), 190-199]. We have generated another soil moisture dataset from the same AMSR-E observed brightness temperature data using the Land Surface Microwave Emission Model (LSMEM) adopting a different estimation method. This paper focuses on a comparison study of soil moisture estimates from the above two methods. The soil moisture data from current AMSR-E product and LSMEM are compared with the in-situ measured soil moisture datasets over the Little River Experimental Watershed (LREW), Georgia, USA for the year 2003. The comparison study was carried out separately for the AMSR-E daytime and night time overpasses. The LSMEM method performed better than the current operational AMSR-E retrieval algorithm in this study. The differences between the AMSR-E and LSMEM results are mostly due to differences in various simplifications and assumptions made for variables in the radiative transfer equations and the soil and vegetation based physical models and the accuracy of the input surface temperature datasets for the LSMEM forward model approach. This study confirms that remote sensing data have the potential to provide useful hydrologic information, but the accuracy of the geophysical parameters could vary depending on the estimation methods. It cannot be concluded from this study whether the soil moisture estimation by the LSMEM approach will perform better in other geographic, climatic or topographic conditions. Nevertheless, this study sheds light on the effects of different approaches for the estimation of geophysical parameters, which may be useful for current and future satellite missions.     

The impact of misregistration on SRTM and DEM image differences

Image differences between Shuttle Radar Topography Mission (SRTM) data and other Digital Elevation Models (DEMs) are often performed for either accuracy assessment or for estimating vegetation height across the landscape. It has been widely assumed that the effect of sub-pixel misregistration between the two models on resultant image differences is negligible, yet this has not previously been tested in detail. The aim of this study was to determine the impact that various levels of misregistration have on image differences between SRTM and DEMs. First, very accurate image co-registration was performed at two study sites between higher resolution DEMs and SRTM data, and then image differences (SRTM–DEM) were performed after various levels of misregistration were systematically introduced into the SRTM data. It was found that: (1) misregistration caused an erroneous and dominant correlation between elevation difference and aspect across the landscape; (2) the direction of the misregistration defined the direction of this erroneous and systematic elevation difference; (3) for sub-pixel misregistration the error due solely to misregistration was greater than, or equal to the true difference between the two models for substantial proportions of the landscape (e.g., greater than 33% of the area for a half-pixel misregistration); and (4) the strength of the erroneous relationship with aspect was enhanced by steeper terrain. Spatial comparisons of DEMs were found to be sensitive to even sub-pixel misregistration between the two models, which resulted in a strong erroneous correlation with aspect. This misregistration induced correlation with aspect is not likely specific to SRTM data only; we expect it to be a generic relationship present in any DEM image difference analysis.     

Gamma-ray remote sensing of aeolian salt sources in the Murray-Darling Basin, Australia

This paper presents a new technique for mapping regional salt sources that has major implications for salinity management in southeastern Australia. This was achieved by analyzing a regional mosaic of airborne gamma-ray emission derivatives and verified by existing airborne electromagnetic and drilling data. A significant correlation was found between aeolian (windblown) materials, upland salts and gamma-ray signatures. This is consistent with the conceptual model that much of the salt in the upland areas of the Murray-Darling Basin is sourced from deposited aeolian materials that have been derived from deflationary events in salt-bearing landscapes in the western arid part of the basin. From gamma-ray emissions, and based on an observed relationship with borehole salinity, concentrated aeolian salt source deposits contained about 0.7% potassium and 10 ppm thorium. Using this signature on normalized data, an Euclidean distance algorithm provided mapping and information relating to salt-mobility pathways over a wide region. The resulting gamma-ray salt source model (GSM) facilitates focussed management of salinity infiltration zones in catchments across the basin.     

珠三角地区咸潮入侵预警预报信息系统的总体设计

珠江口由于淡水径流量小、河口较宽,潮水比较容易进入,每年都会发生不同程度的咸潮现象,咸潮对珠三角城市群构成整体威胁。本文提出预警预报系统设计思想,旨在从计算机软件层面通过预警系统加以模拟实现咸潮发生、发展的整个侵入过程,从而更加科学合理地制定珠三角咸潮的应对措施,为咸潮入侵应急预案提供决策依据。     

Dust source identification using MODIS: A comparison of techniques applied to the Lake Eyre Basin, Australia

The impact of mineral aerosol (dust) in the Earth's system depends on particle characteristics which are initially determined by the terrestrial sources from which the sediments are entrained. Remote sensing is an established method for the detection and mapping of dust events, and has recently been used to identify dust source locations with varying degrees of success. This paper compares and evaluates five principal methods, using MODIS Level 1B and MODIS Level 2 aerosol data, to: (a) differentiate dust (mineral aerosol) from non-dust, and (2) determine the extent to which they enable the source of the dust to be discerned. The five MODIS L1B methods used here are: (1) un-processed false colour composite (FCC), (2) brightness temperature difference, (3) Ackerman's (1997: J.Geophys. Res., 102, 17069-17080) procedure, (4) Miller's (2003:Geophys. Res. Lett. 30, 20, art.no.2071) dust enhancement algorithm and (5) Roskovensky and Liou's (2005: Geophys. Res. Lett. 32, L12809) dust differentiation algorithm; the aerosol product is MODIS Deep Blue (Hsu et al., 2004: IEEE Trans. Geosci. Rem. Sensing, 42, 557-569), which is optimised for use over bright surfaces (i.e. deserts). These are applied to four significant dust events from the Lake Eyre Basin, Australia. OMI AI was also examined for each event to provide an independent assessment of dust presence and plume location. All of the techniques were successful in detecting dust when compared to FCCs, but the most effective technique for source determination varied from event to event depending on factors such as cloud cover, dust plume mineralogy and surface reflectance. Significantly, to optimise dust detection using the MODIS L1B approaches, the recommended dust/non-dust thresholds had to be considerably adjusted on an event by event basis. MODIS L2 aerosol data retrievals were also found to vary in quality significantly between events; being affected in particular by cloud masking difficulties. In general, we find that OMI AI and MODIS AQUA L1B and L2 data are complementary; the former are ideal for initial dust detection, the latter can be used to both identify plumes and sources at high spatial resolution. Overall, approaches using brightness temperature difference (BT10-11) are the most consistently reliable technique for dust source identification in the Lake Eyre Basin. One reason for this is that this enclosed basin contains multiple dust sources with contrasting geochemical signatures. In this instance, BTD data are not affected significantly by perturbations in dust mineralogy. However, the other algorithms tested (including MODIS Deep Blue) were all influenced by ground surface reflectance or dust mineralogy; making it impossible to use one single MODIS L1B or L2 data type for all events (or even for a single multiple-plume event). There is, however, considerable potential to exploit this anomaly, and to use dust detection algorithms to obtain information about dust mineralogy.     

Metric concepts and implications in describing compositional changes for world river's water chemistry

The paper is designed to give the reader an outline that is useful for understanding the importance of distance, as a metric concept, and its implications when compositional (geochemical) data are managed from a statistical point of view in a given sample space. Application examples are shown by considering the construction of confidence regions and mixing models. The analyzed data are related to the chemistry of the most important rivers of the world as referring to the GEMS/WATER Global Register of River Inputs when each sample (river) is represented as a composition. A compositional vector of d parts, x=[x₁,x₂,…,x_d], is defined as a vector in which the only relevant information is contained in the ratios between its components. All the components of the vector are assumed positive and are called parts (variables), while the whole compositional vector, with the sum of the parts equal to a constant, represents the composition. In this case data are not represented by variables free to vary from −∞ to +∞ within a Euclidean space but occupy a restricted part of it called the simplex. The d-part simplex, S^d, is a subset of a d-dimensional real space. In this context the metric of the R space, with the definition of basic algebraic operations and of inner product, norm and distance, thus giving an Euclidean vector space structure, cannot be applied since the scale is relative and not absolute.