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81.
酯硬化水玻璃再生砂循环使用后的性能变化及应对策略 总被引:2,自引:0,他引:2
分析了酯硬化水玻璃砂多次再生使用后,其Na2O含量较大升高、溃散性显著下降、生产厚大铸型(芯)时硬透性变差并出现蠕变现象的原因,探讨了再生砂性能变化后的应对策略。认为:研究开发适于干法再生水玻璃砂的改性水玻璃、对再生砂实施表面处理、对旧砂进行高脱膜率的再生等,应是解决酯硬化水玻璃砂多次再生使用后溃散性恶化问题的主要措施;而控制再生砂的含水量、适当增加有机酯的加入量、CO2气体补充硬化等,是增加型(芯)硬透性和克服蠕变现象的主要方法。 相似文献
82.
83.
衡水市土壤墒情变化特征分析 总被引:3,自引:0,他引:3
于广英 《水科学与工程技术》2015,(1)
衡水水文局对衡水市15处土壤墒情站进行墒情监测,分别对水浇地作物、水浇地白地、旱地作物、旱地白地4种土地情况进行墒情变化分析。通过土壤墒情变化规律分析,为农业抗旱提供科学依据。 相似文献
84.
为提高土壤水分数据同化结果的精度,将基于双集合卡尔曼滤波(Dual Ensemble Kalman Filter,DEnKF)的状态-参数估计方案与简单生物圈模型(simple biosphere model 2,SiB2)相结合,同时更新土壤水分和优化模型参数(土壤属性参数)。选用2008年6月1日~10月29日黑河上游阿柔冻融观测站为参考站,开展了同化表层土壤水分观测数据的实验。研究结果表明:DEnKF可同时优化土壤属性参数和改进土壤水分估计,该方法对表层土壤水分估计的精度0.04高于EnKF算法的精度0.05。当观测数据稀少时,DEnKF算法仍然可以得到较高精度的土壤水分估计,3层土壤水分的估计精度在0.02~0.05之间。 相似文献
85.
针对传统农田灌溉施肥存在的浪费水资源、液体肥不均匀等问题,提出利用欧姆龙SYSMAC C系列可编程控制器和组态王6.55监控软件设计一套PLC灌溉施肥控制系统,利用传感器采集数据控制农田按需灌溉、按量施肥,实现自动灌溉施肥和监控管理,节省人力、实用性强。 相似文献
86.
87.
基于粒子群优化RBF神经网络原油含水率预测 总被引:4,自引:1,他引:3
原油含水率预测对于确定油井水、油层位以及估计原油产量有着非常重要意义。BP神经网络是最近常用的原油含水率预测方法,然而,由于BP神经网络存在容易陷入局部极小值、收敛速度慢等问题,影响了其预测的实用性和准确性,对此,提出基于粒子群优化RBF神经网络(PSO-RBFNN)的原油含水率预测方法,粒子群优化算法用于RBF神经网络参数优化。在分析原油含水率预测的影响因素基础上,建立粒子群优化RBF神经网络的原油含水率预测模型。实验结果表明,在原油含水率预测中,基于粒子群优化RBF神经网络比BP神经网络有着更高的预测精度。 相似文献
88.
Temporal persistence and stability of surface soil moisture in a semi-arid watershed 总被引:2,自引:0,他引:2
Satellite soil moisture products, such as those from Advanced Microwave Scanning Radiometer (AMSR), require diverse landscapes for validation. Semi-arid landscapes present a particular challenge to satellite remote sensing validation using traditional techniques because of the high spatial variability and potentially rapid rates of temporal change in moisture conditions. In this study, temporal stability analysis and spatial sampling techniques are used to investigate the representativeness of ground observations at satellite scale soil moisture in a semi-arid watershed for a long study period (March 1, 2002 to September 13, 2005). The watershed utilized, the Walnut Gulch Experimental Watershed, has a dense network of 19 soil moisture sensors, distributed over a 150 km2 study region. In conjunction with this monitoring network, intensive gravimetric soil moisture sampling conducted as part of the Soil Moisture Experiment in 2004 (SMEX04), contributed to the calibration of the network for large-scale estimation during the North American Monsoon System (NAMS). The sensor network is shown to be an excellent estimator of the watershed average with an accuracy of approximately 0.01 m3/m3 soil moisture. However, temporal stability analysis indicated that while much of the network is stable, the soil moisture spatial pattern, as represented by mean relative difference, is not replicated by the network mean relative difference pattern. Rather, the network is composed of statistical samples. Geophysical aspects of the watershed, including topography and soil type are also examined for their influence on the soil moisture variability and stability. Soil type, as characterized by bulk density, clay and sand content, was responsible for nearly 50% of the temporal stability. Topographic effects were less important in defining representativeness and stability. 相似文献
89.
R. Bindlish T.J. Jackson B. Stankov M.H. Cosh C. Watts V. Lakshmi T. Keefer 《Remote sensing of environment》2008,112(2):375-390
An unresolved issue in global soil moisture retrieval using passive microwave sensors is the spatial integration of heterogeneous landscape features to the nominal 50 km footprint observed by most low frequency satellite systems. One of the objectives of the Soil Moisture Experiments 2004 (SMEX04) was to address some aspects of this problem, specifically variability introduced by vegetation, topography and convective precipitation. Other goals included supporting the development of soil moisture data sets that would contribute to understanding the role of the land surface in the concurrent North American Monsoon System. SMEX04 was conducted over two regions: Arizona — semi-arid climate with sparse vegetation and moderate topography, and Sonora (Mexico) — moderate vegetation with strong topographic gradients. The Polarimetric Scanning Radiometer (PSR/CX) was flown on a Naval Research Lab P-3B aircraft as part of SMEX04 (10 dates of coverage over Arizona and 11 over Sonora). Radio Frequency Interference (RFI) was observed in both PSR and satellite-based (AMSR-E) observations at 6.92 GHz over Arizona, but no detectable RFI was observed over the Sonora domain. The PSR estimated soil moisture was in agreement with the ground-based estimates of soil moisture over both domains. The estimated error over the Sonora domain (SEE = 0.021 cm3/cm3) was higher than over the Arizona domain (SEE = 0.014 cm3/cm3). These results show the possibility of estimating soil moisture in areas of moderate and heterogeneous vegetation and high topographic variability. 相似文献
90.
Evaluation of AMSR-E soil moisture results using the in-situ data over the Little River Experimental Watershed, Georgia 总被引:2,自引:0,他引:2
Alok K. Sahoo Paul R. Houser Craig Ferguson Paul A. Dirmeyer 《Remote sensing of environment》2008,112(6):3142-3152
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. 相似文献