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Hydrological simulation and assessment in a dam–sluice regulated river basin are a complex and challenging issue. In this article, an improved SWAT2000 modelling system was developed that incorporated the Shuffled complex evolution (SCE‐UA) optimization algorithm and the multi‐site and multi‐objective calibration strategy. The implication of multi‐objective is different for different types of outlets, i.e. streamflow for an ordinary outlet, inflow for a sluice, and water storage for a reservoir. Model parameters were redefined to improve model simulations. The surface runoff lag time (SURLAG) was extended as a spatially distributed parameter, and a correction coefficient was introduced to modify the saturated hydraulic conductivity. The modelling system was then applied to the Huai River basin of China under various climatic conditions, including a very dry year (1999), a dry year (1981), an average year (1971), and wet year (1991). In all, 26 dams and 35 sluices were considered, among which about 20 dams/sluices were used for model calibration. The impact assessment primarily focused on the very dry year (1999). The results indicated that the released water from large reservoirs was blocked in the river channels by sluices located downstream. In the very dry year, the dam–sluice operations could result in an increase of the runoff volume during the non‐flood season and a decrease in runoff during the flood season, but the changing magnitude during the non‐flood season was much greater. An important conclusion of this case study is that the sluices in the Sha‐Yin branch located in the north region and the dams in the southern mountainous region above the Wangjiaba Hydrological Station have played the most significant role in regulating the streamflow of the entire river basin. The methods addressed in this article can simulate hydrological regime in the river basins regulated by dams and sluices under different climatic conditions at the whole‐watershed scale. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
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We aim a better understanding of the effect of spring-time snow melt on the remotely sensed scene reflectance by using an extensive amount of optical spectral data obtained from an airborne hyperspectral campaign in Northern Finland. We investigate the behaviour of thin snow reflectance for different land cover types, such as open areas, boreal forests and treeless fells. Our results not only confirm the generally known fact that the reflectance of a melting thin snow layer is considerably lower than that of a thick snow layer, but we also present analyses of the reflectance variation over different land covers and in boreal forests as a function of canopy coverage. According to common knowledge, the highly variating reflectance spectra of partially transparent, most likely also contaminated thin snow pack weakens the performance of snow detection algorithms, in particular in the mapping of Fractional Snow Cover (FSC) during the end of the melting period. The obtained results directly support further development of the SCAmod algorithm for FSC retrieval, and can be likewise applied to develop other algorithms for optical satellite data (e.g. spectral unmixing methods), and to perform accuracy assessments for snow detection algorithms.A useful part of this work is the investigation of the competence of Normalized Difference Snow Index (NDSI) in snow detection in late spring, since it is widely used in snow mapping. We conclude, based on the spectral data analysis, that the NDSI -based snow mapping is more accurate in open areas than in forests. However, at the very end of the snow melting period the behavior of the NDSI becomes more unstable and unpredictable in non-forests with shallow snow, increasing the inaccuracy also in non-forested areas. For instance in peatbogs covered by melting snow layer (snow depth < 30 cm) the mean NDSI -0.6 was observed, having coefficient of variation as high as 70%, whereas for deeper snow packs the mean NDSI shows positive values. 相似文献
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Hydrologic response to future land use change in the Upper Mississippi River Basin by the end of 21st century 总被引：1，自引：0，他引：1 
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下载免费PDF全文 This study demonstrates the spatial variation in hydrologic processes across the Upper Mississippi River Basin (UMRB) by the end of 21st century, by ingesting FOREcasting Scenarios (FORE‐SCE) of Land‐use Change projections into a physics‐based hydrologic model—Soil and Water Assessment Tool. The model is created for UMRB (440,000 km2), using the National Landcover Database of year 2001 and climate data of 1991–2010. Considering 1991–2010 as the baseline reference period, FORE‐SCE projections of year 2091 under three scenarios (A1B, A2, and B1 from the Intergovernmental Panel on Climate Change) are separately assimilated into the calibrated model, whereas climate input is kept the same as in the baseline. Modeling results suggest an increase of 0.5% and 3.5% in the average annual streamflow at the basin outlet (Grafton, Illinois) during 2081–2100, respectively, for A1B and A2, whereas for B1, streamflow would decrease by 1.5%. Under the “worst case” A2 scenario, 6% and 133% increase, respectively, in agricultural and urban areas with 30% depletion of forest and grassland would result into 70% increase in surface runoff, 20% decrease in soil moisture, and 4% decrease in evapotranspiration in certain parts of the basin. Conversion of cropland, forest, or grassland to perennial hay/pasture areas would lower surface runoff by 25% especially in the central region, whereas persistent forest cover in the northern region would cause up to 7% increase in evapotranspiration. The ecosystem in the lower half of UMRB is likely to become adverse, as dictated by a composite water–energy balance indicator. Future land use change extents and resultant hydrologic responses are found significantly different under A2, A1B, and B1 scenarios, which resonates the need for multi‐scenario ensemble assessments towards characterizing a probable future. The spatial variation of hydrologic processes as shown here helps to identify potential “hot spots,” giving ways to adopt more effective policy alternatives at regional level. 相似文献
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针对传统MAD方法在探测钟差粗差方面存在的不足,提出一种基于小波分析的钟差数据粗差探测与处理方法。该方法利用小波变换的多尺度分析能力,将含有粗差的数据分解为低频小波系数和各层的高频小波系数,并在不同时间尺度下进行粗差探测和消除。使用CODE提供的BDS精密钟差数据进行实验,分析不同小波函数及不同分解尺度对预处理效果的影响,并与传统MAD方法比较,发现利用小波分解预处理后的数据在钟差预报方面有明显优势,预报精度平均提高10%。 相似文献
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