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1.
Jeffrey Wade Laura Lautz Christa Kelleher Philippe Vidon Julianne Davis Julio Beltran Casey Pearce 《水文研究》2020,34(26):5340-5353
Beaver dam analogues (BDAs) are a cost-effective stream restoration approach that leverages the recognized environmental benefits of natural beaver dams on channel stability and local hydrology. Although natural beaver dams are known to exert considerable influence on the hydrologic conditions of a stream system by mediating geomorphic processes, nutrient cycling, and groundwater–surface water interactions, the impacts of beaver-derived restoration methods on groundwater–surface water exchange are poorly characterized. To address this deficit, we monitored hyporheic exchange fluxes and streambed porewater biogeochemistry across a sequence of BDAs installed along a central Wyoming stream during the summer of 2019. Streambed fluxes were quantified by heat tracing methods and vertical hydraulic gradients. Biogeochemical activity was evaluated using major ion porewater chemistry and principal component analysis. Vertical fluxes of approximately 1.0 m/day were observed around the BDAs, as was the development of spatially heterogeneous zones of nitrate production, groundwater upwelling, and anaerobic reduction. Strong contrasts in hyporheic zone processes were observed across BDAs of differing sizes. This suggests that structures may function with size-dependent behaviour, only altering groundwater–surface water interactions after a threshold hydraulic step height is exceeded. Patterns of hyporheic exchange and biogeochemical cycling around the studied BDAs resemble those around natural beaver dams, suggesting that BDAs may provide comparable benefits to channel complexity and near-stream function over a 1-year period. 相似文献
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A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted. 相似文献
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Images from satellite platforms are a valid aid in order to obtain distributed information about hydrological surface states and parameters needed in calibration and validation of the water balance and flood forecasting. Remotely sensed data are easily available on large areas and with a frequency compatible with land cover changes. In this paper, remotely sensed images from different types of sensor have been utilized as a support to the calibration of the distributed hydrological model MOBIDIC, currently used in the experimental system of flood forecasting of the Arno River Basin Authority. Six radar images from ERS‐2 synthetic aperture radar (SAR) sensors (three for summer 2002 and three for spring–summer 2003) have been utilized and a relationship between soil saturation indexes and backscatter coefficient from SAR images has been investigated. Analysis has been performed only on pixels with meagre or no vegetation cover, in order to legitimize the assumption that water content of the soil is the main variable that influences the backscatter coefficient. Such pixels have been obtained by considering vegetation indexes (NDVI) and land cover maps produced by optical sensors (Landsat‐ETM). In order to calibrate the soil moisture model based on information provided by SAR images, an optimization algorithm has been utilized to minimize the regression error between saturation indexes from model and SAR data and error between measured and modelled discharge flows. Utilizing this procedure, model parameters that rule soil moisture fluxes have been calibrated, obtaining not only a good match with remotely sensed data, but also an enhancement of model performance in flow prediction with respect to a previous calibration with river discharge data only. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
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The belemnite species Praeactinocamax primus (Arkhangelsky, 1912) and Belemnocamax boweri Crick, 1910 are described from the Cenomanian of the abandoned limestone quarry section of Hoppenstedt (Sachsen-Anhalt, northern Germany). They co-occur in the upper part of a prominent tripartite bioclastic limestone bed associated with the ammonite Acanthoceras rhotomagense, indicating the primus Event of the lower middle Cenomanian A. rhotomagense ammonite Zone. An integrated stratigraphical calibration including carbon stable isotope correlation to southern England suggests that the belemnite event horizon at Hoppenstedt occupies exactly the same chronostratigraphical position as elsewhere, highlighting the strictly isochronous character of the primus Event across northwestern Europe. Furthermore, stratigraphical gaps in the Hoppenstedt succession are evaluated. 相似文献
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This article describes absolute calibration results for both JASON-1 and TOPEX Side B (TSB) altimeters obtained at the Lake Erie calibration site, Marblehead, Ohio, USA. Using 15 overflights, the estimated JASON altimeter bias at Marblehead is 58 ± 38 mm, with an uncertainty of 19 mm based on detailed error analysis. Assuming that the TSB bias is negligible, relative bias estimates using both data from the TSB-JASON formation flight period and data from 48 water level gauges around the entire Great Lakes confirmed the Marblehead results. Global analyses using both the formation flight data and dual-satellite (TSB and JASON) crossovers yield a similar relative bias estimate of 146 ± 59 mm, which agrees well with open ocean absolute calibration results obtained at Harvest, Corsica, and Bass Strait (e.g., Watson et al. 2003). We find that there is a strong dependence of bias estimates on the choice of sea state bias (SSB) models. Results indicate that the invariant JASON instrument bias estimated oceanwide is 71 mm, with additional biases of 76 mm or 28 mm contributed by the choice of Collecte Localisation Satellites (CLS) SSB or Center for Space Research (CSR) SSB model, respectively. Similar analysis in the Great Lakes yields the invariant JASON instrument bias at 19 mm, with the SSB contributed biases at 58 mm or 13 mm, respectively. The reason for the discrepancy is currently unknown and warrants further investigation. Finally, comparison of the TOPEX/POSEIDON mission (1992-2002) data with the Great Lakes water level gauge measurements yields a negligible TOPEX altimeter drift of 0.1 mm/yr. 相似文献
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《海洋技术学报》2024,(1)
利用传感器对水体叶绿素a 浓度进行原位测量是获取实时、连续、长时间序列数据的重要手段。本文在对RBR 传感器和ECO(Environmental Characterization Optics)传感器进行原理分析和线性度、稳定性、重复性等基本性能测试的基础上,利用单一藻种培养液和2020年南海北部海域现场数据校准传感器,并对新的传感器校准系数进行验证。结果表明:两台传感器使用新系数比原出厂系数的叶绿素a 浓度测量准确度有明显提高。RBR 传感器现场数据校准系数的计算结果与叶绿素a 标准值误差最小,平均绝对误差从1.93 μg/L 减小到0.35 μg/L,平均相对误差从55.1%减小到10.9%;ECO 传感器藻液系数明显优于出厂系数和现场数据校准系数的计算结果,平均绝对误差从1.76 μg/L 减小到0.59 μg/L,平均相对误差从50.3%减小到15.1%。传感器测量准确度的提高,可为海洋环境监测、海洋生态灾害预警等工作获取真实可靠数据提供支撑。 相似文献