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Journal of Hydrologic Engineering最新文献

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Predicting Groundwater Levels in Ogallala Aquifer Wells Using Hierarchical Cluster Analysis and Artificial Neural Networks 用层次聚类分析和人工神经网络预测Ogallaa含水层的地下水位
IF 2.4 4区 工程技术 Q2 ENGINEERING, CIVIL Pub Date : 2023-03-01 DOI: 10.1061/jhyeff.heeng-5840
Lairanne Costa de Oliveira, C. Santos, Camilo Allyson Simões de Farias, Richarde Marques da Silva, Vijay Singh
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引用次数: 1
Coupled and Stand-Alone Regional Climate Modeling of Intensive Storms in Western Canada 加拿大西部强风暴的耦合和独立区域气候模拟
4区 工程技术 Q2 ENGINEERING, CIVIL Pub Date : 2023-03-01 DOI: 10.1061/jhyeff.heeng-5872
Kai Ernn Gan, Chun Chao Kuo, Thian Yew Gan, Holger Schüttrumpf, Vijay Singh, Harri Koivusalo
A coupled atmospheric-hydrologic system models the complex interactions between the land surface and the atmospheric boundary layer and the water-energy cycle from groundwater across the land surface to the top of the atmosphere. A regional climate model called weather research forecasting (WRF) was coupled with a land-surface scheme (Noah) to simulate intensive storms in Alberta of Canada. Accounting for the land-atmosphere feedback enhances the predictability of the fine-tuned WRF-Noah system. Soil moisture, vegetation, and land-surface temperature influence latent and sensible heat fluxes and modulate both thermal and dynamical characteristics of land and the lower atmosphere. WRF was set up in a two-way, 3-domain nested framework so that the output of the outermost domain (D1) was used to run the 2nd domain (D2), and the output of D2 was used to run the innermost, 3rd domain (D3). By this 2-way nesting, D3 and D2 provide the feedback to their outer domains (D2 and D1), respectively. D3 was set at a 3-km resolution adequate to simulate convective storms. WRF-Noah was forced with climate outputs from global climate models (GCMs) for the baseline period 1980–2005. A regional frequency analysis and a quantile-quantile bias correction method were applied to develop intensity-duration-frequency (IDF) curves using precipitation data simulated by WRF-Noah. The simulated baseline precipitation of central Alberta agrees well with observed data from a 13-rain gauge network of the City of Edmonton. The 5th-generation mesoscale atmospheric model (MM5) of National Center for Atmospheric Research was also set up in a 3-domain, but 1-way nesting configuration. As expected, after bias correction, precipitation simulated by MM5 was less accurate than that simulated by WRF-Noah. For storms of short durations and return periods of more than 25 years, both MM5 driven by special reports on emission scenario climate scenarios of coupled model inter-comparison project (CMIP3) and WRF-Noah driven by representative concentration pathway climate scenarios of CMIP5 projected storm intensities in central Alberta to increase from the base period to the 2050s and to the 2080s.
一个耦合的大气-水文系统模拟了陆地表面与大气边界层之间复杂的相互作用,以及地下水从陆地表面到大气顶部的水能循环。一个叫做天气研究预报(WRF)的区域气候模式与一个陆地表面方案(Noah)相结合,模拟了加拿大阿尔伯塔省的强烈风暴。考虑陆地-大气反馈增强了微调后的WRF-Noah系统的可预测性。土壤水分、植被和地表温度影响潜热通量和感热通量,调节陆地和低层大气的热力和动力特征。WRF设置在双向3域嵌套框架中,最外层域(D1)的输出用于运行第二域(D2), D2的输出用于运行最内层的第三域(D3)。通过这种双向嵌套,D3和D2分别向它们的外部域(D2和D1)提供反馈。D3的分辨率为3公里,足以模拟对流风暴。WRF-Noah是用1980-2005年基线期全球气候模式(GCMs)的气候输出强迫生成的。利用WRF-Noah模拟的降水资料,采用区域频率分析和分位数-分位数偏差校正方法,绘制了强度-持续时间-频率(IDF)曲线。模拟的阿尔伯塔中部基线降水与埃德蒙顿市13个雨量计网络的观测数据吻合得很好。国家大气研究中心第五代中尺度大气模式(MM5)也采用了三域单向嵌套配置。正如预期的那样,经过偏置校正后,MM5模拟的降水精度低于WRF-Noah模拟的降水。对于短持续时间和回归期大于25年的风暴,由耦合模式比对项目(CMIP3)排放情景气候情景特别报告驱动的MM5和由CMIP5代表性浓度路径气候情景驱动的WRF-Noah预测的阿尔伯塔中部风暴强度从基期到2050年代和2080年代都有所增加。
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引用次数: 0
Limitations of NOAA Atlas 14 Rainfall Statistics in Florida 美国国家海洋和大气管理局阿特拉斯14在佛罗里达州降雨统计的局限性
IF 2.4 4区 工程技术 Q2 ENGINEERING, CIVIL Pub Date : 2023-03-01 DOI: 10.1061/jhyeff.heeng-5887
D. Chin
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引用次数: 0
Determination of Grass Swale Hydrological Performance with Rainfall-Watershed-Swale Experimental Setup 用降雨-流域-沼泽试验装置测定草地湿地水文特性
IF 2.4 4区 工程技术 Q2 ENGINEERING, CIVIL Pub Date : 2023-03-01 DOI: 10.1061/jhyeff.heeng-5824
Kebir Emre Saraçoğlu, C. M. Kazezyılmaz-Alhan
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引用次数: 0
Hydrologic Performance of Permeable Pavers and a Dome Concrete Forming System: A Comparative Study 透水路面和穹顶混凝土成型系统的水文性能比较研究
IF 2.4 4区 工程技术 Q2 ENGINEERING, CIVIL Pub Date : 2023-03-01 DOI: 10.1061/jhyeff.heeng-5804
Tamer Almaaitah, Mikhail Moshe, Alexus Maglalang, D. Joksimovic, James Li
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引用次数: 0
Closure to “Prediction of Flow Resistance in an Open Channel over Movable Beds Using Artificial Neural Network” by Satish Kumar, Jnana Ranjan Khuntia, and Kishanjit Kumar Khatua Satish Kumar、Jnana Ranjan Khutia和Kishanjit Kumar Khatua的“使用人工神经网络预测活动床上明渠的流动阻力”
IF 2.4 4区 工程技术 Q2 ENGINEERING, CIVIL Pub Date : 2023-02-01 DOI: 10.1061/(asce)he.1943-5584.0002225
Satish Kumar, J. R. Khuntia, K. Devi, B. S. Das, K. Khatua
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引用次数: 0
Discussion of “Prediction of Flow Resistance in an Open Channel over Movable Beds Using Artificial Neural Network” by Satish Kumar, Jnana Ranjan Khuntia, and Kishanjit Kumar Khatua Satish Kumar、Jnana Ranjan Khutia和Kishanjit Kumar Khatua关于“利用人工神经网络预测活动床上明渠流动阻力”的讨论
IF 2.4 4区 工程技术 Q2 ENGINEERING, CIVIL Pub Date : 2023-02-01 DOI: 10.1061/(asce)he.1943-5584.0002226
Teng Wu, Xue Lei, Limin Xia, Jie Qin
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引用次数: 0
Uncertainty Analysis and Quantification in Flood Insurance Rate Maps Using Bayesian Model Averaging and Hierarchical BMA 基于贝叶斯模型平均和层次BMA的洪水保险费率图不确定性分析与量化
IF 2.4 4区 工程技术 Q2 ENGINEERING, CIVIL Pub Date : 2023-02-01 DOI: 10.1061/jhyeff.heeng-5851
Tao Huang, V. Merwade
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引用次数: 4
Discussion of “Prediction of Flow Resistance in an Open Channel over Movable Beds Using Artificial Neural Network” by Satish Kumar, Jnana Ranjan Khuntia, and Kishanjit Kumar Khatua Satish Kumar、Jnana Ranjan Khuntia和Kishanjit Kumar Khatua对“利用人工神经网络预测活动河床上明渠的流动阻力”的讨论
IF 2.4 4区 工程技术 Q2 ENGINEERING, CIVIL Pub Date : 2023-02-01 DOI: 10.1061/(asce)he.1943-5584.0002227
Isa Ebtehaj, H. Bonakdari
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引用次数: 0
Flood Seasonality Analysis in Iran: A Circular Statistics Approach 伊朗洪水季节性分析:循环统计方法
IF 2.4 4区 工程技术 Q2 ENGINEERING, CIVIL Pub Date : 2023-02-01 DOI: 10.1061/jhyeff.heeng-5786
Mehdi Bagheri-Gavkosh, S. M. Hosseini
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引用次数: 0
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Journal of Hydrologic Engineering
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