Modelling spatial variations in dissolved oxygen in fine‐grained streams under uncertainty          下载免费PDF全文

Vahid Zahraeifard  Zhiqiang Deng  Ronald Malone 《水文研究》2015,29(2):212-224

This paper presents a novel triple‐layer model, called VART DO‐3L, for simulation of spatial variations in dissolved oxygen (DO) in fine‐grained streams, characterized by a fluid mud (fluff or flocculent) layer (an advection‐dominated storage zone) as the interface between overlying stream water and relatively consolidated streambed sediment (a diffusion‐dominated storage zone). A global sensitivity analysis is conducted to investigate the sensitivity of VART DO‐3L model input parameters. Results of the sensitivity analysis indicate that the most sensitive parameter is the relative size of the advection‐dominated storage zones (A_s/A), followed by a lumped reaction term (R) for the flocculent layer, biological reaction rate (μ_o) in diffusive layer and biochemical oxygen demand concentration (L) in water column. In order to address uncertainty in model input parameters, Monte Carlo simulations are performed to sample parameter values and to produce various parameter combinations or cases. The VART DO‐3L model is applied to the Lower Amite River in Louisiana, USA, to simulate vertical and longitudinal variations in DO under the cases. In terms of longitudinal variation, the DO level decreases from 7.9 mg l at the Denham Springs station to about 2.89 mg l^?1 at the Port Vincent station. In terms of vertical variation, the DO level drops rapidly from the overlying water column to the advection‐dominated storage zone and further to the diffusive layer. The DO level (C_F) in the advective layer (flocculent layer) can reach as high as 40% of DO concentration (C) in the water column. The VART DO‐3L model may be applied to similar rivers for simulation of spatial variations in DO level. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

Piecewise prediction model for watershed‐scale erosion and sediment yield of individual rainfall events on the Loess Plateau,China          下载免费PDF全文

Yu Guo‐Qiang  Zhang Mao‐Sheng  Li Zhan‐Bin  Li Peng  Zhang Xia  Cheng Sheng‐Dong 《水文研究》2014,28(21):5322-5336

Establishing a universal watershed‐scale erosion and sediment yield prediction model represents a frontier field in erosion and soil/water conservation. The research presented here was conducted on the Chabagou watershed, which is located in the first sub‐region of the hill‐gully area of the Loess Plateau, China. A back‐propagation artificial neural model for watershed‐scale erosion and sediment yield was established, with the accuracy of the model, then compared with that of multiple linear regression. The sensitivity degree of various factors to erosion and sediment yield was quantitatively analysed using the default factor test. On the basis of the sensitive factors and the fractal information dimension, the piecewise prediction model for erosion and sediment yield of individual rainfall events was established and further verified. The results revealed the back‐propagation artificial neural network model to perform better than the multiple linear regression model in terms of predicting the erosion modulus, with the former able to effectively characterize dynamic changes in sediment yield under comprehensive factor conditions. The sensitivity of runoff erosion power and runoff depth to the erosion and sediment yield associated with individual rainfall events was found to be related to the complexity of surface topography. The characteristics of such a hydrological response are thus closely related to topography. When the fractal information dimension is greater than the topographic threshold, the accuracy of prediction using runoff erosion power is higher than that of using runoff depth. In contrast, when the fractal information dimension is smaller than the topographic threshold, the accuracy of prediction using runoff depth is higher than that of using runoff erosion power. The developed piecewise prediction model for watershed‐scale erosion and sediment yield of individual rainfall events, which introduces runoff erosion power and runoff depth using the fractal information dimension as a boundary, can be considered feasible and reliable and has a high prediction accuracy. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures          下载免费PDF全文

J. L. McCreight  A. G. Slater  H. P. Marshall  B. Rajagopalan 《水文研究》2014,28(3):933-957

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

Assessing regression‐based statistical approaches for downscaling precipitation over North America          下载免费PDF全文

Jie Chen  François P. Brissette  Robert Leconte 《水文研究》2014,28(9):3482-3504

This paper assesses linear regression‐based methods in downscaling daily precipitation from the general circulation model (GCM) scale to a regional climate model (RCM) scale (45‐ and 15‐km grids) and down to a station scale across North America. Traditional downscaling experiments (linking reanalysis/dynamical model predictors to station precipitation) as well as nontraditional experiments such as predicting dynamic model precipitation from larger‐scale dynamic model predictors or downscaling dynamic model precipitation from predictors at the same scale are conducted. The latter experiments were performed to address predictability limit and scale issues. The results showed that the downscaling of daily precipitation occurrence was rarely successful at all scales, although results did constantly improve with the increased resolution of climate models. The explained variances for downscaled precipitation amounts at the station scales were low, and they became progressively better when using predictors from a higher‐resolution climate model, thus showing a clear advantage in using predictors from RCMs driven by reanalysis at its boundaries, instead of directly using reanalysis data. The low percentage of explained variances resulted in considerable underestimation of daily precipitation mean and standard deviation. Although downscaling GCM precipitation from GCM predictors (or RCM precipitation from RCM predictors) cannot really be considered downscaling, as there is no change in scale, the exercise yields interesting information as to the limit in predictive ability at the station scale. This was especially clear at the GCM scale, where the inability of downscaling GCM precipitation from GCM predictors demonstrates that GCM precipitation‐generating processes are largely at the subgrid scale (especially so for convective events), thus indicating that downscaling precipitation at the station scale from GCM scale is unlikely to be successful. Although results became better at the RCM scale, the results indicate that, overall, regression‐based approaches did not perform well in downscaling precipitation over North America. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

Hydrological impact of a reservoir network in the upper Gan River Basin,China     

Ningpeng Dong  Zhongbo Yu  Chuanguo Yang  Mingxiang Yang  Wenzhuo Wang 《水文研究》2019,33(12):1709-1723

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网络大数据下的中国现代食甜习惯空间分布特征及其影响因素研究     

姚可桢  岳书平 《地球信息科学学报》2020,22(6):1202-1215

饮食地理文化作为地域文化中最具地方特色的重要元素,在现代人口大规模流动背景下呈现出全新的多样化局面,而基于传统认知的“南甜北咸”的地域分异已然不能代表中国现代食甜分布的空间特征。因此,本文采用网络爬虫技术,获取我国大陆31个省会城市共计约2000万条美食消费数据,从传统类菜品、主食类菜品、饮料类和甜品类菜品4个方面计算城市食甜度,在ArcGIS、MySQL软件支持下,借助GIS空间分析和数理统计方法探究我国现代食甜习惯的空间分布特征,分析影响食甜分布的因素。研究发现：① 中国食甜在空间分布上存在显著的地域分异特征,聚类分析评价参数R ²高达0.88,现代食甜习惯总体呈现“东高北中,西微内低”的包围式格局;② 从整体抑或局部角度,在1%显著性水平上莫兰指数均为正,中国食甜分布呈现显著的空间正相关关系,形成特色鲜明的3个地理集聚区,即以苏浙沪闽为主的东南沿海高甜集聚区,以渝黔川为主的西南内陆低甜集聚区和以陕宁为主的西北内陆低甜集聚区;③ 构建了中国现代食甜习惯分布影响因素模型,其拟合精度为0.82,分析结果显示降水、湿度、气温等气象要素及地理位置是影响现代我国食甜空间分布的重要因素。  相似文献   

The exponential decline in saturated hydraulic conductivity with depth: a novel method for exploring its effect on water flow paths and transit time distribution          下载免费PDF全文

A. A. Ameli  J. J. McDonnell  K. Bishop 《水文研究》2016,30(14):2438-2450

The strong vertical gradient in soil and subsoil saturated hydraulic conductivity is characteristic feature of the hydrology of catchments. Despite the potential importance of these strong gradients, they have proven difficult to model using robust physically based schemes. This has hampered the testing of hypotheses about the implications of such vertical gradients for subsurface flow paths, residence times and transit time distribution. Here we present a general semi‐analytical solution for the simulation of 2D steady‐state saturated‐unsaturated flow in hillslopes with saturated hydraulic conductivity that declines exponentially with depth. The grid‐free solution satisfies mass balance exactly over the entire saturated and unsaturated zones. The new method provides continuous solutions for head, flow and velocity in both saturated and unsaturated zones without any interpolation process as is common in discrete numerical schemes. This solution efficiently generates flow pathlines and transit time distributions in hillslopes with the assumption of depth‐varying saturated hydraulic conductivity. The model outputs reveal the pronounced effect that changing the strength of the exponential decline in saturated hydraulic conductivity has on the flow pathlines, residence time and transit time distribution. This new steady‐state model may be useful to others for posing hypotheses about how different depth functions for hydraulic conductivity influence catchment hydrological response. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

Analysis of rectangular piles subjected to lateral loads in nonhomogeneous soil using a Winkler model approach          下载免费PDF全文

Hiroyoshi Hirai 《国际地质力学数值与分析法杂志》2015,39(9):937-968

An analytical approach using a Winkler model based on two lateral soil displacement components in a three‐dimensional soil is investigated to provide analytical solutions of horizontal response of a rectangular pile subjected to lateral loads in nonhomogeneous soil. The two lateral displacement components of a soil surrounding the rectangular pile are represented by the Fourier series of displacement potential functions in the elastic three‐dimensional analysis. The lateral stiffness coefficient of the rectangular pile shaft in nonhomogeneous soil is derived from the rocking stiffness coefficient taking into account rocking rotation of a rigid pile shaft. The relationship between horizontal displacement, rotation, moment, and shear force for the rectangular pile subjected to horizontal loads in nonhomogeneous soil is obtainable in the form of the recurrence equation. The formulation of lateral displacement and rotation for a rectangular pile subjected to lateral loads on the pile base in nonhomogeneous soil is proposed by taking into account Mindlin's equation and the equivalent thickness for soil layers in the equivalent elastic method. The difference of lateral behavior between square and circular piles subjected to lateral loads is insignificant. The effect of aspect ratio of the rectangular pile on the lateral behavior is great for the lower stiffness ratio between pile and soil and the larger length–equivalent diameter ratio. The effect of the value of Poisson's ratio of soil on lateral stiffness coefficient is relatively small except Poisson's ratio close to 0.5. The comparison of the results calculated by the current method for a rectangular pile subjected to lateral loads in nonhomogeneous soil has shown good agreement with those obtained from the analytical methods and the finite element method. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

Linking tracers,water age and conceptual models to identify dominant runoff processes in a sparsely monitored humid tropical catchment          下载免费PDF全文

Christian Birkel  Chris Soulsby 《水文研究》2016,30(24):4477-4493

Assessing catchment runoff response remains a key research frontier because of limitations in current observational techniques to fully characterize water source areas and transit times in diverse geographical environments. Here, we report a study that combines empirical data with modelling to identify dominant runoff processes in a sparsely monitored humid tropical catchment. The analysis integrated isotope tracers into conceptual rainfall–runoff models of varying complexity (from 5 to 11 calibrated parameters) that are able to simulate discharge and tracer concentrations and track the evolving age of stream water exiting the catchment. The model structures can be seen as competing hypotheses of catchment functioning and were simultaneously calibrated against uncertain streamflow gaugings and a 2‐year daily isotope rainfall–runoff record. Comparison of the models was facilitated using global parameter sensitivity analysis and the resulting effect on calibration. We show that a variety of tested model structures reproduced water and tracer dynamics in stream, but the simpler models failed to adequately reproduce both. The resulting water age distributions of the tested models varied significantly with little similarity between the stream water age and stored water age distributions. The sensitivity analysis revealed that only some of the more complex models (from eight parameters) could be better constrained to infer more plausible water age distributions and catchment storage estimates. These models indicated that the age of water stored in the catchment is generally older compared with the age of water fluxes, with evapotranspiration age being younger compared with streamflow. However, the water age distributions followed a similar temporal behaviour dominated by climatic seasonality. Stream water ages increased during the dry season (greater than 1 year) and decreased with increased streamflow (a few weeks old) during the wet season. We further show that the ratios of the streamwater age to stored water age distribution and the water age distribution of actual evapotranspiration to the stored water age distribution from constrained models could potentially serve as useful hydrological indicators of catchment functioning. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

A two‐dimensional coupled hydromechanical discontinuum model for simulating rock hydraulic fracturing          下载免费PDF全文

Yu‐Yong Jiao  Huan‐Qiang Zhang  Xiu‐Li Zhang  Hai‐Bo Li  Qing‐Hui Jiang 《国际地质力学数值与分析法杂志》2015,39(5):457-481

Within the framework of our discontinuous deformation analysis for rock failure algorithm, this paper presents a two‐dimensional coupled hydromechanical discontinuum model for simulating the rock hydraulic fracturing process. In the proposed approach, based on the generated joint network, the calculation of fluid mechanics is performed first to obtain the seepage pressure near the tips of existing cracks, and then the fluid pressure is treated as linearly distributed loads on corresponding block boundaries. The contribution of the hydraulic pressure to the initiation/propagation of the cracks is considered by adding the components of these blocks into the force matrix of the global equilibrium equation. Finally, failure criteria are applied at the crack tips to determine the occurrence of cracking events. Several verification examples are simulated, and the results show that this newly proposed numerical model can simulate the hydraulic fracturing process correctly and effectively. Although the numerical and experimental verifications focus on one unique preexisting crack, because of the capability of discontinuous deformation analysis in simulating block‐like structures, the proposed approach is capable of modeling rock hydraulic fracturing processes. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献