Bed sediment entrainment by streamflow: State of the science

In fluvial sedimentology, bed sediment entrainment by streamflow has a decisive role in controlling several fluvial processes. Owing to its huge practical importance, the subject has been painstakingly explored for over a century. However, a detailed understanding of the mechanism of the bed sediment entrainment phenomenon achieved hitherto is far from complete. The central theme of bed sediment entrainment is occupied by the sediment entrainment threshold, which varies enormously in its qualitative definition, identification and quantification encompassing a broad range of spatiotemporal scales. This article presents the state of the science of the entrainment of non‐cohesive bed sediments under a steady‐unidirectional streamflow. It begins with the diverse definitions and representations of the entrainment threshold criterion from both qualitative and quantitative perspectives, scrutinising its suitability and ambit of applicability. Then, the effects of energetic factors that drive the entrainment threshold criterion are critically appraised. The indispensable mechanisms of bed sediment entrainment, including the theoretical background and modelling strategies, the role of turbulent bursting phenomenon and the phenomenological perspective into the origin of the scaling laws of sediment entrainment, are explained. Throughout the article, special emphasis is given to the strengths and weaknesses of the current state of the science. In addition, a deliberate attempt is made to invoke the thought‐provoking ideas on the multifarious features of bed sediment entrainment. Finally, the innovative perspectives on the bed sediment entrainment are provided and the concluding remarks are made, elucidating the major challenges and suggesting the prospective ways to resolve them as a future scope of research.     

A) A. I. H. S. / A) A. I. H. S.

Abstract

A new method is presented to generate stationary multi-site hydrological time series. The proposed method can handle flexible time-step length, and it can be applied to both continuous and intermittent input series. The algorithm is a departure from standard decomposition models and the Box-Jenkins approach. It relies instead on the recent advances in statistical science that deal with generation of correlated random variables with arbitrary statistical distribution functions. The proposed method has been tested on 11 historic weekly input series, of which the first seven contain flow data and the last four have precipitation data. The article contains an extensive review of the results.

Editor D. Koutsoyiannis

Citation Ilich, N., 2014. An effective three-step algorithm for multi-site generation of stochastic weekly hydrological time series. Hydrological Sciences Journal, 59 (1), 85–98.     

雷达CFAR检测的仿真研究

杂波背景中区分出有用目标回波的恒虚警(CFAR)检测技术,是直接影响雷达性能的关键技术之一。主要研究了CFAR检测的基本理论,重点研究了ML类CFAR算法中的邻近单元平均恒虚警(CA-CFAR)的检测算法,推导了其检测概率和虚警概率表达式,通过计算机仿真比较了在不同窗长情况下的检测门限。     

Supraglacial melt channel networks in the Jakobshavn Isbræ region during the 2007 melt season

Supraglacial channels are an important mechanism for surface water transport over the ablation zone of western Greenland. The first assessment of the spatio‐temporal distribution of surface melt channels and their relationship to supraglacial lakes over the Jakobshavn Isbræ region of Western Greenland was analysed using Landsat Enhanced Thematic Mapper Plus panchromatic images during the 2007 melt season. A total of 1188 melt channels were delineated and show an increase in the number of melt channels throughout the season, reaching a peak on 9 August. Water‐filled melt channels advanced to a maximum elevation of 1647 m on 9 August and attained a minimum average slope of 0.009 on 8 July. The ablation zone demonstrates two hydrologic modes, where crevasse and moulin terminating channels dominate at elevations <800 m and higher‐order channel networks >800 m. Development of higher‐order networks is interrupted by flow divergence due to partitioning of melt water into vertical infiltration through moulins and crevasse fields prevalent at lower elevations. Tributary and connector networks between 800 and 1200 m in elevation are correlated with fewer lake occurrences, lower surface velocities (~50 m a^?1), and ice flow dominated by internal deformation over basal sliding. High‐order channels are associated with lake basins that exceed melt water storage capacity. Evolution of channel networks is coupled to changes in melt water production, runoff, and ice dynamics with implication for the englacial and subglacial environments. © 2013 The Authors. Hydrological Processes Published by John Wiley & Sons, Ltd.     

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

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.     

喜马拉雅山中东段蒙达扛日冰川水文与物质平衡观测研究

2007年7—9月,在喜马拉雅山中东段蒙达扛日冰川进行了水文和物质平衡的同步观测.结果表明：观测期间,蒙达扛日冰川表现为物质负平衡,冰川消融集中在7月和8月中上旬.观测的70 d内,花杆区冰川物质平衡量为-435～-711 mm,日平均值为-6.2～-10.2 mm.d-1,冰川的总物质平衡量为-355 mm.蒙达扛日...     

Modelling landscape evolution

Geomorphology is currently in a period of resurgence as we seek to explain the diversity, origins and dynamics of terrain on the Earth and other planets in an era of increased environmental awareness. Yet there is a great deal we still do not know about the physics and chemistry of the processes that weaken rock and transport mass across a planet's surface. Discovering and refining the relevant geomorphic transport functions requires a combination of careful field measurements, lab experiments, and use of longer‐term natural experiments to test current theory and develop new understandings. Landscape evolution models have an important role to play in sharpening our thinking, guiding us toward the right observables, and mapping out the logical consequences of transport laws, both alone and in combination with other salient processes. Improved quantitative characterization of terrain and process, and an ever‐improving theory that describes the continual modification of topography by the many and varied processes that shape it, together with improved observation and qualitative and quantitative modelling of geology, vegetation and erosion processes, will provide insights into the mechanisms that control catchment form and function. This paper reviews landscape theory – in the form of numerical models of drainage basin evolution and the current knowledge gaps and future computing challenges that exist. Copyright © 2010 John Wiley & Sons, Ltd.     

黑河流域上游寒区水文遥感-地面同步观测试验

介绍了黑河流域上游寒区水文遥感-地而同步观测试验,论述了试验目标与研究内容、试验区的选择设计以及寒区水文长期观测试验.上游试验以理解寒区水文过程、提高寒区定量遥感水平为主旨,以积雪和冻土为主要研究对象,开展了微波辐射计、高光谱成像仪航空遥感和地面同步观测,并选择典型小流域进行长期寒区水文过程观测与研究.试验集中在冰沟积雪小流域、阿柔草场和扁都口裸露耕地3个不蚓地表覆盖区,以积雪和冻土变量与参数的测量为主.同步试验在流域尺度、重点试验区、加密观测区和观测小区4个尺度上展开,分别布置了加密的地面同步观测、通量和气象水文观测、降雨、径流及其它水文要素观测网络;航空飞行传感器分别采用微波辐射计、高光谱成像仪、热红外成像仪和多光谱CCD相机,收集获取了试验区丰富的可见光/近红外、热红外、主被动微波等卫星数据.通过试验,初步构建了上游寒区航空-卫星-地面综合数据集,可以应用于改进和验证寒区陆面/水文过程模型.     

Influences of climate,terrain and land cover on stream salinity in southeastern Australia,and implications for management through reforestation

Reforestation of cleared land has the potential to reduce groundwater recharge, salt mobilization and streamflow. Stream salinity change is the net result of changes in stream salt load and streamflow. The net effect of these changes varies spatially as a function of climate, terrain and land cover. Successful natural resource management requires methods to map the spatial variability of reforestation impacts. We investigated salinity data from 2000 bores and streamflow and salinity measurements from 27 catchments in the Goulburn–Broken region in southeast Australia to assess the main factors determining stream salinity and opportunities for management through reforestation. For groundwater systems of similar geology, relationships were found between average annual rainfall and groundwater salinity and between groundwater salinity and low‐flow salinity. Despite its simplicity, we found that the steady‐state component of a simple conceptual coupled water–salt mass balance model (BC2C) adequately explained the spatial variation in streamflow and salinity. The model results suggest the efficiency of afforestation to reduce stream salinity could be increased by more than an order of magnitude through spatial planning. However, appreciable reductions in stream salinity in large rivers through land cover change alone would still require reforestation on an unprecedented scale. Copyright © 2008 John Wiley & Sons, Ltd.