Modeling the 2D behavior of dry‐stone retaining walls by a fully discrete element method          下载免费PDF全文

James J. Oetomo  Eric Vincens  Fabian Dedecker  Jean‐Claude Morel 《国际地质力学数值与分析法杂志》2016,40(7):1099-1120

The dry‐stone retaining walls (DSRW) have been tipped as a promising solution for sustainable development. However, before recently, their behavior is relatively obscure. In this study, discrete element method (DEM) approach was applied to simulate the plane strain failure of these walls. A commercial DEM package (PFC2D™) was used throughout this study. The authors used a fully discrete approach; thus, both the wall and the backfill were modeled as discrete elements. The methodology for obtaining the micromechanical parameters was discussed in detail; this includes the three mechanical sub‐systems of DSRWs: wall, backfill and interface. The models were loaded progressively until failure, and then the results were compared with the full‐scale experimental results where the walls were loaded, respectively, with hydrostatic load and backfill. Despite its complexity and its intensive calculation time, DEM model can then be used to validate a more simplified approach. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

Rain on snow induced urban floods in cold maritime climate: Risk,indicators and trends     

Hrund Ólöf Andradóttir  Anna Rut Arnardóttir  Tarek Zaqout 《水文研究》2021,35(9):e14298

Urban floods pose a societal and economical risk. This study evaluated the risk and hydro-meteorological conditions that cause pluvial flooding in coastal cities in a cold climate. Twenty years of insurance claims data and up to 97 years of meteorological data were analysed for Reykjavík, Iceland (64.15°N; <100 m above sea level). One third of the city's wastewater collection system is combined, and pipe grades vary from 0.5% to 10%. Results highlight semi-intensive rain (<7 mm/h; ≤3 year return period) in conjunction with snow and frozen ground as the main cause for urban flood risk in a climate which undergoes frequent snow and frost cycles (avg. 13 and 19 per season, respectively). Floods in winter were more common, more severe and affected a greater number of neighbourhoods than during summer. High runoff volumes together with debris remobilized with high winds challenged the capacity of wastewater systems regardless of their age or type (combined vs. separate). The two key determinants for the number of insurance claims were antecedent frost depth and total precipitation volume per event. Two pluvial regimes were particularly problematic: long duration (13–25 h), late peaking rain on snow (RoS), where snowmelt enhanced the runoff intensity, elongated and connected independent rainfall into a singular, more voluminous (20–76 mm) event; shorter duration (7–9 h), more intensive precipitation that evolved from snow to rain. Closely timed RoS and cooling were believed to trigger frost formation. A positive trend was detected in the average seasonal snow depth and volume of rain and snowmelt during RoS events. More emphasis, therefore, needs to be placed on designing and operating urban drainage infrastructure with regard to RoS co-acting with frozen ground. Furthermore, more detailed, routine monitoring of snow and soil conditions is important to predict RoS flood events.  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   

A rain on snow climatology and temporal analysis for the eastern United States     

Lori J. Wachowicz  Thomas L. Mote  Gina R. Henderson 《自然地理学》2020,41(1):54-69

ABSTRACT

Rain-on-snow (ROS) has the potential to produce devastating floods by enhancing runoff from snowmelt. Although a common phenomenon across the eastern United States, little research has focused on ROS in this region. This study used a gridded observational snow dataset from 1960–2009 to establish a comprehensive seasonal climatology of ROS for this region. Additionally, different rain and snow thresholds were compared while considering temporal trends in ROS occurrence at four grid cells representing individual locations. Results show most ROS events occur in MAM (March-April-May). ROS events identified with rainfall >1 cm are more frequent near the east coast and events identified with >1 cm snow loss are more common in higher latitudes and/or elevations. Decreasing trends in DJF (December-January-February) ROS events were identified near the coastal areas, with increasing trends in the northern portion of the domain. Significant decreasing trends in MAM ROS are likewise present on a regional scale. Factors playing a role in snowpack depth and rainfall, such as movement of storm tracks in this region, should be considered with future work to discern mechanisms causing the changes in ROS frequency.  相似文献   

Development of a rain‐on‐snow detection algorithm using passive microwave radiometry          下载免费PDF全文

Caroline Dolant  Alexandre Langlois  Benoit Montpetit  Ludovic Brucker  Alexandre Roy  Alain Royer 《水文研究》2016,30(18):3184-3196

Currently observed climate warming in the Arctic has numerous consequences. Of particular relevance, the precipitation regime is modified where mixed and liquid precipitation can occur during the winter season leading to rain‐on‐snow (ROS) events. This phenomenon is responsible for ice crust formation, which has a significant impact on ecosystems (such as biological, hydrological, ecological and physical processes). The spatially and temporally sporadic nature of ROS events makes the phenomenon difficult to monitor using meteorological observations. This paper focuses on the detection of ROS events using passive microwave (PMW) data from a modified brightness temperature (T_B) gradient approach at 19 and 37 GHz. The approach presented here was developed empirically for observed ROS events with coincident ground‐based PMW measurements in Sherbrooke, Quebec, Canada. It was then tested in Nunavik, Quebec, with the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E). We obtained a detection accuracy of 57, 71 and 89% for ROS detection for three AMSR‐E grid cells with a maximum error of 7% when considering all omissions and commissions with regard to the total number of AMSR‐E passes throughout the winter period. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

Analysis of snowpack dynamics during the spring melt season for a sub‐alpine site using point measurements and numerical modeling          下载免费PDF全文

Mark S. Pleasants  Thijs J. Kelleners  Noriaki Ohara 《水文研究》2017,31(25):4568-4585

Snowpack dynamics through October 2014–June 2017 were described for a forested, sub‐alpine field site in southeastern Wyoming. Point measurements of wetness and density were combined with numerical modeling and continuous time series of snow depth, snow temperature, and snowpack outflow to identify 5 major classes of distinct snowpack conditions. Class (i) is characterized by no snowpack outflow and variable average snowpack temperature and density. Class (ii) is characterized by short durations of liquid water in the upper snowpack, snowpack outflow values of 0.0008–0.005 cm hr^?1, an increase in snowpack temperature, and average snow density between 0.25–0.35 g cm^?3. Class (iii) is characterized by a partially saturated wetness profile, snowpack outflow values of 0.005–0.25 cm hr^?1, snowpack temperature near 0 °C, and average snow density between 0.25–0.40 g cm^?3. Class (iv) is characterized by strong diurnal snowpack outflow pattern with values as high as 0.75 cm hr^?1, stable snowpack temperature near 0 °C, and stable average snow density between 0.35–0.45 g cm^?3. Class (v) occurs intermittently between Classes (ii)–(iv) and displays low snowpack outflow values between 0.0008–0.04 cm hr^?1, a slight decrease in temperature relative to the preceding class, and similar densities to the preceding class. Numerical modeling of snowpack properties with SNOWPACK using both the Storage Threshold scheme and Richards' equation was used to quantify the effect of snowpack capillarity on predictions of snowpack outflow and other snowpack properties. Results indicate that both simulations are able to predict snow depth, snow temperature, and snow density reasonably well with little difference between the 2 water transport schemes. Richards' equation more accurately simulates the timing of snowpack outflow over the Storage Threshold scheme, especially early in the melt season and at diurnal timescales.  相似文献   

An examination of snow redistribution over smooth land‐fast sea ice     

J. Iacozza  D. G. Barber 《水文研究》2010,24(7):850-865

An understanding of temporal evolution of snow on sea ice at different spatial scales is essential for improvement of snow parameterization in sea ice models. One of the problems we face, however, is that long‐term climate data are routinely available for land and not for sea ice. In this paper, we examine the temporal evolution of snow over smooth land‐fast first‐year sea ice using observational and modelled data. Changes in probability density functions indicate that depositional and drifting events control the evolution of snow distribution. Geostatistical analysis suggests that snowdrifts increased over the study period, and the orientation was related to the meteorological conditions. At the microscale, the temporal evolution of the snowdrifts was a product of infilling in the valleys between drifts. Results using two shore‐based climate reporting stations (Paulatuk and Tuktoyuktuk, NWT) suggest that on‐ice air temperature and relative humidity can be estimated using air temperature recorded at either station. Wind speed, direction and precipitation on ice cannot be accurately estimated using meteorological data from either station. The temporal evolution of snow distribution over smooth land‐fast sea ice was modelled using SnowModel and four different forcing regimes. The results from these model runs indicate a lack of agreement between observed distribution and model outputs. The reasons for these results are lack of meteorological measurements prior to the end of January, lack of spatially adequate surface topography and discrepancies between meteorological variables on land and ice. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

Etude isotopique de la pluie et de la neige sur le Mont Liban: premiers résultats / Isotope study of snow and rain on Mount Lebanon: preliminary results     

《水文科学杂志》2013,58(3)

Résumé

Résumé Quelques analyses isotopiques préliminaires ont été réalisées sur les précipitations pluvio-neigeuses, sur un profil de neige et sur deux sources karstiques sur le Mont Liban. Elles confirment la variabilité saisonnière du signal atmosphérique et en particulier que l’excès en deutérium est en relation avec l’origine des masses d’air et avec les recharges de vapeur sur la Méditerranée. Elles montrent également une relative stabilité du signal isotopique du couvert neigeux, peu ou pas influencé par les phénomènes de sublimation, d’évaporation ou de fonte/regel. La participation progressive de la fonte du manteau neigeux à l’alimentation des sources karstiques est qualitativement observée.  相似文献   

Monitoring over‐winter soil water dynamics in a freezing and snow‐covered environment using a thermally insulated tensiometer     

Yukiyoshi Iwata  Tomoyoshi Hirota 《水文研究》2005,19(15):3013-3019

Water potential below a frozen soil layer was continuously monitored over an entire winter period (using thermally insulated tensiometers sheltered in a heated chamber) along with other soil, snow and atmospheric variables. In early winter, the freezing front advanced under a thin snow cover, inducing upward soil water flow in the underlying unfrozen soil. The freezing front started to retreat when the snow cover became thick enough to insulate the soil, resulting in the reversal of the flow direction in the unfrozen zone. These data provide a clear illustration of soil water dynamics, which have rarely been monitored with a tensiometer. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

基于RS与GIS的茂县地区干旱河谷范围变化监测     

周颖智  魏江龙  邵怀勇 《测绘与空间地理信息》2016,(1):38-40

以遥感(RS)和地理信息系统(GIS)为平台,选取茂县地区两期Landsat卫星影像,利用ENVI对影像数据进行监督分类,提取区域内植被覆盖图。利用Arc GIS结合茂县地区河谷分布图,界定出茂县地区两个时期内干旱河谷的分布范围。结果表明:整个茂县地区干旱河谷的范围呈缩减趋势,2005年干旱河谷面积457 619 400 m2,2015年干旱河谷面积为381 971 125 m~2,干旱河谷范围缩减了75 648 275 m~2。准确地界定出干旱河谷的范围变化,对指导川西地区生态环境综合治理以及西部广大山区的生态环境可持续发展战略的实施具有重要意义。  相似文献