Controls on modern alluvial fan processes in the central Alps,northern Italy

Alluvial fan development in Alpine areas is often affected by catastrophic sedimentary processes associated with extreme ?oods events, causing serious risks for people living on the fans. Hazard assessment in these areas depends on proper identi?cation of the dominant sedimentary processes on the fans. Data from a set of 209 alluvial fans from the central Alps of Italy are presented in this paper and analysed with the help of various statistical techniques (linear regression, principal components analysis, cluster analysis, discriminant analysis and logistic regression). First, we used modern sedimentary facies and historical records (?ood events since 15th century), to distinguish between the two dominant sedimentary processes on alluvial fans: debris ?ows and stream?ows. Then, in order to analyse the main controls on past and present fan processes, 36 morphological, geological and land‐use variables were analysed. As with observations for arid‐environment fans, catchment morphology is the most in?uential factor in the study area, whereas geology and land use are minor controls. The role of climatic change and landsliding within the catchments also seems to be very important and is discussed. Statistical techniques also help in differentiating groups of alluvial fans by sets of controlling factors, including stage and type of evolution. Finally, by using discriminant analysis and logistic regression, we classi?ed alluvial fans according to the dominant sedimentary process, with a success rate ranging between 75 and 92 per cent. Copyright © 2004 John Wiley & Sons, Ltd.     

几种空气质量预报方法的预报效果对比分析

目前应用于我国各个城市空气质量预报业务的预报方法主要有三种 :数值模式预报、统计预报和综合经验预报。这几种预报方法都有其各自的优势 ,同时也存在一些不足。应用以上方法对天津市市区进行空气质量业务预报 ,通过实测资料与预报结果进行对比分析 ,给出这几种方法在天津市区空气质量预报中的预报效果客观评价。     

雾探测器现场对比试验方法研究

简单介绍了WT-1型雾探测器的结构和现场试验情况，对试验期间(2001年12月1日至2002年5月31日)所获取的数据进行了统计分析，计算了试验期间雾探测器数据的缺测率、与人工观测数据及芬兰同类产品的测量结果之间的相关系数，三者之间的相符率，以及数据随时间的变化趋势。以数据处理结果为依据，客观地阐述了雾探测器的优缺点，以及与目前世界上普遍认可的芬兰能见度仪之间的差距，同时也提出了雾探测器改进的一些建议。     

泰斯模型的统计分析求解

本文利用统计分析来求解水文地质参数,原理简单,解是唯一的。其基本思想是利用非稳定流抽水试验获得的s-t系列资料,以泰斯公式为参考模型,建立试验系列的非线性统计模型,求解导水系数T和贮水系数μ 。统计模型既可利用目前先进的软件辅助求解,亦可利用一台可编程计算器完成计算。本文借助一个实例,应用MATLAB语言的统计分析工具的多元回归分析模块进行求解,获得了理想的结果。     

Basal sediment evacuation by subglacial meltwater: suspended sediment transport from Haut Glacier d'Arolla,Switzerland

Proglacial suspended sediment transport was monitored at Haut Glacier d'Arolla, Switzerland, during the 1998 melt season to investigate the mechanisms of basal sediment evacuation by subglacial meltwater. Sub‐seasonal changes in relationships between suspended sediment transport and discharge demonstrate that the structure and hydraulics of the subglacial drainage system critically influenced how basal sediment was accessed and entrained. Under hydraulically inefficient subglacial drainage at the start of the melt season, sediment availability was generally high but sediment transport increased relatively slowly with discharge. Later in the melt season, sediment transport increased more rapidly with discharge as subglacial meltwater became confined to a spatially limited network of channels following removal of the seasonal snowpack from the ablation area. Flow capacity is inferred to have increased more rapidly with discharge within subglacial channels because rapid changes in discharge during highly peaked diurnal runoff cycles are likely to have been accommodated largely by changes in flow velocity. Basal sediment availability declined during channelization but increased throughout the remainder of the monitored period, resulting in very efficient basal sediment evacuation over the peak of the melt season. Increased basal sediment availability during the summer appears to have been linked to high diurnal water pressure variation within subglacial channels inferred from the strong increase in flow velocity with discharge. Basal sediment availability therefore appears likely to have been increased by (1) enhanced local ice‐bed separation leading to extra‐channel flow excursions and[sol ]or (2) the deformation of basal sediment towards low‐pressure channels due to a strong diurnally reversing hydraulic gradient between channels and areas of hydraulically less‐efficient drainage. Copyright © 2005 John Wiley & Sons, Ltd.     

Prediction of river water temperature: a comparison between a new family of hybrid models and statistical approaches

River water temperature is a key physical variable controlling several chemical, biological and ecological processes. Its reliable prediction is a main issue in many environmental applications, which however is hampered by data scarcity, when using data‐demanding deterministic models, and modelling limitations, when using simpler statistical models. In this work we test a suite of models belonging to air2stream family, which are characterized by a hybrid formulation that combines a physical derivation of the key equation with a stochastic calibration of parameters. The air2stream models rely solely on air temperature and streamflow, and are of similar complexity as standard statistical models. The performances of the different versions of air2stream in predicting river water temperature are compared with those of the most common statistical models typically used in the literature. To this aim, a dataset of 38 Swiss rivers is used, which includes rivers classified into four different categories according to their hydrological characteristics: low‐land natural rivers, lake outlets, snow‐fed rivers and regulated rivers. The results of the analysis provide practical indications regarding the type of model that is most suitable to simulate river water temperature across different time scales (from daily to seasonal) and for different hydrological regimes. A model intercomparison exercise suggests that the family of air2stream hybrid models generally outperforms statistical models, while cross‐validation conducted over a 30‐year period indicates that they can be suitably adopted for long‐term analyses. Copyright © 2016 John Wiley & Sons, Ltd.     

Assessing regression‐based statistical approaches for downscaling precipitation over North America

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.