Conditional heavy-tail behavior with applications to precipitation and river flow extremes

This article deals with the right-tail behavior of a response distribution \(F_Y\) conditional on a regressor vector \({\mathbf {X}}={\mathbf {x}}\) restricted to the heavy-tailed case of Pareto-type conditional distributions \(F_Y(y|\ {\mathbf {x}})=P(Y\le y|\ {\mathbf {X}}={\mathbf {x}})\), with heaviness of the right tail characterized by the conditional extreme value index \(\gamma ({\mathbf {x}})>0\). We particularly focus on testing the hypothesis \({\mathscr {H}}_{0,tail}:\ \gamma ({\mathbf {x}})=\gamma _0\) of constant tail behavior for some \(\gamma _0>0\) and all possible \({\mathbf {x}}\). When considering \({\mathbf {x}}\) as a time index, the term trend analysis is commonly used. In the recent past several such trend analyses in extreme value data have been published, mostly focusing on time-varying modeling of location or scale parameters of the response distribution. In many such environmental studies a simple test against trend based on Kendall’s tau statistic is applied. This test is powerful when the center of the conditional distribution \(F_Y(y|{\mathbf {x}})\) changes monotonically in \({\mathbf {x}}\), for instance, in a simple location model \(\mu ({\mathbf {x}})=\mu _0+x\cdot \mu _1\), \({\mathbf {x}}=(1,x)'\), but the test is rather insensitive against monotonic tail behavior, say, \(\gamma ({\mathbf {x}})=\eta _0+x\cdot \eta _1\). This has to be considered, since for many environmental applications the main interest is on the tail rather than the center of a distribution. Our work is motivated by this problem and it is our goal to demonstrate the opportunities and the limits of detecting and estimating non-constant conditional heavy-tail behavior with regard to applications from hydrology. We present and compare four different procedures by simulations and illustrate our findings on real data from hydrology: weekly maxima of hourly precipitation from France and monthly maximal river flows from Germany.     

Rates of erosion and landscape change along the Blue Ridge escarpment,southern Appalachian Mountains,estimated from in situ cosmogenic 10Be

The Blue Ridge escarpment, located within the southern Appalachian Mountains of Virginia and North Carolina, forms a distinct, steep boundary between the lower‐elevation Piedmont and higher‐elevation Blue Ridge physiographic provinces. To understand better the rate at which this landform and the adjacent landscape are changing, we measured cosmogenic beryllium‐10 (¹⁰Be) in quartz separated from sediment samples (n = 50) collected in 32 streams and from three exposed bedrock outcrops along four transects normal to the escarpment, allowing us to calculate erosion rates integrated over 10⁴–10⁵ years. These basin‐averaged erosion rates (5.4–49 m Myr^?1) are consistent with those measured elsewhere in the southern Appalachain Mountains and show a positive relationship between erosion rate and average basin slope. Erosion rates show no relationship with basin size or relative position of the Brevard fault zone, a fundamental structural element of the region. The cosmogenic isotopic data, when considered along with the distribution of average basin slopes in each physiographic province, suggest that the escarpment is eroding on average more rapidly than the Blue Ridge uplands, which are eroding more rapidly than the Piedmont lowlands. This difference in erosion rates by geomorphic setting suggests that the elevation difference between the uplands and lowlands adjacent to the escarpment is being reduced but at extremely slow rates. Copyright © 2016 John Wiley & Sons, Ltd.     

利用远震接收函数研究黄海周边地区的地壳上地幔结构

利用宽频带流动台站(YSBSN)记录的远震波形数据和远震接收函数方法,反演了黄海东、西两侧地壳上地幔的S波速度结构.结果表明,莫霍面深度在30~38 km之间变化,位于中方一侧的JNN台下方地壳厚度最大,可以归因于华北板块和扬子板块的碰撞;韩方一侧的地壳厚度自北向南逐渐变厚,但仍然难以厘定朝鲜半岛南部潜在碰撞带的位置,这些问题的解决需要更大范围的流动台站观测.由于部分台站位于巨厚的沉积层和多孔的火山岩之上,与浅部构造的相关性使得接收函数表现出较大振幅的混响,从而影响了来自深部结构的转换震相.     

Assessment of soil factors controlling ephemeral gully erosion on agricultural fields

The soil factor is crucial in controlling and properly modeling the initiation and development of ephemeral gullies (EGs). Usually, EG initiation has been related to various soil properties (i.e. sealing, critical shear stress, moisture, texture, etc.); meanwhile, the total growth of each EG (erosion rate) has been linked with proper soil erodibility. But, despite the studies to determine the influence of soil erodibility on (ephemeral) gully erosion, a universal approach is still lacking. This is due to the complex relationship and interactions between soil properties and the erosive process. A feasible soil characterization of EG erosion prediction on a large scale should be based on simple, quick and inexpensive tests to perform. The objective of this study was to identify and assess the soil properties – easily and quickly to determine – which best reflect soil erodibility on EG erosion. Forty‐nine different physical–chemical soil properties that may participate in establishing soil erodibility were determined on agricultural soils affected by the formation of EGs in Spain and Italy. Experiments were conducted in the laboratory and in the field (in the vicinity of the erosion paths). Because of its importance in controlling EG erosion, five variables related to antecedent moisture prior to the event that generated the gullies and two properties related to landscape topography were obtained for each situation. The most relevant variables were detected using multivariate analysis. The results defined 13 key variables: water content before the initiation of EGs, organic matter content, cation exchange capacity, relative sealing index, two granulometric and organic matter indices, seal permeability, aggregates stability (three index), crust penetration resistance, shear strength and an erodibility index obtained from the Jet Test erosion apparatus. The latter is proposed as a useful technique to evaluate and predict soil loss caused by EG erosion. Copyright © 2018 John Wiley & Sons, Ltd.     

Capture Versus Capture Zones: Clarifying Terminology Related to Sources of Water to Wells

The term capture, related to the source of water derived from wells, has been used in two distinct yet related contexts by the hydrologic community. The first is a water‐budget context, in which capture refers to decreases in the rates of groundwater outflow and (or) increases in the rates of recharge along head‐dependent boundaries of an aquifer in response to pumping. The second is a transport context, in which capture zone refers to the specific flowpaths that define the three‐dimensional, volumetric portion of a groundwater flow field that discharges to a well. A closely related issue that has become associated with the source of water to wells is streamflow depletion, which refers to the reduction in streamflow caused by pumping, and is a type of capture. Rates of capture and streamflow depletion are calculated by use of water‐budget analyses, most often with groundwater‐flow models. Transport models, particularly particle‐tracking methods, are used to determine capture zones to wells. In general, however, transport methods are not useful for quantifying actual or potential streamflow depletion or other types of capture along aquifer boundaries. To clarify the sometimes subtle differences among these terms, we describe the processes and relations among capture, capture zones, and streamflow depletion, and provide proposed terminology to distinguish among them.     

1,4‐Dioxane Soil Remediation Using Enhanced Soil Vapor Extraction (XSVE): II. Modeling

1,4‐Dioxane is a volatile organic compound that is fully miscible in water, allowing it to sequester in vadose zone pore water and serve as a long‐term source of groundwater contamination. Conventional soil vapor extraction (SVE) removes 1,4‐dioxane; however, substantial 1,4‐dioxane can remain even after other colocated chlorinated solvents have been remediated. A field demonstration of “enhanced SVE” (XSVE) with focused extraction and heated injection was conducted at former McClellan AFB, CA, achieving 94% reduction in soil concentrations. A screening‐level tool, HypeVent XSVE, was created to assist in system design and data reduction and to anticipate how operating factors affect XSVE performance (e.g., cleanup level, remediation time, etc.). It assumes well‐mixed conditions, and combines an energy balance, mass balances for water and contaminant, and a temperature‐dependent 1,4‐dioxane Henry's Law constant. User inputs include the target treatment zone size, initial 1,4‐dioxane and soil moisture concentrations, and ambient site and injection/extraction conditions (temperature, humidity). Projections based on inputs representative of demonstration site conditions adequately anticipated the observed macroscopic field results. Sensitivity analyses show that removal increases with increasing heated air injection temperature and relative humidity and decreasing initial soil moisture content.     

A method for uncertainty constraint of catchment discharge and phosphorus load estimates

River discharge and nutrient measurements are subject to aleatory and epistemic uncertainties. In this study, we present a novel method for estimating these uncertainties in colocated discharge and phosphorus (P) measurements. The “voting point”‐based method constrains the derived stage‐discharge rating curve both on the fit to available gaugings and to the catchment water balance. This helps reduce the uncertainty beyond the range of available gaugings and during out of bank situations. In the example presented here, for the top 5% of flows, uncertainties are shown to be 139% using a traditional power law fit, compared with 40% when using our updated “voting point” method. Furthermore, the method is extended to in situ and lab analysed nutrient concentration data pairings, with lower uncertainties (81%) shown for high concentrations (top 5%) than when a traditional regression is applied (102%). Overall, for both discharge and nutrient data, the method presented goes some way to accounting for epistemic uncertainties associated with nonstationary physical characteristics of the monitoring site.