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| 1 | Uncertainty in water resources:introduction to the specia column显示文摘Uncertainty is a defining problem in the understanding of earth systems,especially for water resources (Guillaume et al.,2017).The word 'uncertainty'can have a variety of meanings (Montanari,2007;Beven,2016).Typically,we only understand processes in the confines of laboratory experiments,on a very fine scale in the field,or in vague generalities.This is often the case because the processes are not well understood,or there is not enough data at the appropriate resolution or over a sufficient extent.Scaling issues occur at both spatial and temporal resolution leaving the scientist with imperfect knowledge of a system and poorly defined errors when evaluating it. | S.R.FASSNACHT R.W.WEBB M.MA | 2018 | Frontiers of Earth Science2018，12，4： | 2 |
| 2 | Distribution of snow depth variability显示文摘Snow depth is the easiest snowpack property to measure in the field and is used to estimate the distribution of snow for quantifying snow storage. Often the mean of three snow depth measurements is used to represent snow depth at a location. This location is used as a proxy for an area, typically a digital elevation model (DEM) or remotely sensed pixel. Here, 11, 17, or 21 snow depth measurements were used to represent the mean snow depth of a 30-m DEM pixel. Using the center snow depth measurement for each sampling set was not adequate to represent the pixel mean, and while the use of three snow depth measurements improved the estimate of mean, there is still large error for some pixels. These measurements were then used to determine the variability of snow depth across a pixel. Estimating variability from few points rather than all in a measurement was not sufficient. The sampling size was increased from one to the total per pixel (11, 17, or 21) to determine how many point samples were necessary to approximate the mean snow depth per pixel within five percent. Binary regression trees were constructed to determine which terrain and canopy variables dictated the spatial distribution of the snow depth, the standard deviation of snow depth, and the sample size to within 5% of the mean per pixel. One location was measured in two years just prior to peak accumulation, and it is shown that there was little to no inter-annual consistency in the mean or standard deviation. | S.R.FASSNACHT K.S.J.BROWN E.J.BLUMBERG J.I.LOPEZ MORENO T.P.COVINO M.KAPPAS Y.HUANG V.LEONE A.H.KASHIPAZHA | 2018 | Frontiers of Earth Science2018，12，4： | 0 |
| 3 | The sensitivity of snowpack sublimation estimates to instrument and measurement uncertainty perturbed in a Monte Carlo framework显示文摘The bulk aerodynamic flux equation is often used to estimate snowpack sublimation since it requires meteorological measurements at only one height above the snow surface.However,to date the uncertainty of these estimates and the individual input variables and input parameters uncertainty have not been quantified.We modeled sublimation for three (average snowpack in 2005,deep snowpack in 2011,and shallow snowpack in 2012)different water years (October 1to September 30)at West Glacier Lake watershed within the Glacier Lakes Ecosystem Experiments Site in Wyoming.We performed a Monte Carlo analysis to evaluate the sensitivity of modeled sublimation to uncertainties of the input variables and parameters from the bulk aerodynamic flux equation.Input variable time series were uniformly adjusted by a normally distributed random variable with a standard deviation given as follows:1)the manufacturer's stated instrument accuracy of 0.3℃ for temperature (T),0.3m/s for wind speed (Uz),2%for relative humidity (RH),and 1mb for pressure (P);2)0.0093 m for the aerodynamic roughness length (Zo)based on Zo profiles calculations from multiple heights;and 3)0.08m for measurement height (z).Often z is held constant;here we used a constant z compared to the ground surface,and subsequently altered z to account for the change in snow depth (ds).The most important source of uncertainty was zo,then RH.Accounting for measure- ment height as it changed due to snowpack accumulation ablation was also relevant for deeper snow.Snow surface sublimation uncertainties,from this study,are in the range of 1%to 29% for individual input parameter perturbations.The mean cumulative uncertainty was 41%for the three water years with 55%,37%,and 32%occurring for the wet,average,and low water years.The top three variables (z varying with ds,Zo,and RH)accounted for 74% to 84% of the cumulative sublimation uncertainty. | D.M.HULTSTRAND S.R.FASSNACHT | 2018 | Frontiers of Earth Science2018，12，4： | 0 |
| 4 | Patterns of trends in niveograph characteristics across the western United States from snow telemetry data显示文摘The snowpack is changing across the globe,as the climate warms and changes.We used daily snow water equivalent(SWE)niveograph(time series of SWE)data from 458 snow telemetry(SNOTEL)stations for the period 1982 through 2012.Nineteen indices based on amount,timing,time length,and rates were used to describe the annual temporal evolution in SWE accumulation and ablation.The trends in these annual indices were computed over the time period for each station using the Theil-Sen slope.These trends were then clustered into four groups to determine the spatial pattern of SWE trends.Temperature and precipitation data were extracted from the PRISM data set,due to the shorter time period of temperature measurement at the SNOTEL stations.Results show that SNOTEL stations can be clustered in four clusters according to the observed trends in snow indices.Cluster 1 stations are mostly located in the Eastern-and South-eastern most parts of the study area and they exhibit a generalized decrease in the indices related with peak SWE and snow accumulation.Those stations recorded a negative trend in precipitation and an increase in temperature.Cluster 4 that is mostly restricted to the North and North-west of the study area shows an almost opposite pattern to cluster 1,due to months with positive trends and a more moderate increase of temperature.Stations grouped in clusters 2 and 3 appear mixed with clusters 1 and 4,in general they show very little trends in the snow indices. | S.R.FASSNACHT J.I.LÓPEZ-MORENO | 2020 | Frontiers of Earth Science2020，14，2： | 0 |
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