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The sensitivity of snowpack sublimation estimates to instrument and measurement uncertainty perturbed in a Monte Carlo framework

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Frontiers of Earth science 2018年第4期12卷 728-738页

作者： D.M.HULTsTrAND s.r.fassnacht EAsC-Watershed science Colorado State UniversityFort CollinsCO 80523-1482USA Ess-Watershed science Colorado State UniversityFort CollinsCO 80523-1476USA Cooperative Institute for research in the Atmosphere Fort CollinsCO 80523-1375USA Geographisches Institut Abt.KartographieGIS &FemerkundungGeorg-August-Universitat Gottingen37077GottingenGermany Natural resources Ecology Laboratory Fort CollinsCO 80523-1499USA

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 1 to september 30) at West Glacier Lake watershed within the Glacier Lakes Ecosystem Experiments site in *** 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°C for temperature (T), 0.3 m/s for wind speed (Uz), 2% for relative humidity (rH), and 1 mb for pressure (P); 2) 0.0093 m for the aerodynamic roughness length (z0) based on z0 profiles calculations from multiple heights; and 3) 0.08 m 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 z0, then rH. Accounting for measurement 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, z0, and rH) accounted for 74% to 84% of the cumulative sublimation uncertainty.

关键词： snow sublimation uncertainty aerodynamic methods

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Patterns of trends in niveograph characteristics across the western United states from snow telemetry data

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Frontiers of Earth science 2020年第2期14卷 315-325页

作者： s.r.fassnacht J.I.LÓPEZ-MOrENO Ess-Watershed science Colorado State UniversityFort CollinsCO 80523-1472USA Cooperative Institute for research in the Atmosphere Colorado State UniversityFort CollinsCO 80523-1375USA Natural resources Ecology Laboratory Colorado State UniversityFort CollinsCO 80523-1499USA Instituto Pirenaico de Ecología CSICCampus de Aula Dei 50080Spain

The snowpack is changing across the globe,as the climate warms and *** used daily snow water equivalent(sWE)niveograph(time series of sWE)data from 458 snow telemetry(sNOTEL)stations for the period 1982 through *** indices based on amount,timing,time length,and rates were used to describe the annual temporal evolution in sWE accumulation and *** trends in these annual indices were computed over the time period for each station using the Theil-sen *** trends were then clustered into four groups to determine the spatial pattern of sWE *** and precipitation data were extracted from the PrIsM data set,due to the shorter time period of temperature measurement at the sNOTEL *** show that sNOTEL stations can be clustered in four clusters according to the observed trends in snow *** 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 *** stations recorded a negative trend in precipitation and an increase in *** 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 *** grouped in clusters 2 and 3 appear mixed with clusters 1 and 4,in general they show very little trends in the snow indices.

关键词： snow water equivalent cluster analysis sNOTEL

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Uncertainty in water resources:introduction to the specia column

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Frontiers of Earth science 2018年第4期12卷 649-652页

作者： s.r.fassnacht r.W.WEBB M.MA Ess-Watershed science Colorado State UniversityFort CollinsCO 80523-1476USA Cooperative Institute for research in the Atmosphere Fort CollinsCO 80523-1375USA Natural resources Ecology Laboratory Fort CollinsCO 80523-1499USA Institute of Arctic and Alpine research University of Colorado BoulderBoulderCO 80309-0450USA now with Department of Civil Constructionand Environmental EngineeringUniversity of New MexicoAlbuquerqueNM 87131USA research Base of Karst Eco-environments at Nanehuan in Chongqing Ministry of Nature ResourcesSchool of Geographical SciencesSouthwest UniversityChongqing 400715China

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.

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Distribution of snow depth variability

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Frontiers of Earth science 2018年第4期12卷 683-692页

作者： 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 Ess-Watershed science Colorado State UniversityFort CollinsCO 80523-1476USA Cooperative Institute for research in the Atmosphere Fort CollinsCO 80523-1375USA Natural resources Ecology Laboratory Fort CollinsCO 80523-1499USA Geographisches Institut Georg-August-Universitat Gottingen37077GottingenDeutschland Geosciences Colorado State UniversityFort CollinsCO 80523-1482USA Instituto Pirenaico de Ecologia CSICCampus de Aula Dei50080ZaragozaEspana state Key Laboratory of Flydroscience and Engineering Tsinghua UniversityBeijing 100084China seconda Universita degli studi di Napoli Viale Abramo Lincoln581100Caserta CEItalia

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.

关键词： uncertainty sampling binary regression trees snow telemetry

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Using multispectral landsat and sentinel-2 satellite data to investigate vegetation change at Mount st. Helens since the great volcanic eruption in 1980

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Journal of Mountain science 2018年第9期15卷 1851-1867页

作者： Katharina TELTsCHEr Fabian Ewald fassnacht Institute of Geography and Geoecology Karlsruhe Institute of Technology

Long-term analyses of vegetation succession after catastrophic events are of high interest for an improved understanding of succession dynamics. However, in many studies such analyses were restricted to plot-based measurements. Contrarily, spatially continuous observations of succession dynamics over extended areas and timeperiods are sparse. Here, we applied a change vector analysis(CVA) to investigate vegetation succession dynamics at Mount st. Helens after the great volcanic eruption in 1980 using Landsat. We additionally applied a supervised random forest classification using sentinel-2 data to map the currently prevailing vegetation types. Change vector analysis was performed with the normalized difference vegetation index(NDVI) and the urban index(UI) for three subsequent decades after the eruption as well as for the whole observation time between 1984 and 2016. The influence of topography on the current vegetation distribution was examined by comparing altitude, slope angles and aspect values of vegetation classes derived by the random forest classification. Wilcoxrank-sum test was applied to test for significant differences between topographic properties of the vegetation classes inside and outside of the areas affected by the eruption. For the full time period, a total area of 516 km2 was identified as re-vegetated, whereas the area and magnitude of re-growing vegetation decreased during the three decades and migrated closer to the volcanic crater. Vegetation losses were mainly observed in regions unaffected by the eruption and related mostly to timber harvesting. The vegetation type classification reached a high overall accuracy of approximately 90%. 36 years after the eruption, coniferous and deciduous trees have established at formerly devastated areas dominating with a proportion of 66%, whereas shrubs are more abundant in riparian zones. sparse vegetation dominates at regions very close to the crater. Elevation was found to have a great influence on th

关键词： Mount st. Helens Vegetation change remote sensing Change vector analysis (CVA) supervised classification Topography Density-plots

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scales of snow depth variability in high elevation rangeland sagebrush

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Frontiers of Earth science 2017年第3期11卷 469-481页

作者： Molly E. TEDEsCHE steven r. fassnacht Paul J. MEIMAN Watershed science Department of Ecosystem Science and Sustainability Colorado State University Fort Collins CO 80523-1476 USA Intemational Arctic research Center Water & Environmental Research Center Institute of Northern Engineering University of Alaska Fairbanks Fairbanks AK 99775 USA Cooperative Institute for research in the Atmosphere Fort Collins CO 80523-1375 USA Natural resources Ecology Laboratory Fort Collins CO 80523-1499 USA Abt. Kartographie GIS & Femerkundung Geographisches Institut Georg-August-Universitat Gottingen 37077 Gottingen Germany rangeland Ecosystem science Department of Forest and Rangeland Stewardship Colorado State University Fort Collins CO 80523 USA

In high elevation semi-arid rangelands, sage- brush and other shrubs can affect transport and deposition of wind-blown snow, enabling the formation of snowdrifts. Datasets from three field experiments were used to investigate the scales of spatial variability of snow depth around big mountain sagebrush （Artemisia tridentata Nutt.） at a high elevation plateau rangeland in North Park, Colorado, during the winters of 2002, 2003, and 2008. Data were collected at multiple resolutions （0.05 to 25 m） and extents （2 to 1000 m）. Finer scale data were collected specifically for this study to examine the correlation between snow depth; sagebrush microtopogra- phy, the ground surface, and the snow surface, as well as the temporal consistency of snow depth patterns. Vario- grams were used to identify the spatial structure and the Moran＇s I statistic was used to determine the spatial correlation. results show some temporal consistency in snow depth at several scales. Plot scale snow depth variability is partly a function of the nature of individual shrubs, as there is some correlation between the spatial structure of snow depth and sagebrush, as well as between the ground and snow depth. The optimal sampling resolution appears to be 25-cm, but over a large area, this would require a multitude of samples, and thus a random stratified approach is recommended with a fine measurement resolution of 5-cm.

关键词： snow hydrology high elevation rangelands,spatial statistics variograms snow pack spatial variability,snow drifts

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Headwater regions- Physical, ecological, and social approaches to understand these areas： introduction to the special issue

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Frontiers of Earth science 2017年第3期11卷 443-446页

作者： steven r. fassnacht ryan W. WEBB William E. sANFOrD Ess-Watershed science Colorado State University Fort Collins CO 80523-1476 USA Geographisches Institut Abt. Kartographie GIS & Femerkundung Georg-August-Universitat Gottingen 37077 Gottingen Germany Cooperative Institute for research in the Atmosphere Fort Collins CO 80523-1375 USA Geospatial Centroid Colorado State University Fort Collins CO 80523-1101 USA Natural resources Ecology Laboratory Fort Collins CO 80523-1499 USA Institute of Arctic and Alpine research University of Colorado Boulder Boulder CO 80309-0450 USA Geosciences Colorado State University Fort Collins CO 80523-1482 USA

Around the world human communities and natural ecosystems both rely on headwater regions for vital resources. resources provided include groundwater sto- rage （Flint et al., 2008; Clilverd et al., 2011; Cao et al., 2012） in addition to soil moisture and forest ecosystem dynamics （McNamara et al., 2005; Williams et al., 2009; Jencso and McGlynn, 2011; smith et al., 2011; Harpold et al., 2015; Webb et al., 2015）. Furthermore these regions are ecologically vital zones （schlosser, 1995; Lowe and Likens, 2005） that impact downstream water quality （Peterson et al., 2001; Alexander et al., 2007）.

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spatio-temporal snowmelt variability across the headwaters of the southern rocky Mountains

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Frontiers of Earth science 2017年第3期11卷 505-514页

作者： s.r. fassnacht J.I. LOPEZ-MOrENO C. MA A.N. WEBEr A.K.D. PFOHL s.K. KAMPF M. KAPPAs Ess-Watershed science Colorado State University Fort Collins CO 80523-1476 USA Cooperative Institute for research in the Atmosphere Fort Collins CO 80523-1375 USA Geospatial Centroid at CsU Fort Collins CO 80523-1019 USA Natural resources Ecology Laboratory Fort Collins CO 80523-1499 USA Geographisches Institut Georg-August-Universitat Gottingen 37077 Gottingen Germany Instituto Pirenaico de Ecologia CSIC Campus de Aula Dei 50080 Zaragoza Spain EAsC-Watershed science Colorado State University Fort Collins CO 80523-1482 USA

Understanding the rate of snowmelt helps inform how water stored as snow will transform into streamflow. Data from 87 snow telemetry （sNOTEL） stations across the southern rocky Mountains were used to estimate spatio-temporal melt factors. Decreases in snow water equivalent were correlated to temperature at these monitoring stations for eight half-month periods from early March through late June. Time explained 70% of the variance in the computed snow melt factors. A residual linear correlation model was used to explain subsequent spatial variability. Longitude, slope, and land cover type explained further variance. For evergreen trees, canopy density was relevant to find enhanced melt rates; while for all other land cover types, denoted as non- evergreen, lower melt rates were found at high elevation, high latitude and north facing slopes, denoting that in cold environments melting is less effective than in milder sites. A change in the temperature sensor about mid-way through the time series （1990 to 2013） created a discontinuity in the temperature dataset. An adjustment to the time series yield larger computed melt factors.

关键词： melt sWE temperatutre sNOTEL tempera-ture sensor change

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Uncertainty analysis of hydrological modeling in a tropicalarea using different algorithms

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Frontiers of Earth science 2018年第4期12卷 661-671页

作者： Ammar rAFIEI EMAM Martin KAPPAs steven fassnacht Nguyen Hoang Khanh LINH Department of Cartography GIS and Remote SensingUniversity of Goettingen37077GoettingenGermany Colorado state University Fort CollinsCO 80523-1476USA Faculty of Land resources and Agricultural Environment (FLrAE) Flue University of Agriculture and Forestry (F1UAF)Hue UniversityHue City 0084Vietnam

Hydrological modeling outputs are subject to uncertainty resulting from different sources of errors (e.g., error in input data, model structure, and model parameters), making quantification of uncertainty in hydrological modeling imperative and meant to improve reliability of modeling results. The uncertainty analysis must solve difficulties in calibration of hydrological models, which further increase in areas with data scarcity. The purpose of this study is to apply four uncertainty analysis algorithms to a semi-distributed hydrological model, quantifying different source of uncertainties (especially parameter uncertainty) and evaluate their performance. In this study, the soil and Water Assessment Tools (sWAT) eco-hydrological model was implemented for the watershed in the center of Vietnam. The sensitivity of parameters was analyzed, and the model was calibrated. The uncertainty analysis for the hydrological model was conducted based on four algorithms: Generalized Likelihood Uncertainty Estimation (GLUE), sequential Uncertainty Fitting (sUFI), Parameter solution method (Parasol) and Particle swarm Optimization (PsO). The performance of the algorithms was compared using P-factor and rfactor, coefficient of determination (r2), the Nash sutcliffe coefficient of efficiency (NsE) and Percent Bias (PBIAs). The results showed the high performance of sUFI and PsO with P-factor>0.83, r-factor r2>0.91, NsE>0.89, and 0.18suggest to use sUFI-2 initially to set the parameter ranges, and further use PsO for final analysis. Indeed, the uncertainty analysis must be accounted when the outcomes of the model use for policy or management decisions.

关键词： sWAT-CUP GLUE sUFI2 Parasol PsO Vietnam

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Usingvery long-range terrestriallaser scanner to analyze the temporal consistency of the snowpack distribution in a high mountain environment

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Journal of Mountain science 2017年第5期14卷 823-842页

作者： Juan I.LOPEZ-MOrENO Jesús rEVUELTO E.ALONsO-GONZALEZ Alba sANMIGUEL-VALLELADO steven r.fassnacht Jeffrey DEEMs Enrique MOrAN-TEJEDA Pyrenean Institute of Ecology CSICZaragozaSpain Meteo-France-CNrs CNRM(UMR3589)Centre d'Etudes de la NeigeGrenobleFrance Colorado state University Fort CollinsColoradoUSA National snow and Ice Data Center NSlDCBoulderColorado University of Balearic Islands MallorcaSpain

This study demonstrated the usefulness of very long-range terrestrial laser scanning(TLs) for analysis of the spatial distribution of a snowpack, to distances up to 3000 m, one of the longest measurement range reported to date. snow depth data were collected using a terrestrial laser scanner during 11 periods of snow accumulation and melting,over three snow seasons on a Pyrenean hillslopecharacterized by a large elevational gradient, steep slopes, and avalanche occurrence. The maximum and mean absolute snow depth error found was 0.5-0.6 and 0.2-0.3 m respectively, which may result problematic for areas with a shallow snowpack, but it is sufficiently accurate to determine snow distribution patterns in areas characterized by a thick snowpack. The results indicated that in most cases there was temporal consistency in the spatial distribution of thesnowpack, even in different years. The spatial patterns were particularly similar amongst thesurveys conducted during the period dominated by snow accumulation(generally until end of April), or amongst those conducted during the period dominated by melting processes(generally after mid of April or early May). simple linear correlation analyses for the 11 survey dates, and the application of random Forests analysis to two days representative of snow accumulation and melting periods indicated the importance of topography to the snow distribution. The results also highlight that elevation and the Topographic Position index(TPI) were the main variables explaining the snow distribution, especially during periods dominated by melting. The intra-and inter-annual spatial consistency of the snowpack distribution suggests that the geomorphological processes linked to presence/absence of snow cover act in a similar way in the long term, and that these spatial patternscan be easily identifiedthrough several years of adequate monitoring.

关键词： snow Terrestrial laser scanner(TLs) Topography random Forests spatial distribution Pyrenees

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