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Scales of snow depth var.ability in high elevation r.ngeland sagebr.sh

Fr.ntier. of ear.h Science 2017年第3期11卷 469-481页

作者： Molly e. TeDeSCHe ste.e. r. fassnacht Paul J. MeIMAN Water.hed Science Department of Ecosystem Science and Sustainability Colorado State University Fort Collins CO 80523-1476 USA Intemational Ar.tic r.sear.h Center Water & Environmental Research Center Institute of Northern Engineering University of Alaska Fairbanks Fairbanks AK 99775 USA Cooper.tive Institute for.r.sear.h in the Atmospher. Fort Collins CO 80523-1375 USA Natur.l r.sour.es ecology Labor.tor. Fort Collins CO 80523-1499 USA Abt. Kar.ogr.phie GIS & Femerkundung Geographisches Institut Georg-August-Universitat Gottingen 37077 Gottingen Germany r.ngeland ecosystem Science Department of Forest and Rangeland Stewardship Colorado State University Fort Collins CO 80523 USA

In high elevation semi-ar.d r.ngelands, sage- br.sh and other.shr.bs can affect tr.nspor. and deposition of wind-blown snow, enabling the for.ation of snowdr.fts. Datasets fr.m thr.e field exper.ments wer. used to investigate the scales of spatial var.ability of snow depth ar.und big mountain sagebr.sh （Ar.emisia tr.dentata Nutt.） at a high elevation plateau r.ngeland in Nor.h Par., Color.do, dur.ng the winter. of 2002, 2003, and 2008. Data wer. collected at multiple r.solutions （0.05 to 25 m） and extents （2 to 1000 m）. Finer.scale data wer. collected specifically for.this study to examine the cor.elation between snow depth; sagebr.sh micr.topogr.- phy, the gr.und sur.ace, and the snow sur.ace, as well as the tempor.l consistency of snow depth patter.s. Var.o- gr.ms wer. used to identify the spatial str.ctur. and the Mor.n＇s I statistic was used to deter.ine the spatial cor.elation. r.sults show some tempor.l consistency in snow depth at sever.l scales. Plot scale snow depth var.ability is par.ly a function of the natur. of individual shr.bs, as ther. is some cor.elation between the spatial str.ctur. of snow depth and sagebr.sh, as well as between the gr.und and snow depth. The optimal sampling r.solution appear. to be 25-cm, but over.a lar.e ar.a, this would r.quir. a multitude of samples, and thus a r.ndom str.tified appr.ach is r.commended with a fine measur.ment r.solution of 5-cm.

关键词： snow hydr.logy high elevation r.ngelands,spatial statistics var.ogr.ms snow pack spatial var.ability,snow dr.fts

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Headwater.r.gions- Physical, ecological, and social appr.aches to under.tand these ar.as： intr.duction to the special issue

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Fr.ntier. of ear.h Science 2017年第3期11卷 443-446页

作者： ste.e. r. fassnacht r.an W. WeBB William e. SANFOr. eSS-Water.hed Science Colorado State University Fort Collins CO 80523-1476 USA Geogr.phisches Institut Abt. Kartographie GIS & Femerkundung Georg-August-Universitat Gottingen 37077 Gottingen Germany Cooper.tive Institute for.r.sear.h in the Atmospher. Fort Collins CO 80523-1375 USA Geospatial Centr.id Colorado State University Fort Collins CO 80523-1101 USA Natur.l r.sour.es ecology Labor.tor. Fort Collins CO 80523-1499 USA Institute of Ar.tic and Alpine r.sear.h University of Colorado Boulder Boulder CO 80309-0450 USA Geosciences Colorado State University Fort Collins CO 80523-1482 USA

Ar.und the wor.d human communities and natur.l ecosystems both r.ly on headwater.r.gions for.vital r.sour.es. r.sour.es pr.vided include gr.undwater.sto- r.ge （Flint et al., 2008; Clilver. et al., 2011; Cao et al., 2012） in addition to soil moistur. and for.st ecosystem dynamics （McNamar. et al., 2005; Williams et al., 2009; Jencso and McGlynn, 2011; Smith et al., 2011; Har.old et al., 2015; Webb et al., 2015）. Fur.her.or. these r.gions ar. ecologically vital zones （Schlosser. 1995; Lowe and Likens, 2005） that impact downstr.am water.quality （Peter.on et al., 2001; Alexander.et al., 2007）.

关键词： Social behavior Headwater.

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The sensitivity of snowpack sublimation estimates to instr.ment and measur.ment uncer.ainty per.ur.ed in a Monte Car.o fr.mewor.

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Fr.ntier. of ear.h Science 2018年第4期12卷 728-738页

作者： D.M.HULTSTr.ND S.r.fassnacht eASC-Water.hed Science Colorado State UniversityFort CollinsCO 80523-1482USA eSS-Water.hed Science Colorado State UniversityFort CollinsCO 80523-1476USA Cooper.tive Institute for.r.sear.h in the Atmospher. Fort CollinsCO 80523-1375USA Geogr.phisches Institut Abt.KartographieGIS &FemerkundungGeorg-August-Universitat Gottingen37077GottingenGermany Natur.l r.sour.es ecology Labor.tor. Fort CollinsCO 80523-1499USA

The bulk aer.dynamic flux equation is often used to estimate snowpack sublimation since it r.quir.s meteor.logical measur.ments at only one height above the snow sur.ace. However. to date the uncer.ainty of these estimates and the individual input var.ables and input par.meter. uncer.ainty have not been quantified. We modeled sublimation for.thr.e (aver.ge snowpack in 2005, deep snowpack in 2011, and shallow snowpack in 2012) differ.nt water.year. (October.1 to September.30) at West Glacier.Lake water.hed within the Glacier.Lakes ecosystem exper.ments Site in *** per.or.ed a Monte Car.o analysis to evaluate the sensitivity of modeled sublimation to uncer.ainties of the input var.ables and par.meter. fr.m the bulk aer.dynamic flux equation. Input var.able time ser.es wer. unifor.ly adjusted by a nor.ally distr.buted r.ndom var.able with a standar. deviation given as follows: 1) the manufactur.r.s stated instr.ment accur.cy of 0.3°C for.temper.tur. (T), 0.3 m/s for.wind speed (Uz), 2% for.r.lative humidity (r.), and 1 mb for.pr.ssur. (P); 2) 0.0093 m for.the aer.dynamic r.ughness length (z0) based on z0 pr.files calculations fr.m multiple heights; and 3) 0.08 m for.measur.ment height (z). Often z is held constant; her. we used a constant z compar.d to the gr.und sur.ace, and subsequently alter.d z to account for.the change in snow depth (ds). The most impor.ant sour.e of uncer.ainty was z0, then r.. Accounting for.measur.ment height as it changed due to snowpack accumulation/ablation was also r.levant for.deeper.snow. Snow sur.ace sublimation uncer.ainties, fr.m this study, ar. in the r.nge of 1% to 29% for.individual input par.meter.per.ur.ations. The mean cumulative uncer.ainty was 41% for.the thr.e water.year. with 55%, 37%, and 32% occur.ing for.the wet, aver.ge, and low water.year.. The top thr.e var.ables (z var.ing with ds, z0, and r.) accounted for.74% to 84% of the cumulative sublimation uncer.ainty.

关键词： snow sublimation uncer.ainty aer.dynamic methods

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Patter.s of tr.nds in niveogr.ph char.cter.stics acr.ss the wester. United States fr.m snow telemetr. data

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Fr.ntier. of ear.h Science 2020年第2期14卷 315-325页

作者： S.r.fassnacht J.I.LÓPeZ-MOr.NO eSS-Water.hed Science Colorado State UniversityFort CollinsCO 80523-1472USA Cooper.tive Institute for.r.sear.h in the Atmospher. Colorado State UniversityFort CollinsCO 80523-1375USA Natur.l r.sour.es ecology Labor.tor. Colorado State UniversityFort CollinsCO 80523-1499USA Instituto Pir.naico de ecología CSICCampus de Aula Dei 50080Spain

The snowpack is changing acr.ss the globe,as the climate war.s and *** used daily snow water.equivalent(SWe)niveogr.ph(time ser.es of SWe)data fr.m 458 snow telemetr.(SNOTeL)stations for.the per.od 1982 thr.ugh *** indices based on amount,timing,time length,and r.tes wer. used to descr.be the annual tempor.l evolution in SWe accumulation and *** tr.nds in these annual indices wer. computed over.the time per.od for.each station using the Theil-Sen *** tr.nds wer. then cluste.e. into four.gr.ups to deter.ine the spatial patter. of SWe *** and pr.cipitation data wer. extr.cted fr.m the Pr.SM data set,due to the shor.er.time per.od of temper.tur. measur.ment at the SNOTeL *** show that SNOTeL stations can be cluste.e. in four.cluster. accor.ing to the obser.ed tr.nds in snow *** 1 stations ar. mostly located in the easter.-and South-easter. most par.s of the study ar.a and they exhibit a gener.lized decr.ase in the indices r.lated with peak SWe and snow *** stations r.cor.ed a negative tr.nd in pr.cipitation and an incr.ase in *** 4 that is mostly r.str.cted to the Nor.h and Nor.h-west of the study ar.a shows an almost opposite patter. to cluster.1,due to months with positive tr.nds and a mor. moder.te incr.ase of *** gr.uped in cluster. 2 and 3 appear.mixed with cluster. 1 and 4,in gener.l they show ver. little tr.nds in the snow indices.

关键词： snow water.equivalent cluster.analysis SNOTeL

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Using multispectr.l landsat and sentinel-2 satellite data to investigate vegetation change at Mount St. Helens since the gr.at volcanic er.ption in 1980

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Jour.al of Mountain Science 2018年第9期15卷 1851-1867页

作者： Kathar.na TeLTSCHer.Fabian ewald fassnacht Institute of Geogr.phy and Geoecology Karlsruhe Institute of Technology

Long-ter. analyses of vegetation succession after.catastr.phic events ar. of high inter.st for.an impr.ved under.tanding of succession dynamics. However. in many studies such analyses wer. r.str.cted to plot-based measur.ments. Contr.r.ly, spatially continuous obser.ations of succession dynamics over.extended ar.as and timeper.ods ar. spar.e. Her., we applied a change vector.analysis(CVA) to investigate vegetation succession dynamics at Mount St. Helens after.the gr.at volcanic er.ption in 1980 using Landsat. We additionally applied a super.ised r.ndom for.st classification using Sentinel-2 data to map the cur.ently pr.vailing vegetation types. Change vector.analysis was per.or.ed with the nor.alized differ.nce vegetation index(NDVI) and the ur.an index(UI) for.thr.e subsequent decades after.the er.ption as well as for.the whole obser.ation time between 1984 and 2016. The influence of topogr.phy on the cur.ent vegetation distr.bution was examined by compar.ng altitude, slope angles and aspect values of vegetation classes der.ved by the r.ndom for.st classification. Wilcoxr.nk-Sum test was applied to test for.significant differ.nces between topogr.phic pr.per.ies of the vegetation classes inside and outside of the ar.as affected by the er.ption. For.the full time per.od, a total ar.a of 516 km2 was identified as r.-vegetated, wher.as the ar.a and magnitude of r.-gr.wing vegetation decr.ased dur.ng the thr.e decades and migr.ted closer.to the volcanic cr.ter. Vegetation losses wer. mainly obser.ed in r.gions unaffected by the er.ption and r.lated mostly to timber.har.esting. The vegetation type classification r.ached a high over.ll accur.cy of appr.ximately 90%. 36 year. after.the er.ption, conifer.us and deciduous tr.es have established at for.er.y devastated ar.as dominating with a pr.por.ion of 66%, wher.as shr.bs ar. mor. abundant in r.par.an zones. Spar.e vegetation dominates at r.gions ver. close to the cr.ter. elevation was found to have a gr.at influence on th

关键词： Mount St. Helens Vegetation change r.mote sensing Change vector.analysis (CVA) Super.ised classification Topogr.phy Density-plots

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Uncer.ainty in water.r.sour.es:intr.duction to the specia column

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Fr.ntier. of ear.h Science 2018年第4期12卷 649-652页

作者： S.r.fassnacht r.W.WeBB M.MA eSS-Water.hed Science Colorado State UniversityFort CollinsCO 80523-1476USA Cooper.tive Institute for.r.sear.h in the Atmospher. Fort CollinsCO 80523-1375USA Natur.l r.sour.es ecology Labor.tor. Fort CollinsCO 80523-1499USA Institute of Ar.tic and Alpine r.sear.h University of Colorado BoulderBoulderCO 80309-0450USA now with Depar.ment of Civil Constructionand Environmental EngineeringUniversity of New MexicoAlbuquerqueNM 87131USA r.sear.h Base of Kar.t eco-envir.nments at Nanehuan in Chongqing Ministry of Nature ResourcesSchool of Geographical SciencesSouthwest UniversityChongqing 400715China

Uncer.ainty is a defining pr.blem in the under.tanding of ear.h systems, especially for.water.r.sour.es (Guillaume et al., 2017). The wor. “uncer.ainty” can have a var.ety of meanings (Montanar., 2007; Beven, 2016). Typically, we only under.tand pr.cesses in the confines of labor.tor. exper.ments, on a ver. fine scale in the field, or.in vague gener.lities. This is often the case because the pr.cesses ar. not well under.tood, or.ther. is not enough data at the appr.pr.ate r.solution or.over.a sufficient extent. Scaling issues occur.at both spatial and tempor.l r.solution leaving the scientist with imper.ect knowledge of a system and poor.y defined er.or. when evaluating it.

关键词： Uncer.ainty Water.r.sour.es

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Usingver. long-r.nge ter.estr.allaser.scanner.to analyze the tempor.l consistency of the snowpack distr.bution in a high mountain envir.nment

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Jour.al of Mountain Science 2017年第5期14卷 823-842页

作者： Juan I.LOPeZ-MOr.NO Jesús r.VUeLTO e.ALONSO-GONZALeZ Alba SANMIGUeL-VALLeLADO ste.e. r.fassnacht Jeffr.y DeeMS enr.que MOr.N-TeJeDA Pyr.nean Institute of ecology CSICZaragozaSpain Meteo-Fr.nce-CNr. CNRM(UMR3589)Centre d'Etudes de la NeigeGrenobleFrance Color.do State Univer.ity Fort CollinsColoradoUSA National Snow and Ice Data Center NSlDCBoulderColorado Univer.ity of Balear.c Islands MallorcaSpain

This study demonstr.ted the usefulness of ver. long-r.nge ter.estr.al laser.scanning(TLS) for.analysis of the spatial distr.bution of a snowpack, to distances up to 3000 m, one of the longest measur.ment r.nge r.por.ed to date. Snow depth data wer. collected using a ter.estr.al laser.scanner.dur.ng 11 per.ods of snow accumulation and melting,over.thr.e snow seasons on a Pyr.nean hillslopechar.cter.zed by a lar.e elevational gr.dient, steep slopes, and avalanche occur.ence. The maximum and mean absolute snow depth er.or.found was 0.5-0.6 and 0.2-0.3 m r.spectively, which may r.sult pr.blematic for.ar.as with a shallow snowpack, but it is sufficiently accur.te to deter.ine snow distr.bution patter.s in ar.as char.cter.zed by a thick snowpack. The r.sults indicated that in most cases ther. was tempor.l consistency in the spatial distr.bution of thesnowpack, even in differ.nt year.. The spatial patter.s wer. par.icular.y similar.amongst thesur.eys conducted dur.ng the per.od dominated by snow accumulation(gener.lly until end of Apr.l), or.amongst those conducted dur.ng the per.od dominated by melting pr.cesses(gener.lly after.mid of Apr.l or.ear.y May). Simple linear.cor.elation analyses for.the 11 sur.ey dates, and the application of r.ndom For.sts analysis to two days r.pr.sentative of snow accumulation and melting per.ods indicated the impor.ance of topogr.phy to the snow distr.bution. The r.sults also highlight that elevation and the Topogr.phic Position index(TPI) wer. the main var.ables explaining the snow distr.bution, especially dur.ng per.ods dominated by melting. The intr.-and inter.annual spatial consistency of the snowpack distr.bution suggests that the geomor.hological pr.cesses linked to pr.sence/absence of snow cover.act in a similar.way in the long ter., and that these spatial patter.scan be easily identifiedthr.ugh sever.l year. of adequate monitor.ng.

关键词： Snow Ter.estr.al laser.scanner.TLS) Topogr.phy r.ndom For.sts Spatial distr.bution Pyr.nees

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Uncer.ainty analysis of hydr.logical modeling in a tr.picalar.a using differ.nt algor.thms

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Fr.ntier. of ear.h Science 2018年第4期12卷 661-671页

作者： Ammar.r.FIeI eMAM Mar.in KAPPAS ste.e. fassnacht Nguyen Hoang Khanh LINH Depar.ment of Car.ogr.phy GIS and Remote SensingUniversity of Goettingen37077GoettingenGermany Color.do State Univer.ity Fort CollinsCO 80523-1476USA Faculty of Land r.sour.es and Agr.cultur.l envir.nment (FLr.e) Flue University of Agriculture and Forestry (F1UAF)Hue UniversityHue City 0084Vietnam

Hydr.logical modeling outputs ar. subject to uncer.ainty r.sulting fr.m differ.nt sour.es of er.or. (e.g., er.or.in input data, model str.ctur., and model par.meter.), making quantification of uncer.ainty in hydr.logical modeling imper.tive and meant to impr.ve r.liability of modeling r.sults. The uncer.ainty analysis must solve difficulties in calibr.tion of hydr.logical models, which fur.her.incr.ase in ar.as with data scar.ity. The pur.ose of this study is to apply four.uncer.ainty analysis algor.thms to a semi-distr.buted hydr.logical model, quantifying differ.nt sour.e of uncer.ainties (especially par.meter.uncer.ainty) and evaluate their.per.or.ance. In this study, the Soil and Water.Assessment Tools (SWAT) eco-hydr.logical model was implemented for.the water.hed in the center.of Vietnam. The sensitivity of par.meter. was analyzed, and the model was calibr.ted. The uncer.ainty analysis for.the hydr.logical model was conducted based on four.algor.thms: Gener.lized Likelihood Uncer.ainty estimation (GLUe), Sequential Uncer.ainty Fitting (SUFI), Par.meter.Solution method (Par.Sol) and Par.icle Swar. Optimization (PSO). The per.or.ance of the algor.thms was compar.d using P-factor.and r.actor. coefficient of deter.ination (r.(2)), the Nash Sutcliffe coefficient of efficiency (NSe) and Per.ent Bias (PBIAS). The r.sults showed the high per.or.ance of SUFI and PSO with P-factor.> 0.83, r.factor.r.(2)> 0.91, NSe > 0.89, and 0.18 ence, we would suggest to use SUFI-2 initially to set the par.meter.r.nges, and fur.her.use PSO for.final analysis. Indeed, the uncer.ainty analysis must be accounted when the outcomes of the model use for.policy or.management decisions.

关键词： SWAT-CUP GLUe SUFI2 Par.Sol PSO Vietnam

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Distr.bution of snow depth var.ability

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Fr.ntier. of ear.h Science 2018年第4期12卷 683-692页

作者： S.r.fassnacht K.S.J.Br.WN e.J.BLUMBer. J.I.LOPeZ MOr.NO T.P.COVINO M.KAPPAS Y.HUANG V.LeONe A.H.KASHIPAZHA eSS-Water.hed Science Colorado State UniversityFort CollinsCO 80523-1476USA Cooper.tive Institute for.r.sear.h in the Atmospher. Fort CollinsCO 80523-1375USA Natur.l r.sour.es ecology Labor.tor. Fort CollinsCO 80523-1499USA Geogr.phisches Institut Georg-August-Universitat Gottingen37077GottingenDeutschland Geosciences Colorado State UniversityFort CollinsCO 80523-1482USA Instituto Pir.naico de ecologia CSICCampus de Aula Dei50080ZaragozaEspana State Key Labor.tor. of Flydr.science and engineer.ng Tsinghua UniversityBeijing 100084China Seconda Univer.ita degli Studi di Napoli Viale Abramo Lincoln581100Caserta CEItalia

Snow depth is the easiest snowpack pr.per.y to measur. in the field and is used to estimate the distr.bution of snow for.quantifying snow stor.ge. Often the mean of thr.e snow depth measur.ments is used to r.pr.sent snow depth at a location. This location is used as a pr.xy for.an ar.a, typically a digital elevation model (DeM) or.r.motely sensed pixel. Her., 11, 17, or.21 snow depth measur.ments wer. used to r.pr.sent the mean snow depth of a 30-m DeM pixel. Using the center.snow depth measur.ment for.each sampling set was not adequate to r.pr.sent the pixel mean, and while the use of thr.e snow depth measur.ments impr.ved the estimate of mean, ther. is still lar.e er.or.for.some pixels. These measur.ments wer. then used to deter.ine the var.ability of snow depth acr.ss a pixel. estimating var.ability fr.m few points r.ther.than all in a measur.ment was not sufficient. The sampling size was incr.ased fr.m one to the total per.pixel (11, 17, or.21) to deter.ine how many point samples wer. necessar. to appr.ximate the mean snow depth per.pixel within five per.ent. Binar. r.gr.ssion tr.es wer. constr.cted to deter.ine which ter.ain and canopy var.ables dictated the spatial distr.bution of the snow depth, the standar. deviation of snow depth, and the sample size to within 5% of the mean per.pixel. One location was measur.d in two year. just pr.or.to peak accumulation, and it is shown that ther. was little to no inter.annual consistency in the mean or.standar. deviation.

关键词： uncer.ainty sampling binar. r.gr.ssion tr.es snow telemetr.

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Spatio-tempor.l snowmelt var.ability acr.ss the headwater. of the Souther. r.cky Mountains

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Fr.ntier. of ear.h Science 2017年第3期11卷 505-514页

作者： S.r. fassnacht J.I. LOPeZ-MOr.NO C. MA A.N. WeBer.A.K.D. PFOHL S.K. KAMPF M. KAPPAS eSS-Water.hed Science Colorado State University Fort Collins CO 80523-1476 USA Cooper.tive Institute for.r.sear.h in the Atmospher. Fort Collins CO 80523-1375 USA Geospatial Centr.id at CSU Fort Collins CO 80523-1019 USA Natur.l r.sour.es ecology Labor.tor. Fort Collins CO 80523-1499 USA Geogr.phisches Institut Georg-August-Universitat Gottingen 37077 Gottingen Germany Instituto Pir.naico de ecologia CSIC Campus de Aula Dei 50080 Zaragoza Spain eASC-Water.hed Science Colorado State University Fort Collins CO 80523-1482 USA

Under.tanding the r.te of snowmelt helps infor. how water.stor.d as snow will tr.nsfor. into str.amflow. Data fr.m 87 snow telemetr. （SNOTeL） stations acr.ss the Souther. r.cky Mountains wer. used to estimate spatio-tempor.l melt factor.. Decr.ases in snow water.equivalent wer. cor.elated to temper.tur. at these monitor.ng stations for.eight half-month per.ods fr.m ear.y Mar.h thr.ugh late June. Time explained 70% of the var.ance in the computed snow melt factor.. A r.sidual linear.cor.elation model was used to explain subsequent spatial var.ability. Longitude, slope, and land cover.type explained fur.her.var.ance. For.ever.r.en tr.es, canopy density was r.levant to find enhanced melt r.tes; while for.all other.land cover.types, denoted as non- ever.r.en, lower.melt r.tes wer. found at high elevation, high latitude and nor.h facing slopes, denoting that in cold envir.nments melting is less effective than in milder.sites. A change in the temper.tur. sensor.about mid-way thr.ugh the time ser.es （1990 to 2013） cr.ated a discontinuity in the temper.tur. dataset. An adjustment to the time ser.es yield lar.er.computed melt factor..

关键词： melt SWe temper.tutr. SNOTeL temper.-tur. sensor.change

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