Defining Actual Daily Snowmelt Rates from In Situ Snow Water Equivalent Measurements

被引:1
|
作者
Fassnacht, Steven R. [1 ,2 ,3 ,5 ]
Collados-Lara, Antonio Juan [4 ]
Xu, Kan
Sears, Megan G. [1 ,5 ]
Pulido-Velazquez, David [4 ]
Moran-Tejeda, Enrique [1 ,5 ]
机构
[1] Colorado State Univ, ESS Watershed Sci, Ft Collins, CO 80523 USA
[2] Cooperat Inst Res Atmosphere, Ft Collins, CO 80521 USA
[3] Nat Resources Ecol Lab, Ft Collins, CO 80523 USA
[4] CSIC, Inst Geol & Minero Espana IGME, Granada 18006, Spain
[5] Colorado State Univ, Ecosyst Sci & Sustainabil, Ft Collins, CO 80523 USA
关键词
SNOTEL; southern Rocky Mountains; peak SWE; snow-all-gone; WESTERN; PRECIPITATION; VARIABILITY; COLORADO;
D O I
10.3850/IAHR-39WC2521716X20221464
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Seasonal snow plays an essential role in the Earth's hydrological cycle and melted water flows into rivers, and reservoirs, which will then be managed for different water uses. Research has shown a concerning decline in the snowpack over the last century, but an accurate way to calculate the snowmelt rate has not been developed. Traditionally it is the maximum snow water equivalent (SWE) divided by the time from SVVE peak to complete SWE melt out. However, this does not account for scenarios where melt occurs during the accumulation period or when accumulation occurs during the melt period. Four computational techniques were investigated to improve the traditional snowmelt rate method. Approach 1 removes all melt prior to post-peak accumulation to implement the algorithm. Approach 2 removes the daily melt-accumulation fluctuations to optimize the snowmelt rate. Approach 3 uses snow depth and precipitation as a reference to determine if rain or snow is falling. Approach 4 uses 80% of the peak SWE to replace the peak SWE in the traditional approach to ease computation. While we did not determine the optimal method to estimate the snowmelt rate, Approach 3 seems to be most appropriate as it adjusts SWE to consider the precipitation during melt, rather than removing those values (approaches 1 and 2). This third approach shows less variability in the computed snowmelt rate.
引用
收藏
页码:260 / 267
页数:8
相关论文
共 50 条
  • [21] A virtual network for estimating daily new snow water equivalent and snow depth in the Swiss Alps
    Egli, Luca
    Jonas, Tobias
    Bettems, Jean-Marie
    ANNALS OF GLACIOLOGY, 2010, 51 (54) : 32 - 38
  • [22] POINT MEASUREMENTS OF WATER EQUIVALENT OF SNOW BY USE OF NATURAL RADIATION
    ANDERSON, T
    HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES, 1982, 27 (02): : 254 - 254
  • [23] A NEURAL NETWORK APPROACH TO INVERSION OF SNOW WATER EQUIVALENT FROM PASSIVE MICROWAVE MEASUREMENTS
    CHANG, ATC
    TSANG, L
    NORDIC HYDROLOGY, 1992, 23 (03) : 173 - 182
  • [24] Improving Snow Water Equivalent Maps With Machine Learning of Snow Survey and Lidar Measurements
    Broxton, Patrick D.
    van Leeuwen, Willem J. D.
    Biederman, Joel A.
    WATER RESOURCES RESEARCH, 2019, 55 (05) : 3739 - 3757
  • [25] Monitoring of Snow Water Equivalent and Snowmelt Through Space-Borne Synthetic Aperture Radar Techniques
    Pettinato, S.
    Bovenga, F.
    Santi, E.
    Paloscia, S.
    Baroni, F.
    Belmonte, A.
    Refice, A.
    Argentiero, I.
    Colombo, R.
    Di Mauro, B.
    Bramati, G.
    Marin, C.
    Cuozzo, G.
    De Gregorio, L.
    Notarnicola, C.
    Callegari, M.
    Barella, R.
    Pasian, Marco
    Lodigiani, M.
    Cremonese, E.
    2022 52ND EUROPEAN MICROWAVE CONFERENCE (EUMC), 2022,
  • [26] Monitoring of Snow Water Equivalent and Snowmelt Through Space-Borne Synthetic Aperture Radar Techniques
    Pettinato, S.
    Bovenga, F.
    Santi, E.
    Paloscia, S.
    Baroni, F.
    Belmonte, A.
    Refice, A.
    Argentiero, I.
    Colombo, R.
    Di Mauro, B.
    Bramati, G.
    Marin, C.
    Cuozzo, G.
    De Gregorio, L.
    Notarnicola, C.
    Callegari, M.
    Barella, R.
    Pasian, Marco
    Lodigiani, M.
    Cremonese, E.
    2022 52ND EUROPEAN MICROWAVE CONFERENCE (EUMC), 2022,
  • [27] Monitoring of snow water equivalent and snowmelt through space-borne synthetic aperture radar techniques
    Pettinato, S.
    Bovenga, F.
    Santi, E.
    Paloscia, S.
    Baroni, F.
    Belmonte, A.
    Refice, A.
    Argentiero, I.
    Colombo, R.
    Di Mauro, B.
    Bramati, G.
    Marin, C.
    Cuozzo, G.
    De Gregorio, L.
    Notarnicola, C.
    Callegari, M.
    Barella, R.
    Pasian, M.
    Lodigiani, M.
    Cremonese, E.
    2022 52ND EUROPEAN MICROWAVE CONFERENCE (EUMC), 2022, : 91 - 94
  • [28] Toward advanced daily cloud-free snow cover and snow water equivalent products from Terra-Aqua MODIS and Aqua AMSR-E measurements
    Gao, Yang
    Xie, Hongjie
    Lu, Ning
    Yao, Tandong
    Liang, Tiangang
    JOURNAL OF HYDROLOGY, 2010, 385 (1-4) : 23 - 35
  • [29] Snow water equivalent retrieval in a Canadian boreal environment from microwave measurements using the HUT snow emission model
    Roy, V
    Goïta, K
    Royer, A
    Walker, AE
    Goodison, BE
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2004, 42 (09): : 1850 - 1859
  • [30] Fusing daily snow water equivalent from 1980 to 2020 in China using a spatiotemporal XGBoost model
    Sun, Liyang
    Zhang, Xueliang
    Xiao, Pengfeng
    Wang, Huadong
    Wang, Yunhan
    Zheng, Zhaojun
    JOURNAL OF HYDROLOGY, 2024, 632