DOI: 10.5194/hess-21-1-2017
Scopus记录号: 2-s2.0-85009127199
论文题名: Rain or snow: Hydrologic processes, observations, prediction, and research needs
作者: Harpold A ; A ; , Kaplan M ; L ; , Zion Klos P ; , Link T ; , McNamara J ; P ; , Rajagopal S ; , Schumer R ; , Steele C ; M
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2017
卷: 21, 期: 1 起始页码: 1
结束页码: 22
语种: 英语
Scopus关键词: Atmospheric humidity
; Complex networks
; Digital storage
; Hydrology
; Precipitation (meteorology)
; Remote sensing
; Snow
; Hydrological modeling
; Initial and boundary conditions
; Measurement interval
; Mixed phase precipitation
; Near surface air temperature
; Near surface precipitation
; Scientific cooperation
; Spatio-temporal variation
; Rain
; air temperature
; analytical method
; boundary condition
; complex terrain
; hydrological modeling
; hydrometeorology
; model validation
; precipitation (climatology)
; prediction
; rainfall
; remote sensing
; spatiotemporal analysis
; uncertainty analysis
; watershed
英文摘要: The phase of precipitation when it reaches the ground is a first-order driver of hydrologic processes in a watershed. The presence of snow, rain, or mixed-phase precipitation affects the initial and boundary conditions that drive hydrological models. Despite their foundational importance to terrestrial hydrology, typical phase partitioning methods (PPMs) specify the phase based on near-surface air temperature only. Our review conveys the diversity of tools available for PPMs in hydrological modeling and the advancements needed to improve predictions in complex terrain with large spatiotemporal variations in precipitation phase. Initially, we review the processes and physics that control precipitation phase as relevant to hydrologists, focusing on the importance of processes occurring aloft. There is a wide range of options for field observations of precipitation phase, but there is a lack of a robust observation networks in complex terrain. New remote sensing observations have the potential to increase PPM fidelity, but generally require assumptions typical of other PPMs and field validation before they are operational. We review common PPMs and find that accuracy is generally increased at finer measurement intervals and by including humidity information. One important tool for PPM development is atmospheric modeling, which includes microphysical schemes that have not been effectively linked to hydrological models or validated against near-surface precipitation-phase observations. The review concludes by describing key research gaps and recommendations to improve PPMs, including better incorporation of atmospheric information, improved validation datasets, and regional-scale gridded data products. Two key points emerge from this synthesis for the hydrologic community: (1) current PPMs are too simple to capture important processes and are not well validated for most locations, (2) lack of sophisticated PPMs increases the uncertainty in estimation of hydrological sensitivity to changes in precipitation phase at local to regional scales. The advancement of PPMs is a critical research frontier in hydrology that requires scientific cooperation between hydrological and atmospheric modelers and field scientists. © Author(s) 2017. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79311
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Natural Resources and Environmental Science, University of Nevada, 1664 N. Virginia Street, Reno, NV, United States; Division of Hydrologic Sciences, Desert Research Institute, 2215 Raggio Parkway, Reno, NV, United States; Department of Forest, Rangeland, and Fire Sciences, University of Idaho, 875 Perimeter Drive, Moscow, ID, United States; Department of Geosciences, Boise State University, 1910 University Dr., Boise, ID, United States; Jornada Experimental Range, New Mexico State University, Las Cruces, NM, United States
Recommended Citation:
Harpold A,A,, Kaplan M,et al. Rain or snow: Hydrologic processes, observations, prediction, and research needs[J]. Hydrology and Earth System Sciences,2017-01-01,21(1)