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Case Study: Class I Areas in the Mountainous West
Published in Timothy J. Sullivan, Aquatic Effects of Acidic Deposition, 2019
Average annual precipitation varies from about 41 cm at Jackson to about 154 cm near the summit of the Teton Mountains. Average annual snowfall varies from about 2 m at Jackson to over 7.7 m at high elevation. Snowmelt generally peaks in May and June. Surface winds display a wide range of prevailing directions and mean speeds depending on the topography and elevation of the site (Dirks and Martner, 1982). At the higher locations, the prevailing winds are consistently from the southwest.
Water Quality and Security
Published in Barry L. Johnson, Maureen Y. Lichtveld, Environmental Policy and Public Health, 2017
Barry L. Johnson, Maureen Y. Lichtveld
Global climate change affects a variety of factors associated with drought. There is high confidence that increased temperatures will lead to more precipitation falling as rain rather than snow, earlier snow melt, and increased evaporation and transpiration. Thus the risk of hydrological and agricultural drought increases as temperatures rise. Much of the U.S. Mountain West has experienced declines in spring snowpack, especially since mid-century. These declines are related to a reduction in precipitation falling as snow (with more falling as rain), and a shift in timing of snowmelt. Earlier snowmelt, associated with warmer temperatures, can lead to water supply being increasingly out of phase with water demands. While there is some variability in the models for western North America as a whole, climate models unanimously project increased drought in the U.S. Southwest. The Southwest is considered one of the more sensitive regions in the world for increased risk of drought caused by climate change [10].
Public Perception and Economic Issues
Published in Julie Kerr, Introduction to Energy and Climate, 2017
Climate change may increase the risk of drought in some areas and the risk of extreme precipitation and flooding in others. Increased temperatures would alter the timing of snowmelt, affecting the seasonal availability of water. Although many trees are resilient to some degree of drought, increases in temperature could make future droughts more damaging than those experienced in the past. Drought increases wildfire risk, since dry trees and shrubs provide fuel for fires. Drought also reduces trees’ ability to produce sap, which protects them from destructive insects, such as pine beetles. So what do all these predictions really mean? Disturbances can interact with one another—or with changes in temperature and precipitation—to increase risks to forests (EPA, 2016a).
A novel fine-resolution snow depth retrieval model to reveal detailed spatiotemporal patterns of snow cover in Northeast China
Published in International Journal of Digital Earth, 2023
Yanlin Wei, Xiaofeng Li, Lingjia Gu, Xingming Zheng, Tao Jiang
Seasonal snow cover, a meteorological component of the cryosphere, has important implications for surface energy balance because of its high reflectivity and low heat conduction (Bhatti, Koike, and Shrestha 2016; Estilow, Young, and Robinson 2015). Many previous studies indicated that seasonal snow cover has many impacts on hydrology, agriculture, ecology, and human activities (Iwata et al. 2010; Qin et al. 2020; Zhang and Ma 2018). Additionally, snowmelt is a valuable freshwater resource feeding river and lake and has a significant influence on soil moisture, soil drought, and flooding in spring (Pulliainen et al. 2020; Yang et al. 2020b). Being one of the main snow-covered areas in China, Northeast China is strongly sensitive to climate change and is an important region for agricultural production (Li et al. 2014; Li et al. 2022b; Qi et al. 2020). Therefore, it is essential to understand the temporal variations and spatial distribution patterns of the snow cover in Northeast China. The snow depth (SD) indicates the amount of snow cover for a given region. A high-quality and long-term SD dataset is a key requirement for the monitoring of climate change and the analysis of snow dynamics (Hori et al. 2017; Luojus et al. 2021).
Potential predictability of Eurasian spring snow water equivalent in IAP AGCM4 hindcasts
Published in Atmospheric and Oceanic Science Letters, 2020
Hong CHEN, He ZHANG, Yanling ZHAN
Snow is an important component of the global climate system. With its high albedo and low conductivity, snow can directly impact the local surface energy balance and thus modulate climate anomalies (Dewey 1977). Furthermore, snowmelt can serve as an important source of freshwater recharge, which can influence soil moisture and groundwater. Previous studies have been devoted to understanding the important influence of the Eurasian snow condition on climate anomalies over East Asia by diagnosing observational datasets and numerical simulation (e.g. Liu and Luo 1990; Wu and Qian 2003; Zhao, Zhou, and Liu 2007; Li, Wu, and Zhu 2009). For example, Wu, Yang, and Zhang (2009) reported that increasing Eurasian spring (seasonal mean of March–April–May) snow water equivalent (SWE) is significantly associated with reducing precipitation in southeastern China. Mu and Zhou (2010) found that the winter snow cover over northern Eurasia has remarkable negative correlations with summer rainfall in the region to the south of the Yangtze River. Wu and Kirtman (2007) also revealed the important impact of spring SWE over Eurasia on the spring and summer rainfall in China.
Modelling historical and potential future climate impacts on Keremeos Creek, an Okanagan-Similkameen watershed, British Columbia, Canada: Part I. Forecasting change in spring and summer water supply and demand
Published in Canadian Water Resources Journal / Revue canadienne des ressources hydriques, 2019
Shaghayegh Mirmasoudi, James Byrne, Ryan MacDonald, Daniel Johnson, Roland Kroebel
Snowmelt started in April, achieved a peak in June, and then decreased through July and August for the 1961–1990 period (Figures 3 and 8). However, the onset of snowmelt may shift earlier in March with the primary spring water volume between March and June with a peak in May for RCP 4.5 in the 2020’s. This pattern is the same for all other projected periods and emission scenarios except RCP 8.5 in the 2080’s, in which the timing of snowmelt may start 2 months earlier and extend through February and May with a peak in April (Figure 8). An earlier timing of snowmelt in all projected periods and emission scenarios is to be expected as a function of air temperature increasing over the watershed (Figure 5 and 8). According to the results, the peak water volume at the outlet of the watershed may decrease for RCP 4.5 and 8.5 in the 2020’s, 2050’s, and 2080’s relative to the average historical value (Figure 8). The reduction in peak water volume at the outlet of the watershed is a function of possible decreases in the ratio of snow to total monthly precipitation due to air temperature increases in all emission scenarios and projected periods (Knowles et al. 2006; Schnorbus et al. 2014; Elsner et al. 2010; Mote et al. 2005). If more of the precipitation occurs as rain than snow, the available water storage in the form of snow decreases and results in lower snowmelt runoff (MacDonald et al. 2011).