Bioscience Evidence 2024, Vol.14, No.4, 161-171 http://bioscipublisher.com/index.php/be 165 projected to transition to mixed rain and snow, with high flows occurring more frequently in late autumn and winter rather than spring (Clifton et al., 2018). These changes are driven by increased temperatures and altered precipitation patterns, which will significantly modify snow dynamics and reduce snowpack accumulation (Clifton et al., 2018; Collados-Lara et al., 2019). 5.3 Implications for water resource management The reduction in snowpack due to climate change poses significant challenges for water resource management. Snowpack acts as a natural reservoir, storing water during the cold season and releasing it during warmer months. Reduced snowpack makes drought harder to predict and jeopardizes irrigated agriculture, particularly in regions heavily dependent on snowmelt runoff, such as high-mountain Asia, Central Asia, western Russia, the western US, and the southern Andes (Qin et al., 2020; Vano et al., 2020). Effective water management strategies will need to adapt to these changes by improving current practices and developing new methods to ensure water availability throughout the year (Clifton et al., 2018; Qin et al., 2020). 5.4 Implications for ecosystem health and function Changes in snowpack dynamics have profound effects on ecosystem health and function. Reduced snowpack and altered snowmelt patterns influence soil microclimate, affecting soil temperature and moisture levels, which in turn impact carbon and nitrogen cycling processes (Wilson et al., 2020). In northern forest ecosystems, declining snowpack depth and duration lead to colder and more variable soil temperatures, affecting soil biogeochemistry, microbial activity, vegetation, and fauna (Sanders‐DeMott and Templer, 2017; Sanders-DeMott et al., 2019). Additionally, increased atmospheric humidity under warming conditions can lead to more frequent and intense winter melt events, further altering the timing and availability of water for ecosystems (Harpold and Brooks, 2018). These changes highlight the need for comprehensive climate change experiments that consider both winter and growing season dynamics to better understand and predict ecosystem responses (Sanders-DeMott and Templer, 2017). By understanding and addressing the impacts of climate change on snowpack dynamics, water resource management, and ecosystem health, we can develop more effective strategies to mitigate these effects and ensure the sustainability of vital water and ecological resources. 6 Monitoring and Modeling of Snowpack 6.1 Techniques for snowpack measurement and monitoring Accurate measurement and monitoring of snowpack are crucial for effective water resource management and understanding ecosystem functions. Traditional methods include in situ measurements such as snow pillows and snow courses, which provide point-specific data on snow water equivalent (SWE) (Pelak et al., 2022). However, these methods are often limited by their spatial coverage and accessibility, particularly in high mountain regions. Recent advancements have introduced innovative techniques such as the combination of ground-penetrating radar with terrestrial LiDAR scanning to estimate the spatial distribution of liquid water content in seasonal snowpacks. This method allows for non-destructive, high-resolution measurements of snowpack properties, capturing rapid changes at sub-daily timescales (Webb et al., 2018). Additionally, the use of lake water pressure to measure changes in SWE over large areas offers a novel approach that inherently senses changes over the entire lake surface, providing data that is directly comparable to the grid cells of weather and climate models (Pritchard et al., 2021). 6.2 Advances in snowpack modeling Snowpack modeling has seen significant advancements, particularly with the integration of data assimilation techniques. For instance, the CrocO_v1.0 system uses an ensemble data assimilation approach to ingest various snowpack observations, improving the accuracy of snowpack simulations by reducing uncertainties (Cluzet et al., 2021). Similarly, the development of the Spatial Modeling for Resources Framework (SMRF) has streamlined the process of generating input forcing data for snow models, making it computationally efficient and suitable for both research and operational applications (Havens et al., 2017).
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