Bioscience Evidence 2024, Vol.14, No.4, 161-171 http://bioscipublisher.com/index.php/be 168 7.4 Recommendations for future policy directions Future policy directions should focus on enhancing the resilience of snow-dependent water systems through a combination of technological innovation, sustainable land use practices, and international cooperation. Key recommendations include: Investing in Advanced Monitoring Technologies: Policies should support the development and implementation of advanced snowpack monitoring technologies, such as the blending of precipitation gauge and snow pillow data, to improve SWE predictions and water management (Pelak et al., 2022). Promoting Sustainable Land Use Practices: Policies should encourage the preservation of forested watersheds and other critical snowpack areas to maintain snowpack depth and duration, which are essential for ecosystem functions and water resources (Sanders-DeMott et al., 2019). Enhancing International Cooperation: Transboundary water resources require coordinated management efforts. Policies should facilitate international data sharing and collaborative research to address the global challenges of snowpack conservation (Fayad et al., 2017). Adapting to Climate Change: Policies should incorporate climate change projections into water management strategies, focusing on regions most at risk from changing snowmelt patterns. This includes developing alternative water supplies and adjusting agricultural practices to ensure water availability (Qin et al., 2020). By implementing these recommendations, policymakers can help ensure the sustainable management of snowpack resources, thereby supporting water resource management and ecosystem functions in the face of climate change. 8 Challenges and Future Research Directions 8.1 Current challenges in snowpack research and management One of the primary challenges in snowpack research is the accurate estimation of snow water equivalent (SWE) at basin scales. This is complicated by the low density of precipitation gauges in high mountain regions, which are often below the snowline, leading to significant spatial interpolation errors (Pelak et al., 2022). Additionally, the predictability of seasonal water resources is declining due to reduced snowpack, particularly in lower-elevation coastal areas, which are most impacted by warming (Livneh and Badger, 2020). The variability in snowmelt timing and the shift from snow to rain due to rising temperatures further complicate water resource management (Mote et al., 2018; Simpkins et al., 2018). 8.2 Gaps in existing knowledge There are significant gaps in understanding the spatial distribution and temporal dynamics of liquid water content within snowpacks. Current methods lack the ability to non-destructively estimate these properties at fine spatial and temporal scales (Webb et al., 2018). Furthermore, the impact of atmospheric humidity on snowpack ablation and the interaction between humidity, solar radiation, and temperature in controlling snowmelt are not fully understood (Harpold and Brooks, 2018). The role of internal climate variability in future snow resource potential also remains uncertain, with many basins showing potential for both increases and decreases in snow supply (Mankin et al., 2015). 8.3 Priority areas for future research Future research should prioritize the development of improved snow observation networks at high elevations to enhance the representation of precipitation patterns in hydrologic models (Pelak et al., 2022). There is also a need to better understand the effects of forest management practices, such as thinning, on snow accumulation and melt dynamics (Krogh et al., 2020). Additionally, research should focus on the development of robust climate risk management strategies that account for the full range of internal climate variability and the critical role of atmospheric humidity in snowpack dynamics (Mankin et al., 2015; Harpold and Brooks, 2018). Integrating interdisciplinary approaches to link climate change drivers with water-energy-food nexus and related ecosystem processes is also essential (Liu et al., 2016).
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