Climate change is altering snow accumulation and ablation dynamics in snow-dependent regions worldwide, reshaping runoff timing and water availability for people, ecosystems, and agriculture. Water year 2026 offers a preview, with high-elevation snowpack across the western US losing roughly 40% of peak SWE in a single fortnight at elevations long assumed to be buffered against warming. As the world’s largest consumer of freshwater, irrigated agriculture is particularly exposed, and these hydrologic changes impose substantial economic costs. Here, we demonstrate how hydrology and economics can be combined to assign costs to snow-driven hydrologic change, using irrigated agriculture in the western United States as a large, snow-dependent test case. By integrating reduced-form economic models with climate model projections spanning approximately 2–5 °C of warming, we estimate that irrigated cropland could decline by 27–46% by the end of the century, while agricultural profitability—proxied using land rental rates—declines by 11–26%, corresponding to annual losses of approximately $8.2–$14.7 billion.

However, agricultural retirement in response to these economic pressures without integration across hazards risks unintended consequences. Through analysis of four decades of fire perimeter data (1984-present) and aerial imagery, we show that cropland is 2x less likely to be on the inside of a fire perimeter than any other land-cover type, suggesting an important landscape-scale buffering effect. Using drought and wildfire as an example, this finding demonstrates how cropland abandonment—a strategy that may enhance drought resilience—could amplify fire risk if poorly coordinated, illustrating how rational responses to one hazard can inadvertently increase exposure to others. Results underscore the complexity of adaptation under compounding climate risks and the importance of working in partnership with decision-makers to leverage expanding data and modeling capabilities for locally-relevant solutions.

Addressing this complexity requires research matched not only to the scales at which land and water management decisions are made, but to the varied and often competing pressures those decisions must weigh. We propose that archetypes—a framework from social-ecological systems research—could help accelerate matching adaptation strategies to specific decision-making contexts by explicitly accounting for governance capacity and behavioral dynamics. This approach has not been widely explored in adaptation to changes in mountain snow resources but could enable rapid, locally-relevant guidance.