Mountain social‐ecological systems (MtSES) are vital to humanity, providing ecosystem services to over half the planet's human population. Despite their importance, there has been no global assessment of threats to MtSES, even as they face unprecedented challenges to their sustainability. With survey data from 57 MtSES sites worldwide, we test a conceptual model of the types and scales of stressors and ecosystem services in MtSES and explore their distinct configurations according to their primary economic orientation and land use. We find that MtSES worldwide are experiencing both gradual and abrupt climatic, economic, and governance changes, with policies made by outsiders as the most ubiquitous challenge. Mountains that support primarily subsistence‐oriented livelihoods, especially agropastoral systems, deliver abundant services but are also most at risk. Moreover, transitions from subsistence‐ to market‐oriented economies are often accompanied by increased physical connectedness, reduced diversity of cross‐scale ecosystem services, lowered importance of local knowledge, and shifting vulnerabilities to threats. Addressing the complex challenges facing MtSES and catalyzing transformations to MtSES sustainability will require cross‐scale partnerships among researchers, stakeholders, and decision makers to jointly identify desired futures and adaptation pathways, assess trade‐offs in prioritizing ecosystem services, and share best practices for sustainability. These transdisciplinary approaches will allow local stakeholders, researchers, and practitioners to jointly address MtSES knowledge gaps while simultaneously focusing on critical issues of poverty and food security.

Glacierized mountains are often referred to as our world's water towers because glaciers both store water over time and regulate seasonal stream flow, releasing runoff during dry seasons when societies most need water. Ice loss thus has the potential to affect human societies in diverse ways, including irrigation, agriculture, hydropower, potable water, livelihoods, recreation, spirituality, and demography. Unfortunately, research focusing on the human impacts of glacier runoff variability in mountain regions remains limited, and studies often rely on assumptions rather than concrete evidence about the effects of shrinking glaciers on mountain hydrology and societies. This article provides a systematic review of international research on human impacts of glacier meltwater variability in mountain ranges worldwide, including the Andes, Alps, greater Himalayan region, Cascades, and Alaska. It identifies four main areas of existing research: (1) socioeconomic impacts; (2) hydropower; (3) agriculture, irrigation, and food security; and (4) cultural impacts. The article also suggests paths forward for social sciences, humanities, and natural sciences research that could more accurately detect and attribute glacier runoff and human impacts, grapple with complex and intersecting spatial and temporal scales, and implement transdisciplinary research approaches to study glacier runoff. The objective is ultimately to redefine and reorient the glacier-water problem around human societies rather than simply around ice and climate. By systematically evaluating human impacts in different mountain regions, the article strives to stimulate cross-regional thinking and inspire new studies on glaciers, hydrology, risk, adaptation, and human–environment interactions in mountain regions.

Thorn, J. P. R., J. A. Klein, C. Steger, K. A. Hopping, C. Capitani, C. M. Tucker, A. W. Nolin, R. S. Reid, R. Seidl, V. S. Chitale, and R. Marchant. 2020. A systematic review of participatory scenario planning to envision mountain social-ecological systems futures. Ecology and Society 25(3):6. https://doi.org/10.5751/ES-11608-250306

Snow is a critical component of the climate system, provides fresh water for millions of people globally, and affects forest and wildlife ecology. Snowy regions are typically data sparse, especially in mountain environments. Remotely-sensed snow cover data are available globally but are challenging to convert into accessible, actionable information. SnowCloudMetrics is a web portal for on-demand production and delivery of snow information including snow cover frequency (SCF) and snow disappearance date (SDD) using Google Earth Engine (GEE). SCF and SDD are computed using the Moderate Resolution Imaging Spectroradiometer (MODIS) Snow Cover Binary 500 m (MOD10A1) product. The SCF and SDD metrics are assessed using 18 years of Snow Telemetry records at more than 750 stations across the Western U.S. SnowCloudMetrics provides users with the capacity to quickly and efficiently generate local-to-global scale snow information. It requires no user-side data storage or computing capacity, and needs little in the way of remote sensing expertise. SnowCloudMetrics allows users to subset by year, watershed, elevation range, political boundary, or user-defined region. Users can explore the snow information via a GEE map interface and, if desired, download scripts for access to tabular and image data in non-proprietary formats for additional analyses. We present global and hemispheric scale examples of SCF and SDD. We also provide a watershed example in the transboundary, snow-dominated Amu Darya Basin. Our approach represents a new, user-driven paradigm for access to snow information. SnowCloudMetrics benefits snow scientists, water resource managers, climate scientists, and snow related industries providing SCF and SDD information tailored to their needs, especially in data sparse regions.

Snow is Earth's most climatically sensitive land cover type. Traditional snow metrics may not be able to adequately capture the changing nature of snow cover. For example, April 1 snow water equivalent (SWE) has been an effective index for streamflow forecasting, but it cannot express the effects of midwinter melt events, now expected in warming snow climates, nor can we assume that station‐based measurements will be representative of snow conditions in future decades. Remote sensing and climate model data provide capacity for a suite of multi‐use snow metrics from local to global scales. Such indicators need to be simple enough to “tell the story” of snowpack changes over space and time, but not overly simplistic or overly complicated in their interpretation. We describe a suite of spatially explicit, multi‐temporal snow metrics based on global satellite data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and downscaled climate model output for the U.S. We describe and provide examples for Snow Cover Frequency (SCF), Snow Disappearance Date (SDD), At‐Risk Snow (ARS), and Frequency of a Warm Winter (FWW). Using these retrospective and prospective snow metrics, we assess the current and future snow‐related conditions in three hydroclimatically different U.S. watersheds: the Truckee, Colorado Headwaters, and Upper Connecticut. In the two western U.S. watersheds, SCF and SDD show greater sensitivity to annual differences in snow cover compared with data from the ground‐based Snow Telemetry (SNOTEL) network. The eastern U.S. watershed does not have a ground‐based network of data, so these MODIS‐derived metrics provide uniquely valuable snow information. The ARS and FWW metrics show that the Truckee Watershed is highly vulnerable to conversion from snowfall to rainfall (ARS) and midwinter melt events (FWW) throughout the seasonal snow zone. In comparison, the Colorado Headwaters and Upper Connecticut Watersheds are colder and much less vulnerable through mid‐ and late‐century.