Snow and ice in the desert: reflections from a decade of connecting cryospheric science with communities in the semiarid Chilean Andes

Citizen science and related engagement programmes have proliferated in recent years throughout the sciences but have been reasonably limited in the cryospheric sciences. In the semiarid Andes we at the Centro de Estudios Avanzados en Zonas Áridas have developed a range of initiatives together with the wider community and stakeholder institutions to improve our understanding of the role snow and ice play in headwater catchments. In this paper we reflect on ongoing engagement with communities living and working in and near study sites of cryospheric science in northern Chile as a strategy that can both strengthen the research being done and empower local communities

Climate change impacts on mountain snowpack presented in a knowledge to action framework

Throughout many of the world’s mountain ranges snowpack accumulates during the winter and into the spring, providing a natural reservoir for water. As this reservoir melts, it fills streams and recharges groundwater for over 1 billion people globally. Despite its importance to water resources, our understanding of the storage capacity of mountain snowpack is incomplete. This partial knowledge limits our abilities to assess the impact that projected climate conditions will have on mountain snowpack and water resources. While understanding the effect of projected climate on mountain snowpack is a global question, it can be best understood at the basin scale. It is at this level that decision makers and water resource managers base their decisions and require a clarified understanding of basin's mountain snowpack. The McKenzie River Basin located in the central-western Cascades of Oregon exhibits characteristics typical of many mountain river systems globally and in the Pacific Northwestern United States. Here snowmelt provides critical water supply for hydropower, agriculture, ecosystems, recreation, and municipalities. While there is a surplus of water in winter, the summer months see flows reach a minimum and the same groups have to compete for a limited supply. Throughout the Pacific Northwestern United States, current analyses and those of projected future climate change impacts show rising temperatures, diminished snowpacks, and declining summertime streamflow. The impacts of climate change on water resources presents new challenges and requires fresh approaches to understanding problems that are only beginning to be recognized. Climate change also presents challenges to decision makers who need new kinds of climate and water information, and will need the scientific research community to help provide improved means of knowledge transfer. This dissertation quantified the basin-wide distribution of snowpack across multiple decades in present and in projected climate conditions, describing a 56% decrease in mountain snowpack with regional projected temperature increases. These results were used to develop a probabilistic understanding of snowpack in projected climates. This section described a significant shift in statistical relations of snowpack. One that would be statistically likely to accumulate every 3 out of 4 years would accumulate in 1 out of 20 years. Finally this research identifies methods to improved knowledge transfer from the research community to water resource professionals. Implementation of these recommendations would enable a more effective means of dissemination to stakeholders and policy makers. While this research focused only on the McKenzie River Basin, it has regional applications. Processes affecting snowpack in the McKenzie River Basin are similar to those in many other maritime, forested Pacific Northwest watersheds. The framework of this research could also be applied to regions outside of the Pacific Northwestern United States to gain a similar level of understanding of climate impacts on mountain snowpack

Quantifying the Effect of River Ice Surface Roughness on Sentinel-1 SAR Backscatter

Satellite-based C-band synthetic aperture radar (SAR) imagery is an effective tool to map and monitor river ice on regional scales because the SAR backscatter is affected by various physical properties of the ice, including roughness, thickness, and structure. Validation of SAR-based river ice classification maps is typically performed using expert interpretation of aerial or ground reference images of the river ice surface, using visually apparent changes in surface roughness to delineate different ice classes. Although many studies achieve high classification accuracies using this qualitative technique, it is not possible to determine if the river ice information contained within the SAR backscatter data originates from the changes in surface roughness used to create the validation data, or from some other ice property that may be more relevant for ice jam forecasting. In this study, we present the first systematic, quantitative investigation of the effect of river ice surface roughness on C-band Sentinel-1 backscatter. We use uncrewed aerial vehicle-based Structure from Motion photogrammetry to generate high-resolution (0.03 m) digital elevation models of river ice surfaces, from which we derive measurements of surface roughness. We employ Random Forest models first to repeat previous ice classification studies, and then as regression models to explore quantitative relationships between ice surface roughness and Sentinel-1 backscatter. Classification accuracies are similar to those reported in previous studies (77–96%) but poor regression performance for many surface roughness metrics (5–113% mean absolute percentage errors) indicates a weak relationship between river ice surface roughness and Sentinel-1 backscatter. Additional work is necessary to determine which physical ice properties are strong controls on C-band SAR backscatter

Characterizing Rain/snow Partitioning in Mountain Watersheds for Present-day and Future Projected Climates

The western slope of the Oregon Cascades receives up to 3500 mm of precipitation annually, with a majority falling between the months of November-March. In this maritime climate, the partitioning of precipitation between rain and snow is highly sensitive to temperature. Climate models generally agree that winter temperatures in the Pacific Northwest will increase in the next few decades. In this model-based study we apply a classification system based upon rain-snow probability, seasonal precipitation variability, land cover, landscape position, and geology for sub-basins of the McKenzie River Basin. Using a “delta” approach, we apply monthly projected changes in temperature and precipitation to the meteorological data that forces a spatially distributed snow model. The model distributes precipitation over the landscape as rain or snow depending on grid cell temperature. The metric for rain-snow probability uses the dimensionless ratio of Snow Water Equivalent (SWE) to precipitation (P; with the ratio referred to as SWE/P hereafter). This metric minimizes the effects of variable precipitation, while still accounting for impacts of warmer temperatures on snowmelt. Combining SWE/P likelihood with landscape metrics provides a probabilistic approach characterizing sub-basins and their spatiotemporal responses to warmer temperatures.Presented at The Oregon Water Conference, May 24-25, 2011, Corvallis, OR

Autonomous Aerial Vehicles (AAVs) as a Tool for Improving the Spatial Resolution of Snow Albedo Measurements in Mountainous Regions

We present technical advances and methods to measure effective broadband physical albedo in snowy mountain headwaters using a prototype dual-sensor pyranometer mounted on an Autonomous Aerial Vehicle (an AAV). Our test flights over snowy meadows and forested areas performed well during both clear sky and snowy/windy conditions at an elevation of ~2650 m above mean sea level (MSL). Our AAV-pyranometer platform provided high spatial (m) and temporal resolution (sec) measurements of effective broadband (310–2700 nm) surface albedo. The AAV-based measurements reveal spatially explicit changes in landscape albedo that are not present in concurrent satellite measurements from Landsat and MODIS due to a higher spatial resolution. This AAV capability is needed for validation of satellite snow albedo products, especially over variable montane landscapes at spatial scales of critical importance to hydrological applications. Effectively measuring albedo is important, as annually the seasonal accumulation and melt of mountain snowpack represent a dramatic transformation of Earth’s albedo, which directly affects headwaters’ water and energy cycles

SnowCloudMetrics: Snow Information for Everyone

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

SnowCloudHydro—A New Framework for Forecasting Streamflow in Snowy, Data-Scarce Regions

We tested the efficacy and skill of SnowCloud, a prototype web-based, cloud-computing framework for snow mapping and hydrologic modeling. SnowCloud is the overarching framework that functions within the Google Earth Engine cloud-computing environment. SnowCloudMetrics is a sub-component of SnowCloud that provides users with spatially and temporally composited snow cover information in an easy-to-use format. SnowCloudHydro is a simple spreadsheet-based model that uses Snow Cover Frequency (SCF) output from SnowCloudMetrics as a key model input. In this application, SnowCloudMetrics rapidly converts NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover product (MOD10A1) into a monthly snow cover frequency for a user-specified watershed area. SnowCloudHydro uses SCF and prior monthly streamflow to forecast streamflow for the subsequent month. We tested the skill of SnowCloudHydro in three snow-dominated headwaters that represent a range of precipitation/snowmelt runoff categories: the Río Elqui in Northern Chile; the John Day River, in the Northwestern United States; and the Río Aragón in Northern Spain. The skill of the SnowCloudHydro model directly corresponded to snowpack contributions to streamflow. Watersheds with proportionately more snowmelt than rain provided better results (R2 values: 0.88, 0.52, and 0.22, respectively). To test the user experience of SnowCloud, we provided the tools and tutorials in English and Spanish to water resource managers in Chile, Spain, and the United States. Participants assessed their user experience, which was generally very positive. While these initial results focus on SnowCloud, they outline methods for developing cloud-based tools that can function effectively across cultures and languages. Our approach also addresses the primary challenges of science-based computing; human resource limitations, infrastructure costs, and expensive proprietary software. These challenges are particularly problematic in countries where scientific and computational resources are underdeveloped.This research was funded by an incubator grant from the Earth Science Information Partners (ESIP) and by NASA grant No. NNX16AG35G.Peer reviewe