This proposal will build on our existing legacy of citizen scientist engagement in alaska and extend this engagement to stakeholders in the pacific northwest, to study variations in alpine…

This federal contract award, with ID NNX17AI20A, was granted by the Shared Services Center (an alternate name for the federal agency) to the Alaska Department of Natural Resources (DNR) as the prime contractor. The contract, valued at $150,766.00, is part of the Citizen Science for Earth Systems Program (CSESP) 2017 and aims to build on existing citizen scientist engagement in Alaska to extend this to the Pacific Northwest region, with a focus on studying variations in alpine snowpacks. The project will leverage smartphone and cloud-based technologies to coordinate a network of citizen scientists, including avalanche specialists and local recreationists, to collect observations on snow depth and grain size. This data will be integrated with NASA's remote sensing datasets and used to validate models and improve the understanding of snow processes in remote regions. The contract does not have a set-aside designation.

This proposal will build on our existing legacy of citizen scientist engagement in alaska and extend this engagement to stakeholders in the pacific northwest, to study variations in alpine snowpacks. During the past several years, we have built a highly collaborative network of scientists spanning multiple government, academic, and industry partners. This work has enabled US to acquire new information on the distribution of snow across remote alpine regions of alaska. To date we have involved citizen scientists, including avalanche specialists and local recreationists, in an informal way on a number of small-scale projects. Here we propose to build a citizen-scientist infrastructure supported by smartphone and cloud-based technologies and coordinated by expert snow scientists. Our project scientists will train this community to generate observations of snow depth and grain size. This work will provide information on snow properties in alpine regions that our existing remote observations have been unable to capture and will help validate remotesensing products and improve the quality of our runoff models. We will link observations and model output to a wide range of NASA remote observations. Our integrative approach, linking field-based and remote observations with model simulations, provides the best method to advance understanding of snow across remote regions of alaska and the pacific northwest. Our work is motivated by the fact that snow is linked to a wide range of hydrological processes, natural hazards, and ecosystem services. Snow is a unique form of precipitation because IT facilitates the storage of water on the landscape for release at a later time, both in seasonal snowpacks and within glacier systems, providing a source of runoff during periods without precipitation. Changes to these water delivery systems alter chemical and physical stream properties, with impacts on aquatic habitat. In addition, extreme events can lead to flooding, creating potentially serious socio-economic consequences. Water management decisions aimed at mitigating these hazards requires accurate predictive models of the timing and magnitude of snowmelt runoff. Despite the importance of snow on the landscape, numerous challenges have limited our ability to measure and model snow processes. Snow is a highly complex substance that metamorphoses rapidly in time and can vary over short spatial scales as IT interacts with the landscape and is redistributed by wind. A central challenge is matching model resolution and complexity to the availability of observations needed for accurate model calibration. Such observations are usually located at low-elevation weather stations and do not accurately sample high-alpine areas where snowpacks are deepest. To overcome these challenges, NASA has invested considerable resources to measure snow properties from airborne and spaceborne platforms, including measurements of snow depth (altimetry), snow water equivalence (active and passive radar) and snow mass balance (gravimetry). Each of these approaches has provided some insight into snowpack evolution, but no single approach provides the detailed information that by the modeling community needs. The activities of snow recreationists in high-alpine regions presents a unique opportunity for US to collect snowpack information and to engage the public in NASA s earth-science mission. Citizen scientists will collect snow-depth data using snow probes and will take photographs of snow crystals using their smartphones. The participants will enter their data into a citizen-science tool imbedded in mountainhub, a smartphone application developed by our partners avatech. We will leverage our existing database and web services deployed in the cloud to visualize the observations, providing rapid feedback to the citizen-scientist community. Data will be ingested into our existing hydrological simulation models and integrated with NASA remote-sensing datasets.