Get started
Work

Assimilation of citizen science data in snowpack modeling using a new snow data set: Community Snow Observations

Year: 2021
Type: article
Abstract: A physically based snowpack evolution and redistribution model was used to test the effectiveness of assimilating crowd-sourced snow depth measurements collected by citizen scientists. The Community Snow Observations (CSO; https://communitysnowobs.org/, last access: 11 August 2021) project gathers, stores, and distributes measurements of snow depth recorded by recreational users and snow professionals in high mountain environments. These citizen science measurements are valuable since they come from terrain that is relatively undersampled and can offer in situ snow information in locations where snow information is sparse or nonexistent. The present study investigates (1) the improvements to model performance when citizen science measurements are assimilated, and (2) the number of measurements necessary to obtain those improvements. Model performance is assessed by comparing time series of observed (snow pillow) and modeled snow water equivalent values, by comparing spatially distributed maps of observed (remotely sensed) and modeled snow depth, and by comparing fieldwork results from within the study area. The results demonstrate that few citizen science measurements are needed to obtain improvements in model performance, and these improvements are found in 62 % to 78 % of the ensemble simulations, depending on the model year. Model estimations of total water volume from a subregion of the study area also demonstrate improvements in accuracy after CSO measurements have been assimilated. These results suggest that even modest measurement efforts by citizen scientists have the potential to improve efforts to model snowpack processes in high mountain environments, with implications for water resource management and process-based snow modeling.
DOI: 10.5194/hess-25-4651-2021
Authors: Ryan L. Crumley, David F. Hill, Katreen Wikstrom Jones, Gabriel J. Wolken, Anthony A. Arendt, Christina M. Aragon, Christopher Cosgrove, the Community Snow Observations Participants
Institutions: Oregon State University, Los Alamos National Laboratory, University of Washington Applied Physics Laboratory, Alaska Division of Geological and Geophysical Surveys, University of Alaska Fairbanks, International Arctic Research Center, University of Washington
Language: English
Open Access status: gold
Estimated APC paid: $1,725
Funders: National Aeronautics and Space Administration, Alfred P. Sloan Foundation, Washington Research Foundation, University of Washington, Consortium of Universities for the Advancement of Hydrologic Science, Los Alamos National Laboratory
Award: 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.
OpenAlex ID: W3086228570
FWCI: 1.231
Cites: 129
Cited by: 22
Related to: 10