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Abstract Air temperature, ground temperature and relative humidity data were collected in a longitudinal transect of the Nooksack watershed at varying elevations from 500 to 1800 m above sea level. Data were collected by anchoring sensors from trees above winter snow levels and shaded from direct solar radiation. Paired sensors were also buried 3 cm under ground near each air temperature sensor to determine snow absence or presence. Select sites included relative humidity sensors to indicate whether precipitation was occurring. Data were collected every 3-4 h from December 2015 to Sept 2018 (with ongoing collection). Code for analysis of daily mean, minimum, maximum, and temperature change with elevation (lapse rates) are available on Github (https://doi.org/10.5281/zenodo.3239539). The sensor download and intermediate data products are available on HydroShare at (http://www.hydroshare.org/resource/222e832d3df24dea9bae9bbeb6f4219d) with publicly accessible visualization available from the Nooksack Observatory at data.cuahsi.org. Hydrologic models are generally structured with a single annual average lapse rate parameter which assumes a linear temperature gradient with elevation. The daily data (2016-2018) is used as part of ongoing studies on the non-linear dynamics and temporal variability of temperature with elevation to improve assessments of watershed function and salmon habitat.
Value of the Data
• The air temperature (Ta), relative humidity (RH), and temperature lapse rate (TLR) data in this collection is the first dataset of this kind available at this location for calibrating and validating downscaled hydrological models in the glaciated North Fork Nooksack River watershed, which drains into Whatcom County, Washington State.
• This dataset can be used to more accurately model snow covered area (SCA), snow depth (SD), and snow water equivalent (SWE) in the Nooksack River watershed.
• The Nooksack Observatory serves as a prototype for developing collaboratively supported microclimatology networks.
• This dataset can be downloaded from the CUAHSI HydroClient and Hydroshare for water and atmospheric research in a range of disciplines in need of microclimatology observations and networks, including mountain hydrology, snow accumulation and melt dynamics, climatology, water resources management, drought and fire forecasts, and mountain ecology.
Data in Brief Journal Publication: Bandaragoda, C., Beaulieu, J., Cristea, N. and Beveridge, C., 2020. Elevation distributed micro-climatology data in a coastal glaciated watershed. Data in Brief, p.105578. https://doi.org/10.1016/j.dib.2020.105578
Data Resource: Beaulieu, J., C. Bandaragoda, C. Beveridge, N. Cristea (2020). Nooksack Temperature Lapse Rate Study 2016-2018, HydroShare, http://www.hydroshare.org/resource/222e832d3df24dea9bae9bbeb6f4219d
Code: Beveridge C. , N. Cristea, Bandaragoda, C., J. Beaulieu. (2019, June 5). nooksack-indian-tribe/CurvyLapseRate: Alpha release to create DOI for Nooksack Indian Tribe (Version v0.0.1-alpha). Zenodo. http://doi.org/10.5281/zenodo.3239539
Land Acknowledgement: The Coast Salish people are the indigenous inhabitants of Western Washington. The Nooksack Watershed, from the peak of Mount Baker to the Bellingham Bay, is the unceded ancestral land of the Nooksack Tribe and Lummi Nation. They are still here, continuing to honor and bring to light their ancient heritage. The University of Washington acknowledges the Coast Salish peoples of this land, the land which touches the shared waters of all tribes and bands within the Suquamish, Tulalip and Muckleshoot nations.
[1] J.D. Lundquist, F. Lott Using inexpensive temperature sensors to monitor the duration and heterogeneity of snow-covered areas Water Resour. Res., 44 (2008), 10.1029/2008WR007035 W00D16 Google Scholar
[2] P.H. Stone, J.H. Carlson Atmospheric lapse rate regimes and their parameterization J. Atmos. Sci., 36 (3) (1979), pp. 415-423 CrossRefView Record in ScopusGoogle Scholar
[3] J.R. Minder, P.W. Mote, J.D. Lundquist Surface temperature lapse rates over complex terrains: lessons from the Cascade mountains J. Geophys. Res., 115 (2010), p. D14122, 10.1029/2009JD013493 Google Scholar
[4] A.F. Hamlet, P.W. Mote, M.P. Clark, D.P. Lettenmaier Effects of temperature and precipitation variability on snowpack trends in the western United States J. Clim., 18 (21) (2005), pp. 4545-4561 View Record in ScopusGoogle Scholar [5] P.W. Mote, S. Li, D.P. Lettenmaier, M. Xiao, R. Engel Dramatic declines in snowpack in the western US Npj Clim. Atmos. Sci., 1 (1) (2018), p. 2 Google Scholar
[6] C. Frans, E. Istanbulluoglu, D.P. Lettenmaier, A.G. Fountain, J. Riedel Glacier recession and the response of summer streamflow in the Pacific Northwest United States, 1960–2099 Water Resour. Res., 54 (2018), pp. 6202-6225, 10.1029/2017WR021764 CrossRefView Record in ScopusGoogle Scholar
[7] E.P. Maurer, L. Brekke, T. Pruitt, P.B. Duffy Fine‐resolution climate projections enhance regional climate change impact studies Eos Trans. Am. Geophys. Union, 88 (47) (2007), p. 504 504 CrossRefView Record in ScopusGoogle Scholar
[8] E.P. Salathé Jr., A.F. Hamlet, C.F. Mass, S.Y. Lee, M. Stumbaugh, R. Steed Estimates of twenty-first-century flood risk in the Pacific Northwest based on regional climate model simulations J. Hydrometeorol., 15 (5) (2014), pp. 1881-1899 View Record in ScopusGoogle Scholar
[9] B. Livneh, T.J. Bohn, D.W. Pierce, F. Munoz-Arriola, B. Nijssen, R. Vose, L. Brekke A spatially comprehensive, hydrometeorological data set for Mexico, the US, and Southern Canada 1950–2013 Sci. Data, 2 (2015), Article 150042 View Record in ScopusGoogle Scholar
[10] B. Henn, M.S. Raleigh, A. Fisher, J.D. Lundquist A comparison of methods for filling gaps in hourly near-surface air temperature data J. Hydrometeorol., 14 (3) (2013), pp. 929-945 View Record in ScopusGoogle Scholar
[11] S. Manabe, R.F. Strickler Thermal Equilibrium of the Atmosphere with a Convective Adjustment J. Atmos. Sci., 21 (1964), pp. 361-385, 10.1175/1520-0469 CrossRefView Record in ScopusGoogle Scholar