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Nooksack Temperature Lapse Rate Study 2016-2018

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Created: Nov 10, 2017 at 1:52 a.m.
Last updated: Feb 05, 2024 at 10:40 a.m.
DOI: 10.4211/hs.2d9787bf36d04c9383e595d179f9298b
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Air temperature, ground temperature and relative humidity data was collected in a longitudinal transect of the Nooksack watershed at varying elevations from 500 - 1800 m above sea level. Data was 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. Selected sites included relative humidity sensors to indicate whether precipitation was occuring. Data was collected every 3-4 hours from May 2015 to Sept 2018 (with ongoing collection). Code for processing daily mean, minimum, maximum, and rates of temperature changes with elevation is available on Github ( The sensor download and intermediate data products are available on HydroShare with publicly accessible visualization available from the Nooksack Observatory at

Subject Keywords



Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
North Fork Nooksack River Watershed
North Latitude
East Longitude
South Latitude
West Longitude


Start Date:
End Date:




Code for processing sub-daily temperature sensor data for analysis of elevation distributed micro-climatology processes in a coastal glaciated watershed. Code allows for visualization and publication of daily data in multiple formats (1) daily data in python dictionaries for analysis (2) daily data for visualization and download on HydroServer at (ODM format), and (3) archive publication on the HydroShare repository (ODM2 format)).

Air temperature and relative humidity were collected on a longitudinal transect in the North Fork Nooksack River Watershed in Whatcom County, Washington using methods described in (Hubbart et al., 2005; Lundquist & Cayan, 2007; Lundquist & Lott, 2008). This code is useful for assessment of observations, especially lapse rates with increasing elevation and microclimatic influences. Every hydrologic model that we know of (circa 2019) uses a linear annual lapse rate (e.g. -6.5 C/km clear sky assumption; - 4.5 C/km North Cascades Mountain Range (Minder et al., 2010) to represent the change in temperature with elevation. We are collecting empirical evidence to test the hypothesis that high elevation temperature lapse rates in glaciated mountain peaks, especially in maritime climates, are linear. They may be curvy.

Preliminary data analysis from the North Fork Nooksack (northern flank of Mt. Baker) shows a consistent annual average (-4.4 C/km) to previous work (Minder et al., 2010). However, at the lowest elevations (~664m -1056m) the annual average lapse rate is ~ 2.3 C/km; at mid elevations (1056m- 1575m) the annual average lapse rate is ~ 5.4 C/km; at higher elevations (1575m-1743m) the annual average lapse rate is ~ -7.2 C/km. If mountain lapse rates are significantly different from modeled rates, this is expected to have significant implications for high elevation snow, glacier, and hydrologic model predictions.

Curvy is something to be proud of. -Paloma Faith

Citation suggestions:

Data Publication: Bandaragoda, C., J. Beaulieu, N. Cristea, C. Beveridge, (2019). elevation distributed micro-climatology data in a coastal glaciated watershed, Data in Brief, (in preparation).

Data Resource: J. Beaulieu, Bandaragoda, C., C. Beveridge, N. Cristea (2019). Nooksack Temperature Lapse Rate Study, HydroShare,

Code: C. Beveridge, 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.


Hubbart, J., Link, T., Campbell, C., & Cobos, D. (2005). Evaluation of a low‐cost temperature measurement system for environmental applications. Hydrological Processes: An International Journal, 19(7), 1517-1523.

Lundquist, J. D., & Cayan, D. R. (2007). Surface temperature patterns in complex terrain: Daily variations and long‐term change in the central Sierra Nevada, California. Journal of Geophysical Research: Atmospheres, 112(D11).

Lundquist, J.D. and Lott, F. 2008. Using inexpensive temperature sensors to monitor the duration and heterogeneity of snow-covered areas. Water Resour. Res., 44, W00D16, doi: 10.1029/2008WR007035.

Minder, J.R., Mote, P.W., Lundquist, J.D. 2010. Surface temperature lapse rates over complex terrains: Lessons from the Cascade Mountains. J. Geophys. Res. 115, D14122, doi:10.1029/2009JD013493.

Data Services

The following web services are available for data contained in this resource. Geospatial Feature and Raster data are made available via Open Geospatial Consortium Web Services. The provided links can be copied and pasted into GIS software to access these data. Multidimensional NetCDF data are made available via a THREDDS Data Server using remote data access protocols such as OPeNDAP. Other data services may be made available in the future to support additional data types.

Related Resources

This resource is referenced by
This resource is referenced by Data in Brief publication
The content of this resource is derived from Sara Damiano, Anthony Aufdenkampe, Jeff Horsburgh, David Valentine, Amber Jones, Jacob Meline, … David Tarboton. (2019, May 13). ODM2/YODA-File: v0.1-alpha: Initial alpha release for testing (Version v0.1-alpha). Zenodo.
This resource has been replaced by a newer version Beaulieu, J., C. Bandaragoda, C. Beveridge, N. Cristea (2020). Nooksack Temperature Lapse Rate Study 2016-2018, HydroShare,


Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
Bureau of Indian Affairs Tribal Resilience Program
Bureau of Indian Affairs Rights Protection Implementation
National Science Foundation PREEVENTS TRACK 2: Integrated Modeling of Hydro-Geomorphic Hazards: Floods, Landslides and Sediment 1663859
National Science Foundation Collaborative Research: SI2-SSI: Cyberinfrastructure for AdvancinHydrologic Knowledge through Collaborative Integration of Data Science, Modeling and Analysis 1664061


People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.

Name Organization Address Phone Author Identifiers
Susan Dickerson-Lange Natural Systems Design
Jessica Lundquist University of Washington WA, US 2066857594
Jennifer Lennon
Oliver Grah Nooksack Indian Tribe 5016 Deming Rd. 3605925140

How to Cite

Beaulieu, J., C. Bandaragoda, C. Beveridge, N. Cristea (2019). Nooksack Temperature Lapse Rate Study 2016-2018, HydroShare,

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