2. Field Data Acquisition - nooksack-indian-tribe/CurvyLapseRate GitHub Wiki

Abstract

Air temperature, ground temperature and relative humidity data were collected in a longitudinal transect of the Nooksack watershed at varying elevations from 500 - 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 hours from May 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 (https://doi.org/10.4211/hs.2d9787bf36d04c9383e595d179f9298) 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; these observations were collected to study the non-linear dynamics of temperature with elevation.

Background

The data collection scheme was designed to fill a critical gap in understanding the temperature lapse rates used in mountain watershed predictions using physical models. In the absence of temperature observations, climatological and hydrological models often assume a constant temperature lapse rate, such as -6.5 °C km-1 (Stone and Carlson, 1979 [2]; moist adiabatic lapse rate) to describe clear sky tropospheric temperature variability. In the coastal mountains of the North Cascades, Minder et al. (2010) [3] found annual temperature variability of -4.5 °C km-1.

Temperature lapse rate uncertainty on the order of 1-2°C km-1 has implications for understanding long-term water availability, drought and fire forecasts, and ecosystem adaptability in mountain watersheds like the State of Washington Water Resource Inventory Area 1 (WRIA1; Figure 1a). The Nooksack Tribe and Lummi Nation are two tribal governments in WRIA1 leading scientific and resource management efforts towards improving future estimates of instream flows in the Nooksack River that are crucial for salmonid population revival and longevity.

Field Work

Sensor field deployment methods were adapted from Lindquist and Lott (2008)[1] and Minder et al., (2010). Our sensor location design deployed air temperature sensors on a longitudinal transect 150-400 m apart in elevation in order to calculate the change in Ta with increasing elevation in the NFN (approximately one site per grid cell in Figure 1d). RH was collected at three locations (NFN1, NFN5, and NFN7) along the transect to determine variability in cloud cover and precipitation. Ta and RH were collected in tandem at 3-hour intervals with Maxim DS 1923 iButton sensors. Ta was collected without RH at 4-hour intervals with Maxim DS 1921 iButton sensors at sites NFN2, NFN4, and NFN6. Sensors were secured to trees with twine 3-7 m above the ground to ensure they were above the winter snowpack. The sensors were located within dense stands of trees and shaded with white plastic funnels to block direct solar radiation and allow air flow to the sensor. Paired ground temperature sensors were buried at least 3 cm below the ground surface near Ta and RH sensors in order to detect the absence or presence of snow. The sensors were wrapped in plastic to protect from water damage, then encased in small PVC tee fittings to protect from damage by environmental factors.

Interactive map of monitoring sites

Results

Our NFN watershed dataset from the northern flank of Mt. Baker has an annual average temperature lapse rates (-4.22 C/km), consistent with previous work in the North Cascades (-4.5 C/km in Minder et al., 2010). However, at the lowest elevations (~501m -1269m) the annual average lapse rate is ~ -4.88 deg C/km, while at higher elevations (1269m-1743m) the annual average lapse rate is ~ -3.13 deg C/km (Figure 3b). If mountain lapse rates are significantly different from assumed rates in meteorological and hydrology models, this is expected to have significant implications for high elevation snow, glacier, and hydrologic model predictions.