Peter D. Blanken University of Colorado. 26 Jan 2017 Geo-Informa(cs and Space Technology Development Agency Bangkok, Thailand
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1 Integra(ng Remote Sensing and Eddy Covariance Observa(ons for Microclimate Studies Peter D. Blanken University of Colorado 26 Jan 2017 Geo-Informa(cs and Space Technology Development Agency Bangkok, Thailand Photo by Chris Hadfield, courtesy of NASA 13-March
2 Table of Contents 2
3
4 1. McMaster University, Hamilton, Ontario 4
5 2. Churchill, Manitoba 5
6 6
7 3. University of Bri(sh Columbia: Soil Science 7
8 4. Boreal Aspen Forest: Prince Albert Na(onal Park 8
9 5. Great Slave Lake, Yellowknife NWT: GEWEX-MAGS 9
10 6. Great Bear Lake, Arc(c Circle 10
11 7. University of Colorado 11
12 12
13 13
14 7. Colorado: Wetlands and Prairies 14
15 8. Lauren(an Great Lakes: Back to Start 15
16 Thailand 16
17 Table of Contents 17
18 1. The atmosphere is heated from the surface. 2. The radiaion balance largely determines the available energy. 3. The available energy is pariioned through the energy balance. 4. The energy balance determines the surface temperature. We need to measure the small spa0al scale, and fast 0me scale, varia0ons at the Earth s surface. 18
19 19
20 Inputs - Outputs = Change in Storage (S +L ) (S +L +λe + H + G) = ρc p dt dt z 20
21 The Measurements 21
22 How do we measure net radia(on? Directly with a net radiometer : Two types 2 thermopile surfaces 4-componet 22
23 Fluxes? Eddy covariance method Covariance between verical wind speed (w) and scalar of interest: For example, evaporaion: λe = λwʹqʹ Heat Water vapor CO 2 Wind 23
24 What about beneath the surface? 24
25 25
26 White Shoal L. Michigan L. Huron Spectacle Reef o C 26
27 Table of Contents 27
28 Challenge: How do we connect satellite observaions to small-scale surface processes? How do we connect surface measurements to satellite observaions? 28
29 29
30 30
31 Good informaion obtained from remote sensing at the large spaial scale (landscape) can be used to understand small spaial scale processes. 31
32 Examples of how remote sensing has been used to help interpret micrometeorological measurements: 1. VegetaIon: Alpine Wetlands 2. Lakes: LaurenIan Great Lakes 32
33 33
34 Journal of Geophysical Research: Biogeosciences Volume 119, Issue 7, pages , 21 JUL 2014 DOI: /2014JG hbp://onlinelibrary.wiley.com/doi/ /2014jg002648/full#jgrg20245-fig
35 Journal of Geophysical Research: Biogeosciences Volume 119, Issue 7, pages , 21 JUL 2014 DOI: /2014JG hbp://onlinelibrary.wiley.com/doi/ /2014jg002648/full#jgrg20245-fig
36 Journal of Geophysical Research: Biogeosciences Volume 119, Issue 7, pages , 21 JUL 2014 DOI: /2014JG hbp://onlinelibrary.wiley.com/doi/ /2014jg002648/full#jgrg20245-fig
37 37
38 Compare simultaneous ice-covered to ice-free condi(ons 28 March 2015 Ice cover is highly variable through space Whitefish Bay White Shoal Lake Michigan Mackinaw City Spectacle Reef Lake Huron 78 km 38
39 39
40 What is going on under the surface? 40
41 Shortgrass Steppe Prairie Loca0on: Northeastern Colorado Leaf Area Index: 1.15 m 2 m -2 Study: Impacts of Grazing Past 50 yrs: Total harvested area increased 5%; Amount of irrigated land increased by 73% 41
42 Frac(on of Vegeta(on Cover Impact On: Temperature Moisture RespiraIon 42
43 Soil Temperature Differences % Bare
44 Soil Respira(on 44
45 Montane Ponderosa Pine Forest Hi Meadow Loca0on: Colorado Rocky Mountains Leaf Area Index: 3.0 m 2 m -2 Study: Unburned vs. Severe Burned Fire Jefferson County Unburned Site Burned Site 45
46 Temperature (Degrees Celsius) Temperature (Degrees Celsius) Time (Day of Year) Time (Day of Year) 46
47 Do these small-scale differences affect the surface fluxes? Can remote sensing help us?
48 How representative is a point? The spatial variability of surface energy fluxes across short distances in a sand-sagebrush ecosystem Fig. 3. The flux footprint, i.e. source area, of the turbulent flux measurements collected at each of the Mobile Station locations is shown. 48
49 Change in Surface Temperature ( o C/day) Using daily MODIS calibrated with lighthouse measurements
50 Niwot Ridge Subalpine Forest 50
51 Nov 2009 Nov 2013 Species Age 46% Subalpine Fir - 28% Engelmann Spruce 26% Lodgepole Pine ~ 100 years LAI ~ 4.2 m 2 m -2 Canopy Height Canopy Gap FracIon ~11.4 m ~ 17 % 51
52 Annual Cycles of NEE 52
53 New Measurements: LIDAR Near Tower 53
54 54
55 55
56 Summary 56
57 Thank you for your aaen(on. And thank you to my kind hosts at GISTDA. 57
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