1 Introduction
A fine-scale (1-meter resolution) water balance model was developed to examine the influence of fine-scale variation in soils and topography on climate at sites selected for planting and direct seeding of whitebark pine. The climatic water balance accounts for the simultaneous availability of water and energy as well as the influence of terrain and soils on climate in a way that is proximal to the biophysical environment experienced by plants (Stephenson, 1998). This report focuses on two measures from the climatic water balance - Actual Evapotranspiration (AET), which represents the length and magnitude of growing conditions favorable to plants, and Climatic Water Deficit (CWD), which is a measure of drought stress representing plant water demands unmet by available water.
The climatic water balance has been used to understand environmental variability within relatively coarse climate grid cells to accurately model plant distributions (Lutz et al., 2010). Fine-scale terrain features have been shown to reveal patterns in the water balance across the landscape that are masked by coarser digital elevation models (DEM), such as the persistence of fine-scale mesic habitat under climate projections using fine-scale LiDAR DEM data compared with the coarser 30m SRTM DEM (Dyer, 2019). The USGS 1m LiDAR product was selected for this study.
Climate data for historical periods is sourced from the gridMET climate dataset (Abatzoglou, 2013), and climate projections from the MACA dataset (Abatzoglou & Brown, 2012). MACA projections are downscaled using gridMET data, so data from the two models are directly comparable without bias correction (Tercek et al., 2023).
Several caveats apply to this product. Temperature and precipitation data are not available at high-resolutions in this study system and all available gridded climate data products have uncertainty in areas of complex topography such as mountains regions. Additionally, temperature inversions, cold air drainage, and snow-drift are not considered in the model or climate data used with the model. Gridded climate products assign an elevation to each grid cell, which can vary considerably from the actual elevation of any point within the cell, with commensurate influence on the accuracy of the temperature estimates for any point within that grid cell (Tercek et al., 2021). We apply the average of the North and South slope lapse rates obtained from temperature dataloggers in Yellowstone National Park reported by Tercek et al. (2021) as a correction for this issue, however exact lapse rates can vary across time and location and this correction does not account for fine-scale factors affecting air temperature such as cold air drainage and temperature inversions. Relative patterns in the water balance across the landscape come with a higher degree of certainty than absolute estimates of AET and CWD. Any use of this model needs to be carefully considered with these uncertainties in mind against actual patterns of vegetation composition observed in the field. Relative importance of AET and CWD to vegetation on the site will also depend on whether the location is energy- or water-limited.
Future studies can improve on the models here by incorporating more accurate estimates of temperature and precipitation in mountainous terrain. Low-cost temperature dataloggers can be deployed to obtain more accurate temperature estimates that account for fine-scale patterns such as cold air drainage and temperature inversions (Holden et al., 2011).
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## Reading layer `GRTE_WBP_ModelingAreas_merged' from data source
## `/home/steve/OneDrive/core_areas/data/StephenHuysman_GRTE_WBP_ModelingAreas/GRTE_WBP_ModelingAreas_merged.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 6 features and 6 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY, XYZ
## Bounding box: xmin: 514602.9 ymin: 4835126 xmax: 518367.2 ymax: 4849686
## z_range: zmin: 0 zmax: 0
## Projected CRS: NAD83 / UTM zone 12N2 Core Areas
Figure 2.1: YELL and GRTE Core Areas in WBP Climate Space. This figure shows historical (2000-2019) AET/CWD average annual sums for known WBP occurences from the WPBR monitoring dataset. Historical data for points within the GYE are highlighted in blue, the rest of the WBP range shown in grey. Historical (green), RCP4.5 (darkviolet), and RCP8.5 (red) projections are also shown for the YELL and GRTE WBP core areas, as well as the Burroughs Creek planting site in the Shoshone NF. RCP4.5 and RCP8.5 projections are ensemble averages provided by the ‘summary layers’ product from the NPS Gridded Water Balance dataset. This figure visualizes the potential shifts in climate space that these sites could experience in the future, bracketed by the emission scenarios modeled under the RCP4.5 and RCP8.5 scenarios. These data were generated from the 1km NPS gridded water balance model, and as such, absolute values of AET and CWD cannot be compared to the values generated from the 1m model presented here.
3 Static East
3.1 Topographic & Soil Features
## Reading layer `grte_modelingareas_ssurgo' from data source
## `/home/steve/OneDrive/core_areas/data/input/soil/grte_modelingareas_ssurgo.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 41 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 514602.9 ymin: 4835125 xmax: 518367.2 ymax: 4849686
## Projected CRS: NAD83 / UTM zone 12N
Figure 3.1: Static East Direct Seeding Polygon with Hillshade from 1m LiDAR DEM
| elev_mean | elev_max | elev_min | slope_mean | slope_max | slope_min | aspect_mean |
|---|---|---|---|---|---|---|
| 2825.52 | 2889.88 | 2747.71 | 22.47 | 74.97 | 0 | 143.8 |
Figure 3.2: Static East Soil Water Holding Capacity (WHC, mm) at 25 cm soil depth.
3.2 Historical Water Balance
3.2.1 Baseline
Historical climate data are average across the years 2002-2022. This represents historical “baseline” conditions against with other climatic summaries can be compared.
Figure 3.3: Static East Historical Water Balance (2002-2022)
Figure 3.4: Static East Historical Water Balance (2002-2022)
3.2.2 1988 Drought Year
1988 is selected as an example of a drought year for the location. This year was characterized by extreme drought levels leading to widespread wildfires across the GYE. This serves as an example of an extreme year against which other climatic summaries can be compared.
Figure 3.5: Static East Drought Year (1988) annual AET and CWD
Figure 3.6: Static East Drought Year (1988) annual AET and CWD
3.3 Projected Water Balance
Four GCM/Emission Scenario combinations were selected to bracket the range of plausible future climates based on ‘Warm/Wet’, ‘Warm/Dry’, ‘Hot/Wet’, and ‘Hot/Dry’ scenarios determined by changes in annual Temperature and Precipitation. These show the most extreme projections of future climate. For visualizations of future AET and CWD, only the “best” and “worst” case scenarios are showing for the sake of brevity. Climate projections are summarized across the years 2075-2099 to represent “end-of-century” conditions. Given the long amounts of time needed for WBP trees to reach reproductive maturity, this is approximately the time period that trees planted today would be producing cones.
| Future | GCM | Scenario | Note |
|---|---|---|---|
| Warm Wet | MRI-CGCM3 | RCP8.5 | |
| Hot Wet | CanESM2 | RCP8.5 | ‘Best-Case’ AET Scenario (Highest) |
| Warm Dry | MRI-CGCM3 | RCP4.5 | ‘Worse-Case’ AET Scenario (Lowest), ‘Best-Case’ CWD Scenario (Lowest) |
| Hot Dry | HadGEM2-CC365 | RCP8.5 | ‘Worse-Case’ CWD Scenario (Highest) |
(#fig:static_east-projections-1)Projected Water Balance
(#fig:static_east-projections-2)Projected Water Balance
3.4 Climate Summary
Figure 3.7: Static East Climate summaries. AET and CWD distributions for all 1m pixels in the avalanche polygon are shown for 1988 (Drought Year), 2002-2022 (Historical Baseline), and 2075-2099 projections for ‘Warm/Wet’, ‘Warm/Dry’, ‘Hot/Wet’, ‘Hot/Dry’ scenarios
Figure 3.8: Static East Climate summaries. AET and CWD distributions for all 1m pixels in the avalanche polygon are shown for 1988 (Drought Year), 2002-2022 (Historical Baseline), and 2075-2099 projections for ‘Warm/Wet’, ‘Warm/Dry’, ‘Hot/Wet’, ‘Hot/Dry’ scenarios