Emerging Stress and Relative Resiliency of Giant Sequoia Groves Experiencing Multiyear Dry Periods in a Warming Climate

Abstract

The relative greenness and wetness of Giant Sequoia (Sequoiadendron giganteum) groves and the surrounding Sierra Nevada, California forests were investigated using patterns in vegetation indices from Landsat imagery for the period 1985–2015. Vegetation greenness (normalized difference vegetation index) and thus forest biomass in groves increased by about 6% over that 30 year period, suggesting a 10% increase in evapotranspiration. No significant change in the surrounding nongrove forest was observed. In this period, local temperature measurements showed an increase of about 2.2°C. The wetness of groves (normalized difference wetness index) showed no overall long‐term trend but responded to changes in annual water‐year precipitation and temperature. The long‐term trends of grove greenness and wetness varied by elevation, with the lower rain‐snow transition elevation zone (1,700–2,100 m) marking a change from an increasing trend at lower elevations to a decreasing trend at higher elevations. The 2011–2015 drought brought an unprecedented drop in grove wetness, over 5 times the 1985–2010 standard deviation, and wetness in SEGI groves dropped 50% more than in nongrove areas. Overall, the wetness and greenness of SEGI groves showed a larger response to the warming climate and drought than nongrove areas. The influence of droughts on the wetness of SEGI groves reflected effects of both the multidecadal increase in forest biomass and the effects of warmer drought‐year temperatures on the evaporative demand of current grove vegetation, plus sufficient regolith water storage of rain and snowmelt to sustain that vegetation through seasonal and multiyear dry periods.

Evapotranspiration Mapping for Forest Management in California's Sierra Nevada

“…An interesting result of this work is that net evapotranspiration change due to fire in the American River watershed was ∼20% less than that reported by Roche et al (2018). This is the result of (i) using the USGS Tier 1 Collection 1 of Landsat data vs. the precollection data, and (ii) using a normalization that is more representative of the entire vegetation range of California than that of Su et al (2017). While there may be an impact to the annual NDVI average due to variable snow and cloud cover filtering of mid-and high-elevation areas, the improved regression results relative to that used earlier gives us confidence in the current results.…”

“…In a southern Sierra headwater catchment, Oroza et al (2018) found topographic wetness index (TWI) to be an explanatory variable for spatial patterns of soil-water storage only during the dry summer period, after drawdown of soil water following the wet winter and spring periods. Similarly, across a broad area of the southern Sierra, Su et al (2017) found that TWI was not a good predictor of patterns in moisture stress (NDMI) or greenness (NDVI) from Landsat. Using a rich suite of soilmoisture data from headwater catchments in the American and Merced River basins, Saksa et al (2017) modeled catchmentscale water balances, with no significant lateral redistribution.…”

“…Additional filtering for shadows was accomplished by masking pixels for which values in the red, shortwave infrared 1, and shortwave infrared 2 bands were <1%, and values in the infrared band were <5% reflectance. All NDVI grids derived from Landsat 7 (L7 ETM+ ) and Landsat 8 (L8 OLI ) were then homogenized to Landsat 5 Thematic Mapper (L5 TM ) using the methods of Su et al (2017), by the following equations:…”

Estimating evapotranspiration change due to forest treatment and fire at the basin scale in the Sierra Nevada, California

“…Pixels were filtered using the Landsat Collection‐1 Level‐1 Quality‐Assessment Band (CFMask, Foga et al, ) removing all pixels with possible clouds, cloud shadows, or snow contamination. NDVI values were further constrained to a range of 0.2 and 1.0 in order to remove largely unvegetated areas from the analysis (Carlson, Perry, & Schmugge, ). Homogenize Landsat Thematic Mapper (Landsat 5 or LT‐5) and Landsat Operational Land Imager (Landsat 8 or LC8) values to Landsat Enhanced Thematic Mapper values (Landsat 7 or LE‐7) using the following equations (Su et al, , Figure S3): Temporally interpolate the data. First, smooth the resultant time series using a centred moving average spanning five observation dates.…”

Soil microbial communities associated with giant sequoia: How does the world's largest tree affect some of the world's smallest organisms?

“…Over the last century, fire suppression has threatened giant sequoia regeneration by minimizing canopy gaps and exposure of mineral soil, two important factors for germination of this shade intolerant tree (York, Battles, Eschtruth, & Schurr, 2011). Future conditions marked by increased and prolonged drought are expected to put additional stress on giant sequoia (Su et al, 2017). Fortunately, their charisma has deemed them one of the seven natural wonders of the United States (DeFries, 2013), and together, the ecological and cultural importance of the giant sequoia has promoted their current protection by both state and national agencies (Aune, 1994;Leisz, 1994).…”