Water-stress patterns of giant-sequoia groves during multi-year droughts

Abstract:

Giant-sequoia groves in California’s Sierra Nevada have adapted to periodic droughts and fires. However, the recent hot drought (2012-2015) caused unprecedented stress in the southern Sierra Nevada, including in groves. To better understand current and project future stress patterns, we used a water-balance approach based on annual precipitation minus evapotranspiration (P – ET) at a 30-m resolution, and built a relation between P – ET with forest die-off patterns measured by aerial detection survey. By scaling up point evapotranspiration measurements to the area encompassing the main 78 groves through Landsat-based NDVI (Normalized Difference Vegetation Index), we analyzed the water stress at both grove-average and 30-m-pixel scales for the period 1985-2018. Analysis of the 2012-2015 and a second recent (1987-1992) drought showed that the giant-sequoia groves encountered more-severe water stress with more negative cumulative P – ET (– 400 mm) than surrounding non-grove areas. The greatest grove-average water stress was in smaller groves representing end-member landscape attributes. The cumulative P – ET during droughts was closely related to forest die-off and water stress from Landsat (NDMI, Normalized Difference Moisture Index), providing an important, but relatively simple, approach to map vulnerability pattern. For longer-term drought scenarios, a deep-learning model was developed to accurately predict ET at the pixel scale based on precipitation, temperature, historical NDVI, water deficit, and other topographic attributes. The model analysis suggested that historical mean NDVI and maximum water deficit play important roles in predicting ET. Drought scenarios of 8 and 12 years were examined by repeating the 2012-2015 drought conditions. Results indicated that grove areas need ~4 years to recover to pre-drought ET after a drought. In future drought scenarios, the grove die-off can also be estimated using its close relation to the predicted cumulative P – ET. However, our approach does not point stress by tree species, and giant sequoia trees are mixed with other conifer species in the groves. With this analysis approach, we can identify the relative vulnerability of giant-sequoia groves, project overall drought effects, and better manage the grove areas in a warming climate.