Recent California tree mortality portends future increase in drought-driven forest die-off

Droughts are among the worst climate hazards society faces, creating economic losses of tens to hundreds of billion US dollars per year (Mishra and Singh 2010, European Commission 2012). Forests are especially vulnerable to drought in a warming world, as higher temperatures increase atmospheric moisture demand, evapotranspiration and soil drying and tree die-off (Allen et al 2010, Williams et al 2013, Allen et al 2015, Fettig et al 2019). Future droughts are projected to become longer, more severe and frequent in many regions (Wehner et al 2011, Seneviratne et al 2012, Madakumbura et al 2019), with consequent impacts on vegetation structure and function. An improved understanding of forest response to drought is needed to better predict the impact of climate change on forested ecosystems at large scales. Such capabilities would help forest managers anticipate patterns of tree vulnerability to drought and proactively allocate resources and time at local and landscape scales (Heinimann 2010, Keenan 2015).

Studies investigating the causes of tree die-off have advanced our knowledge of forest tolerance to drought, and the underlying interactions of biological, physiological and environmental factors. Robust statistical relationships between drought induced tree mortality and various metrics of drought intensity have often revealed ecological thresholds (Anderegg et al 2015, Paz-Kagan et al 2017, Young et al 2017, Goulden and Bales 2019). These plant physiological and environmental predictors of mortality reflect the processes and properties that mediate the translation of precipitation deficit to physiological damage and ultimate tree death (Anderegg et al 2013).

The patterns of die-off ultimately depend on both drought intensity and duration, and processes that occur over a range of timescales (Mcdowell et al 2008). The biological response to drought stress has been investigated intensively at the scale of individual plants based on theoretical (Mcdowell et al 2008) and experimental (Barbeta and Peñuelas 2016) analyses, but studies that focus on multidecadal records and at landscape scales and larger are scarce. Given potentially large spatial variations in drought tolerance and drought severity across landscapes, such larger scale studies are needed. For example, while large variation in vegetation stress and tree mortality were noted across the Sierra Nevada landscape during California's 2012–2015 drought it is unclear whether the variation arises from forest drought tolerance (e.g. local water availability or plant physiological factors) or drought severity.

Comparisons of drought duration and remotely-sensed vegetation properties can provide an opportunity to investigate plant response and tolerance to drought at large spatial scales. The Normalized Difference Vegetation Index (NDVI) shows strong responses to droughts lasting 2–4 months in arid and humid biomes, and 8–10 months in semiarid and sub humid biomes in the world (Vicente-Serrano et al 2013). In a regional setting, a comparison between California forest water balance and canopy density, as measured by canopy water content (CWC) (Asner et al 2016), showed a comparatively long time scale response, with coniferous vegetation tolerating drought for several years before accelerated die-off (Brodrick et al 2019). Despite the insights from such comparisons, the controlling factors (e.g. environmental and tree physiological properties) behind drought tolerance have not been fully explored. Efforts to forecast the long-term impact of drought are also complicated by the likelihood that climate, environmental conditions, and vegetation distributions may be different in the coming century (Kelly and Goulden 2008, Parks et al 2018, Holsinger et al 2019).

Here we explore the timescale of drought response for coniferous forests in California's Sierra Nevada (Myers et al 2000, Bales et al 2011). This region experienced a severe drought in 2012–2015 that was followed by widespread die-off in 2015–2016. We used data from pre-2012 to identify the duration of drought that was best correlated with anomalies in canopy density time series as measured by the Normalized Difference Moisture Index (NDMI) (hereafter referred to as the drought sensitivity timescale or DST). We then investigate how the pre-2012 DST interacted with drought severity to produce the observed spatial and temporal patterns of die-off during the 2012–2015 drought. Finally, we use output from state-of-the-art global climate simulations to examine how changes in multiyear droughts may amplify future die-off episodes.

2.1. Data

2.2. Procedures

3.1. Spatial patterns and the interpretation of DST

The drought sensitivity timescale (DST) was calculated across the Sierra Nevada (see figure 2 for geographic domain) based on the relationship between SPI and NDMI (figure 1(a)). Different forest types in the Sierra Nevada have different DST values, with mesic stands having DSTs around 3–4 years (see supplementary discussion). The longest drought time scales (>4 years) are seen in an elevation band around 1500–2500 m and a latitude range of 36–39°N. Lower or higher elevation forests generally have shorter timescales (<2 years), depending on latitude. The timescale generally decreases with increasing elevation south of 38.5°N. Alternative combinations of drought and canopy moisture indices revealed time scales that agreed with the spatial distribution obtained from the SPI-NDMI analysis (supplementary figures 5–7).

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A simple interpretation of DST is that it represents the number of years required to empty a previously full, subsurface water storage under steady rate conditions. If this interpretation is correct, we should be able to multiply the mean drawdown rate by DST to predict subsurface water storage depth. We can quantify the subsurface storage depth as the maximum rootzone drying during the recent past (2003–2010) (Maeda et al 2015, Wang-Erlandsson et al 2016) (see supplementary discussion). Carrying out the multiplication described above, we find that the simple interpretation of DST holds remarkably well for Sierran forests (supplementary figure 8). We further explore the drivers of DST and its interpretation in supplementary discussion. DST may be shaped by complicated environmental, climate and biological processes across temporal and spatial scales, but preliminary analysis shows that the factors thought to control plant water drawdown (Fellows and Goulden 2017) such as mean precipitation, evapotranspiration, potential evapotranspiration, plant available water content, can account for DST variation (from a random forest regression model with R² = 0.79, supplementary figure 9, see supplementary discussion). This confirms that DST reflects the plant accessible subsurface water buffer.

3.2. Usefulness of DST as a vegetation stress predictor

We investigated the relationship between pre-2012 DST and the subsequent trajectory of vegetation stress and mortality during the 2012–2015 drought. The triangles in figures 1(b)–(d) show the progressive deepening of drought with elevation. This is measured by the normalized precipitation minus evapotranspiration (PR-ET; see triangles in figures 1(b)–(d)) of each water year, accumulated since the beginning of the drought in 2012. Cumulative PR-ET during a drought can be linked to subsurface drying (Goulden and Bales 2019). Cumulative PR-ET deficits in three water years (2012–2014) were severe, as shown by negative values in all elevation bins below 2700 m. By 2014, the PR-ET deficit had become extraordinarily large below 2700 m. Figures 1(b)–(d) represent the antecedent drought conditions affecting vegetation in the subsequent year, as portrayed in the companion panels (figures 1(e)–(g)). Figures 1(e)–(g) show DST as a function of elevation, i.e. a collapsed version of figure 1(a). In 2013, the year after the beginning of the drought, there were only modest losses of canopy moisture at all elevations (figure 1(e)). By 2014 canopy moisture loss increased for elevations where the normalized cumulative PR-ET was negative (figure 1(f)), and by 2015 extreme canopy loss was seen at these elevations (figure 1(g)).

In 2013, after 1 year of drought, some elevations showed moderate vegetation stress (figure 1(e)), but no systematic relationship between DST and vegetation stress. However, as the drought lengthened, vegetation stress became most apparent at those elevations where DST is lowest and drought severity is high, i.e. orange/reddish dots in figure 1(f) (Elevations above about 2700 m are characterized by a low DST, but they did not experience severe drought and hence remained unaffected). By 2015, after an even longer period of extreme precipitation deficit (at least 3 years), even elevations with the longest DST showed signs of extreme canopy moisture loss. Only the highest elevations avoided moisture loss (figures 1(b)–(d)). Canopy moisture loss precedes tree mortality and could ultimately trigger it (Brodrick and Asner 2017, Paz-Kagan et al 2017, Goulden and Bales 2019). The predictive power of DST for vegetation conditions is apparent in directly measured tree mortality, as shown in figures 1(e)–(g). Very low tree mortality is seen in 2013, similar to the background rate (Byer and Jin 2017). By 2014 values were slightly higher for those elevations with signs of vegetation stress, and relatively low DST values (orange/reddish dots in figure 1(f)). By 2015 a dramatic increase in tree mortality is seen everywhere except the highest elevations.

These results suggest that both DST and drought severity are useful predictors of the progression of vegetation stress during drought. To test this conclusion further, we examine the geographical variation in tree mortality at lower elevations of the Sierra Nevada (figure 1(a)). Figure 2(b) shows the observed DTPA at the end of the drought. In the low elevations of the Southern Sierra, where DST is low (figure 1(a), supplementary figure 6), the die-off is high. By contrast, the low elevations of the Northern Sierra, where DST is similarly low, little tree mortality occurred. Clearly the early signs of vegetation stress at low elevations (figure 1(f)), and the higher levels of eventual tree mortality (figure 1(g)) came from the southern portion of the Sierra Nevada. This spatial pattern appears to reflect the greater precipitation shortfall in the south relative to the north (figure 2(a)).

To test this hypothesis, we predicted the spatial distribution of tree die-off assuming the entire Sierra Nevada faced a precipitation deficit as large as that seen in the southern part (i.e. 4 year SPI = −2.5). The hypothetical distribution is generated with a model of DTPA based on random forest regression. As predictors, we use direct and indirect factors contributing to drought severity (4 year SPI, mean potential evaporation during the drought, mean evapotranspiration, basal area) and drought tolerance to tree die-off (DST, plant available water capacity) (model R² = 0.64). The model predicts that mesic and dry forests in central and northwestern regions of the Sierra Nevada (figure 1(a)) suffer marked tree die-off (figure 2(c)), consistent with their low DST values. Thus, differences in drought severity can account for the differences in tree mortality in those low elevation zones where DTS is comparably low.

To assess the potential of droughts similar to the 2012–2015 California drought in the future, we analyzed future projections of multiyear precipitation variability in California simulated by ten state-of the art GCMs (see supplementary table 1) participating in the CMIP6 (Eyring et al 2015). The simulated changes in 4 year SPI occurrence from historical (1969–2019) to future (2050–2100) under a 'no emissions reduction policy' warming scenario, SSP585 (Eyring et al 2015, O'Neill et al 2016), are shown in figure 3. Under climate change, the precipitation distribution shifts systematically, resulting in increased probability of severe multiyear droughts (figure 3(a)). Examining scenarios associated with lower greenhouse gas emissions (figure 3(b)), we see that the probability of severe droughts similar to 2012–2015 increases with growing emissions. But even for the lowest emissions scenarios, this increase in frequency is statistically significant at 99% compared to the historical simulations. We also note that the future may bring droughts even more severe than that in 2012–2015, as seen in the emergence of 4 year SPI anomalies more negative than −2.0 in the SSP585 scenario (figure 3(a)). Such deep droughts would bring very dry conditions to large swaths of the Sierra Nevada, killing trees over areas with low DST, as in the hypothetical case of figure 2(c).

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Using multiple drought and vegetation indices spanning many decades, we obtain a drought sensitivity timescale, DST, which can also be interpreted as the plant water buffer. We find that forests on the low-elevation western slopes and high-elevation eastern slopes of the Sierra Nevada have the shortest DST, and hence the least tolerance to drought. When DST is combined with drought severity, it can be used to map the progression of vegetation stress during the 2012–2015 drought. The low elevation slopes were the first to respond to the drought as it deepened, consistent with their low DST values. However, the largest ultimate response, and the greatest tree mortality, occurred in the southern low elevations. We demonstrate that this enhanced response can be explained by the spatial patterns of drought severity. The low elevation forests in the central and northern Sierra Nevada are also vulnerable to drought and would have likely experienced extensive dieback if the 2012–2015 drought had extended further north.

Previous work suggests interannual precipitation variability in California will increase in the future (Swain et al 2018). This work, combined with the results shown here demonstrating the connection between tree mortality and multiyear drought, raises the question of whether Sierra Nevada forests will experience greater tree mortality in the future. We found that multiyear droughts in California will increase with increasing greenhouse gasses based on state-of-the-art climate model simulations from the CMIP6 project (Eyring et al 2015). The distribution of multiyear precipitation anomalies shifts to a drier regime, and the likelihood of a 4 year drought as deep (or deeper) than the 2012–2015 event increases by up to a factor of three by the end of the century. These results imply a future increase in the likelihood of tree mortality in the Sierra Nevada, especially in areas with a low DST. The low-elevation central and northern Sierran forests did not exhibit die-off in the 2012–2015 event; but this does not mean they are not vulnerable to drought. Future droughts will almost certainly be distributed differently in space from the 2012–2015 event (as were the pre-2012 droughts which allowed us to diagnose the sensitivity of the low-elevation central and northern forests with low DST).

We note the warmer temperatures accompanying these projected droughts (Dai 2011, 2013), and that a limitation of our study is that our estimates of future drought ignore this warming effect. Warming will increase potential and actual evapotranspiration, which will further increase drought stress when precipitation is low; thus, our estimates represent a lower range of possible increases to drought intensity. Conversely, it is possible that plant physiological responses to elevated CO₂ may mitigate tree mortality impacts of drought, though it is unclear how much tolerance to mortality such responses confer (Swann et al 2016, Sperry et al 2019). Lastly, future climate projections are based on coarse resolution GCMs, and the spatial patterns of climate variables in this region of complex topography are inadequately represented. Future research can refine our analysis using downscaled climate model data, as well as state-of-the-art land surface and vegetation models to simulate evapotranspiration changes and physiological responses to CO₂.

Our results imply future changes in Sierra Nevada ecology. Previous work has shown that projected changes in mean climate would be associated with shifts in vegetation distribution (Holsinger et al 2019). Changes in mean conditions may lead to a decline in Sierra Nevada forests assuming current vegetation types migrate to new preferred climate zones (Parks et al 2018). These ecological transitions may be accelerated by changes in extreme events. Previous work has shown that tree mortality markedly impacts subsequent species composition and forest structure (e.g. Cobb et al 2017). Future increases in forest-die-off frequency and magnitude would likewise be associated with large impacts. The increase in tree mortality may increase fuel load and wildfire risk (Ruthrof et al 2016, Stephens et al 2018). Our results point to the low elevation western slopes of the Sierra Nevada as a hotspot of increasing die-off. Conversely, other sub-regions with high drought tolerance may be more tolerant to ecological change, and may even become refugia (Morelli et al 2016, Mclaughlin et al 2017). The drought timescale metric and results we show potentially provide information that may aid conservation planning.

We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP6, and we thank the climate modeling groups for producing and making available their model output. We also thank the Earth System Grid Federation (ESGF) for archiving the data and providing access, and various funding agencies who support CMIP6 and ESGF. This research was funded by the University of California Laboratory Fees Research Program. CDK is supported by the Department of Energy, Office of Science, Office of Biological and Environmental Research through the Early Career Research Program administered by the Regional and Global Model Analysis Program. CAN was supported by the Ridge to Reef Graduate Training Program funded by NSF-NRT award DGE-1735040. GDM would like to thank Neil Berg for discussions on early ideas of this paper.

Climate model data were obtained from the CMIP6 archive, accessed via the ESGF at https://esgf-node.llnl.gov/projects/cmip6/. The data that support the findings of this study are available upon reasonable request from the authors.