Tipping point of a conifer forest ecosystem under severe drought

Abstract

Drought-induced tree mortality has recently received considerable attention. Questions have arisen over the necessary intensity and duration thresholds of droughts that are sufficient to trigger rapid forest declines. The values of such tipping points leading to forest declines due to drought are presently unknown. In this study, we have evaluated the potential relationship between the level of tree growth and concurrent drought conditions with data of the tree growth-related ring width index (RWI) of the two dominant conifer species (Pinus edulis and Pinus ponderosa) in the Southwestern United States (SWUS) and the meteorological drought-related standardized precipitation evapotranspiration index (SPEI). In this effort, we determined the binned averages of RWI and the 11 month SPEI within the month of July within each bin of 30 of RWI in the range of 0-3000. We found a significant correlation between the binned averages of RWI and SPEI at the regional-scale under dryer conditions. The tipping point of forest declines to drought is predicted by the regression model as SPEI tp = −1.64 and RWI tp = 0, that is, persistence of the water deficit (11 month) with intensity of −1.64 leading to negligible growth for the conifer species. When climate conditions are wetter, the correlation between the binned averages of RWI and SPEI is weaker which we believe is most likely due to soil water and atmospheric moisture levels no longer being the dominant factor limiting tree growth. We also illustrate a potential application of the derived tipping point (SPEI tp = −1.64) through an examination of the 2002 extreme drought event in the SWUS conifer forest regions. Distinguished differences in remote-sensing based NDVI anomalies were found between the two regions partitioned by the derived tipping point.

Stock Volume Dependency of Forest Drought Responses in Yunnan, China

“…10.2) (Esri, Environmental Systems Research Institute, New York, NY, USA). As July is the most conducive for forest growth, and the trees in this month are most sensitive to moisture status [10,28], only the EVI for July in each year was selected to represent the status of forest growth in that year. Furthermore, the start time of the MOD13A1-EVI was 2001 [52], and the deadline of SPEIbase ver.…”

“…The effect of water balance on forest growth clearly shows hysteresis [26], and its length depends on the vegetation type, which shows distinct spatial patterns. Some researchers have found that it takes many months before the effects of moisture are apparent [10,26], and the length of such hysteresis can be a year [28,29] or even 2-3 years [63]. Due to the hysteretic effects of moisture, the SPEI was not calculated for a given time scale initially; instead, it was calculated for multiple time scales.…”

“…The length of hysteresis in the forest drought response changes with the type of forest [27,28] as the cumulative effect of water deficit or surplus that will influence forest growth is different for different types of forests [10,28,29]. If the differences in forest attributes are ignored, the SPEI that is assigned artificially on a fixed time scale cannot reflect the differences in the forest drought response accurately, thereby affecting the accuracy of evaluating and predicting such responses.…”

“…Based on the correlations between meteorological data, the annual tree rings, and remote sensing vegetation index, Wu et al reported that forests (trees and shrubs) took more than two years to respond to drought in the temperate zone of the northern hemisphere [27]. Thus, if the differences in response characteristics (e.g., the length of hysteresis) of forests with different stock volume densities are fully taken into account, the response of forests to drought can be evaluated more reliably [10,28,29].…”

Ecosystem Drought Response Timescales from Thermal Emission versus Shortwave Remote Sensing

“…Most previous studies of drought response timescales have been local (e.g., north-eastern Spain, parts of the United States, Thailand) or limited to select ecosystems or plant species (e.g., conifer forests in the southwest United States) [22][23][24][25][26][27][28]. A number of these studies indicate that there is variation in the strength and lag time of peak correlation between precipitation and remotely sensed LST and land surface reflectivity indices for different ecosystems.…”