Water memory effects and their impacts on global vegetation productivity and resilience

Abstract

Memory effects refer to the impacts of antecedent climate conditions on current vegetation productivity. This temporal linkage has been found to be strong in arid and semi-arid regions. However, the dominant climatic factors that determine such patterns are still unclear. Here, we defined’water-memory effects’ as the persistent effects of antecedent precipitation on the vegetation productivity for a given memory length (from 1 to up to 12 months). Based on satellite observations and climate data, we quantified the length of water-memory effects and evaluated the contributions of antecedent precipitation on current vegetation. Our results showed that vegetation productivity was highly dependent on antecedent precipitation in arid and semi-arid regions. The average length of water memory was approximately 5.6 months. Globally, water-memory effects could explain the geographical pattern and strength of memory effects, indicating that precipitation might be the dominant climatic factor determining memory effects because of its impact on water availability. Moreover, our results showed vegetation in regions with low mean annual precipitation or a longer water memory has lower engineering resilience (i.e. slower recovery rate) to disturbances. These findings will enable better assessment of memory effects and improve our understanding of the vulnerability of vegetation to climate change.

Revisiting assessments of ecosystem drought recovery

“…Our estimates of drought recovery time can be biased by anthropogenic activities and the possible shifts in species composition (Engelbrecht et al 2007, Tong et al 2018. Currently, drought recovery is tracked from drought ending time, but the drought legacy effects are reported to be prevalent in dry ecosystems (Liu et al 2018b), which could bias the drought recovery time. An alternative approach that may be worth to explore in future is to identify GPP extremes first (Zscheischler et al 2013) and track the drought recovery from the maximum negative GPP magnitude (He et al 2018).…”

“…To characterize ecosystem recovery from drought, it is necessary to identify drought events that actually decrease ecosystem productivity. Drought indices are a simple way to identify drought events and are widely used in assessing drought impacts on ecosystem production (Vicente-Serrano et al 2013, Ma et al 2015, Huang et al 2016, Liu et al 2018a, 2018b. Climatebased drought indices, such as the Standardized Precipitation index (SPI) and SPEI, are easy to calculate, as they only need climate data as input and can be used for long-term drought recovery analysis.…”

“…However, in some tropical and northern high latitude regions, more than 80% of drought events do not decrease GPP. Most likely because ecosystem productivity is not limited by water but temperature or radiation in these regions (Nemani et al 2003, Liu et al 2018b, and the droughts identified may not be severe enough to limit plant photosynthesis. Uncertainties in using DSI to identify droughts could be related to the included vegetation greenness information (NDVI).…”

Broad Consistency Between Satellite and Vegetation Model Estimates of Net Primary Productivity Across Global and Regional Scales

“…Our results show that the sign of the response of NPP S and NPP M to SPEI3 is positive over most vegetated land areas during 1982–2011. In particular, the NPP M and NPP S are significantly coupled with the SPEI3 in arid, semiarid, and subhumid regions because water availability is the limiting factor for vegetation growth in these regions (Beer et al, ; Liu et al, ; Nemani et al, ). Both the NPP S and NPP M are less correlated with drought in most cold humid regions, which may be attributed to a more important role of temperature in explaining NPP variability (Chen et al, ; Huang et al, ).…”

Response to the Referee report

“…However, these classification schemes are not based on the type of response of vegetation to climate dynamics. Recent advances in understanding vegetation response to climate variability highlight the importance of revealing the sensitivity of ecosystems to climate conditions; see Nemani et al (2003), De Keersmaecker et al (2015, Seddon et al (2016), Papagiannopoulou et al (2017b), or Liu et al (2018. Therefore, a step beyond these previous studies is a spatial characterization of the vegetation dynamics that are induced by climate variability so that ecosystems of similar response to climate anomalies can be unveiled.…”