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

Nonlinear time effects of vegetation response to climate change: Evidence from Qilian Mountain National Park in China

Vegetation responses to climate change are typically nonlinear with varied time effects, yet current research lacks comprehensiveness and precise definitions, hindering a deeper understanding of the underlying mechanisms. This study focuses on the mountain-type Qilian Mountain National Park (QMNP), investigating the characteristics and patterns of these nonlinear time effects using a generalized additive model (GAM) based on MODIS-NDVI, growing season temperature, and precipitation data. The results show that 1) The time effects of climate change on vegetation exhibit significant spatial variations, differing across vegetation types and topographic conditions. Accounting for optimal time effects can increase the explanatory power of climate on vegetation change by 6.8 %. Precipitation responses are mainly characterized by time-lag and time-accumulation effects, notably in meadows and steppes, while temperature responses are largely cumulative, especially in steppes. The altitude and slope significantly influence the pattern of vegetation response to climate, particularly in areas with high altitudes and steep slopes. 2) There is a significant nonlinear relationship between vegetation growth and both precipitation and temperature, with the nonlinear relationship between precipitation and vegetation being stronger than that with temperature, particularly in the western and central regions of the park. Different vegetation types exhibit significant variations in their response to climate change, with deserts and steppes being more sensitive to precipitation. 3) Precipitation is the primary driver of vegetation change in the QMNP, particularly for high-elevation vegetation and herbaceous vegetation. The complex temporal patterns of vegetation response to climate change in the QMNP not only deepen the understanding of the intricate relationship between regional vegetation and climate variability but also provide a methodological reference for global studies on vegetation responses to climate change.

Characteristics and drivers of vegetation productivity sensitivity to increasing CO2 at Northern Middle and High Latitudes

Understanding and accurately predicting how the sensitivity of terrestrial vegetation productivity to rising atmospheric CO2 concentration (β) is crucial for assessing carbon sink dynamics. However, the temporal characteristics and driving mechanisms of β remain uncertain. Here, observational and CMIP6 modeling evidence suggest a decreasing trend in β at the Northern Middle and High Latitudes during the historical period of 1982-2015 (-0.082 ± 0.005% 100 ppm-1 year-1). This decreasing trend is projected to persist until the end of the 21st century (-0.082 ± 0.005% 100 ppm-1 year-1 under SSP370 and -0.166 ± 0.006% 100 ppm-1 year-1 under SSP585). The declining β indicates a weakening capacity of vegetation to mitigate warming climates, posing challenges for achieving the temperature goals of the Paris Agreement. The rise in vapor pressure deficit (VPD), that triggers stomata closure and weakens photosynthesis, is considered as the dominated factor contributing to the historical and future decline in β, accounting for 62.3%-75.2% of the effect. Nutrient availability and water availability contribute 15.7%-21.4% and 8.5%-16.3%, respectively. These findings underscore the significant role of VPD in shaping terrestrial carbon sink dynamics, an aspect that is currently insufficiently considered in many climate and ecological models.

Responses of vegetation cover to hydro-climatic variations in Bosten Lake Watershed, NW China

Amidst the backdrop of global climate change, it is imperative to comprehend the intricate connections among surface water, vegetation, and climatic shifts within watersheds, especially in fragile, arid ecosystems. However, these relationships across various timescales remain unclear. We employed the Ensemble Empirical Mode Decomposition (EEMD) method to analyze the multifaceted dynamics of surface water and vegetation in the Bosten Lake Watershed across multiple temporal scales. This analysis has shed light on how these elements interact with climate change, revealing significant insights. From March to October, approximately 14.9-16.8% of the areas with permanent water were susceptible to receding and drying up. Both the annual and monthly values of Bosten Lake's level and area exhibited a trend of initial decline followed by an increase, reaching their lowest point in 2013 (1,045.0 m and 906.6 km2, respectively). Approximately 7.7% of vegetated areas showed a significant increase in the Normalized Difference Vegetation Index (NDVI). NDVI volatility was observed in 23.4% of vegetated areas, primarily concentrated in the southern part of the study area and near Lake Bosten. Regarding the annual components (6 < T < 24 months), temperature, 3-month cumulative NDVI, and 3-month-leading precipitation exhibited the strongest correlation with changes in water level and surface area. For the interannual components (T≥ 24 months), NDVI, 3-month cumulative precipitation, and 3-month-leading temperature displayed the most robust correlation with alterations in water level and surface area. In both components, NDVI had a negative impact on Bosten Lake's water level and surface area, while temperature and precipitation exerted positive effects. Through comparative analysis, this study reveals the importance of temporal periodicity in developing adaptive strategies for achieving Sustainable Development Goals in dryland watersheds. This study introduces a robust methodology for dissecting trends within scale components of lake level and surface area and links these trends to climate variations and NDVI changes across different temporal scales. The inherent correlations uncovered in this research can serve as valuable guidance for future investigations into surface water dynamics in arid regions.

Long-term evidence of differential resistance and resilience of grassland ecosystems to extreme climate events

Grassland ecosystems are affected by the increasing frequency and intensity of extreme climate events (e.g., droughts). Understanding how grassland ecosystems maintain their functioning, resistance, and resilience under climatic perturbations is a topic of current concern. Resistance is the capacity of an ecosystem to withstand change against extreme climate, while resilience is the ability of an ecosystem to return to its original state after a perturbation. Using the growing season Normalized Difference Vegetation Index (NDVI_gs, an index of vegetation growth) and the Standardized Precipitation Evapotranspiration Index (a drought index), we evaluated the response, resistance, and resilience of vegetation to climatic conditions for alpine grassland, grass-dominated steppe, hay meadow, arid steppe, and semi-arid steppe in northern China for the period 1982-2012. The results show that NDVI_gs varied significantly across these grasslands, with the highest (lowest) NDVI_gs values in alpine grassland (semi-arid steppe). We found increasing trends of greenness in alpine grassland, grass-dominated steppe, and hay meadow, while there were no detectable changes of NDVI_gs in arid and semi-arid steppes. NDVI_gs decreased with increasing dryness from extreme wet to extreme dry. Alpine and steppe grasslands exhibited higher resistance to and lower resilience after extreme wet, while lower resistance to and higher resilience after extreme dry conditions. No significant differences in resistance and resilience of hay meadow under climatic conditions suggest the stability of this grassland under climatic perturbations. This study concludes that highly resistant grasslands under conditions of water surplus are low resilient, but low resistant ecosystems under conditions of water shortage are highly resilient.

A perception of the nexus "resistance, recovery, resilience" of vegetations responded to extreme precipitation pulses in arid and semi-arid regions: A case study of the Qilian Mountains Nature Reserve, China

Unprecedented pulses of extreme precipitation due to climate change are causing significant stresses and impacts on regional and even global ecosystems. However, the relationship of vegetation response to this disturbance is unclear, such as phase characteristics, extent, timing, and degree. We summarize the nexus between vegetation resistance, recovery, and resilience under three stages of extreme precipitation pulses: duration, lagging, and post-disturbance, and then construct a pragmatic scheme to quantify and validate this complex relationship based on precipitation and Normalized Difference Vegetation Index (NDVI) data for the Qilian Mountains Nature Reserve (QMNR) from 2000 to 2020. The results show that the four extreme precipitation pulses were spring 2010 (118.98 mm), summer 2007 (312.25 mm), autumn 2010 (109.74 mm), and winter 2018 (6.84 mm). Extreme precipitations had a significant effect on vegetation in at least 98.5 % of the area, and there was also a two-month time lag effect. Specifically, the percentage of negative vegetation resistance in the face of four seasons of extreme precipitation pulses was 18.3 %, 2.0 %, 15.4 %, and 21.7 %, respectively, compared to negative recovery rates of 4.8 %, 11.9 %, 17.8 % and 10.2 % respectively, resilience was even more severe, with 20.1 %, 10.9 %, 16.1 % and 16.3 % of vegetation failing to rebound to normal levels within two months. The negative resistance, negative recovery, and weak resilience of vegetation under short-term extreme precipitation pulses are approximately 4.8, 3.7, and 5.3 times more fierce than long-term vegetation degradation. A total of 62 % of the four seasonal areas of severe negative resistance, severe negative recovery, and weak resilience were located in areas of moderate and significant steepness, which confirms that extreme precipitation pulses cause serious degradation of vegetation. Response of vegetation under extreme precipitation pulses is perceived, quantified, and validated in this study, which is essential for addressing climate change.

Biodiversity showed positive effects on resistance but mixed effects on resilience to climatic extremes in a long-term grassland experiment

Understanding the role of biodiversity in maintaining ecosystem functioning and stability under increasing frequency and magnitude of climatic extremes has fascinated ecologists for decades. Although growing evidence suggests that biodiversity affects ecosystem productivity and buffers ecosystem against climatic extremes, it remains unclear whether the stability of an ecosystem is caused by its resistance against disturbances or resilience towards perturbations or both. In attempting to explore how species richness affects resistance and resilience of above-ground net primary productivity (ANPP) against climatic extremes, we analyzed the grassland ANPP of the long-running (1997-2020) Bayreuth Biodiversity experiment in Germany. We used the Standardized Precipitation Evapotranspiration Index to identify climatic conditions based on 5- and 7-class classifications of climatic conditions. Mixed-effects models and post-hoc test show that ANPP varied significantly among different intensities (e.g. moderate or extreme) and directions (e.g. dry or wet) of climatic conditions, with the highest ANPP in extreme wet and the lowest in extreme dry conditions. Resistance and resilience of ANPP to climatic extremes in different intensities were examined by linear-mixed effects models and we found that species richness increased ecosystem resistance against all dry and wet climatic extremes, but decreased ecosystem resilience towards all dry climatic extremes. Species richness had no effects on ecosystem resilience towards wet climatic extremes. When the five level of species richness treatment (i.e., 1, 2, 4, 8, and 16 species) were considered, the relationships between species richness and resistance and resilience of ANPP under extreme wet and dry conditions remained similar. Our study emphasizes that plant communities with greater species richness need to be maintained to stabilize ecosystem productivity and increase resistance against different climatic extremes.

Patterns of post-drought recovery are strongly influenced by drought duration, frequency, post-drought wetness, and bioclimatic setting

Understanding vegetation recovery after drought is critical for projecting vegetation dynamics in future climates. From 1997 to 2009, Australia experienced a long-lasting drought known as the Millennium Drought (MD), which led to widespread reductions in vegetation productivity. However, vegetation recovery post-drought and its determinants remain unclear. This study leverages remote sensing products from different sources-fraction of absorbed photosynthetically active radiation (FPAR), based on optical data, and canopy density, derived from microwave data-and random forest algorithms to assess drought recovery over Australian natural vegetation during a 20-year period centered on the MD. Post-drought recovery was prevalent across the continent, with 6 out of 10 drought events seeing full recovery within about 6 months. Canopy density was slower to recover than leaf area seen in FPAR. The probability of full recovery was most strongly controlled by drought return interval, post-drought hydrological condition, and drought length. Full recovery was seldom observed when drought events occurred at intervals of 3 months or less, and moderately dry (standardized water balance anomaly [SWBA] within [-1, -0.76]) post-drought conditions resulted in less complete recovery than wet (SWBA > 0.3) post-drought conditions. Press droughts, which are long term but not extreme, delayed recovery more than pulse droughts (short term but extreme) and led to a higher frequency of persistent decline. Following press droughts, the frequency of persistent decline differed little among biome types but peaked in semi-arid regions across aridity levels. Forests and savanna required the longest recovery times for press drought, while grasslands were the slowest to recover for pulse drought. This study provides quantitative thresholds that could be used to improve the modeling of ecosystem dynamics post-drought.

Drought Affected Ecosystem Water Use Efficiency of a Natural Oak Forest in Central China

Global climate models project more frequent drought events in Central China. However, the effect of seasonal drought on ecosystem water use efficiency (WUE) and water regulation strategy in Central China’s natural forests is poorly understood. This study investigated variations in WUE associated with drought in a natural oak (Quercus aliena) forest in Central China from 2017 to 2020 at several timescales based on continuous CO2 and water vapor flux measurements. Results showed that the 4-year mean gross ecosystem production (GEP), evapotranspiration (ET) and WUE of the natural oak forest was 1613.2 ± 116 g Cm−2, 637.8 ± 163.3 mm and 2.6 ± 0.68 g Ckg−1 H2O, with a coefficient of variation (CV) of 7.2%, 25.6% and 26.4%, respectively. The inter-annual variation in WUE was large, primarily due to the variation in ET caused by seasonal drought. Drought increased WUE distinctly in summer and decreased it slightly in autumn. During summer drought, surface conductance (gs) usually decreased with an increase in VPD, but the ratios of stomatal sensitivity (m) and reference conductance (gsref) were 0.21 and 0.3 molm−2s−1ln(kPa)−1 in the summer of 2019 and 2020. Strong drought can also affect ecosystem WUE and water regulation strategy in the next year. Decrease in precipitation in spring increased annual WUE. These results suggested that drought in different seasons had different effects on ecosystem WUE. Overall, our findings suggest that the natural oak forest did not reduce GEP by increasing WUE (i.e., reducing ET) under spring and summer drought, which could be due to its typical anisohydric characteristics, although it can also reduce stomatal opening during long-term drought.

Effects of Drought on Vegetation Productivity of Farmland Ecosystems in the Drylands of Northern China

Previous research on the effects of drought on vegetation productivity seldom distinguished the different responses of vegetation ecosystems to drought under different management practices and different land use systems. Studies investigating whether irrigation can buffer the negative impacts of drought on vegetation usually used discontinuous yield data in distribution. In this study, the trends in drought and vegetation productivity in farmlands in the drylands of northern China (DNC) from 2000 to 2018 were studied using the self-calibrated Palmer drought severity index (scPDSI) and enhanced vegetation index (EVI). The differences in the impact of drought on vegetation productivity in irrigated farmland, rainfed farmland, and natural vegetation areas were quantified. The results showed that the growing season scPDSI and EVI showed an increasing trend from 2000 to 2018. Significant correlations between drought anomalies and EVI anomalies were found in both arid drylands and semi-arid drylands. In addition, irrigation mitigated 59.66% of the negative impact caused by drought on irrigated farmland EVI in the growing season. The impact of drought on irrigated farmland EVI in the growing season was 19.98% lower than that on natural vegetation EVI. The impact of drought on natural vegetation EVI was 49.59% lower than that on rainfed farmland EVI. The results of this study refine the vegetation response to drought under different land management practices and land use patterns.

Lagged precipitation effect on plant productivity is influenced collectively by climate and edaphic factors in drylands

Lagged precipitation effect explains a large proportion of annual aboveground net primary productivity in some dryland ecosystems. Using satellite-derived plant productivity and precipitation datasets in the Northern Hemisphere drylands during 2000-2018, we identify 1111 pixels mainly located in the Tibetan Plateau, the western US, and Kazakhstan where productivities are significantly correlated with previous-year precipitation (hereafter, the lagged type). Differences in climatic and edaphic factors between the lagged and unlagged (pixels where productivities are not correlated with previous-year precipitation) types are evaluated. Permutational multivariate analysis of variance shows that the two types differ significantly regarding six climatic and edaphic factors. Compared to unlagged type, water availability, soil organic carbon, total nitrogen, field capacity, silt content and radiation are more sensitive to changes in precipitation in lagged type. Water availability is the most important factor for distinguishing the two types, followed by soil organic carbon, total nitrogen, field capacity, soil texture, and radiation. Our study suggests that the altered sensitivities of several climatic and edaphic factors to precipitation collectively affect the lagged effect of precipitation on productivity in drylands.

Annual Green Water Resources and Vegetation Resilience Indicators: Definitions, Mutual Relationships, and Future Climate Projections

Satellites offer a privileged view on terrestrial ecosystems and a unique possibility to evaluate their status, their resilience and the reliability of the services they provide. In this study, we introduce two indicators for estimating the resilience of terrestrial ecosystems from the local to the global levels. We use the Normalized Differential Vegetation Index (NDVI) time series to estimate annual vegetation primary production resilience. We use annual precipitation time series to estimate annual green water resource resilience. Resilience estimation is achieved through the annual production resilience indicator, originally developed in agricultural science, which is formally derived from the original ecological definition of resilience i.e., the largest stress that the system can absorb without losing its function. Interestingly, we find coherent relationships between annual green water resource resilience and vegetation primary production resilience over a wide range of world biomes, suggesting that green water resource resilience contributes to determining vegetation primary production resilience. Finally, we estimate the changes of green water resource resilience due to climate change using results from the sixth phase of the Coupled Model Inter-comparison Project (CMIP6) and discuss the potential consequences of global warming for ecosystem service reliability.

Identifying Dynamic Memory Effects on Vegetation State Using Recurrent Neural Networks

Vegetation state is largely driven by climate and the complexity of involved processes leads to non-linear interactions over multiple time-scales. Recently, the role of temporally lagged dependencies, so-called memory effects, has been emphasized and studied using data-driven methods, relying on a vast amount of Earth observation and climate data. However, the employed models are often not able to represent the highly non-linear processes and do not represent time explicitly. Thus, data-driven study of vegetation dynamics demands new approaches that are able to model complex sequences. The success of Recurrent Neural Networks (RNNs) in other disciplines dealing with sequential data, such as Natural Language Processing, suggests adoption of this method for Earth system sciences. Here, we used a Long Short-Term Memory (LSTM) architecture to fit a global model for Normalized Difference Vegetation Index (NDVI), a proxy for vegetation state, by using climate time-series and static variables representing soil properties and land cover as predictor variables. Furthermore, a set of permutation experiments was performed with the objective to identify memory effects and to better understand the scales on which they act under different environmental conditions. This was done by comparing models that have limited access to temporal context, which was achieved through sequence permutation during model training. We performed a cross-validation with spatio-temporal blocking to deal with the auto-correlation present in the data and to increase the generalizability of the findings. With a full temporal model, global NDVI was predicted with R² of 0.943 and RMSE of 0.056. The temporal model explained 14% more variance than the non-memory model on global level. The strongest differences were found in arid and semiarid regions, where the improvement was up to 25%. Our results show that memory effects matter on global scale, with the strongest effects occurring in sub-tropical and transitional water-driven biomes.