Annual precipitation explains variability in dryland vegetation greenness globally but not locally

Time-lag and accumulation responses of vegetation growth to average and extreme precipitation and temperature events in China between 2001 and 2020

Climate change is often closely related to vegetation dynamics; time lag (Tlag) and accumulative effects (Tacc) are non-negligible phenomena when studying the interaction between climate and vegetation. But, amidst the escalating frequency of extreme climatic events, the quantification of temporal effects (Teffects) of such extremes on vegetation remains scarce. This research quantifies the Tlag and Tacc responses of China's vegetation to episodes of extreme temperature and precipitation since the early 2000s, utilizing daily meteorological data series. Overall, the precipitation in China has become wetter, and nighttime temperatures have risen significantly. The proportion of areas with Teffects ranged from 1.15 % to 15.95 %, and the correlation coefficient between the climate indices and the Normalized Difference Vegetation Index (NDVI) increased by 0.05 to 0.38 when considering the Teffects, compared to not considering it. The Tacc of vegetation had the strongest response (70.74-88.01 %) to extreme events among all the tested climate indices. Moreover, the Tacc of consecutive climate events had a greater impact on vegetation growth than individual climate event. The average Tacc for extreme temperature and extreme precipitation was 1.7-3.09 months and 2.17-3.25 months, respectively. Events like the over 95 % (R95p) and 99 % (R99p) percentile heavy precipitation and the maximum precipitation amount in one day (Rx1day) caused significant Teffects on NDVI. In addition, 90 % of grasslands exhibit Tacc, mainly contributed by the extreme precipitation indices (55.7 %), while the Teffects of forests were stronger than those of extreme temperature. Furthermore, NDVI was more affected by annual precipitation than by extreme precipitation, but the opposite was true for temperature. The results of this study highlight the importance of considering the Tlag and Tacc when predicting the effects of climate change on vegetation dynamics.

Restoration of vegetation in the Yellow River Basin of Inner Mongolia is limited by geographic factors

Studying the relationships between vegetation cover and geography in the Mongolian region of the Yellow River Basin will help to optimize local vegetation recovery strategies and achieve harmonious human relations. Based on MOD13Q1 data, the spatial and temporal variations in fractional vegetation cover (FVC) in the Mongolian Yellow River Basin during 2000-2020 were investigated via trend and correlative analysis. The results are as follows: (1) From 2000 to 2020, the vegetation cover in the Mongolian section of the Yellow River Basin recovered well, the mean increase in the FVC was 0.001/a, the distribution of vegetation showed high coverage in the southeast and low coverage in the northwest, and 31.19% of the total area showed an extremely significant and significant increase in vegetation cover. (2) The explanatory power of each geographic factor significantly differed. Precipitation, soil type, air temperature, land use type and slope were the main driving factors influencing the spatial distribution of the vegetation cover, and for each factor, the explanatory power of its interaction with other factors was greater than that of the single factor. (3) The correlation coefficients between FVC and temperature and precipitation are mainly positive. The mean value of the FVC and its variation trend are characterized by differences in terrain and soil characteristics, population density and land use. Land use conversion can reflect the characteristics of human activities, and positive effects, such as returning farmland to forest and grassland and afforestation of unused land, promote the significant improvement of regional vegetation, while negative effects, such as urban expansion, inhibit the growth of vegetation.

Interannual precipitation variability dominates the growth of alpine grassland above-ground biomass at high elevations on the Tibetan Plateau

The impact of global climate change on mountainous regions with significant elevational gaps is complex and often unpredictable. In particular, alpine grassland ecosystems, are experiencing changes in their spatial patterns along elevational gradients, which increases their vulnerability to degradation. Therefore, a more detailed understanding of spatiotemporal changes in alpine grassland productivity along elevational gradients and an elevation-dependent characterization of the effects of climatic variables on grassland productivity dynamics are essential. Thus, we conducted a study in the Tibetan Plateau, where we collected 2251 above-ground biomass (AGB) observations collected from 1986 to 2020. Mean annual temperature (TMP), annual precipitation (PRE), interannual precipitation variability (CVP), and snowmelt (SNMM) were chosen as influential variables. Using the Random Forest algorithm, we generated an AGB raster dataset covering the period 1989-2020 based on earth observation data at 30 m resolution to examine the dynamics of alpine grasslands and their response to climate change with respect to elevation. The results showed that the AGB of alpine grassland on the Tibetan Plateau was 49.17 g/m2. We observed an increasing trend in grassland AGB at high elevations, with a growth rate of about 0.28 g/m2 per year within the interval of 3100-4800 m. However, above the elevation of approximately 4400-4600 m, we observed a decoupling trend between grassland AGB and TMP. Moreover, at most elevations, the proportion of maximum partial correlation coefficients for CVP, PRE, and SNMM surpassed that of TMP. We found the dominant role of precipitation variability on grassland AGB dynamics, with 22.80 % and 18.86 % for CVP+ and CVP-, respectively. The proportion of CVP+ did not vary much at different elevations, whereas the proportion of CVP- increased with elevation, varying between 12.85 and 30.25 %. In the future, precipitation on the Tibetan plateau is expected to increase, potentially reversing its original positive impact.

Dominance of soil moisture over aridity in explaining vegetation greenness across global drylands

Drylands are one of the most sensitive areas to climate change. Despite being characterized by water scarcity and low precipitation, drylands support a wide range of green biodiversity and nearly 40 % of the global population. However, the climate change impacts on dryland characteristics and vegetation dynamics are debatable as the reasons remain poorly understood. Here, we use hydro-meteorological variables from ERA5 reanalysis and GIMMS-NDVI to analyze the changes in dryland aridity and vegetation greenness in the eight selected global dryland regions. The total dryland area (excluding hyperarid) has increased by 12 %, while arid, semiarid, and dry sub-humid areas have increased by 10.5 %, 8 %, and 25 %, respectively. We find a significant increase in aridity in drylands across the globe, except for South Asia. A decrease (increase) in precipitation is the major driver for a significant increase (decrease) in dryland aridity, with a notable contribution from climate warming. Despite decreasing trends in precipitation, vegetation greenness has significantly increased in most dryland regions due to increased soil moisture. Cropland expansion in Europe, Asia, and Australia resulted in the maximum increase in NDVI (Normalized Difference Vegetation Index) in dryland regions. The highest increase, with a ΔNDVI of 0.075, was observed in South Asia. The enhanced vegetation greenness observed is attributed to the expansion of croplands in recent decades, which has increased soil moisture. Overall, we show that monitoring soil moisture variability can provide a more robust explanation for vegetation greenness in the global drylands than aridity change. Moreover, human interventions of climatic alteration through land use change practices, such as cropland expansion, cannot be ignored while explaining the ecosystem dynamics of the drylands.

Mesic vegetation persistence: A new approach for monitoring spatial and temporal changes in water availability in dryland regions using cloud computing and the sentinel and Landsat constellations

Climate change and anthropogenic activity pose severe threats to water availability in drylands. A better understanding of water availability response to these threats could improve our ability to adapt and mitigate climate and anthropogenic effects. Here, we present a Mesic Vegetation Persistence (MVP) workflow that takes every usable image in the Sentinel (10-m) and Landsat (30-m) archives to generate a dense time-series of water availability that is continuously updated as new images become available in Google Earth Engine. MVP takes advantage of the fact that mesic vegetation can be used as a proxy of available water in drylands. Our MVP workflow combines a novel moisture-based index (moisture change index - MCI) with a vegetation index (Modified Chlorophyll Absorption Ratio Vegetation Index (MCARI2)). MCI is the difference in soil moisture condition between an individual pixel's state and the dry and wet reference reflectance in the image, derived using 5th and 95th percentiles of the visible and shortwave infra-red drought index (VSDI). We produced and validated our MVP products across drylands of the western U.S., covering a broad range of elevation, land use, and ecoregions. MVP outperforms NDVI, a commonly-employed index for mesic ecosystem health, in both rangeland and forested ecosystems, and in mesic habitats with particularly high and low vegetation cover. We applied our MVP product at case study sites and found that MVP more accurately characterizes differences in mesic persistence, late-season water availability, and restoration success compared to NDVI. MVP could be applied as an indicator of change in a variety of contexts to provide a greater understanding of how water availability changes as a result of climate and management. Our MVP product for the western U.S. is freely available within a Google Earth Engine Web App, and the MVP workflow is replicable for other dryland regions.

Changes in vegetation in China's drylands are closely related to afforestation compared with climate change

The response of vegetation to climate change and human activities has attracted considerable attention. However, quantitative studies on the effects of climate change and human activities on dryland vegetation in different seasons remain unclear. This study investigated the impacts of precipitation, temperature, soil water storage (SWS) (top [0-7 cm], shallow [7-28 cm], and middle [28-100 cm] layers), vapor pressure deficit (VPD), and afforestation on vegetation as well as their relative contribution rates during the rainy season ([RS], June to September), dry season ([DS], November to April), transition season ([TS], May and October), and all year period (AY) in China's drylands from 2001 to 2020 using the first-difference method. Areas with precipitation and SWS showing significant positive correlation with dryland vegetation (p < 0.05) were found to be larger in RS than in DS and TS, and the positive effect of SWS increased with soil depth in the 0-28 cm interval. Increasing VPD induced a significant negative effect on vgetation during RS but it was not predominant in DS and TS. Afforestation showed an extremely significant positive correlated with dryland vegetation across >60 % of China's dryland areas (p < 0.01), but this improvement was found to be limited to regions with the highest afforestation area. Moreover, dryland vegetation dynamics were driven by afforestation in all seasons, with contribution rates of 64.23 %-71.46 %. The effects of SWS and VPD on vegetation driven by precipitation and temperature exceeded the direct effects of precipitation and temperature. Among climatic factors, VPD showed a major regulating effect on dryland vegetation at the top and shallow soil layers in almost all seasons, whereas the relative contribution rate of SWS increased with soil layer. The findings can provide a scientific reference for the sustainable development and protection of drylands under global warming.

Spatiotemporal variation in sensitivity of urban vegetation growth and greenness to vegetation water content: Evidence from Chinese megacities

Understanding the sensitivity of vegetation growth and greenness to vegetation water content change is crucial for elucidating the mechanism of terrestrial ecosystems response to water availability change caused by climate change. Nevertheless, we still have limited knowledge of such aspects in urban in different climatic contexts under the influence of human activities. In this study, we employed Google Earth Engine (GEE), remote sensing satellite imagery, meteorological data, and Vegetation Photosynthesis Model (VPM) to explore the spatiotemporal pattern of vegetation growth and greenness sensitivity to vegetation water content in three megacities (Beijing, Shanghai, and Guangzhou) located in eastern China from 2001 to 2020. We found a significant increase (slope > 0, p < 0.05) in the sensitivity of urban vegetation growth and greenness to vegetation water content (SLSWI). This indicates the increasing dependence of urban vegetation ecosystems on vegetation water resources. Moreover, evident spatial heterogeneity was observed in both SLSWI and the trends of SLSWI, and spatial heterogeneity in SLSWI and the trends of SLSWI was also present among identical vegetation types within the same city. Additionally, both SLSWI of vegetation growth and greenness and the trend of SLSWI showed obvious spatial distribution differences (e.g., standard deviations of trends in SLSWI of open evergreen needle-leaved forest of GPP is 14.36 × 10-2 and standard deviations of trends in SLSWI of open evergreen needle-leaved forest of EVI is 10.16 × 10-2), closely associated with factors such as vegetation type, climatic conditions, and anthropogenic influences.

Dryland sensitivity to climate change and variability using nonlinear dynamics

Primary productivity response to climatic drivers varies temporally, indicating state-dependent interactions between climate and productivity. Previous studies primarily employed equation-based approaches to clarify this relationship, ignoring the state-dependent nature of ecological dynamics. Here, using 40 y of climate and productivity data from 48 grassland sites across Mongolia, we applied an equation-free, nonlinear time-series analysis to reveal sensitivity patterns of productivity to climate change and variability and clarify underlying mechanisms. We showed that productivity responded positively to annual precipitation in mesic regions but negatively in arid regions, with the opposite pattern observed for annual mean temperature. Furthermore, productivity responded negatively to decreasing annual aridity that integrated precipitation and temperature across Mongolia. Productivity responded negatively to interannual variability in precipitation and aridity in mesic regions but positively in arid regions. Overall, interannual temperature variability enhanced productivity. These response patterns are largely unrecognized; however, two mechanisms are inferable. First, time-delayed climate effects modify annual productivity responses to annual climate conditions. Notably, our results suggest that the sensitivity of annual productivity to increasing annual precipitation and decreasing annual aridity can even be negative when the negative time-delayed effects of annual precipitation and aridity on productivity prevail across time. Second, the proportion of plant species resistant to water and temperature stresses at a site determines the sensitivity of productivity to climate variability. Thus, we highlight the importance of nonlinear, state-dependent sensitivity of productivity to climate change and variability, accurately forecasting potential biosphere feedback to the climate system.

As above, not so below: Long-term dynamics of net primary production across a dryland transition zone

Drylands are key contributors to interannual variation in the terrestrial carbon sink, which has been attributed primarily to broad-scale climatic anomalies that disproportionately affect net primary production (NPP) in these ecosystems. Current knowledge around the patterns and controls of NPP is based largely on measurements of aboveground net primary production (ANPP), particularly in the context of altered precipitation regimes. Limited evidence suggests belowground net primary production (BNPP), a major input to the terrestrial carbon pool, may respond differently than ANPP to precipitation, as well as other drivers of environmental change, such as nitrogen deposition and fire. Yet long-term measurements of BNPP are rare, contributing to uncertainty in carbon cycle assessments. Here, we used 16 years of annual NPP measurements to investigate responses of ANPP and BNPP to several environmental change drivers across a grassland-shrubland transition zone in the northern Chihuahuan Desert. ANPP was positively correlated with annual precipitation across this landscape; however, this relationship was weaker within sites. BNPP, on the other hand, was weakly correlated with precipitation only in Chihuahuan Desert shrubland. Although NPP generally exhibited similar trends among sites, temporal correlations between ANPP and BNPP within sites were weak. We found chronic nitrogen enrichment stimulated ANPP, whereas a one-time prescribed burn reduced ANPP for nearly a decade. Surprisingly, BNPP was largely unaffected by these factors. Together, our results suggest that BNPP is driven by a different set of controls than ANPP. Furthermore, our findings imply belowground production cannot be inferred from aboveground measurements in dryland ecosystems. Improving understanding around the patterns and controls of dryland NPP at interannual to decadal scales is fundamentally important because of their measurable impact on the global carbon cycle. This study underscores the need for more long-term measurements of BNPP to improve assessments of the terrestrial carbon sink, particularly in the context of ongoing environmental change.

Should time-lag and time-accumulation effects of climate be considered in attribution of vegetation dynamics? Case study of China's temperate grassland region

Although the time-lag and time-accumulation effects (TLTAEs) of climatic factors on vegetation growth have been investigated extensively, the uncertainties caused by disregarding TLTAEs in the attribution analysis of long-term changes in vegetation remain unclear. This hinders our understanding of the associated changes in ecosystems and the effects of climate change. In this study, using multiple methods, we evaluate the biases of attribution analyses of vegetation dynamics caused by the non-consideration of TLTAEs in the temperate grassland region (TGR) of China from 2000 to 2019. Based on the datasets of the normalized difference vegetation index (NDVI), temperature (TMP), precipitation (PRE), and solar radiation (SR), the temporal reaction patterns of vegetation are analyzed, and the relationships among these variables under two scenarios (considering and disregarding TLTAEs) are compared. The results indicate that most areas of the TGR show a greening trend. A time-lag or time-accumulation effect of the three climatic variables is observed in most areas with significant spatial differences. The lagged times of the vegetation response to PRE are particularly prominent, with an average of 2.12 months in the TGR. When the TLTAE is considered, the areas where changes in the NDVI are affected by climatic factors expanded significantly, whereas the explanatory power of climate change on NDVI change increased by an average of 9.3% in the TGR; these improvements are more prominent in relatively arid areas. This study highlights the importance of including TLTAEs in the attribution of vegetation dynamics and the assessment of climatic effects on ecosystems.

Dynamic Changes and Influencing Factors of Vegetation in the "Green Heart" Zone of the Chang-Zhu-Tan Urban Agglomeration during the Past 21 Years

As a policy, protected green space in the rapidly developing the Chang-Zhu-Tan Urban Agglomeration is of great practical significance to study the vegetation changes and influencing factors in the Green Heart area. In this paper, data processing, grading and area statistics were carried out for the maximum value of normalized differential vegetation index (NDVI) from 2000 to 2020. Combined with Theil-Sen median trend analysis and Mann-Kendall, the change trend of long-time series NDVI was studied, and investigation of NDVI influencing factors, processes and mechanisms using geographical detectors. The results showed that: (1) The spatial distribution characteristics of NDVI in the study area were high in the middle and inlaid transition between adjacent grades. Except for the low grades, the distribution of NDVI in other grades was relatively scattered, and the overall trend of NDVI change was rising. (2) Population density was the main factor affecting NDVI changes, with an explanatory power of up to 40%, followed by elevation, precipitation and minimum temperature. (3) The influence of influencing factors on the change of NDVI was not the result of independent action of a single factor, but the result of the interaction between human factors and natural factors, and the factor combinations with greater interaction had significant differences in the spatial distribution of NDVI.

Revegetation affects the response of land surface phenology to climate in Loess Plateau, China

Land surface phenology (LSP), defined as the plant's growth rhythm retrieved from satellite sensing products, is proven to shift with climate change and affect the carbon cycles of terrestrial ecosystems. Global afforested area is largely increasing and consequently affecting local and global climate. However, how and to what extent revegetation affects LSP remains relatively unexplored. Here we investigated the difference in four LSPs (i.e., greenup, maturity, senescence, and dormancy) and the response of LSP to climate between restored and native vegetation on Loess Plateau, China, where a remarkable process of vegetation restoration happened during 1982-2015. Most study regions showed a longer growing season (LOS) over time, specifically, with a slight delay in greenup but a relatively large delay in senescence. We found that air temperature was the dominant factor affecting greenup and maturity, while precipitation mostly controlled the senescence and dormancy in the study area. Under similar climate conditions, the LSP of restored vegetation (i.e., restored forest and grassland) showed a significant difference (p < 0.05) from native ones during 1999-2015. Compared to the native forest, restored forest from cropland and grassland showed a delayed greenup date by 0.3 and 3.6 days (p < 0.05) and an advanced dormancy date of 6.6 and 9.0 days (p < 0.05), respectively. Furthermore, the restored vegetation became less sensitive to air temperature than native vegetation, while the restored forest was more sensitive to precipitation, and its growth was affected by the water limitation to a larger extent in the study area. Our study highlights the necessity of considering land use management and its effect on the LSP change to better understand the effect of afforestation on global climate and carbon cycles.

The role of climate, vegetation, and soil factors on carbon fluxes in Chinese drylands

Drylands dominate the trend and variability of the land carbon (C) sink. A better understanding of the implications of climate-induced changes in the drylands for C sink-source dynamics is urgently needed. The effect of climate on ecosystem C fluxes (gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem productivity (NEP)) in drylands has been extensively explored, but the roles of other concurrently changing factors, such as vegetation conditions and nutrient availability, remain unclear. We used eddy-covariance C-flux measurements from 45 ecosystems with concurrent information on climate (mean annual temperature (MAT) and mean annual precipitation (MAP)), soil (soil moisture (SM) and soil total nitrogen content (soil N)), and vegetation (leaf area index (LAI) and leaf nitrogen content (LNC)) factors to assess their roles in C fluxes. The results showed that the drylands in China were weak C sinks. GPP and ER were positively correlated with MAP, while they were negatively correlated with MAT. NEP first decreased and then increased with increasing MAT and MAP, and 6.6 °C and 207 mm were the boundaries for the NEP response to MAT and MAP, respectively. SM, soil N, LAI, and MAP were the main factors affecting GPP and ER. However, SM and LNC had the most important influence on NEP. Compared with climate and vegetation factors, soil factors (SM and soil N) had a greater impact on C fluxes in the drylands. Climate factors mainly affected C fluxes by regulating vegetation and soil factors. To accurately estimate the global C balance and predict the response of ecosystems to environmental change, it is necessary to fully consider the discrepant effects of climate, vegetation, and soil factors on C fluxes, as well as the cascade relationships between different factors.

Multifaceted responses of vegetation to average and extreme climate change over global drylands

Average climatic events describe the occurrence of weather or climate at an average value, whereas extreme events are defined as events that exceed the upper or lower threshold value of statistical or observational average climatic events. This study investigated the impacts of both average climate change (ACC) (i.e., average precipitation, temperature, and potential evapotranspiration [PET]) and extreme climate change (ECC) (i.e., five precipitation and five temperature extremes) on dryland vegetation based on the Normalized Difference Vegetation Index (NDVI). The spatial divergences of ACC and ECC in affecting changes in NDVI over drylands were determined using the geographical detector model. In this study, the growth of vegetation in 40.29 % of global drylands was driven by average precipitation and this dominant effect also occurred in all the plant species, particularly shrubs. However, the sensitivity of grassland to average precipitation exceeded that of most of the woody vegetation. The average temperature and PET controlled 28.64 % and 31.07 % of the changes in NDVI, respectively. Precipitation extremes (except for consecutive dry days and consecutive wet days) and warm temperature extremes (WTE) had positive influences on dryland vegetation, and the effect of WTE on NDVI exceeded that of the remaining temperature extremes. Temperature extremes exerted more significant effects than precipitation extremes for changes in the grassland NDVI. In contrast, the variations in shrub NDVI were primarily dominated by precipitation extremes. We also found that the impacts of parts of average and extreme climatic factors on vegetation had changed over time. Furthermore, temperature extremes had far exceeded the average temperature in affecting vegetation growth at the spatial scale, and this action gradually intensified from 1982 to 2015. The influences of all precipitation extremes were weaker than those of the average precipitation. Those can offer scientific references for ecosystem protection in drylands.

Ecological restoration programs dominate vegetation greening in China

Many ecological restoration programs have been implemented in China during the last two decades. At the same time, the vegetation has turned green significantly in China. However, few studies have directly evaluated the contribution of the ecological restoration programs to vegetation greening in comparison with the contribution of climate change using high-resolution data of afforestation areas at the national scale. We used newly compiled high-resolution data on yearly forest plantation and mountain closure, the daily climate data from the 2480 meteorological stations and GIMMS 3g NDVI data. We used a multiple linear regression model to compare the influence of temperature, precipitation, and ecological restoration programs on NDVI dynamics. We then used the hierarchical variance partitioning method to evaluate the relative contribution of temperature, precipitation, and ecological restoration programs on NDVI dynamics. We found a significant greening trend in China from 1999 to 2015 with an annual increase rate of 0.0017 yr^-1 in the mean growing season NDVI. The ecological restoration programs dominated the vegetation greening in northern China and the southern coastal regions, indicating a good performance of restoration programs in these regions. In contrast, temperature or precipitation dominated the vegetation greening in southwestern China, Inner Mongolia and the implementation regions of several ecological restoration programs in northeastern China. Among the ecological restoration programs except the Three-North Shelterbelt Forest Program, the effect of ecological restoration programs on vegetation greening was stronger than the total effects of temperature and precipitation changes. Our study presents a systematic assessment on the contribution of ecological restoration programs to the vegetation greening in China, accessed the role on vegetation greening of different ecosystem restoration programs. We analyzed the reasons for the differences in the contribution of different ecological restoration programs to vegetation greening and provided insights facilitating policy makers to prioritize future restoration planning.

Prevalence and drivers of abrupt vegetation shifts in global drylands

The constant provision of plant productivity is integral to supporting the liability of ecosystems and human wellbeing in global drylands. Drylands are paradigmatic examples of systems prone to experiencing abrupt changes in their functioning. Indeed, space-for-time substitution approaches suggest that abrupt changes in plant productivity are widespread, but this evidence is less clear using observational time series or experimental data at a large scale. Studying the prevalence and, most importantly, the unknown drivers of abrupt (rather than gradual) dynamical patterns in drylands may help to unveil hotspots of current and future dynamical instabilities in drylands. Using a 20-y global satellite-derived temporal assessment of dryland Normalized Difference Vegetation Index (NDVI), we show that 50% of all dryland ecosystems exhibiting gains or losses of NDVI are characterized by abrupt positive/negative temporal dynamics. We further show that abrupt changes are more common among negative than positive NDVI trends and can be found in global regions suffering recent droughts, particularly around critical aridity thresholds. Positive abrupt dynamics are found most in ecosystems with low seasonal variability or high aridity. Our work unveils the high importance of climate variability on triggering abrupt shifts in vegetation and it provides missing evidence of increasing abruptness in systems intensively managed by humans, with low soil organic carbon contents, or around specific aridity thresholds. These results highlight that abrupt changes in dryland dynamics are very common, especially for productivity losses, pinpoint global hotspots of dryland vulnerability, and identify drivers that could be targeted for effective dryland management.

Local-scale factors matter for tree cover modelling in Mediterranean drylands

Forests contribute directly to ecosystem structure and functioning, maintaining biodiversity, acting as a climate regulator and reducing desertification. To better manage forests, it is essential to have high-resolution forest models and appropriate spatial-explicit variables able to explain tree cover at different scales, including the management scale. Most tree cover models rely only on broad-scale variables (>500 m), such as macroclimate, while only few studies include also local-scale variables (<500 m). This study aimed to identify the importance of local-scale factors relative to broad-scale factors and identify the environmental variables at different scales that explain tree cover in oak woodlands in Mediterranean drylands. Sixty sites previously identified as being covered with Holm oak or Cork oak were stratified by precipitation. Normalized Difference Vegetation Index, used here as a surrogate of tree cover, was modelled using simultaneously broad-scale factors (macroclimate) and local-scale factors (microclimatic and edaphic conditions). The percentage of variance explained by local- and broad-scale factors and the effect size of each environmental variable on tree cover was determined for the study site. It was found that local-scale factors and their interaction with broad-scale factors explained more variance than broad-scale factors alone. The most important local-scale factors explaining tree cover were elevation, potential solar radiation, used as a surrogate of microclimatic conditions, and wetness evaluated terrain used as an indicator of water flow accumulation. The main broad-scale factors were related to temperature and precipitation. The effect of some local-scale variables in tree cover seems to increase in areas where water as a limiting factor is more important. This study demonstrates the critical importance of including local-scale factors in multi-scale modelling of tree cover to obtain better predictions. These models will support well-suited forest management decisions, such as reforestation and afforestation plans to reverse evergreen oaks decline in Mediterranean drylands.

Vegetation Greenness Dynamics in the Western Greater Khingan Range of Northeast China Based on Dendrochronology

Understanding the vegetation greenness dynamics in the forest–steppe transition zone is essential for ecosystem management, and in order to study ecological changes in the region. This study provides a valuable record of the vegetation greenness dynamics in the western Greater Khingan Range over the past 193 years (1826–2018) based on tree-ring data represented by the normalized difference vegetation index (NDVI). The reconstructed vegetation greenness dynamics record contains a total of 32 years of high vegetation greenness and 37 years of low vegetation greenness, together occupying 35.8% of the entire reconstructed period (193 years). Climate (precipitation) is the main influence on the vegetation greenness dynamics at this site, but human activities have also had a significant impact over the last few decades. The magnitude, frequency, and duration of extreme changes in vegetation greenness dynamics have increased significantly, with progressively shorter intervals. Analyses targeting human behavior have shown that the density of livestock, agricultural land area, and total population have gradually increased, encroaching on forests and grasslands and reducing the inter-annual variability. After 2002, the government implemented projects to return farmland to its original ecosystems, and for the implementation of new land management practices (which are more ecologically related); as such, the vegetation conditions began to improve. These findings will help us to understand the relationship between climate change and inter- and intra- annual dynamics in northeastern China, and to better understand the impact of human activities on vegetation greenness dynamics.

Characterizing the Response of Vegetation Cover to Water Limitation in Africa Using Geostationary Satellites

Hydrological interactions between vegetation, soil, and topography are complex, and heterogeneous in semi-arid landscapes. This along with data scarcity poses challenges for large-scale modeling of vegetation-water interactions. Here, we exploit metrics derived from daily Meteosat data over Africa at ca. 5 km spatial resolution for ecohydrological analysis. Their spatial patterns are based on Fractional Vegetation Cover (FVC) time series and emphasize limiting conditions of the seasonal wet to dry transition: the minimum and maximum FVC of temporal record, the FVC decay rate and the FVC integral over the decay period. We investigate the relevance of these metrics for large scale ecohydrological studies by assessing their co-variation with soil moisture, and with topographic, soil, and vegetation factors. Consistent with our initial hypothesis, FVC minimum and maximum increase with soil moisture, while the FVC integral and decay rate peak at intermediate soil moisture. We find evidence for the relevance of topographic moisture variations in arid regions, which, counter-intuitively, is detectable in the maximum but not in the minimum FVC. We find no clear evidence for wide-spread occurrence of the "inverse texture effect" on FVC. The FVC integral over the decay period correlates with independent data sets of plant water storage capacity or rooting depth while correlations increase with aridity. In arid regions, the FVC decay rate decreases with canopy height and tree cover fraction as expected for ecosystems with a more conservative water-use strategy. Thus, our observation-based products have large potential for better understanding complex vegetation-water interactions from regional to continental scales.