Vegetation Greening and Climate Change Promote Multidecadal Rises of Global Land Evapotranspiration

A global dataset of terrestrial evapotranspiration and soil moisture dynamics from 1982 to 2020

Quantifying terrestrial evapotranspiration (ET) and soil moisture dynamics accurately is crucial for understanding the global water cycle and surface energy balance. We present a novel, long-term dataset of global ET and soil moisture derived from the newly developed Simple Terrestrial Hydrosphere model, version 2 (SiTHv2). This ecohydrological model, driven by multi-source satellite observations and hydrometeorological variables from reanalysis data, provides daily global ET-related estimates (e.g., total ET, plant transpiration, soil evaporation, intercepted evaporation) and three-layer soil moisture dynamics at a 0.1° spatial resolution. Validation with in-situ measurements and comparisons with mainstream global ET and soil moisture products demonstrate robust performance of SiTHv2 in both magnitude and temporal dynamics of ET and soil moisture at multiple scales. The comprehensive water path characterization in the SiTHv2 model makes this seamless dataset particularly valuable for studies requiring synchronized water budget and vegetation response to water constraints. With its long-term coverage and high spatiotemporal resolution, the SiTHv2-derived ET and soil moisture product will be suitable to support analyses related to the hydrologic cycle, drought assessment, and ecosystem health.

Contrasting patterns of water use efficiency and annual radial growth among European beech forests along the Italian peninsula

Tree mortality and forest dieback episodes are increasing due to drought and heat stress. Nevertheless, a comprehensive understanding of mechanisms enabling trees to withstand and survive droughts remains lacking. Our study investigated basal area increment (BAI), and δ13C-derived intrinsic water-use-efficiency (iWUE), to elucidate beech resilience across four healthy stands in Italy with varying climates and soil water availability. Additionally, fist-order autocorrelation (AR1) analysis was performed to detect early warning signals for potential tree dieback risks during extreme drought events. Results reveal a negative link between BAI and vapour pressure deficit (VPD), especially in southern latitudes. After the 2003 drought, BAI decreased at the northern site, with an increase in δ13C and iWUE, indicating conservative water-use. Conversely, the southern sites showed increased BAI and iWUE, likely influenced by rising CO2 and improved water availability. In contrast, the central site sustained higher transpiration rates due to higher soil water holding capacity (SWHC). Despite varied responses, most sites exhibited reduced resilience to future extreme events, indicated by increased AR1. Temperature significantly affected beech iWUE and BAI in northern Italy, while VPD strongly influenced the southern latitudes. The observed increase in BAI and iWUE in southern regions might be attributed to an acclimation response.

Global water use efficiency saturation due to increased vapor pressure deficit

The ratio of carbon assimilation to water evapotranspiration (ET) of an ecosystem, referred to as ecosystem water use efficiency (WUEeco), is widely expected to increase because of the rising atmospheric carbon dioxide concentration (Ca). However, little is known about the interactive effects of rising Ca and climate change on WUEeco. On the basis of upscaled estimates from machine learning methods and global FLUXNET observations, we show that global WUEeco has not risen since 2001 because of the asymmetric effects of an increased vapor pressure deficit (VPD), which depressed photosynthesis and enhanced ET. An undiminished ET trend indicates that rising temperature and VPD may play a more important role in regulating ET than declining stomatal conductance. Projected increases in VPD are predicted to affect the future coupling of the terrestrial carbon and water cycles.

Differences in mucilage properties and stomatal sensitivity of locally adapted Zea mays in relation with precipitation seasonality and vapour pressure deficit regime of their native environment

With ongoing climate change and the increase in extreme weather events, especially droughts, the challenge of maintaining food security is becoming ever greater. Locally adapted landraces of crops represent a valuable source of adaptation to stressful environments. In the light of future droughts-both by altered soil water supply and increasing atmospheric water demand (vapor pressure deficit [VPD])-plants need to improve their water efficiency. To do so, plants can enhance their access to soil water by improving rhizosphere hydraulic conductivity via the exudation of mucilage. Furthermore, plants can reduce transpirational water loss via stomatal regulation. Although the role of mucilage and stomata regulation on plant water management have been extensively studied, little is known about a possible coordination between root mucilage properties and stomatal sensitivity as well as abiotic drivers shaping the development of drought resistant trait suits within landraces. Mucilage properties and stomatal sensitivity of eight Mexican landraces of Zea mays in contrast with one inbred line were first quantified under controlled conditions and second related to water demand and supply at their respective site of origin. Mucilage physical properties-namely, viscosity, contact angle, and surface tension-differed between the investigated maize varieties. We found strong influences of precipitation seasonality, thus plant water availability, on mucilage production (R 2 = .88, p < .01) and mucilage viscosity (R 2 = .93, p < .01). Further, stomatal sensitivity to increased atmospheric water demand was related to mucilage viscosity and contact angle, both of which are crucial in determining mucilage's water repellent, thus maladaptive, behavior upon soil drying. The identification of landraces with pre-adapted suitable trait sets with regard to drought resistance is of utmost importance, for example, trait combinations such as exhibited in one of the here investigated landraces. Our results suggest a strong environmental selective force of seasonality in plant water availability on mucilage properties as well as regulatory stomatal effects to avoid mucilage's maladaptive potential upon drying and likely delay critical levels of hydraulic dysfunction. By this, landraces from highly seasonal climates may exhibit beneficial mucilage and stomatal traits to prolong plant functioning under edaphic drought. These findings may help breeders to efficiently screen for local landraces with pre-adaptations to drought to ultimately increase crop yield resistance under future climatic variability.

Effects of climate change on vegetation dynamics of the Qinghai-Tibet Plateau, a causality analysis using empirical dynamic modeling

Given the vital role of the Qinghai-Tibet Plateau (QTP) as water tower in Asia and regulator for regional and even global climate, the relationship between climate change and vegetation dynamics on it has received considerable focused attention. Climate change may influence the vegetation growth on the plateau, but clear empirical evidence of such causal linkages is sparse. Herein, using datasets CRU-TS v4.04 and AVHHR NDVI from 1981 to 2019, we quantify causal effects of climate factors on vegetation dynamics with an empirical dynamical model (EDM) -- a nonlinear dynamical systems analysis approach based on state-space reconstruction rather than correlation. Results showed the following: (1) climate change promotes the growth of vegetation on the QTP, and specifically, this favorable influence of temperature is stronger than precipitation's; (2) the direction and strength of climate effects on vegetation varied over time, and the effects are seasonally different; (3) a significant increase in temperature and a slight increase in precipitation are beneficial to vegetation growth, specifically, NDVI will increase within 2% in the next 40 years with the climate trend of warming and humidity. Besides the above results, another interesting finding is that the two seasons in which precipitation strongly influence vegetation in the Three-River Source region (part of the QTP) are spring and winter. This study provides insights into the mechanisms by which climate change affects vegetation growth on the QTP, aiding in the modeling of vegetation dynamics in future scenarios.

Climate-driven vegetation greening further reduces water availability in drylands

Climate change alters surface water availability (WA; precipitation minus evapotranspiration, P - ET) and consequently impacts agricultural production and societal water needs, leading to increasing concerns on the sustainability of water use. Although the direct effects of climate change on WA have long been recognized and assessed, indirect climate effects occurring through adjustments in terrestrial vegetation are more subtle and not yet fully quantified. To address this knowledge gap, here we investigate the interplay between climate-induced changes in leaf area index (LAI) and ET and quantify its ultimate effect on WA during the period 1982-2016 at the global scale, using an ensemble of data-driven products and land surface models. We show that ~44% of the global vegetated land has experienced a significant increase in growing season-averaged LAI and climate change explains 33.5% of this greening signal. Such climate-induced greening has enhanced ET of 0.051 ± 0.067 mm year^-2 (mean ± SD), further amplifying the ongoing increase in ET directly driven by variations in climatic factors over 36.8% of the globe, and thus exacerbating the decline in WA prominently in drylands. These findings highlight the indirect impact of positive feedbacks in the land-climate system on the decline of WA, and call for an in-depth evaluation of these phenomena in the design of local mitigation and adaptation plans.

Four decades of hydrological response to vegetation dynamics and anthropogenic factors in the Three-North Region of China and Mongolia

Afforestation has been initiated in Northeast Asia to improve ecological status. The responses of the regional hydrological cycle to vegetation restoration remain insufficiently explored. This study uses a variety of satellite-derived vegetation variables and hydrological cycle components to scan the eco-hydrological regimes in the Three-North Region of China and Mongolia during the past four decades. We observe that vegetation productivity increases mainly in North China (NC), Northeast China (NEC), Northwest China (NWC), and the north of Inner Mongolia and Mongolia. Precipitation and runoff show a decreasing trend (-0.4 mm/year and - 0.6 mm/year, respectively), yet they are less correlated to the normalized difference vegetation index and leaf area index. Along with increasing vegetation productivity, evapotranspiration increases (0.05 mm/year) obviously in NC and NEC, while root soil moisture (-0.001 m³/m³/year) and terrestrial water storage (-2.0 mm/year) decrease in NC and parts of NEC and NWC. The correlation coefficient between evapotranspiration and vegetation variables is up to 0.73. Collectively, results imply one potential adverse response of terrestrial water fluxes to increasing vegetation. Independent ecological and hydrological datasets further corroborate our work. Climatic factors (i.e., downward shortwave radiation and air temperature) and human activities (i.e., aerosol optical depth, carbon dioxide, and water withdrawal) substantially affect regional hydrological cycles. Considering the increasing vegetation productivity in the Three-North Region of China and Mongolia is likely to continue in the 21st century based on the Sixth Coupled Model Intercomparison Project (CMIP6) simulations, the terrestrial water fluxes may undergo deficit pressure. Overall, this study comprehensively investigates the vegetation and hydrology interplays, and provides a reference for protecting and improving ecological-hydrological conditions in Northeast Asia.

Inter-annual variations of vegetation dynamics to climate change in Ordos, Inner Mongolia, China

To reveal the characteristics of climate change and the controlling factors for vegetation dynamics in the Ordos, Inner Mongolia, China, 34 years (1982–2015) of regional climate variables and vegetation dynamics were investigated. The results show that: Annual mean air temperature (TMP) significantly increased with a linear slope of 0.473°C/10yr. Annual precipitation (PRE) had a non-significant positive trend nearly 5 times lower than the trend of potential evapotranspiration (PET). The average Normalized Difference Vegetation Index (NDVI) computed for the region was found to show a significant positive trend (6.131×10⁻⁴/yr). However, all climate variables displayed non-significant correlations with NDVI at annual scale. The reduction of desert and the increase of grassland over the past decades were accountable for the increased NDVI. Principal components analysis revealed that the regional climate change can be characterized as changes in temperature, humidity and the availability of radiant energy. Based on principal components regression coefficients, NDVI was mostly sensitive to humidity component, followed by growing season warmth (WMI). Spatially, 93.1% of the pixels displayed positive trend and 61.8% of the pixels displayed significant change over the past decades. Both principal regression analysis and partial correlation analysis revealed that NDVI in eastern part of Ordos was sensitive to TMP, whereas, NDVI in southern and western areas of Ordos displayed the high sensitivity to combined effects of PRE and cloud coverage (CLD). Partial correlation analyses also revealed that TMX was a surrogate for aridity, TMN was a representative of humidity, and temperature variations below the threshold of 5°C (CDI) were less important than WMI. We conclude that regional climate change can be characterized by warming and increased aridity. The significant positive trend of regional NDVI and the non-significant correlations between NDVI and climate variables at annual scale suggests the hidden role of the human activities.

Anthropogenically driven climate and landscape change effects on inland water carbon dynamics: What have we learned and where are we going?

Inland waters serve as important hydrological connections between the terrestrial landscape and oceans but are often overlooked in global carbon (C) budgets and Earth System Models. Terrestrially derived C entering inland waters from the watershed can be transported to oceans but over 83% is either buried in sediments or emitted to the atmosphere before reaching oceans. Anthropogenic pressures such as climate and landscape changes are altering the magnitude of these C fluxes in inland waters. Here, we synthesize the most recent estimates of C fluxes and the differential contributions across inland waterbody types (rivers, streams, lakes, reservoirs, and ponds), including recent measurements that incorporate improved sampling methods, small waterbodies, and dried areas. Across all inland waters, we report a global C emission estimate of 4.40 Pg C/year (95% confidence interval: 3.95-4.85 Pg C/year), representing a 13% increase from the most recent estimate. We also review the mechanisms by which the most globally widespread anthropogenically driven climate and landscape changes influence inland water C fluxes. The majority of these drivers are expected to influence terrestrial C inputs to inland waters due to alterations in terrestrial C quality and quantity, hydrological pathways, and biogeochemical processing. We recommend four research priorities for the future study of anthropogenic alterations to inland water C fluxes: (1) before-and-after measurements of C fluxes associated with climate change events and landscape changes, (2) better quantification of C input from land, (3) improved assessment of spatial coverage and contributions of small inland waterbodies to C fluxes, and (4) integration of dried and drawdown areas to global C flux estimates. Improved measurements of inland water C fluxes and quantification of uncertainty in these estimates will be vital to understanding both terrestrial C losses and the "moving target" of inland water C emissions in response to rapid and complex anthropogenic pressures.

Evolution and Climate Drivers of NDVI of Natural Vegetation during the Growing Season in the Arid Region of Northwest China

Vegetation plays an important role in linking water, atmosphere, and soil. The dynamic change in vegetation is an important indicator for the regulation of the terrestrial carbon balance and climate change. This study applied trend analysis, detrended correlation analysis, and the Hierarchical Partitioning Algorithm (HPA) to GIMMS NDVI3g data, meteorological data, and natural vegetation types for the period 1983 to 2015 to analyze the temporal and spatial changes in NDVI during the growing season and its driving factors in the arid region of northwestern China. The results showed that: (1) the growing season length (GSL) was delayed, with a regional trend of 8 d/33 a, due to a significant advancement in the start of the growing season (SOS, −7 d/33 a) and an insignificant delay to the end of growing season (EOS, 2 d/33 a). (2) The regional change in NDVI was mainly driven by temperature and precipitation, contributing to variations in NDVI of forest of 36% and 15%, respectively, and in the NDVI of grassland, of 35% and 21%, respectively. In particular, changes to forested land and medium-coverage grassland (Mgra) were closely related to temperature and precipitation, respectively. (3) The spatial distribution of the mean NDVI of forest was closely related with precipitation, temperature, and solar radiation, with these meteorological variables explaining 20%, 15%, and 10% of the variation in NDVI, respectively. Precipitation and solar radiation explained 29% and 17% of the variation in the NDVI of grassland, respectively. The study reveals the spatial–temporal evolution and driving mechanism of the NDVI of natural vegetation in the arid region of Northwest China, which can provide theoretical and data support for regional vegetation restoration and conservation.

Vegetation Responses to Climate Change and Anthropogenic Activity in China, 1982 to 2018

Climate change and human activities significantly affect vegetation growth in terrestrial ecosystems. Here, data reconstruction was performed to obtain a time series of the normalized difference vegetation index (NDVI) for China (1982−2018) based on Savitzky−Golay filtered GIMMS NDVI3g and MOD13A2 datasets. Combining surface temperature and precipitation observations from more than 2000 meteorological stations in China, Theil−Sen trend analysis, Mann−Kendall significance tests, Pearson correlation analysis, and residual trend analysis were used to quantitatively analyze the long-term trends of vegetation changes and their sources of uncertainty. Significant spatial and temporal heterogeneity was observed in vegetation changes in the study area. From 1982 to 2018, the vegetation showed a gradually increasing trend, at a rate of 0.5%·10 a−1, significantly improving (37.15%, p < 0.05) more than the significant degradation (7.46%, p < 0.05). Broadleaf (0.66) and coniferous forests (0.62) had higher NDVI, and farmland had the fastest rate of increase (1.02%/10 a−1). Temperature significantly affected the vegetation growth in spring (R > 0; p < 0.05); however, the increase in summer temperatures significantly inhibited (R < 0; p < 0.05) the growth in North China (RNDVI-tem = −0.379) and the Qinghai−Tibetan Plateau (RNDVI-tem = −0.051). Climate change has highly promoted the growth of vegetation in the plain region of the Changjiang (Yangtze) River (3.24%), Northwest China (1.07%). Affected by human activities only, 49.89% of the vegetation showed an increasing trend, of which 22.91% increased significantly (p < 0.05) and 9.97% decreased significantly (p < 0.05). Emergency mitigation actions are required in Northeast China, Xinjiang, Northwest China, and the Qinghai−Tibetan Plateau. Therefore, monitoring vegetation changes is important for ecological environment construction and promoting regional ecological protection.

Revisiting the cumulative effects of drought on global gross primary productivity based on new long-term series data (1982-2018)

Drought has broad and deep impacts on vegetation. Studies on the effects of drought on vegetation have been conducted over years. Recently, the cumulative effect of drought is recognized as another key factor affecting plant growth. However, global-scale studies on this phenomenon are still lacking. Thus, based on new satellite based gross primary productivity (GPP) and multi-temporal scale Standardized Precipitation Evapotranspiration Index data sets, we explored the cumulative effect duration (CED) of drought on global vegetation GPP and analyzed its variability across elevations and climatic zones. The main findings were as follows: (1) The cumulative effect of drought on GPP was widespread, with an average CED of 4.89 months. (2) CED of drought on GPP varied among vegetation types. Specifically, grasslands showed the longest duration, with an average value of 5.28 months, followed by shrublands (5.09 months), wetlands (5.03 months), croplands (4.85 months), savannas (4.58 months), and forestlands (4.57 months). (3) CED of drought on GPP changes with climate conditions. It decreased with the decrease of precipitation in the driest month (P_dry ) and mean annual precipitation in tropical and arid climate zones, respectively. In both temperate and cold climate zones, CED of drought on GPP was shorter in areas with dry winter than that in areas with dry summer. It increased with the decrease of mean annual air temperature in tropical climate zones and decreased with the increase of summer temperature in temperate and cold climatic zones. (4) With increasing elevation, CED of drought on GPP showed a pattern of increasing (0-3000 m), then decreasing (3000-5000 m), and increasing again (>5000 m). Our findings highlight the heterogeneity of CED of drought on GPP, owing to differences in vegetation types, long-term hydrothermal conditions, elevation, etc. The results could deepen our understanding of the effects of drought on global vegetation.

Analysis of Water Yield Changes from 1981 to 2018 Using an Improved Mann-Kendall Test

Water yield (WY) refers to the difference between precipitation and evapotranspiration (ET), which is vital for available terrestrial water. Climate change has led to significant changes in precipitation and evapotranspiration on a global scale, which will affect the global WY. Nevertheless, how terrestrial WY has changed during the past few decades and which factors dominated the WY changes are not fully understood. In this study, based on climate reanalysis and remote sensing data, the spatial and temporal patterns of terrestrial WY were revisited from 1981 to 2018 globally using an improved Mann-Kendall trend test method with a permutation test. The response patterns of WY to precipitation and ET are also investigated. The results show that the global multi-year mean WY is 297.4 mm/a. Based on the traditional Mann-Kendall trend test, terrestrial WY showed a significant (p < 0.05) increase of 5.72% of the total valid grid cells, while it showed a significant decrease of 7.68% of those. After correction using the calibration method, the significantly increasing and decreasing areas are reduced by 10.52% and 10.58% of them, respectively. After the correction, the confirmed increase and decrease in WY are mainly located in Africa, eastern North America and Siberia, and parts of Asia and Oceania, respectively. The dominant factor for increasing WY is precipitation, while that for decreasing WY was the combined effect of precipitation and evapotranspiration. The achievements of this study are beneficial for improving the understanding of WY in response to hydrological variables in the context of climate change.

Temporal and Spatial Changes in Evapotranspiration and Its Potential Driving Factors in Mongolia over the Past 20 Years

Evapotranspiration (ET) plays an important role in the study of regional long-term water cycles. The water cycle in Mongolia has been seriously affected by global warming and the intensification of human activities. A significant relationship exists between climate factors and ET. In this paper, the temporal and spatial fluctuations and stability of ET in Mongolia from 2001 to 2020 were studied by using MOD16A2 ET, MOD13A2 NDVI and the climate data of ERA5-Land. ET trends were analysed by using the Breaks for Additive Season and Trend (BFAST) software package, Theil–Sen median trend analysis, Mann–Kendall method and Hurst index. The correlations between ET and temperature (Tem), precipitation (Pre), net solar radiation (Nsr), soil moisture (Swl) and human activities were determined by partial correlation analysis and a geographic detector. In the past 20 years, ET increased significantly in 49.4% of Mongolia, and NDVI also showed a significant increasing trend. BFAST detected two mutation years. ET decreased rapidly from 2006 to 2007 and increased rapidly from 2015 to 2016. In addition to winter, the meteorological factor that had a significant positive impact on ET in the east and west was Pre, whereas the impact of Tem was more obvious in central Mongolia. In winter, Tem had a great impact on ET. In the vegetation growing season, the joint action of NDVI and Pre greatly positively contributed to ET. The geographical detector showed that the influence of annual human factors on ET was weakened by changes in NDVI and Pre. In the growing season, Tem and Nsr increased nonlinearly to ET, and other natural and human factors showed bivariate enhancement. These results will help to understand the responses of ET changes to natural factors and human activities in Mongolia and provide data support for future research on ET and the water cycle.

Intra-Annual Variability of Evapotranspiration in Response to Climate and Vegetation Change across the Poyang Lake Basin, China

Improving understanding of changes in intra-annual variability (IAV) of evapotranspiration (ET) and the underlying drivers is an essential step for modeling hydrological processes in response to global change. Previous studies paid special attention to climatic regulations of IAV of ET. However, ignoring the role of landscape characteristics (e.g., vegetation coverage) can introduce great uncertainty in the explanation of ET variance. In this work, the Poyang Lake Basin, which is a typical humid basin in China, was taken as the study area. It has experienced an obvious climate change and revegetation since the 1980s. Here, trends of IAV of ET and their responses to four climatic variables (i.e., air temperature, precipitation, downward shortwave radiation and wind speed) and vegetation coverage were explored from 1983 to 2014. The results show that IAV of ET exhibited contrary trends during the past decades. It significantly (p < 0.05) declined with a significant linear slope of −0.52 mm/year before 2000, and then slightly increased (slope = 0.06 mm/year, p > 0.05) over the basin, which was generally consistent with the IAV of temperature and radiation. The proposed variables could well capture the change in IAV of ET, while their dominators were different during the two contrasting phases mentioned above. The IAV of radiation and temperature dominated the change of the IAV of ET over 77.82% and 35.14% of the basin, respectively, before and after the turning point. Meanwhile, the rapid increase in vegetation coverage, which was associated with afforestation, significantly (p < 0.05) reduced IAV of ET over about 35% of the study area. The achievements of this study should be beneficial for a sophisticated awareness of responses of intra-annual variability of ET to climate and land cover changes at the basin scale.

Effects of Climate Change on Vegetation Growth in the Yellow River Basin from 2000 to 2019

A changing climate has been posing significant impacts on vegetation growth, especially in the Yellow River Basin (YRB) where agriculture and ecosystems are extremely vulnerable. In this study, the data for normalized difference vegetation index (NDVI) obtained from moderate-resolution imaging spectroradiometer (MODIS) sensors and climate data (precipitation and temperature) derived from the national meteorological stations were employed to examine the spatiotemporal differences in vegetation growth and its reaction to climate changes in the YRB from 2000–2019, using several sophisticated statistical methods. The results showed that both NDVI and climatic variables exhibited overall increasing trends during this period, and positive correlations at different significant levels were found between temperature/precipitation and NDVI. Furthermore, NDVI in spring had the strongest response to temperature/precipitation, and the correlation coefficient of NDVI with temperature and precipitation was 0.485 and 0.726, respectively. However, an opposite situation was detected in autumn (September to November) since NDVIs exhibited the weakest responses to temperatures/precipitation, and the NDVI’s correlation with both temperature and precipitation was 0.13. This indicated that, compared to other seasons, increasing the temperature and precipitation has the most significant effect on NDVI in spring (March to May). Except for a few places in the northern, southern, and southwestern regions of the YRB, NDVI was positively correlated with precipitation in most areas. There was an inverse relationship between NDVI and temperature in most parts of the central YRB, especially in summer (June to August) and growing season (May to September); however, there was a positive correlation in most areas of the YRB in spring. Finally, continuous attention must be given to the influence of other factors in the YRB.

Multi-scale assessment of water security under climate change in North China in the past two decades

Climate change is projected to affect the hydrological cycles in China, while the effects are expected to vary spatiotemporally. Understanding the variations in water security conditions and their sensitivity to climatic variables is crucial for assessing regional ecosystem responses to climate change. In the present study, we estimated the water yield capacity, an important indicator of water security in North China (NC), at a spatial resolution of 1 km during the last two decades based on the Budyko framework and quantified the sensitivity of water yield change to climate change among different vegetation types. The results showed that the performances of the Budyko framework were reliable both at the pixel scale and across large watersheds. The annual water yield in North China was estimated to be 7.61 ± 2.67 ∗ 10¹⁰ m³/yr, with an average mean water yield (MWY) of 49.51 ± 17.49 mm/yr. The spatial pattern of mean water yield change (MWYC) exhibited high heterogeneity; 46% of the study region was dominated by an increasing trend, while 9.84% was statistically significant (P < 0.05). Compared with temperature, the water yield capacity was more sensitive to precipitation variation. A consistent trend of variation was found in cropland between water yield and precipitation, while negative sensitivity coefficients were found in natural vegetation types. The variation in sensitivity coefficients (Swyp) in natural vegetation showed that in regions with a decrease in precipitation, the variation in water yield capacity also decreased, while in regions with an increase in precipitation from 0 to 8 mm/yr, the water yield capacity first decreased and then increased with precipitation. Our findings suggest that grass and shrubs would be more beneficial to regional water security in North China's revegetation, while afforestation would provide protection for the regional environment from extreme rainfall events.

Assessment of Canopy Conductance Responses to Vapor Pressure Deficit in Eight Hazelnut Orchards Across Continents

A remarkable increase in vapor pressure deficit (VPD) has been recorded in the last decades in relation to global warming. Higher VPD generally leads to stomatal closure and limitations to leaf carbon uptake. Assessing tree conductance responses to VPD is a key step for modeling plant performances and productivity under future environmental conditions, especially when trees are cultivated well outside their native range as for hazelnut (Corylus spp.). Our main aim is to assess the stand-level surface canopy conductance (G _surf ) responses to VPD in hazelnut across different continents to provide a proxy for potential productivity. Tree sap flow (Fd) was measured by Thermal dissipation probes (TDP) probes (six per sites) in eight hazelnut orchards in France, Italy, Georgia, Australia, and Chile during three growing seasons since 2016, together with the main meteorological parameters. We extracted diurnal Fd to estimate the canopy conductance G _surf. . In all the sites, the maximum G _surf occurred at low values of VPD (on average 0.57 kPa) showing that hazelnut promptly avoids leaf dehydration and that maximum leaf gas exchange is limited at relatively low VPD (i.e., often less than 1 kPa). The sensitivity of the conductance vs. VPD (i.e., -dG/dlnVPD) resulted much lower (average m = -0.36) compared to other tree species, with little differences among sites. We identified a range of suboptimal VPD conditions for G _surf maximization (G _surf > 80% compared to maximum) in each site, named "VPD₈₀," which multiplied by the mean G _surf might be used as a proxy for assessing the maximum gas exchange of the orchard with a specific management and site. Potential gas exchange appeared relatively constant in most of the sites except in France (much higher) and in the driest Australian site (much lower). This study assessed the sensitivity of hazelnut to VPD and proposed a simple proxy for predicting the potential gas exchange in different areas. Our results can be used for defining suitability maps based on average VPD conditions, thus facilitating correct identification of the potentially most productive sites.

A bibliometric analysis of carbon exchange in global drylands

Drylands refer to regions with an aridity index lower than 0.65, and billions of people depend on services provided by the critically important ecosystems in these areas. How ecosystem carbon exchange in global drylands (CED) occurs and how climate change affects CED are critical to the global carbon cycle. Here, we performed a comprehensive bibliometric study on the fields of annual publications, marked journals, marked institutions, marked countries, popular keywords, and their temporal evolution to understand the temporal trends of CED research over the past 30 a (1991-2020). We found that the annual scientific publications on CED research increased significantly at an average growth rate of 7.93%. Agricultural Water Management ranked first among all journals and had the most citations. The ten most productive institutions were centered on drylands in America, China, and Australia that had the largest number and most citations of publications on CED research. "Climate change" and climate-related (such as "drought", "precipitation", "temperature", and "rainfall") research were found to be the most popular study areas. Keywords were classified into five clusters, indicating the five main research focuses on CED studies: hydrological cycle, effects of climate change, carbon and water balance, productivity, and carbon-nitrogen-phosphorous coupling cycles. The temporal evolution of keywords further showed that the areas of focus on CED studies were transformed from classical pedology and agricultural research to applied ecology and then to global change ecological research over the past 30 a. In future CED studies, basic themes (such as "water", "yield", and "salinity") and motor themes (such as "climate change", "sustainability", and "remote sensing") will be the focus of research on CED. In particular, multiple integrated methods to understand climate change and ecosystem sustainability are potential new research trends and hotspots.

Climatic and landscape changes as drivers of environmental feedback that influence rainfall frequency in the United States

Previous studies have identified regions where the occurrence of rainfall significantly increases or decreases the probability for subsequent rainfall over periods that range from a few days to several weeks. These observable phenomena are termed "rainfall feedback" (RF). To better understand the land-atmosphere interactions involved in RF, the behavior of RF patterns was analyzed using data from 1849 to 2016 at ~3000 sites in the contiguous United States. We also considered changes in major land-use types and applied a geographically weighted regression model technique for analyzing the predictors of RF. This approach identified non-linear and spatially non-stationary relationships between RF, climate, land use, and land type. RF patterns in certain regions of the United States are predictable by modeling variables associated with climate, season, and land use. The model outputs also demonstrate the extent to which the effect of precipitation, temperature, and land use on RF depend on season and location. Specifically, major changes were observed for land use associated with agriculture in the western United States, which had negative and positive influences on RF in summer and winter, respectively. In contrast, developed land in the eastern United States correlated with positive RF values in summer but with negative ones in winter. We discuss how changes in climate and land use would be expected to affect land-atmosphere interactions, as well as the possible role that physical mechanisms and rain-enhanced bioaerosol emissions may play in the spatiotemporal changes observed for historical patterns of rainfall frequency in the United States.

Continuous warming shift greening towards browning in the Southeast and Northwest High Mountain Asia

Remote sensing and ground vegetation observation data show that climate warming promotes global vegetation greening, and the increase in air temperature in High Mountain Asia (HMA) is more than twice the global average. Under such a drastic warming in climate, how have the vegetation dynamics in HMA changed? In this study, we use the Normalized Difference Vegetation Index (NDVI) from 1982 to 2015 to evaluate the latest changes in vegetation dynamics in HMA and their climate-driving mechanisms. The results show that over the past 30 years, HMA has generally followed a "warm-wet" trend, with temperatures charting a continuous rise. During 1982-1998 precipitation increased (1.16 mm yr-1), but depicted to reverse since 1998 (- 2.73 mm yr-1). Meanwhile, the NDVI in HMA increased (0.012 per decade) prior to 1998, after which the trend reversed and declined (- 0.005 per decade). The main reason for the browning of HMA vegetation is the dual effects of warming and precipitation changes. As mentioned, the increase in air temperature in HMA exceeds the global average. The increase of water vapor pressure deficit caused by global warming accelerates the loss and consumption of surface water, and also aggravates the soil water deficit. That is to say, the abnormal increase of land evapotranspiration far exceeds the precipitation, and the regional water shortage increases. Climate change is the primary factor driving these vegetation and water dynamics, with the largest proportion reaching 41.9%.

Quantifying Urban Vegetation Dynamics from a Process Perspective Using Temporally Dense Landsat Imagery

Urban vegetation can be highly dynamic due to the complexity of different anthropogenic drivers. Quantifying such dynamics is crucially important as it is a prerequisite to understanding its social and ecological consequences. Previous studies have mostly focused on the urban vegetation dynamics through monotonic trends analysis in certain intervals, but not considered the process which provides important insights for urban vegetation management. Here, we developed an approach that integrates trends with dynamic analysis to measure the vegetation dynamics from the process perspective based on the time-series Landsat imagery and applied it in Shenzhen, a coastal megacity in southern China, as an example. Our results indicated that Shenzhen was turning green from 2000–2020, even though a large-scale urban expansion occurred during this period. Approximately half of the city (49.5%) showed consistent trends in greening, most of which were located in the areas within the ecological protection baseline. We also found that 35.3% of the Shenzhen city experienced at least a one-time change in urban greenness that was mostly caused by changes in land cover types (e.g., vegetation to developed land). Interestingly, 61.5% of these lands showed trends in greening in the recent change period and most of them were distributed in build-up areas. Our approach that integrates trends analysis and dynamic process reveals information that cannot be discovered by monotonic trends analysis alone, and such information can provide insights for urban vegetation planning and management.

Estimation of Evapotranspiration and Its Components across China Based on a Modified Priestley–Taylor Algorithm Using Monthly Multi-Layer Soil Moisture Data

Although soil moisture (SM) is an important constraint factor of evapotranspiration (ET), the majority of the satellite-driven ET models do not include SM observations, especially the SM at different depths, since its spatial and temporal distribution is difficult to obtain. Based on monthly three-layer SM data at a 0.25° spatial resolution determined from multi-sources, we updated the original Priestley Taylor–Jet Propulsion Laboratory (PT-JPL) algorithm to the Priestley Taylor–Soil Moisture Evapotranspiration (PT-SM ET) algorithm by incorporating SM control into soil evaporation (Es) and canopy transpiration (T). Both algorithms were evaluated using 17 eddy covariance towers across different biomes of China. The PT-SM ET model shows increased R2, NSE and reduced RMSE, Bias, with more improvements occurring in water-limited regions. SM incorporation into T enhanced ET estimates by increasing R2 and NSE by 4% and 18%, respectively, and RMSE and Bias were respectively reduced by 34% and 7 mm. Moreover, we applied the two ET algorithms to the whole of China and found larger increases in T and Es in the central, northeastern, and southern regions of China when using the PT-SM algorithm compared with the original algorithm. Additionally, the estimated mean annual ET increased from the northwest to the southeast. The SM constraint resulted in higher transpiration estimate and lower evaporation estimate. Es was greatest in the northwest arid region, interception was a large fraction in some rainforests, and T was dominant in most other regions. Further improvements in the estimation of ET components at high spatial and temporal resolution are likely to lead to a better understanding of the water movement through the soil–plant–atmosphere continuum.

A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm

Evapotranspiration (ET) is a vital part of the hydrological cycle and the water–energy balance. To explore the characteristics of five typical remote sensing evapotranspiration datasets and provide guidance for algorithm development, we used reconstructed evapotranspiration (Recon) data based on ground and GRACE satellite observations as a benchmark and evaluated five remote sensing datasets for 592 watersheds across the continental United States. The Global Land Evaporation Amsterdam Model (GLEAM) dataset (with bias and RMSE values of 23.18 mm/year and 106.10 mm/year, respectively), process-based land surface evapotranspiration/heat flux (P-LSH) dataset (bias = 22.94 mm/year and RMSE = 114.44 mm/year) and the Penman–Monteith–Leuning (PML) algorithm generated ET dataset (bias = −17.73 mm/year and RMSE = 108.97 mm/year) showed the better performance on a yearly scale, followed by the model tree ensemble (MTE) dataset (bias = 99.45 mm/year and RMSE = 141.32 mm/year) and the moderate-resolution imaging spectroradiometer (MODIS) dataset (bias = −106.71 mm/year and RMSE = 158.90 mm/year). The P-LSH dataset outperformed the other four ET datasets on a seasonal scale, especially from March to August. Both PML and MTE showed better overall accuracy and could accurately capture the spatial variability of evapotranspiration in arid regions. The P-LSH and GLEAM products were consistent with the Recon data in middle-value section. MODIS and MTE had larger bias and RMSE values on a yearly scale, whereby the MODIS and MTE datasets tended to underestimate and overestimate ET values in all the sections, respectively. In the future, the aim should be to reduce bias in the MODIS and MTE algorithms and further improve seasonality of the ET estimation in the GLEAM algorithm, while the estimation accuracy of the P-LSH and MODIS algorithms should be improved in arid regions. Our analysis suggests that combining artificial intelligence algorithms or data-driven algorithms and physical process algorithms will further improve the accuracy of ET estimation algorithms and the quality of ET datasets, as well as enhancing their capacity to be applied in different climate regions.

Spatiotemporal Modeling of Coniferous Forests Dynamics along the Southern Edge of Their Range in the Central Russian Plain

Forests with predominance of Norway spruce (Picea abies (L.) H. Karst.) and Scots pine (Pinus sylvestris L.) within the hemiboreal zone are considered as secondary communities formed under long-term human activity (logging, plowing, fires and silviculture). This study raises the question—how stable is current state of coniferous forests on the southern border of their natural distribution in the center of Eastern Europe using the example of the Moscow region (MR)? The object of the study are spruce and pine forests in different periods of Soviet and post-Soviet history within the Moscow Region (MR). The current proportion of spruce forests is 21.7%, and the proportion of pine forests is 18.5% from total forest area according to our estimates. The direction and rate of forest succession were analyzed based on current composition of populations of the main forest-forming species (spruce, pine, birch, aspen, oak, linden, and ash) based on ground-based research materials collected in 2006–2019. This allowed to develop the dynamic model (DM) of forest communities with the participation of Norway spruce and Scots pine for several decades. Assessment of the spatial distribution of coniferous communities is based on field data and spatial modeling using remote sensing data—Landsat 8 mosaic for 2020. In parallel, a retrospective model (RM) of the spatial-temporal organization of spruce and pine forests for a 30-year period was developed using two Landsat 5 mosaics. For this, nine different algorithms were tested and the best one for this task was found—random forest. Geobotanical relevés were used as a training sample combined with the 2006–2012 mosaic; the obtained spectral signatures were used for modeling based on the 1984–1990 mosaic. Thus, two multi-temporal spatial models of coniferous formations have been developed. Detailed analysis of the structure of spruce and pine forests based on field data made it possible to track trends of successional dynamics for the first time, considering the origin of communities and the ecological conditions of habitats. As a result, ideas about the viability of spruce and pine cenopopulations in different types of communities were formulated, which made possible to develop a dynamic model (DM) of changes in forest communities for future. Comparison of the areas and nature of changes in the spatial structure of coniferous formations made possible to develop the RM. Comparison of two different-time models of succession dynamics (DM and RM) makes possible to correct the main trends in the transformation of coniferous forests of natural and artificial origin under the existing regime of forestry. A set of features was identified that indicates risk factors for coniferous forests in the region. A further decrease of the spruce and pine plantations and increase of the spruce-small-leaved and deciduous formations are expected in the study area. The proportion of pine-spruce forests does not exceed 3% of the area and can be considered as the most vulnerable type of forest.

A 10 per cent increase in global land evapotranspiration from 2003 to 2019

Accurate quantification of global land evapotranspiration is necessary for understanding variability in the global water cycle, which is expected to intensify under climate change^1-3. Current global evapotranspiration products are derived from a variety of sources, including models^4,5, remote sensing^6,7 and in situ observations^8-10. However, existing approaches contain extensive uncertainties; for example, relating to model structure or the upscaling of observations to a global level¹¹. As a result, variability and trends in global evapotranspiration remain unclear¹². Here we show that global land evapotranspiration increased by 10 ± 2 per cent between 2003 and 2019, and that land precipitation is increasingly partitioned into evapotranspiration rather than runoff. Our results are based on an independent water-balance ensemble time series of global land evapotranspiration and the corresponding uncertainty distribution, using data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) satellites¹³. Variability in global land evapotranspiration is positively correlated with El Niño-Southern Oscillation. The main driver of the trend, however, is increasing land temperature. Our findings provide an observational constraint on global land evapotranspiration, and are consistent with the hypothesis that global evapotranspiration should increase in a warming climate.

Downscaling GRACE total water storage change using partial least squares regression

The Gravity Recovery And Climate Experiment (GRACE) satellite mission recorded temporal variations in the Earth’s gravity field, which are then converted to Total Water Storage Change (TWSC) fields representing an anomaly in the water mass stored in all three physical states, on and below the surface of the Earth. GRACE provided a first global observational record of water mass redistribution at spatial scales greater than 63000 km². This limits their usability in regional hydrological applications. In this study, we implement a statistical downscaling approach that assimilates 0.5° × 0.5° water storage fields from the WaterGAP hydrology model (WGHM), precipitation fields from 3 models, evapotranspiration and runoff from 2 models, with GRACE data to obtain TWSC at a 0.5° × 0.5° grid. The downscaled product exploits dominant common statistical modes between all the hydrological datasets to improve the spatial resolution of GRACE. We also provide open access to scripts that researchers can use to produce downscaled TWSC fields with input observations and models of their own choice.

Measurement(s)	Gravity
Technology Type(s)	gravity field theory • computational modeling technique
Factor Type(s)	geographic location • temporal interval
Sample Characteristic - Environment	water body
Sample Characteristic - Location	global

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13503114

Disentangling the Regional Climate Impacts of Competing Vegetation Responses to Elevated Atmospheric CO2

Biophysical vegetation responses to elevated atmospheric carbon dioxide (CO2) affect regional hydroclimate through two competing mechanisms. Higher CO2 increases leaf area (LAI), thereby increasing transpiration and water losses. Simultaneously, elevated CO2 reduces stomatal conductance and transpiration, thereby increasing rootzone soil moisture. Which mechanism dominates in the future is highly uncertain, partly because these two processes are difficult to explicitly separate within dynamic vegetation models. We address this challenge by using the GISS ModelE global climate model to conduct a novel set of idealized 2×CO2 sensitivity experiments to: evaluate the total vegetation biophysical contribution to regional climate change under high CO2; and quantify the separate contributions of enhanced LAI and reduced stomatal conductance to regional hydroclimate responses. We find that increased LAI exacerbates soil moisture deficits across the sub-tropics and more water-limited regions, but also attenuates warming by ∼0.5-1°C in the US Southwest, Central Asia, Southeast Asia, and northern South America. Reduced stomatal conductance effects contribute ∼1°C of summertime warming. For some regions, enhanced LAI and reduced stomatal conductance produce nonlinear and either competing or mutually amplifying hydroclimate responses. In northeastern Australia, these effects combine to exacerbate radiation-forced warming and contribute to year-round water limitation. Conversely, at higher latitudes these combined effects result in less warming than would otherwise be predicted due to nonlinear responses. These results highlight substantial regional variation in CO2-driven vegetation responses and the importance of improving model representations of these processes to better quantify regional hydroclimate impacts.

The Allocation of Water Resources in the Midstream of Heihe River for the "97 Water Diversion Scheme" and the "Three Red Lines"

Rapid economic and societal development increases resource consumption. Understanding how to balance the discrepancy between economic and social water use and ecological water use is an urgent problem to be solved, especially in arid areas. The Heihe River is the second-largest inland river in China, and this problem is notable. To ensure the downstream ecological water use, the “Water Distribution Plan for the Mainstream of the Heihe River” (97 Water Diversion Scheme) controls the discharge of Yingluo Gorge and Zhengyi Gorge, while the “Opinions of applying the strictest water resources control system” (Three Red Lines) restricts the water use. With the development of the economy and agriculture in the midstream, Zhengyi Gorge’s discharge cannot reach the Heihe River’s ecological water downstream. This paper is under the constraints of the “97 Water Diversion Scheme” of Heihe River and the “Three Red Lines” of the total water use control index for Zhangye County. We constructed a water resource allocation model for the midstream of Heihe River to reasonably allocate water resources in the Heihe River’s midstream and downstream. This model is divided into three parts: Establish the mathematical equation, simulate the water consumption under the different inflow conditions, and ensure each water user’s demand. The result showed that if we fail to confine total water consumption in the midstream, through the reasonable allocation of water resources, the real water use and water consumption of the middle Heihe River will be greater than the “97 Water Diversion Scheme” and the “Three Red Lines.” If we confine water consumption, they will be within the “97 Water Diversion Scheme” and the “Three Red Lines,” at the same time, they can reach the downstream of the Heihe River’s ecological water. Besides, under the premise of satisfying the economic water and ecological water downstream of the Heihe River, returning farmland to wasteland and strengthening water-saving measures will improve water efficiency and be more conducive to allocating water resources.

Attribution of Long-Term Evapotranspiration Trends in the Mekong River Basin with a Remote Sensing-Based Process Model

Using the Global Land Surface Satellite (GLASS) leaf area index (LAI), the actual evapotranspiration (ETa) and available water resources in the Mekong River Basin were estimated with the Remote Sensing-Based Vegetation Interface Processes Model (VIP-RS). The relative contributions of climate variables and vegetation greening to ETa were estimated with numerical experiments. The results show that the average ETa in the entire basin increased at a rate of 1.16 mm year−2 from 1980 to 2012 (36.7% of the area met the 95% significance level). Vegetation greening contributed 54.1% of the annual ETa trend, slightly higher than that of climate change. The contributions of air temperature, precipitation and the LAI were positive, whereas contributions of solar radiation and vapor pressure were negative. The effects of water supply and energy availability were equivalent on the variation of ETa throughout most of the basin, except the upper reach and downstream Mekong Delta. In the upper reach, climate warming played a critical role in the ETa variability, while the warming effect was offset by reduced solar radiation in the Mekong Delta (an energy-limited region). For the entire basin, the available water resources showed an increasing trend due to intensified precipitation; however, in downstream areas, additional pressure on available water resources is exerted due to cropland expansion with enhanced agricultural water consumption. The results provide scientific basis for practices of integrated catchment management and water resources allocation.

Temporal Stability of Vegetation Cover across the Loess Plateau Based on GIMMS during 1982-2013

The Loess Plateau, covering approximately 640,000 km2, has experienced the most severe soil erosion in the world. A greening tendency has been noticed since implementing the Grain to Green Program (GTGP), which may prevent further soil erosion. Therefore, understanding the underpinning basis of greening stability and persistence is important for sustainable improvement. Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) datasets for 1982-2013 were used to investigate the temporal stability and persistent time (PT) of vegetation over the Loess Plateau, utilizing the coefficient of variation (CV) and the estimation of tendencies of vegetation greening starting from the selected reference conditions. Two periods from 1982 to 1999 (as the reference period) and 2000 to 2013 were selected by considering the GTGP since 1999. The results indicate that: (1) A significant increase in vegetation cover occurred in the low NDVI area (NDVI < 0.3), with a high fluctuation from 2000 to 2013 compared with the reference period. Moreover, the fluctuation in vegetation is more related to precipitation variation since 1999. (2) Most areas recovered in the greening trend of the first period starting in 2009, occurring in 28.7% (2628 of 9148) of the total area. (3) The revegetated areas have a low PT and a high CVvi, that is, the revegetated areas need a long time to recover from disturbances. Therefore, we identify the sensitive areas with PT = 4; further management needs to be implemented for sustainable development in these areas. These results provide a method to quantify the stability and persistence of the complex interactions between vegetation greenness and environmental changes, particularly in fragile areas.

Impact of Three Gorges Reservoir Water Impoundment on Vegetation–Climate Response Relationship

In recent years, the impact of global climate change and human activities on vegetation has become increasingly prominent. Understanding vegetation change and its response to climate variables and human activities are key tasks in predicting future environmental changes, climate changes and ecosystem evolution. This paper aims to explore the impact of Three Gorges Reservoir (TGR) water impoundment on the vegetation–climate response relationship in the Three Gorges Reservoir Region (TGRR) and its surrounding region. Firstly, based on the SPOT/VEGETATION NDVI and ERA5 reanalysis datasets, the correlation between climatic factors (temperature and precipitation) and NDVI was analyzed by using partial correlation coefficient method. Secondly, nonlinear fitting method was used to fit the mapping relationship between NDVI and climatic factors. Then, the residual analysis was conducted to evaluate the impact of TGR impoundment on vegetation–climate response relationship. Finally, sensitivity index (SI), sensitivity variation index (SVI) and difference index (DI) were defined to quantify the variation of vegetation–climate response relationship before and after water impoundment. The results show that water impoundment might have some impacts on the response of vegetation–climate, which gradually reduced with increasing distance from the channel; comparing with the residual analysis method, the SI and DI index methods are more intuitive, and combining these two methods may provide new ideas for the study of the impact of human activities on vegetation.

Evaluation of Evapotranspiration Models Using Different LAI and Meteorological Forcing Data from 1982 to 2017

We evaluated the performance of three global evapotranspiration (ET) models at local, regional, and global scales using the multiple sets of leaf area index (LAI) and meteorological data from 1982 to 2017 and investigated the uncertainty in ET simulations from the model structure and forcing data. The three ET models were the Simple Terrestrial Hydrosphere model (SiTH) developed by our team, the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL), and the MODerate Resolution Imaging Spectroradiometer (MODIS) ET algorithm (MOD16). Comparing the observed with simulated monthly ET by the three models over 43 Fluxnet sites, we found that SiTH overestimated ET for forests with mean slope from 1.25 to 1.67, but it performed better than the other two models over short vegetation. MOD16 and PT-JPL models simulated well for forests but poorly in dryland biomes (slope = 0.25~0.55; R2 = 0.02~0.46). At the catchment scale, all models performed well, except for some tropical and high latitudinal catchments, with NSE values lower than 0 and RMSE and MAE values far beyond their mean values. At the global scale, SiTH highly overestimated ET in tropics, while PT-JPL slightly underestimated ET between 30°N and 60°N and MOD16 underestimated ET between 15°S and 30°S. Generally, the PT-JPL provided the better performance than SiTH and MOD16 models. This study also revealed that the estimated ET by SiTH and especially PT-JPL model were influenced by the uncertainty in meteorological data, and the estimated ET was performed better using MERRA-2 datasets for PT-JPL and using ERA5 datasets for SiTH. While the estimated ET by MOD16 were relatively sensitive to LAI data. In addition, our results suggested that the GLOBMAP and GIMMS datasets were more suitable for long-term ET simulations than the GLASS dataset.

Land Use Pattern and Vegetation Cover Dynamics in the Three Gorges Reservoir (TGR) Intervening Basin

The Three Gorges Reservoir (TGR) intervening basin is one of the most important, ecologically fragile and sensitive areas in the upper reaches of the Yangtze River. Since the completion and operation of the TGR, the change of the ecological environment in this region—with vegetation as an indicator—has been a consistent focus of attention. Based on the six phases of land use data and normalized difference vegetation index (NDVI), temperature and precipitation data from 1998 to 2017, the change and trend of land use and vegetation cover in the TGR intervening basin were analyzed quantitatively by using a transition matrix, linear regression and partial correlation analysis. The area of unchanged land use type is 56,565 km2, accounting for 97.27% of the total area of the basin. The vegetation coverage with NDVI as the indicator showed a significant upward trend, with a growth rate of 7.5%/10a. The impact of temperature on vegetation was greater than that of precipitation on vegetation. The non-linear fitting curve of NDVI to temperature and precipitation rose with the time course of TGR impoundment, although the mechanism remains to be studied further. In general, climate change, ecological restoration measures, urbanization and reservoir impoundment did not significantly change the spatial distribution pattern of land use and the climate driving mechanism of vegetation growth in the TGR intervening basin.

The Reliability of Global Remote Sensing Evapotranspiration Products over Amazon

As a key component of terrestrial water cycle, evapotranspiration (ET), specifically over the Amazon River basin, is of high scientific significance. However, due to the sparse observation network and relatively short observational period of eddy covariance data, large uncertainties remain in the spatial-temporal characteristics of ET over the Amazon. Recently, a great number of long-term global remotely sensed ET products have been developed to fill the observation gap. However, the reliabilities of these global ET products over the Amazon are unknown. In this study, we assessed the consistency of the magnitude, trend and spatial pattern of Amazon ET among five global remotely sensed ET reconstructions. The magnitudes of these products are similar but the long-term trends from 1982 to 2011 are completely divergent. Validation from the eddy covariance data and water balance method proves a better performance of a product grounded on local measurements, highlighting the importance of local measurements in the ET reconstruction. We also examined four hypotheses dealing with the response of ET to brightening, warming, greening and deforestation, which shows that in general, these ET products respond better to warming and greening than to brightening and deforestation. This large uncertainty highlights the need for future studies focusing on ET issues over the Amazon.

Impacts of climate and vegetation leaf area index changes on global terrestrial water storage from 2002 to 2016

Terrestrial water storage (TWS) has a major impact on the water, energy, and biogeochemical fluxes of the land surface. The spatiotemporal features of TWS variations, as well as the roles of climate change and human activities in TWS variations, have been of key concerns and need to be further investigated. Here, using the data products from the Gravity Recovery and Climate Experiment and the Global Land Data Assimilation System, together with some auxiliary data on climate, evapotranspiration, and vegetation leaf area index (LAI), we investigated the spatiotemporal variations of global TWS and how TWS was partitioned into its different components. We further quantified the sensitivity of TWS to changes in climate and vegetation LAI, as well as the impacts of climate and vegetation LAI changes on TWS during 2002-2016. The results showed that global TWS declined at a rate of 0.04 mm month^-1 during 2002-2016 with a spatially explicit pattern and a distinct seasonal pattern, although the trend was negligible before October 2009 and only became obvious after that. With the seasonal variations of precipitation, temperature, and LAI together, the seasonal variations of TWS were able to be explained by ≥50% in the tropical and subtropical regions. With the joint changes in LAI, temperature, and precipitation, TWS increased by ≤ ~10 mm month^-1 in the subarctic and inland temperate regions, and some tropical and subtropical regions; by contrast declined by ≤ ~10 mm month^-1 in some regions such as the Central Africa. Our study shed light on the spatiotemporal characteristic of the TWS variations and quantified the impacts of climate and vegetation LAI changes on TWS. The findings can provide not only important evidence for the effectiveness of past ecosystem management intervention programs in water retention service but also guidance for planning ecosystem management intervention programs in the near future.

Plant responses to rising vapor pressure deficit

Recent decades have been characterized by increasing temperatures worldwide, resulting in an exponential climb in vapor pressure deficit (VPD). VPD has been identified as an increasingly important driver of plant functioning in terrestrial biomes and has been established as a major contributor in recent drought-induced plant mortality independent of other drivers associated with climate change. Despite this, few studies have isolated the physiological response of plant functioning to high VPD, thus limiting our understanding and ability to predict future impacts on terrestrial ecosystems. An abundance of evidence suggests that stomatal conductance declines under high VPD and transpiration increases in most species up until a given VPD threshold, leading to a cascade of subsequent impacts including reduced photosynthesis and growth, and higher risks of carbon starvation and hydraulic failure. Incorporation of photosynthetic and hydraulic traits in 'next-generation' land-surface models has the greatest potential for improved prediction of VPD responses at the plant- and global-scale, and will yield more mechanistic simulations of plant responses to a changing climate. By providing a fully integrated framework and evaluation of the impacts of high VPD on plant function, improvements in forecasting and long-term projections of climate impacts can be made.

Materials and technical innovations in 3D printing in biomedical applications

3D printing is a rapidly growing research area, which significantly contributes to major innovations in various fields of engineering, science, and medicine. Although the scientific advancement of 3D printing technologies has enabled the development of complex geometries, there is still an increasing demand for innovative 3D printing techniques and materials to address the challenges in building speed and accuracy, surface finish, stability, and functionality. In this review, we introduce and review the recent developments in novel materials and 3D printing techniques to address the needs of the conventional 3D printing methodologies, especially in biomedical applications, such as printing speed, cell growth feasibility, and complex shape achievement. A comparative study of these materials and technologies with respect to the 3D printing parameters will be provided for selecting a suitable application-based 3D printing methodology. Discussion of the prospects of 3D printing materials and technologies will be finally covered.

Global vulnerability of soil ecosystems to erosion

CONTEXT

Soil erosion is one of the main threats driving soil degradation across the globe with important impacts on crop yields, soil biota, biogeochemical cycles, and ultimately human nutrition.

OBJECTIVES

Here, using an empirical model, we present a global and temporally explicit assessment of soil erosion risk according to recent (2001-2013) dynamics of rainfall and vegetation cover change to identify vulnerable areas for soils and soil biodiversity.

METHODS

We used an adaptation of the Universal Soil Loss Equation together with state of the art remote sensing models to create a spatially and temporally explicit global model of soil erosion and soil protection. Finally, we overlaid global maps of soil biodiversity to assess the potential vulnerability of these soil communities to soil erosion.

RESULTS

We show a consistent decline in soil erosion protection over time across terrestrial biomes, which resulted in a global increase of 11.7% in soil erosion rates. Notably, soil erosion risk systematically increased between 2006 and 2013 in relation to the baseline year (2001). Although vegetation cover is central to soil protection, this increase was mostly driven by changes in rainfall erosivity. Globally, soil erosion is expected not only to have an impact on the vulnerability of soil conditions but also on soil biodiversity with 6.4% (for soil macrofauna) and 7.6% (for soil fungi) of these vulnerable areas coinciding with regions with high soil biodiversity.

CONCLUSIONS

Our results indicate that an increasing proportion of soils are degraded globally, affecting not only livelihoods but also potentially degrading local and regional landscapes. Similarly, many degraded regions coincide with and may have impacted high levels of soil biodiversity.

An Evaluation of Vegetation Health in and around Southern African National Parks during the 21st Century (2000–2016)

Roughly 65% of the African continent is classified as savanna. Such regions are of critical importance given their high levels of biological productivity, role in the carbon cycle, structural differences, and support of large human populations. Across southern Africa there are 79 national parks within savanna landscapes. Understanding trends and factors of vegetation health in these parks is critical for proper management and sustainability. This research strives to understand factors and trends in vegetation health from 2000 to 2016 in and around the 79 national parks across southern Africa. A backward stepwise regression was used to understand the factors (e.g., precipitation, population density, and presence of transfrontier conservation areas) affecting the normalized difference vegetation index (NDVI) during the 21st century. There was a statistically significant positive (p < 0.05) relationship between mean annual precipitation and NDVI, and a significant negative relationship between population density and NDVI. To monitor vegetation trends in and around the parks, directional persistence, a seasonal NDVI time series-based trend analysis, was used. Directional persistence is the net accumulation of directional change in NDVI over time in a given period relative to a fixed benchmarked period. Parks and buffer zones across size classes were compared to examine differences in vegetation health. There was an overwhelmingly positive trend throughout. Additionally, national parks, overall, had higher amounts of positive persistence and lower amounts of negative persistence than the surrounding buffer zones. Having higher positive persistence inside of parks indicates that they are functioning favorably relative to the buffer zones in terms of vegetation resilience. This is an important finding for park managers and conservation overall in Southern Africa.

Response of Vegetation to Changes in Temperature and Precipitation at a Semi-Arid Area of Northern China Based on Multi-Statistical Methods

Hydrothermal and climatic conditions determine vegetation productivity and its dynamic changes. However, the legacy effect and the causal relationships between these climatic variables and vegetation growth are still unclear, especially in the dry regions. Based on multi-statistical methods, including bivariate correlation analysis and composite Granger causality tests, we investigated the correlation, causality, and lag length between temperature/precipitation and the vegetation growth (Normalized Difference Vegetation Index, NDVI) in three typical sub-watersheds in the Luanhe River Basin, China. The results show that: (1) Precipitation and temperature are the Granger causes of NDVI variation in the study catchment; (2) temperature and precipitation are not strictly positively correlated with NDVI during growing seasons along with the whole sequence, and excessive warmth and precipitation inhibits vegetative growth; (3) the lag length of vegetation growth in response to temperature/precipitation was shorter in agriculture areas (~2 months) than the forest-dominant area, which have indicated 3–4 months lag length; and (4) anthropogenic disturbance did not result in notable negative effects on vegetation growth at the Luanhe River Basin. Our study further suggests that use of these multi-statistical methods could be a valuable approach for comprehensively understanding the correlation between vegetation growth and climatic variations. We have also provided an avenue to bridge the gaps between stationary and non-stationary sequence, as well as to eliminate pseudo regression problems. These findings provide critical information for developing cost-efficient policies and land use management applications for forest conservation in arid and semi-arid area.

Quantifying the Impacts of Climate Change and Vegetation Variation on Actual Evapotranspiration Based on the Budyko Hypothesis in North and South Panjiang Basin, China

Actual evapotranspiration (Ea) plays a key role in the global water and energy cycles. The accurate quantification of the impacts of different factors on Ea change can help us better understand the driving mechanisms of Ea in a changing environment. Climate change and vegetation variations are well known as two main factors that have significant impacts on Ea change. Our study used three differential Budyko-type equations to quantify the contributions of climate change and vegetation variations to Ea change. First, in order to establish the relationship between the parameter n, which usually presents the land surface characteristics in the Budyko-type equations, with basic climatic variables and the Normalized Difference Vegetation Index (NDVI), the stepwise linear regression method has been used. Then, elasticity and contribution analyses were performed to quantify the contributions of different numbers of climatic factors and NDVI to Ea change. The North and South Panjiang basin in China was selected to investigate the efficiency of the modified Budyko-type equations and quantify the impacts of climate change and vegetation variations on Ea change. The empirical formal of the parameter n established in this study can be used to simulate the parameter n and Ea for the study area. The results of the elasticity and contribution analyses suggest that climate change contributed (whose average contribution is 149.6%) more to Ea change than vegetation variation (whose average contribution is −49.4%). Precipitation, NDVI and the maximum temperature are the major drivers of Ea change, while the minimum temperature and wind speed contribute the least to Ea change.

A Healthy Park Needs Healthy Vegetation: The Story of Gorongosa National Park in the 21st Century

Understanding trends or changes in biomass and biodiversity around conservation areas in Africa is important and has economic and societal impacts on the surrounding communities. Gorongosa National Park, Mozambique was established under unique conditions due to its complex history. In this study, we used a time-series of Normalized Difference Vegetation Index (NDVI) to explore seasonal trends in biomass between 2000 and 2016. In addition, vegetation directional persistence was created. This product is derived from the seasonal NDVI time series-based analysis and represents the accumulation of directional change in NDVI relative to a fixed benchmark (2000–2004). Trends in precipitation from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) was explored from 2000–2016. Different vegetation covers are also considered across various landscapes, including a comparison between the Lower Gorongosa (savanna), Mount Gorongosa (rainforest), and surrounding buffer zones. Important findings include a decline in precipitation over the time of study, which most likely drives the observed decrease in NDVI. In terms of vegetation persistence, Lower Gorongosa had stronger positive trends than the buffer zone, and Mount Gorongosa had higher negative persistence overall. Directional persistence also varied by vegetation type. These are valuable findings for park managers and conservationists across the world.

Multi-Temporal Variabilities of Evapotranspiration Rates and Their Associations with Climate Change and Vegetation Greening in the Gan River Basin, China

Understanding the spatial-temporal dynamics of evapotranspiration in relation to climate change and human activities is crucial for the sustainability of water resources and ecosystem security, especially in regions strongly influenced by human impact. In this study, a process-based evapotranspiration (ET) model in conjunction with the Global Land Surface Satellite (GLASS) LAI dataset was used to characterize the spatial-temporal pattern of evapotranspiration from 1982 to 2016 over the Gan River basin (GRB), the largest sub-basin of the Poyang Lake catchment, China. The results showed that the actual annual ET (ETa) weakly increased with an annual trend of 0.88 mm year−2 from 1982 to 2016 over the GRB, along with a slight decline in annual potential ET (ETp). On an ecosystem scale; however, only the evergreen broadleaved forest and cropland presented a positive ETa trend, while the rest of the ecosystems demonstrated negative trends of ETa. Both correlation analysis and sensitivity analysis revealed a close relationship between ETa inter-annual variability and energy availability. Attribution analysis illustrated that contributions of climate change and vegetation greening on the ETa trend were −0.48 mm year−2 and 1.36 mm year−2, respectively. Climate change had a negative impact on the ETa trend over the GRB. However, the negative effects have been offset by the positive effects of vegetation greening, which mainly resulted from the large-scale revegetation in forestland and agricultural practices in cropland. It is concluded that large-scale afforestation and agricultural management were the main drivers of the long-term evolution of water consumption over the GRB. This study can improve our understanding of the interactive effects of climate change and human activities on the long-term evolution of water cycles.

Evaluating the Impacts of Climate Change and Vegetation Restoration on the Hydrological Cycle over the Loess Plateau, China

In recent decades, both observation and simulation data have demonstrated an obvious decrease in runoff and soil moisture, with increasing evapotranspiration, over the Loess Plateau. In this study, we employed a Variable Infiltration Capacity model coupled with scenario simulation to explore the impact of change in climate and land cover on four hydrological variables (HVs) over the Loess Plateau, i.e., evapotranspiration (ET), runoff (Runoff), shallow soil moisture (SM1), and deep soil moisture (SM2). Results showed precipitation, rather than temperature, had the closest relationship with the four HVs, with r ranging from 0.76 to 0.97 (p < 0.01), and this was therefore presumed to be the dominant climate-based driving factor in the variation of hydrological regimes. Vegetation conversion, from cropland and grassland to woodland, significantly reduced runoff and increased soil moisture consumption, to sustain an increased ET, and, assuming that the reduction of SM2 is entirely evaporated, we can attribute 71.28% ± 18.64%, 65.89% ± 24.14% of the ET increase to the water loss of SM2 in the two conversion modes, respectively. The variation in HVs, induced by land cover change, were higher than the expected climate change with respect to SM1, while different factors were selected to determine HVs variation in six catchments, due to differences in the mode and intensity of vegetation conversion, and the degree of climate change. Our findings are critical for understanding and quantifying the impact of climate change and vegetation conversions, and provide a further basis for the design of water resources and land-use management strategies with respect to climate change, especially in the water-limited Loess Plateau.

Spatiotemporal Variability of Actual Evapotranspiration and the Dominant Climatic Factors in the Pearl River Basin, China

Evapotranspiration is a vital component of the land surface process, thus, a more accurate estimate of evapotranspiration is of great significance to agricultural production, research on climate change, and other activities. In order to explore the spatiotemporal variation of evapotranspiration under global climate change in the Pearl River Basin (PRB), in China, this study conducted a simulation of actual evapotranspiration (ETa) during 1960–2014 based on the variable infiltration capacity (VIC) model with a high spatial resolution of 0.05°. The nonparametric Mann–Kendall (M–K) test and partial correlation analysis were used to examine the trends of ETa. The dominant climatic factors impacting on ETa were also examined. The results reveal that the annual ETa across the whole basin exhibited a slight but not significant increasing trend during the 1960–2014 period, whereas a significant decreasing trend was found during the 1960–1992 period. At the seasonal scale, the ETa showed a significant upward trend in summer and a significant downward trend in autumn. At the spatial scale, the ETa generally showed a decreasing, but not significant, trend in the middle and upper stream of the PRB, while in the downstream areas, especially in the Pearl River Delta and Dongjiang River Basin, it exhibited a significant increasing trend. The variation of the ETa was mainly associated with sunshine hours and average air pressure. The negative trend of the ETa in the PRB before 1992 may be due to the significant decrease in sunshine hours, while the increasing trend of the ETa after 1992 may be due to the recovery of sunshine hours and the significant decrease of air pressure. Additionally, we found that the “paradox” phenomenon detected by ETa mainly existed in the middle-upper area of the PRB during the period of 1960–1992.

The FLUXCOM ensemble of global land-atmosphere energy fluxes

Although a key driver of Earth’s climate system, global land-atmosphere energy fluxes are poorly constrained. Here we use machine learning to merge energy flux measurements from FLUXNET eddy covariance towers with remote sensing and meteorological data to estimate global gridded net radiation, latent and sensible heat and their uncertainties. The resulting FLUXCOM database comprises 147 products in two setups: (1) 0.0833° resolution using MODIS remote sensing data (RS) and (2) 0.5° resolution using remote sensing and meteorological data (RS + METEO). Within each setup we use a full factorial design across machine learning methods, forcing datasets and energy balance closure corrections. For RS and RS + METEO setups respectively, we estimate 2001–2013 global (±1 s.d.) net radiation as 75.49 ± 1.39 W m⁻² and 77.52 ± 2.43 W m⁻², sensible heat as 32.39 ± 4.17 W m⁻² and 35.58 ± 4.75 W m⁻², and latent heat flux as 39.14 ± 6.60 W m⁻² and 39.49 ± 4.51 W m⁻² (as evapotranspiration, 75.6 ± 9.8 × 10³ km³ yr⁻¹ and 76 ± 6.8 × 10³ km³ yr⁻¹). FLUXCOM products are suitable to quantify global land-atmosphere interactions and benchmark land surface model simulations.

Design Type(s)	modeling and simulation objective • factorial design
Measurement Type(s)	energy
Technology Type(s)	machine learning
Factor Type(s)	machine learning • Energy Balance • temporal_interval • geographic location
Sample Characteristic(s)	Earth (Planet) • land • vegetation layer • climate system

Machine-accessible metadata file describing the reported data (ISA-Tab format)