Explore the vast range of titles available.

Process-based models of tree-ring width are used both for reconstructing past climates and for projecting changes in growth due to climate change. Since soil moisture observations are unavailable at appropriate spatial and temporal scales, these models generally rely on simple water budgets driven in part by temperature-based potential evapotranspiration (PET) estimates, but the choice of PET model could have large effects on simulated soil moisture, moisture stress, and radial growth. Here, I use four different PET models to drive the VS-Lite model and evaluate the extent to which they differ in both their ability to replicate observed growth variability and their simulated responses to projected 21st century warming. Across more than 1200 tree-ring width chronologies in the conterminous United States, there were no significant differences among the four PET models in their ability to replicate observed radial growth, but the models differed in their responses to 21st century warming. The temperature-driven empirical PET models (Thornthwaite and Hargreaves) simulated much larger warming-induced increases in PET and decreases in soil moisture than the more physically realistic PET models (Priestley–Taylor and Penman–Monteith). In cooler and more mesic regions with relatively minimal moisture constraints to growth, the models simulated similarly small reductions in growth with increased warming. However, in dry regions, the Thornthwaite- and Hargreaves-driven VS-Lite models simulated an increase in moisture stress roughly double that of the Priestley–Taylor and Penman–Monteith models, which also translated to larger simulated declines in radial growth under warming. While the lack of difference in the models’ ability to replicate observed radial growth variability is an encouraging sign for some applications (e.g. attributing changes in growth to specific climatic drivers), the large differences in model responses to warming suggest that caution is needed when applying the temperature-driven PET models to climatic conditions with large trends in temperature.

As forest demographics are altered by the global decline of old trees and reforestation efforts, younger trees are expected to have an increasingly important influence on carbon sequestration and forest ecosystem functioning. However, the relative resilience of these younger trees to climate change stressors is poorly understood. Here we examine age-dependent drought sensitivity of over 20,000 individual trees across five continents and show that younger trees in the upper canopy layer have larger growth reductions during drought. Angiosperms show greater age differences than gymnosperms, and age-dependent sensitivity is more pronounced in humid climates compared with more arid regions. However, younger canopy-dominant trees also recover more quickly from drought. The future combination of increased drought events and an increased proportion of younger canopy-dominant trees suggests a larger adverse impact on carbon stocks in the short term, while the higher resilience of younger canopy-dominant trees could positively affect carbon stocks over time.The authors analyse the impacts of drought on tree growth for various species of various ages to assess the influences of forest demographic shift on future drought responses. The increasing proportion of young trees showing greater growth reduction to drought raises concern on future carbon storage.

Lidar‐derived forest structural diversity (FSD) metrics—including measures of forest canopy height, vegetation arrangement, canopy cover (CC), structural complexity and leaf area and density—are increasingly used to describe forest structural characteristics and can be used to infer many ecosystem functions. Despite broad adoption, the importance of spatial resolution (grain and extent) over which these structural metrics are calculated remains largely unconsidered. Often researchers will quantify FSD at the spatial grain size of the process of interest without considering the scale dependency or statistical behaviour of the FSD metric employed. We investigated the appropriate scale of inference for eight lidar‐derived spatial metrics—CC, canopy relief ratio, foliar height diversity, leaf area index, mean and median canopy height, mean outer canopy height, and rugosity (RT)‐‐representing five FSD categories—canopy arrangement, CC, canopy height, leaf area and density, and canopy complexity. Optimal scale was determined using the representative elementary area (REA) concept whereby the REA is the smallest grain size representative of the extent. Structural metrics were calculated at increasing canopy spatial grain (from 5 to 1000 m) from aerial lidar data collected at nine different forested ecosystems including sub‐boreal, broadleaf temperate, needleleaf temperate, dry tropical, woodland and savanna systems, all sites are part of the National Ecological Observatory Network within the conterminous United States. To identify the REA of each FSD metric, we used changepoint analysis via segmented or piecewise regression which identifies significant changepoints for both the magnitude and variance of each metric. We find that using a spatial grain size between 25 and 75 m sufficiently captures the REA of CC, canopy arrangement, canopy leaf area and canopy complexity metrics across multiple forest types and a grain size of 30–150 m captures the REA of canopy height metrics. However, differences were evident among forest types with higher REA necessary to characterize CC in evergreen needleleaf forests, and canopy height in deciduous broadleaved forests. These findings indicate the appropriate range of spatial grain sizes from which inferences can be drawn from this set of FSD metrics, informing the use of lidar‐derived structural metrics for research and management applications.

Rising atmospheric CO₂ and warming spring temperatures increase vegetation growth and the terrestrial carbon sink. However, drought, heat stress, phenology, and resource limitations may stabilize or limit theses projected increases. We investigate the balance between these amplifying and stabilizing ecological factors by asking whether enhanced early-season growth leads to continued late-season growth. Using the Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) dataset, we identify three seasonal growth patterns based on early- and peak-season positive LAI anomalies: (1) amplification, where late-season LAI anomalies exceed earlier ones; (2) weak stabilization, where late-season anomalies remain similar or slightly lower; and (3) strong stabilization, where late-season anomalies become negative. Weak and strong stabilization events dominate across 67% and 26% of Northern Hemisphere ecosystems above 30°N, respectively. The absence of any trend in amplifying or stabilizing events suggests stabilizing factors seasonally offset CO₂ and temperature-induced spring greening. Terrestrial biosphere models underestimate strong stabilization and overestimate amplification events. This inconsistency arises from the models’ underestimation late-season LAI sensitivity to precipitation in water-limited regions; overlook negative legacy effects of early enhanced LAI on late-season soil moisture via evapotranspiration losses in energy-limited regions. Our findings suggest water/heat stress and resource limitations limit greening and the land carbon sink. Rising CO₂ and warming enhance vegetation greening, but drought, heat stress, and resource limits constrain this trend. Here, the authors show that within a year, increased early- and peak- season greenness often leads to late-season declines, highlighting water/heat stress limits on greening and the carbon sink.

Paciorek and Wehner raise important questions around our use of the Mann‐Kendall nonparametric trend test on smoothed data for analyzing long‐term hydrometeorological trends in Zhang et al. (2021, https://doi.org/10.1029/2020gl092293). We thank them for initiating this important conversation and their gracious cooperation in exploring the issues addressed in their comment. In this reply we confirm the inflation of significant p‐values by our choice to smooth, illustrate the relatively minor impacts on the main conclusions of our paper, and add our voices to those of Paciorek and Wehner in highlighting the lack of methodology for hypothesis testing across multiple stations that have spatial structure (i.e., testing for regionally consistent trends). Plain Language Summary Our colleagues Drs. Paciorek and Wehner have raised concerns about our paper (Zhang et al., 2021, https://doi.org/10.1029/2020gl092293), which showed widespread increases in the duration of drought events over the last five decades in the western United States. They point out that our decision to smooth the data using a moving average inflated the number of weather stations at which the trends toward longer droughts were deemed significant by a statistical test. We agree with them on this point, and we have recomputed all our results using unsmoothed data to determine the impacts. We find that for most stations and regions, trend magnitudes remained largely unchanged, with many stations nearby one another often suggesting similar trends. Finally, we agree with Paciorek and Wehner that there is a lack of statistical methods to test such coherent regional patterns, and we caution that over‐reliance on p‐values limits the power of regional data to identify important climate trends. Key Points We agree that smoothing to 5‐year moving windows introduced serial correlation into time series of annual statistics of daily rainfall data, inflating the number of weather stations individually showing significant trends (p < 0.05) with the Mann‐Kendall test Recomputation with unsmoothed values produced substantially the same dry intervals trend magnitude and direction at most stations individually and had only minimal impacts on dry interval trends computed for National Ecological Observatory Network domains using the Regional Kendall test No perfect statistical approach leverages the capacity of coherent regional patterns among spatially correlated weather stations, and an over‐reliance on p‐values as a binary (significant vs. insignificant) determinant of trends limits the power of regional data

Short-term forecasts of vegetation activity are currently not well constrained due largely to our lack of understanding of coupled climate-vegetation dynamics mediated by complex interactions between atmospheric teleconnection patterns. Using ecoregion-scale estimates of North American vegetation activity inferred from remote sensing (1982-2015), we examined seasonal and spatial relationships between land surface phenology and the atmospheric components of five teleconnection patterns over the tropical Pacific, north Pacific, and north Atlantic. Using a set of regression experiments, we also tested for interactions among these teleconnection patterns and assessed predictability of vegetation activity solely based on knowledge of atmospheric teleconnection indices. Autumn-to-winter composites of the Southern Oscillation Index (SOI) were strongly correlated with start of growing season timing, especially in the Pacific Northwest. The two leading modes of north Pacific variability (the Pacific-North American, PNA, and West Pacific patterns) were significantly correlated with start of growing season timing across much of southern Canada and the upper Great Lakes. Regression models based on these Pacific teleconnections were skillful predictors of spring phenology across an east-west swath of temperate and boreal North America, between 40°N-60°N. While the North Atlantic Oscillation (NAO) was not strongly correlated with start of growing season timing on its own, we found compelling evidence of widespread NAO-SOI and NAO-PNA interaction effects. These results suggest that knowledge of atmospheric conditions over the Pacific and Atlantic Oceans increases the predictability of North American spring phenology. A more robust consideration of the complexity of the atmospheric circulation system, including interactions across multiple ocean basins, is an important step towards accurate forecasts of vegetation activity.

Drylands exert a strong influence over global interannual variability in carbon and water cycling due to their substantial heterogeneity over space and time. This variability in ecosystem fluxes presents challenges for understanding their primary drivers. Here we quantify the sensitivity of dryland gross primary productivity and evapotranspiration to various hydrometeorological drivers by synthesizing eddy covariance data, remote sensing products and land surface model output across the western United States. We find that gross primary productivity and evapotranspiration derived from eddy covariance are most sensitive to soil moisture fluctuations, with lesser sensitivity to vapour pressure deficit and little to no sensitivity to air temperature or light. We find that remote sensing data accurately capture the sensitivity of eddy covariance fluxes to soil moisture but largely over-predict sensitivity to atmospheric drivers. In contrast, land surface models underestimate sensitivity of gross primary productivity to soil moisture fluctuations by approximately 45%. Amid debates about the role of increasing vapour pressure deficit in a changing climate, we conclude that soil moisture is the primary driver of US dryland carbon–water fluxes. It is thus imperative to both improve model representation of soil water limitation and more realistically represent how atmospheric drivers affect dryland vegetation in remotely sensed flux products. Soil moisture is the primary driver of variability in dryland carbon and water cycling, according to a synthesis of eddy covariance, remote sensing and land surface model data from the western United States.

The Colorado River Basin (CRB) includes seven states and provides municipal and industrial water to millions of people across all major southwestern cities both inside and outside the basin. Agriculture is the largest part of the CRB economy and crop production depends on irrigation, which accounts for about 74% of the total water demand cross the region. A better understanding of irrigation water demands is critically needed as temperatures continue to rise and drought intensifies, potentially leading to water shortages across the region. Yet, past research on irrigation dynamics has generally utilized relatively low spatiotemporal resolution datasets and has often overlooked the relationship between climate and management decisions such as land fallowing, i.e., the practice of leaving cultivated land idle for a growing season. Here, we produced annual estimates of fallow and active cropland extent at high spatial resolution (30 m) from 2001 to 2017 by applying the fallow-land algorithm based on neighborhood and temporal anomalies (FANTA). We specifically focused on diverse CRB agricultural regions: the lower Colorado River planning (LCRP) area and the Pinal and Phoenix active management areas (PPAMA). Utilizing ground observations collected in 2014 and 2017, we found an overall classification accuracy of 88.9% and 87.2% for LCRP and PPAMA, respectively. We then quantified how factors such as climate, district water rights, and market value influenced: (1) annual fallow and active cropland extent and (2) annual cropland productivity, approximated by integrated growing season NDVI (iNDVI). We found that for the LCRP, a region of winter cropping and senior (i.e., preferential) water rights, active cropland productivity was positively correlated with cool-season average vapor pressure deficit (R = 0.72; p < 0.01). By contrast, for the PPAMA, a region of summer cropping and junior water rights, annual fallow and active cropland extent was positively correlated with cool-season aridity (precipitation/potential evapotranspiration) (R = 0.46; p < 0.05), and active cropland productivity was positively correlated with warm-season aridity (precipitation/potential evapotranspiration) (R = 0.42; p < 0.01). We also found that PPAMA cropland productivity was more sensitive to aridity when crop prices were low, potentially due to the influence of market value on management decisions. Our analysis highlights how biophysical (e.g., temperature and precipitation) and socioeconomic (e.g., water rights and crop market value) factors interact to explain seasonal patterns of cropland extent, water use and productivity. These findings indicate that increasing aridity across the region may result in reduced cropland productivity and increased land fallowing for some regions, particularly those with junior water rights.

Recent climate change and vegetation greening have important implications for global terrestrial hydrological cycles and other ecosystem functions, raising concerns about the watershed water supply capacity for large water diversion projects. To address this emerging concern, we built a hybrid model based on the Coupled Carbon and Water (CCW) and Water Supply Stress Index (WaSSI) models and conducted a case study on the upper Han River basin (UHRB) in Central China that serves as the water source area to the middle route of the South-to-North Water Diversion Project (SNWDP). Significant vegetation greening occurred in the UHRB during 2001–2018, largely driven by the widespread afforestation in the region, with the normalized difference vegetation index increasing at a rate of 0.5±0.1 % yr−1 (p<0.05) but with no significant trends in climate during the same period (albeit with large interannual variability). Annual water yield greatly decreased, and vegetation greening alone induced a significant decrease in water yield of 3.2±1.0 mm yr−1 (p<0.05). Vegetation greening could potentially reduce the annual water supply by 7.3 km3 on average, accounting for 77 % of the intended annual water diversion volume of the SNWDP. Although vegetation greening can bring enormous ecosystem goods and services (e.g., carbon sequestration and water quality improvement), it could aggravate the severity of hydrological drought. Our analysis indicated that vegetation greening in the UHRB reduced about a quarter of water yield on average during drought periods. Given the future warming and drying climate is likely to continue to raise evaporative demand and exert stress on water availability, the potential water yield decline induced by vegetation greening revealed by our study needs to be taken into account in the water resources management over the UHRB while reaping other benefits of forest protection and ecological restoration.

Terrestrial ecosystem primary productivity is a key indicator of ecosystem functions, including, but not limited to, carbon storage, provision of food and fiber, and sustaining biodiversity. However, measuring terrestrial ecosystem primary productivity in the field is extremely laborious and expensive. Optical remote sensing has revolutionized our ability to map terrestrial ecosystem primary productivity over large areas ranging from regions to the entire globe in a repeated, cost-efficient manner. This progress report reviews the theory and practice of mapping terrestrial primary productivity using optical remotely sensed data. Terrestrial ecosystem primary productivity is generally estimated with optical remote sensing via one of the following approaches: (1) empirical estimation from spectral vegetation indices; (2) models that are based on light-use-efficiency (LUE) theory; (3) models that are not based on LUE theory, but the biophysical processes of plant photosynthesis. Among these three, models based on LUE are the primary approach because there is a solid physical basis for the linkage between fraction of absorbed photosynthetically active radiation (fAPAR) and remotely sensed spectral signatures of vegetation. There has been much inconsistency in the literature with regard to the appropriate value for LUE. This issue should be resolved with the ongoing efforts aimed at direct mapping of LUE from remote sensing. At the same time, major efforts have been dedicated to mapping vegetation canopy biochemical composition via imaging spectroscopy for use in process-based models to estimating primary productivity. In so doing, optical remote sensing will continue to play a vital role in global carbon cycle science research.