Changes In Leaf Area Index Research Articles

Attributing trend in naturalized streamflow to temporally explicit vegetation change and climate variation in the Yellow River basin of China

Abstract. The naturalized streamflow, i.e., streamflow without water management effects, in the Yellow River basin (YRB) has been significantly decreased at a rate of -3.71×108 m3 yr−1 during 1982–2018, although annual precipitation experienced an insignificantly positive trend. Explicit detection and attribution of naturalized streamflow are critical to manage limited water resources for the sustainable development of ecosystems and socio-economic systems. The effects from temporally explicit changes of climate variables and underlying surfaces on the streamflow trend were assessed using the variable infiltration capacity (VIC) model prescribed with continuously dynamic leaf area index (LAI) and land cover. The results show a sharp increase of the LAI trend and land use change as a conversion of cropland into forest grass in the basin. The decrease in naturalized streamflow can primarily be attributed to the vegetation changes including an interannual LAI increase and intra-annual LAI temporal pattern change, which account for the streamflow reduction of 1.99×108 and 0.45×108 m3 yr−1, respectively. The impacts of the LAI change are largest at the subregion of Longmen–Huayuankou where the LAI increasing trend is high and land use change is substantial. Attribution based on simulations with multiyear average LAI changes obviously underestimates the impacts of the interannual LAI change and intra-annual LAI temporal change on the natural streamflow trend. Overall, the effect of climate variation on streamflow is slight because the positive effect from precipitation and wind speed changes was offset by the negative effect from increasing temperature. Although climate variation is decisive for streamflow change, this study suggests that change in underlying surfaces has imposed a substantial trend on naturalized streamflow. This study improves the understanding of the spatiotemporal patterns and the underlying mechanisms of natural streamflow reduction across the YRB between 1982 and 2018.

Effects of satellite LAI data on modelling land surface temperature and related energy budget in the Noah-MP land surface model

• Satellite LAI scheme can provide more accurate vegetation dynamic information. • Satellite LAI scheme improved LST simulation in Noah-MP land surface model. • Mechanism of LAI on LST simulation was revealed by energy budget analysis in model. Leaf area index (LAI) is essential for modelling the land surface temperature (LST) and related energy budget in the land surface models (LSMs). Due to the lack of a parameterization scheme based on satellite LAI data, it is unclear whether satellite LAI data can improve the performance of Noah with multi-parameterization (Noah-MP) LSM to model LST in China land data assimilation system (CLDAS). Here, by using satellite LAI data reconstructed by the advanced deep learning method, we established a new vegetation scheme based on satellite data for the Noah-MP LSM in the CLDAS. We quantitatively evaluated simulation effects of different vegetation schemes including the parameter table scheme (TAB), the dynamic vegetation scheme (DVEG), and the satellite LAI data scheme (OBS), on modelling LAI, LST and their related energy budget in China from 2016 to 2018. We found that except for the cropland, the LAI of the vegetation in growing season observed by satellite was lower than that simulated by the Noah-MP LSM. Validations based on the automatic weather stations in the vegetation-covered area showed that compared with the TAB and DVEG scheme, the OBS scheme improved the negative bias and high unbiased root mean square error (ubRMSE) of simulated LST. Compared with the TAB and DVEG scheme, the optimal improvements of the OBS scheme for annual mean bias and annual mean ubRMSE were 0.32 °C and 0.17 °C, respectively. We further revealed the mechanism of LAI on LST simulation in the Noah-MP LSM by decomposed temperature metric method. With the adoption of the new satellite LAI-based scheme, the absorbed radiation (SW), sensible heat flux (H), latent heat flux (LE), and ground heat flux (G) simulated by the Noah-MP LSM tend to change in different directions, leading to an overall increase in the net energy absorbed by the surface, which is consistent with the LST changes. The LAI changes of dense vegetation affect the LST simulation mainly through the competition of the absorbed shortwave radiation and turbulent heat fluxes. Due to the influence of exposed soil, the LAI changes of sparse vegetation mainly affect the LST simulation mainly through the interaction of the absorbed shortwave radiation and ground heat flux. Our study highlighted that the application of satellite LAI data has the potential to improve simulations of multiple land surface processes in the Noah-MP LSM.

Irrigation and warming drive the decreases in surface albedo over High Mountain Asia

Human and climate induced land surface changes resulting from irrigation, snow cover decreases, and greening impact the surface albedo over High Mountain Asia (HMA). Here we use a partial information decomposition approach and remote sensing data to quantify the effects of the changes in leaf area index, soil moisture, and snow cover on the surface albedo in HMA, home to over a billion people, from 2003 to 2020. The study establishes strong evidence of anthropogenic agricultural water use over irrigated lands (e.g., Ganges–Brahmaputra) which causes the highest surface albedo decreases (≤ 1%/year). Greening and decreased snow cover from warming also drive changes in visible and near-infrared surface albedo in different areas of HMA. The significant role of irrigation and greening in influencing albedo suggests the potential of a positive feedback cycle where albedo decreases lead to increased evaporative demand and increased stress on water resources.

Assessment of vegetation change on the Mongolian Plateau over three decades using different remote sensing products.

As a major component of temperate steppes in the Eurasian continent, the Mongolian Plateau (MP) plays a pivotal role in the East Asian and global carbon cycles. This paper describes the use of five remote sensing indices derived from satellite data to characterize vegetation cover on MP, namely: gross primary production (GPP), net primary production (NPP), normalized difference vegetation index (NDVI), leaf area index (LAI) and fractional vegetation cover (FVC). It is found that GPP, NPP, and NDVI exhibit increasing trends, whereas LAI and FVC present decreasing trends on the MP since 1982. The different indices highlight discrepancies in the spatial pattern of vegetation growth, with the greatest increase in the southeast of MP. Only 3.4% of the total land area of MP exhibited consistent trends in the indices (0.1% degradation and 3.3% growth, P<0.01), with the synchronous change of both LAI and NPP exhibiting higher consistency than that of raw NDVI and NPP. Understanding of the characteristics and status of vegetation change on the MP has far-reaching implications for its ecological protection management, and climate change mitigation.

An Automated Hemispherical Scanner for Monitoring the Leaf Area Index of Forest Canopies

The leaf area index (LAI) is an important structural parameter of plant canopies used in terrestrial biosphere models. Optical methods are commonly used for measuring LAI due to their non-destructive nature, convenience, and rapidity. In the present study, a novel instrument, named the Automated Hemispherical Scanner (AHS), was developed to measure plant area index (PAI) for monitoring daily changes in LAI in forest ecosystems. In the AHS, an optical sensor driven by a pair of servomotors is used to observe hemispherical light transmission continuously at adjustable intervals, and a blue filter is used to reduce the multiple scattering effect of light on the measured transmission. A set of algorithms was developed to screen the direct radiation transmitted through the canopy and to compute the transmissions from the diffuse radiation at seven zenith (0–60) and seven azimuth (0–150) angles for calculating PAI. Field experiments were conducted to verify the reliability of the AHS in three forests of Northeast China against an existing instrument named the LAI-2200 Plant Canopy Analyzer. The PAI values obtained using the AHS agreed well (R2 = 0.927, root mean square error = 0.41) with those from the LAI-2200. Since both instruments use the same gap fraction theory for calculating the PAI from diffuse radiation transmissions obtained from multiple angles, the agreement of these two instruments means that the AHS can reliably measure the transmittance of diffuse radiation and the theory has been implemented correctly. Compared with LAI-2200, the AHS has the advantage of automated and continuous measurements, and therefore it is suitable for monitoring variations in PAI over extended periods, such as the whole growing season. Compared with widely used digital photographic techniques, the AHS also avoids the requirement of determining a suitable photographic exposure, which is often problematic in the field with variable sky conditions. With these advantages, the AHS could be deployed in forest growth monitoring networks.

Estimating hydrological consequences of vegetation greening

• LAI increased significantly up to 72.2% of the whole YRB area in 1981–2018. • Vegetation greening decreased SW and Q, but increased ET in selected catchments. • Sensitivities of the changes in Q and ET to the changes in LAI increased as LAI grows. • Sensitivity of ET and Q to LAI changes increased as the climate became drier. Large-scale afforestation program has alleviated environmental problems to some extent in China. However, the response of hydrological processes to vegetation greening at different catchments remains unclear. This study identified the impact of vegetation changes on runoff (Q), evapotranspiration (ET), and soil water storage (SW), as well as their relationships and sensitivities based on a coupled SWAT-PML model for 23 catchments from 2000 to 2018 in the Yellow River Basin (YRB). Results show that leaf area index (LAI) was significantly ( p < 0.05) increased in 72.2% of the YRB area. Vegetation greening decreased SW and Q, but increased ET in all catchments. The effects of vegetation greening on Q and ET were contrasting with axisymmetric fluctuation distribution. The changes in SW, ET, and Q were strongly correlated with the changes in LAI. Sensitivity of the changes in ET and Q to LAI changes increased when the climate becomes drier. Our study suggests that the vegetation greening followed by the afforestation policy implementation has caused gradually increasing impacts on Q and ET. However, such effects are spatially heterogeneous due to different degrees of increase in LAI and aridity conditions. Given the water crisis problem in the YRB, afforestation activity should be taken into consideration of the increasing water demand and the negative effects of vegetation greening on water resources in the future.

Separating climate change and vegetation dynamics contributions to soil drying over drylands of China: A case study in the West Liao River Basin

AbstractSignificant soil drying has been reported in the semi‐arid Three‐North Region of China since the 1980s, yet its dominant drivers and the potential effects on dryland ecosystems are still poorly documented. In this work, the improved Vegetation Interface Processes (VIP) model was employed to characterize the spatio‐temporal pattern of the root‐zone soil moisture (RZSM) from 1982 to 2015 over the West Liao River Basin (WLRB). Results showed that RZSM decreased in most parts of the WLRB along with precipitation decline and vegetation greening. Sensitivities of RZSM to climate and vegetation dynamics exhibited obvious spatial and seasonal heterogeneity. Averaged over the whole basin, sensitivity of RZSM to precipitation (SP) is the highest. Significant seasonal differences were found in the sensitivity of RZSM to leaf area index (LAI) (SLAI). SLAI values in spring and autumn were generally negative throughout the WLRB, but prominent positive SLAI values were observed in the north‐west forest in summer, owing to the increased drier‐and‐browner associations in summer induced by the earlier spring greenness. Attribution analysis showed that relative contributions of precipitation, air temperature and LAI changes to the growing‐season RZSM (RZSMGS) drying trend were 69.0%, 17.5% and 13.5 %, respectively. It can be inferred that precipitation decrease dominated the soil drying over the WLRB, while air warming and vegetation greening accelerated this drying. In a future warming scenario without substantial increase in water use efficiency (WUE), dryland ecosystems might suffer more severe biomass losses caused by complex soil–vegetation–atmosphere interactions. This study can improve our understanding of the terrestrial–atmospheric feedbacks and their possible effects on dryland ecosystems.

Evaluation of satellite Leaf Area Index in California vineyards for improving water use estimation

Remote sensing estimation of evapotranspiration (ET) directly quantifies plant water consumption and provides essential information for irrigation scheduling, which is a pressing need for California vineyards as extreme droughts become more frequent. Many ET models take satellite-derived Leaf Area Index (LAI) as a major input, but how uncertainties of LAI estimations propagate to ET and the partitioning between evaporation and transpiration is poorly understood. Here we assessed six satellite-based LAI estimation approaches using Landsat and Sentinel-2 images against ground measurements from four vineyards in California and evaluated ET sensitivity to LAI in the thermal-based two-source energy balance (TSEB) model. We found that radiative transfer modeling-based approaches predicted low to medium LAI well, but they significantly underestimated high LAI in highly clumped vine canopies (RMSE ~ 0.97 to 1.27). Cubist regression models trained with ground LAI measurements from all vineyards achieved high accuracy (RMSE ~ 0.3 to 0.48), but these empirical models did not generalize well between sites. Red edge bands and the related vegetation index (VI) from the Sentinel-2 satellite contain complementary information of LAI to VIs based on near-infrared and red bands. TSEB ET was more sensitive to positive LAI biases than negative ones. Positive LAI errors of 50% resulted in up to 50% changes in ET, while negative biases of 50% in LAI caused less than 10% deviations in ET. However, even when ET changes were minimal, negative LAI errors of 50% led to up to a 40% reduction in modeled transpiration, as soil evaporation and plant transpiration responded to LAI change divergently. These findings call for careful consideration of satellite LAI uncertainties for ET modeling, especially for the partitioning of water loss between vine and soil or cover crop for effective vineyard irrigation management.

Spatiotemporal changes and driving factors of vegetation in 14 different climatic regions in the global from 1981 to 2018.

Climate change affects the change of vegetation, and the analysis of vegetation change and its drivers in different globe climate zones is important for ecological conservation, energy balances, and climate change in different global climate zones. Based on the vegetation leaf area index (LAI) and climate factor datasets, this paper uses an integrated empirical model decomposition, sensitivity rate, contribution rate, and geographic detector analysis method to study the vegetation drivers and their changes in 14 different climate zones around the globe from 1981 to 2018. The results showed that (1) Vegetation changes were sensitive to precipitation and evapotranspiration in arid climate zones and to temperature and soil temperature in cold climate zones. In the tundra climate zone, the sensitivity of vegetation change to temperature was higher than that to precipitation and evapotranspiration. (2) Soil moisture has the highest contribution to vegetation change, and the areas with absolute contribution rates over 60% account for 50.26% of the total area of global vegetation cover. The areas with high contributions of temperature and soil temperature to the LAI are mainly distributed in the Northern Hemisphere, which indicates that temperature has a high contribution to vegetation change in low-temperature environments. (3) The areas with significant increasing trends for the global vegetation LAIs accounted for approximately 15.32% of the total global vegetation cover (slope ≥ 0.01), which are mainly located in equatorial savannahs with dry winters, warm temperate climates with dry winters, and warm temperate climates with fully humid climatic zones. (4) The LAIs were dominated by medium-high fluctuations and sustainable increasing changes, which accounted for 61.27% and 69.34% of the total global vegetation cover area, respectively. (5) Globally, the driving factors influencing LAI changes are specific humidity, temperature, soil temperature, evapotranspiration, precipitation, and soil moisture in descending order, with the largest interaction effect of specific humidity and soil moisture on LAI changes. This research provides a scientific basis for vegetation change monitoring, driving mechanisms, and ecological protection in different climate regions around the globe.

Analysis and prediction of global vegetation dynamics: past variations and future perspectives

Spatiotemporal dynamic vegetation changes affect global climate change, energy balances and the hydrological cycle. Predicting these dynamics over a long time series is important for the study and analysis of global environmental change. Based on leaf area index (LAI), climate, and radiation flux data of past and future scenarios, this study looked at historical dynamic changes in global vegetation LAI, and proposed a coupled multiple linear regression and improved gray model (CMLRIGM) to predict future global LAI. The results show that CMLRIGM predictions are more accurate than results predicted by the multiple linear regression (MLR) model or the improved gray model (IGM) alone. This coupled model can effectively resolve the problem posed by the underestimation of annual average of global vegetation LAI predicted by MLR and the overestimate predicted by IGM. From 1981 to 2018, the annual average of LAI in most areas covered by global vegetation (71.4%) showed an increase with a growth rate of 0.0028 a–1; of this area, significant increases occurred in 34.42% of the total area. From 2016 to 2060, the CMLRIGM model has predicted that the annual average global vegetation LAI will increase, accounting for approximately 68.5% of the global vegetation coverage, with a growth rate of 0.004 a−1. The growth rate will increase in the future scenario, and it may be related to the driving factors of the high emission scenario used in this study. This research may provide a basis for simulating spatiotemporal dynamic changes in global vegetation conditions over a long time series.

Experimental throughfall reduction has little effect on shoot and needle developmental patterns or leaf area dynamics in a young longleaf pine (Pinus palustris Mill.) plantation

Reduced precipitation and, consequently, low soil moisture are known to limit tree growth and function by affecting shoot and foliage development, as well as canopy-scale leaf area dynamics and litterfall. Longleaf pine (Pinus palustris Mill.) is considered one of the most drought-resistant forest species in the southeastern U.S. and could serve as a pathway to increase drought resistance of forests in the region. Still, reduced precipitation and low soil moisture impacts on longleaf canopy phenology and growth are not well understood. Over three years (2017–2019), we determined the effects of a 40% throughfall reduction (TR40) (relative to ambient throughfall treatment, TR0) on shoot and foliage phenology and growth in a young (12–14 yr. old) longleaf pine plantation. Each year, we repeatedly measured shoot and needle lengths on primary and secondary branch axes of multiple trees in each treatment plot. We fit growth curves for each tree and branch axis to estimate shoot and needle growth rate, growth start and cessation dates, growth duration (growth start - growth cessation), and final shoot and needle lengths. At the plot level, we documented temporal patterns of leaf area index (LAI) and litterfall to determine whether branch-scale phenological and growth responses to reduced water availability corresponded with temporal changes in LAI and litterfall. We observed significant and consistent differences in shoot and needle elongation patterns between primary and secondary branches. Timing of needle development varied among years and was generally later each successive year. However, shoot elongation patterns were relatively consistent across years. Although soil moisture was lower under throughfall reduction, shoot and needle growth patterns were not affected. LAI and litterfall patterns were also not affected by throughfall reduction. Our results indicate that reductions in rainfall amount (without changes in rainfall frequency or timing) may have little impact on shoot and needle phenology, canopy development, and litter production in established longleaf plantations.

Investigating the response of leaf area index to droughts in southern African vegetation using observations and model simulations

Abstract. In many regions of the world, frequent and continual dry spells are exacerbating drought conditions, which have severe impacts on vegetation biomes. Vegetation in southern Africa is among the most affected by drought. Here, we assessed the spatiotemporal characteristics of meteorological drought in southern Africa using the standardized precipitation evapotranspiration index (SPEI) over a 30-year period (1982–2011). The severity and the effects of droughts on vegetation productiveness were examined at different drought timescales (1- to 24-month timescales). In this study, we characterized vegetation using the leaf area index (LAI) after evaluating its relationship with the normalized difference vegetation index (NDVI). Correlating the LAI with the SPEI, we found that the LAI responds strongly (r=0.6) to drought over the central and southeastern parts of the region, with weaker impacts (r&lt;0.4) over parts of Madagascar, Angola, and the western parts of South Africa. Furthermore, the latitudinal distribution of LAI responses to drought indicates a similar temporal pattern but different magnitudes across timescales. The results of the study also showed that the seasonal response across different southern African biomes varies in magnitude and occurs mostly at shorter to intermediate timescales. The semi-desert biome strongly correlates (r=0.95) to drought as characterized by the SPEI at a 6-month timescale in the MAM (March–May; summer) season, while the tropical forest biome shows the weakest response (r=0.35) at a 6-month timescale in the DJF (December–February; hot and rainy) season. In addition, we found that the spatial pattern of change of LAI and SPEI are mostly similar during extremely dry and wet years, with the highest anomaly observed in the dry year of 1991, and we found different temporal variability in global and regional responses across different biomes. We also examined how well an ensemble of state-of-the-art dynamic global vegetation models (DGVMs) simulate the LAI and its response to drought. The spatial and seasonal response of the LAI to drought is mostly overestimated in the DGVM multimodel ensemble compared to the response calculated for the observation-based data. The correlation coefficient values for the multimodel ensemble are as high as 0.76 (annual) over South Africa and 0.98 in the MAM season over the temperate grassland biome. Furthermore, the DGVM model ensemble shows positive biases (3 months or longer) in the simulation of spatial distribution of drought timescales and overestimates the seasonal distribution timescales. The results of this study highlight the areas to target for further development of DGVMs and can be used to improve the models' capability in simulating the drought–vegetation relationship.

Wildfire controls on land surface properties in mixed conifer and ponderosa pine forests of Sierra Nevada and Klamath mountains, Western US

This study examines the post-fire biogeophysical and biochemical dynamics after several high-severity wildfires that occurred in mixed conifer and ponderosa pine forest types in the Sierra Nevada and Klamath Mountains regions between 1986 and 2017. We found a consistent pattern of reduced leaf area index (LAI) in the first year after fire, followed by gradual recovery over the subsequent 25 years. Recovery rate varied between forest types. For example, average summer LAI for 16-25 years post-fire was 88% of the pre-fire average for mixed conifers in the Sierra Nevada, 64% for ponderosa pine in the Sierra Nevada, and 83% for mixed conifers in Klamath Mountains (63, 35, and 64% in winter, respectively). The slower recovery of LAI in ponderosa pine could be due to poor species diversity and drier climate. Summer and winter albedo increased progressively until 12 years post-fire in Sierra Nevada, while it continued to increase until 25 years post-fire in Klamath Mountains. Ponderosa pine had the highest summer (0.148 ± 0.001) and winter (0.5 ± 0.0033) albedos. Post-fire changes in evapotranspiration (ET) and gross primary productivity (GPP) were consistent with the changes in LAI. Both summer and winter ET and GPP returned to pre-fire levels by 25 years after fire in mixed conifers of both regions, while the ET and GPP did not recover to pre-fire levels in ponderosa pine. Wildfires increased the land surface temperature (LST) immediately after fire in summer. This effect was significantly higher in mixed conifers of the Sierra Nevada (11 ± 0.03 °C) compared to Klamath Mountains (7 ± 0.01 °C). Our results suggest that reduced ET, consistent with less leaf area and its associated reduced evaporative cooling is the main factor controlling the immediate post-fire warming effect of wildfires in these regions. The findings reported here can be used to understand ecological responses to wildfire in these and nearby ecoregions as they represent mean historical behavior across multiple wildfire events.

Mathematical Models of Leaf Area Index and Yield for Grapevines Grown in the Turpan Area, Xinjiang, China

The Leaf Area Index (LAI) strongly influences crop biomass production and yields. The variation characteristic of LAI and the development of crop growth models can provide a theoretical basis for predicting crops’ water consumption, fruit quality and yields. This paper analyzes the relationship between measurements of aboveground grape biomass and trends in LAI and dry biomass production in grapes grown in the Turpan area. The LAI changes in grapes were estimated using the modified logistic model, the modified Gaussian model, the log-normal model, the cubic polynomial model, and the Gaussian model. Universal models of LAI were established in which the applied irrigation quota was applied to calculate the maximum LAI. The relationship between the irrigation quota and biomass production, yields, and the harvest index was investigated. The developed models could accurately predict the LAI of grapevines grown in an extremely arid area. However, the Gaussian and cubic polynomial models produced less accurate results than the other models tested. The Michaelis–Menten model analyzed the relationship between biomass and LAI, providing a numerical method for predicting dynamic changes in grapevine LAI. Moreover, the crop biomass increased linearly with the irrigation quota for quotas between 6375 and 13,200 m3/hm. This made it possible to describe the grape yield and harvest index with a quadratic polynomial function, which increases during the early stages of the growing season and then decreases. The analyses of the relationship between yield and harvest index provide important theoretical insights that can be used to improve water use efficiency in grape cultivation and to identify optimal irrigation quotas.

Effect of Leaf Area Index on Green Facade Thermal Performance in Buildings

Green facades applied on a building’s envelope allow achieving the building’s passive thermal control and energy consumption reduction. These are complex systems and many site- and plant-specific parameters influence their energy behavior. The leaf area index (LAI) is a relevant plant characteristic to consider. Solar shading and latent heat loss of plant evapotranspiration are the two main cooling mechanisms. The aim of this study was to assess the cooling effect provided by an evergreen south oriented green facade in summer in a Mediterranean area and to investigate what happens when LAI changes. Experimental data were used to calculate the cooling effect provided by the facade. Simulations with different LAI values were performed to determine the related cooling effect. The canopy solar transmissivity decreased by 54% for every LAI unit increase. LAI significantly influenced the green facade cooling performance. As LAI increased, solar shading and latent heat increased; this was relevant until an upper limit value of 6. An exponential equation to calculate the mean extinction coefficient (km), and a polynomial relationship, with very good agreement, were proposed to calculate shading and latent heat as function of LAI. The findings of this research can effectively contribute to fill still existing gaps on green facades’ energy performance and to the energy simulation of buildings equipped with them.

Climate change impacts on crop water productivity and net groundwater use under a double-cropping system with intensive irrigation in the Haihe River Basin, China

Facing the high possibility of future severe water resources scarcity, the Haihe River Basin in the North China Plain is an important region for climate change studies. This study employed an improved SWAT model with a Management Allowed Depletion (MAD) auto-irrigation method to assess the impacts of future climate change on water cycle and crop growth of irrigated winter wheat and summer maize in the Haihe River Basin, China. A total of 27 Global Climate Models (GCMs) from the latest Coupled Model Intercomparison Project 6 (CMIP6) and two Shared Socioeconomic Pathways (SSP2–4.5 and SSP5–8.5) were used to drive the SWAT-MAD model to simulate crop growth and water balance components for two future periods of 2041–2070 (2050s) and 2071–2100 (2080s). Data from almost all GCMs showed an increase in annual mean maximum air temperature, minimum air temperature, total precipitation, and solar radiation across the Haihe River Basin. Winter wheat yields increased by 15.6% (SSP245) and 16.3% (SSP585) by 2050s, and by 17.1% (SSP245) and 10.0% (SSP585) by 2080s, respectively. Summer maize yields increased by 8.9% (SSP245) and 9.6% (SSP585) by 2050s, and by 12.6% (SSP245) and 6.1% (SSP585) by 2080s, respectively. The change of leaf area index (LAI) indicated an advance in phenology of crops, which might be related to the increase in temperature. In the four future climate scenarios, precipitation, percolation, and surface runoff increased by 19.16–57.25 mm, 7.26–22.18 mm, and 1.45–4.87 mm during winter wheat growing season, 125.41–205.24 mm, 51.03–96.37 mm, and 33.97–68.69 mm during summer maize growing season. However, the irrigation amounts decreased by 18.82–83.74 mm and 35.71–50.92 mm during wheat and maize growing seasons, respectively. The annual Net Groundwater Use (NGU) decreased with time and the crop water productivity (CWP) increased in the future, which have positive effects on the sustainable development in this double-cropping system with intensive irrigation management.

Does growing atmospheric CO2 explain increasing carbon sink in a boreal coniferous forest?

The terrestrial net ecosystem productivity (NEP) has increased during the past three decades, but the mechanisms responsible are still unclear. We analyzed 17 years (2001–2017) of eddy‐covariance measurements of NEP, evapotranspiration (ET) and light and water use efficiency from a boreal coniferous forest in Southern Finland for trends and inter‐annual variability (IAV). The forest was a mean annual carbon sink (252 [±42] gC m‐2 a‐1), and NEP increased at rate +6.4–7.0 gC m‐2 a‐1 (or ca. +2.5% a‐1) during the period. This was attributed to the increasing gross‐primary productivity GPP and occurred without detectable change in ET. The start of annual carbon uptake period was advanced by 0.7 d a‐1, and increase in GPP and NEP outside the main growing season contributed ca. one‐third and one‐fourth of the annual trend, respectively. Meteorological factors were responsible for the IAV of fluxes but did not explain the long‐term trends. The growing season GPP trend was strongest in ample light during the peak growing season. Using a multi‐layer ecosystem model, we showed that direct CO2 fertilization effect diminishes when moving from leaf to ecosystem, and only 30–40% of the observed ecosystem GPP increase could be attributed to CO2. The increasing trend in leaf‐area index (LAI), stimulated by forest thinning in 2002, was the main driver of the enhanced GPP and NEP of the mid‐rotation managed forest. It also compensated for the decrease of mean leaf stomatal conductance with increasing CO2 and LAI, explaining the apparent proportionality between observed GPP and CO2 trends. The results emphasize that attributing trends to their physical and physiological drivers is challenged by strong IAV, and uncertainty of LAI and species composition changes due to the dynamic flux footprint. The results enlighten the underlying mechanisms responsible for the increasing terrestrial carbon uptake in the boreal zone.

Contrasting responses of woody and grassland ecosystems to increased CO2 as water supply varies.

Experiments show that elevated atmospheric CO2 (eCO2) often enhances plant photosynthesis and productivity, yet this effect varies substantially and may be climate sensitive. Understanding if, where and how water supply regulates CO2 enhancement is critical for projecting terrestrial responses to increasing atmospheric CO2 and climate change. Here, using data from 14 long-term ecosystem-scale CO2 experiments, we show that the eCO2 enhancement of annual aboveground net primary productivity is sensitive to annual precipitation and that this sensitivity differs between woody and grassland ecosystems. During wetter years, CO2 enhancement increases in woody ecosystems but declines in grass-dominated systems. Consistent with this difference, woody ecosystems can increase leaf area index in wetter years more effectively under eCO2 than can grassland ecosystems. Overall, and across different precipitation regimes, woody systems had markedly stronger CO2 enhancement (24%) than grasslands (13%). We developed an empirical relationship to quantify aboveground net primary productivity enhancement on the basis of changes in leaf area index, providing a new approach for evaluating eCO2 impacts on the productivity of terrestrial ecosystems.

Regional variation in seagrass complexity drives blue crab Callinectes sapidus mortality and growth across the northern Gulf of Mexico

Seagrass meadows provide greater predator refuge and resource availability than unvegetated habitats and generally improve the survival and growth rates of associated animals. Few studies, however, have examined how these relationships might vary at a region-wide spatial scale. The blue crabCallinectes sapidusis a commercially important species that uses turtlegrassThalassia testudinumhabitats, but it is unclear if blue crab use of seagrass habitats varies across the northern Gulf of Mexico (GOM), USA. We conducted synoptic predation and growth experiments at 6 turtlegrass-dominated estuaries in the northern GOM to evaluate the role of seagrass structural complexity on juvenile (9.7-44 mm carapace width) blue crab mortality due to predation and growth. Relationships of blue crab predation and growth rate with seagrass shoot density, canopy height, temperature, and seagrass leaf area index (LAI) were evaluated using linear and generalized linear mixed effects models. Mortality rates due to predation (50 ± 9.6% [mean ± SD] crabs eaten, n = 286) showed negative relationships with LAI across the northern GOM. Conversely, mean crab growth rate (0.513 ± 0.317 mm d-1) varied across the northern GOM but was independent of seagrass shoot density or canopy height. We found that: (1) turtlegrass-dominated beds with a greater seagrass LAI provided more effective cover for juvenile blue crabs across the northern GOM, (2) blue crabs across multiple GOM estuaries responded similarly to changes in LAI, and (3) blue crab growth varied across the northern GOM and was likely dependent on regional factors unrelated to structural complexity. This study illustrates the value of conducting synchronous field-based experiments across broad spatial scales to identify regional patterns and the influence of regional versus local drivers.

Estimating NDVI and LAI as a precursor for monitoring air pollution along the BBN industrial corridor of Himachal Pradesh, India

The research conducted is to establish the role of Normalized Difference Vegetative Index (NDVI) and Leaf Area Index (LAI) as a tool for monitoring air pollution through resolution multispectral images for Baddi Barotiwala Nalagarh (BBN Industrial Corridor). The air pollution is estimated in terms of Respirable Suspended Particulate Matter (RSPM). The LAI was estimated through high-resolution Multi-spectral imageries for the years (2001 to 2021). LAI analysis reveals that the majority of the study is covered under moderate to very high plant canopy density region. Analysis of the 2016 image revealed vegetation indices ranging between −0.9 to 2. Decrease of the forest vegetation quality was observed between 2001 and 2021 also causes the increase in the absorption of air pollutants while at the same time. In the year 2009 the RSPM of the areas has been registered at an average of 76 ug/m3. Between 2010 and 2011 the average air quality registered at 106.67, 105.50, 83.33. The results indicate the air quality has been worsening through the years (2009 and 2020) RSPM of these regions has doubled increasing the toxicity of the pollutants. Analysis of the decadal change in NDVI and LAI shows the reduction of the annual forest coverage of the BBN industrial corridor coupled with the decrease in the air quality of the region. The study shows a clear relation between decreasing vegetation cover and the air quality of the region.