Changes In Leaf Area Index Research Articles

Changes in Leaf Area Index, Leaf Mass Ratio, Net Assimilation Rate, Relative Growth Rate and Specific Leaf Area Two Cultivars of Fig (Ficus Carica L.) Treated Under Different Concentrations of Brassinolide

There is limited information and insures for next studies on exogenous brassinolide application fig cultivar of Masui Dauphine (MD) and Improved Brown Turkey (IBT). Therefore, the research objective was to determine the impact of concentrations of exogenous brassinolide application on Leaf Area Index (LAI), Leaf Mass Ratio (LMR), Net Assimilation Rate (NAR), Relative Growth Rate (RGR), and Specific Leaf Area (SLA) of fig. Fig resources were propagated using stem cuttings and were transferred into 3:2:1 mixed soil (topsoil:organic matters:sand) media. Two cultivars of fig treated with brassinolide (control, 50, 100 and 200 ml/L) were repeated four times and constructed by Split Plot Randomized Complete Block Design. Changes in the parameter of LAI, LMR, NAR, RGR, and SLA were investigated monthly. There was a significant effect of fig variety alone on LAI, SLA and LMR but it wasn’t affected on NAR and RGR. SLA and LMR were influenced by brassinolide but not on LAI, NAR and RGR. Cultivar IBT showed higher responses to these parameters than cultivar MD after receiving brassinolide treatment. The interaction between brassinolide and variety was significant on LAI, SLA and LMR of fig except in the parameters of NAR and RGR.

A framework to quantify impacts of elevated CO2 concentration, global warming and leaf area changes on seasonal variations of water resources on a river basin scale

With concerns about future catastrophic disasters, elevated CO2 concentration (eCO2), global warming and vegetation changes have received much attention globally. In recent years, studies have suggested controlling climate change should be based on regional impacts. However, regional quantitative assessments of respective and combination influences of CO2, temperature and vegetation variations on water resources remain limited. To address this issue, a comprehensive framework is developed. This framework integrates a dynamic vegetation simulation approach, a biosphere hydrological model and a variance-based sensitivity analysis approach which enables identification of predominant influential factors including their combinations. A medium-sized river basin in northeast China is used to illustrate the application based on different CO2 concentration and temperature rise assumptions. Validation using MODIS products shows the proposed approach can simulate Leaf Area Index (LAI) changes well. Results show sum of the influences of LAI and temperature variations on transpiration are more than 1.6 times higher than physiological effects of eCO2 in growing seasons, which implies more water could be transpired than conserved with eCO2, global warming and LAI changes. Results also show runoff increases with eCO2 and decreases with increasing temperatures, but LAI changes play a dominate role in runoff decrease in hot summer. Therefore, considering LAI changes is of utmost importance in projections of future water resources, and severity of future water shortage could be underestimated if LAI changes are neglected. This research is useful in gaining insights into the complicated impacts of eCO2, global warming and LAI changes on regional water security and sustainability.

Modeling water and heat transfer in soil-plant-atmosphere continuum applied to maize growth under plastic film mulching

Based on our previous work modeling crop growth (CropSPAC) and water and heat transfer in the soil-plant-atmosphere continuum (SPAC), the model was improved by considering the effect of plastic film mulching applied to field-grown maize in North-west China. In CropSPAC, a single layer canopy model and a multi-layer soil model were adopted to simulate the energy partition between the canopy and water and heat transfer in the soil, respectively. The maize growth module included photosynthesis, growth stage calculation, biomass accumulation, and participation. The CropSPAC model coupled the maize growth module and SPAC water and heat transfer module through leaf area index (LAI), plant height and soil moisture condition in the root zone. The LAI and plant height were calculated from the maize growth module and used as input for the SPAC water and heat transfer module, and the SPAC module output for soil water stress conditions used as an input for maize growth module. We used <i>r</i>_<i>s</i>, the representation of evaporation resistance, instead of the commonly used evaporation resistance <i>r</i>_<i>s</i>0 to reflect the change of latent heat flux of soil evaporation under film mulching as well as the induced change in energy partition. The model was tested in a maize field at Yingke irrigation area in North-west China. Results showed reasonable agreement between the simulations and measurements of LAI, above-ground biomass and soil water content. Compared with the original model, the modified model was more reliable for maize growth simulation under film mulching and showed better accuracy for the LAI (with the coefficient of determination <i>R</i>² = 0.92, the root mean square of error RMSE= 1.23, and the Nush-Suttclife efficiency E_ns = 0.87), the above-ground biomass (with <i>R</i>² = 0.96, RMSE= 7.17 t·ha⁻¹ and E_ns = 0.95) and the soil water content in 0–1 m soil layer (with <i>R</i>² = 0.78, RMSE= 49.44 mm and E_ns = 0.26). Scenarios were considered to simulate the influence of future climate change and film mulching on crop growth, soil water and heat conditions, and crop yield. The simulations indicated that the change of LAI, leaf biomass and yield are negatively correlated with temperature change, but the growing degree-days, evaporation, soil water content and soil temperature are positively correlated with temperature change. With an increase in the ratio of film mulching area, the evaporation will decrease, while the impact of film mulching on crop transpiration is not significant. In general, film mulching is effective in saving water, preserving soil moisture, increasing soil surface temperature, shortening the potential growth period, and increasing the potential yield of maize.

Utjecaj gustoće sklopa na lisnu površinu, interval od prašenja do svilanja, prinos i komponente prinosa inbred linija kukuruza i križanaca srodnih linija

Yield of maize inbreds is often a limiting factor in hybrid seed production. Thus, the main aim was to evaluate the effect of plant density on leaf area index (LAI), anthesis to silking interval (ASI), grain yield and yield components of inbreds and their sister-lines at two locations over two growing seasons. Inbreds Bc 279 and Bc 64, and their corresponding sister-lines Bc 279026-22 and Bc 822-1064, respectively, were grown at 60,000 and 86,000 plants*ha-1. When compared to inbreds, grain yields improved for sister-lines by an average of 1188 kg*ha-1 (21%) for Bc 279026-22 and 4816 kg*ha-1 (141%) for Bc 822-1064 primarily due to more kernel rows per ear and kernels per ear row. This greater kernel number per ear and per plant was associated with larger LAI for Bc 279026-22, while Bc 822-1064 out-yielded inbred Bc 64 despite of similar LAI and ASI. Inbred Bc 64 and its sister-line Bc 822-1064 increased LAI similarly at higher plant density, but the former showed limited yield increment because of a large increase in barren plants. In contrast, inbred Bc 279 and its sister-line Bc 279026-22 responded similarly for grain yields and LAI across plant densities. A growing season × location × plant density interaction indicated various grain yield responses to plant densities under different environmental conditions. Higher plant density significantly improved grain yields for sister-lines and inbred Bc 279, but had limited advantage for inbred Bc 64; these genotype-specific responses could not be always explained by changes in LAI and ASI.

A Coupled Model for Simulating Water and Heat Transfer in Soil-Plant-Atmosphere Continuum with Crop Growth

Crop growth is influenced by the energy partition and water–heat transfer in the soil and canopy, while crop growth affects the land surface energy distribution and soil water-heat dynamics. In order to simulate the above processes and their interactions, a new model, named CropSPAC, was developed considering both the growth of winter wheat and the water–heat transfer in Soil-Plant-Atmosphere Continuum (SPAC). In CropSPAC, the crop module depicts the dynamic changes of leaf area index (LAI), crop height, and the root distribution and outputs them to the SPAC module, while the latter outputs soil moisture conditions for the crop module. CropSPAC was calibrated and validated by field experiment of winter wheat in Yongledian, Beijing, with five levels of irrigation treatments, namely W0 (0 mm), W1 (60 mm), W2 (110 mm), W3 (170 mm), and W4 (230 mm). Results show that CropSPAC could predict the soil water and temperature distribution, and winter wheat growth with acceptable accuracy. For example, for the 0–1 m soil water storage, the R2 for W0, W1, W2, W3, and W4 is 0.90, 0.88, 0.90, 0.91, and 0.79, and the root mean square error (RMSE) is 17.24 mm, 27.65 mm, 20.47 mm, 22.35 mm, and 12.88 mm, respectively. For soil temperature along the soil profile, the R2 ranges between 0.96 and 0.98, and the RMSE between 1.22 °C and 1.94 °C. For LAI, the R2 varied from 0.76 to 0.96, and the RMSE from 0.52 to 0.67. We further compared the simulation results by CropSPAC and its two detached modules, i.e., crop and the SPAC modules. Results demonstrate that the coupled model could better reflect the interactions between crop growth and soil moisture condition, more suitable to be used under deficit irrigation conditions.

Influence of automatic exposure on the estimation of seasonal variations in leaf area index measured by digital hemispherical photograph.

Automatic exposure is one of the important error sources during measurement of leaf area index (LAI) by digital hemispherical photography (DHP). This study was conducted in a mixed broadleaved-Korean pine (Pinus koraiensis) forest, a secondary birch (Betula platyphylla) forest, a Korean pine plantation and a Dahurian larch (Larix gmelinii) plantation in the Xiaoxing'an Mountains. LAI was measured using DHP and LAI-2200 plant canopy analyzer in the middle of June to September. We compared LAI values measured through these two methods, and then tested whether the forest type and study period had a significant influence on the correlations between the measured values of those two methods. We constructed empirical models for correcting the errors caused by automatic exposure for LAI values measured through DHP at different study periods in different forest types. The results showed that LAI from DHP was underestimated by 20%-49% rela-tive to that from LAI-2200 in four study periods of the four forest types. Forest type had no significant effect on the construction of empirical models between these two measuring methods of LAI, whereas study period showed significant effects. Two classified empirical models (A and B) were constructed, which were suitable for correcting the LAI from DHP in June and September, July and August in four forest types, respectively. After being corrected by the classified empirical models, LAI from DHP of the four forest types increased by 45%-79%, and the measurement accuracy could be improved to 83%-94%. Classified empirical models between LAI from DHP and LAI-2200 could effectively correct the influence of automatic exposure on DHP and greatly improve its mea-surement accuracy, and provide a technical support for rapid and effective measurement of seasonal changes of LAI in different forest types.

Typhoon-induced changes in rainfall interception loss from a tropical multi-species ‘reforest’

Typhoon Haiyan made landfall in the central Philippines on 8 November 2013 as one of the strongest tropical cyclones ever. It also affected a 23-year-old multi-species ‘reforest’ at Manobo near Tacloban City on Leyte Island. As part of a larger hydrological investigation of the impacts of reforestation on streamflow response in the Tacloban area, gross rainfall (P), throughfall (TF; 24 roving collectors) and stemflow (SF; 12 trees) were monitored at Manobo between June 2013 and May 2014. Leaf Area Index (LAI) above each TF collector was measured regularly. Total rainfall interception losses (I) were determined using Gash’s revised analytical model for three consecutive periods:(i) pre-Haiyan (baseline), (ii) post–Haiyan (damaged canopy), and (iii) after initial canopy recovery. Modeled I was 18% of P before disturbance, 12% for the period with the most extensive canopy damage, and 17.5% after initial canopy recovery. Stemflow was low, and weighted mean values accounted for 2.7%, 1.3% and 2.0% of P for the respective periods. Contrasts in period-average values of I reflected changes in LAI as well as wet-canopy evaporation rates. Storm-based TF fractions at the 5 m × 5 m sub-plot scale were inversely related to LAI, especially for small storms and low rainfall intensities. Inferred hourly rates of wet-canopy evaporation showed a strong positive relationship to hourly rainfall intensity during large storms. The revised analytical model was also run using pre-disturbance parameter values for the entire year to assess the overall effect of canopy damage on I. Estimated annual losses with and without canopy disturbance were 514 mm (15% of P) and 572 mm (17%), respectively. Thus, observed and inferred changes in rainfall partitioning after canopy disturbance and initial recovery were comparatively modest, likely because the measurement site was relatively sheltered from the winds during typhoon passage and re-foliation relatively rapid. However, given the predicted increase in occurrence of ‘super-typhoons’ due to continued global warming and oceanic freshening, the structure of forests in affected regions can be expected to be modified, with potential consequences for rainfall partitioning and hydrological response.

Coupling between surface ozone and leaf area index in a chemical transport model: strength of feedback and implications for ozone air quality and vegetation health

Abstract. Tropospheric ozone is an air pollutant that substantially harms vegetation and is also strongly dependent on various vegetation-mediated processes. The interdependence between ozone and vegetation may constitute feedback mechanisms that can alter ozone concentration itself but have not been considered in most studies to date. In this study we examine the importance of dynamic coupling between surface ozone and leaf area index (LAI) in shaping ozone air quality and vegetation. We first implement an empirical scheme for ozone damage on vegetation in the Community Land Model (CLM) and simulate the steady-state responses of LAI to long-term exposure to a range of prescribed ozone levels (from 0 to 100 ppb). We find that most plant functional types suffer a substantial decline in LAI as ozone level increases. Based on the CLM-simulated results, we develop and implement in the GEOS-Chem chemical transport model a parameterization that computes fractional changes in monthly LAI as a function of local mean ozone levels. By forcing LAI to respond to ozone concentrations on a monthly timescale, the model simulates ozone–LAI coupling dynamically via biogeochemical processes including biogenic volatile organic compound (VOC) emissions and dry deposition, without the complication from meteorological changes. We find that ozone-induced damage on LAI can lead to changes in ozone concentrations by −1.8 to +3 ppb in boreal summer, with a corresponding ozone feedback factor of −0.1 to +0.6 that represents an overall self-amplifying effect from ozone–LAI coupling. Substantially higher simulated ozone due to strong positive feedbacks is found in most tropical forests, mainly due to the ozone-induced reductions in LAI and dry deposition velocity, whereas reduced isoprene emission plays a lesser role in these low-NOx environments. In high-NOx regions such as the eastern US, Europe, and China, however, the feedback effect is much weaker and even negative in some regions, reflecting the compensating effects of reduced dry deposition and reduced isoprene emission (which reduces ozone in high-NOx environments). In remote, low-LAI regions, including most of the Southern Hemisphere, the ozone feedback is generally slightly negative due to the reduced transport of NOx–VOC reaction products that serve as NOx reservoirs. This study represents the first step to accounting for dynamic ozone–vegetation coupling in a chemical transport model with ramifications for a more realistic joint assessment of ozone air quality and ecosystem health.

Interannual variability of leaf area index of an evergreen conifer stand was affected by carry-over effects from recent climate conditions

Despite the relevance of leaf area index (LAI) to forest productivity, few studies have focused on the interannual variability of LAI of an evergreen stand and its relationship with stand growth and meteorological factors. We estimated the change in LAI of an evergreen conifer (Chamaecyparis obtusa) stand over 19 years from a dataset using allometric methods. The LAI varied between 7.1 and 8.8 m2 m−2, with a 95% confidence interval of <1.1 m2 m−2 over the 19 years. This LAI range was maintained such that the gradual increase in leaf area (LA) of the largest trees counterbalanced the gradual loss in LA of the smallest trees. Meanwhile, more trees showed a temporary decrease in LA in years with low summer precipitation. The LAI and current-year mean temperature for July and August (TJA) were weakly correlated, whereas the correlation coefficient increased (r = 0.93) when LAI was correlated with the moving average TJA over the previous 6 years, which agreed with the estimated turnover time of canopy foliage. The annual stem biomass growth rate was significantly positively correlated with summer precipitation, but not with LAI. These results will be useful for refining models in studies on forest growth and global climate change.

Estimating the leaf area of an individual tree in urban areas using terrestrial laser scanner and path length distribution model

Urban leaf area measurement is crucial to properly determining the effect of urban trees on micro-climate regulation, heat island effect, building cooling, air quality improvement, and ozone formation. Previous works on the leaf area measurement have mainly focused on the stand level, although the presence of individual trees is more common than forests in urban areas. The only feasible ways for an operational non-destructive leaf area measurement, namely, optical indirect methods, are mostly limited in urban areas because light path is constantly intercepted by surrounding buildings or other objects. A terrestrial laser scanner (TLS), which can extract an individual tree by using its unique distance information, provides a possibility for indirectly measuring the leaf area index (LAI) in urban areas. However, indirect LAI measurement theory, which uses the cosine of an observation zenith angle for path-length correction, is incompatible for an individual tree because the representative projected area of LAI changes while the observation zenith angle changes, thus making the results incomparable and ambiguous. Therefore, we modified a path length distribution model for the leaf area measurement of an individual tree by replacing the traditional cosine path length correction for a continuous canopy with real path length distribution. We reconstructed the tree crown envelope from a TLS point cloud and calculated a real path length distribution through laser pulse-envelope intersections. Consequently, leaf area density was separated from the path length distribution model for leaf area calculation. Comparisons with reference measurement for an individual tree showed that the TLS-derived leaf area using the path length distribution is insensitive to the scanning resolution and agrees well with an allometric measurement with an overestimation from 5 m2 to 18 m2 (3–10%, respectively). Results from different stations are globally consistent, and using a weighted mean for different stations by sample numbers further improves the universality and efficiency of the proposed method. Further automation of the proposed method can facilitate a rapid and operational leaf area extraction of an individual tree for urban climate modeling.

Factors controlling changes in evapotranspiration, runoff, and soil moisture over the conterminous U.S.: Accounting for vegetation dynamics

While remotely-sensed data reveal enhanced vegetation growth under a warming climate, how such vegetation dynamics could alter regional hydrology is not fully understood. In this study, we investigated how changes in leaf area index (LAI) and climatic variability alter long-term trends and seasonal variability of evapotranspiration (ET), runoff (R), and soil moisture (SM) over the conterminous United States (CONUS) during the period 1983–2009 by using the Variable Infiltration Capacity (VIC) model with transient remotely-sensed LAI inputs. Factor-controlled simulation experiments reveal that while precipitation is the dominant factor controlling long-term trends and inter-annual fluctuations of water fluxes, other factors (temperature, LAI, and atmospheric CO2) make significant contributions to long-term trends, particularly over specific regions. Changes in LAI result in minor overall contributions to the inter-annual variability of ET, R, and SM over the CONUS as a whole; however, they do make a more significant contribution over certain regions, such as the Midwest. Furthermore, changing LAI is the second most important factor in controlling variability in ET and SM, particularly over the cool season. Anomalies of LAI during El Nino Southern Oscillation (ENSO) phases result in no significant correlations with anomalies in R and SM, but result in positive correlations with ET, indicating that vegetation dynamics can have a significant impact on the hydrologic cycle over continental scales.

Interdependent Dynamics of LAI-Albedo across the Roofing Landscapes: Mongolian and Tibetan Plateaus

The Mongolian Plateau (MP) and Tibetan Plateau (TP) have experienced higher-than-global average warming in recent decades, resulting in many significant changes in ecosystem structure and function. Among them are the leaf area index (LAI) and albedo, which play a fundamental role in understanding many causes and consequences of land surface processes and climate. Here, we focused on the spatiotemporal changes of LAI, albedo, and their spatiotemporal relationships on the two roofing landscapes in Eurasia. Based on the MODIS products, we investigated the spatiotemporal changes of albedo(VIS, NIR and SHO) and LAI from 2000 through 2016. We found that there existed a general negative logarithmic relationship between LAI and three measures of albedo on both plateaus. No significant relationship was found for LAI-albedoNIR on the TP, due to more complex land surface canopy characteristics affected by the NIR reflection there. During 2000–2016, overall, annual mean LAI increased significantly by 119.40 × 103 km2 on the MP and by 28.35 × 103 km2 on the TP, while the decreased areas for annual mean albedoVIS were 585.59 × 103 km2 and 235.73 × 103 km2 on the MP and TP, respectively. More importantly, the LAI-albedo relationships varied substantially across the space and over time, with mismatches found in some parts of the landscapes. Substantial additional efforts with observational and/or experimental investigations are needed to explore the underlying mechanisms responsible for these relationships, including the influences of vegetation characteristics and disturbances.

Drought and thinning have limited impacts on evapotranspiration in a managed pine plantation on the southeastern United States coastal plain

Managed and natural coastal plain forests in the humid southeastern United States exchange large amounts of water and energy with the atmosphere through the evapotranspiration (ET) process. ET plays an important role in controlling regional hydrology, climate, and ecosystem productivity. However, long-term studies on the impacts of forest management and climatic variability on forest ET are rare, and our understanding of both external and internal drivers on seasonal and interannual ET variability is incomplete. Using techniques centered on an eddy covariance method, the present study measured year-round ET flux and associated hydrometeorological variables in a drained loblolly pine (Pinus taeda L.) plantation on the lower coastal plain of North Carolina, U.S. We found that annual ET was relatively stable (1076 ± 104 mm) in comparison to precipitation (P) (1168 ± 216 mm) during the 10-year study period when the site experienced extreme climate (2007–2008) and forest thinning (2009). At the seasonal time scale, mean ET/P varied between 0.41 and 1.51, with a mean value of 1.12 ± 0.23 and 0.72 ± 0.16 for the growing and dormant seasons, respectively. The extreme drought during 2007–2008 (mean annual P, 854 mm) only resulted in a slight decrease (∼8%) in annual ET owing to the shallow groundwater common to the study area. Although changes in leaf area index and canopy structure were large after the stand was 50% thinned in the fall of 2009, mean annual ET was similar and averaged 1055 mm and 1104 mm before (2005, 2006 and 2009) and after (2010–2015) thinning, respectively. Data suggested that annual ET recovered within two years of the thinning as a result of rapid canopy closure and growth of understory. Further analysis indicated that available energy was the key driver of ET: approximately 69% and 61% of the monthly variations in ET were explained by net radiation during the dormant and growing seasons, respectively. Overall, we concluded that drought and forest thinning had limited impacts on seasonal and annual ET in this energy limited forest ecosystem with shallow groundwater. The results from this study help to better understand regional ecohydrological processes and projecting potential effects of forest management and extreme climate on water and carbon cycles.

Monitoring ecosystem reclamation recovery using optical remote sensing: Comparison with field measurements and eddy covariance

Time series remote sensing vegetation indices derived from SPOT 5 data are compared with vegetation structure and eddy covariance flux data at 15 dry to wet reclamation and reference sites within the Oil Sands region of Alberta, Canada. This comprehensive analysis examines the linkages between indicators of ecosystem function and change trajectories observed both at the plot level and within pixels. Using SPOT imagery, we find that higher spatial resolution datasets (e.g. 10 m) improves the relationship between vegetation indices and structural measurements compared with interpolated (lower resolution) pixels. The simple ratio (SR) vegetation index performs best when compared with stem density-based indicators (R2 = 0.65; p < 0.00), while the normalised difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI) are most comparable to foliage indicators (leaf area index (LAI) and canopy cover (R2 = 0.52–0.78; p > 0.02). Fluxes (net ecosystem production (NEP) and gross ecosystem production (GEP)) are most related to NDVI and SAVI when these are interpolated to larger 20 m × 20 m pixels (R2 = 0.44–0.50; p < 0.00). As expected, decreased sensitivity of NDVI is problematic for sites with LAI > 3 m2 m−2, making this index more appropriate for newly regenerating reclamation areas. For sites with LAI < 3 m2 m−2, trajectories of vegetation change can be mapped over time and are within 2.7% and 3.3% of annual measured LAI changes observed at most sites. This study demonstrates the utility of remote sensing in combination with field and eddy covariance data for monitoring and scaling of reclaimed and reference site productivity within and beyond the Oil Sands Region of western Canada.

Evaluating the Interplay Between Biophysical Processes and Leaf Area Changes in Land Surface Models.

Land Surface Models (LSMs) are essential to reproduce biophysical processes modulated by vegetation and to predict the future evolution of the land‐climate system. To assess the performance of an ensemble of LSMs (JSBACH, JULES, ORCHIDEE, CLM, and LPJ‐GUESS) a consistent set of land surface energy fluxes and leaf area index (LAI) has been generated. Relationships of interannual variations of modeled surface fluxes and LAI changes have been analyzed at global scale across climatological gradients and compared with those obtained from satellite‐based products. Model‐specific strengths and deficiencies were diagnosed for tree and grass biomes. Results show that the responses of grasses are generally well represented in models with respect to the observed interplay between turbulent fluxes and LAI, increasing the confidence on how the LAI‐dependent partition of net radiation into latent and sensible heat are simulated. On the contrary, modeled forest responses are characterized by systematic bias in the relation between the year‐to‐year variability in LAI and net radiation in cold and temperate climates, ultimately affecting the amount of absorbed radiation due to LAI‐related effects on surface albedo. In addition, for tree biomes, the relationships between LAI and turbulent fluxes appear to contradict the experimental evidences. The dominance of the transpiration‐driven over the observed albedo‐driven effects might suggest that LSMs have the incorrect balance of these two processes. Such mismatches shed light on the limitations of our current understanding and process representation of the vegetation control on the surface energy balance and help to identify critical areas for model improvement.

Quantifying grazing patterns using a new growth function based on MODIS Leaf Area Index

Monitoring grazing activities on grassland is crucial for ensuring sustainable grassland development and for protecting it from grazing-led degradation. The Leaf Area Index (LAI), which measures leaf coverage over a surface area, is commonly used as a proxy for grassland condition. However, current studies focus on the year-round or seasonal aggregated LAI change rather than the change that can be attributed explicitly to grazing, which is the important indicator for quantifying grassland grazing. This paper presents a new exponential growth function under grazing with an estimation algorithm, the purpose of which is to extract grazing-led LAI changes for every 8 days' satellite observations. All the analyses are based on the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2H products. An improved MODIS LAI and an expected LAI are produced separately, considering both current and previous grazing-led LAI changes. The differences between expected LAI and improved LAI are then converted to the equivalent carbon mass of grazed material. This grazed carbon mass is then aggregated within the growing season, and compared with the expected carbon mass consumed by livestock (calculated from statistics yearbooks). In addition, Net Primary Productivity (NPP) is produced using the improved LAI, simulated by a Light Use Efficiency with Vegetation Photosynthesis Model (LUE-VPM). This is compared with the NPP produced by LUE-VPM based on original MODIS LAI, MODIS NPP products (MOD17A2H) and grassland monitoring stations' in situ measured data. Results show that the NPP calculated from the improved LAI is statistically the same as in situ converted NPP with a p-value equalling 0.998 (the RMSE between the two is 97.77 gC/m2). Conversely, the p-value between converted in situ measured carbon mass and the MODIS NPP product is 0.011 (the RMSE between the two is 133.98 gC/m2), indicating they are statistically different. The results detailed in this paper provide precise and almost real-time grassland grazing monitoring information for policy makers managing grassland.

Water use efficiency and evapotranspiration partitioning for three typical ecosystems in the Heihe River Basin, northwestern China

It is crucial to improve water use efficiency (WUE) and the transpiration fraction of evapotranspiration (T/ET) for water conservation in arid regions. As a link between carbon and water cycling, WUE is defined as the ratio of gross primary productivity (GPP) and ET at the ecosystem scale. By incorporating the effect of vapor pressure deficit (VPD) on WUE, two underlying WUE (uWUE) formulations, i.e. a potential uWUE (uWUEp = GPP·VPD0.5/T) and an apparent uWUE (uWUEa = GPP·VPD0.5/ET), were proposed recently. Both uWUEp and uWUEa can be estimated from eddy covariance measurements, and the ratio of uWUEa and uWUEp was then used to estimate T/ET. This new method for ET partitioning was applied to three typical ecosystems in the Heihe River Basin. Growing season T/ET at the Daman site (0.63) was higher than that at the Arou and Huyanglin sites (0.55) due to the application of plastic film mulching. The effect of leaf area index (LAI) on seasonal variations in T/ET was strong for Arou (R2 = 0.74) and Daman (R2 = 0.76) sites, but weak for Huyanglin (R2 = 0.44) site. Daily T/ET derived using the uWUE method agreed with that using the isotope and lysimeter/eddy covariance methods during the peak growth season at the Daman site. The estimated T using the uWUE method showed consistent seasonal and diurnal patterns and magnitudes with that using the sap flow method at the Huyanglin site. In addition, the uWUE method can estimate ecosystem T/ET without scaling issues, and can effectively capture T/ET variations in relation to LAI changes and the abrupt T/ET changes in response to individual irrigation events. These advantages make the uWUE method effective for ET partitioning at the ecosystem scale, and can be used for water resources management by predicting seasonal pattern of actual plant water requirements to support irrigation strategies in arid regions.

Attribution and Statistical Parameterization of the Sensitivity of Surface Ozone to Changes in Leaf Area Index Based On a Chemical Transport Model

AbstractMany studies have shown that global land cover change can significantly affect surface ozone air quality, albeit still with great uncertainties due to the complex pathways involved. In this study, we develop a framework to systematically examine the effects of any changes in foliage density as represented by leaf area index (LAI) on surface ozone concentration. We perform a series of perturbation experiments using the GEOS‐Chem chemical transport model. The spatial variability of the simulated results is used as a proxy to build a statistical model to quantify the sensitivity of surface ozone to LAI changes, which is found to arise mostly from the associated changes in dry deposition velocity and isoprene emission rate, whereas other factors and second‐order effects are negligible. The spatial variations of ozone responses to LAI changes are found to be the most correlated with anthropogenic NOx emission, biogenic isoprene emission, wind speed, ozone concentration, baseline LAI, and changes in LAI. We also show that the sign of change in surface ozone under future LAI changes for a given location can be inferred by distinguishing between three different regimes based on local anthropogenic NOx emission and LAI. The statistical model is optimized so that it is applicable to a wide range of LAI changes and can be used as a quick assessment tool to estimate the impacts of various land use policies on ozone air quality, and a diagnostic tool to estimate the relative contribution of different pathways toward the overall ozone‐LAI relationship.

Predicting canopy biophysical properties and sensitivity of plant carbon uptake to water limitations with a coupled eco-hydrological framework

Variations in soil water availability and atmospheric water demand impact seasonal canopy dynamics are often represented by the fraction of photosynthetically active radiation (FPAR) and leaf area index (LAI) under a coupled eco-hydrologic framework. Changes in FPAR and LAI lead to changes in evapotranspiration (ET) and gross primary productivity (GPP), coupling the water and carbon cycles. In this study, a predictive Dynamic Canopy Biophysical Properties (DCBP) model is adapted to predict daily FPAR and LAI forced by observed and modeled meteorological and root-zone soil moisture conditions, respectively. Vegetation green-up and die-off responses to temperature (T), vapor pressure deficit (VPD), soil water potential (ψsoil), and photoperiod (Pht) are modeled through a modified form of the growing season index (GSI). The DCBP model parameterizations of seasonality (T and Pht) and intraseasonal water stress (VPD and ψsoil) are calibrated separately for distinct plant functional types (PFTs) and soil types using a Bayesian estimator. To investigate the impact of dynamic phenology on modeled GPP and hydrologic processes, phenology predicted by the DCBP model is input to the Duke Coupled Hydrology Model with Prognostic Vegetation (DCHM-PV), and hydrologic conditions are input to the DCBP model to specify water availability constraints. The coupled model framework was evaluated against AmeriFlux tower data and remotely sensed FPAR and LAI products. Sensitivity analysis of the predicted daily FPAR, LAI, and GPP to the diurnal cycle of root zone water indicates that mid-day soil water availability is the primary control on seasonality across different PFTs and soil textures in the DCBP model. Further, calibrated parameters describing plant-water relations change significantly depending on whether the inference period used in the data assimilation includes persistent meteorological drought, thus effectively resulting in distinct plant water use strategies in the DCHM-PV. The dynamics of water stress recovery are examined by mapping seasonal phenology into the phase space of soil water stress and carbon uptake. In the Southeast U.S., simulated annual differences in GPP can be as high as 350 g C/m2/year with ET increases up to 125 mm/year during wet years. These values represent first order estimates of the dynamics of plant-water use feedbacks on the water and carbon budgets, and highlight the need to incorporate vegetation-specific phenology responses to water availability in order to accurately estimate the terrestrial carbon storage component of the global carbon budget at local and regional scales.

Impact of Earth Greening on the Terrestrial Water Cycle

Leaf area index (LAI) is increasing throughout the globe, implying Earth greening. Global modeling studies support this contention, yet satellite observations and model simulations have never been directly compared. Here, for the first time, a coupled land–climate model was used to quantify the potential impact of the satellite-observed Earth greening over the past 30 years on the terrestrial water cycle. The global LAI enhancement of 8% between the early 1980s and the early 2010s is modeled to have caused increases of 12.0 ± 2.4 mm yr−1 in evapotranspiration and 12.1 ± 2.7 mm yr−1 in precipitation—about 55% ± 25% and 28% ± 6% of the observed increases in land evapotranspiration and precipitation, respectively. In wet regions, the greening did not significantly decrease runoff and soil moisture because it intensified moisture recycling through a coincident increase of evapotranspiration and precipitation. But in dry regions, including the Sahel, west Asia, northern India, the western United States, and the Mediterranean coast, the greening was modeled to significantly decrease soil moisture through its coupling with the atmospheric water cycle. This modeled soil moisture response, however, might have biases resulting from the precipitation biases in the model. For example, the model dry bias might have underestimated the soil moisture response in the observed dry area (e.g., the Sahel and northern India) given that the modeled soil moisture is near the wilting point. Thus, an accurate representation of precipitation and its feedbacks in Earth system models is essential for simulations and predictions of how soil moisture responds to LAI changes, and therefore how the terrestrial water cycle responds to climate change.