Vegetation Model Research Articles

Was the steppe bison a grazing beast in Pleistocene landscapes?

The history and palaeoecology of the steppe bison (Bison priscus) remain incompletely understood despite its widespread distribution. Using dental microwear textural analysis (DMTA) and vegetation modelling, we reconstructed the diet and assessed the habitat of steppe bison inhabiting Eurasia and Alaska since the Middle Pleistocene. During the Late Pleistocene, steppe bison occupied a variety of biome types: from the mosaic of temperate summergreen forest and steppe/temperate grassland (Serbia) to the tundra biomes (Siberia and Alaska). Despite the differences in the identified biome types, the diet of steppe bison did not differ significantly among populations in Eurasia. DMTA classified it as a mixed forager in all populations studied. The DMTA of Bb1 bison-a recently identified genetically extinct sister-clade of Bison bonasus-was typical of a highly grazing bovid species and differed from all B. priscus populations. The results of the study temper the common perception that steppe bison were grazers in steppe habitats. The dietary plasticity of the steppe bison was lower when compared with modern European bison and may have played an important role in its extinction, even in the stable tundra biome of eastern Siberia, where it has survived the longest in all of Eurasia.

Evaluation of the coupling of EMACv2.55 to the land surface and vegetation model JSBACHv4

Abstract. We present the coupling of the Jena Scheme for Biosphere–Atmosphere Coupling in Hamburg version 4 (JSBACHv4) to the ECHAM/MESSy Atmospheric Chemistry (EMAC) model. With JSBACH, the soil water bucket model in EMAC is replaced by a diffusive hydrological transport model for soil water that includes water storage and infiltration in five soil layers, preventing soil from drying too rapidly and reducing biases in soil temperature and moisture. A three-layer soil scheme is implemented, and phase changes in water in the soil are considered. The leaf area index (LAI) climatology in EMAC has been substituted with a phenology module calculating the LAI. Multiple land cover types are included to provide a state-dependent surface albedo, which accounts for the absorption of solar radiation by vegetation. Plant net primary productivity, leaf area index and surface roughness are calculated according to the plant functional types. This paper provides a detailed evaluation of the new coupled model based on observations and reanalysis data, including ERA5/ERA5-Land datasets, Global Precipitation Climatology Project (GPCP) data and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. Land surface temperature (LST), terrestrial water storage (TWS), surface albedo (α), net top-of-atmosphere radiation flux (RadTOA), precipitation (precip), leaf area index (LAI), fraction of absorbed photosynthetic active radiation (FAPAR) and gross primary productivity (GPP) are evaluated in particular. The strongest correlation (r) between reanalysis data and the newly coupled model is found for LST (r=0.985, with an average global bias of −1.546 K), α (r=0.947, with an average global bias of −0.015) and RadTOA (r=0.907, with an average global bias of 3.56 W m−2). Precipitation exhibits a correlation with the GPCP dataset of 0.523 and an average global bias of 0.042 mm d−1. The LAI optimisation yields a correlation of 0.637 with observations and a global mean deviation of −0.212. FAPAR and GPP exemplify two of the many additional variables made available through JSBACH in EMAC. FAPAR and observations show a correlation of 0.663, with an average global difference of −0.223, while the correlation for GPP and observations is 0.564 and the average global difference is −0.001 kg carbon km−1. Benefiting from the numerous added features within the simulated land system, the representation of soil moisture is improved, which is critical for vegetation modelling. This improvement can be attributed to a general increase in soil moisture and water storage in deeper soil layers and a closer alignment of simulated TWS with observations, mitigating the previously widespread problem of soil drought. We show that the numerous newly added components strongly improve the land surface, e.g. soil moisture, TWS and LAI, while surface parameters, such as LST, surface albedo or RadTOA, which were mostly prescribed according to climatologies, remain similar. The coupling of JSBACH brings EMAC a step closer towards a holistic comprehensive Earth system model and extends its versatility.

Trigger thresholds and their dynamics of vegetation production loss under different atmospheric and soil drought conditions

Many evidences have shown that both atmospheric and soil droughts can constrain vegetation growth and further threaten its ability to sequester carbon. However, the trigger thresholds of vegetation production loss under different atmospheric and soil drought conditions are still unknown. In this study, we proposed a Copula and Bayesian equations-based framework to investigate trigger thresholds of various vegetation production losses under different atmospheric and soil drought conditions. The trigger thresholds dynamics and their possible causes were also investigated. To achieve this goal, we first simulated the gross primary production, soil moisture, and vapor pressure deficit over China during 1961–2018 using an individual-based, spatially explicit dynamic global vegetation model. The main drivers of the dynamic change in trigger thresholds were then explored by Random Forest model. We found that soil drought caused greater stress on gross primary production loss than atmospheric drought, with a larger impact area and higher probability of damage. In terms of spatial distribution, the risk probability of gross primary production loss was higher in eastern China than in western China, and the drought trigger threshold was also smaller in eastern China. In addition, the trigger thresholds for atmospheric and soil drought in most regions exhibited a decreasing trend from 1961 to 2018, while the CO2 fertilization enhanced the drought tolerance of vegetation. The reduction in CO2 fertilization effect slowed down the downward trend of trigger threshold for soil drought, while the increase in temperature exacerbated the downward trend of trigger threshold for atmospheric drought. This study highlighted the larger effect of soil drought on vegetation production loss than atmospheric drought and implied that climate change can modulate the trigger threshold of vegetation production losses under drought conditions. These findings provide scientific guidance for managing the increasing risk of drought on vegetation and optimizing watershed water allocation.

Progress of vegetation modelling and future research prospects

Progress of vegetation modelling and future research prospects

Numerical and experimental investigations on wave transmission reduction using vegetation models

Numerical and experimental investigations were conducted using vegetation models with varying leaf thicknesses, meadow lengths, and friction coefficients, to evaluate the efficacy of vegetation in reducing wave transmission under different wave heights, periods, and submergence conditions. A modified shallow-water model considering the effect of friction coefficient as a function of several wave characteristics was developed. The model was solved numerically using a staggered finite volume model. The numerical model was validated using experimental data. Good agreement was observed between the experimental and numerical results, particularly in terms of the wave amplitude and phase. A genetic algorithm was used to derive empirical formulas using the friction coefficient as a function of different vegetation and wave parameters. The results showed that increasing the leaf thickness increased the friction coefficient and reduced the wave transmission. However, increasing the meadow length had a greater effect than increasing the leaf thickness. An emergent meadow covered the entire water column. Hence, it yielded the highest friction coefficient and wave transmission reduction among all tested submergence conditions. A sensitivity analysis was performed to assess the effects of the wave height, wave period, and leaf thickness. The results indicated that the maximum reduction in wave transmission was achieved under emergent conditions with high wave energies, short wavelengths, and thick leaves. This was attributed to enhanced wave–vegetation interaction, wave breaking, and energy dissipation. The observations of this study will aid coastal engineers in selecting the optimal leaf height, leaf thickness, and meadow length to achieve the desired reduction in wave transmission.

Future climate doubles the risk of hydraulic failure in a wet tropical forest.

Future climate presents conflicting implications for forest biomass. We evaluate how plant hydraulic traits, elevated CO2 levels, warming, and changes in precipitation affect forest primary productivity, evapotranspiration, and the risk of hydraulic failure. We used a dynamic vegetation model with plant hydrodynamics (FATES-HYDRO) to simulate the stand-level responses to future climate changes in a wet tropical forest in Barro Colorado Island, Panama. We calibrated the model by selecting plant trait assemblages that performed well against observations. These assemblages were run with temperature and precipitation changes for two greenhouse gas emission scenarios (2086-2100: SSP2-45, SSP5-85) and two CO2 levels (contemporary, anticipated). The risk of hydraulic failure is projected to increase from a contemporary rate of 5.7% to 10.1-11.3% under future climate scenarios, and, crucially, elevated CO2 provided only slight amelioration. By contrast, elevated CO2 mitigated GPP reductions. We attribute a greater variation in hydraulic failure risk to trait assemblages than to either CO2 or climate. Our results project forests with both faster growth (through productivity increases) and higher mortality rates (through increasing rates of hydraulic failure) in the neo-tropics accompanied by certain trait plant assemblages becoming nonviable.

Free water surface constructed wetlands: review of pollutant removal performance and modeling approaches.

Free water surface constructed wetlands (FWSCWs) for the treatment of various wastewater types have evolved significantly over the last few decades. With an increasing need and interest in FWSCWs applications worldwide due to their cost-effectiveness and other benefits, this paper reviews recent literature on FWSCWs' ability to remove different types of pollutants such as nutrients (i.e., TN, TP, NH4-N), heavy metals (i.e., Fe, Zn, and Ni), antibiotics (i.e., oxytetracycline, ciprofloxacin, doxycycline, sulfamethazine, and ofloxacin), and pesticides (i.e., Atrazine, S-Metolachlor, imidacloprid, lambda-cyhalothrin, diuron 3,4-dichloroanilin, Simazine, and Atrazine) that may co-exist in wetland inflow, and discusses approaches for simulating hydraulic and pollutant removal processes. A bibliometric analysis of recent literature reveals that China has the highest number of publications, followed by the USA. The collected data show that FWSCWs can remove an average of 61.6%, 67.8%, 54.7%, and 72.85% of inflowing nutrients, heavy metals, antibiotics, and pesticides, respectively. Optimizing each pollutant removal process requires specific design parameters. Removing heavy metal requires the lowest hydraulic retention time (HRT) (average of 4.78days), removing pesticides requires the lowest water depth (average of 0.34m), and nutrient removal requires the largest system size. Vegetation, especially Typha spp. and Phragmites spp., play an important role in FWSCWs' system performance, making significant contributions to the removal process. Various modeling approaches (i.e., black-box and process-based) were comprehensively reviewed, revealing the need for including the internal process mechanisms related to the biological processes along with plants spp., that supported by a further research with field study validations. This work presents a state-of-the-art, systematic, and comparative discussion on the efficiency of FWSCWs in removing different pollutants, main design factors, the vegetation, and well-described models for performance prediction.

Distribution of the Velocity Profile via Analytical and Three-Dimensional Numerical Vegetation Modeling

Distribution of the Velocity Profile via Analytical and Three-Dimensional Numerical Vegetation Modeling

Rainfall seasonality dominates critical precipitation threshold for the Amazon forest in the LPJmL vegetation model

Understanding the Amazon Rainforest's response to shifts in precipitation is paramount with regard to its sensitivity to climate change and deforestation. Studies using Dynamic Global Vegetation Models (DGVMs) typically only explore a range of socio-economically plausible pathways. In this study, we applied the state-of-the-art DGVM LPJmL to simulate the Amazon forest's response under idealized scenarios where precipitation is linearly decreased and subsequently increased between current levels and zero. Our results indicate a nonlinear but reversible relationship between vegetation Above Ground Biomass (AGB) and Mean Annual Precipitation (MAP), suggesting a threshold at a critical MAP value, below which vegetation biomass decline accelerates with decreasing MAP. We find that approaching this critical threshold is accompanied by critical slowing down, which can hence be expected to warn of accelerating biomass decline with decreasing rainfall. The critical precipitation threshold is lowest in the northwestern Amazon, whereas the eastern and southern regions may already be below their critical MAP thresholds. Overall, we identify the seasonality of precipitation and the potential evapotranspiration (PET) as the most important parameters determining the threshold value. While vegetation fires show little effect on the critical threshold and the biomass pattern in general, the ability of trees to adapt to water stress by investing in deep roots leads to increased biomass and a lower critical threshold in some areas in the eastern and southern Amazon where seasonality and PET are high. Our findings underscore the risk of Amazon forest degradation due to changes in the water cycle, and imply that regions that are currently characterized by higher water availability may exhibit heightened vulnerability to future drying.

Coordination between degree of isohydricity and depth of root water uptake in temperate tree species

In an increasingly dry environment, it is crucial to understand how tree species use soil water and cope with drought. However, there is still a knowledge gap regarding the relationships between species-specific stomatal behaviour, spatial root distribution, and root water uptake (RWU) dynamics.Our study aimed to investigate above- and below-ground aspects of water use during soil drying periods in four temperate tree species that differ in stomatal behaviour: two isohydric tracheid-bearing conifers, Scots pine and Norway spruce, and two more anisohydric deciduous species, the diffuse-porous European beech, and the ring-porous Downy oak. From 2015 to 2020, soil-tree-atmosphere-continuum parameters were measured for each species in monospecific forests where trees had no access to groundwater. The hourly time series included data on air temperature, vapor pressure deficit, soil water potential, soil hydraulic conductivity, and RWU to a depth of 2 m. Analysis of drought responses included data on stem radius, leaf water potential, estimated osmotically active compounds, and drought damage.Our study reveals an inherent coordination between stomatal regulation, fine root distribution and water uptake. Compared to conifers, the more anisohydric water use of oak and beech was associated with less strict stomatal closure, greater investment in deep roots, four times higher maximum RWU, a shift of RWU to deeper soil layers as the topsoil dried, and a more pronounced soil drying below 1 m depth. Soil hydraulic conductivity started to limit RWU when values fell below 10−3 to 10−5 cm/d, depending on the soil. As drought progressed, oak and beech may also have benefited from their leaf osmoregulatory capacity, but at the cost of xylem embolism with around 50 % loss of hydraulic conductivity when soil water potential dropped below −1.25 MPa.Consideration of species-specific water use is crucial for forest management and vegetation modelling to improve forest resilience to drought.

Bomb radiocarbon evidence for strong global carbon uptake and turnover in terrestrial vegetation.

Vegetation and soils are taking up approximately 30% of anthropogenic carbon dioxide emissions because of small imbalances in large gross carbon exchanges from productivity and turnover that are poorly constrained. We combined a new budget of radiocarbon produced by nuclear bomb testing in the 1960s with model simulations to evaluate carbon cycling in terrestrial vegetation. We found that most state-of-the-art vegetation models used in the Coupled Model Intercomparison Project underestimated the radiocarbon accumulation in vegetation biomass. Our findings, combined with constraints on vegetation carbon stocks and productivity trends, imply that net primary productivity is likely at least 80 petagrams of carbon per year presently, compared with the 43 to 76 petagrams per year predicted by current models. Storage of anthropogenic carbon in terrestrial vegetation is likely more short-lived and vulnerable than previously predicted.

Modeling drought mortality and resilience of savannas and forests in tropical Asia

The projected increase of drought occurrence in many tropical and sub-tropical regions globally under future climates will affect terrestrial ecosystems, particularly by increasing drought-induced plant mortality. The capacity to simulate drought mortality in vegetation models is therefore essential to understand future ecosystem dynamics. Using the trait-based vegetation model aDGVM2, we assessed drought mortality and resilience in tropical Asia under climate change. We conducted model simulations for ten sites in tropical Asia, representing a biogeographic gradient. Responses of vegetation attributes and mortality rates were simulated until 2099 for hypothetical drought scenarios and recovery times were calculated. Model simulations showed biomass dieback during drought due to increased plant mortality, primarily among tall and old trees. Drought responses were related to hydraulic traits and associated ecological strategies. Despite severe drought impacts, recovery was possible, but recovery times differed between ecosystem attributes. We conclude that the aDGVM2 enhances our ability to understand drought impacts in tropical ecosystems. The model can simulate increased mortality during drought in a trait- and individual-based modeling framework. It indicated drought resilience of forests and adaptation to drought by changes in community trait composition and the demographic structure. Yet, further model improvements are required to better represent drought impact and recovery.

How to map biomes: Quantitative comparison and review of biome‐mapping methods

AbstractBiomes are large‐scale ecosystems occupying large spaces. The biome concept should theoretically facilitate scientific synthesis of global‐scale studies of the past, present, and future biosphere. However, there is neither a consensus biome map nor universally accepted definition of terrestrial biomes, making joint interpretation and comparison of biome‐related studies difficult. “Desert,” “rainforest,” “tundra,” “grassland,” or “savanna,” while widely used terms in common language, have multiple definitions and no universally accepted spatial distribution. Fit‐for‐purpose classification schemes are necessary, so multiple biome‐mapping methods should for now co‐exist. In this review, we compare biome‐mapping methods, first conceptually, then quantitatively. To facilitate the description of the diversity of approaches, we group the extant diversity of past, present, and future global‐scale biome‐mapping methods into three main families that differ by the feature captured, the mapping technique, and the nature of observation used: (1) compilation biome maps from expert elicitation, (2) functional biome maps from vegetation physiognomy, and (3) simulated biome maps from vegetation modeling. We design a protocol to measure and quantify spatially the pairwise agreement between biome maps. We then illustrate the use of such a protocol with a real‐world application by investigating the potential ecological drivers of disagreement between four broadly used, modern global biome maps. In this example, we quantify that the strongest disagreement among biome maps generally occurs in landscapes altered by human activities and moderately covered by vegetation. Such disagreements are sources of bias when combining several biome classifications. When aiming to produce realistic biome maps, biases could be minimized by promoting schemes using observations rather than predictions, while simultaneously considering the effect of humans and other ecosystem engineers in the definition. Throughout this review, we provide comparison and decision tools to navigate the diversity of approaches to encourage a more effective use of the biome concept.

Climate and vegetation change impacts on future conterminous United States water yield

Future water availability is influenced by both climate and associated vegetation dynamics. This study coupled vegetation projections from a dynamic global vegetation model (MC2) with an ecohydrological model, Water Supply Stress Index (WaSSI), to predict water yield at the 8-digit Hydrologic Unit Code (HUC8) watershed level for the conterminous United States (CONUS) for the 21st century. We considered two contrasting warming scenarios (Representative Concentration Pathways 8.5 and 4.5) and accounted for simulation uncertainty by using a large ensemble of climate model outputs. The coupled model projects a decrease in water yield across much of CONUS, especially towards end-century (2080–2099) under RCP 8.5 (warmer scenario), reaching up to −30% at the regional level, relative to the 2008–2027 baseline period. Overall, the projected water yield reduction under RCP 8.5 is roughly twice as high as under RCP 4.5. Substantial changes in water yield for watersheds in the central and southeastern United States are expected by mid-century (2040–2059), reaching up to −40% (RCP 4.5) and −75% (RCP 8.5) at at the century’s end (2080–2099), relative to 2008–2027, respectively. Climate change, rather than vegetation change, strongly dominates the projected future changes in water yield, with contributions typically one order of magnitude higher. For a small number of watersheds, the effects of vegetation change can mitigate or exacerbate the effects of climate change on water yield. Our simulation results suggest a widespread increase in aridity and evaporative indices and a decrease in soil moisture, especially under RCP 8.5. Our integrated modeling results can inform policy makers and resource development planners quantitative information of future water availability.

Spatial patterns and variations in leaf traits of alpine plants on the interior Tibetan Plateau

Spatial patterns and environmental gradients in plant traits can elucidate the relationships between plant characteristics, environmental factors, and ecosystem functions and can be used to develop the next generation of vegetation models for predicting vegetation distribution and functions. Studies of Plant traits across spatial scales have advanced significantly worldwide, including on the Tibetan Plateau (TP), the highest and largest plateau globally. Previous research on plant traits on the TP has been limited to specific areas, leaving the overall trend across the plateau unclear. In this study, we examined seven leaf traits—leaf thickness (LT), leaf dry matter content (LDMC), leaf area (LA), specific leaf area (SLA), and leaf carbon (LCC), nitrogen (LNC), and phosphorus (LPC) contents—in 1314 individuals at 237 sites within the alpine vegetation region above 3000 m on the TP to determine their distribution patterns across three spatial dimensions and their relationships with environmental factors. Our results indicated that, compared with Chinese plants, TP plants exhibited significantly greater LT, LDMC, and LNC but lower LA, LCC, and LPC. Except for SLA and LCC, most leaf traits showed significant spatial variability. Climate and soil explained 29.23 % of the total variations in traits. Radiation had the most pronounced effect on LDMC, LA, LNC, and LPC, whereas soil pH primarily constrained LT and LCC. Precipitation influenced most traits both directly and indirectly, with substantial overall effects. Temperature and soil nutrient had minimal influence on the regulation of leaf traits across most of the TP. Our findings suggest that alpine plants typically employ a conservative strategy to cope with the harsh conditions on the TP, but this strategy varies at broader spatial scales and longer environmental gradients, particularly in extreme environments such as the highest alpine regions.

Consistent time allocation fraction to vegetation green-up versus senescence across northern ecosystems despite recent climate change

Extended growing season lengths under climatic warming suggest increased time for plant growth. However, research has focused on climatic impacts to the timing or duration of distinct phenological events. Comparatively little is known about impacts to the relative time allocation to distinct phenological events, for example, the proportion of time dedicated to leaf growth versus senescence. We use multiple satellite and ground-based observations to show that, despite recent climate change during 2001 to 2020, the ratio of time allocated to vegetation green-up over senescence has remained stable [1.27 (± 0.92)] across more than 83% of northern ecosystems. This stability is independent of changes in growing season lengths and is caused by widespread positive relationships among vegetation phenological events; longer vegetation green-up results in longer vegetation senescence. These empirical observations were also partly reproduced by 13 dynamic global vegetation models. Our work demonstrates an intrinsic biotic control to vegetation phenology that could explain the timing of vegetation senescence under climate change.

Allometric estimation models for aboveground and belowground biomass of pre-fire and post-fire vegetation in Scots pine forests

Abstract Wildfires have recently destroyed large areas of forest in Central Europe. After a fire, the post-fire vegetation succession rebuilds previously lost biomass and carbon. Nevertheless, ground vegetation and tree regeneration, as well as belowground biomass, are largely neglected in allometric biomass models. Moreover, most models are calibrated for undisturbed forests, which differ from burned forests in terms of site conditions and species composition. We adapted a model using plant cover and shoot length of herb, graminoid and bryophyte species (PhytoCalc), and a single tree model using shoot length and root collar diameter of juvenile trees to estimate aboveground biomass (AGB) of post-fire species and growth groups, and extended them to estimate belowground biomass. We sampled the most dominant species from the herb and moss layer and the tree regeneration of burned and unburned Scots pine (Pinus sylvestris L.) forests in NE Germany. We successfully calibrated post-fire vegetation models and showed that models for undisturbed forests from the literature mostly underestimate post-fire AGB. Using models from the literature for clear-cuts often improved the estimation of post-fire AGB, but still failed to estimate AGB accurately for some species. Inaccurate estimation of post-fire biomass with these other models can be explained by high irradiance and increased nutrient availability on burned sites, which affect tissue and wood density. We showed that own species- and growth group-specific allometric models calibrated to post-fire vegetation are required to correctly estimate the total (above- and belowground) biomass of post-fire vegetation needed to calculate carbon storage.

Contributions of China's terrestrial ecosystem carbon uptakes to offsetting CO2 emissions under different scenarios over 2001–2060

China is committed to achieving carbon neutrality by 2060, which requires that CO2 emissions from fossil fuels and industrial processes (referred to as CO2 emissions) be offset by carbon uptake through its terrestrial ecosystems. This study quantified the contributions of China's terrestrial carbon sinks to offsetting CO2 emissions over 2001–2060 by combining vegetation model simulations and multiple datasets of CO2 emission projections across different future emission scenarios. Our results showed that China's CO2 emissions will increase from 0.99 Pg C yr−1 in 2001 to >4 Pg C yr−1 in 2060 under the SSP3–7.0 and SSP5–8.5 scenarios; however, for SSP1–2.6, CO2 emissions will eventually decrease to 0.50 Pg C yr−1. Conversely, China's net terrestrial carbon sink was projected to increase from −0.14 Pg C yr−1 (2000s) to [−0.35, −0.40] Pg C yr−1 (2050s) under different SSP scenarios based on vegetation modelling. As a result, China's net terrestrial carbon sink will contribute only approximately 10% to offsetting its CO2 emissions under scenarios SSP3–7.0 and SSP5–8.5, implying an almost certain failure of the carbon-neutral strategy. In contrast, under scenario SSP1–2.6, approximately 50%–80% of CO2 emissions can be offset by the terrestrial carbon sink by 2060, in line with the possible success of the carbon-neutral goal. This study underscores the critical role of terrestrial carbon sink in achieving carbon neutrality in China and hightlights the remaining challenges for further reducing CO2 emissions.

Predicting the Emergence of Localised Dihedral Patterns in Models for Dryland Vegetation

Localised patterns are often observed in models for dryland vegetation, both as peaks of vegetation in a desert state and as gaps within a vegetated state, known as ‘fairy circles’. Recent results from radial spatial dynamics show that approximations of localised patterns with dihedral symmetry emerge from a Turing instability in general reaction–diffusion systems, which we apply to several vegetation models. We present a systematic guide for finding such patterns in a given reaction–diffusion model, during which we obtain four key quantities that allow us to predict the qualitative properties of our solutions with minimal analysis. We consider four well-established vegetation models and compute their key predictive quantities, observing that models which possess similar values exhibit qualitatively similar localised patterns; we then complement our results with numerical simulations of various localised states in each model. Here, localised vegetation patches emerge generically from Turing instabilities and act as transient states between uniform and patterned environments, displaying complex dynamics as they evolve over time.

Methodological advancement in deriving primary productivity and ecosystem respiration fluxes across different biomes

In this paper, we introduce a methodology that can improve the estimations of Gross Primary Productivity (GPP) and ecosystem Respiration (Reco) processes at a regional scale. This method is based on a satellite data-driven approach which is suitable for regions like India where there exists a serious shortage of ground-based observations of biospheric carbon fluxes (e.g., Eddy Covariance (EC) flux measurements). We relied on the Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance for capturing vegetation dynamics in the Light-Use Efficiency (LUE)-based vegetation model. Further, we utilised recently available satellite-based Solar-Induced Fluorescence (SIF) and other variables such as Soil Moisture (SM) and Soil Temperature (ST) to refine the predictions of GPP and Reco. The methodology involves establishing a relationship between SIF and GPP for different vegetation classes over India. The SIF-GPP relationship established across the biomes was then used to correct the GPP fluxes simulated by the LUE-based model. Similarly, the ecosystem respiration estimations by the model have undergone refinement by incorporating ST and SM information. This innovative method shows remarkable potential to improve biospheric CO2 uptake and release, especially for in situ data-constrained regions like India.• SIF-based information is introduced to a light-use efficiency-based vegetation model.• SIF-GPP relationship is established for major biomes across India.• SM and ST information is incorporated into the Reco simulations in the model.