Author response to referee comments on egusphere-2024-978

Reliable modeling of carbon and water fluxes is essential for understanding the terrestrial carbon and water cycles and informing policy strategies aimed at constraining carbon emissions and improving water use efficiency. We used an assimilation framework (LPJ-Vegetation and soil moisture Joint Assimilation, or LPJ-VSJA) to improve gross primary production (GPP) and evapotranspiration (ET) estimates globally. The terrestrial biosphere model that we used is the integrated model – LPJ-PM coupled from the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM) and a hydrology module (i.e., the updated Priestley–Taylor Jet Propulsion Laboratory model, PT-JPLSM). Satellite-based soil moisture products derived from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) and leaf area index (LAI) from the global Land and Ground satellite (GLASS) product were assimilated into LPJ-PM to improve GPP and ET simulations using a Proper Orthogonal Decomposition-based ensemble four-dimensional variational assimilation method (PODEn4DVar). The joint assimilation framework LPJ-VSJA achieved the best model performance (with an R2 of 0.91 and 0.81 and an RMSD reduced by 50.4 % and 38.4 % for GPP and ET, respectively, compared with those of LPJ-DGVM at the monthly scale). The assimilated GPP and ET demonstrated a better performance in the arid and semiarid regions (GPP: R2 = 0.73, ubRMSD = 1.05 g C m−2 d−1; ET: R2 = 0.73, ubRMSD = 0.61 mm d−1) than in the humid and sub-dry humid regions (GPP: R2 = 0.61, ubRMSD = 1.23 g C m−2 d−1; ET: R2 = 0.66; ubRMSD = 0.67 mm d−1). The ET simulated by LPJ-PM that assimilated SMAP or SMOS had a slight difference, and the ET that assimilated SMAP soil moisture data was more improved than that assimilated SMOS data. Our global simulation modeled by LPJ-VSJA was compared with several global GPP and ET products (e.g., GLASS GPP, GOSIF GPP, GLDAS ET, GLEAM ET) using the triple collocation (TC) method. Our products, especially ET, exhibited advantages in the overall error distribution (estimated error (μ): 3.4 mm month−1; estimated standard deviation of μ: 1.91 mm month−1). Our research showed that the assimilation of multiple datasets could reduce model uncertainties, while the model performance differed across regions and plant functional types. Our assimilation framework (LPJ-VSJA) can improve the model simulation performance of daily GPP and ET globally, especially in water-limited regions.

Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling

Integrating leaf functional traits improves modelled estimates of carbon and water fluxes at a subtropical evergreen conifer forest

Accurate high-resolution actual evapotranspiration (ET) and gross primary production (GPP) information is essential for understanding the large-scale water and carbon dynamics. However, substantial uncertainties exist in the current ET and GPP datasets in China because of insufficient local ground measurements used for model constrain. This study utilizes a water-carbon coupled model, Penman-Monteith-Leuning Version 2 (PML-V2), to estimate 500 m ET and GPP at a daily scale. The parameters of PML-V2 (China) were well-calibrated against observations of 26 eddy covariance flux towers across nine plant functional types in China, indicated by Nash–Sutcliffe Efficiency (NSE) of 0.75 and Root Mean Square Error (RMSE) of 0.69 mm d−1 for daily ET respectively, and NSE of 0.82 and RMSE of 1.71 g C m-2 d−1 for daily GPP. The model estimates get a small bias of 6.28 % and a high NSE of 0.82 against water‐balance annual ET estimates across 10 major river basins in China. Further evaluations suggest that the newly developed product outperforms its global version and other typical products (MOD16A2, SEBAL, GLEAM, MOD17A2H, VPM, and EC-LUE) in estimating both ET and GPP. Moreover, PML-V2 (China) accurately monitors the intra-annual variations in ET and GPP in the croplands with a dual-cropping system. Using the new data showed that, over the last 20 years, the annual GPP and water use efficiency experienced a significant (p < 0.001) increase (8.51 g C m-2 yr-1 and 0.02 g C mm-1 H2O yr-1, respectively), but annual ET showed a non-significant (p > 0.05) increase (0.65 mm yr-1). This indicates that vegetation in China exhibits a huge potential for carbon sequestration with little cost in water resources. The PML-V2 (China) product provides a great opportunity for academic communities and various agencies for scientific studies and applications, freely available at http://dx.doi.org/10.11888/Terre.tpdc.272389.

An accurate estimation of vegetation Gross Primary Productivity (GPP), which is the amount of carbon taken up by vegetation through photosynthesis for a given time and area, is critical for understanding terrestrial-atmosphere CO2 exchange processes, ecosystem functioning, and as well as ecosystem responses and adaptations to climate change. Earliest studies, based on ground, airborne and satellite Sun-Induced chlorophyll Fluorescence (SIF) observations have recently revealed close relationships with GPP at different spatial and temporal scales and across different plant functional type (PFT). However, questions remain regarding whether there is a unique relationship between SIF and GPP across different sites and PFT and how can we improve GPP estimates using solely remotely sensed data. Using concurrent measurements of daily TROPOMI (TROPOspheric Monitoring Instrument) SIF (daily SIFd), daily MODIS Terra and Aqua spectral reflectance, and vegetation indices (VIs, notably NDVI (normalized difference vegetation index), NIRv (near-infrared reflectance of vegetation) and PRI (photochemical reflectance index)) and daily tower-based GPP across eight major different PFT, including mixed forests, deciduous broadleaf forests, croplands, evergreen broadleaf forests, evergreen needleleaf forests, grassland, open shrubland, and wetland, the strength of the linear relationships between tower-based GPP and SIFd at 40 ICOS (Integrated Carbon Observation Systems) flux sites was investigated, and the synergy between these variables to improve GPP estimates using a data-driven modelling approach was evaluated. The results revealed that the strength of the linear relationship between GPP and SIFd was strongly site-specific and PFT-dependent. Furthermore, the GLM (Generalized Linear Model) model, fitted between SIFd, GPP, site and vegetation type as categorical variables, further supported this site-and PFT-dependent relationship between GPP and SIFd. This study also showed that the spectral reflectance bands (RF-R), SIFd plus spectral reflectance (RF-SIF-R) models explained over 80 % of the seasonal and interannual variations in GPP, whereas the SIFd plus VIs (RF-SIF-VI) model reproduced only 75 % of the tower-based GPP variance. In addition, the relative importance results demonstrated that the spectral reflectance bands in the near-infrared, red and SIFd appeared as the most influential and dominant factors determining GPP predictions, indicating the importance of canopy structure, biochemical properties and vegetation functioning on GPP estimates. Overall, this study provides insights into understanding the strength of the relationships between GPP and SIF and the use of the spectral reflectance and SIFd to improve GPP across sites and PFT.

Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data

Interannual variations in GPP in forest ecosystems in Southwest China and regional differences in the climatic contributions

Partitioning evapotranspiration (ET) into evaporation (E) and transpiration (T) is essential for understanding the global hydrological cycle and improving water resource management. However, ecosystem‐level ET partitioning remains challenging. Here we proposed a novel ET partitioning method that uses the unified stomatal conductance model to estimate T:ET by calculating the ratio of the ecosystem water use efficiency (WUEeco) to leaf WUE (WUEleaf) using half‐hourly flux data. The WUEleaf values estimated by the unified stomatal conductance model agree with an independently measured ratio of hourly photosynthetic rate to T rate (R2 = 0.69). The sensitivity of T:ET to the key parameter g1 varied among different plant functional types (PFTs), but the T:ET variations for each PFT were all controlled within 20% when g1 altered within its 95% confidence interval. The mean annual T:ET was highest for evergreen broadleaf forests (0.63), followed by deciduous broad forests (0.62), grasslands (0.52), evergreen needleleaf forests (0.43) and woody savannas (0.40). C3 croplands had higher T:ET (0.65) than C4 croplands (0.48). Seasonal variations in T:ET varied across PFTs and the leaf area index explained about 50% of the variation in seasonal T:ET. Our method is not only consistent with other three EC‐based methods: Z16, N18, and L19 (R = 0.92, 0.94, and 0.68), but also shows high correlations to sap flow‐based T (R = 0.70) at three different forest sites. The method developed in this study provides a feasible and universal approach for ET partitioning of global EC sites, improving the understanding of ecosystem T characteristics across climates and PFTs.

Combined MODIS land surface temperature and greenness data for modeling vegetation phenology, physiology, and gross primary production in terrestrial ecosystems

Solar-induced chlorophyll fluorescence (SIF) offers significant potential as a novel approach for quantifying carbon and water cycling in coastal wetland ecosystems across multiple spatial scales. However, the mechanisms governing these biogeochemical processes remain insufficiently understood, largely due to the periodic influence of tidal inundation. In this study, we investigated the effects and underlying mechanisms of meteorological and tidal factors on the relationships between canopy-level solar-induced chlorophyll fluorescence at 760 nm (SIF760) and key ecosystem processes, including gross primary productivity (GPP) and evapotranspiration (ET), in coastal wetlands. These processes are critical components of the ecosystem carbon and water cycles. Our approach involved a comparative analysis of simulations from the Soil Canopy Observation, Photochemistry and Energy Fluxes (SCOPE) model with field measurements. The results showed that: (1) simulations of SIF760 improved following observation-based calibration of the fluorescence photosynthesis module in the SCOPE model; (2) under optimal moisture and temperature conditions (VPD 1.2–1.4 kPa and temperatures of 20–23 °C for air, soil, and water), the simulations of GPP, ET, and SIF760 were most accurate, although salinity stress reduced performance. GPP simulations tended to overestimate under drought stress but improved at higher air temperatures (30–32 °C); (3) during tidal inundation, the SIF760-GPP relationship weakened while the SIF760-ET strengthened. The range of significant correlations between SIF760, water levels, and temperature narrowed, with both relationships becoming more complex due to salinity stress. These findings suggest that tidal inundation can alleviate temperature stress on photosynthesis and transpiration; however, it also decreases photosynthetic efficiency and alters radiative transfer processes due to elevated salinity and water levels. These factors are critical considerations when using SIF to monitor GPP and ET dynamics in coastal wetlands. This study demonstrated that the tidal dynamics significantly affected the SIF760-GPP and SIF760-ET relationships, underscoring the necessity of incorporating tidal influences in the application of SIF remote sensing for monitoring GPP and ET dynamics. The results of this study not only contribute to a deeper understanding of the mechanisms linking SIF760 with GPP and ET but also provide new insights into the development and refinement of SIF-based remote sensing for carbon quantification in coastal blue-carbon ecosystems on a large-scale domain.

Improved global estimations of gross primary productivity of natural vegetation types by incorporating plant functional type

Estimating gross primary productivity (GPP) is important for simulating the subsequent carbon cycle elements and assessing the capacity of terrestrial ecosystems to support the sustainable development of human society. Light use efficiency (LUE) models were widely used to estimate GPP due to their concise model structures. However, quantifying LUEmax (maximum light use efficiency) and representing the responses of photosynthesis to environmental factors are still subject to large uncertainties, which lead to substantial errors in GPP simulations. In this study, we developed a hybrid model based on machine learning and a LUE model for GPP estimates. This hybrid model was built by targeting LUE with a machine learning approach, namely multi-layer perceptron (MLP), and then, estimating GPP within a LUE model framework with the MLP-based LUE and other required inputs. We trained the hybrid LUE (H-LUE) model and then, compared it against two conventional LUE models, the vegetation photosynthesis model (VPM) and vegetation photosynthesis and respiration model (VPRM), regarding GPP estimation, using tower-based daily-scale observations from 180 flux sites that cover nine different plant function types (PFTs). The results revealed better performance (R2 = 0.86 and RMSE = 1.79 gC m−2 d−1 on the test dataset) of the H-LUE model compared to the VPM and VPRM. Evaluations of the three models under four different extreme conditions consistently revealed better performance of the H-LUE model, indicating greater adaptability of the model to varied environments in the context of climate change. Furthermore, we also found that the H-LUE model can reasonably represent the responses of the LUE to meteorological variables. Our study revealed the reliable and robust performance of the developed hybrid LUE when simulating GPP across global biomes, providing references for developing better hybrid GPP models.

Relative to flat surfaces, mountain terrains modify solar radiation absorbed by vegetation on sloping surfaces, causing changes in mass and energy fluxes, including gross primary productivity (GPP) and evapotranspiration (ET). However, these changes are generally ignored in regional and global ecosystem models and their magnitudes have not been systematically evaluated. In this study, we first validated the Biosphere‐atmosphere Exchange Process Simulator (BEPS) model against measured GPP and ET over mountainous sites, and then applied it to a mountainous region (Fujian Province, China). In BEPS, the topographic effects are systematically considered in the following steps: (1) the satellite‐derived leaf area index (LAI) is projected to sloping surfaces, (2) canopy radiative transfer is modeled relative to the normal to the slope, and (3) the modeled fluxes are reprojected from sloping to horizontal surfaces. Step (1) decreases LAI as sloping surfaces are larger than the corresponding horizontal surfaces, but Step (3) increases fluxes in the opposite way. Because of the nonlinear relationships between fluxes and LAI, GPP and ET simulations without considering the topographic effects are always underestimated, especially on sunlit slopes. The underestimation increases with increasing slope, and for slopes greater than 40°, GPP is underestimated by 11% and ET by 33%, suggesting that existing global GPP and ET products could have been significantly underestimated in mountainous regions.

Assessing potential future changes in arctic and boreal plant species productivity, ecosystem composition, and canopy complexity is essential for understanding environmental responses under expected altered climate forcing. We examined potential changes in the dominant plant functional types (PFTs) of the sedge tundra, shrub tundra, and boreal forest ecosystems in ecotonal northern Alaska, USA, for the years 2003-2100. We compared energy feedbacks associated with increases in biomass to energy feedbacks associated with changes in the duration of the snow-free season. We based our simulations on nine input climate scenarios from the Intergovernmental Panel on Climate Change (IPCC) and a new version of the Terrestrial Ecosystem Model (TEM) that incorporates biogeochemistry, vegetation dynamics for multiple PFTs (e.g., trees, shrubs, grasses, sedges, mosses), multiple vegetation pools, and soil thermal regimes. We found mean increases in net primary productivity (NPP) in all PFTs. Most notably, birch (Betula spp.) in the shrub tundra showed increases that were at least three times larger than any other PFT. Increases in NPP were positively related to increases in growing-season length in the sedge tundra, but PFTs in boreal forest and shrub tundra showed a significant response to changes in light availability as well as growing-season length. Significant NPP responses to changes in vegetation uptake of nitrogen by PFT indicated that some PFTs were better competitors for nitrogen than other PFTs. While NPP increased, heterotrophic respiration (RH) also increased, resulting in decreases or no change in net ecosystem carbon uptake. Greater aboveground biomass from increased NPP produced a decrease in summer albedo, greater regional heat absorption (0.34 +/- 0.23 W x m(-2) x 10 yr(-1) [mean +/- SD]), and a positive feedback to climate warming. However, the decrease in albedo due to a shorter snow season (-5.1 +/- 1.6 d/10 yr) resulted in much greater regional heat absorption (3.3 +/- 1.24 W x m(-2) x 10 yr(-1)) than that associated with increases in vegetation. Through quantifying feedbacks associated with changes in vegetation and those associated with changes in the snow season length, we can reach a more integrated understanding of the manner in which climate change may impact interactions between high-latitude ecosystems and the climate system.

Abstract. Aerosol- and cloud-induced changes in diffuse light have important impacts on the global land carbon cycle, as they alter light distribution and photosynthesis in vegetation canopies. However, this effect remains poorly represented or evaluated in current land surface models. Here, we add a light partitioning module and a new canopy light transmission module to the ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) land surface model (trunk version, v5453) and use the revised model, ORCHIDEE_DF, to estimate the fraction of diffuse light and its effect on gross primary production (GPP) in a multilayer canopy. We evaluate the new parameterizations using flux observations from 159 eddy covariance sites over the globe. Our results show that, compared with the original model, ORCHIDEE_DF improves the GPP simulation under sunny conditions and captures the observed higher photosynthesis under cloudier conditions in most plant functional types (PFTs). Our results also indicate that the larger GPP under cloudy conditions compared with sunny conditions is mainly driven by increased diffuse light in the morning and in the afternoon as well as by a decreased vapor pressure deficit (VPD) and decreased air temperature at midday. The observations show that the strongest positive effects of diffuse light on photosynthesis are found in the range from 5 to 20 ∘C and at a VPD &lt; 1 kPa. This effect is found to decrease when the VPD becomes too large or the temperature falls outside of the abovementioned range, which is likely due to the increasing stomatal resistance to leaf CO2 uptake. ORCHIDEE_DF underestimates the diffuse light effect at low temperature in all PFTs and overestimates this effect at high temperature and at a high VPD in grasslands and croplands. The new model has the potential to better investigate the impact of large-scale aerosol changes and long-term changes in cloudiness on the terrestrial carbon budget, both in the historical period and in the context of future air quality policies and/or climate engineering.