Comment on essd-2022-183

<strong class="journal-contentHeaderColor">Abstract.</strong> Evapotranspiration (ET) and gross primary production (GPP) are critical fluxes contributing to the energy, water, and carbon exchanges between the atmosphere and the land surface. Land surface models such as the Community Land Model v5 (CLM5) quantify these fluxes, contribute to a better understanding of climate change's impact on ecosystems, and estimate the state of carbon budgets and water resources. Past studies have shown the ability of CLM5 to model ET and GPP magnitudes well but emphasized systematic underestimations and lower variability than in the observations. Here, we evaluate the simulated ET and GPP from CLM5 at the grid scale (CLM5_grid) and the plant functional type (PFT) scale (CLM5_PFT) with observations from eddy covariance stations from the Integrated Carbon Observation System (ICOS) over Europe. For most PFTs, CLM5_grid and CLM5_PFT compared better to ICOS than publicly available reanalysis data and estimates obtained from remote sensing. CLM5_PFT exhibited a low systematic error in simulating the ET of the ICOS measurements (average bias of -5.05 %), implying that the PFT-specific ET matches the magnitude of the observations closely. However, CLM5_PFT severely underestimates GPP, especially in deciduous forests (bias of -43.76 %). Furthermore, the simulated ET and GPP distribution moments across PFTs in CLM5_grid and CLM5_PFT, reanalyses, and remote sensing data indicate an underestimated spatiotemporal variability compared to the observations across Europe. These results are essential insights for further evaluations in CLM5 by pointing to the limitations of CLM5 in simulating the spatiotemporal variability of ET and GPP across PFTs.

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

Changbai Mountain Nature Reserve (CNR) is a typical temperate forest ecosystem, and gross primary production (GPP) of which is closely related to topography and climate change. Research on the spatio-temporal variations and influencing factors of GPP in the CNR is of great significance for assessing growth status of vegetation and the quality of ecological environment. We calculated GPP in CNR using the vegetation photosynthesis model (VPM), and analyzed the influences of slope, altitude, temperature, precipitation, and total radiation. The results showed that the range of annual average GPP in CNR was 63-1706 g C·m-2·a-1 from 2000 to 2020 and that GPP decreased with the increases of altitude. Temperature played the most important role in driving the spatial varia-tion of GPP, with a significant positive correlation with GPP. During the study period, the overall annual GPP showed a significant increase trend in CNR, with an average annual increase of 13 g C·m-2·a-1. The areas with increase of annual GPP accounted for 79.9% of the total area, and the area proportion of annual GPP increase differed in each plant functional type. Annual precipitation was significantly negatively correlated with GPP in 43.2% of CNR, while annual mean temperature and annual total radiation were significantly positively correlated with GPP in 47.2% and 82.4% of CNR. GPP would increase continuously in CNR under the scenario of future global warming.

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.

Modeling Carbon and Water Budgets in the Lushi Basin with Biome-BGC

Terrestrial gross primary productivity (GPP) varies greatly over time and space. A better understanding of this variability is necessary for more accurate predictions of the future climate-carbon cycle feedback. Recent studies have suggested that variability in GPP is driven by a broad range of biotic and abiotic factors operating mainly through changes in vegetation phenology and physiological processes. However, it is still unclear how plant phenology and physiology can be integrated to explain the spatiotemporal variability of terrestrial GPP. Based on analyses of eddy-covariance and satellite-derived data, we decomposed annual terrestrial GPP into the length of the CO2 uptake period (CUP) and the seasonal maximal capacity of CO2 uptake (GPPmax). The product of CUP and GPPmax explained >90% of the temporal GPP variability in most areas of North America during 2000-2010 and the spatial GPP variation among globally distributed eddy flux tower sites. It also explained GPP response to the European heatwave in 2003 (r(2) = 0.90) and GPP recovery after a fire disturbance in South Dakota (r(2) = 0.88). Additional analysis of the eddy-covariance flux data shows that the interbiome variation in annual GPP is better explained by that in GPPmax than CUP. These findings indicate that terrestrial GPP is jointly controlled by ecosystem-level plant phenology and photosynthetic capacity, and greater understanding of GPPmax and CUP responses to environmental and biological variations will, thus, improve predictions of GPP over time and space.

Studies on the spatiotemporal dynamics in ecosystem carbon and water exchanges are essential in predicting the effects of climate change on regional carbon and energy budgets. Using the eddy covariance technique, carbon and water fluxes were observed in a typical winter wheat ecosystem (WWE) and an agroforest ecosystem (AFE) in the southern Loess Plateau from 2004 to 2010. The seasonal and inter-annual variability in gross primary productivity (GPP), net ecosystem exchange (NEE), evapotranspiration (ET), and water use efficiency (WUE) were examined and the main influencing factors were identified using the Pearson correlation. The results indicate that the seasonal GPP and NEE showed a bimodal distribution in WWE, while this was unimodal in AFE. The sinusoidal function did well in the characterization of seasonal ET dynamics for both ecosystems, with the determination coefficients being 0.85 and 0.94, respectively. In WWE and AFE, the annual mean GPP were 724.33 and 723.08 g C m−2 a−1, respectively, and the corresponding ET were 392.22 and 410.02 mm a−1. However, the difference in NEE between the two ecosystems was obvious, NEE were −446.28 and −549.08 g C m−2 a−1, respectively, showing a stronger carbon sink in AFE. There were strong coupling relationships between the GPP and ET of both ecosystems; the overall slopes were 1.71 and 1.69, respectively. The seasonal trend of WUE was bimodal in WWE, with peak values of 3.94 and 3.65 g C kg−1 H2O, occurring in November and April, respectively. However, the monthly WUE in AFE had one single peak of 4.07 g C kg−1 H2O in January. Photosynthetically active radiation (PAR) and soil temperature (Ts) were most positively correlated with GPP, net radiation (Rn) and Ts were the major factors influencing ET, while vapor pressure deficit (VPD) and soil water content (SWC) were the major influencing factors for WUE. These results provide observational support for regional carbon neutrality simulations.

When assessing the socio‐economic impacts of climate change, it is sensible to make targeted climate projections for regions of high population density and economy activity. Much of human activity is concentrated at river basins, yet it has been difficult to resolve the complex boundaries of these basins in coarse resolution global climate models. The latest high‐resolution observation and climate projection datasets enable such basin‐based evaluations now, and this study assesses the historical and projected climate changes over three major river basins in China—the Yellow, Yangtze and Pearl River basins. Based on CN05.1 dataset, the Yellow River basin has significantly warmed by about 1.8°C over the past five decades, far more than the other two basins. The change in temperature extremes has been as severe, with the annual maxima of daily maximum temperatures (TXx) increasing by 1.5°C, and the annual minima of daily minimum temperatures (TNn) increasing by 2.5°C. Precipitation over the Yangtze River has significantly increased by about 0.2 mm·day−1, while changes over the other two basins were not statistically significant. The uncertainty in the change of precipitation was greater than that of temperature. A selection of simulations from the Fifth and Sixth Coupled Model Intercomparison Projects (CMIP5 and CMIP6) were validated against the CN05.1 dataset for the historical period of 1961–2018. Changes in temperature indices were well‐reproduced, but changes in precipitation indices poorly so. CMIP6 models performed better than the CMIP5 models. Both CMIP5 and CMIP6 multi‐model ensembles (MMEs) projected about 1.0–2.0°C warming over China and the three river basins by 2015–2050. Both MMEs projected wetting trends over most parts of China and the three river basins. Both warming and wetting were projected to accelerate with time, particularly warming over the Yellow River basin, and wetting over the Pearl River basin.

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

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.

Abstract. Attributing changes in evapotranspiration (ET) and gross primary productivity (GPP) is crucial for impact and adaptation assessment of the agro-ecosystems to climate change. Simulations with the VIP model revealed that annual ET and GPP slightly increased from 1981 to 2013 over the North China Plain. The tendencies of both ET and GPP were upward in the spring season, while they were weak and downward in the summer season. A complete factor analysis illustrated that the relative contributions of climatic change, CO2 fertilization, and management to the ET (GPP) trend were 56 (−32) %, −28 (25) %, and 68 (108) %, respectively. The decline of global radiation resulted from deteriorated aerosol and air pollution was the principal cause of GPP decline in summer, while air warming intensified the water cycle and advanced the plant productivity in the spring season. Generally, agronomic improvements were the principal drivers of crop productivity enhancement.

Climate and management impacts on the spatiotemporal dynamics of water-carbon fluxes in the North China Plain

Known as the “Third Pole” and “Early-warning region” of the world, the Qinghai-Tibetan Plateau (QTP) had an intense increase in surface temperature and marked change in the amplitude of diurnal temperature (ADT), which could have significant influences on the ecosystem carbon cycle. The increasing rate of the mean daily minimum temperature (MinTa) was about two times higher than that of maximum temperature (MaxTa) in the last five decades, and this asymmetric pattern has resulted in a smaller ADT, which substantially affected the plant phenology and vegetation productivity. The reduction in the ADT caused by global climate change would have a profound impact on the carbon balance of alpine ecosystems. However, the response of carbon budgets to the ADT over the QTP remained unclear. Here, we analyzed the 15-a growing seasonal (June−September) carbon fluxes (measured by the eddy covariance [EC] technique) in alpine meadow at the southern foot of Qilian Mountains, which is one of the most extensive vegetation types on the QTP. This study aimed to clarify how carbon fluxes respond to ADT at different temporal (daily, monthly and annual) scales in alpine meadow and to understand their potential response to future climate change. The results indicated that both the MaxTa and MinTa showed bell-shaped seasonal patterns, whereas the ADT failed to show an obvious trend during the growing season from 2002 to 2016. Besides, there was a non-significant increase in annual MaxTa, MinTa and ADT ( P >0.05). Meanwhile, daily gross primary productivity (GPP) and ecosystem respiration (Re) exhibited a single-peaked trend that increased and then decreased, whereas daily net ecosystem CO2 exchange (NEE) showed a v-shaped trend. The alpine meadow ecosystem is a carbon sink during the growing season, and the annual NEE, GPP, and Re were −230.4±17.3, 668.8±25.5, 438.3±27.5 g C m–2, respectively. Moreover, the annual GPP and Re of alpine meadow in the growing season showed a significant increase trend ( P P >0.05). Annual CO2 fluxes were not related annual ADT. Only annual MaxTa exerted significant influence on variations in annual GPP and Re. Interestingly, the slopes of GPP and Re with respect to MaxTa were similar, also indicating the little impact of MaxTa on annual NEE. On a monthly scale, ADT exerted a negligible influence on CO2 fluxes ( P >0.05), but there were significant correlations between MinTa and MaxTa with CO2 fluxes. On a daily scale, there was a significant quadratic relationship between daily NEE and ADT during the whole growing season ( P P

Water stress can positively or negatively impact grape yield and yield quality, and there is a need for wine growers to accurately regulate water use. In a four-year study (2010–2013), energy balance fluxes were measured with an eddy-covariance (EC) system in a North Carolina vineyard (Vitis vinifera cv. Chardonnay), and evapotranspiration (ET) and the Crop Water Stress Index (CWSI) calculated. A multiple linear regression model was developed to upscale ET using air temperature (Ta), vapor pressure deficit (VPD), and Landsat-derived Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI). Daily ET reached values of up to 7.7 mm day−1, and the annual ET was 752 ± 59 mm, as measured with the EC system. The grapevine CWSI was between 0.53–0.85, which indicated moderate water stress levels. Median vineyard EVI was between 0.22 and 0.72, and the EVI range (max–min) within the vineyard was 0.18. The empirical models explained 75%–84% of the variation in ET, and all parameters had a positive linear relationship to ET. The Root Mean Square Error (RMSE) was 0.52–0.62 mm. This study presents easily applicable approaches to analyzing water dynamics and ET. This may help wine growers to cost-effectively quantify water use in vineyards.

Ecosystem water use efficiency in a warm-temperate mixed plantation in the North China