Evapotranspiration Partitioning Based on Leaf and Ecosystem Water Use Efficiency

Understanding the differences in carbon and water vapor fluxes of spatially distributed evergreen needleleaf forests (ENFs) is crucial for accurately estimating regional or global carbon and water budgets and when predicting the responses of ENFs to current and future climate. We compared the fluxes of ten AmeriFlux ENF sites to investigate cross-site variability in net ecosystem exchange of carbon (NEE), gross primary production (GPP), and evapotranspiration (ET). We used wavelet cross-correlation analysis to examine responses of NEE and ET to common climatic drivers over multiple timescales and also determined optimum values of air temperature (T a) and vapor pressure deficit (VPD) for NEE and ET. We found larger differences in the NEE spectra than in the ET spectra across sites, demonstrating that spatial (site-to-site) variability was larger for NEE than for ET. The NEE and ET were decoupled differently across ENF sites because the wavelet cospectra between ET and climate variables were similar at all sites, while the wavelet cospectra between NEE and climate variables were higher (i.e., closer coupling between NEE and climatic drivers) in semi-arid and Mediterranean sites than in other sites. Ecosystem water use efficiency (EWUE) based on annual GPP/ET ranged from 1.3 ± 0.18 to 4.08 ± 0.62 g C mm−1 ET, while EWUE based on annual net ecosystem production (NEP)/ET ranged from 0.06 ± 0.04 to 1.02 ± 0.16 g C mm−1 ET) among ENFs. Responses of NEE and ET to T a varied across climatic zones. In particular, for ENF sites in semi-arid and Mediterranean climates, the maximum NEE and ET occurred at lower ranges of T a than in sites with warm and humid summers. The optimum T a and VPD values were higher for ET than for NEE, and ET was less sensitive to high values of T a and VPD. Large spatial variability in carbon and water vapor fluxes among ENFs and large variations in responses of NEE and ET to major climate variables among climatic zones necessitate sub-plant functional type parameterization based on climatic zones to better represent climate sensitivity of ENFs and to reduce uncertainty in model predictions.

Water and energy flux densities of co‐located cellulosic feedstocks, switchgrass (Panicum virgatum L.) and high biomass sorghum (Sorghum bicolor L. Moench), were measured using the eddy covariance technique during the 2012 and 2013 growing seasons to quantify and compare evapotranspiration (ET) and ecosystem water use efficiency (EWUE). Peak growing season ET from the switchgrass field was approximately 6 mm d−1 in both years; whereas, the sorghum ET reached 6.7 in 2012 and 4.8 mm d−1 in 2013. While both species showed similar water use patterns during the active growing period, seasonal cumulative ET was higher in switchgrass due to a longer growing season. Monthly ET integrals were strongly correlated with monthly integrals of gross primary production (GPP) and net ecosystem production (NEP) for both species, suggesting a strong linkage between C gain and water loss over the season. Different methods of EWUE estimations yielded different EWUE values. The ratio of seasonal sums of GPP to ET yielded EWUE of 9.41 to 11.32 and 8.98 to 9.17 g CO2 mm−1 ET in switchgrass and sorghum, respectively. The ratio of seasonal sums of NEP to ET was 2.75 to 2.81 and 2.06 to 2.18 g CO2 mm−1 ET in switchgrass and sorghum, respectively. Slightly larger EWUE in switchgrass than sorghum shows that the switchgrass was more efficient than the sorghum on using water to gain C. Results suggest that larger seasonal C uptake by switchgrass was enough to offset its higher water use due to the longer growing season. Eddy covariance technique can be used to assess bioenergy crops ecosystem water use efficiency. Swithcgrass has higher ET than sorghum due to longer growing season. Greater seasonal carbon uptake by switchgrass than sorghum was enough to offset higher water use.

Evapotranspiration partitioning and its interannual variability over a winter wheat-summer maize rotation system in the North China Plain

<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.

Partitioning evapotranspiration (ET) into vegetation transpiration (T) and soil evaporation (E) is challenging, but it is key to improving the understanding of plant water use and changes in terrestrial ecosystems. Considering that the transpiration of vegetation at night is minimal and can be negligible, we established a machine learning model (i.e., extreme gradient boosting algorithm (XGBoost)) for soil evaporation estimation based on night-time evapotranspiration observation data from eddy covariance towers, remote sensing data, and meteorological reanalysis data. Daytime T was consequently calculated as the difference between the total evapotranspiration and predicted daytime soil evaporation. The soil evaporation estimation model was validated based on the remaining night-time ET data (i.e., model test dataset), the non-growing season ET data of the natural ecosystem, and ET data during the fallow periods of croplands. The validation results showed that XGBoost had a better performance in E estimation, with the average overall accuracy of NSE 0.657, R 0.806, and RMSE 11.344 W/m2. The average annual T/ET of the examined ten ecosystems was 0.50 ± 0.08, with the highest value in deciduous broadleaf forests (0.68 ± 0.11), followed by mixed forests (0.61 ± 0.04), and the lowest in croplands (0.40 ± 0.08). We further examined the impact of the leaf area index (LAI) and vapor pressure deficit (VPD) on the variation in T/ET. Overall, at the interannual scale, LAI contributed 28% to the T/ET variation, while VPD had a small (5%) influence. On a seasonal scale, LAI also exerted a stronger impact (1~90%) on T/ET compared to VPD (1~77%). Our study suggests that the XGBoost machine learning model has good performance in ET partitioning, and this method is mainly data-driven without prior knowledge, which may provide a simple and valuable method in global ET partitioning and T/ET estimation.

Grasslands in Inner Mongolia are important for livestock farming while ecosystem functioning and water consumption are dominated by evapotranspiration (ET). In this paper we studied the spatiotemporal distribution and variability of ET and its components in Inner Mongolian grasslands over a period of 10 years, from 2002 to 2011. ET was modelled pixel-wise for more than 3000 1 km2 pixels with the physically-based hydrological model BROOK90. The model was parameterised from eddy-covariance measurements and daily input was generated from MODIS leaf area index and surface temperatures. Modelled ET was also compared with the ET provided by the MODIS MOD16 ET data.The study showed ET to be highly variable in both time and space in Inner Mongolian grasslands. The mean coefficient of variation of 8-day ET in the study area varied between 25% and 40% and was up to 75% for individual pixels indicating a high innerannual variability of ET. Generally, ET equals or exceeds P during the vegetation period, but high precipitation in 2003 clearly exceeded ET in this year indicating a recharge of soil moisture and groundwater. Despite the high interannual and innerannual variations of spatial ET, the study also showed the existence of an intrinsic long-term spatial pattern of ET distribution, which can be explained partly by altitude and longitude (R2 = 0.49). In conclusion, the results of this research suggest the development of dynamic and productive rangeland management systems according to the inherent variability of rainfall, productivity and ET in order to restore and protect Inner Mongolian grasslands.

Vegetation exerts profound influences on evapotranspiration (ET) partitioning. Many studies have demonstrated the positive impact of vegetation cover on the ratio of transpiration (T) to ET. Whether it is universally true with regard to different vegetation types and different sites is understudied. In this study, five sites in Northern China with different vegetation types were selected for comparison study.ET partitioning is conducted using an approach based on the concept of the underlying water use efficiency with eddy covariance measurements. The results show various patterns of vegetation’s effects over ET partitioning and, when compared with existing studies, also reveal a new relationship between the T/ET ratio and Normalized Difference Vegetation Index (NDVI) at some of the sites. At the alpine meadow site, the T/ET ratio gradually increase when NDVI is low and rapidly increase as NDVI go beyond a certain value, whereas at the arid shrub site, the T/ET ratio rapidly increase when NDVI is low and plateaus at a certain value when NDVI reaches a relatively high value. In deciduous forest, the T/ET ratio becomes unresponsive to NDVI beyond a threshold value. This study also reveals that irrigation schemes play a major role in determining the correlation between the T/ET ratio and NDVI because the T/ET ratio becomes well correlated with NDVI in case of flood irrigation and irrelevant to NDVI in the case of mulch drip irrigation. Furthermore, this study helps us to understand ET partitioning under different sites and different human activities such as irrigation. These findings can help policymakers to better understand the connection between vegetation and climate change or human activities and provide significant information for water management policy.

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

Considering spatio-temporal dynamics of soil water with evapotranspiration partitioning helps to clarify water utilization characteristics of summer maize under deficit irrigation

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.

Models overestimate ecosystem water use efficiency for northern permafrost regions

Effects of soil water content on forest ecosystem water use efficiency through changes in transpiration/evapotranspiration ratio

Scarce water resources are available in the arid and semi-arid areas of Northwest China, where significant water-related challenges will be faced in the coming decades. Quantitative evaluations of the spatio-temporal dynamics in ecosystem water use efficiency (WUE), as well as the underlying environmental controls, are crucial for predicting future climate change impacts on ecosystem carbon-water interactions and agricultural production. However, these questions remain poorly understood in this typical region. By means of continuous eddy covariance (EC) measurements and time-series MODIS data, this study revealed the distinct seasonal cycles in gross primary productivity (GPP), evapotranspiration (ET), and WUE for both grassland and cropland ecosystems, and the dominant climate factors performed jointly by temperature and precipitation. The MODIS WUE estimates from GPP and ET products can capture the broad trend in WUE variability of grassland, but with large biases for maize cropland, which was mainly ascribed to large uncertainties resulting from both GPP and ET algorithms. Given the excellent biophysical performance of the MODIS-derived enhanced vegetation index (EVI), a new greenness model (GR) was proposed to track the eight-day changes in ecosystem WUE. Seasonal variations and the scatterplots between EC-based WUE and the estimates from time-series EVI data (WUEGR) also certified its prediction accuracy with R2 and RMSE of both grassland and cropland ecosystems over 0.90 and less than 0.30 g kg−1, respectively. The application of the GR model to regional scales in the near future will provide accurate WUE information to support water resource management in dry regions around the world.

Enhanced water use efficiency in global terrestrial ecosystems under increasing aerosol loadings

Evapotranspiration partitioning and water productivity of rainfed maize under contrasting mulching conditions in Northwest China