Global estimation of evapotranspiration using a leaf area index-based surface energy and water balance model

Billesbach

et al. 2015

Ecological Modelling

A B S T R A C TAccurate simulation of terrestrial gross primary production (GPP), the largest global carbon flux, benefits our understanding of carbon cycle and its source of variation. This paper presents a novel light use efficiency-based GPP model called the terrestrial ecosystem carbon flux model (TEC) driven by MODIS FPAR and climate data coupled with a precipitation-driven evapotranspiration (E) model (Yan et al., 2012). TEC incorporated a new water stress factor, defined as the ratio of actual E to Priestley and Taylor (1972) potential evaporation (E PT ). A maximum light use efficiency (e*) of 1.8 gC MJ À1 and 2.76 gC MJ À1 was applied to C3 and C4 ecosystems, respectively. An evaluation at 18 eddy covariance flux towers representing various ecosystem types under various climates indicates that the TEC model predicted monthly average GPP for all sites with overall statistics of r = 0.85, RMSE = 2.20 gC m À2 day À1 , and bias = À0.05 gC m À2 day À1 . For comparison the MODIS GPP products (MOD17A2) had overall statistics of r = 0.73, RMSE = 2.82 gC m À2 day À1 , and bias = À0.31 gC m À2 day À1 for this same set of data. In this case, the TEC model performed better than MOD17A2 products, especially for C4 plants. We obtained an estimate of global mean annual GPP flux at 128.2 AE 1.5 Pg C yr À1 from monthly MODIS FPAR and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA reanalysis data at a 1.0 spatial resolution over 11 year period from 2000 to 2010. This falls in the range of published land GPP estimates that consider the effect of C4 and C3 species. The TEC model with its new definition of water stress factor and its parameterization of C4 and C3 plants should help better understand the coupled climate-carbon cycle processes.

Section: Eddy Covariance Datamentioning

confidence: 99%

Section: Modis Lai/fpar Products (Mod15a2)mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants

Yan

Billesbach

et al. 2015

Ecological Modelling

“…In recent years, several studies have quantified the spatial patterns of AET at regional (e.g., Li et al, 2014;Liu et al, 2013a;Wang et al, 2013) and global scales (e.g., Mu et al, 2011;Yan et al, 2012;Yuan et al, 2010) using process-based or remote sensing models. Although the spatial patterns described by different models generally agreed well with each other for a given region (Mueller et al, 2011;Liu et al, 2013a), large obvious uncertainties in AET values still remain.…”

Section: Introductionmentioning

confidence: 99%

Spatial variation in annual actual evapotranspiration of terrestrial ecosystems in China: Results from eddy covariance measurements

Zheng

et al. 2016

J. Geogr. Sci.

Abstract:Understanding the spatial variation in annual actual evapotranspiration (AET) and its influencing factors is crucial for a better understanding of hydrological processes and water resources management. By synthesizing ecosystem-level observations of eddy-covariance flux sites in China (a total of 61 sites), we constructed the most complete AET dataset in China up to now. Based on this dataset, we quantified the statistic characteristics of AET and water budgets (defined as the ratio of AET to annual mean precipitation (MAP), AET/MAP) of terrestrial ecosystems in China. Results showed that AET differed significantly among both different vegetation types and climate types in China, with overall mean AET of 534.7±232.8 mm yr -1 . AET/MAP also differed significantly among different climate types, but there were no distinct differences in AET/MAP values across vegetation types, with mean AET/MAP of 0.82±0.28 for non-irrigated ecosystems. We further investigated how the main climatic factors and vegetation attributes control the spatial variation in AET. Our findings revealed that the spatial variation of AET in China was closely correlated with the geographical patterns of climate and vegetation, in which the effects of total annual net radiation (R n ), MAP and mean annual air temperature (MAT) were dominant. Thus, we proposed an empirical equation to describe the spatial patterns of AET in China, which could explain about 84% of the spatial variation in AET of terrestrial ecosystems in China. Based on the constructed dataset, we also evaluated the uncertainties of five published global evapotranspiration products in simulating 1392 Journal of Geographical Sciences site-specific AET in China. Results showed that large biases in site-specific AET values existed for all five global evapotranspiration products, which indicated that it is necessary to involve more observation data of China in their parameterization or validation, while our AET dataset would provide a data source for it.

“…The ARTS E 0 module and the simple snowmelting module in section 2.1 are adopted from ARTS E model [Yan et al, 2012]. The self-calibrating PDSI model [Wells et al, 2004] used in this study can be found in Appendix A (Calculation of Self-Calibrating PDSI model).…”

Section: Arts Pdsi Modelmentioning

confidence: 99%

Development of a remotely sensing seasonal vegetation‐based Palmer Drought Severity Index and its application of global drought monitoring over 1982–2011

Yan

et al. 2014

JGR Atmospheres

Vegetation effects are currently disregarded in Palmer Drought Severity Index (PDSI), and the sensitivity of PDSI to the choice of potential evaporation (E P ) parameterization is often a concern. We developed a revised self-calibrating PDSI model that replaces E P with leaf area index-based total evapotranspiration (ARTS E 0 ). It also included a simple snowmelt module. Using a unique satellite leaf area index data set and climate data, we calculated and compared ARTS E 0 , three other types of E P (i.e., Thornthwaite E P_Th , Allen E P_Al , and Penman-Monteith E P_PM ), and corresponding PDSI values (i.e., PDSI_ARTS, PDSI_Th, PDSI_Al, and PDSI_PM) for the period 1982-2011. The results of PDSI_ARTS, PDSI_Al, and PDSI_PM show that global land became wetter mainly due to increased precipitation and El Niño-Southern Oscillation (ENSO) effect for the period, which confirms the ongoing intensification of global hydrologic cycle with global temperature increase. However, only PDSI_Th gave a trend of global drying, which confirms that PDSI_Th overestimates the global drying in response to global warming; i.e., PDSI values are sensitive to the parameterizations for E p . Thus, ARTS E 0 , E P_Al , and E P_PM are preferred to E P_Th in global drought monitoring. In short, global warming affects global drought condition in two opposite ways. One is to contribute to the increases of E P and hence drought; the other is to increase global precipitation that contributes to global wetting. These results suggest that precipitation trend and its interaction with global warming and ENSO should be given much attention to correctly quantify past and future trends of drought.