Incorporating diffuse radiation into a light use efficiency and evapotranspiration model: An 11-year study in a high latitude deciduous forest

Abstract The fraction of diffuse photosynthetic active radiation (PAR) reaching the land surface is one of the biophysical factors regulating carbon and water exchange between terrestrial ecosystems and the atmosphere. This is especially relevant for high latitude ecosystems, where cloudy days are prevalent. Without considering impacts of diffuse PAR, traditional ‘top-down’ models of ecosystem gross primary productivity (GPP) and evapotranspiration (ET), which use satellite remote sensing observations, are biased towards clear sky conditions. This study incorporated a cloudiness index (CI), an index for the fraction of diffuse PAR, into a joint ‘top-down’ model that uses the same set of biophysical constraints to simulate GPP and ET for a high latitude temperate deciduous forest. To quantify the diffuse PAR effects, CI along with other environmental variables derived from an eleven-year eddy covariance data set were used to statistically explore the independent and joint effects of diffuse PAR on GPP, ET, incident light use efficiency (LUE), evaporative fraction (EF) and ecosystem water use efficiency (WUE). The independent and joint effects of CI were compared from global sensitivity analysis of the ‘top-down’ models. Results indicate that for independent effects, CI increased GPP, LUE, ET, EF and WUE. Analysis of joint effects shows that CI mainly interacted with the radiation intercepted in the canopy (PAR, net radiation and leaf area index) to influence GPP, ET and WUE. Moreover, Ta and vapor pressure saturation deficit played a major role for the joint influence of CI on LUE and EF. In the growing season from May to October, variation in CI accounts for 11.9%, 3.0% and 7.8% of the total variation of GPP, ET and transpiration, respectively. As the influence of CI on GPP is larger than that on ET, this leads to an increase in WUE with CI. Joint GPP and ET model results showed that when including CI, the root mean square errors (RMSE) of daily GPP decreased from 1.64 to 1.45 g C m−2 d−1 (11.7% reduction) and ET from 15.79 to 14.50 W m−2 (8.2% reduction). Due to the interaction of diffuse PAR with plant canopies, the largest model improvements using CI for GPP and ET occurred during the growing season and for the transpiration component, as suggested by comparisons to sap flow measurements. Furthermore, our study suggests a potential biophysical mechanism, not considered in other studies: under high diffuse PAR conditions, due to the increased longwave emission from clouds, canopy temperature gets higher and enhances GPP and transpiration in this temperature-limited high latitude ecosystem.

[1]  T. Vesala,et al.  Biophysical controls on CO2 fluxes of three Northern forests based on long-term eddy covariance data , 2008 .

[2]  Hideki Kobayashi,et al.  Integration of MODIS land and atmosphere products with a coupled‐process model to estimate gross primary productivity and evapotranspiration from 1 km to global scales , 2011 .

[3]  Y. Malhi,et al.  Diffuse radiation and cloud fraction relationships in two contrasting Amazonian rainforest sites , 2010 .

[4]  S. T. Gower,et al.  A cross‐biome comparison of daily light use efficiency for gross primary production , 2003 .

[5]  P. Ceccato,et al.  Actual evapotranspiration in drylands derived from in-situ and satellite data: Assessing biophysical constraints , 2013 .

[6]  Ching Chun Li.  Path Analysis: A Primer , 1977 .

[7]  D. Baldocchi,et al.  Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites , 2008 .

[8]  Zhuguo Ma,et al.  Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China , 2014 .

[9]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .

[10]  Maosheng Zhao,et al.  Development of a global evapotranspiration algorithm based on MODIS and global meteorology data , 2007 .

[11]  L. Stroosnijder,et al.  Improving water use efficiency in drylands , 2012 .

[12]  A. Steiner,et al.  Aerosol-induced thermal effects increase modelled terrestrial photosynthesis and transpiration , 2005 .

[13]  Dan Liu,et al.  Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database , 2014 .

[14]  M. Mccabe,et al.  Multi-site evaluation of terrestrial evaporation models using FLUXNET data , 2014 .

[15]  P. Cox,et al.  Impact of changes in diffuse radiation on the global land carbon sink , 2009, Nature.

[16]  Matthew F. McCabe,et al.  The WACMOS-ET project – Part 2: Evaluation of global terrestrial evaporation data sets , 2015 .

[17]  I. Sobola,et al.  Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .

[18]  Hans Peter Schmid,et al.  Photosynthetic and Water Use Efficiency Responses to Diffuse Radiation by an Aspen-Dominated Northern Hardwood Forest , 2004, Forest Science.

[19]  A. Granier,et al.  A new method of sap flow measurement in tree stems , 1985 .

[20]  Joe Landsberg,et al.  Physiological ecology of forest production , 1986 .

[21]  Yanlian Zhou,et al.  Development of a two-leaf light use efficiency model for improving the calculation of terrestrial gross primary productivity , 2013 .

[22]  Matthew F. McCabe,et al.  The WACMOS-ET project – Part 1: Tower-scale evaluation of four remote-sensing-based evapotranspiration algorithms , 2015 .

[23]  Martin Navrátil,et al.  Impact of clear and cloudy sky conditions on the vertical distribution of photosynthetic CO2 uptake within a spruce canopy , 2012 .

[24]  Niels Otto Jensen,et al.  Increasing net CO2 uptake by a Danish beech forest during the period from 1996 to 2009 , 2011 .

[25]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[26]  Gordon B. Bonan,et al.  Ecological Climatology: Concepts and Applications , 2002 .

[27]  S. Weiss.  Vertical and temporal distribution of insolation in gaps in an old-growth coniferous forest. , 2000 .

[28]  T. A. Black,et al.  Global parameterization and validation of a two‐leaf light use efficiency model for predicting gross primary production across FLUXNET sites , 2016 .

[29]  T. Vesala,et al.  Advantages of diffuse radiation for terrestrial ecosystem productivity , 2002 .

[30]  G. Hammer,et al.  Radiation use efficiency increases when the diffuse component of incident radiation is enhanced under shade , 1998 .

[31]  S. Los,et al.  The impact of diffuse sunlight on canopy light‐use efficiency, gross photosynthetic product and net ecosystem exchange in three forest biomes , 2007 .

[32]  A. Bondeau,et al.  Comparing global models of terrestrial net primary productivity (NPP): analysis of differences in light absorption and light‐use efficiency , 1999 .

[33]  Rasmus Houborg,et al.  Intercomparison of a ‘bottom-up’ and ‘top-down’ modeling paradigm for estimating carbon and energy fluxes over a variety of vegetative regimes across the U.S. , 2009 .

[34]  D. Baldocchi,et al.  Effects of diffuse radiation on canopy gas exchange processes in a forest ecosystem , 2008 .

[35]  D. C. Uprety,et al.  Carbon dioxide , 2017, Reactions Weekly.

[36]  Y. Wanga,et al.  A two-leaf model for canopy conductance , photosynthesis and partitioning of available energy I : Model description and comparison with a multi-layered model , 1998 .

[37]  G. Wieczorek,et al.  A comparative analysis of simulated and observed landslide locations triggered by Hurricane Camille in Nelson County, Virginia , 2008 .

[38]  Alfredo R. Huete,et al.  Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests , 2016, Science.

[39]  R. Leuning.  A critical appraisal of a combined stomatal‐photosynthesis model for C3 plants , 1995 .

[40]  R. Leuning,et al.  A two-leaf model for canopy conductance, photosynthesis and partitioning of available energy I:: Model description and comparison with a multi-layered model , 1998 .

[41]  Eric F. Wood,et al.  Global estimates of evapotranspiration for climate studies using multi-sensor remote sensing data: Evaluation of three process-based approaches , 2011 .

[42]  Xingguo Mo,et al.  Simulating evapotranspiration and photosynthesis of winter wheat over the growing season , 2001 .

[43]  Maosheng Zhao,et al.  A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production , 2004 .

[44]  P. Ciais,et al.  Europe-wide reduction in primary productivity caused by the heat and drought in 2003 , 2005, Nature.

[45]  J. Monteith.  SOLAR RADIATION AND PRODUCTIVITY IN TROPICAL ECOSYSTEMS , 1972 .

[46]  J. Orgill,et al.  Correlation equation for hourly diffuse radiation on a horizontal surface , 1976 .

[47]  Christian Wirth,et al.  Seasonal and annual variations in the photosynthetic productivity and carbon balance of a central Siberian pine forest , 2002 .

[48]  C. Jacobs,et al.  Direct and Diffuse Radiation in the Shallow Cumulus–Vegetation System: Enhanced and Decreased Evapotranspiration Regimes , 2017 .

[49]  M. Torn,et al.  Separating the effects of phenology and diffuse radiation on gross primary productivity in winter wheat , 2016 .

[50]  Eric F. Wood,et al.  Multi‐model, multi‐sensor estimates of global evapotranspiration: climatology, uncertainties and trends , 2011 .

[51]  S. Bassow,et al.  HOW ENVIRONMENTAL CONDITIONS AFFECT CANOPY LEAF‐LEVEL PHOTOSYNTHESIS IN FOUR DECIDUOUS TREE SPECIES , 1998 .

[52]  D. Baldocchi.  Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future , 2003 .

[53]  C.J.T. Spitters,et al.  Separating the diffuse and direct component of global radiation and its implications for modeling canopy photosynthesis Part II. Calculation of canopy photosynthesis , 1986 .

[54]  R. Lemeur,et al.  Extinction of net radiation in different crop canopies , 1969 .

[55]  J. Lhomme,et al.  A THEORETICAL BASIS FOR THE PRIESTLEY-TAYLOR COEFFICIENT , 1997 .

[56]  I. Noble,et al.  On the direct effect of clouds and atmospheric particles on the productivity and structure of vegetation , 2001, Oecologia.

[57]  R. Monson,et al.  Temperature as a control over ecosystem CO2 fluxes in a high-elevation, subalpine forest , 2003, Oecologia.

[58]  Hans Peter Schmid,et al.  The role of sky conditions on gross primary production in a mixed deciduous forest , 2011 .

[59]  Paola Annoni,et al.  Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index , 2010, Comput. Phys. Commun..

[60]  David Y. Hollinger,et al.  Carbon dioxide exchange between an undisturbed old-growth temperate forest and the atmosphere , 1994 .

[61]  S. Seneviratne,et al.  Vegetation‐mediated impacts of trends in global radiation on land hydrology: a global sensitivity study , 2011 .

[62]  A. J. Dolman,et al.  Radiation, temperature, and leaf area explain ecosystem carbon fluxes in boreal and temperate European forests , 2005 .

[63]  Allison L. Dunn,et al.  A long‐term record of carbon exchange in a boreal black spruce forest: means, responses to interannual variability, and decadal trends , 2007 .

[64]  K. Taylor.  Summarizing multiple aspects of model performance in a single diagram , 2001 .

[65]  S. Seneviratne,et al.  Role of land surface processes and diffuse/direct radiation partitioning in simulating the European climate , 2011 .

[66]  A. Ibrom,et al.  Effects of climate variability and functional changes on the interannual variation of the carbon balance in a temperate deciduous forest , 2011 .

[67]  Shaoqiang Wang,et al.  Improving the light use efficiency model for simulating terrestrial vegetation gross primary production by the inclusion of diffuse radiation across ecosystems in China , 2015 .

[68]  J. Randerson,et al.  Terrestrial ecosystem production: A process model based on global satellite and surface data , 1993 .

[69]  Robert J. Beaver,et al.  Path Analysis: A Primer. , 1976 .

[70]  Peter R. J. North,et al.  Control of atmospheric particles on diffuse radiation and terrestrial plant productivity , 2012 .

[71]  Manfred Owe,et al.  On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces , 1993 .

[72]  Rasmus Houborg,et al.  Regional simulation of ecosystem CO2 and water vapor exchange for agricultural land using NOAA AVHRR and Terra MODIS satellite data - application to Zealand, Denmark , 2004 .

[73]  Biophysical controls on CO2 fluxes of three Northern forests based on long-term eddy covariance data , 2008 .

[74]  A. Ibrom,et al.  Remote sensing based evapotranspiration and runoff modeling of agricultural, forest and urban flux sites in Denmark: from field to macro-scale. , 2009 .

[75]  Jean-Francois Lamarque,et al.  Predicted change in global secondary organic aerosol concentrations in response to future climate, emissions, and land use change , 2008 .

[76]  Tim R. McVicar,et al.  Evaluation of the remote-sensing-based DIFFUSE model for estimating photosynthesis of vegetation , 2014 .

[77]  Dennis D. Baldocchi,et al.  Response of a Deciduous Forest to the Mount Pinatubo Eruption: Enhanced Photosynthesis , 2003, Science.

[78]  S. Nilsson,et al.  Satellite-based terrestrial production efficiency modeling , 2009, Carbon balance and management.

[79]  J. H. Awbery.  Temperature Control , 2019, Nature.

[80]  Andrew E. Suyker,et al.  Variations in the influence of diffuse light on gross primary productivity in temperate ecosystems , 2015 .

[81]  N. Jensen,et al.  Two years of continuous CO2 eddy-flux measurements over a Danish beech forest , 2001 .