Suitability of modelled and remotely sensed essential climate variables for monitoring Euro-Mediterranean droughts

Abstract. Two new remotely sensed leaf area index (LAI) and surface soil moisture (SSM) satellite-derived products are compared with two sets of simulations of the ORganizing Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE) and Interactions between Soil, Biosphere and Atmosphere, CO2-reactive (ISBA-A-gs) land surface models. We analyse the interannual variability over the period 1991–2008. The leaf onset and the length of the vegetation growing period (LGP) are derived from both the satellite-derived LAI and modelled LAI. The LGP values produced by the photosynthesis-driven phenology model of ISBA-A-gs are closer to the satellite-derived LAI and LGP than those produced by ORCHIDEE. In the latter, the phenology is based on a growing degree day model for leaf onset, and on both climatic conditions and leaf life span for senescence. Further, the interannual variability of LAI is better captured by ISBA-A-gs than by ORCHIDEE. In order to investigate how recent droughts affected vegetation over the Euro-Mediterranean area, a case study addressing the summer 2003 drought is presented. It shows a relatively good agreement of the modelled LAI anomalies with the observations, but the two models underestimate plant regrowth in the autumn. A better representation of the root-zone soil moisture profile could improve the simulations of both models. The satellite-derived SSM is compared with SSM simulations of ISBA-A-gs only, as ORCHIDEE has no explicit representation of SSM. Overall, the ISBA-A-gs simulations of SSM agree well with the satellite-derived SSM and are used to detect regions where the satellite-derived product could be improved. Finally, a correspondence is found between the interannual variability of detrended SSM and LAI. The predictability of LAI is less pronounced using remote sensing observations than using simulated variables. However, consistent results are found in July for the croplands of the Ukraine and southern Russia.

[1]  Jan Polcher,et al.  Sensitivity of the West African hydrological cycle in ORCHIDEE to infiltration processes , 2008 .

[2]  Vincent Rivalland,et al.  Modelling forest transpiration and CO2 fluxes—response to soil moisture stress , 2004 .

[3]  Fabienne Maignan,et al.  Evaluation of a Global Vegetation Model using time series of satellite vegetation indices , 2011 .

[4]  Yi Y. Liu,et al.  Evaluating global trends (1988–2010) in harmonized multi‐satellite surface soil moisture , 2012 .

[5]  J. Berry,et al.  A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species , 1980, Planta.

[6]  Yi Y. Liu,et al.  Trend-preserving blending of passive and active microwave soil moisture retrievals , 2012 .

[7]  A. J. Dolman,et al.  The Pilot Phase of the Global Soil Wetness Project , 1999 .

[8]  T. Vesala,et al.  Reduction of ecosystem productivity and respiration during the European summer 2003 climate anomaly: a joint flux tower, remote sensing and modelling analysis , 2007 .

[9]  Alina Barbu,et al.  The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes , 2012 .

[10]  Olivier Dupont,et al.  Heat and drought 2003 in Europe: a climate synthesis , 2006 .

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

[12]  Markus Reichstein,et al.  Analyzing the causes and spatial pattern of the European 2003 carbon flux anomaly using seven models , 2007 .

[13]  J. Deardorff.  A Parameterization of Ground-Surface Moisture Content for Use in Atmospheric Prediction Models , 1977 .

[14]  Jean-Philippe Gastellu-Etchegorry,et al.  A canopy radiative transfer scheme with explicit FAPAR for the interactive vegetation model ISBA‐A‐gs: Impact on carbon fluxes , 2013 .

[15]  Edwin W. Pak,et al.  An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data , 2005 .

[16]  J. Goudriaan,et al.  Photosynthesis, CO2 and Plant Production , 1985 .

[17]  A. Perrier,et al.  SECHIBA : a new set of parameterizations of the hydrologic exchanges at the land-atmosphere interface within the LMD atmospheric general circulation model , 1993 .

[18]  Pierre Friedlingstein,et al.  A global prognostic scheme of leaf onset using satellite data , 2000 .

[19]  Shilong Piao,et al.  A global analysis of soil moisture derived from satellite observations and a land surface model , 2012 .

[20]  G. Collatz,et al.  Coupled Photosynthesis-Stomatal Conductance Model for Leaves of C4 Plants , 1992 .

[21]  W. Wagner,et al.  Evaluation of the ESA CCI soil moisture product using ground-based observations , 2015 .

[22]  Gérard Dedieu,et al.  TURC: A diagnostic model of continental gross primary productivity and net primary productivity , 1996 .

[23]  P. Ciais,et al.  Analyzing the causes and spatial pattern of the European 2003 carbon flux anomaly in Europe using seven models , 2007 .

[24]  Aaron Boone,et al.  The Influence of the Inclusion of Soil Freezing on Simulations by a Soil–Vegetation–Atmosphere Transfer Scheme , 2000 .

[25]  S. Seneviratne,et al.  Investigating soil moisture-climate interactions in a changing climate: A review , 2010 .

[26]  C. Müller,et al.  Modelling the role of agriculture for the 20th century global terrestrial carbon balance , 2007 .

[27]  Jean-Christophe Calvet,et al.  Use of agricultural statistics to verify the interannual variability in land surface models: a case study over France with ISBA-A-gs , 2011 .

[28]  Adrian Barbu,et al.  Assimilation of surface soil moisture into a multilayer soil model: design and evaluation at local scale , 2013 .

[29]  Wouter Dorigo,et al.  Patterns and drivers of Araucaria araucana forest growth along a biophysical gradient in the northern Patagonian Andes: Linking tree rings with satellite observations of soil moisture , 2014 .

[30]  Roger Jones,et al.  Regional climate projections , 2007 .

[31]  Jean-Pierre Wigneron,et al.  An interactive vegetation SVAT model tested against data from six contrasting sites , 1998 .

[32]  Jan Polcher,et al.  Modelling root water uptake in a complex land surface scheme coupled to a GCM , 1998 .

[33]  S. Running,et al.  Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data , 2002 .

[34]  J. Deardorff.  Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation , 1978 .

[35]  C. Albergel,et al.  An evaluation of ASCAT surface soil moisture products with in-situ observations in Southwestern France , 2008 .

[36]  W. Wagner,et al.  An Intercomparison of ERS-Scat and AMSR-E Soil Moisture Observations with Model Simulations over France , 2009 .

[37]  Jean-Louis Roujean,et al.  ECOCLIMAP-II/Europe: a twofold database of ecosystems and surface parameters at 1 km resolution based on satellite information for use in land surface, meteorological and climate models , 2012 .

[38]  Yi Y. Liu,et al.  Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals , 2011 .

[39]  Ross E. McMurtrie,et al.  Modelling the yield of Pinus radiata on a site limited by water and nitrogen , 1990 .

[40]  I. C. Prentice,et al.  A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system , 2005 .

[41]  I. C. Prentice,et al.  Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model , 2003 .

[42]  Dominique Carrer,et al.  Verification of the new ECMWF ERA-Interim reanalysis over France , 2010 .

[43]  O. Hagolle,et al.  LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm , 2007 .

[44]  Frédéric Baret,et al.  A Terrestrial Surface Climate Data Record for Global Change Studies , 2009 .

[45]  Heather McNairn,et al.  Evaluation of soil moisture extremes for agricultural productivity in the Canadian prairies , 2012 .

[46]  Konstantine P. Georgakakos,et al.  Potential value of operationally available and spatially distributed ensemble soil water estimates for agriculture , 2006 .

[47]  Jean-Philippe Vidal,et al.  Multilevel and multiscale drought reanalysis over France with the Safran-Isba-Modcou hydrometeorological suite , 2009 .

[48]  Jean-Christophe Calvet,et al.  Inclusion of a Third Soil Layer in a Land Surface Scheme Using the Force–Restore Method , 1999 .

[49]  R. Pachauri.  Climate change 2007. Synthesis report. Contribution of Working Groups I, II and III to the fourth assessment report , 2008 .

[50]  Véronique Ducrocq,et al.  Studying the Hydrological Cycle in the Mediterranean: Fourth Hydrological Cycle in Mediterranean Experiment Workshop; Bologna, Italy, 8–10 June 2010 , 2010 .

[51]  Frédéric Baret,et al.  GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production , 2013 .

[52]  W. Wagner,et al.  A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data , 1999 .

[53]  Ranga B. Myneni,et al.  Temperature and Snow-Mediated Moisture Controls of Summer Photosynthetic Activity in Northern Terrestrial Ecosystems between 1982 and 2011 , 2014, Remote. Sens..

[54]  Fabienne Maignan,et al.  Constraining a global ecosystem model with multi-site eddy-covariance data , 2012 .

[55]  Elke Hertig,et al.  The climate of the Mediterranean region in future climate projections , 2012 .

[56]  Jordi Font,et al.  The HyMeX (Hydrological cycle in the Méditerranean Experiment) program: The specific context of oceanography , 2009 .

[57]  W. Wagner,et al.  Skill and Global Trend Analysis of Soil Moisture from Reanalyses and Microwave Remote Sensing , 2013 .

[58]  M. Déqué,et al.  Anthropogenic climate change over the Mediterranean region simulated by a global variable resolution model , 2003 .

[59]  C. Albergel,et al.  Assimilation of Soil Wetness Index and Leaf Area Index into the ISBA-A-gs land surface model: grassland case study , 2011 .

[60]  Alina Barbu,et al.  Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France , 2013 .

[61]  Jean-Christophe Calvet,et al.  Investigating soil and atmospheric plant water stress using physiological and micrometeorological data , 2000 .

[62]  Dominique Carrer,et al.  Impact of precipitation and land biophysical variables on the simulated discharge of European and Mediterranean rivers , 2012 .

[63]  Thomas J. Jackson,et al.  Soil moisture retrieval from AMSR-E , 2003, IEEE Trans. Geosci. Remote. Sens..

[64]  Aaron Boone,et al.  Local evaluation of the Interaction between Soil Biosphere Atmosphere soil multilayer diffusion scheme using four pedotransfer functions , 2011 .

[65]  Jean-Louis Roujean,et al.  Past and future scenarios of the effect of carbon dioxide on plant growth and transpiration for three vegetation types of southwestern France , 2007 .

[66]  P. Friedlingstein,et al.  Toward an allocation scheme for global terrestrial carbon models , 1999 .

[67]  Fabienne Maignan,et al.  Modelling LAI, surface water and carbon fluxes at high-resolution over France: comparison of ISBA-A-gs and ORCHIDEE. , 2011 .

[68]  F. Baret,et al.  LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: validation and comparison with MODIS collection 4 products , 2007 .

[69]  Y. Kerr.  Soil moisture from space: Where are we? , 2007 .

[70]  F. Baret,et al.  Performances of neural networks for deriving LAI estimates from existing CYCLOPES and MODIS products , 2008 .

[71]  F. Baret,et al.  GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products , 2013 .

[72]  Jean-Pierre Wigneron,et al.  Modelling LAI at a regional scale with ISBA-A-gs: comparison with satellite-derived LAI over southwestern France , 2009 .

[73]  D. Baldocchi.  ‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems , 2008 .

[74]  Wouter Dorigo,et al.  Potential and limitations of multidecadal satellite soil moisture observations for selected climate model evaluation studies , 2013 .

[75]  C. Jacobs,et al.  Stomatal behaviour and photosynthetic rate of unstressed grapevines in semi-arid conditions , 1996 .

[76]  Dick Dee,et al.  Low‐frequency variations in surface atmospheric humidity, temperature, and precipitation: Inferences from reanalyses and monthly gridded observational data sets , 2010 .

[77]  Jean-Louis Roujean,et al.  Ability of the land surface model ISBA‐A‐gs to simulate leaf area index at the global scale: Comparison with satellites products , 2006 .

[78]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[79]  J. Tenhunen,et al.  Inverse modeling of seasonal drought effects on canopy CO2/H2O exchange in three Mediterranean ecosystems , 2003 .

[80]  Jean-Christophe Calvet,et al.  Modelling CO2-enrichment effects using an interactive vegetation SVAT scheme , 2001 .

[81]  W. Wagner,et al.  Monitoring multi-decadal satellite earth observation of soil moisture products through land surface reanalyses , 2013 .

[82]  N. Gobron,et al.  Simultaneous assimilation of satellite and eddy covariance data for improving terrestrial water and carbon simulations at a semi-arid woodland site in Botswana , 2012 .

[83]  R. Giering,et al.  Consistent assimilation of MERIS FAPAR and atmospheric CO2 into a terrestrial vegetation model and interactive mission benefit analysis , 2011 .