Discussions concerning global change typically concentrate on future climatic changes promulgated by changes in atmospheric chemistry, most notably increases in the so-called greenhouse gases such as CO2, CH4, and and N2O. Although the energy exchange characteristics of the Earth’s surface are an important component of climate models, the idea that changes in the terrestrial surface could also be a causal factor in climatic changes has not received much attention. Sensitivity studies with GCM’s suggested that regional climate can be dramatically changed by severe deforestation. Dickinson and Henderson-Sellers (1988) simulated the Amazon basin with complete forest cover, and then replaced with degraded grasslands. Th degraded grasslands reduced evapotranspiration so much that surface temperatures were predicted to increase by 3-5 degrees. Walker et al. (1995) used the deforestation statistics of Skole and Tucker (1993) and estimated that precipitation had been reduced by 1.2mm/day due to reductions in ET of 18% caused by landcover changes

Churkina, Galina

Hibbard, Kathy

Nemani, Ramakrishna R.

Running, Steven W

English

University of Montana

University of Montana ScholarWorks at University of Montana Numerical Terradynamic Simulation Group Publications Numerical Terradynamic Simulation Group 11-1996 The influence of landcover change on global terrestrial biogeochemistry Steven W. Running University of Montana - Missoula Ramakrishna R. Nemani Kathy Hibbard Galina Churkina Follow this and additional works at: https://scholarworks.umt.edu/ntsg_pubs Let us know how access to this document benefits you. Recommended Citation Running, S. W., Nemani R. R., Hibbard K. A., and Churkina G. The influence of landcover change on global terrestrial biogeochemistry. Proceedings of IGBP/BAHC-LUCC Joint Inter-Core Projects Symposium on Interactions Between the Hydrological Cycle and Land Use/Cover, November 4-7, 1996, Kyoto, Japan. This Conference Proceeding is brought to you for free and open access by the Numerical Terradynamic Simulation Group at ScholarWorks at University of Montana. It has been accepted for inclusion in Numerical Terradynamic Simulation Group Publications by an authorized administrator of ScholarWorks at University of Montana. For more information, please contact scholarworks@mso.umt.edu. The influence of landcover change on global terrestrialbiogeochemistrySteven W. Running, Ramakrishna Nemani, Kathy Hibbard and Galina ChurkinaThe University of Montana. School of Forestry, Missoula, MT 59812, USAINTRODUCTION , . ,  .D iscussions concerning global change typically concentrate on future climatic changes  promulgated by changes in atmospheric chemistry, m ost notably increases in ® ,green house g a s e s  such  as CO2 . CH4 . and N2 O. Although the energy exchange charactenstics of the Earth’s  surface are an important component of climate m odels, the idea that changes in the terrestrial surface could also be a causal factor in climatic changes has not attention. Sensitivity studies with GCM’s su ggested  that region^ c  imate ^ n  be dramatically changed by severe  deforestation. Dickinson and Henderson-Sellers (1988) simulated the  Am azon basin with com plete forest cover, and then replaced with degraded grasslands. Th degraded grasslands reduced evapotranspiration so  much that surface temperatures wer predicted to increase by 3-5®. Walker et al (1995) u sed  the deforestation s ta tij ic s  of Skole and Tucker (1993) and estimated that precipitation had been reduced by 1.2mm/day due toreductions in ET of 18% caused  by landcover ch a n g es . ,M ost global m odels of terrestrial biogeochem istry have used potential vegetation to describe the terrestrial landcover (Melillo et al 1993, VEMAP 1995). Models of global vegetation  biogeography also are computing a potential vegetation, that d oes not inco^orate human- induced landcover c h a n g e  (Prentice et al 1993 , N ielson 1995, Woodward et al 1996).R ecent advances have been m ade in quantifying the global extent of landcover change. Nemani and Running (1995) compared landcover a s  im aged by cujrent satellites ag^ ^ st a  climatically defined potential landcover that theoretically would exist without human disturbance. Nemani et al (1996) related these landcover ch an ges to radiometnc surface temperatures^, showing regions w here mid-afternoon surface tem peratures can now reach as much as 16 warmer than the original vegetated surfaces. Implications of th ese  changes in energy partitioning include increased m esosca le  tuitulence (Pielke et al 1996). In this paP®'' w® con seq u en ces of the landcover changes quantified by Nemani and Rurining (1995) on the terrestrial biogeochem ical cycles. W e will calculate annual carbon, water and nitrogen budgets of the terrestrial biosphere for both the potential landcover, and the existing la i^ o v e r  and contrast the results, particularly in areas where large differences becom e evident. This analysis should give so m e first estim ates of whether hum ans are degrading the biospheric productivity, and whether substantial imbalances in hydrologic regim es may have been generated by landcover ch an ges, and contributed to flooding, droughts and other water m anagem ent problems.METHODSLand Cover C h ance . . .To allow for global comparability and satellite evaluation, w e define vegetation only in terms of biome type and leaf area index, two of the m ost irriportant characteristics used  to represent vegetation in current climate and carbon m odels. Biome type differentiates arnong deciduous and evergreen forests, shrubs, crops and gra sses . Figure 1 show s the existing biome distribution found by Nemani and Running (1995) from satellite analysis.Proceedings o f IGBP/BAHC-LUCC Joint Inter-Core Projects Symposixim Kyoto, Japan, 1996—  6 —Figure 1 Map of current global biome distribution generated  from AVHRR satellite imagery using the procedures in Nemani and Running (1995) that incorporate NDVI and surface temperature to classify  vegetation types.Biome distributior changesare strongest in the tem perate zones, despite popular press Jhave highest landcover changes (Figure 2). However, som e regie • P r-.gg*- rroolandshave shown virtually com plete landcover conversion from ongin p(Myers and Turner 1993, Dale 1994).Figure 2 Areal changes In land surface area for each biome type relative to potential landcover. The change trajectory is plotted on the NDVI vs Surface temperature axes used by Nemani and Running to define biome types in Rgure 1. Forests typically have the highest NDVI and low est surface temperature, b ecau se  of strong latent heat exchange and aerodynamic mixing. Shorter stature vegetation have both lower NDVI and higher maximum annual surface temperatures.♦7 I.0 $SHRUB'.CROPSu  32 ':TCMPHHATc ’  IV t  •  *2%0.7S0.500.25NDVIindex (area of leaves per unit ground area, L «) provides a sim ple m easure of plant o a X  i n »  ignores the oom piexities of oanopy Snergy and m ass e io h a n g e  prooesses betw een piants andW e define land cover changes in terms of ch an ges in biome V̂P ’ anthrooooenlcpotential natural vegetation, vegetation that would have „  x  ̂ Actual LAIs are disturbances, (Potential L4I) and current vegetation, (Actual LA).calculated a s  f o l i o s .  vegetation that would have existed before large scalehuman “ e ^ b r g ^ a ^  a p o t e L l  biom e map a n d l Sassociating vegetation with long-term climate (Leemans npnnraohic biomecharacteristics (Zobler 1986), produced at 0 .5x0 .5  degree  distribution Is based  on specific physiological resp on ses o P VPtolerance, growing sea so n  heat sum s and drought stress (Peen i . . leaf areaclimate and soils data, w e also computed geographic vanations in p . . , = eauillbriumIndex based  on well established principles of hvHminnir eouilibrium theorv(Neilson 1995; Woodward 1987; Nemani and Running 1989a). c Zsu g g ests  that plants adjust their leaf area to optimize the u se  w i e r  balanceexam ple areas with longer growing sea so n s  of optimum p—  iconditions support higher leaf area. This definition of vegetation naturald isturbances such  as fire, insect and d is e a s e s , and wind extrernes rniiprtPri durino. A ctual LAI: W e used the NOAA/'Global Vegetation index (GVl) data collected dunng1985-90 m apped to a 10 minute grid. A s w e are interested in companng only the that a particular area can achieve under potential versus actual conditions, w e  first com puted a yearly m aximum NDVI for the six years and then averaged the six maximum values. W e assum e  this six year average maximum value to minimize the impact of cloud contamination atm osphe  influence and inter-annual variability in climate. Finally, NDV for ^ c h  • ^ • 9 _produced by taking an average of all the 10 minute gnd cells within each 0.5x0.5  a r e ^  account for the differences in structural and optical properties among different vegetation t ^ e s  w e u sed  sep arate NDVl-U^l relations for grass (LA1=NDV1* 1.71+0.48, A sraret al  ̂985), needle  leaf (LAI=:(NDV1/0.31)^.26, Spanner et al 1990, Nemani and Running 1989a) and broadl^eaf canopies (LAI={NDVl/0.26)^2, Pierce et al 1993). The empirica relations based on ^eld studies were found to corroborate theoretically derived forms of NDVl-LAl relations (Asrar et al 1992, Sellers e t al 1994).Global E cosystem  Simulation System  ( G F S S v sl m odeling *W e u sed  B10ME-3GC. an ecosystem  simulation model, to estim ate potentia all-sided leaf area index for each  Ix ld egree grid cell (Hunt et al 1996). The model u ses  daily climate (solar radiation, humidity, air temperature and rainfall), soil (texture and depth) and yeg  (biome, LAI) information to compute carbon (photosynthesis and respiration) and hydrojogic budgets (interception, evaporation, transpiration, outflow). Soil decomposition and nitrogen mobilization are a lso  calculated. Snow pack dynamics are simulated using air temperatures an precipitation. Seasonal canopy phenology is computed from daylength and minimum airtem perature thresholds. . . . x , w ■ xDaily simulations were run. and summarized to annual totals of global net primaryproduction and hydrologic runoff, two variables of high significance to human econom ic activity.A first simulation w as run defining potential landcover and A l ,  effectively quantifying t ebenchmark biogeochem istry of Earth without human disturbance. Second the simulations wererun again with all climate and parameter fields identical except that existing landcover and A ldatalayers w ere used . The difference b etw een  the two resulting outputs w as then m apped.RESULTS  ̂ ,Global hydrologic changes resulting from landcover change activity are predom iriantly^  two types. Historically forested areas that have been  converted to cropland produce le ss  ET b eca u se  of lower A l ,  resulting in runoff in creases as high as 50cm. Temperate forests have ha the h ighest percentage conversion to agriculture (40%, s e e  Figure 2). However cropland converted from tropical forests in high precipitation clim ates may have the greatest proWems dueto increased  flooding potential. The region of highest hydrologic c h ^ g e  appears to be SoutheastAsia. T he secon d  major hyrologic change occurs w hen increased ET has occurred where desert lands h ave b een  irrigated, as evidenced by higher observed LAI tl^n  natural climate can support. This hydrologic change is m ost ev id en tjr v t^  W estern United States.Figure 3 Simulated global changes in terrestrial hydrologic runoff resulting from anthropogenic changes in vegetation landcover. Landcover change is defined as a change in satellite observed A l  compared to a climatically defined potential A l .cmr2 (» o :itKZo:m o:-HO:•KKO;■yPARTIAL REFERENCESAsrar, G., E.T. Kanemasu and M. Yoshida. (1985). Estimates of leaf area index from spectra! reflectance of Wheat under different cultural practices and solar angle. Rem ote Sensing of Environment, 17; 1 - l l .Asrar, G., Myneni, R.B., and Choudhury, B.J. (1992). Spatial heterogeneity in vegetation canopies and remote sen sin g  of Absorbed Photosynthetically Active Radiation: A modeling study. R em ote S e n sin g  of Environment, 41, 85-101.Dale, V.H. (Ed.) (1994). Effects of land-use change on atmospheric CO2 concentrations: Southeast Asia a s  a C a se  Study. Ecological Studies Vol 101. Springer-Verlag. 384pp.Hunt, E.R., Jr., S.C. Piper, R. Nemani, C.D. Keeling, R.D. Otto, and S.W. Running. (1996). Global net carbon exchange and intra-annual atmospheric CO2 concentrations Predicted by an Ecosystem  P rocess Model and Three-Dimensional Atmospheric Transport Model. Global Biogeochem ical C ycles 10(3): 431-456.Melillo, J.M., McGuire, A.D., Kicklighter, D.W., Moore III, B., Vorosmarty, C.J., and Schloss, A.L  (1993). Global climate change and terrestrial net primary production. Nature 363, 234- 240.Myer, W.B., and B.L Turner (eds). (1993). Global Land-Use/Land-Cover Change: A global perspective, Cambridge University Press, New York.Nemani, R., P ierce,L L , Running,S.W ., Goward,S.N. (1993). Developing satellite derived estim ates of surface moisture status. J. Appl. Meteorology 32:548-557.Nemani, R., S.W. Running, and R.A. Pielke. (1996). Global vegetation cover changes from coarse  resolution satellite data. Journal of G eophysical R esearch 101 (D3): 7157-7162.Nemani, R. and S.W. Running. (1995). Satellite monitoring of global land cover changes and their impact on climate. Climate C hange 31 :39 5 -4 1 3 .Neilson, R.P. (1995). A model for predicting continental-scale vegetation distribution and water balance. Ecological Applications 5, 362-385.Pierce, L L , J. Walker, T.Dowling, T. McVicar, T. Hatton, S. Running and J. Coughlan.(1993). Ecohydrological ch an ges in Murray-Darling basin III. A simulation of regional hydrological ch a n g es. J. Appl. Ecol., 30: 283-294.Prentice, C., Cramer, W., Harrison, S ., Leem ans, R., Monserud, R., Solomon, R. (1993). A global biome model based  on plant physiology and dom.inance, soil properties and climate. Journal of Biogeography 19, 117-134.Running, S.W ., T. Loveland and LL. Pierce. (1994). A vegetation classification logic based on rem ote sensing  for u se  in global biogeochem ical m odels. Ambio, 23:77-81.Running, S.W ., C.O.Justice, V .Salom onson, D.Hall, J.Barker, Y.J.Kaufmann, A.H.Strahler, A.R.Huete, J-P.Muller, V.Vanderbilt, Z.M.Wan, P.Teillet, D .Cam eggie. (1994) Terrestrial remote sensing science and algorithms planned for EOS/MODIS. Int. J. Rem ote Sensing, 15: 3587-3620.Sellers, P.J., S . Los, C.Tucker, C .Justice, D. Dazlich, G. Collatz and D. Randall. (1994). A revised land surface parameterization (SiB2) for atmospheric GCMs. Part 2: The generation of global fields of terrestrial biophysical parameters from satellite data. Int. J. R em ote Sensing, 15: 3519 -3546 .Spanner, M.A., L L  Pierce, S.W. Running and D .L  Peterson. (1990). The seasonality of AVHRR data of temperate coniferous forests: relation to leaf area index. Rem ote Sensing of Environment, 33: 97-112.VEMAP Members. (1995). V egetation/ecosystem  modeling and analysis project (VEMAP): Comparing biogeography and biogeochem istry models in a continental-scale study of terrestrial ecosystem  resp on ses to climate change and CO2 doubling. Global Biogeochem ical Cycles 9: 407 -437 .Woodward, F.I., T.M. Smith, and W .R. Emanuel. (1995). A global primary productivity and phytogeography model. Global B iogeochem ical Cycles 9:471-490.

The influence of landcover change on global terrestrial biogeochemistry

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The influence of landcover change on global terrestrial biogeochemistry

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