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Impacts of Dynamic Aerodynamic and Thermodynamic Parameters Over Rainfed Maize Agroecosystem on Simulating Land Surface Process

Author: CaiFu
Tutor: ZhouGuangSheng
School: Chinese Academy of Meteorological Sciences
Course: Meteorology
Keywords: rainfed maize agroecosystem albedo surface roughness dynamic parameter land surface process simulation
CLC: S513
Type: PhD thesis
Year: 2012
Downloads: 61
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Abstract


Surface albedo (α) controls directly distribution of solar radiation energy between the earthsurface and atmosphere, and it is a very important thermal parameter used to calculateexchanges of energy and materials between terrestrial ecosystems and atmosphere.Aerodynamics roughness (z0) and zero plane displacement (d) are significant dynamicparameters influencing flux exchanges bwteeen terrestrial ecosystems and atmosphere.Accurate description of these parameters can improve simulation accuracy of the exchanges ofenergy and materials between terrestrial ecosystems and atmosphere as well as meteorologicalelements. Usually, α,z0and d are expressed with fixed value during the same kind ofunderlying surface in existing land surface models, and do not consider the change with time.Rainfed maize agroecosystem is a typical and representative underlying surface type innortheast China because its extreme changes in surface construction and properties includingcanopy height (h), leaf area index (LAI) and vegetation coverage (FVEG) with growth ofmaize cause variations of α, z0and d and then lead to changes in a series of physical processsuch as distributing and transferring processes of radiation, water and heat. Based oncontinuous observation data of land-atmosphere flux exchanges, meteorological and biologicalelements during2006-2008from Jinzhou agricultural ecosystem research station, dynamiccharacteristics and relationships with relevant influence factors of α,z0and d in rainfed maizewhole growth period are analyzed and their dynamic parameterization schemes are set up andused to improve BATS1e. At the last, the effect of improved model on simulating land surfaceprocess is investigated. The main conclusions are listed as follows.(1) Revealing the sensitivity of BATS1e to dynamic LAI, FVEG, α and z0. BATS1e is ableto simulate reasonable daily pattern and interdiurnal change of surface soil temperature(Tg), netabsorbed solar energy flux(Frs) and sensible heat flux(Hs) as well as undesirable surface soilwater content(SWC) and latent heat flux(λE) especially on no-precipitation day. The simulationerrors is greater and decreasing when LAI and FVEG are smaller and approaching ground truth,indicating that it is very necessary for increase simulation precision to use more real parametersettings in the model. Dynamic assignment of z0, LAI and FVEG plays an important role inimproving simulation precision respectively to Tg, Hsand Tg, Hs, λE, SWC and Tg, frs, Hs,SWC. On the whole, every variable is sensitive to parameter dynamic when rainfed maize ecosystem surface change from bare soil to vegetation. In addition, α assigned with dynamicvalue affects to varying degrees to simulation of Tg, λE and Hs, especially the latter.(2) Developing a dynamic parameterization scheme of α based on solar altitude(hθ), SWCand LAI. The bare soil α scheme founded considering respectively logarithm and linearrelationship between α and hθand SWC is better than those considering other relationships andis able to simulate diurnal pattern of α with smaller error in most of the non-growing seasonexcept early spring. In the growing season, the simulation precision of α scheme founded withstatistical regression method considering respectively logarithm, linear and exponentialrelationship between α and hθ, SWC and LAI those play an important role to α is higher thanthose considering other relationships. For the limitation of data, the scheme underestimatesevidently α in most of the study periods especially in vegetative growth phase of maize. AsFVEG is introduced and used to bestow weighing to soil and vegetation, the synthesis modelwhose simulation error decreases significantly in whole growing season especially invegetative growth phase is able to reflect seasonal variation of α and has dynamic simulationability, which change an untrue hypothesis that vegetation α is only fixed value in many landsurface model and makes the model universal-adapted to simulate dynamic α in differentphases of rainfed maize ecosystem. Compared with the double-layer and simplifieddouble-layer model of α, simulation ability of the synthesis model is stronger than that ofanother two in most of time expect in later growing period when is weaker than that ofsimplified double-layer model.(3) Evaluating the simulation of improving BATS1e model through introducing thesynthesis model and dynamic LAI. The results show that the improving model realizesdynamic simulation of α whose annual simulation error decreases obviously and the simulationvalue is more accurate for dynamic LAI introduced in growing season, which improves thesimulation precision of radial component such as frs whose annual improving quantity(IQ)accounts for1.7percent of annual global radiation, net absorb long wave radiation(frl) in Mayand June when FVEG changes quickly and nr in growing season and daytime,respectively. IQof yearly and monthly Tgis0.62K and above1K, respectively. Concidering difference ofcanopy heat flux between bare soil and vegetation, their simulation results analyzedrespectively show that simulation process of heat flux by the improve model is more close tothe fact including Hswhose improvement is the most obvious, especially in the growing seasonin June and August when underlying surface characteristics change evidently than in non-growing season, and λE whose improvement is less than the former and circumstance isconsistent mainly with Hs, but simulation error is large because of notable underestimation ofSWC on no precipitation day, which demonstrates that expression of soil water content ofBATS1e must be improved, as well as soil heat flux(G) whose simulation precision is higher in the non-growing season than in the growing season and the explaining ability of simulation toobservation increases4%. Yet simulation process of the primary model is consistent with thefact in trend seen from the outside but is an indisguise in facts.(4) Establishing dynamic parameterization schemes of z0and d by the optimizationmethods. It is found that z0from bulk Rickardson number (Ri) considering different heightscombination is discrepant, decrease with increasing Riand is reasonable simulated with theheight combination of2m and10m. z0is smaller than0.2m before tasseling stage and comes tothe maximum about0.4m before and after milk stage. d value begins to appear10days afterjointing when h is1.4m height and at this time its height is0.8to1m then1to1.4m aftertasseling stage. For magnitude and change trend, z0and d in this study are consistent withrelated research results. Before d appears, negative exponent and positive linear relationshipsbetween z0and wind speed, LAI, h are found. Simulation precision of z0parameterizationscheme considering accumulation form of the effect of h and wind speed on z0is highest. Afterd appears, relationship between wind speed and z0+d is more notable than those between windspeed and z0or d. At the same time, positive exponent relationships between z0or d and LAI orh are found. LAI and h have more influence to z0than d and z0+d for the former greater thanthe latter. In addition, d/h and z0/h is0.4to0.54and0.1to0.14respectively. Before h comes tothe maximum, d/h and z0/h are decreasing and increasing with LAI respectively. Simulationprecisions of z0and d parameterization model considering multiplicative form of exponentrelationships between z0and LAI, wind speed as well as d and h, wind speed respectively arehighest, respectively. When h is invariable, simulation precisions of d and z0parameterizationmodel is decreasing and can’t be simulated respectively as a result of small variation of LAIand greater measure error of wind speed for the former and smaller diurnal variation for thelatter.(5) Evaluating the simulation of improving BATS1e model through introducing dynamicaerodynamic parameter scheme. The simulation precision of each component of land surfaceheat flux is in different degree improved with the order of G, Hsand λE whose growing seasonIQ account for1.24,0.36and0.19percent of global radiation respectively when originalBATS1e model is modified with newly-built dynamic z0and d parameterization scheme. IQ ofG, Hsand λE are larger in July and August, in August and September, in July and Augustaccounting for2.35and3.36,1.68and0.4as well as0.67and2.29percent of monthly globalradiation respectively than in other months. Furthermore, we come to a conclusion that d isable to be ignored when that is smaller than1.6m because of slow response from BATS1emodel.(6) Evaluating the simulation of improving BATS1e model through introducing dynamicaerodynamic and thermodynamic parameter scheme. Simulation precision of α, nr and Tgare improved when original BATS1e model is simultaneously modified with newly-built dynamicα, z0and d parameterization scheme. As a result, simulation of each component of land surfaceheat flux is improved with the order of Hs, G, and λE. Considering contribution of eachparameter, α dynamic parameterization contributes more than that of z0and d to nr and Hs, onthe contrary, z0and d dynamic parameterization contribute more than that of α to simulation ofTg, λE and G. Simulation precision of λE decreases in some periods though thatparameterization of α, z0and d are improved, which is owed to SWC unreasonably simulatedby original BATS1e model. Those show that simulation of some variables may be improvedbut those of another deteriorative when one or several parameters are mended because thefictitious balance situation canceling out simulation errors between different variables andmaking simulation result seem to be true is destroyed by improving of some variables, whichleads error to amplify. But, the model is being more reasonably improved. As a result,enlargement of simulation error does n’t absolutely represent that the improvement of model isinvalid. Altogether, many processes in land surface model are still not perfect and in need to beimproved

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CLC: > Agricultural Sciences > Crop > Cereal crops > Corn ( maize )
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