rückkopplungen,an,der, landoberfläche - aktuelles · rückkopplungen,an,der, landoberfläche....
TRANSCRIPT
Thilo StreckBiogeophysik
Ins4tut für Bodenkunde und StandortslehreUniversität Hohenheim, Stu@gart
Rückkopplungen an der Landoberfläche
Typischer Ablauf in der Anpassungsforschung
Pflanzenwachs-‐tumsmodell
ÖkonomischeBewertung
Handlungs-‐opDonen
Böden, Landnutzung
Management
ÖkonomischeRahmenbedingungen
Entwicklungs-‐szenarien
Globale KlimaprojekDon
Regionale KlimaprojekDon
1. Wie genau sind Klimamodelle?
Warrach-‐Sagi et al. (2012, subm.)
Spa
tial p
df
modeledobserved
Precipitation, mm/monthSommerniederschläge (JJA), 1990-‐2008, Vergleich von WRF331-‐NOAH (12 km Auflösung) mit DWD REGNIE-‐Datensatz
2. Wie genau sind Pflanzenwachstumsmodelle?
Sommergerste, 7 Standorte in Nord-‐ und MiWeleuropa,44 Wachstumsperioden
R.P. Rötter et al. / Field Crops Research 133 (2012) 23–36 31
OBSERVEDWOFOST
STICSMONICAHERMESFASSET
DSSAT!CERESDAISY
CROPSYSTAPES!ACE
0 2000 4000 6000 8000
Grain yield [kg ha!1, dry matter]
a b
c d
WOFOST
STICS
MONICA
HERMES
FASSET
DSSAT!CERES
DAISY
APES!ACE
0 500 1500 2500 3500
Root biomass [kg ha!1, dry matter]
WOFOST
STICS
MONICA
HERMES
FASSET
DSSAT!CERES
DAISY
CROPSYST
APES!ACE
0 5000 10000 15000 20000
TAGB [kg ha!1, dry matter]
WOFOST
STICS
MONICA
HERMES
FASSET
DSSAT!CERES
DAISY
CROPSYST
APES!ACE
0.3 0.4 0.5 0.6 0.7
Harve st index
Fig. 5. Box-and-whisker plots of (a) grain yield estimates of models and observations, (b) root biomass estimates, (c) maximum above-ground biomass estimates, and (d)harvest indices of the models – among the simulated sites and years (N = 44). Boxes delimit the inter-quartile range (25–75 percentiles) and whiskers show the high and lowextreme values.
rank correlation coefficients of 0.552 and 0.49, respectively, wereperforming best for all seasons (N = 44), while for Verovany site(N = 14), models DSSAT-CERES, WOFOST and HERMES showedhighest rank correlation coefficients (0.539, 0.537 and 0.488,respectively).
4. Discussion
4.1. Uncertainty levels
Our results from this barley model comparison show that sim-ulated grain yields vary widely, ranging from 1700 to 8100 kg ha!1
for all sites and seasons, being similar to the observed range
(2400–8100 kg ha!1). However, there were considerable differ-ences in estimates for individual sites and years among the models(Figs. 3–5 and 7). Under conditions of limited data available for cal-ibration (as in this blind test), uncertainty ranges in yield estimatesfrom individual models are mostly not acceptable and beyond themeasurement error of about 10–15% found in field experiments(Joernsgaard and Halmoe, 2003). This result is similar to the winterwheat study by Palosuo et al. (2011) and confirms that the differ-ences in estimates of grain yield between models, and betweenthe models and field observations have not much decreased whencompared to earlier model comparisons for wheat, where yieldswere off by 20% and more (Goudriaan et al., 1994; Jamieson et al.,1998).
Ertrag in kg ha-1
RöPer et al. (2012, verändert)
R.P. Rötter et al. / Field Crops Research 133 (2012) 23–36 29
APES!ACE
020
0040
0060
0080
00 CROPSYST DAISY
DSSAT!CERES
020
0040
0060
0080
00 FASSET HERME S
MONICA
020
0040
0060
0080
00 STICS
0 2000 4000 6000 8000
WOFOST
0 2000 4000 6000 8000MEAN
0 2000 4000 6000 80000
2000
4000
6000
8000
LedniceVerovanyBratislavaFlakkebjergJyndeva dFoulumJokioinen
Obse
rved
grain
yield
[kg
ha!1
, dry
matt
er]
Simulated grain yield [kg ha!1, dry matter]
Fig. 3. Simulated and observed grain yield estimates [kg ha!1, dry matter] for 44 studied growing seasons. Simulation results are shown for nine individual models andmulti-model means. Different study sites are depicted with different symbols. The 1:1 line is shown, representing perfect agreement.
Exceptions include Bratislava in 1994, Flakkeberg in 2006 andJokioinen in 2005.
At the two Czech sites, the “best model” HERMES estimatesyields slightly better than the MMM (Fig. 7). Overall, however,the MMM is a slighly better predictor than HERMES as indicatedby RMSE and IA (Fig. 4). Two other models, DAISY and WOFOSTalmost perform as well as the “best model”. However, their “bestperformances” look quite different, as we found when plotting yieldestimates by the individual models vis a vis observed (not shown)
as in Fig. 7. Except for Bratislava site, DAISY tends to underesti-mate observed yields and remains below the MMM. This is mostpronounced for the Czech and Finnish sites. WOFOST, on the otherhand, in most cases overestimates observed yields, on average byabout 1000 kg ha!1.
For all growing seasons, and for Verovany site separately,we also calculated Spearman’s rank correlations (not shown) toexamine how well the models are in reproducing the order ofobserved yields. Models DAISY and WOFOST, with Spearman’s
Gem
esse
n (k
g ha
-1)
Simuliert (kg ha-1)
+ 25%
-‐ 25%
MulU Model Mean
Symbole = Standorte
3. Wie sieht es aus mit Rückkopplungen?
BodenVegetaDon
AkteureLandnutzung
Standort-‐bedingungen
ÖkonomischeRahmenbedingungen
Globale Entwicklung
Globales Klima
Regionales Klima
Prozesse und Rückkopplungen im Landsystem
Atmosphärische Grenzschicht
WolkenbildungNiederschlag
Boden-‐bearbeitung,Management
Bestandes-‐entwicklungLAI, Albedo
Bodenwasser-‐haushalt
Energiebilanz
Landnutzungs-‐entscheidungen
Einstrahlung
FOR 1695
Einfluss der Bodenfeuchte auf die regionale Niederschlagsverteilung
Niederschlag (m
m)
Avissar und Liu (1996)
WeWer-‐ und Klimamodelle
MM5
NOAH
WRF
CLM
COSMO
Terra ML
Atmosphärenmodelle
Landoberflächenmodelle
WRF
NOAH-‐MP
Modellkomplexität und DatenbedarfModell „richDg“ Modell „nicht ganz richDg“
692 AGRONOMY JOURNAL, VOL. 88, SEPTEMBER-OCTOBER 1996
ters, for example those defining hydraulic conductivity,with increasing spatial resolution. If the flow is occurringpreferentially, however, for example in continuous mac-ropores, or if there are perched water tables that resultin lateral flow in the soil, then the one-dimensional modelis inappropriate, and persisting with it while increasingthe level of spatial detail is futile.
It is in the realm of environmental physics, though,that we probably do know enough about the structureof the main processes for us to be reasonably confidentof our predictions, at least where they concern theaboveground microenvironment within crop canopies.A good example of success in this area is that of Berryet al. (1991), whose simulation of the environment closeto the Surface of transpiring corn (Zea mays L.) leavesgave good insights into the interactions between preyand predator mites.
Reynolds and Acock (1985), following R.V. O’Neill,have discussed sources of error in relation to the complex-ity of models. They dissected the notional total error intotwo components, one arising from errors in estimatingparameters, the other arising from systematic bias re-sulting from oversimplifying. They postulated that cumu-lative errors in the parameters grow with the number ofthe parameters as a model becomes more complex. Andthey postulated that this systematic bias (which is similarto what I have been calling erroneous structure) decreasesas complexity increases. Figure 3a, adapted from theirFig. 5 illustrates this argument. Their argument is con-vincing where we are sure of the fundamental structureof the system-for example, adding a wing mirror tothe simulated model of a car will improve our prospectsof predicting the overall aerodynamic drag. The aerody-namic principles are well understood. However, if thestructure is fundamentally wrong, as it could be in theexample of photosynthetically driven growth illustratedin Fig. 2, then no amount of complexity will improvethe structural error. There will be an irreducible mini-mum error, as illustrated by the dotted asymptote inFig. 3b.
Occasionally, though, the structure seems to be sowrong that no amount of adjusting of the parametersenables the model to fit the data. When that happens,we have moved beyond the realm of validation and arein a position to discover something new. A good example
a b
Complexity ComplexityFig. 3. Notional components or’prediction error in modeis of increasing
complexity: (a) when the structure of the system is well understood;(b) when the structure is wrong, with the irreducible structuralerror represented by the dotted asymptote (after Reynolds andAcock, 1985). Complexity and error increase away from the inter-eept.
is the problem that the CERES models met with theirroutine for the withdrawal of water from the subsoil (J.T.Ritchie, personal communication, 1983). This routinegreatly overestimated the rate of uptake by the roots,even when the measured root length density was used.The disagreement stimulated research into alternativestructures for the routine: for example, that the rootswere not uniformly distributed through the given layerof soil, but were clumped into preexisting pores or cracks(Passioura, 1991).
Another example comes from Loomis et al. (1976),whose sugar beet (Beta vulgaris L.) model failed whenthey changed plant density, owing to its having the wrongstructure for partitioning assimilate between root andshoot. This failure stimulated work on reciprocal graftsbetween beet (large root, small leaves) and chard (smallroot, large leaves) that showed that the voracious appetiteof a small fraction of the cells in the root of the beetlargely determined the size of the axis (Rapoport andLoomis, 1986).
Even if the structure is right, as it might be in someof the leaching models when they are applied to soilsin which the flow is essentially one-dimensional, themodels can rarely be applied with confidence to a field,because the parameters vary greatly in space. We haveto assume average values of, say, the hydraulic conduc-tivity to apply the Richards equation, and because thisequation is not linear, the averaging is an art rather thana well-defined procedure, and often works poorly.
EDUCATIONSo far, the part played by the large mechanistic simula-
tion models of crops, those that aspire to occupy thescientific end of the spectrum, seems to have been largelyone of self-education for the developer. Perhaps this isinevitable: these models are typically so complex thatnobody but the developer is likely to have the enthusiasmto dip inside them. Thus, they are not transmissible toothers in the sense that the research described in a typicalresearch paper is transmissible. We do not know enoughabout the structure of the soil-plant-atmosphere systemto expect such models to be accurate, except perhaps inthe domain of the aboveground microenvironment. Theyare too complex to be tested as entities, but talenteddevelopers enhance their understanding of the interac-tions that occur and that may be far from obvious. Theformidable understanding of the interacting processeswithin plants, or between plants and their environment,that is evident in the writings of, for example, R.S.Loomis or J.M. Norman, has undoubtedly been honedby their developing mechanistic simulation models (see,for example, Loomis and Connor, 1992; Norman, 1989).At best, comprehensive mechanistic models of cropsgive structural insights to their developers. At worst,they are merely time-wasting ceremony. There is littlepoint, for example, in trying to cope with the structuraldifficulty illustrated in Fig. 2 by creating a simulationmodel that combines both scenarios. Such a model wouldmerely be an elaborate shopping list of disposable param-eters having no predictive value.
Passioura (1996)
Räumliche Auflösung und Modellfehler
0 1 2 3 4 5 6 7 8 9 10
0
1
2
3
4
5
6
7
8
9
10
Räumliche Auflösung
Kum
ulat
iver
Feh
ler
Topographie
Gesamt
Boden-Vegetation
Fehler: Abweichung zwischen SimulaUon und Messung
Höhe über NN in m
Topographie Südwestdeutschlandsbei unterschiedicher Auflösung
Auflösung 7 km Auflösung 1 km
Bauer et al. (2007)
Niederschlag in SüdwestdeutschlandSommer 2005, simuliert mit MM5-‐NOAH
Auflösung 7 km Auflösung 1 km
Bauer et al. (2007)
mm/Tag
Räumliche Auflösung regionaler KlimasimulaDonenSchär et al. (2004)56 km GiPer
THE FUTURE OF DRY AND WET SPELLS IN EUROPE
linear trend from the seasonal mean values of bothperiods (Raisanen, 2002). As a measure for interannualvariability, the mean of the squared deviations of theseasonal values from the linear fit (mean-squared error;MSE) is further used and the projected changes arecalculated as ratios between future and baseline MSE.The pooled residuals of the eight models are used toassess the statistical significance of the seasonal changesin interannual variability. The pooled residuals partlyshow severe deviations from normality and, therefore, theFligner–Killeen test is applied which proofed to be mostrobust against non-normally distributed data (Conoveret al., 1981).
Significance levels lower than 1, 1–5, 5–10 and greaterthan 10% are termed as highly significant, significant,weakly significant and insignificant, respectively.
Uncertainties of the projected changes are quantifiedby two different measures. First, the inter-model stan-dard deviation is calculated. With the information of themulti-model mean change, probabilities of the projectedchanges can be derived. According to the normal dis-tribution, about 68% of the climate change signals liewithin ±1 standard deviation, while already about 95%lie within ±2 standard deviations. Second, the percentageof models which coincide in the sign of change is calcu-lated as a nonparametric uncertainty measure. Applyingthe confidence terminology of the 4th Assessment Reportof the Intergovernmental Panel on Climate Change (IPCC4AR; Solomon et al., 2007), very high confidence, highconfidence, and medium confidence are reached if at least90% (in our study all 8 models), at least 80% (6 models),
and at least 50% (4 models) coincide in the sign ofchange, respectively.
4. Results and discussion
4.1. Changes in the mean, their significance,and uncertainty
For air temperature, Figure 5 displays maps of the errorcorrected seasonal multi-model mean change betweenbaseline (1961–1990) and future (2021–2050) periodover Europe. Air temperature changes are positive in allseasons, and the most responsive regions are the north-eastern parts of Europe in winter and southern Europein summer. The centred values in each box of Figure 9indicate the seasonal multi-model mean changes of thenine subregions. The magnitude of the change is lowestfor IL in summer with +1.04 K while the most sensi-tive regions (SC in winter and MD in summer) showa warming of +2.43 K. The brightness of the coloursof Figure 9 represents the level of significance of the t-test. It can be seen that highly significant shifts towardsincreasing air temperature are obtained for all seasons andsubregions. Concerning the uncertainty of the changes,all models agree in increasing air temperature (lower leftvalues in each box of Figure 9) and, therefore, very highconfidence can be attributed to the changes for all sea-sons and subregions. The inter-model standard deviation(lower right values in each box of Figure 9) is a factortwo to three (even more for some cases) smaller than themulti-model mean change, underpinning high confidenceof the projected changes.
–20 0 20
–20 0 20
5070
5070
(a)
–20 0 20
–20 0 20
5070
5070
(b)
–20 0 20
–20 0 20
5070
5070
(c)
–20 0 20
–20 0 2050
7050
70(d)
1.0
1.5
2.0
2.5
[K]
Figure 5. Multi-model mean change between 1961 and 1990, and 2021 and 2050 of the error-corrected RCMs for seasonal mean airtemperature for: (a) winter (DJF), (b) spring (MAM), (c) summer (JJA), and (d) autumn (SON). This figure is available in colour online at
wileyonlinelibrary.com/journal/joc
Copyright ! 2011 Royal Meteorological Society Int. J. Climatol. (2011)
Heinrich & Gobiet (2011)ENSEMBLES-‐KonsorUum25 km GiPer
Kotlarski (2011)CORDEX-‐KonsorUum12 km GiPer
FOR 1695 u.a.2 km GiPer
•••
Böden in LandoberflächenmodellenBoden-
einheiten
BodenübersichtskarteBÜK 1000
72
Cosmo-DE (DWD) 7NOAH/NOAH-MP 13
Bodenarten in CLM
Bodenübersichtskarte BÜK 1000
%T%S
Fernerkundung mit mulDtemporalen RapidEye-‐Daten
grassland corn winter crop rapeseed root crops other
Figure 4. CRFmulti-classification superimposed by GIS cropland
object borders (black borders) 6.5 Detailed results of CRFmulti
The association potential in CRFmulti is the context-free result of a separate Maximum-Likelihood-classification for each epoch (Equation 3). A section of the ML-classification result for the four individual epochs is displayed in Figure 5 a)-d). For most of the pixels the classification results for the epochs differ, sometimes three or even four different classes are assigned. Moreover the classification result within many fields is inhomogeneous. Overall the classification accuracy for the single epochs is 59.6%.
a) b)
c) d)
e) f)
grassland corn winter crop rapeseed root crops other
Figure 5 a)-d) Results of ML-classification for t=1...4; e) Result of CRFmulti-classification; f) Reference
By use of the temporal interaction potential (Equations 5-9) these results are set in temporal context and the overall accuracy is increased to 84.2%. Results of the CRFmulti-classification and the corresponding reference can be seen in Figure 5 e) and f).
7. CONCLUSION
In this work we presented two CRF-based approaches for multitemporal crop type analysis and tested them on RapidEye data of 4 epochs. Even with using just very few simple features, we achieved a classification accuracy of far over 80% for six crop type classes (grassland, corn, winter crop, rapeseed, root crops and other crops) with both approaches. Both of them performed better than a SVM-classification that served as a benchmark with the “classic approach” CRFall being slightly better than CRFmulti. Nevertheless the CRFmulti approach generally has a higher potential for any kind of multitemporal analysis. Because of its flexibility in the definition of the temporal interaction potential, it is also applicable for tasks of change detection or multi-scale analysis..
ACKNOWLEDGEMENT
The implementation of the CRF-classification is based on “UGM: A Matlab toolbox for probabilistic undirected graphical models” by Mark Schmidt, http://people.cs.ubc.ca/~schmidtm/ Software/UGM.html. The research is funded by the Federal Ministry of Economics and Technology (BMWi) via the German Aerospace Center (DLR e.V.) under the funding number 50EE0914 and by the German Science Foundation (Deutsche Forschungs-gemeinschaft) under grant HE 1822/22-1.
REFERENCES
AdV, Arbeitsgemeindschaft der Vermessungsverwaltungen der Länder der Bundesrepublik Deutschland, 1997. ATKIS – Amtlich Topographisch-Kartographisches Informationssystem, Germany. http://www.atkis.de (accessed 2011-04-01).
Bishop, C. M., 2006. Pattern recognition and machine learning. 1st edition, Springer New York.
Burges, C. J. C., 1998. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2, pp. 121–167.
Bruzzone, L., Cossu, R., Vernazza, G., 2004. Detection of land-cover transitions by combining multidate classifiers. Pattern Recognition Letters, 25(13), pp. 1491-1500.
Chang, C.C. and Lin, C.J., 2001 LIBSVM: a library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm/, accessed: 2011-04-01.
De Wit, A. J. W. and Clevers, J. G. P. W., 2004. Efficiency and accuracy of per-field classification for operational crop mapping. International Journal of Remote Sensing, 25(20), pp. 4091–4112.
Feitosa, R. Q., Costa, G. A. O. P., Mota, G. L. A., Pakzad, K., Costa, M. C. O., 2009. Cascade multitemporal classification
Müller & Hoberg (2011)
Reference
SVM-classification
CRFall-classification
CRFmulti-classification
Legend: grassland corn winter crop rapeseed root crops other
Figure 3. Reference data and classification results in comparison 6.2 Description of manual reference
A manual reference is used that was made in the same year 2010 like the satellite images. It consists of 121 separate fields of an area of about 322ha and has been acquired by field walking. The portion of the individual crop types of the whole area is 12% grassland, 21% corn, 28% winter crop, 11% rapeseed, 11% root crops and 17% other crops. In the process an existing GIS was used to define the borders of single fields. The manual reference builds the training and evaluation sample for the test classifications. 6.3 CRF vs. SVM
For our tests we applied the cross-validation method by separation the learning sample into two equal parts. Tables 1-4 show the results for the two CRF-approaches and the SVM-classification. Overall 129001 pixels were classified. Both of the CRF-based approaches slightly outperform the SVM classification with CRFall being best. For all approaches the overall accuracy is far over 80%, only the class grassland is classified with lower accuracy in each case. The classification results for a section of 21 fields are displayed in Figure 3. In a next step, the majority of pixels belonging to a class in each reference fields was determined. This gives an idea of how good this approach is suited for classifying complete fields, ignoring classification errors at their borders. Applying CRFall 108 of the 121 reference fields were classified correctly (89.3%), with CRFmulti 102 fields were correct (84,3%). In general there are two main reasons for misclassifications: At first some fields show an “untypical” appearance for their class, e.g. most of the fields of one class are already harvested at one time of image acquisition but on some fields the crop is still present. Second some fields are very slender. So by using our feature extraction in an 11*11 window, their characteristics become blurred.
Cla Ref Gra Cor Win Rap Roo Oth
Gra 74.8 2.4 13.1 5.7 2.9 1.1 Cor 0.2 93.4 0 4.0 1.8 0.6 Win 1.0 3.3 88.7 2.7 1.9 2.3 Rap 0 4.5 0 95.5 0 0 Roo 0.8 2.3 0.5 0.1 95.2 1.0 Oth 2.7 6.8 4.0 0.4 9.4 76.7
Table 1. Confusion matrix for CRFall-classification
Cla Ref Gra Cor Win Rap Roo Oth
Gra 63.2 11.1 13.8 7.1 3.2 1.6 Cor 0.1 90.6 0.1 4.5 4.0 0.7 Win 2.1 0 94.9 0 0 2.9 Rap 0.8 17.7 0.2 81.2 0 0.2 Roo 0.3 13.5 0.1 0 83.5 2.7 Oth 1.5 7.4 3.4 0.3 7.1 80.4
Table 2. Confusion matrix for CRFmulti-classification
Cla Ref Gra Cor Win Rap Roo Oth
Gra 70.9 0.5 8.9 12.9 0.3 6.6 Cor 1.8 76.6 0.1 12.3 0.4 8.9 Win 3.2 0.2 87.7 0.6 0.1 8.1 Rap 4.3 6.5 0.5 88.6 0 0.1 Roo 1.1 3.7 1.6 0.5 83.4 8.9 Oth 2.4 0.6 6.3 0.5 5.3 84.9
Table 3. Confusion matrix for SVM-classification (Cla=classification, Ref=Reference, Gra=Grassland, Cor=Corn, Win=winter crop, Rap=rapeseed, Roo=root crops, Oth=other
crops.)
overall accuracy kappa CRFall 87.4 0.85
CRFmulti 84.2 0.81 SVM 82.7 0.79
Table 4. Overview on overall accuracy and kappa coefficient 6.4 Comparison to GIS data
To evaluate the results concerning geometric accuracy we superimposed them with a national GIS dataset more specifically the German Authoritative Topographic Cartographic Information System (ATKIS). Among other sources ATKIS data are collected using aerial photography with a resolution of 20cm or 40cm supported by ground truth data, and set to be used in scale between 1:10.000 and 1:25.000. Objects of interest are point, line and area based objects listed at (AdV, 1997) with a minimum mapping unit of 0.1 ha to 1 ha. The geometric accuracy is 3m. Figure 4 illustrates that the class borders of the CRF-based approaches fit to boundaries of the GIS-dataset quite well.
RapidEye
Confusion matrix
Daten der StaDsDschen Landesämter
28
Tabelle 2: Übersicht über die erhobenen Merkmalskomplexe Merkmalskomplex 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Rechtsform, Nutzung Gesamtfläche Anbau Ackerland Stillgelegte Fl., Zwischenfrucht-anbau Ökologischer Landbau Rinder, Schweine, Schafe Pferde, Geflügel Arbeitskräfte Personengruppen Arbeitskräfte Einzelpersonen gepachtete Fläche, Jahrespacht Neupachten der letzten 2 Jahre, Flächen mit Pachtpreisänderung Haupt/Nebenerwerb Gewinnermittlung, Umsatzbesteuerung Außerbetriebliche. Erwerbs-/ Unterhaltsquellen Wirtschaftsdünger Außerldw. Einkommen Umweltleistungen Pfluglose Bearbeitung Maschinenaus-stattung Ausbildung Hofnachfolge
Total Repräsentativ
28
Tabelle 2: Übersicht über die erhobenen Merkmalskomplexe Merkmalskomplex 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Rechtsform, Nutzung Gesamtfläche Anbau Ackerland Stillgelegte Fl., Zwischenfrucht-anbau Ökologischer Landbau Rinder, Schweine, Schafe Pferde, Geflügel Arbeitskräfte Personengruppen Arbeitskräfte Einzelpersonen gepachtete Fläche, Jahrespacht Neupachten der letzten 2 Jahre, Flächen mit Pachtpreisänderung Haupt/Nebenerwerb Gewinnermittlung, Umsatzbesteuerung Außerbetriebliche. Erwerbs-/ Unterhaltsquellen Wirtschaftsdünger Außerldw. Einkommen Umweltleistungen Pfluglose Bearbeitung Maschinenaus-stattung Ausbildung Hofnachfolge
Total Repräsentativ
• Forschungsdaten-‐zentrum seit 2002
• Zugriff auf anonymisierte Einzeldaten, z.B. aus der Agrarstruktur-‐erhebung, seit 2009
17
Tabelle 1: Exemplarische Beispiele nationaler und internationaler Plattformen für die Langzeitbeobachtung terrestrischer Systeme
Plattform Kompartiment Landnutzung Skala Status Exp. Obs. Hyp RS Laufzeit* Org. Nationale Plattformen - Deutschland ICOS-D PE/BIO/AT W/A/G P/F G - + + - 2008-2031 BMBF Boden-Dauerbeobachtung PE/BIO/HY W/A/G P/F L - + - - Länder Landwirtschaftliche Dauerversuche
PE/BIO A/G P/F L + + - Länder
ICP-Forest Level PE/BIO/AT W P/F L - + - - Länder TERENO PE/BIO/AT/HY/GE W/A/G P/F/E/R L + + + + Start 2008 HGF GCEF PE/BIO A/G P/F G + - + - Start 2012 HGF COSYNA BIO/AT/HY Meer R L - + - + Start 2009 HGF DFG-Expl. PE/BIO W/A/G P/F/ L + + + - Start 2006 DFG Agrarmeteologisches Netzwerk
PE/BIO W/A/G P/F L - + - - DWD
LTER-D PE/BIO W/A/G P/F/ L - + + + LTER Nationale Plattformen – Andere Länder MISTRALS PE/BIO/AT/HY W/A/G P/F/R L + + + + 2007-2020 CNRS,
France NATIONAL CRITICAL ZONE OBSERVATORY PROGRAME
PE/BIO/AT/HY W/A/G P/F/E/R L + + + - Start 2007 USA
NEON PE/BIO/AT/HY W/A/G P/F/E/R/G L - + -/+ + USA Internationale Plattformen ANAEE PE/BIO W/A/G P/F/R G + + + + 2010 Start der
Vorbereitungsphase ESFRI
ICOS PE/BIO/AT/HY W/A/G P/F/R/G L - + + - Start 2014 ESFRI IAGOS AT/BIO - R L - + + - Start 2012 ESFRI NOHA PEBIO/AT/HY W/A/G P/F/E/R G + + + + ESFRI SOILTREC PE/HY W/A/G P L + + + - EU LTER-Europe PE/BIO/HY W/A/G P/F/E/R L - + - - LTER TERENO-MED PE/BIO/AT/HY W/A/G P/F/E G + + + + Start 2012 HGF DF
G, AG „Terrstrisc
he Infrastruktur“
Plaformen• TERENO (HGF)• Biodiversitätsexploratorien (DFG)• ICOS-‐D (BMBF), Bodendauerbeobachtung (Länder)• MISTRALS (F), CriUcal Zone Obs. (USA), TERENO-‐MED (INT)
Großprojekte• TR 32 „PaPerns in Soil-‐VegetaUon-‐Atmosphere Systems“ (DFG)• PAK 346/FOR 1695 „Regional Climate Change“ (DFG)• FOR 1598 „Catchments As Organised Systems (CAOS)“ (DFG)• REKLIM „Regionale Klimaänderungen“ (HGF)
Untersuchungsgebiete der Forschergruppe 1695„Regional Climate Change“
Kraichgau• Hügellandschao mit Lößböden, intensiv bewirtschaoet
• JahresmiPeltemperatur: 9 °C• Jahresniederschlag: 720-‐830 mm• LNF/Ackerland: 53%/83%• Weizen, Gerste, Körnermais, Zuckerrüben
MiWlere Schwäbische Alb• Karstlandschao mit überwiegend flachgrün-‐digen Böden, extensiv bewirtschaoet
• JahresmiPeltemperatur: 6-‐7 °C• Jahresniederschlag: 800-‐1000 mm• LNF/Ackerland: 52%/47%• Sommergerste, Wintergerste, Winterweizen
EC 1 EC 2 EC 3
Katharinentalerhof, Kraichgau
Flussmessungen im Krauchgau und auf der Alb
In zwei Landschaoen jeweils drei Eddy-‐Kovarianz-‐StaUonen auf Ackerland mit WePerstaUonen, TDRs, Tensiometern etc.
Energieflüsse über verschiedenen Ackerfrüchten
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Swabian Alb Kraichgau
a Winter Wheat 2009Q
H,
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dWinter Rape 2010
gMaize 2011
b
QE,
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-2
e
h
c
Bow
en R
atio
Day of Year
f
Day of Year
i
Day of Year
LE (W
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H (W
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Alb
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eman
n, In
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sen,
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enhe
im
Bowen-‐
Verhältnis
~Verdu
nstung
Ernte
Energieflüsse über verschiedenen Ackerfrüchten
Wiz
eman
n, F
OR
169
5, U
niv.
Hoh
enhe
im
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a Winter Wheat 2009Winter Wheat 2011
QH,
Wm
-2
dWinter Rape 2010Winter Rape 2009
gMaize 2011Maize 2010
b
QE,
Wm
-2
e
h
c
Bow
en R
atio
Day of Year
f
Day of Year
i
Day of Year
LE (W
m-‐2)
H (W
m-‐2)
H/LE
Bowen-‐
Verhältnis
~Verdu
nstung
Ernte
Schwäbische Alb
RN JH LE H
Abreife Ernte
MonatsgemiWelte Tagesverläufe der Energieflüsseüber Winterweizen
Kraichgau 2009
Steffens (2010)
constantRc,min
Ingwersen et al. (2011)
time-variableRc,min
Verdunstungsfluss über Winterweizen
EC flux measurement
NOAH land surface model
Rc,min Minimaler Stomatawiderstand
Das Landoberflächenmodell NOAH kann die Entwicklungsdynamik von Ackerfrüchten und damit die EnergieauHeilung an der Landoberfläche nach
Einsetzen der Abreife nicht richDg darstellen
SimulaUon mit Landoberflächen-‐modell NOAH
Vergleich von Pflanzen-‐ und Landoberflächenmodellen
Gayler et al. (2012, subm.)
WochenmiWelwerteAlle Modelle anhand von Boden-‐ und Pflanzendatenkalibriert
week of the year14 16 18 20 22 24 26 28 30 32
LHF
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-2)
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Messungen
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nt h
eat f
lux
(W m
-2)
Kopplung WRF-‐NOAH-‐GECROS
Ingwersen, FOR 1695, Univ. Hohenheim
WRF
NOAH
Atmosphärenmodell
Landoberflächenmodell
PflanzenmodellGECROS
Kalibrierung von NOAH-‐GECROS
Winterweizen
Ingw
ersen, FOR 1695, U
niv. Hoh
enhe
im
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Messung Standard-Parametrisierung Nach Kalibrierung
E
ntw
ickl
ungs
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ium
Grüner LAI
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ttflä
chen
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x
Tag des Jahres
Totaler LAI
Bes
tand
eshö
he (m
)
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nert
rag
(dt/h
a)
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Latenter Wärmefluss Sensibler Wärmefluss
EnergieauHeilung über Winterweizen
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Eddy-Kovarianzmessung NOAH-GECROS
Late
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luss
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-2)
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ärm
eflu
ss (W
m-2)
Stunde des Tages
Ingw
ersen, FOR 1695, U
niv. Hoh
enhe
im
(H-korrigiert)
Mittelwerte Mai-Juli
Entscheidungmodell
Wie reagieren die landwirtschaHlichen Betriebe?
Troo
st, B
erger, FO
R 1695, U
niv. Hoh
enhe
im
Erwartungsbildung (Lernen)
Anpassung der ProdukDon
Klimawandel
Anpassung der BetriebsausstaWung
kurzfris4g
langfris4g
Zusammenfassung• Die derzeiUge Qualität regionaler KlimaprojekUonen und von diesen abgeleiteter ErtragsprojekUonen sollte man, gerade in der Anpassungsforschung, kriUsch sehen.
• Die regionalen KlimaprojekUonen werden jedoch konUnuierlich besser, vor allem durch die Erhöhung der räumlichen Auflösung.
• Die Abbildung von Prozessen und Rückkopplungen an der Landoberfläche (Boden-‐VegetaUon-‐Atmosphäre) in regionalen Klimamodellen kann und muss verbessert werden, indem vorhandene Daten besser genutzt werden und mehr Prozess-‐wissen in die Modelle eingearbeitet wird.
• Wechselwirkungen zwischen Klima und Landnutzung sollten in KlimaprojekUonen berücksichUgt werden.
Vielen Dank für Ihre Aufmerksamkeit!Vielen Dank auch an:
Joachim Ingwersen, KrisUna Imukova, Maxim PoltoradnevInsDtut für Bodenkunde und Standortslehre, Biogeophysik, UHOH
SebasUan GaylerWater & Earth System Science (WESS) Competence Cluster, Tübingen
Volker Wulfmeyer, Hans-‐Dieter Wizemann, Kirsten Warrach-‐Sagi, Thomas SchwitallaInsDtut für Physik und Meteorologie, UHOH
Petra Högy, Andreas FangmeierInsDtut für LandschaUs-‐ und Pflanzenökologie, UHOH
ChrisUan Troost, Thomas BergerInsDtut für Agrarökonomik und SozialwissenschaUenin den Tropen und Subtropen, UHOH