Application for a Grant within the Research Unit 1695 ... · Project P2 Soil-plant-atmosphere interactions at the regional scale Short Title Soil-Plant-Atmosphere Interactions Prof

Application for a Grant within the Research Unit 1695

Agricultural Landscapes under Global Climate Change – Processes and Feedbacks on a Regional Scale –

Project P2

Soil-plant-atmosphere interactions at the regional scale

Short Title

Soil-Plant-Atmosphere Interactions

Prof. Dr. Thilo Streck Institute of Soil Science and Land Evaluation (310d)

Chair of Biogeophysics University of Hohenheim 70593 Stuttgart, Germany Tel.: ++49-711-459-22796 Fax: ++49-711-459-23117

E-Mail: [email protected]

July 2014

DFG form 54.011 – 7/13 FOR 1695 / Project P2 page 1 of 6

Proposal Data and Obligations – FOR 1695 Project P2 Thilo Streck, Stuttgart

I. Proposal data 1 Type of proposal Programme

Research grants [ ] Research Unit FOR 1695

Individual proposal [X] Coordination proposal [ ]

Proposal category

New proposal [ ] Renewal proposal [X] 2 Proposal information

2.1 Title/duration

Title (in German) Boden-Pflanze-Atmosphäre Interaktionen auf der regionalen Skala Title (in English) Soil-plant-atmosphere interactions at the regional scale 36 months

2.2 Subject classification Subject area: 207-01 Soil Sciences


2.3 Supplemental Classification

[ ] Transfer Project [ ] Cooperation with Developing Countries [ ] Long-Term Project [ ] Biodiversity

2.4 Keywords

Keywords (in German): Regionaler Klimawandel; System Boden-Pflanze; Landoberflächenmodellierung; biogeo-physikalische Prozesse Keywords (in English): Regional climate change; soil-plant system; land surface modeling; biogeophysical processes

2.5 Countries

Not applicable

2.6 Summary Summary (in German) Gekoppelte Atmosphären-Landoberflächen-Modelle sind wichtige Werkzeuge, um die Auswirkungen des Klimawandels auf der regionalen Skala abzuschätzen. Die Qualität regionaler Klimasimulationen hängt wesentlich von einer guten Beschreibung der Landoberflächen-austauschprozesse ab. Hier spielen die Wechselwirkungen zwischen Böden, Pflanzen und der Atmosphäre eine Schlüsselrolle. In der ersten Phase haben wir das Pflanzenwachstumsmodell GECROS mit dem Landoberflächenmodell NOAHMP gekoppelt. In der zweiten Phase werden wir die Leistungsfähigkeit und Robustheit von NOAHMP-GECROS im Hinblick darauf testen, wie gut Bodenwasserhaushalt, Pflanzenwachstum und Landoberflächenaustausch vom Modell abgebildet werden. Die Überprüfung und nötigenfalls Weiterentwicklung wird anhand der Langzeitdaten von unseren Eddy-Kovarianz-Stationen und regionalen Bodenwassermessnetzen erfolgen. Über das Atmosphären-Landoberflächen-Pflanzenwachstums-Modell ALCM (NOAHMP-GECROS gekoppelt mit WRF) ist NOAHMP-GECROS ein wesentlicher Bestandteil des zu entwickelnden Integrierten Landsystem-Modellsystems (ILMS). In enger Zusammenarbeit mit den anderen Projekten der Forschergruppe werden wir mit ILMS Rückkopplungen in Landsystemen (Kraichgau und Schwäbische Alb) unter dem Klimawandel untersuchen. Innerhalb dieser Zusammenarbeit werden wir unter anderem klären, bis zu welchem Detail Prozesse im Bereich Boden-Pflanze abgebildet werden müssen, um Landschaftsfunktionen wie Pflanzenproduktion und Wasserhaushalt ausreichend genau zu simulieren.


Summary (in English) Coupled atmosphere-land surface models are important tools for assessing the impact of global climate change on a regional scale. The quality of regional climate simulations is critically dependent on an accurate representation of land surface exchange processes. Soil-plant-atmosphere interactions play a key role here. In the first phase, we have integrated the advanced crop growth model GECROS with the land surface model NOAHMP. In the second phase, we will test the capability and robustness of NOAHMP-GECROS with regard to simulating soil water regime, crop growth and land surface exchange. Model testing and, if necessary, further development are based on our long-term eddy covariance and soil water measurements. As part of the Atmosphere-Land surface-Crop Model ALCM (NOAHMP-GECROS coupled with WRF) NOAHMP-GECROS is an important component of the Integrated Land-system Model System (ILMS). In cooperation with the other projects of the Research Unit, ILMS will be used to study feedbacks in the land system (Kraichgau and Swabian Alb) under climate change. Within this joint effort, we will investigate, among others, the question in which detail soil processes and crop growth have to be considered to reliably simulate landscape functions such as crop production and water regime.

3 Participating individuals

3.1 Applicant

Academic degree/title: Prof. Dr.

First name: Thilo

Last name: Streck

Nationality: German

Gender: m [X] f [ ]

Date of birth: 11.02.1960

German-speaking: y [X] n [ ]

E-mail address: [email protected]

Telephone: ++49-711-459-22796

Address of the institution that will host the proposed project: University of Hohenheim Institute of Soil Science and Land Evaluation (310) Emil-Wolff-Str. 27 70599 Stuttgart, Germany


3.2 Other participating individuals

Academic degree/title: Dr.

First name: Joachim

Last name: Ingwersen

Nationality: German

Gender: m [X] f [ ]

Date of birth: 25.02.1969

German-speaking: y [X] n [ ]

E-mail address: [email protected]

Telephone: ++49-711-459-23675

Address: University of Hohenheim Institute of Soil Science and Land Evaluation (310) Emil-Wolff-Str. 27 70599 Stuttgart, Germany 4 Participating institutions None


II. Concluding Information In submitting a proposal to the Deutsche Forschungsgemeinschaft (DFG), all applicants agree to • adhere to the rules of good scientific practice. • have adhered to the guidelines regarding publication lists and bibliographies. • inform the DFG immediately of any changes to the information provided in this proposal. • observe all relevant laws, regulations and guidelines that pertain to the project and in

particular to attain all necessary approvals, certifications, etc., in a timely manner. • use the grant exclusively and in a targeted manner to realise the funded project, to conform

to the relevant regulations of the DFG in the use and accounting of funds, and in particular not to use the grant to finance core support.

• submit research progress reports according to the dates specified in the award letter and to present financial accounts to the DFG detailing the use of funds.

• and if applicable o inform the DFG immediately if funding for this project is requested from a third party.

Proposals requesting major instrumentation and/or those previously submitted to a third party must be mentioned in the Project Description under Additional Information.

o inform your university’s DFG liaison officer about the proposal submission. o inform the head office of the Max Planck Society about the proposal submission should the

project be carried out at a Max-Planck-Institute. o plan and conduct any experiments involving humans, including identifiable samples

taken from humans and identifiable data, in compliance with the most current versions of the German Embryo Protection Act (Embryonenschutzgesetz), Stem Cell Act (Stammzellgesetz), Pharmaceutical Drugs Act (Arzneimittelgesetz), Medical Devices Act (Medizinproduktegesetz), and Declaration of Helsinki.

o adhere to the regulations and provisions of the Animal Protection Act (Tierschutzgesetz) and the Experimental Animals Ordinance (Versuchstierverordnung).

o if the research project, or parts thereof, are subject to the Convention on Biological Diversity, to follow the Guidelines for Funding Proposals Concerning Research Projects within the Scope of the Convention on Biological Diversity (CBD).

o adhere to the provisions of the Genetic Engineering Act (Gentechnikgesetz) with regard to experiments involving genetically modified organisms (GMO).

[X] I accept the foregoing conditions and obligations. [X] I agree to:

the DFG’s electronic processing and storage of data provided in conjunction with this proposal. This information may be passed to reviewers and the DFG bodies as part of the DFG’s review and decision-making process.

having all address and communication data (e.g. telephone, fax, e-mail, internet website), as well as information on the content of this research project (e.g. title, summary, keywords, international cooperation), if approved, published in the DFG's project database GEPRIS (http://www.dfg.de/gepris) and - in excerpts (grant holder’s name, institution and location) - in the “Programmes and Projects” section of the DFG’s electronic annual report (http://www.dfg.de/jahresbericht). I/We understand that the electronic publication of this information may be opposed by contacting the appropriate programme contact no later than four weeks from receipt of the award letter.


Before this proposal can be processed, the DFG requires signatures from all applicants listed above certifying that they accept and will comply with the conditions and obligations as stated. Multiple applicants may sign and submit this form jointly; they may also submit a signed copy of this form separately. ______________________________________________________________________________ 15.07.2014/Stuttgart Thilo Streck Signature

P2 Soil-Plant-Atmosphere Interactions

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Project Description – FOR 1695 Project P2 Thilo Streck, Stuttgart

Project Description 1 State of the art and preliminary work State of the art:

Weather and climate simulations are critically dependent on an accurate representation of land surface exchange processes, which are simulated by so-called land surface models (LSMs). LSMs simulate 1D water and energy fluxes in soil and vegetation. The central purpose of a LSM in weather and climate simulations is to partition net radiation into latent, sensible, and soil heat flux. This partitioning controls the formation of the boundary layer, cloud formation, and the distribution of rainfall. The energy partitioning between sensible and latent heat depends on the physical and physiological state of the land surface (RADDATZ, 2007). Physical state is characterised by variables such as albedo, roughness length, or soil texture, the physiological state by plant variables such as leaf area index (LAI), stomatal resistance, or rooting depth. The earth's surface is described by a set of different land use types, cropland being one of them. In regional atmosphere - land surface models, cropland may cover a considerable part of the simulation domain. In 2013, the fraction of cropland of the total area of the European Union was about 25%. In Germany this fraction is with 33% above average (STATISTICAL OFFICE OF THE

EUROPEAN UNION, 2014). In reality, cropland stands for a collection of crops differing in sowing and harvest date, rooting depth, leaf area and senescence dynamics, water use efficiency, and phenology. It may consist of summer and winter crops, C3 and C4 plants, legumes and non-legumes, etc. Each crop may show distinct seasonal dynamics. After sowing, the distribution of living roots is close to zero across the entire soil profile. After germination, roots start to penetrate the soil, and at the end of the vegetation period the root system may reach a depth of one or several metres. The green LAI is zero before emerging and may peak to values of 5 or 6 during the vegetation period, dropping suddenly back to zero after harvest. After harvest, the field is fallow for a certain period of time. In some situations, e.g., when a summer crop follows up a winter crop, fallow may last several months.

A widely used LSM is the NOAH LSM (CHEN & DUDHIA, 2001). Its development began about 25 years ago. Since then, it has undergone continuous improvement. The NOAH LSM was designed to be coupled with the Mesoscale Meteorology Model 5 (MM5; DUDHIA, 1993) and was later coupled with the Weather Research and Forecasting (WRF) model (see, e.g., SKAMAROCK

et al., 2008). The WRF model is intended to be used from the large eddy simulation scale up to the global scale. In local to regional weather forecast simulations the NOAH LSM is typically applied to grid cell sizes of 3 x 3 km2 to 20 x 20 km2. At such scales the model design must be a compromise between detailedness of process description and data availability. As a consequence, in the NOAH model some simplified modelling and parameterisation schemes are used. In the NOAH LSM, for example, the seasonal dynamics of the physiological properties of the land surface, i.e. the properties of the vegetation, is not simulated explicitly and weather-driven, but simulated based on external satellite-derived maps of LAI and green vegetation fraction (GVF). INGWERSEN et al. (2011) could show that in cereal-dominated croplands this simplification leads to a considerably biased energy partitioning, in particular during the ripening stage. Recently, the NOAH LSM has been extended by multiple-physics options (NOAHMP) and an improved implementation to consider land surface heterogeneities (NIU et al., 2011). In NOAHMP the land surface heterogeneity is described with a semi-tile subgrid scheme. This means that shortwave radiation transfer is computed over the entire grid cell, while longwave radiation, latent heat, sensible heat and ground heat flux are computed separately over two tiles

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(vegetated or bare ground area). Among the bunch of multi-physics options, the user can choose, for example, between two schemes for computing the stomatal resistance: 1) the empirical Jarvis scheme, which was already implemented in previous NOAH LSM versions, and 2) the photosynthesis-based Ball-Berry scheme.

Because in agricultural landscapes crop development and phenology play a key role in energy partitioning, there have been several attempts to combine LSMs with crop models (CMs). LOKUPITIYA et al. (2009) coupled the Simple Biosphere (SiB) model with a crop-specific phenology model (SiBcrop). Driven by photosynthetic carbon assimilation computed in SiB, SiBcrop delivers daily values of LAI and the absorbed fraction of incident visible light to SiB. By this coupling, the prediction of onset and end of growing season, harvest, interannual variability associated with crop rotation, daytime carbon uptake and day-to-day variability in carbon exchange could be improved. VAN DEN HOOF et al. (2010) extended the Joint UK Land Environment Simulator (JULES) by a dynamic crop growth module. Most parts were taken from the SUCROS CM (GOUDRIAAN & VAN LAAR, 1994), a precursor of GECROS CM (YIN & VAN

LAAR, 2005). Without any calibration the correlation between measured and simulated latent and sensible heat flux could be considerably improved. Similar results were recently reported by SONG et al. (2013), who coupled a dynamic crop growth model to the ISAM LSM. Also in other disciplines such as hydrology (HOUSKA et al., 2014) or irrigation engineering (LI et al. 2013) the importance of crop growth dynamics and phenology in coupled model systems has been recognised. None of the above LSM-CM, however, has yet been coupled to an atmosphere model and an analysis of the feedback between crop growth dynamics, land surface exchange and atmospheric processes is lacking.

Crop models are in general set up as systems of first-order ordinary differential equations. Overall growth is driven by photosynthetically active radiation (PAR). Assimilation is a function of the LAI, which consequently is a key factor in CMs. The development of each plant organ is represented by one differential equation. In most CMs, assimilates are distributed to the plant organs (roots, shoots, leaves, ears, etc.) by simple switch functions. One of the difficulties is that the switch functions change with crop development and environmental conditions. The transition between the different phenological stages, the number of which depends on the specific model, is controlled by biological time, which in turn depends on temperature. In practice, biological time is generally approximated by temperature sums (degree days). To our knowledge, the only CM that simulates the partitioning between roots and shoots in a mechanistic, physiological way is the GECROS model. It computes root-shoot partitioning based on the functional balance theory (e.g., YIN & SCHAPENDONK, 2004). This approach assumes that there is a quantitative proportionality between the activity of the roots (which supply N) and the activity of the shoot (which supply C). Moreover, it is assumed that plants optimise their behaviour by maximising the relative growth rate. This approach makes the plants plastic in adjusting the distribution between organ systems in response to external water or nitrogen stress or changing ambient CO2 concentrations.

The test and improvement of CMs and LSMs require long-term field data, in the context of this Research Unit in particular those of latent and sensible heat flux. The eddy covariance (EC) method has become the standard technique for their measurement. The EC method assumes fully turbulent exchange of energy and water vapour between land surface and atmosphere. Under this condition and involving some further assumptions, the energy and water vapour fluxes can be computed from the covariance of the vertical wind speed and the respective scalars, air temperature or air humidity. A general problem of the EC method, however, is that the energy balance involving net radiation, soil heat flux and the turbulent fluxes of sensible and latent heat is in general not closed. The energy gap typically ranges between 10 and 30%. Possible reasons discussed in literature (FOKEN, 2008; among others) can be assigned to two types: I) measurement errors, and II) errors due to invalidity of assumptions. There is growing evidence that measuring errors cannot fully explain the systematical energy gap of the EC flux data (FOKEN, 2008). Type II errors include unconsidered energy storage terms or neglected energy fluxes such as photosynthesis, which are usually not determined with conventional EC systems. Basic assumptions underlying the EC method might be severely violated during night or due to mesoscale circulations induced by landscape-scale heterogeneity. Renewing the


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hypothesis of KANDA et al. (2004) or STEINFELD et al. (2007) the latter was recently suggested by MAUDER et al. (2013). Due to their low frequency mesoscale circulations cannot be detected with a single EC station and the typical averaging time of half an hour.

Several authors have stressed that it is an urgent research task to resolve the energy closure problem of EC flux data in particular for the case that the data are used to test and parameterise LSMs (FALGE et al., 2005; EL MAYAAR et al., 2008; LEUNING et al., 2012; WIZEMANN et al., 2014). Recently, LEUNING et al. (2012) postulated that it is possible to close the energy balance of EC flux data at half-hourly time scale by careful attention to all sources of measurement and data processing errors in the EC system. In this context they emphasise in particular the role of minor terms such as energy storage in soil and biomass, the energy consumption by photosynthesis and advective flux divergences, which are usually not considered in EC measurements.

Report on own previous work:

In addition to the financial support by DFG, we were able to raise funds of the Erasmus Mundus programme for an external PhD student in P2 (Kristina Imukova, M.Sc.). Imukova executed the research assigned to the work packages of the Doctoral Researcher.

Work Package 1 (Doctoral Researcher + Postdoctoral Researcher + Technician): Eddy covariance measurements of surface energy fluxes

Project P2 continued the operation of the three EC stations from PAK 346 at Katharinentalerhof. Until the end of the first phase, the Research Unit will have a six-year time record of surface energy and net CO2 fluxes. This data set forms a firm basis for testing and calibrating the model components. The farmer and owner of the fields EC1 to EC3 (Mr. Bosch) grew winter wheat, silage maize and winter rape during the last years. Weather data such as global radiation, air temperature, wind speed, or humidity were recorded over the whole year in a half-hourly resolution. The half-hourly measurement of surface energy and net CO2 fluxes was performed from the beginning of the growing period until end of November. Due to the high power consumption of the open path CO2 and H2O analyser (LI-7500), the latent heat and CO2 flux measurements had to be interrupted over the winter months. Weather and flux data were delivered to Projects P1, P3, P6, P7 and P9 and were fed into the FOR database. A detailed comparison of the energy and water fluxes between the Kraichgau and Swabian Alb sites and between different years is given in WIZEMANN et al. (2014).

As discussed already above, a problem of the EC method is that the energy balance involving net radiation, soil heat flux and the turbulent fluxes of sensible and latent heat is in general not closed. In modelling studies the energy balance of eddy covariance (EC) flux data is usually post-closed, i.e. the measured turbulent fluxes are adjusted as to close the energy balance. At the current state of knowledge, however, because it is not clear how to partition the missing energy, the modeller has to make an assumption at this point. Most often it is assumed that the missing turbulent fluxes have the same Bowen ratio as the measured fluxes. This method is known as the Bowen ratio approach (BARR et al., 1994; BLANKEN et al., 1997; TWINE et al., 2000. A second, less often applied method is to fully assign the missing energy to the latent heat flux (LE post-closure method; FALGE et al., 2005, CHEN et al., 2007). In few studies, the authors decided to use the raw flux data without closing the energy balance. This decision was made because either the authors were not interested in total fluxes but in flux patterns (CARRER et al., 2012) or they had doubts in the correctness of the Bowen ratio method (STAUDT et al., 2010). Recently, based on arguments raised by FOKEN (2008) and experimental findings of MAUDER &

FOKEN (2006), a fourth method has been proposed, which has been termed the sensible heat flux method (H post-closure method, INGWERSEN et al., 2011). FOKEN (2008) argued that large eddies (mesoscale circulations), which cannot be captured by a single EC station and a covariance averaging time of half an hour as mentioned above, may significantly contribute to the total turbulent flux. MAUDER & FOKEN (2006) observed that the energy residual vanished almost completely if the flux averaging time was extended from 30 min (short-wave eddies) over 24 h to 5 days (long-wave eddies). Interestingly, the averaging time had a minor effect on the

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latent heat flux but the sensible heat flux nearly doubled. Hence, in that data set the energy gap could be mainly assigned to sensible heat. Note that in this case the Bowen-ratio method would yield too high latent heat fluxes and too low sensible heat fluxes.

When models are tested against measured EC flux data, the standard is 1) to adjust the EC flux data with one post-closure method, usually with the Bowen-ratio method, 2) to state this method in the Material and Methods section, and 3) to evaluate model performance based on this single data set, neglecting the possible substantial error originating from the post-closure method (GERKEN et al., 2012; ALAVI et al., 2010; INGWERSEN et al., 2011). We have developed a new approach allowing to clearly communicating the possible systematic error of EC flux data. We propose to use the difference between EC flux data adjusted by the Bowen-ratio and the H-post-closure method as a proxy for the possible systematic error due to the unknown nature of the energy balance gap. Figure 1 illustrates this approach. The grey band between the two lines is the post-closure method uncertainty band (PUB). In the case of latent heat flux, the data adjusted with the Bowen-ratio method form the upper bound. In the case of the sensible heat flux, the contrary holds true. The upper bound is formed by the H-adjusted data. Additionally, the LE-adjusted data are plotted as open squares, and the non-adjusted raw data are plotted as open circles. Note that with the H-method, the adjusted and non-adjusted latent heat fluxes are identical, and that with the LE method, raw sensible heat fluxes are the same as the adjusted ones. Furthermore, to indicate the measurement error due to instrumental noise and the number of independent observations used in calculating the covariances, the random error is plotted as error bars on the measured raw fluxes. The PUBs shown in Figure 1, moreover, clearly demonstrate that further efforts are needed to narrow the PUB of EC flux data. A promising way could be to measure and account for additional fluxes neglected so far in standard setups (see WP2 of work programme).

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Work Package 2 (Doctoral researcher): Nature of the energy balance gap of EC flux data

We compared the EC heat flux data experimentally against an independent method, the soil water balance method, to determine evapotranspiration (ET) from the components of the water balance equation (Eq. 1). This method does not depend on an a priori assumption on the composition of the energy residual. The water balance equation of a soil reads as follows:


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ET = P – S – SP – SR Eq. 1

where P stands for rainfall, and SP for seepage (negative: capillary rise, positive: vertical drainage). The symbol SR denotes surface runoff, and S stands for the change in soil water storage over the balancing period. The unit of each term is mm (L m-2). By determining the quantity of the terms on the right-hand-side of Eq. [1], ET can be assed. The term S was measured at sixteen locations within the footprint of the EC station. The locations were distributed within the footprint following a stratified random sampling design. At each position, a capacitive soil moisture probe (SM-1, Adcon Telemetry GmbH, Austria) was installed. The SM-1 probe measures the soil water content down to 90 cm in 10 cm intervals. Additionally, during three sampling campaigns soil samples were taken down to 1.5 m depth in 0.1 m intervals, and the gravimetric water content (and so S) was determined. The original idea to measure the vertical soil water distribution additionally with a neutron probe was refused, because it turned out that the strict safety regulations, required when a device containing a highly radioactive source (241Americium) is operated outdoors, hampered a flexible scheduling of the sampling campaigns. Seepage was estimated by means of the Darcy-Buckingham law. The hydraulic gradient was measured with four soil matric potential probes (257-L, Campbell Scientific, USA). Two of them were installed in 1.3 m depth and the other two in 1.5 m depth. The hydraulic conductivity function was estimated from soil texture and bulk density data with the help of a pedotransfer function (Rosetta lite). Based on field observations of the last years, SR can be expected to be minor under the usual weather conditions during the main vegetation period. In 2012, the best agreement between the soil water balance method and the EC method was achieved when the H-post-closure method was applied. Practically this means that the raw latent heat flux data are used for computing the cumulative evapotranspiration (Table 1). In 2013, the experiment was hampered by an unusually intensive rainstorm event. Within two days (31 May – 1st June), 108 mm of rain fell. After this rain event, in an open well located at the south border of field EC1 the water level rose to terrain level, and we observed evidence for SR. Therefore, this period had to be excluded from data evaluation. The soil water balance method could only be applied for the period between 18 June and 31 July. In this period, ET measured with the soil water balance method was in closer agreement with that using Bowen ratio post-closure method. This contrasting result may indicate that there exists no universal post-closure method. This statement, however, needs to be further tested against additional field measurements. In 2014, the experiment will be repeated a third time in the frame of the master thesis of Mrs Jamie Smith at EC1 (silage maize).

Table 1. Comparison of cumulative evapotranspiration data determined with the EC method and the soil water balance method. The EC flux data were either post-closed with the sensible heat (H) flux method or the Bowen ratio method. ET was measured over winter wheat fields.

Period Cumulative evapotranspiration in mm

Soil water balance method

EC method (H post-closed)

EC method (Bowen ratio post-closed)

25.4 – 15.6.2012 17125 182 233

15.6 – 27.7.2012 12518 137 167

25.4 – 27.7.2012 29643 319 400

18.6 – 31.7.2013 18625 145 202

In the frame of a master thesis (Anna Horsche), we investigated the contribution of low and high frequency flux losses to the energy balance gap. With our dataset, the turbulent flux data of EC3 of the year 2012 (winter wheat), we could not confirm the findings of MAUDER & FOKEN (2006). The extension of the interval to compute the covariance did not resolve the residual of

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the energy balance closure. At the high frequency edge of the spectra a part of the flux is lost due to the resolution of sampling (Nyquist frequency). This high frequency signal loss was quantified with a procedure that uses the statistics from the co-spectrum to estimate the amount of high-frequency flux that remains unmeasured (WOLF & LACA, 2007). We found that H was stronger affected by the high frequency loss than LE. While the high frequency loss of LE was on average 1%, that of H was on average 3% and reached values of up to 10%. The high frequency loss was largest during the day. Furthermore, we found a better closure of the energy balance with daily averaging implying that storage terms contribute to the energy balance gap.

Comparative soil water content measurements using a cosmic-ray soil moisture probe were performed in cooperation with Prof. S. Oswald (University of Potsdam, Germany) in the vegetation period 2013 at EC1. The capacitive SM-1 soil moisture probes of the Adcon sensor network were calibrated against volumetric determined water contents thermo-gravimetrically in the frame of a soil science module (Michael Hevart). We found good agreement between both methods. The cosmic-ray probe was well suited to measure the soil water dynamics in topsoil at the scale of a field. Its use to measure soil water storage was limited because of the shallow penetration depth (10-20 cm) of the slow neutrons. An experiment with Lidar measurements has been scheduled for the first two weeks of August 2014 (see Project P1).

Work Package 3 (Doctoral Researcher): Remote sensing of the green vegetation fraction (GVF)

We determined the dynamics of the green vegetation fraction (GVF) using high-resolution (5 m x 5 m) RapidEye satellite images from the geospatial information provider Blackbridge (former RapidEye AG) at the field and regional scale. In January 2012, we applied for RapidEye satellite images by submitting a proposal entitled “Determining the green vegetation fraction from RapidEye data for use in regional climate simulations” to the German Aerospace Center (DLR). On 6 March 2012, the proposal was approved by the DLR (grant number: RESA project 505). The GVF was computed from the RapidEye satellite images based on a two-endmember spectral mixing model (GUTMAN & IGNATOV, 1998).

At the field scale, the variability of GVF was caused, among others, by management differences (sowing date, soil tillage etc.). On the regional scale (here Kraichgau region), we showed for the first time that the variability of GVF is largely driven by differences in the phenological development among crops. At that scale, we observed a bimodal distribution of GVF (Figure 2) for all months, which can be attributed to the distinctly different phenological development of early- and late-covering crops (e.g., winter wheat vs. silage maize). Our results imply that in agricultural landscapes, in which farmers perform a multiple-year crop rotation of early- and late covering crops, simulation results of a land surface model such as NOAHMP can be improved by splitting up the land use class “cropland” into these two sub-classes (IMUKOVA et al., 2014). From our EC measurements it is evident that early- and late-covering crops show pronounced differences in the seasonal dynamics of energy- and water exchange with the atmosphere (WIZEMANN et al., 2014).


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Figure 2. Histograms of the green vegetation fractions (GVF) over cropland in the Kraichgau region in 2012 and 2013. GVF data were derived from RapidEye satellite images (resolution: 5 m x 5 m). The red and green lines show fitted bi-modal Gaussian distributions.

Work Package 4 (Postdoctoral Researcher, Technician): Soil moisture networks in Kraichgau and Swabian Alb

The operation of the two soil moisture networks established in PAK 346 (POLTORADNEV et al., 2014) has been continued during the first phase of the Research Unit. For the use of the rainfall and soil water data in HRLDAS, it was originally planned to interpolate the point measurements to the grid configuration of WRF-NOAH. Because the step to integrate NOAHMP into HRLDAS had to be skipped (see WP8 below), the interpolation work was cancelled, and we focused on the analysis of the spatial and temporal variability of soil water dynamics at the regional scale. Spatial and temporal variability of soil water content (SWC) is usually expressed as the coefficient of variation (CV) or standard deviation (SD) of SWC. CV as a function of the ensemble mean SWC (CVθ(θ)) usually shows a maximum in the intermediate SWC range, i.e., the curve has a convex shape (VEREECKEN et al., 2007; BROCCA et al., 2012; ROSENBAUM et al., 2012). The opposite shape (concave) is only rarely observed (BOGENA et al., 2010; MITTELBACH

& SENEVIRATNE, 2012). In the ploughing horizons of Kraichgau and Swabian Alb, the CVθ(θ) relationship forms combinations of convex and concave hyperbolas. Our results suggest that the CVθ(θ) relationship is constrained by two anchor points: CVθθ at pF 4.2 and saturation. In Kraichgau, the CV of SWC is higher than in the Swabian Alb. More pronounced drying branches were observed in 2010 due to a long dry period. In general, CV increases during dry periods and decreases in rewetting periods. Clockwise hysteretic CVθ(θ) trajectories were generated by intense rainfall events. Drying branches of CVθ(θ) tend to be convex, while rewetting branches have in most cases a concave shape. The CVθ of drying branches is always lower than the CVθ of the subsequent rewetting branch. Differently from ROSENBAUM et al. (2012) and VIVONI et al. (2010) we observed this behaviour not only in the intermediate range of θ but over its entire range.

Work Package 5 (Postdoctoral Researcher): Improving the simulation of soil water dynamics in the NOAH LSM

In WP5, we extended NOAHMP by a dynamic exponential root distribution model and implemented it as a new multi-physics option. For this purpose, the dynamic root growth module of the crop model SPASS (WANG & ENGEL, 2000; GAYLER et al., 2013) was transferred to NOAHMP under the guidance of Sebastian Gayler from the competence cluster “Water and Earth System Science” (WESS). The implementation of the new root model clearly enhanced the performance of NOAHMP (GAYLER et al., 2014). The improvement was stronger for the deep loess soils of Kraichgau than for the shallow soils of Swabian Alb. Similar results were

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obtained in the model intercomparison study of WÖHLING et al. (2014). Here, we found that crop growth models that describe transpiration and root water uptake in a mechanistic way, such as SPASS or GECROS, performed better than the simpler models (LEACHN, CERES, SUCROS, CLM3.5). Moreover, in the study of GAYLER et al. (2014) we systematically searched for the best multi-physics combinations for our two studies sites. In total, we tested 720 multi-physics combinations with regard to their performance of simulating the latent and sensible heat flux, ground heat flux and soil water dynamics based on the field data of EC1 and EC6 (GAYLER et al., 2014). We found, among many other results, that in none of the 25 best model ensembles the simple dynamic leaf model had been used. The newly implemented Ball-Berry scheme outperformed the Jarvis scheme, and the soil moisture response function taken from CLM or SSiB was better suited to simulate soil water dynamics and energy fluxes than the approach implemented in NOAH.

Work Package 6 (Postdoctoral Researcher): Calibrating EXPERTN-GECROS

This calibration of EXPERTN-GECROS, in cooperation with P7 and P8, was continuously pursued during the first phase of the Research Unit based on the plant data of P4 and weather data of P1 and P2. Using data of the years 2009, 2010 and 2011, EXPERTN-GECROS was calibrated to winter wheat, silage maize, and winter rape. Additionally, winter and spring barley were calibrated to plant data from LTZ Augustenberg. The dataset of the LTZ Augustenberg, compiled by P7, contained only development stage (BBCH) and yield data. In 2012, at EC4 and EC5 winter and spring barley were grown and the standard monitoring program was performed by P4 (external funding). These data will be used in the next months to further improve the calibration of winter and spring barley.

As already discussed in the progress report of PAK 346, standard GECROS performs well with summer crops but has deficiencies with winter crops. Winter crops develop slowly but continuously over the winter months, so that in early spring biomass and LAI are systematically overestimated. Furthermore, the calibrated set of parameters was different for the two model regions pointing at structural model deficiencies in GECROS. After a thorough analysis of the model equations (in cooperation with P5), it turned out that the reason was the algorithm used in standard GECROS to compute the deficiency-driven nitrogen demand, i.e. the amount of nitrogen required to maintain the actual nitrogen concentration in the plant to a critical nitrogen concentration (ncri). The critical nitrogen concentration is computed with the help of an empirical equation:

ncri = ncri0 e-0.4 Eq. 2

Here, is the development stage. This equation leads to the situation that plants have their maximum N demand during the juvenile phase, which is reasonable for summer crops but not for winter crops. To account for winter dormancy, plant growth was down-regulated by reducing the N demand to 1% of its usual value as long as is below 0.25 (BBCH 22). When this critical development stage is over, N demand is computed with the standard equation (Eq. 2). This model modification fixed the problem, and the GECROS model is now capable of simulating crop dynamics at the Kraichgau and Swabian Alb sites with a unique set of parameters, which demonstrates the robustness of our model modification.

To further test the capabilities of EXPERTN-GECROS against that of other crop models, Projects P2 and P5 joined the International Agricultural Model Intercomparison Project (AgMIP) in 2011. The first AgMIP study aimed at quantifying the uncertainty in yield predictions under climate change (ASSENG et al., 2013). An ensemble of 27 wheat models, three of them run by P2 and P5 and one by WESS, were compared and analysed. Modellers had the task to forecast the yield for four experimental sites representative for the International Maize and Wheat Improvement Center (CIMMYT) mega-environments. Based on single-year experiments for the Netherlands, Argentina, India, and Australia in a first run the modellers were allowed to calibrate the model using a low-information dataset. This means that sowing date, date of BBCH 65 and 91, N fertilisation rate and irrigation, but no yield data, were provided. In a second run, models were calibrated based on a high-information (complete) dataset (management, biomass


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dynamics, yield, LAI, N content of grain, soil water content etc.). In the last step, calibrated models were used to simulate the effect of temperature and CO2 rise on yield. Based on the low-information dataset, none of the 27 models was able to predict the yield for all four mega-environments within the mean experimental variation (coefficient of variation: 13.5%). Based on the high-information dataset 10 models, among them GECROS, were able to simulate the yield within this range for all 4 sites. This underlines the value of comprehensive field observations. With regard to the forecast of temperature and CO2 fertilisation effects on yield, the study showed that an ensemble of 4-5 crop models was needed to reduce the coefficient of variation of simulated yields to mean experimental variation. The more extreme the changes of environmental conditions, the larger the model ensemble needed to match this criterion. Because in climate simulations multi-ensemble runs are limited by computational power and time, in the Research Unit a reliable and robust calibration of GECROS is mandatory. This can only be achieved on the basis of a multiple-year dataset of crop and weather data. Every additional dataset that can be considered in the calibration and validation will improve the predictive power of the model.

Work Package 7 (Postdoctoral Researcher): Calibration and validation of the coupled 1D NOAH LSM – GECROS CM

At the end of PAK 346, the coupling of NOAH and GECROS had been successfully completed. In April 2012, however, the developers of NOAH at National Centre of Atmospheric Research (NCAR, Boulder, USA) released a new version of NOAH, named NOAHMP. Among others, NOAHMP was extended by a semi-tile approach and it now contains a photosynthesis-based Ball-Berry scheme to compute canopy resistance. Because of these significant improvements, the Research Unit decided to switch from NOAH to NOAHMP. This decision made it necessary to redo the coupling of the LSM and GECROS. It was completed in October 2012. The concept of model coupling is depicted in Figure 3.

Figure 3. Schematic representation of the coupling of the land surface model NOAHMP and the crop growth model GECROS

Equal to EXPERTN-GECROS, NOAHMP-GECROS was extended by routines for vernalisation and winter dormancy. Based on plant data provided by Project P4, it was calibrated for one major winter and one major summer crop (winter wheat and silage maize). NOAHMP-GECROS reproduced well the vegetation dynamics of winter wheat and silage maize in Kraichgau and Swabian Alb (Table 2 and Figure 4). The overall modelling efficiency (EF) for these two crops was 94% (winter wheat) and 87% (silage maize). With this novel model it is now possible to simulate the seasonal crop development in a dynamic, weather-driven way.

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Table 2. Model performance of NOAHMP-GECROS for winter wheat and silage maize in Kraichgau and Swabian Alb. The model was calibrated on the basis of a two-year data set (2010-2011). Variable Winter wheat Silage maize

EF RMSE Bias EF RMSE Bias

Development stage (-) 0.98 0.10 -0.08 0.95 0.05 -0.01 Leaf area index (m2/m2) 0.83 0.53 0.34 0.51 0.63 -0.01 Plant height (m) 0.95 0.04 -0.06 0.92 0.14 -0.17 Vegetative biomass (dt/ha) 0.02 30.8 0.10 0.91 0.39 0.65 Generative biomass (dt/ha) 0.95 2.30 -3.26 0.88 2.56 -6.40 Overall 0.94 0.185 -0.018 0.87 0.101 -0.019

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Figure 4. NOAHMP-GECROS simulations (red and blue line) of the vegetation dynamics of winter wheat in 2010 (top row) and 2011 (bottom row). The color indicates the study site: Kraichgau (red) and Swabian Alb (blue). The dotted symbols are measurements. Except the development stage, all data are normalised to the maximum of their observation group.

With the new coupled model NOAHMP-GECROS it is now possible to compute energy balance terms that are not considered in current LSMs, namely canopy storage and energy consumption by photosynthesis. Simulations show that on a monthly diurnal average both fluxes may sum up to about 30 W m-2 (Figure 5). On single days and on a half-hourly basis the simulated fluxes were as large as 55 W m-2 (data not shown). Not surprisingly, the energy flux of the two components was highest in June and lowest in April. Averaged over the entire simulation period, these two fluxes contributed to about 5% of the energy balance.


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Work Package 8 (Postdoctoral Researcher): Transferring the NOAH–GECROS CM into HRLDAS

At the time of applying for the Research Unit in 2011, NCAR had announced at their website the speedy release of NOAH-HRLDAS. The release was however delayed until May 2014. In the proposal it was planned to transfer the calibrated NOAH–GECROS first to HRLDAS before integrating the new package into WRF-NOAH system. Because of the delay this step had to be skipped and the new packages have been directly linked to WRF.

In the application it was announced to replace the 1:5,000,000 scale FAO-UNESCO Soil Map of the World (FAO; FAO, 1981) by the 1:1,000,000 scale Soil Overview Map of Germany (BÜK 1000; Bundesanstalt für Geowissenschaften und Rohstoffe (BGR)). In 2012, however, the Harmonised World Soil Database (HWSD) (FAO/IIASA/ISRIC/ISSCAS/JRC, 2012) was released. The HWSD is a 30-arc-second raster database with over 16,000 different soil mapping units. It combines existing regional and national updates of soil information worldwide (SOTER, ESD, Soil Map of China, WISE) with the information contained within the FAO-UNESCO Soil Map of the World. In close consultation with P1, we decided to integrate the BÜK instead of FAO into HWSD. The BÜK differentiates between 71 soil units and gives the dominant soil texture for each unit. The texture classes of the BÜK were reclassified to USDA soil texture classes as required by WRF-NOAHMP. The main differences between the FAO soil map and BÜK 1000 are that in the FAO map the soil texture in the loess areas of Germany are classified as “loam” and as “silt loam” in the BÜK, and that the Geest areas of the Northern Lowland, which are also classified as “loam” in the FAO map, are classified as “loamy sand” in the BÜK. The HWSD-BÜK soil texture map was handed over to Project P1, who integrated the novel soil texture map into WRF-NOAHMP.

Work Package 9 (Postdoctoral Researcher): Evaluation of coupled WRF-NOAHMP-GECROS simulations

NOAHMP-GECROS was handed over to Project P1, which is currently working on integrating NOAHMP-GECROS in the WRF environment. Until the end of 2014, first WRF-NOAHMP-GECROS simulations will be available and used to study in-depth how the implementation of a mechanistic crop growth model, and the more detailed description of soil-plant-atmosphere interactions connected herewith, into WRF-NOAHMP affects land surface exchange processes and the regional soil water dynamics. This work will be performed in close cooperation and reconcilement with the researchers of P1.

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1.1 Project-related publications

1.1.1 Articles published by outlets with scientific quality assurance, book publications, and works accepted for publication but not yet published

WIZEMANN, H.D., INGWERSEN, J., HÖGY, P., WARRACH-SAGI. K., STRECK, T., WULFMEYER, V., 2014. Three-Year Observations of Water Vapor and Energy Fluxes over Agricultural Crops in two Regional Climates of Southwest Germany. Meteorol. Z., accepted for publication on 8 July 2014; cf. manuscript on enclosed CD-ROM.

GAYLER, S., WÖHLING,T., GRZESCHIK, M., INGWERSEN, J., WIZEMANN, H.-D., WARRACH-SAGI, K., HÖGY, P., ATTINGER, S., STRECK, T., WULFMEYER, V., 2014. Incorporating dynamic root growth enhances the performance of NOAHMP at two contrasting winter wheat field sites. Water Resour. Res. 50, 1337-1356.

WÖHLING,T., GAYLER, S., PRIESACK, E., INGWERSEN, J., WIZEMANN, H.-D., HÖGY, P., CUNTZ, M., ATTINGER, S., WULFMEYER, V., STRECK, T., 2014. Multiresponse, multiobjective calibration as a diagnostic tool to compare accuracy and structural limitations of five coupled soil-plant models and CLM3.5. Water Resour. Res. 49, 8200-8221.

ASSENG, S., EWERT, F., ROSENZWEIG, C., JONES, J.W., HATFIELD, J.L., RUANE, A.C., BOOTE, K.J., THORBURN, P.J., ROTTER, R.P., CAMMARANO, D., BRISSON, N., BASSO, B., MARTRE, P., AGGARWAL, P.K., ANGULO, C., BERTUZZI, P., BIERNATH, C., CHALLINOR, A.J., DOLTRA, J., GAYLER, S., GOLDBERG, R., GRANT, R., HENG, L., HOOKER, J., HUNT, L.A., INGWERSEN, J., IZAURRALDE, R.C., KERSEBAUM, K.C., MULLER, C., NARESH KUMAR, S., NENDEL, C., O'LEARY, G., OLESEN, J.E., OSBORNE, T.M., PALOSUO, T., PRIESACK, E., RIPOCHE, D., SEMENOV, M.A., SHCHERBAK, I., STEDUTO, P., STOCKLE, C., STRATONOVITCH, P., STRECK, T., SUPIT, I., TAO, F., TRAVASSO, M., WAHA, K., WALLACH, D., WHITE, J.W., WILLIAM,S J.R., WOLF, J., 2013. Uncertainties in simulating wheat yields under climate change. Nat. Clim. Change 3, 827-832.

AURBACHER, J., PARKER, P. S., CALBERTO SÁNCHEZ, G. A., STEINBACH, J., REINMUTH, E., INGWERSEN, J., DABBERT, S., 2013. Influence of climate change on short term management of field crops – A modelling approach. Agr. Syst. 119, 44-57.

GAYLER, S., INGWERSEN, J., PRIESACK, E., WÖHLING, T., WULFMEYER, V., STRECK, T., 2013. Assessing the relevance of subsurface processes for the simulation of evapotranspiration and soil moisture dynamics with CLM3.5: Comparison with field data and crop model simulations. Environ. Earth Sci. 69, 415-427.

INGWERSEN, J., STEFFENS, K., HÖGY, P., WARRACH-SAGI, K., ZHUNUSBAYEVA, D., POLTORADNEV, M., GÄBLER, R., WIZEMANN, H.-D., FANGMEIER, A., WULFMEYER, V., STRECK, T., 2011. Comparison of NOAH simulations with eddy covariance and soil water measurements at a winter wheat stand. Agric. Forest Meteorol. 151, 345-355.

1.1.1 Other publications None 1.1.2 Patents None

2 Objectives and work programme

2.1 Anticipated total duration of the project

This application builds on Project P2 which has been funded by DFG since 01 February 2012. Application period: 36 months Beginning of funding: 01.02.2015

2.2 Objectives

Weather and climate simulations are critically dependent on an accurate representation of land surface exchange processes. Soil-plant-atmosphere interactions play a key role here. The


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overall goal of P2 is to improve our understanding of processes and fluxes in the soil-plant-atmosphere system and to improve their description in land surface models such as NOAHMP. Emphasis is put on cropland. In the first phase we coupled the land surface model NOAHMP and the crop growth model GECROS. Based on continuous, long-term eddy covariance and soil water and crop measurements the coupled model was tested and calibrated. In the second phase of FOR 1695, the validity and robustness of NOAHMP-GECROS will be investigated. Through the Atmosphere-Land surface-Crop Model (ALCM) the coupled model is an important component of the Integrated Land-system Model System (ILMS), which will be used in cooperation with the other projects of the Research Unit to study feedbacks in the land system under global climate change.

2.3 Work programme incl. proposed research methods

Work Package 1 (Technician + Doctoral Researcher): Eddy covariance measurements of surface energy fluxes

Long-term measurements are fundamental to study the dynamics of (agro)-ecosystems (DFG, 2013). Therefore, Project P2 plans to continue the operation of the three EC stations at Katharinentalerhof in Kraichgau. The maintenance work will be performed by the technician (30% of a full position) and the doctoral researcher. The energy and CO2 flux data of the next three years will be the basis for validating the model components developed during the first phase. The flux, weather and soil data will be handed over to P1, P3, P5, P6, P8, and P9. A drawback of the previous years was that the LI-7500 sensor had to be switched off over the winter months, so that it was not possible to test the performance of NOAHMP-GECROS with regard to the energy and water fluxes during this period. Particularly with regard to the newly implemented winter dormancy routine it would be important to have such data. Therefore, we intend to extend the solar power supply of one EC station by a hydrogen fuel cell system that provides sufficient power over the winter months. Moreover, we will install at each station a webcam mounted at the mast of the net radiation sensor to take photos from the canopy to determine the GVF as described in the progress report (WP3). Within the view field of the webcam P4 will determine bi-weekly the chlorophyll content of leaves using a SPAD measuring device.

Work Package 2 (Doctoral researcher): Narrowing the post-closure method uncertainty band of EC flux measurements

We plan to continue our research on the nature of the energy balance gap of EC flux measurement. Besides the comparison of the latent heat flux measurements of the EC method against those obtained from the soil water balance method as performed in 2012 and 2013, we additionally aim at quantifying the contribution of minor terms, usually not considered in the energy balance, to the energy gap. We do not expect that these minor terms will fully close the energy balance but hypothesise that it will significantly enhance the closure. Minor terms to be measured are energy storage in canopy, energy consumption by photosynthesis, and loss of high frequency fluxes (see WP2 of report).

Within the footprint of one EC station we will install our Adcon sensor network consisting of 16 soil moisture probes (SM1, depth: 0.9 m, resolution: 0.1 m) over the vegetation periods 2015 and 2016. Close to the position of the probes the soil water profile down to 1.5 m will be determined four times over the vegetation period by augering and the water content will be measured gravimetrically as reference. Energy storage in the canopy will be determined by placing three high-precision radiation-shielded temperature probes (UTL dataloggers) in a bottom, a mid and a top position at 6 selected positions. Total biomass and its water content will be determined by P4 in a bi-weekly interval. Energy consumption by photosynthesis (derived from gross primary production, GPP) will be computed from the CO2 flux measurements of the EC system (net ecosystem exchange NEE). Ecosystem respiration (ER) will be computed from the temperature response function of measured night-time CO2 fluxes. To further separate ER into above- and below-ground respiration, P4 will perform leaf-chamber measurements over night-time at selected development stages. Moreover, we will determine the loss of high frequency fluxes as described in the progress report of WP1.

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Work Package 3 (Doctoral Researcher): Quantifying spatial variability within the footprint of the EC stations

During the first phase of the Research Unit, we observed that the canopy within the footprint of EC2 and EC6 did not develop fully homogeneously. In the eastern part of the EC2 field, for example, in all years along a north-south strip the canopy did not develop as well as in the surrounding. At EC6, in some years the canopy developed at some patches of the field not as well as elsewhere. We will map the within-field variability with the help of the non-invasive electric magnetic induction (EMI) measuring technique and perform a comprehensive footprint analysis to evaluate whether the observed within-field variability affects water, energy and CO2 fluxes and energy balance closure. The EMI measurements will be conducted once in early spring and once shortly after harvest by the Agrosphere group of the Institute of Bio- and Geoscience (Dr. L. Weihermüller) of Forschungszentrum Jülich, Germany. In spring 2014 (8 April – 11 April), Dr. Weihermüller performed first EMI measurements (CMD MiniExplorer, Gf instruments, Czech Republic) at EC1 and EC2. EMI measurements were calibrated and offset-corrected with the help of apparent soil resistance measurements performed with a multi-channel electrical resistance tomograph (ERT) (Syscal Pro, IRIS instruments, France). Moreover, the EMI data will be used to identify “cold” spots within these EC fields, and P2 will test whether GECROS is able to reproduce the differences in local crop growth. Project P4 will add these plots to their routine measurement programme. Project P9 will measure the abundance and function of soil organisms (Cmic, PLFA content, and soil enzyme activities) in the ploughing horizon two times per year, Project P3 will determine, among others, at these sites the mineral N content in 0-90 cm depth. Project P2 will take soil samples in a bi-weekly interval and determine the soil water content.

Work Package 4 (Doctoral Researcher, Technician): Soil moisture networks in Kraichgau and Swabian Alb

The long-term operation of the two soil moisture networks will be continued. The maintenance of the network is labour intensive (detailed work plan see 4.1), therefore a technician (30% of a full position) is indispensable. The soil moisture network delivers essential and unique data for testing the land surface model component of the ILMS (Integrated Landsystem Model System). The data of the soil moisture networks will be used to verify the spin-up of the WRF (ALCM0.9) evaluation run (see P1), and to assess the quality of the first climate projection (2015-2025) with regard to soil water dynamics for the years 2015 and 2016. Moreover, the soil moisture and temperature data of the two networks are needed by P9, who will perform litter decomposition experiments at selected stations, and P3, who will use the data to force their C/N model.

Work Package 5 (Postdoctoral Researcher): Parameterisation of EXPERTN-GECROS and NOAHMP-GECROS for durum, soybean, sunflower, and some missing crops

In the first phase, we parameterised EXPERTN-GECROS for the major crops of the current rotations and NOAHMP-GECROS for winter wheat and silage maize. In the second phase, we will additionally work on the parameterisation of crops that have the potential to enter the crop rotation in future. Durum, soybean, and sunflower are promising candidates. The three crops are currently investigated in the frame of a breeding experiment at the experimental station “Heidfeldhof” by the research group ”Legumes and Sunflowers” (Dr. V. Hahn) and “Wheat” (Dr. F. Longin) of Landessaatzuchtanstalt (State Plant Breeding Institute). These two research groups will cooperate with us. P4 will measure bi-weekly leaf area index, BBCH, and plant height. Management as well as grain and straw yield data will be gathered by the State Plant Breeding Institute. P2 will install TDR and temperature probes at each field in 3 depths. Weather data will be taken from the meteorological station of the university, which is located in about 2 km distance. Based on these field data and the historical yield and BBCH data from 2000-2014 (State Plant Breeding Institute) EXPERTN-GECROS and NOAHMP-GECROS will be parameterised for these three new crops. Moreover, based on the dataset that was used to calibrate EXPERTN-GECROS for winter barley, summer barley and winter rape, NOAHMP-GECROS will be calibrated for these crops. The parameterisations will be handed over to P3, P6, P7 and P8.


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Work Package 6 (Postdoctoral Researcher): Validation and uncertainty assessment of EXPERTN-GECROS and NOAHMP-GECROS in offline mode

In cooperation with P5, P7 and P8, the calibration work of EXPERTN-GECROS and NOAHMP-GECROS has continuously been pursued during the first phase of the Research Unit based on the incoming data. At the end of the first phase, both models will be calibrated on the basis of a five-year data set (2009-2014). The data acquired in the second phase will be used to validate the models. The field data will be acquired by Projects P1, P2, P3, and P4. P1 and P2 determine surface energy fluxes, CO2 fluxes and weather data, P3 provides Nmin data, and P4 records bi-weekly crop data (LAI, biomass of stem, leaf and grain, nitrogen leaf content, plant height, and BBCH development stage) at EC1-EC6. Moreover, we will perform a comprehensive uncertainty analysis based on the Generalised Likelihood Uncertainty Estimation (GLUE) (BEVEN & BINLEY, 1992; BANNWARTH et al., 2014).

Work Package 7 (Postdoctoral Researcher): Testing the robustness of NOAHMP-GECROS at the scale of Germany

So far, NOAHMP-GECROS has been successfully tested for the Kraichgau and Swabian Alb. In the Atmosphere-Land Surface-Crop Model 2.6 (ALCM2.6) simulations (see Section 1.4.1 of the FOR 1695 proposal), however, WRF-NOAHMP-GECROS will be applied to the entire inner domain including Germany. Therefore, in WP7 we will test the robustness and performance of NOAHMP-GECROS for other regions of Germany. For that purpose, we will use weather and EC data of the four TERENO observatories (Northeastern German Lowland OV, Harz/Central German Lowland OV, Eifel/Lower Rhine Valley OV, and Bavarian Alps/pre-Alps OV) operated by the Helmholtz Centre. The TERENO observatories follow an open data access philosophy. Basic monitoring data at Level 2 (Reviewed and formatted data with quality flags assigned) and Level 3 (Gap-filled data, derived data, spatially and/or temporally aggregated data) are freely accessible. NOAHMP-GECROS will be forced with the half-hourly weather data of the EC stations, and the simulated energy and water fluxes will be compared to the observed EC fluxes. Moreover, we will perform hyper-ensemble simulations (N~1,000) of combinations of multi-physics options to identify the best combination of multi-physics options. Work Package 8 (Postdoctoral Researcher): Simulating feedbacks in the land system under climate change and the impact on landscape functions

Next to P8, P1 and P5, Project P2 will contribute to implementing ALCM (Atmosphere-Land surface-Crop-Model) and ILMS. P2 is responsible for all questions regarding NOAHMP-GECROS. We will focus on evaluating landscape functions such as crop production, water regime, and CN cycling (with others). ALCM projections will be thoroughly analysed in close cooperation with P1 to study the biophysical feedbacks between agricultural landscapes and the atmosphere under climate change. Moreover, we will study the coupling strength (KOSTER et al., 2002) between crop growth dynamics, soil water dynamics, and land surface exchange processes. An important methodological research question will be to clarify the detail in which soil and crop growth processes have to be described to produce reliable projections. In cooperation with P3 and P5 a sensitivity analysis will be performed to study the need to include CN cycling in the ALCM of ILMS to assess the potential impact on atmospheric processes via water and energy feedbacks derived from alterations in C- and N-cycling and hence crop performance and water use. P2 will further be involved in the setup and analysis of BEMS (Bio-Economic Model System) simulations coordinated by P8 to assess adaptation options and policy measures as well as the impact of extreme events on adaptation.

In projection mode ALCM requires sowing and harvest dates. P2 will implement and test the simplified crop management routines delivered by P7. In the ALCM2.6 simulations the land cover type “croplands” will be split into an early-covering winter crop (winter wheat), and a late-covering summer crop (silage maize). P2 will generate a land cover map of this type for P1.

Research Unit 1695

P2-B16

Timetable of activities Year 1 2 3

Postdoctoral Researcher

Parameterisation of EXPERTN-GECROS and NOAHMP-GEC-ROS for durum, soybean, sunflower, and missing crops (WP5)

Validation and uncertainty assessment of EXPERTN-GECROS and NOAHMP-GECROS in offline mode (WP6)

Testing the robustness of NOAHMP-GECROS (WP7)

Projections of regional climate change and its impact on agricultural landscapes (WP8)

Preparation of publications and final report

Doctoral Researcher

Literature reading, training on instruments and EC data processing, ordering of equipment

Operation of the EC stations, processing and evaluation of flux data (WP1)

Narrowing the post-closure method uncertainty band of EC flux measurements (WP2)

Quantifying the spatial variability within the footprint of the EC stations (WP3)

Operation of soil moisture networks in Kraichgau and Swabian Alb (WP4)

Preparation of publications and of final report

2.4 Data handling

Within FOR 1695, a common data management strategy has been implemented. In general, raw data (such as input data for climate simulations, field data, farm surveys, etc.) are managed by the project that generated or retrieved it and stored for further processing as appropriate. A metadata directory which lists information on all available data has been established on the intranet section of the FOR 1695 homepage. All data that is shared by several projects is stored in a joint MySQL-Database maintained by Project P8. Details on data storage and management can be found in Section 1.5 of the FOR 1695 proposal.

The raw data of the EC stations and weather data of the central experimental sites in Kraichgau are stored on a Raid system maintained by P1. The processed EC flux and weather data are fed into the central MySQL server. Data of the soil moisture network are locally stored, daily backed up, and managed on an addVANTAGE Pro Server (Oracle 10g Standard Edition Database, Oracle, USA). Also in the latter case, the processed data are regularly transferred to the central MySQL server.

2.5 Other information

2.6 Descriptions of proposed investigations involving experiments on humans, human materials or animals

Not applicable

2.7 Information on scientific and financial involvement of international cooperation partners

Not applicable


P2-B17

3 Bibliography ALAVI, N., BERG, A.A., WARLAND, J.S., PARKIN, G., VERSEGHY, D., BARTLETT, P., 2010. Evaluating the

impact of assimilating soil moisture variability data on latent heat flux estimation in a land surface model. Can. Water Resour. J. 35, 157-172.

ASSENG, S., EWERT, F., ROSENZWEIG, C., JONES, J.W., HATFIELD, J.L., RUANE, A.C., BOOTE, K.J., THORBURN, P.J., RÖTTER, R.P., CAMMARANO, D., BRISSON, N., BASSO, B., MARTRE, P., AGGARWAL, P.K., ANGULO, C., BERTUZZI, P., BIERNATH, C., CHALLINOR, A.J., DOLTRA, J., GAYLER, S., GOLDBERG, R., GRANT, R., HENG, L., HOOKER, J., HUNT, L.A., INGWERSEN, J., IZAURRALDE, R.C., KERSEBAUM, K.C., MÜLLER, C., NARESH KUMAR, S., NENDEL, C., O'LEARY, G., OLESEN, J.E., OSBORNE, T.M., PALOSUO, T., PRIESACK, E., RIPOCHE, D., SEMENOV, M.A., SHCHERBAK, I., STEDUTO, P., STÖCKLE, C., STRATONOVITCH, P., STRECK, T., SUPIT, I., TAO, F., TRAVASSO, M., WAHA, K., WALLACH, D., WHITE, J.W., WILLIAMS, J.R., WOLF, J., 2013. Uncertainty in simulating wheat yields under climate change. Nat. Clim. Change. 3, 827-832.

AURBACHER, J., PARKER, P.S., CALBERTO SÁNCHEZ, G.A., STEINBACH, J., REINMUTH, E., INGWERSEN, J., DABBERT, S., 2013. Influence of climate change on short term management of field crops – A modelling approach. Agr. Syst. 119, 44-57.

BANNWARTH, M., HUGENSCHMIDT, C., SANGCHAN, W., LAMERS, M., INGWERSEN, J., ZIEGLER, A.D., STRECK, T., 2014. Simulation of stream flow components in a mountainous catchment in northern Thailand with SWAT, using the ANSELM calibration approach. Hydrol. Process. in print, DOI: 10.1002/hyp.10268.

BARR, A.G., KING, K.M., GILLESPIE, T.J., DEN HARTOG, G., NEUMANN, H.H., 1994. A comparison of Bowen ratio and eddy correlation sensible and latent heat flux measurements above deciduous forest. Bound-Lay. Meteorol. 71, 21-41.

BEVEN, K., BINLEY, A., 1992. The future of distributed models: model calibration and uncertainty prediction. Hydrol.Process. 6, 279-298.

BLANKEN, P.D., BLACK, T.A., YANG, P.C., NEUMANN, H.H., NESIC, Z., STAEBLER, R., DEN HARTOG, G., NOVAK, M.D., LEE, X., 1997. Energy balance and canopy conductance of a boreal aspen forest: Partitioning overstory and understory components. J. Geophys. Res. D: Atmospheres. 102, 28915-28927.

BOGENA, H.R., HERBST, M., HUISMAN, J.A., ROSENBAUM, U., WEUTHEN, A., VEREECKEN, H., 2010. Potential of wireless sensor networks for measuring soil water content variability. Vadose Zone J. 9, 1002-1013.

BROCCA, L., TULLO, T., MELONE, F., MORAMARCO, T., MORBIDELLI, R., 2012. Catchment scale soil moisture spatial-temporal variability. J. Hydrol. 422-423, 63-75.

CARRER, D., LAFONT, S., ROUJEAN, J., CALVET, J., MEUREY, C., LE MOIGNE, P., TRIGO, I.F., 2012. Incoming solar and infrared radiation derived from METEOSAT: Impact on the modeled land water and energy budget over France. J. Hydrometeorol. 13, 504-520.

CHEN, F. F., DUDHIA, J., 2001. Coupling and advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Weather Rev. 129, 569–585.

CHEN, F. F., MANNING, K. W., LEMONE, M. A., TRIER, S. B., ALFIERI, J. G., ROBERTS, R., TEWARI, M., NIYOGI, D., HORST, T. W., ONCLEY, S. P., BASARA, J. B., BLANKEN, P. D., 2007. Description and evaluation of the characteristics of the NCAR high-resolution land data assimilation system. J. Appl. Meteorol. Clim. 46, 694–713.

DFG, 2013. Langzeitperspektiven und Infrastruktur der terrestrischen Forschung Deutschlands – ein systemischer Ansatz. www.dfg.de.

DUDHIA, J., 1993. A nonhydrostatic version of the Penn State-NCAR mesoscale model: Validation tests and simulation of an Atlantic cyclone and cold front. Mon. Weather Rev. 121, 1493–1513.

EL MAAYAR, M., CHEN, J.M., PRICE, D.T., 2008. On the use of field measurements of energy fluxes to evaluate land surface models. Ecol. Model. 214, 293-304.

FALGE, E. E., RETH, S., BRÜGGEMANN, N., BUTTERBACH-BAHL, K., GOLDBERG, V., OLTCHEV, A., SCHAAF, S., SPINDLER, G., STILLER, B., QUECK, R., KÖSTNER, B., BERNHOFER, C., 2005. Comparison of surface energy exchange models with eddy flux data in forest and grassland ecosystems of Germany. Ecol. Model. 188, 174–216.

FOKEN, T.T., 2008. The energy balance closure problem: An overview. Ecol. Appl. 18, 1351–1367. GAYLER, S., INGWERSEN, J., PRIESACK, E., WÖHLING, T., WULFMEYER, V., STRECK, T., 2013. Assessing the

relevance of subsurface processes for the simulation of evapotranspiration and soil moisture dynamics with CLM3.5: Comparison with field data and crop model simulations. Environ. Earth Sci. 69, 415-427.

GAYLER, S., WÖHLING, T., GRZESCHIK, M., INGWERSEN, J., WIZEMANN, H.-D., WARRACH-SAGI, K., HÖGY, P., ATTINGER, S., STRECK, T., WULFMEYER, V., 2014. Incorporating dynamic root growth enhances the performance of Noah-MP at two contrasting winter wheat field sites. Water Resour. Res. 50, 1337-1356.

Research Unit 1695

P2-B18

GERKEN, T., BABEL, W., HOFFMANN, A., BIERMANN, T., HERZOG, M., FRIEND, A.D., LI, M., MA, Y., FOKEN, T., GRAF, H., 2012. Turbulent flux modelling with a simple 2-layer soil model and extrapolated surface temperature applied at Nam Co Lake basin on the Tibetan Plateau. Hydrol. Earth Syst. Sc. 16, 1095-1110.

GOUDRIAAN, J., VAN LAAR, H.H., 1994. Modelling potential crop growth processes, Kluwer Academic Publishers Dordrecht.

GUTMAN, G., IGNATOV, A., 1998. The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. Int. J. Remote Sensing. 19, 1533-1543.

HOUSKA, T., MULTSCH, S., KRAFT, P., FREDE, H., BREUER, L., 2014. Monte Carlo-based calibration and uncertainty analysis of a coupled plant growth and hydrological model. Biogeosciences 11, 2069-2082.

IMUKOVA, K., INGWERSEN, J., STRECK, T., 2014. Determining the spatial and temporal dynamics of the green vegetation fraction of croplands using high-resolution RapidEye satellite images. Agric. For. Meteorol., submitted on 14 July 2014; cf. manuscript on enclosed CD-ROM.

INGWERSEN, J., STEFFENS, K., HÖGY, P., WARRACH-SAGI, K., ZHUNUSBAYEVA, D., POLTORADNEV, M., GÄBLER, R., WIZEMANN, H.-D., FANGMEIER, A., WULFMEYER, V., STRECK, T., 2011. Comparision of NOAH simulations with eddy covariance and soil water measurements at a winter wheat stand. Agric. For. Meteorol. 151, 345-355.

KANDA, M.A., J.C.WEYNGAARD, M.O.LETZEL, S. RAASCH, T. WATANABE, 2004. LES study of the energy imbalance problem with eddy covariance fluxes. Bound.-Lay. Meteorol. 110, 922-381

KOSTER, R.D., DIRMEYER, P.A., HAHMANN, A.N., IJPELAAR, R., TYAHLA, L., COX, P., SUAREZ, M.J., 2002. Comparing the degree of land-atmosphere interaction in four atmospheric general circulation models. J. Hydrometeorol. 3, 363-375.

LEUNING, R., VAN GORSEL, E., MASSMAN, W.J., ISAAC, P.R., 2012. Reflections on the surface energy imbalance problem. Agric. For. Meteorol. 156, 65-74.

LI, Y., ZHOU, J., KINZELBACH, W., CHENG, G., LI, X., ZHAO, W., 2013. Coupling a SVAT heat and water flow model, a stomatal-photosynthesis model and a crop growth model to simulate energy, water and carbon fluxes in an irrigated maize ecosystem. Agric. For. Meteorol. 176, 10-24.

LOKUPITIYA, E.E., DENNING, S., PAUSTIAN, K., BAKER, I., SCHAEFER, K., VERMA, S., MEYERS, T., BERNACCHI, C., SUYKER, A., FISCHER, M., 2009. Incorporation of crop phenology in Simple Biosphere Model (SiBcrop) to improve land-atmosphere carbon exchanges from croplands. Biogeosciences Discussions 6, 1903–1944.

MAUDER, M.M., FOKEN, T.T., 2006. Impact of post-field data processing on eddy covariance flux estimates and energy balance closure. Meteorol. Z. 15, 597–609.

MAUDER, M., CUNTZ, M., DRÜE, C., GRAF, A., REBMANN, C., SCHMID, H.P., SCHMIDT, M., STEINBRECHER, R., 2013. A strategy for quality and uncertainty assessment of long-term eddy-covariance measurements. Agric. For. Meteorol. 169, 122-135.

MITTELBACH, H., SENEVIRATNE, S.I., 2012. A new perspective on the spatio-temporal variability of soil moisture: Temporal dynamics versus time-invariant contributions. Hydrol. Earth Syst. Sc. 16, 2169-2179.

NIU, G.Y., YANG, Z.L., MITCHELL, K.E., CHEN, F., EK, M.B., BARLAGE, M., KUMAR, A., MANNING, K., NIYOGI, D., ROSERO, E., TEWARI, M., XIA, Y., 2011. The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res. D: Atmospheres. 116 (12), D12109.

POLTORADNEV, M., INGWERSEN, J., STRECK, T., 2014. Calibration and application of Aquaflex TDT soil water probes to measure the soil water dynamics of agricultural topsoils in Southwest Germany. J. Irrig. Drain. E., submitted on 14 May 2014; cf. manuscript on enclosed CD-ROM.

RADDATZ, R.L., 2007. Evidence for the influence of agriculture on weather and climate through the transformation and management of vegetation: Illustrated by examples from the Canadian Prairies. Agric. For. Meteorol. 142, 186–202.

ROSENBAUM, U., BOGENA, H.R., HERBST, M., HUISMAN, J.A., PETERSON, T.J., WEUTHEN, A., WESTERN, A.W., VEREECKEN, H., 2012. Seasonal and event dynamics of spatial soil moisture patterns at the small catchment scale. Water Resour. Res. 48, W10544.

SKAMAROCK, W.C., KLEMP, J.B., DUDHIA, J., GILL, D.O., BARKER, D.M., DUDA, HUANG, WANG, POWERS, 2008. A description of the Advanced Research WRF Version 3. No. NCAR Tech Notes-475+STR.

SONG, Y., JAIN, A.K., MCISAAC, G.F., 2013. Implementation of dynamic crop growth processes into a land surface model: Evaluation of energy, water and carbon fluxes under corn and soybean rotation. Biogeosciences 10, 8039-8066.

STATISTICAL OFFICE OF THE EUROPEAN UNION, 2014. Eurostat. Available: http://epp.eurostat.ec.europa.eu. [02.07.2014].

STAUDT, K., FALGE, E., PYLES, R.D., PAW U, K.T., FOKEN, T., 2010. Sensitivity and predictive uncertainty of the ACASA model at a spruce forest site. Biogeosciences 7, 3685-3705.


P2-B19

STEINFELD, G., LETZTEL, M.O., RAASCH, S., KANDA, M., INAGAKI, A., 2007. Spatial representativeness of single tower measurements and the imbalance problem with eddy-covariance fluxes: results of a large-eddy simulation study. Bound.-Lay. Meteorol. 123, 77-98.

TWINE, T.E., KUSTAS, W.P., NORMAN, J.M., COOK, D.R., HOUSER, P.R., MEYERS, T.P., PRUEGER, J.H., STARKS, P.J., WESELY, M.L., 2000. Correcting eddy-covariance flux underestimates over a grassland. Agric. For. Meteorol. 103, 279–300.

VAN DEN HOOF, C., HANERT, E., VIDALE, P. L., 2011. Simulating dynamic crop growth with an adapted land surface model - JULES-SUCROS: Model development and validation. Agric. For. Meteorol. 151, 137-153.

VEREECKEN, H., KAMAI, T., HARTER, T., KASTEEL, R., HOPMANS, J., VANDERBORGHT, J., 2007. Explaining soil moisture variability as a function of mean soil moisture: A stochastic unsaturated flow perspective. Geophys. Res. Lett. 34 (22).

VIVONI, E.R., RODRÍGUEZ, J.C., WATTS, C.J., 2010. On the spatiotemporal variability of soil moisture and evapotranspiration in a mountainous basin within the North American monsoon region. Water Resour.Res. 46 (2).

WANG, E., ENGEL, T., 2000. SPASS: A generic process-oriented crop model with versatile windows interfaces. Environ. Modell. Softw. 15, 179-188.

WOLF, A., LACA, E.A., 2007. Cospectral analysis of high frequency signal loss in eddy covariance measurements. Atmos. Chem. Phys. Discuss. 7, 13151-13173.

WIZEMANN, H.D., INGWERSEN, J., HÖGY, P., WARRACH-SAGI. K., STRECK, T., WULFMEYER, V., 2014. Three-Year Observations of Water Vapor and Energy Fluxes over Agricultural Crops in two Regional Climates of Southwest Germany. Meteorol. Z., accepted for publication on 8 July 2014; cf. manuscript on enclosed CD-ROM.

WÖHLING,T., GAYLER, S., PRIESACK, E., INGWERSEN, J., WIZEMANN, H.-D., HÖGY, P., CUNTZ, M., ATTINGER, S., WULFMEYER, V., STRECK, T., 2014. Multiresponse, multiobjective calibration as a diagnostic tool to compare accuracy and structural limitations of five coupled soil-plant models and CLM3.5. Water Resour. Res. 49, 8200-8221.

YIN, X., SCHAPENDONK, A.H.C.M., 2004. Simulating the partitioning of biomass and nitrogen between roots and shoot in crop and grass plants. NJAS 51, 407–425.

YIN, X., VAN LAAR, H.H., 2005. Crop systems dynamics – An ecophysiological simulation model for genotype-by-environment interactions. Wageningen Academic Publishers, Wageningen, NL.

Research Unit 1695

P2-B20

4 Requested modules/funds

Basic Module

4.1 Funding for staff Cost Category Sum in € Postdoctoral Researcher

(100%) 36 months 190.800

Service Assistant (60%) 36 months 78.840

Student assistance 36 months (80 hours per month)

36.691

SUBTOTAL 4.1 306.331

4.2 Funding for direct project costs

4.2.1 Equipment up to €10,000, software and consumables Sum in €

1 Fuel cell power supply system for one EC station (WP 1) 8.000

3 Webcams with weather resistant housing 900

Spare sensors (2 heat flux plates, 1 TDR probes, 1 temperature probes, 15 matric potential sensors)

3.400

6 Batteries (12 V, 250 Ah) for 3 EC stations (2 per station) 1.800

3 TDT sensors 1.800

6 solar panels (9V, 5 W) 1.100

42 battery packs for RTUs 2.700

3 Soil water and temperature monitoring stations (WP 5) 9.600

Wires, cable binder, tape, marking sticks, methanol, and others

1.000

SUBTOTAL 30.300

4.2.2 Travel Sum in € Travels to the central research site, maintenance of the

eddy covariance stations 6.490

Maintenance of the regional soil moisture network 15.053

Travel and hotel cost for two persons for performing the EMI measurements at EC2 and EC6

1.938

National/international conferences > see Project PZ

SUBTOTAL 23.481

4.2.3 Visiting researchers Sum in € None requested

SUBTOTAL 0

4.2.4 Experimental animals Sum in € Not applicable

SUBTOTAL 0

4.2.5 Other costs Sum in € Calibration of three H2O/CO2 Licor open path sensors 4.000

Correction data for the DGPS 2.400

Data transfer costs for regional soil moisture network 1.900

Allowance payments for farmers 3.150

Rent for trencher and transporter (1 day) 350

Arc-GIS licence 500

SUBTOTAL 12.300

4.2.6 Publication costs > see Project PZ

SUBTOTAL 4.2 0

4.3 Funding for instrumentation (> € 10,000 per item) Sum in €

SUBTOTAL 4.3 0

GRAND TOTAL 372.412


P2-B21

4.1 Funding for staff

Project P2 requests funding for a postdoctoral researcher (100%) and a service assistant (60%). Both employees will be contracted for 3 years. As in the first phase of FOR 1695 (and also in PAK 346), the doctoral researcher will be funded externally.

The postdoctoral researcher will be responsible for parameterising EXPERTN-GECROS and NOAHMP-GECROS for missing and new crops (WP5). He/she will validate the calibrated EXPERTN-GECROS and NOAHMP-GECROS against the field data acquired in Phase 2 and perform the uncertainty analysis outlined in WP6. Moreover, the postdoctoral researcher will test the robustness of NOAHMP-GECROS on the basis of EC flux measurements from TERENO sites (WP7). In close cooperation with the other projects, he/she will be involved in the implementation of ALCM and ILMS and responsible for all questions regarding NOAHMP-GECROS and its parameterisation.

The externally funded doctoral researcher will be responsible for setting up and running and evaluating the field experiments (WP2+3). He/she will evaluate the quality of the ALCM spin-up and climate projection with regard to the simulation of the soil water dynamics in topsoils of Kraichgau and Alb (WP4). Additionally, he/she will support the technician in operating the EC stations at Katharinentalerhof in Kraichgau (WP1). The calculation of the EC flux data is in his/her responsibility. He/she will be intensively supervised by the postdoctoral researcher of P2.

The service assistant (technician; Benedikt Prechter), supported by the PhD student, maintains the EC stations (WP1, 30% of a full position). He/she pre-processes the EC raw data, compiles the weather and soil data and regularly transfers the data to the Raid system and central MySQL server. Additionally, the technician is indispensable for maintaining the soil moisture networks (WP4, 30% of a full position). From beginning of April until mid of October, the technician travels to the Kraichgau or the Swabian Alb stations once per week. About 10 stations can be visited and maintained per day (control and, if necessary, cleaning of the rain gauge and solar panel, removal of weeds, exchange of desiccant, soil sampling for checking the calibration, etc.). This maintenance interval results in an about monthly maintenance of each station. During the harvest and sowing period the maintenance interval must be shortened. The technician will travel to Kraichgau and Swabian Alb once per week. He will remove the plant material from the station after harvest, grub the sensor areas, and level the terrain after ploughing. During this period the technician stays in close touch with the farmers, to coordinate the activities and in particular the sowing in the sensor area. Student Research Assistance The project requests funding for student assistance of 36 months x 80 hours/month. The student assistance supports the project in setting up and running the field experiments and the EC stations. Additionally, he/she supports the technician in maintaining the soil moisture networks during peak times (harvest: July-October, sowing period: March-April). Moreover, the student assistance supports the postdoctoral researcher in compiling the data and configuration files needed for model calibration and validation, and he/she is in charge for the operation of the central Adcon data server of the soil moisture network. The student assistance checks by remote-control once per week all 42 stations for correct functioning and identifies and labels data gaps. 4.2 Funding for direct project costs 4.2.1 Equipment up to €10,000, software and consumables The fuel cell power supply is needed to operate one of the three EC stations over the winter months. In the second phase we intend to determine the GVF at each EC station with the help of a webcam mounted to the mast of the net radiation sensor. The capacity of batteries (2 per station) of the EC stations has decreased and they need to be replaced. Over the last three years at the EC stations 2 heat flux plates, 1 TDR probe and 1 thermistor probe broke or were damaged. Moreover, all 15 matric potential sensors need to be replaced because of the

Research Unit 1695

P2-B22

wastage of the internal gypsum tablet. At 3 stations of the regional soil moisture networks the TDT sensor was damaged by tillage and 6 solar panels broke. Moreover, the rechargeable battery pack of the 42 stations aged over the last 6 years and need to be replaced. Additional three soil water and temperature monitoring stations are needed at the durum, soybean, and sunflower sites (WP5). 4.2.2 Travel

Weekly travels to the Kraichgau EC stations during 3 years; transportation costs: 156 weeks x 1 travel/week x 130 km/travel x 0.32 €/km

6,490 €

Maintenance and operation of regional soil moisture network over a period of 3 years; transportation costs: 84 travels (Swabian Alb) x 220 km/travel x 0.32 €/km; 84 travels (Kraichgau) x 340 km/travel x 0.32 €/km

15,053 €

EMI measurements: Travel and hotel costs for two persons for two stays of a week (hotel costs: 2 persons x 14 days x 50 €/person/day = 1,400 €; travels costs: 2 travels by car Jülich-Pforzheim-Nellingen-Jülich, 2 x 840 km x 0,32 €/km= 461 €)

1,938 €

Total 4.4 23,481 €

The EC stations at the Katharinentalerhof will be maintained weekly. The maintenance schedule of the soil moisture networks is a follows: From April to mid of July (14 weeks) the technician travels to Kraichgau or Swabian Alb once per week (7 travels to Kraichgau, 7 travels to Swabian Alb). During the harvest and sowing period from mid of July to mid of October (16 weeks) the maintenance intervals must be shortened. The technician will travel once per week to Kraichgau and Swabian Alb (16 travels to Kraichgau, 16 travels to Swabian Alb). After the peak period from mid of October until the end of the year (10 weeks), the maintenance interval will be shorted again (5 travels to Kraichgau, 5 travels to Swabian Alb). Over the wintertime the stations will not be regularly maintained. Over three years the above maintenance schedule results in 84 travels to each study region.

4.2.3 Visiting researchers Not applicable

4.2.4 Experimental animals Not applicable

4.2.5 Other costs The manufacturer of the CO2/H2O open-path analyser recommends recalibrating the sensor every two years. The calibration plus spare part costs for three sensors amount to 2,000 €. Recalibration will be performed in spring 2015 and 2017. For the operation of the DGPS, which will be used to locate the position of the sensors of the field and the regional soil moisture network, SAPOS correction data are needed. The annual fee for these data and costs for transmission by mobile radio amounts to 800 € per year. The data transfer costs for the regional soil moisture network (re-charge of SIM cards) amount to 15 € per station and year. Over 3 years this results in data transfer costs of 1,890 € in total. The farmers affected by the soil moisture networks get an annual allowance of 25 € per station summing up to 3,150 € over three years (42 stations x 25 €/station/year x 3 years). Damaged TDT sensors will be replaced against new ones. For the installation a trencher and a transporter has to be rented for one day. (350 €). One Arc-GIS licence for the postdoctoral researcher (500 €) is required.

4.2.6 Project-related publication expenses

As many journals have a limit in number of pages and some journals collect a publication fee, a budget for publication will allow the team to submit journal papers without facing financial restrictions. For publication costs (3 years per 750,- €) see Project PZ.


P2-B23

4.3 Funding for instrumentation (> € 10,000 per item) Not applicable

5 Project requirements

5.1 Employment status information

Streck, Thilo, Professor, permanent position

5.2 First-time proposal data

Not applicable

5.3 Composition of the project group

Name, acad. title, position

Department of the university or non-university institution

Work performed in the project in hours/week

Salary Scale

Employ-ment status

Core support

Scientific Staff

1. Streck, T., Prof. Dr., Principal Investigator

2. Ingwersen, J., Dr., Researcher

Institute of Soil Science and Land Evaluation

4

6

W3

TV-L E14

Perm.

Perm.

Non-Scientific Staff

3. Sibylle Schulz, Secretary

4. Strohm, E., Dipl.-Ing. agr., Technical Assistant

Institute of Soil Science and Land Evaluation

2

3

BAT9/7

BAT5c

Perm.

Perm.

5.4 Cooperation with other researchers

5.4.1 Researchers with whom you have agreed to cooperate on this project

Cooperation within FOR 1695 We will closely collaborate with P1, P3, P5, P6, P7 and P8 with regard to the integration of the ILMS. Moreover, P1 and P2 closely cooperate in operating the EC stations and processing the EC flux data. Performing the field experiments will be a joint effort of P1, P2, P3, P4, and P9. All modelling aspects with regard to EXPERTN will be done in close cooperation with P5, and the latest calibrated parameter sets will be exchanged with P3, P6, P7 and P8. Moreover, P2 will support P1 and P3 in transferring the new soil texture and SOM maps into the required WRF format and aggregation level. P6 and P7 will be assisted in all issues concerning the application and calibration of EXPERTN-GECROS. External cooperation

The EMI measurements will be performed by the Agrosphere group of the Institute of Bio- and Geoscience, Forschungszentrum Jülich (Dr. L. Weihermüller).

Dr. Joachim Ingwersen Dr. Joachim Ingwersen is currently holding the postdoctoral researcher position in P2, but he will move on to a permanent senior lecturer faculty position in the Biogeophysics group on 01 October 2014. He has established the measurement systems and is the developer of NOAHMP-GECROS. Dr. J. Ingwersen will stay closely involved in the project. He will support the postdoctoral researcher in all issues related to NOAHMP-GECROS and EXPERTN-GECROS and supervise the doctoral researcher in processing the EC flux and other experimental data and manuscript preparation.

Research Unit 1695

P2-B24

5.4.2 Researchers with whom you have collaborated scientifically within the past three years

Prof. Dr. Xiaotang Ju, Key Lab. of Plant-Soil Interactions, China Agricultural University, Beijing, China Prof. Dr. R. K. Panda, Agricultural and Food Eng. Dept., Indian Institute of Technology, Kharagpur, India Prof. Dr. Prasak Thavornyutikarn, Department of Chemistry, Chiang Mai University, Thailand Prof. Dr. Nguyen Van Vien, Dept. of Plant Pathology, Hanoi University of Agriculture, Vietnam Prof. Dr. A.D. Ziegler, Department of Geography, National University of Singapore, Singapore Dr. Fabrice Martin-Laurent, Institut National de la Recherche Agronomique (INRA), Dijon, France Dr. Naoise Nunan, Biogéochimie et écologie des milieux continentaux (BioEMCo), ENS, Paris, France Prof. Dr. Michael Schloter, Inst. of Soil Ecology, Helmholtz-Zentrum München, Germany Prof. Dr. A. Kumar, Indian Council of Agricultural Research (ICAR), Directorate of Water Management, Bhubaneshvar, India Prof. Dr. J. Alarcón, Centro de Edafología y Biología Applicada del Segur (CEBAS-CSIC), Murcia, Spain Prof. Dr. Frans Huibers, Wageningen University and Research Center, Netherlands Prof. Dr. S. Fiedler, Geographisches Institut, Johannes-Gutenberg-Universität Mainz, Germany Prof. Dr. M. Kazda, Institut für Systemtische Botanik und Ökologie, Universität Ulm, Germany Prof. Dr. P. Grathwohl, Prof. Dr. O. Cirpka, Universität Tübingen, Germany, Prof. Dr. S. Attinger, Borchardt, Prof. Dr. O. Kolditz, Prof. Dr. G. Teutsch, Prof. Dr. H.J. Vogel, UFZ Halle/Leipzig, Germany, Prof. Dr. S. Wieprecht, Prof. Dr. B. Nowak, Universität Stuttgart, Germany, and others (WESS)

5.5 Scientific equipment

3 Eddy-covariance stations (EC1-3) 2 Regional soil moisture networks (42 stations) 1 Field soil moisture network (16 stations) 1 Differential global positioning system (DGPS) 22 UTL temperature data loggers

5.6 Project-relevant interests in commercial enterprises Not applicable 6 Additional information Not applicable


P2-C1

C APPENDICES

Appendix 1: Short CV of the applicant and list of his 10 most important publications Prof. Dr. Thilo Streck Speaker of Research Unit 1695 Leader of Projects P2 and PZ Institute of Soil Science and Land Evaluation (310d) Chair of Biogeophysics University of Hohenheim 70593 Stuttgart Tel: ++49-711-459-22796 Fax: ++49-711-459-23117 E-Mail: [email protected] Date of birth: 11.02.1960 Scientific Career 1980–1982 Studies in Sociology and Philosophy, University of Marburg, Germany

1982–1984 Studies in Agricultural Sciences, University of Giessen, Germany

1985–1988 Studies in Agricultural Sciences, University of Göttingen, Germany

1988–1992 Research associate (PhD student), Institute of Geoecology, Technical University Braunschweig, Germany

1992–1993 Postdoc, Dept. of Soil and Environmental Sciences, University of California, Riverside, CA, USA

1993–2000 Researcher and Lecturer, Institute of Geoecology, Technical University Braunschweig, Germany

Since 2001 Professor of Biogeophysics, Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, Germany

2002–2006 Managing Director of the Institute of Soil Science and Land Evaluation, University of Hohenheim

2009–2013 Cofounder and Member of the Management Board of the Water & Earth System Science Research Institute (WESS), Tübingen

2013– Managing Director of the Institute of Soil Science and Land Evaluation, University of Hohenheim

List of peer-reviewed publications 1. BANNWARTH, M., HUGENSCHMIDT, C., SANGCHAN, W., LAMERS, M., INGWERSEN, J., ZIEGLER, A.D.,

STRECK, T., 2014. Simulation of stream flow components in a mountainous catchment in northern Thailand with SWAT, using the ANSELM calibration approach. Hydrol. Process., in print, DOI: 10.1002/hyp.10268.

2. GAYLER, S., WÖHLING, T., GRZESCHIK, M., INGWERSEN, J., WIZEMANN, H.-D., WARRACH-SAGI, K., HÖGY, P., ATTINGER, S., STRECK, T., WULFMEYER, V., 2014. Incorporating dynamic root growth enhances the performance of NOAHMP at two contrasting winter wheat field sites. Water Resour. Res. 50, 1337-1356.

3. WÖHLING, T., GAYLER, S., PRIESACK, E., INGWERSEN, J., WIZEMANN, H.-D., HÖGY, P., CUNTZ, M., ATTINGER, S., WULFMEYER, V., STRECK, T., 2013. Multiresponse, multiobjective calibration as a diagnostic tool to compare accuracy and structural limitations of five coupled soil-plant models and CLM3.5. Water Resour. Res. 49, 8200-8221.

Research Unit 1695

P2-C2

4. GAYLER, S., INGWERSEN, J., PRIESACK, E., WÖHLING, T., WULFMEYER, V., STRECK, T., 2013. Assessing the relevance of subsurface processes for the simulation of evapotranspiration and soil moisture dynamics with CLM3.5: Comparison with field data and crop model simulations. Environ. Earth. Sci. 69, 415–427.

5. ASSENG, S. et al., 2013. Uncertainties in simulating wheat yields under climate change. Nature Climate Change 3, 827–832.

6. PAGEL, H., INGWERSEN, J., POLL, C., KANDELER, E., STRECK, T., 2013. Micro-scale modeling of pesticide degradation coupled to carbon turnover in the detritusphere: Model description and sensitivity analysis. Biogeochemistry 42, 1879–1887.

7. DUFFNER, A., INGWERSEN, J., HUGENSCHMIDT, C., STRECK, T., 2012. Pesticide transport pathways from a sloped litchi orchard to an adjacent tropical stream as identified by hydrograph separation. J. Environ. Qual. 41, 1315–1323.

8. INGWERSEN, J., STEFFENS, K., HÖGY, P., WARRACH-SAGI, K., ZHUNUSBAYEVA, D., POLTORADNEV, M., GÄBLER, R., WIZEMANN, H.-D., FANGMEIER, A., WULFMEYER, V., STRECK, T., 2011. Comparison of NOAH simulations with eddy covariance and soil water measurements at a winter wheat stand. Agric. For. Meteorol. 151, 345-355.

9. FISHKIS, O., INGWERSEN, J., LAMERS, M., DENYSENKO, D., STRECK, T., 2010. Phytolith transport in soil: A field study using fluorescent labelling. Geoderma 157, 27–36.

10. WAN, Y. J., JU, X. T., INGWERSEN, J., SCHWARZ, U., STANGE, C. F., ZHANG, F.S., STRECK, T., 2009. Gross nitrogen transformations and related nitrous oxide emissions in an intensively used calcareous soil. Soil Sci. Soc. Am. J. 73, 102–112.


P2-C3

Appendix 2: Full text of unpublished work cited in Sections 1.1 and 3 of the project proposal

IMUKOVA, K., INGWERSEN, J., STRECK, T., 2014. Determining the spatial and temporal dynamics of the green vegetation fraction of croplands using high-resolution RapidEye satellite images. Agric. For. Meteorol., submitted on 14 July 2014; cf. manuscript on enclosed CD-ROM.

POLTORADNEV, M., INGWERSEN, J., STRECK, T., 2014. Calibration and application of Aquaflex TDT soil water probes to measure the soil water dynamics of agricultural topsoils in Southwest Germany. J. Irrig. Drain. E., submitted on 14 May 2014; cf. manuscript on enclosed CD-ROM.

WIZEMANN, H.D., INGWERSEN, J., HÖGY, P., WARRACH-SAGI, K., STRECK, T., WULFMEYER, V., 2014. Three-year observations of water vapor and energy fluxes over agricultural crops in two regional climates of Southwest Germany. Meteorol. Z., accepted on 08 July 2014; cf. manuscript on enclosed CD-ROM.

Documents

Application for a Grant within the Research Unit 1695 ... · Project P2 Soil-plant-atmosphere interactions at the regional scale Short Title Soil-Plant-Atmosphere Interactions Prof