Coupling the land surface model Noah-MP with the generic crop growth model Gecros: Model description, calibration and validation

Agricultural and Forest Meteorology 262 (2018) 322–339

Contents lists available at ScienceDirect

Agricultural and Forest Meteorology journal homepage: www.elsevier.com/locate/agrformet

Coupling the land surface model Noah-MP with the generic crop growth model Gecros: Model description, calibration and validation

T

⁎

J. Ingwersena, , P. Högyb, H.D. Wizemannc, K. Warrach-Sagic, T. Strecka a

Institute of Soil Science and Land Evaluation, Universität Hohenheim, 70593 Stuttgart, Germany Institute of Landscape and Plant Ecology, Universität Hohenheim, 70593 Stuttgart, Germany c Institute of Physics and Meteorology, Universität Hohenheim, 70593 Stuttgart, Germany b

A R T I C LE I N FO

A B S T R A C T

Keywords: Land surface modeling Crop model Regional climate model Soil-plant-atmosphere feedback Maize Winter wheat

Interactions between vegetation and atmosphere have a large impact on weather and climate. During the last decade, enormous efforts have been made to improve the representation of vegetation dynamics in land surface models (LSM). The present study extends the LSM Noah-MP by the dynamic crop growth model Gecros that enables simulating the development of crop stands in a weather-driven manner. This extension is a pre-requisite to simulate two-way climate-crop interactions in climate projections. Based on a comprehensive five-year dataset on energy- and water fluxes, and soil water and crop data from two different climate regions of southwest Germany, we adapted the crop growth model Gecros, integrated it with Noah-MP, calibrated the coupled model for winter wheat and maize and tested its robustness in multiple-year validation runs against independent measurements. This sound data set yielded a robust parameterization that performed well both in calibration and in validation runs over in total 16 seasons. Due to pronounced differences in phenology among maize cultivars, wheat simulations were better than maize simulations. The simulated dynamics in leaf area index of wheat and maize differed largely from the one used in standard Noah-MP simulations. The new model yielded pronounced differences in the partitioning of evapotranspiration into transpiration and soil evaporation. The added value of the improved description of vegetation dynamics needs to be evaluated in high-resolution coupled crop-climate simulations in future.

1. Introduction Weather and climate are strongly affected by the vegetation-atmosphere interactions (McPherson, 2007). Water, energy and momentum transfer across the land surface is simulated using land surface models (LSMs). LSMs are essential components of weather and climate models, where they are linked with atmospheric models. In the LSM component of regional climate models (RCMs), vegetation dynamics are prescribed in lookup tables or in gridded maps with effective plant properties for each raster cell. As a consequence, the development of the leaf area index (LAI), for example, is the same in each year. The dynamics are “frozen” and hence do not depend on the prevailing weather conditions. The difference between the real development of vegetation and its representation in LSMs is arguably the highest for croplands. This is relevant because the share of crop land on the land surface is high in many parts of the world, although it may vary from country to country. In 2013, for example, it was 57% in Denmark, but only 6% in Sweden (Statistisches Bundesamt, 2017). Furthermore, in RCMs working with

⁎

lookup tables, every raster cell belonging to a certain land cover class has the same LAI dynamics. Accordingly, regional differences, for example in crop development within the simulation domain due to variation in temperature (e.g., originating from different elevation) cannot be represented. Errors in sensible heat flux (H) and latent heat flux (LE) resulting from this coarseness translate into changes in regional air and surface temperature, evolution of the atmospheric boundary layer, and spatial distribution of rainfall. This coarseness is particularly problematic in climate change studies because it is obvious that crop development, for example, will respond to climatic changes. In general, a warmer climate will accelerate crop development, will shift drilling and harvesting dates and might even induce changes in crop rotation (Aurbacher et al., 2013). A widely used LSM worldwide is Noah (Chen and Dudhia, 2001a, b). It is the land surface component of the Weather Research and Forecasting (WRF) model (see e.g., Skamarock et al., 2008). Recently, the Noah LSM has been extended to Noah-MP by introducing a number of so-called multiple-physics options (Niu et al., 2011). For example,

Corresponding author. E-mail address: [email protected] (J. Ingwersen).

https://doi.org/10.1016/j.agrformet.2018.06.023 Received 26 September 2017; Received in revised form 21 June 2018; Accepted 24 June 2018 0168-1923/ © 2018 Elsevier B.V. All rights reserved.

Agricultural and Forest Meteorology 262 (2018) 322–339

J. Ingwersen et al.

biases in the range of ± 1.5 °C. Though this is a fairly well match with observational data, these biases may be a challenge for their application as forcing data in CMs. Therefore, the relevant output of the climate model (temperature, precipitation, etc.) is usually adjusted for the bias (Hawkins et al., 2013). A widely applied method is to add the difference between RCM and observations over a reference period to the future RCM data. Obviously, an implicit assumption of this approach is that the bias stays constant in time (Bakker et al., 2014). Particularly during the last decade, the modelling community has exerted enormous efforts to improve the representation of crops in LSM (Lokupitiya et al., 2009; Osborne et al., 2009; Liang et al., 2012; Tsarouchi et al., 2014; Lu et al., 2015; Wu et al., 2016; Liu et al., 2016), pointing to the high relevance of this subject. To our knowledge, the first study that incorporated a CM into a LSM was that of Tsvetsinskaya et al. (2001). The authors incorporated the physiological CM CERESMaize into the Biosphere-Atmosphere Transfer Scheme (BATS) and examined the effect of seasonal crop development on mesoscale heat, moisture, and momentum fluxes over the central Great Plains region of North America. They found statistically significant changes in H and LE. The effects were more pronounced in dry than in moist years. An example that incorporated a CM of the Dutch school is the study by van den Hoof et al. (2010). The authors extended the Joint UK Land Environment Simulator (JULES) by SUCROS (Goudriaan and van Laar, 1994), which is a precursor of Gecros (Yin and van Laar, 2005). Without any calibration, the correlation between measured and simulated LE and H was considerably improved. Very recently, Liu et al. (2016) introduced dynamic growth simulations and field management for two summer crops (corn and soybean) into Noah-MP (Noah-MP-Crop). Corn and soybean are preponderant crops over the central U.S. The CM component builds, among others, on DSSAT routines. The present study moves in a similar direction: it extends the LSM Noah-MP by the dynamic crop growth model Gecros that allows simulating the development of two major crops of mid Europe (maize (summer crop) and winter wheat (winter crop)) in a weatherdriven way. This extension is a pre-requisite to simulate two-way climate-crop interactions in future WRF-Noah-MP-Gecros climate projections. Based on a comprehensive five-year dataset on energy- and water fluxes, soil water, and crop data from two different climate regions of southwest Germany, we adapted the crop growth model Gecros, integrated it with Noah-MP, calibrated the coupled model, and tested its robustness in multiple-year validation runs against independent measurements. Furthermore, we investigated the sensitivity of the model on temperature biases and discuss how to handle this issue in coupled crop-climate models.

the user can choose between two schemes for computing the stomatal resistance: the Jarvis scheme, involving constraint functions, and the photosynthesis-based Ball-Berry scheme. If the user works with the land cover map of the US Geological Survey (USGS), Noah-MP distinguishes in total 27 classes. None of the classes is assigned to pure cropland. Cropland and pasture are aggregated into the same land cover class “cropland and pasture”. This land cover class is further classified into irrigated, non-irrigated and mixed dryland/irrigated. Classification is based on management differences with respect to irrigation, but it is not based on differences among crops, although they might differ substantially in their seasonal plant development. In the northern temperate zone, for example, annual crops can basically be grouped into early-covering crops (e.g., winter rape or winter wheat) and late-covering crops (e.g., maize or sugar beet) (Imukova et al., 2015). An earlycovering crop such as winter wheat has an LAI in the range of 4–6 already in May (see Results section). Its root system penetrates the soil down to one meter or even more (Fan et al., 2016). In contrast, a latecovering crop such as maize has potentially emerged in early May. The soil surface is only sparsely covered with vegetation (LAI < 1) (see Results section), and the root system has advanced to the first decimeters (Abendroth et al., 2011). Moreover, as outlined above, the land surface characteristics of croplands change drastically over the season. While the Bowen ratio of a winter wheat stand is about 0.2 in May and June, it increases considerably during the grain maturation of the crop. Shortly before harvest the Bowen ratio can reach values up to four (Wizemann et al., 2014). Dynamic crop growth simulation models (crop models (CMs), hereafter) are widely used to simulate crop yield as a function of environmental factors and limitations due to water or nutrient stress. Almost all models have developed from two schools, founded by C.T. de Wit in the Netherlands and J.T. Ritchie in the US. The models SUCROS and ORYZA (Goudriaan and van Laar, 1994), the modelling package WOFOST (World Food Studies) (Boogaard et al., 1998), or the model Gecros (Yin and van Laar, 2005) belong to the Dutch school. The bestknown models from the US school are CERES, SOYGRO and CROPGRO, which have been combined with other models into the modelling package DSSAT (Decision Support System for Agrotechnology Transfer) (Jones et al., 2003). One main difference among the models is the degree of mechanistic detail which processes are described at. For example, the C3 photosynthesis model of Farquhar et al. (1980) is a rather mechanistic approach, while the concept of light use efficiency (Brisson et al., 2003) employed by many CMs must be regarded as purely empirical. In general, CMs are set up as systems of first-order ordinary differential equations (Müller, 1999). Overall, growth is driven by photosynthetically active radiation (PAR). Assimilation is a function of the LAI, which consequently is a key variable 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.) based on lookup tables as a function of the development stage. The transition between the different phenological stages is controlled by biological time, which depends on temperature. 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 (see Chapter 2.2). This approach makes the plants plastic in adjusting the distribution between organ systems in response to external factors such as water or nitrogen stress or changing atmospheric CO2 concentrations. During the last decades, CMs have been applied more and more widely to assess the effect of climate change on future yields. A frequently applied approach is to use projections of global or RCMs to force the CM with future weather data (Bakker et al., 2014). The output of these climate models, however, is subject to inherent biases. Depending on region and season, an ERA-Interim-driven EURO-CORDEX RCM ensemble consisting of 17 simulations at a grid scale of 12 or 50 km (Kotlarski et al., 2014), for example, had mean temperature

2. Model description and coupling 2.1. The Noah-MP land surface model Noah-MP describes land surface heterogeneity with a semi-tile subgrid scheme. This means that shortwave radiation transfer is computed over the entire grid cell, whereas longwave radiation, LE, H and ground heat flux are computed separately over two tiles: a fractional vegetated area (Fveg) and a fractional bare ground area (1-Fveg) (Niu et al., 2011). The fractional vegetated area is either taken from satellite data with a resolution of about 16 km (0.15°) or is computed from the leaf area index (LAI). Depending on the land cover dataset selected for the simulation, the vegetated tile is assigned to one out of 20 to 28 land use classes. Among the many multi-physics options the user can choose between two schemes for computing the stomatal resistance (rs): 1) the empirical Jarvis scheme, which had already been part of previous Noah versions, and 2) the photosynthesis-based Ball-Berry scheme. The rs is a key variable for transpiration. It strongly controls energy partitioning at the land surface. Surface energy fluxes are computed using resistor network theory. The LE of the vegetation canopy LEv, for example, is computed as 323

Agricultural and Forest Meteorology 262 (2018) 322–339

J. Ingwersen et al.

LEv =

ρCp ⎛ LAIsun LAIsha ⎞ + ⎜ ⎟ (esat , v (Tv )−eav ) γ ⎝ rb + rs, sun rb + rs, sha ⎠

where mV and mR are crop-specific minimum number of days for the pre-seed period and seed filling phase, respectively. g(T) is a bellshaped nonlinear function that takes values between 0 and 1. The function needs the input of the cardinal temperature base (Tb), optimum (To) and ceiling temperature (Tc). During the seed filling phase, the same cardinal temperatures as for the pre-seed phase are used with the restriction that T is set to To if T > To to avoid a decline in the development rate at higher temperatures. This approach accounts for a shortened seed filling duration when plants are exposed to high temperatures (Yin and van Laar, 2005, p. 42). The phenological response to photoperiod is considered in the model by the parameter psen, being positive for short-day crops and negative for long-day crops (Yin and van Laar, 2005, p. 40–41) The partitioning of assimilates between root and shoot is simulated based on the functional balance theory (Yin and Schapendonk, 2004). This approach incorporates a general mechanism that enables simulating how a crop controls root-shoot partitioning so as to maximize its relative carbon gain. If the biomass gain is limited by carbon, then the plant invests more into the shoot, if nitrogen is limiting, the plants partitions more assimilates to the roots. In this way, Gecros is much more flexible in its response to environmental stress than the precursor model SUCROS, in which assimilate partitioning is controlled by tabulated functions dependent on development stage. Within-shoot carbon partitioning is computed based on the priority setting that carbon goes first to the seed. If available carbon exceeds the demand of the seed, it goes to the structural stem and, if this demand is met, to the leaf. Any further available carbon is transported to the shoot reserve pool. The daily demands of the sinks (growing seed and structural stem) are calculated from a differential form of a sigmoid function for asymmetric determinate growth. The shape of the function is controlled by the parameters υm and υe, which are the phenological stage at which the growth rate peaks and the stage at which growth ends, respectively (Yin et al., 2003):

(1)

where ρ, Cp and γ are air density (kg m−3), dry-air specific heat capacity ( = 1005 J kg-1 K-1), and the psychrometric constant (Pa K-1), respectively. LAIsun and LAIsha are sunlit and shaded LAI, and rs,sun and rs,sha are stomatal resistances (s m-1) of sunlit and shaded leaves. The symbol rb stands for the leaf boundary layer’s resistance (s m-1). Esat,v (Pa) is the saturation vapor pressure within the canopy as a function of the canopy temperature Tv (K), and eav is the water vapor pressure (Pa) within the canopy. 2.2. The generic crop growth model Gecros Gecros is a generic photosynthesis-based crop growth model specifically designed to simulate genotype-by-environment interactions. A comprehensive description of the model is given in Yin and van Laar (2005). This and the following chapter focus particularly on those model parts, functions, and parameters that were relevant for model calibration (Chapters 4.2 and 4.3). In brief, Gecros uses the leaf photosynthesis model of Farquhar et al. (1980), in which the potential gross photosynthesis rate (Pp, g CO2 m−2 leaf s-1) is computed as the minimum of the two rates Vc and Vj (rate of carboxylation per unit leaf area limited by Rubisco activity and electron transport (g CO2 m−2 s-1), respectively):

Pp = 44 × 10−6 (1−Γ*/ Cc ) min (Vc , Vj )

(2)

Here, Γ* is the CO2 compensation point in the absence of dark respiration (μmol mol−1), and Cc is the CO2 concentration at the carboxylation site (μmol mol−1). Both rates are computed as Michaelis-Menten kinetics, in which the maximum rates (Vcmax and Jmax) are linear functions of the leaf nitrogen content n:

Vcmax = χvcn (n−nb)

(3a)

Jmax = χjn (n−nb)

Cϑi = ωi Cmax

(3b)

Here, nb defines the base or minimum value of n at or below which leaf photosynthesis is zero, and χvcn and χjn are the slopes of these linear relationships. Based on Pp, the potential conductance of the leaf for CO2 (gc,p, m s−1) is computed as

gc, p = (Pp−Rd )[(273 + Tl )/0.53717]/(Ca−Ci )

−1

(7)

(8) −2

where ρ stands for stem dry weight per unit of plant height (g m m ), Hmax is maximum plant height (m), and fc, V denotes the carbon fraction in the vegetative organs (g g-1). -1

2.3. Gecros modifications

(5)

The original Gecros code runs on a daily time step, and the atmospheric forcing requires daily data on cumulated global radiation, minimum and maximum temperature, mean vapour pressure, mean wind speed and total precipitation. To cover the diurnal course of temperature and global radiation the subroutines for computing the daily gross photosynthesis (TOTPT) and the daily amount of thermal day (TUNIT) contain daily integration schemes that compute daily mean rates as a weighted mean of five rates computed at different day times. As Noah-MP runs on an arbitrary time step, these integration schemes were removed, and in the Fortran code all rates and fluxes were converted from per day to per second. Furthermore, the original Gecros was developed for summer crops. This means that the original model does not consider vernalization and winter dormancy, which are important processes for winter crops. Vernalization slows down the phenological development over the winter period. It was implemented applying the approach of Streck et al. (2003). Here, the temperature response of the vernalization rate is calculated with the same response function used for computing the phenological development but with the

−1

0< υ<1 υ≥1

ϑm /(ϑe − ϑm)

⎟

Cmax = ρ Hmax fc, V

(4)

where rbw (s m ) and rt (s m ) are the boundary layer and turbulent resistance to water transfer, respectively. When the water supply from the rooted soil layers does not meet potential transpiration, transpiration is reduced to the amount of water available for plant uptake in the root zone, and actual photosynthesis and leaf stomatal resistance are adjusted accordingly. To scale leaf photosynthesis to the canopy scale, a two-leaf model is applied. The canopy is divided into sunlit and shaded leaves and each fraction is simulated separately. The phenological development of the crop is described with the unitless variable υ (development stage), which takes the value of 0 at seedling emergence, 1 at the start of seed filling (flowering), and 2 at maturity. The daily development rate is computed based on a temperature response function. For a non-photosensitive crop, ω is computed as

g (T )/ mV ω=⎧ ⎨ ⎩ g (T )/ mR

⎜

Here, Cϑi is the carbon demand at stage ϑi , ωi is the development rate at stage ϑi , and Cmax is the total demand of carbon at the end of growth of the organ. For stem growth,

where Rd is dark respiration per unit leaf area (g CO2 m−2 s-1), Tl (K) is leaf temperature, and Ca and Ci are the ambient and intercellular CO2 concentration (μmol mol-1), respectively. The stomatal resistance to water transfer in the absence of water stress is related to gc,p by

rsw, p = (1/ gc, p−1.3rbw−rt )/1.6

(2ϑe−ϑm )(ϑe−ϑi ) ⎛ ϑi ⎞ ϑe (ϑe−ϑm )2 ⎝ ϑe ⎠

(6) 324

Agricultural and Forest Meteorology 262 (2018) 322–339

J. Ingwersen et al.

cardinal temperatures Tb,v=−1.3 °C, To,v = 4.9 °C, and Tc,v = 15.7 °C. The rate of vernalization is integrated over time to the vernalization day (VD)

VD5 f (VD) = 5 VD0.5 + VD5

Table 1 Parameters of the germination and emergence routine. Symbol

(9)

where VD0.5 are the vernalization days when crops shows one-half of the response of fully vernalized plants. Streck et al. (2003) fixed for VD0.5 of winter wheat a value of 22.5 days, so that ω is computed as follows:

⎧ g (T ) f (VD)/ mV 0 < υ < 1 0.4 < υ < 1 ω = g (T )/ mV ⎨ ( )/ υ≥1 g T m R ⎩

(10)

Winter dormancy was implemented in the model by down-regulating the nitrogen demand over winter time. In Gecros, crop nitrogen demand (Ndem) is calculated as the maximum of deficiency-driven and activity-driven nitrogen demand (NdemD and NdemA, respectively). The model, however, does not allow Ndem to exceed the upper threshold of nitrogen uptake (Nmaxup). A key variable for computing Ndem is the amount of nitrogen required to maintain the actual nitrogen concentration in the plant above a critical nitrogen concentration (ncri). In Gecros, the critical nitrogen concentration is computed as: ncri = ncri0 e−0.4υ

Source code variable

Definition

Value

Unit

Wheat Tb,em

TBEM

2.0

°C

Em,a Em,b Dd

EMA EMB DD

Base temperature of emergence Intercept of threshold function Slope of threshold function Drilling depth

40.0 10.2 2.5

°C °C cm−1 cm

Maize Tb,em

TBEM

8.0

°C

Em,a Em,b Dd

EMA EMB DD

Base temperature of emergence Intercept of threshold function Slope of threshold function Drilling depth

6.0 15.0 5.0

°C °C cm−1 cm

senescent LAI including stems). This dynamics is controlled by the fraction of carbon transferred from senescent leaves to the litter pool (rlit). In the original Gecros code, rlit is hardcoded (rlit = 0.1). To make this parameter accessible to the automated calibration, rlit was declared as a variable in the source code. The coupling between Noah-MP and Gecros is illustrated in Fig. 1. The LSM Noah-MP transfers the atmospheric forcing and the leaf boundary layer and aerodynamic resistance to Gecros. Gecros computes plant growth and returns the total and green LAI, and the stomatal resistance of the sunlit and shaded leaves and returns their value to Noah-MP. While plant height and rooting depth are static in Noah-MP, in Noah-MP-Gecros also these entities are considered as state variables. Gecros separates soil into a rooted and an unrooted zone. The rooted zone, the zone from which water is extracted by the crop, continuously expands until the maximum rooting depth (see Chapter 3.5) is reached.

(11) −1

Here, ncri0 (g N g ) is the initial nitrogen concentration in living leaves. This approach leads to the situation that plants have their maximum nitrogen demand during the juvenile phase, which is reasonable for summer crops but not for winter crops. Therefore, plant growth was down-regulated over the winter time by reducing the nitrogen demand to 1% of its usual value (Eq. (12a)) as long as υ is below 0.225 (BBCH scale 20; Meier, 2001):

3. Material and methods When the critical development stage υcrit is over, Ndem is computed by the standard equation (Eq. (12b)). The value of 1% was derived by manual calibration in the frame of pre-simulations. Moreover, it is assumed that the crop has always sufficient access to nitrogen, meaning that Ndem can always be fully provided by the soil nitrogen pool, which is not explicitly simulated in the model. Furthermore, Gecros was extended for a germination and emergence routine. In the original version, the crop emerges directly on the day of sowing. In the real world, the time needed for germination and emergence usually varies between two and four weeks, depending on soil temperature, soil wetness and crop. In Gecros, we introduced a routine used in the CERES crop growth model (Jones and Kiniry, 1986). For time steps exceeding the base temperature Tb,em, thermal days of emergence (TTem) were cumulated as

TTem =

∑ (Ts,1−Tb,em)

3.1. Study region and central study sites The study region and the central study sites are described in detail in Wizemann et al. (2014). In brief, measurements were performed in southwest Germany in the Federal State of Baden-Württemberg. To cover the climatic extremes in this area, two contrasting regions were selected: Kraichgau (KR) and Swabian Alb (SA; in German ‘Schwäbische

(13)

where Ts,1 is the soil temperature in the first layer. If TTem exceeds the crop-specific threshold value (Em,th), the crop emerges. The threshold value Em,th is taken as a linear function of the drilling depth (Dd)

Em, th = Em, a + Em, b Dd

(14)

Here, Em,a and Em,b are plant-specific empirical coefficients. Parameters of the emergence routine are given in Table 1. Test simulations revealed that during ripening Gecros tended to overestimate the decline of the total leaf area index (TLAI, green plus

Fig. 1. Coupling scheme of Noah-MP-Gecros. 325

Agricultural and Forest Meteorology 262 (2018) 322–339

J. Ingwersen et al.

Table 2 Field management of the sites used for calibration and validation. Field

Year

Region$

Cultivar

Pre-crop

Drill date (dd.mm)

Total N fertilization (kg N/ha)

Number of applications of agrochemicals*

Day of harvest

Yield (t/ ha)

Winter wheat EC3 EC5 EC1 EC4 EC3 EC1 EC3 EC4

2010c 2010c 2011c 2011c 2012v 2013v 2014v 2014v

KR SA KR SA KR KR KR SA

Cubus Pamier Akteur Akteur Akteur Aktuer JB Asano Orcas

Maize Maize Maize Winter Maize Winter Winter Winter

22.10 18.09 19.10 22.09 17.10 17.10 25.10 08.10

220 244 170 206 252 179 197 202

1/1/1/0 1/2/0/1 2/1/0/0 2/2/1/0 2/1/0/1 2/2/0/1 2/1/0/0 2/1/0/1

05.08.2010 26.08.2010 28.07.2011 20.08.2011 01.08.2012 04.08.2013 04.08.2014 23.08.2014

7.1 7.9 8.4 8.0 7.8 8.6 8.3 9.0

Maize EC1

2010v

KR

Cannavaro

274

1/0/0/0

14.10.2010

42.4

2010

SA

Fernandez

23.04

180

1/0/0/0

06.10.2010

39.4

EC3

2011

c

KR

Cannavaro

18.04

223

1/0/0/0

03.10.2011

58.5

EC5

2011v

SA

Agro-Yoko

25.04

206

1/0/0/0

04.10.2011

60.0

EC2

2012c

KR

Cannavaro

02.05

273

1/0/0/0

18.09.2012

56.5

EC5 EC6

c

2012 2013c

SA SA

Amanatidis Agro-Yoko

28.04 26.04

237 255

1/0/0/0 2/0/0/0

07.10.2012 04.10.2013

50.6 n.d.

EC1

2014v

KR

Grosso

Wheat/ mustard Wheat/ mustard Wheat/ mustard Wheat/ mustard Wheat/ phacelia Maize/bare Barley/ mustard Wheat/ mustard

17.04

v

12.04

185

1/0/0/0

09.10.2014

52.6

EC6

rape rape rape rape

$

KR: Kraichgau, SA: Swabian Alb. Data set was used for calibration. v Data set was used for validation. * Agrochemicals are listed in the following order: herbicide, fungicide, insecticide, growth regulator. c

instrumentation and EC data processing see Ingwersen et al. (2011) and Wizemann et al. (2014). Due to a technical failure of a data logger, no EC flux data are available from EC1 in August 2010. In the same year, at EC6 a sporadic failure of the sonic anemometer remained undiscovered over the entire vegetation period; thus, no continuous EC flux data are available for that field. The energy balance of EC data is normally not closed. At the current state of knowledge, it is not clear how to correctly partition the missing energy. Eddy flux data therefore contain some uncertainty due to the unknown nature of the energy balance gap, which should be considered in model evaluation and the interpretation of simulation results. We considered this issue by constructing post-closure method uncertainty bands (PUB) (Ingwersen et al., 2015). This method essentially designates the differences between non-adjusted flux data and flux data adjusted with the three post-closure methods (Bowen ratio, LE and H method). Over some short periods the PUB disappeared, because the measured fluxes had an energy access over several hours, that means that the turbulent energy (sum of LE and H) was larger than the available energy (net radiation minus ground heat flux). This situation was not considered in Ingwersen et al. (2015). In the present study, in such a case, we computed the upper bound by adding the measurement error to the measured flux.

Alb’). KR is a fertile hilly loess region. It covers altitudes between 100 and 400 m above sea level (a.s.l.). Due to its basin position, KR is characterized by a mild climate with an annual mean temperature of more than 9 °C, making it one of the warmest regions in Germany. Mean annual precipitation ranges between 720 (west) and 830 mm (east and south). The characteristic soil of KR is a Luvisol (WRB (2006); Parabraunerde according to KA5 (2005)). The low mountain range of SA is a plateau approximately 220 km long and 40 km wide. The altitude of SA ranges between 700 m and about 1000 m a.s.l. Due to the higher elevation, the climate is distinctly colder and harsher than in KR. The mean annual temperature is about 6–7 °C. The mean annual precipitation ranges from 800 to 1000 mm. The underlying Jurassic limestone promoted a deep and persistent karst formation. Because of the fissured underground, the groundwater table is very deep. The predominant soil type of SA is a Leptosol (WRB (2006); Rendzina according to KA5 (2005)). The thickness of the solum, which usually has a clayey loam texture, typically does not exceed 0.3 m. In each region one central study area was established in spring 2009. The central study area in KR (48.9 °N and 8.7 °E, 319 m a.s.l.) is located north of the city Pforzheim close to ‘Katharinentaler Hof’. The central study area in SA is near the village ‘Nellingen’ (48.5 °N and 9.8 °E, 690 m a.s.l.). Each central study area consists of three arable fields. The fields (14.9–23.6 ha) are managed and operated by local farmers. An eddy covariance (EC) station was installed in the center of each of the six fields. The fields were named according to the EC station: EC1, EC2 and EC3 at the KR site and EC4, EC5 and EC6 at the SA site.

3.3. Plant rating At each field, five plots of 4 m2 were randomly selected and permanently marked to track phenological stage (BBCH), TLAI, plant height, and plant biomass. Non-destructive measurements in wheat were performed at least in four-weekly intervals during winter. During the main growth period starting in early spring, phenology, TLAI and plant height were measured about biweekly until crop maturity at the central square meter of every subplot. An LAI-2000 Plant Canopy Analyzer (LI-COR Biosciences Inc., USA) was used to measure TLAI.

3.2. Eddy covariance measurements Surface energy fluxes (net radiation, H, LE, and soil heat flux in W m−2) were measured by the EC technique. For details on 326

Agricultural and Forest Meteorology 262 (2018) 322–339

J. Ingwersen et al.

BBCH stage was assigned to the development stage ω as described in Lenz (2007). In between, BBCH stages were linearly interpolated. Intermediate harvests of total aboveground biomass took place at stem elongation (decimal code (DC) 31 according to BBCH scale) and full flowering (DC 65) using five extra plants per plot. At final harvest (crop maturity), the biomass of the central square meter of each subplot was cut at ground level and separated into vegetative and generative fractions. Plant material was dried at 60 °C (vegetative parts) and 28 °C (generative parts) to constant weight and dry weight determined. Generative parts were manually threshed to determine crop yield. The vegetative fractions were cut using a chaff cutter and homogeneously mixed. Randomly picked material of vegetative parts and of harvestable products was milled using a laboratory mixer mill (MM 301, Retsch, Haan, Germany). All fractions were analyzed for carbon and nitrogen as described in Högy et al. (2009).

Table 3 Settings of the multi-physics options used in Noah-MP simulations.

3.4. Field management

LAI: leaf area index; FVEG: fractional vegetated are.

Field management is summarized in Table 2. On SA, winter wheat was drilled between 18 September and 8 October. In KR, wheat was drilled between 17 and 34 days later. The wheat cultivars Cubus and JB Asana are early-mid ripening cultivars (ripening number 4), Pamier and Orcas are mid ripening cultivars (ripening number 5) and Akteur is a late-mid ripening cultivar (ripening number 6). Akteur was grown in both KR and SA. Total nitrogen fertilization ranged between 170 and 252 kg N ha−1 and averaged 209 kg N ha-1. Usually, the crop stand was treated twice with herbicides. The first application was usually performed in autumn, the second in early summer. Insecticides were applied only at EC3 in 2010 and at EC4 in 2011. In KR, wheat was harvested between 28 July and 5 August. On SA, harvest took place 19–23 days later. Wheat yield on SA averaged 8.3 t ha−1 and ranged between 7.9 t ha−1 and 9 t ha−1. In KR, the yield averaged 8.0 t ha−1, ranging between 7.1 t ha−1 and 8.6 t ha−1. In the seasons with winter rape as a pre-crop, the average wheat yield was 0.7 t ha−1 higher than after maize. In KR, maize was drilled between 12 April and 2 May, on SA usually about one week later. The cultivars grown in KR and SA cover the full range of ripening groups or rather heat demand groups. Amanatidis is an early ripening cultivar (S220), while Cannavaro, which was grown only in KR, is a late-ripening and stay-green cultivar (S310). Grosso, Fernandez, and Agro Yoko are mid-early ripening cultivars (S250, S240, S250, respectively). Total nitrogen fertilization on maize ranged between 180 and 274 kg N ha−1, averaging 229 kg N ha−1. Usually, herbicides were applied only once in early summer. Fungicides and insecticides were applied in none of the five years. In KR, maize was harvested between 18 September and 14 October. The harvest date differed between 1 and 19 days between the two regions. The maize yield averaged 51.4 t ha−1 (fresh matter), ranging between 42.1 t ha−1 on EC1 in 2010 and 60.0 t ha−1 on EC5 in 2011.

temperatures and soil water contents were initialised with measured data. Winter wheat simulations were run from 1 September to harvest. Maize simulations were started on 1 January. Sowing dates were taken from the management data (Table 2).

Multi-physics option

Setting

Vegetation model Soil moisture factor for stomatal resistance Runoff and groundwater model Sensible heat exchange coefficient Super-cooled liquid water

opt_dveg = 3: table LAI, calculate FVEG opt_btr = 1: Noah

Radiation transfer scheme Lower boundary of soil temperature Snow/soil temperature time scheme

opt_run = 1: TOPMODEL-based simple groundwater model opt_sfc = 1: Based on Monin-Obukov similarity theory opt_frz = 1: General form of the of the freezingpoint depression equation (NY06) opt_rad = 2: Gaps = 0 opt_tbot = 2: Constant temperature opt_stc = 1: Semi-implicit

3.6. Model parameterization, calibration and validation Automatic parameter estimation was performed using UCODE_2005 (Poeter et al., 2005). UCODE_2005 performs inverse simulations by computing parameter values that minimize a weighted least-square objective function using the Levenberg-Marquardt algorithm. Calibration data were grouped into six groups (BBCH, TLAI, vegetative and generative biomass, total plant nitrogen content, and plant height). Before parameter optimization, data of each group were normalized to the maximum value of the respective group. In the objective function all data groups were evenly weighted. Composite scaled sensitivities were calculated using forward differencing, and convergence was reached if parameter values between iterations changed by less than 5%. In the first step, the phenological parameters mV, mR, and psen were calibrated based on the BBCH data. As first guess, we used the default values (see Tables 6 and 7). In a second step, phenological parameters were fixed and additional plant parameters were calibrated. Here, in general, we used a step-by-step forward approach. The optimization started with the parameter that had the highest local scaled composite sensitivity. In the next step the parameter with the second highest scaled composite sensitivity was added, etc. If a newly introduced parameter showed either (1) a high parameter correlation or (2) its confidence interval ranged over several orders of magnitude or (3) its estimated value run out of realistic ranges, the parameter was removed from the optimization and was fixed at its default value. For both crops, calibration was based on at least two years of data records for both regions. For winter wheat the dataset was split chronologically into calibration and validation subsets. The datasets of the years 2010 and 2011 (EC3-2010, EC5-2010, EC1-2011, and EC42011) were used for calibration. The datasets of the years 2012, 2013 and 2014 were used for validation. Because on SA no winter wheat was grown in 2012 and 2013, the validation is based on three KR seasons (EC3-2012, EC1-2013, and EC3-2014) and only one SA season (EC42014). For the reason that in KR maize development was unusually delayed in 2010, probably due to a plant disease (see Discussion), the dataset EC1-2010 was forced to be part of the validation dataset. The remaining maize datasets were randomly assigned to the calibration or validation subset as follows: Maize was calibrated based on the datasets EC3-2011, EC2-2012, EC5-2012, and EC6-2013. Validation was performed with the independent datasets EC1-2010, EC5-2011, EC6-2010, and EC1-2014, which had not been used for calibration. For evaluating model performance, we used modelling efficiency (EF), root mean

3.5. Model setup Noah-MP-Gecros was forced with half-hourly weather data measured at the EC stations (wind speed, air temperature, air humidity, pressure, incoming shortwave radiation, incoming longwave radiation, and precipitation). The settings of the multi-physics options are given in Table 3. Based on soil texture measurements (Wizemann et al., 2014), the soil type index was set to 4 (silt loam) at the KR sites. On SA, the soil type index was set to 11 (silty clay) at EC4 and EC6. At station EC5, the soil type index was set to 8 (silty clay loam). At all sites, the soil profile was discretized into seven layers: 0–0.05 m, 0.05–0.1 m, 0.1–0.2 m, 0.2–0.3 m, 0.3–0.6 m, 0.6–0.9 m and 0.9–2.0 m. At the KR loess sites, the maximum rooting depth was set to 1.3 m. Soils of the SA have usually a shallow solum, which is underlain by weathered and fissured parent material (Jurassic limestone) (Wizemann et al., 2014). Therefore, at EC4-EC6 we limited the rooting depth to 0.45 m. Soil 327

Agricultural and Forest Meteorology 262 (2018) 322–339

J. Ingwersen et al.

Table 4 Temperature anomalies between the years 2010–2014. All values in °C.

half-hourly air temperatures increased or decreased by 1.5 °C, respectively. This value was chosen as it equals the mean bias of the ERAInterim-driven EURO-CORDEX RCM ensemble mentioned in the Introduction. All other settings were the same as described in Chapter 3.5. For compensating the impact of the temperature bias on the crop simulation, we tested the following approach: instead of correcting the air temperature for the bias (which is normally done and, in our case, would of course yield the original results), we adjusted the phenological parameters to the temperature bias of the RCM. We adjusted mV and mR based on the rule of proportion. By way of example, in case of mV, this yields:

square error (RMSE), and bias. For definition and calculation see, for example, Ingwersen et al. (2011). 3.7. Model comparison: Noah-MP versus Noah-MP-Gecros We compared the Noah-MP-Gecros validation results for wheat (EC3-2012, EC1-2013, EC3-2014, and EC4-2014) and maize (EC1-2010, EC5-2011, and EC1-2014) with Noah-MP (version 1.1) simulations of evapotranspiration. Noah-MP was forced with half-hourly weather data from the EC stations. In the simulations the land use dataset USGS was used. This dataset contains five cropland related land use classes: 1) dryland cropland and pasture, 2) irrigated cropland and pasture, 3) mixed dryland/irrigated cropland and pasture, 4) cropland/grassland mosaic, and 5) cropland/woodland mosaic. As the model region is in the temperate zone and cropland is not irrigated, we used cropland/ grassland mosaic (vegetation type index = 5) in our simulation. Besides the multi-physics settings given in Table 3 the canopy stomatal resistance was computed with the Ball-Berry scheme (opt_crs = 1). Soil settings were the same as described in Chapter 3.5. Soil temperatures and soil water contents were initialised with measured data.

mV , adjusted = mV

3.8. Temperature sensitivity and bias correction

4.1. Temperature and rainfall variability between 2010 and 2014

To investigate the robustness of Noah-MP-Gecros with respect to temperature biases, we performed a simple sensitivity analysis. For the season 2012, a season with no pronounced temperature anomalies (Table 4), we ran Noah-MP-Gecros for winter wheat and maize with

Tables 4 and 5 show air temperature and rainfall anomalies over the period 2010–2014. In KR, mean temperature was 9.5 °C, the annual precipitation 872 mm, whereas on SA the annual temperature was 2.4 °C lower than in KR. Mean annual rainfall was similar in both regions.

Tveg−Tb + Tbias Tveg−Tb

(15)

Here, Tveg (°C) is the observed mean temperature over the vegetation period, Tb (°C) stands for the base temperature of maize, and Tbias (°C) is the temperature bias of the RCM. 4. Results

328

Agricultural and Forest Meteorology 262 (2018) 322–339

J. Ingwersen et al.

Table 5 Rainfall anomalies between 2010 and 2014. All values in mm.

4.1.1. Wheat seasons – Kraichgau 2011 and 2014 were the warmest wheat seasons (March to June). Other than in 2011, in 2014 high temperatures were accompanied by low rainfalls. The by far coldest and wettest season took place in 2013. The wheat season 2010 was also colder than the five-year mean but it was not as wet as in 2013. The year 2012 closely matches the five-year mean temperature, and total precipitation was also close to the five-

year mean. Periods with exceptionally low rainfall (anomaly > 40 mm) occurred in June–July 2010, May 2012, and May–June 2014.

4.1.2. Wheat seasons – Swabian Alb Other than in KR, the warmest wheat season on SA was not 2014 but 2012. Rainfall during the 2012 season matched closely the five-year mean. The seasons 2011 and 2014 were similarly warm, but with

Table 6 Optimized parameters values for winter wheat. Values in brackets: standard error (SE). Symbol

Source code variable

Definition

Default value

Optimized Value (SE)

Unit

TO mV mR psen βL ρ υcrit ncri0 Sla ϑm χvcn

TOD MTDV MTDR PSEN BLD CDMHT DSCRIT LNCI SLA0 PMEH XVN

25 34.7* 23.1* 0.0* 50* 460 n.a. 0.05 0.028 0.8 62*

22.5$ 46.12 (2.46) 40.22 (2.15) −0.104 (0.009) 25.58 (6.00) 492.6 (61.0) 0.225$ 0.03586 (0.001) 0.0237 (0.001) 0.6468 (0.0316) 24.96 (1.00)

°C d d h−1 degree g m−2 m-1 1 g g−1 m2 g−1 1 μmol s-1 g−1

rlit

RLVDS

Optimum temperature for phenology Minimum thermal day for vegetative growth phase Minimum thermal day for reproductive growth phase Photoperiod sensitivity of phenological development Leaf angle (from horizontal) Stem dry weight per unit of plant height Critical development stage that breaks winter dormancy Initial N concentration in living leaves Specific leaf area constant Fraction of sigmoid curve inflexion in entire plant height growth period Slope of linear relationship between maximum rate of rubisco-limited carboxylation and leaf nitrogen Fraction of the transfer of C from dead leaves to litter pool

0.1

0.0904 (0.011)

d−1

n.a.: not applicable. The parameter was newly introduced to Gecros. $ Parameter set by expert guess. * Default values are available only for pea (Pisum sativum L.). 329

Agricultural and Forest Meteorology 262 (2018) 322–339

J. Ingwersen et al.

Table 7 Optimized parameters values for maize. Values in brackets: standard error (SE). Symbol mV1

Source code variable MTDV1

Definition

Default value *

Minimum thermal days for vegetative growth phase in Kraichgau

34.7

*

mV2

MTDV2

Minimum thermal days for vegetative growth phase on Swabian Alb

34.7

mR1

MTDR1

Minimum thermal days for reproductive growth phase in Kraichgau

23.1*

mR2

MTDR2

Minimum thermal days for reproductive growth phase on Swabian Alb

23.1*

ncri0

LNCI

Initial N concentration in living leaves

0.05

ϑm

PMEH

Fraction of sigmoid curve inflexion in entire plant height growth period

0.8

nSO

SEEDNC

Seed nitrogen content

0.046*

Optimized value (SE)

Unit

37.71 (0.85) 28.50 (0.45) 18.97 (1.31) 14.04 (1.13) 0.051 (0.001) 0.364 (0.0228) 0.0176 (0.0005)

d d d d g g−1 1 g g−1

* Default values are available only for pea (Pisum sativum L.).

rainfalls below average. As in KR, the by far coldest season occurred in 2013, followed by a longer period of low rainfall from July and August. Among the five years, the season 2010 was the wettest. Longer periods with low rainfall besides the one in 2013 took place in May–June 2011, August-September 2011, and May–June 2014.

χ

4.1.3. Maize seasons – Kraichgau 2010 and 2013 were the coldest maize seasons (May–September), and 2012 was the year with the warmest maize season. In 2011 and 2014, temperature was also above average, but with some outstanding cold months in July 2011 and August 2014. Periods with low rainfall occurred in June–July 2010, September 2011, May and August 2012, July–August 2013, and May–June 2014. 4.1.4. Maize seasons – Swabian Alb As in KR, 2010 and 2013 were the coldest maize seasons on SA. In 2010, cold temperatures were combined with above-average rainfall sums. In 2013, the situation was different. While May and June were also wetter than the five-year mean, in July and August a longer period with low rainfall followed the wet phase. The 2012 season was the warmest and, similar to KR, the seasons 2011 and 2014 were also warmer than average but with some exceptionally cold months (July 2011 and August 2014). Additional periods with low rainfall occurred in May–June 2011, August-September 2011, and May–June 2014.

χ

4.2. Calibration and validation – winter wheat Besides the phenological parameters (mV, mR, psen; see Eq. (6)), seven plant parameters were calibrated (Table 6): 1) the initial nitrogen content in leaves ncri0, 2) the slope of the linear relationship between maximum carboxylation rate and leaf nitrogen χvcn, 3) fraction of sigmoid curve inflexion during the entire plant height growth period ϑm , 4) specific leaf area Sla, 5) stem dry weight per unit of plant height ρ, 6) leaf angle (from horizontal) βL, and 7) the fraction of carbon transferred from senescent leaves to litter pool rlit. Parameter correlation was in an acceptable range in all cases. Highest parameter correlation showed up between ncri0 and χvcn (r=−0.76) as well as between ncri0 and Sla (r = 0.72). Among the seven plant parameters, ncri0 and χvcn were by far the most sensitive ones. The scaled sensitivities (in brackets) ranked in the following order: ncri0 (1.0), χvcn (0.98), ϑm (0.57), Sla (0.31), ρ (0.18), βL (0.05), and rlit (0.03). This ranking shows that ncri0 (see Eq. (11)) is a key parameter in Gecros. It controls the initial nitrogen content in leaves. The higher the leaf nitrogen content, the higher the photosynthesis rate; the higher the photosynthesis rate, the more assimilates are produced and the faster the plant develops. The parameter χvcn (Eq. (3a)) is also a high-leverage parameter. It controls mainly the canopy dynamics during the maturity phase. With the default value of

Fig. 2. Effect of lowering the slope of linear relationship between maximum rate of Rubisco-limited carboxylation and leaf nitrogen (χvcn) from the default value of 62 to 25 μmol CO2 s−1 g−1 N on the simulated dynamics of green leaf area index (GLAI) and evapotranspiration (ET).

62 μmol CO2 s−1 g−1 N, the canopy ripened too fast and evapotranspiration (ET) broke down too early (Fig. 2), already in mid-June. By reducing χvcn to 25 μmol CO2 s−1 g−1 N, the phase of senescence 330

Agricultural and Forest Meteorology 262 (2018) 322–339

J. Ingwersen et al.

Fig. 3. Calibration runs for winter wheat.

was extended and cell death in leaves was slowed down. The parameter ρ (Eq. (8)) determines the final stem biomass and, with that, the formation of vegetative biomass. The parameter ϑm (see Eq. (7)) controls the within-shoot partitioning. The smaller ϑm , the earlier more assimilates are partitioned to the stem instead of to the leaves. Sla defines how much leaf area is built up per gram dry leaf biomass, thereby controlling the LAI dynamics. The parameter βL is used to compute the extinction coefficient for beam radiation, which controls the vertical distribution of sunlit and shaded leaves within the canopy (Yin and van Laar, 2005; p. 14–16 and Appendix E). Tuning βL enabled adjusting the maximum level of LAI. The parameter rlit controls the fraction of carbon transferred from senescent leaves to the litter pool and becomes effective during the maturing phase by controlling the decline of TLAI. The optimum temperature of phenological development (To) and the critical development stage at which winter dormancy is broken (υcrit) were not automatically calibrated with UCODE_2005 but pre-set according to expert guess (see Discussion). Fig. 3 shows the result of the calibration for winter wheat, and Table 8 summarizes the corresponding performance measures. Development stage and plant height are simulated very well. The dynamics of the LAI as well as the generative biomass are matched well. The model has deficiencies in reproducing the measured vegetative biomass and straw nitrogen content. Regarding the grain nitrogen content, the performance of the model must be regarded as poor. The inter-annual

Table 9 Performance of the validation runs for winter wheat and maize in Kraichgau and Swabian Alb. Variable

Development stage (-) Leaf area index (m2/m2) Plant height (m) Vegetative biomass (dt/ha) Generative biomass (dt/ha) Straw N content (kg N/ha) Grain N content (kg N/ha) Total N content (kg N/ha)

Winter wheat

Maize

EF

RMSE

Bias

EF

RMSE

Bias

0.98 0.84 0.89 0.43 0.52 0.06 0.75 –

0.09 0.74 0.13 28.7 13.1 8.17 24.75 –

0.00 0.15 −0.10 −23.3 6.07 −3.38 −24.33 –

0.93 −1.01 0.76 −16.3 0.77 – – −0.44

0.16 1.51 0.50 47.8 16.8 – – 92.8

−0.03 0.69 0.41 13.2 −13.9 – – −61.0

EF: modelling efficiency, RMSE: root mean square error.

Table 8 Performance of the calibration runs for winter wheat and maize in Kraichgau and Swabian Alb. Variable

Development stage (-) Leaf area index (m2/m2) Plant height (m) Vegetative biomass (dt/ha) Generative biomass (dt/ha) Straw N content (kg N/ha) Grain N content (kg N/ha) Total plant N content (kg N/ha) Latent heat flux (W/m2)

Winter wheat

Maize

EF

RMSE

Bias

EF

RMSE

Bias

0.99 0.81 0.96 0.32 0.86 0.52 −1.28 – 0.63

0.08 0.76 0.07 26.7 15.5 7.6 32.7 – 69.2

−0.01 0.25 −0.04 0.55 4.3 −0.8 −30.5 – 26.8

0.95 0.68 0.83 0.88 0.86 – – 0.97 –

0.14 0.78 0.51 15.8 10.4 – – 11.3 –

0.02 0.24 0.30 −2.4 19.7 – – −10.5 –

Fig. 4. Measured and simulated total leaf area index dynamics of winter wheat in the validation runs.

variation of the grain nitrogen content is not well matched. While the measured grain nitrogen content ranges between 167.2–221.2 kg N ha−1, the simulated nitrogen contents vary only between 150.6 and 184.7 kg N ha−1, and the nitrogen content is underestimated by on

EF: modelling efficiency, RMSE: root mean square error. 331

Agricultural and Forest Meteorology 262 (2018) 322–339

J. Ingwersen et al.

Fig. 5. Measured and simulated evapotranspiration in the wheat validation phase. The grey band indicates the post-closure method uncertainty band.

average 30.5 kg N ha−1 (data not shown). In the validation phase the model performs for the development stage, LAI and plant height similarly well as in the calibration phase (Table 9). Again, the development stage is matched best. With 13.1 dt ha−1, the RMSE of the generative biomass is somewhat lower than in the calibration phase (15.5 dt ha−1). While the nitrogen content in grain is fairly well matched, the inter-annual variation of the straw nitrogen content is not well reproduced, resulting in a low EF of 0.06; the RMSE of the grain and straw nitrogen content, however, are in the same order of magnitude as in the calibration phase. Fig. 4 shows the measured and simulated LAI dynamics for the four validation seasons. Both the inter-annual variation and the inter-regional differences are well reproduced. The delayed development of LAI on SA versus KR is well described in 2014. Also, the contrasting LAI development in KR between the year with a cold spring (2013) and the year with a warm spring (2014) is well simulated. The abrupt and sudden LAI decline in 2012 between day of the year (DOY) 164 and DOY 181 from 4.7 to 3.3 is not reproduced by the model. In the validation run EC3-2012, the simulated ET is within the PUB or within the range of the lower error bar during most days (96% coverage) (Fig. 5). Until late June the simulated ET fluxes tend to be located at the upper bound of the PUB. End of June, with the onset of the ripening phase, this change. ET tends to be at the lower bound of the PUB. At the strong transition from high to low ET rates on 2 and 3 July, the model slightly underestimates measured ET. In the EC1-2013 validation run, the percentage of coverage is similar as in 2012 (1 of 91 above, 4 of 91 below). 97% of the simulated ET fall within the observed range. Similar to 2012, there are periods when the simulated fluxes tend to be at the upper bound and other periods when the simulated fluxes tend to be at the lower bound. The timing and the break-down of ET during senescing of the wheat stand is well simulated in both years. In the validation year 2014, the model performance with regard to ET is less good than in 2012 and 2013. In KR at EC3, the simulated ET falls

within the PUB on only 84 days of in total 103 days (82%). At most of these non-matching days the model overestimates ET. Only on four days is the simulated ET below the lower bound of the observation. Both KR and SA exhibit one striking period: in June there is a 14-day period (11 to 24 June) in which the model continuously overestimates ET. On SA, this overestimation is stronger than in KR. 4.3. Calibration and validation – maize Due to the strong heat demand differences between maize cultivars grown in KR and on SA (see Chapter 3.4), the phenological parameters mV and mR had to be optimized separately for each region. As expected, both parameter values were distinctly lower on SA than in KR (Table 7). Only three additional plant parameters were optimized: as in wheat the parameters ncri0 and ϑm and, additionally, the seed nitrogen content nSO. The parameter nSO controls the intra-shoot nitrogen partitioning (Yin and van Laar, 2001; p. 32–33) and primarily affects the formation of generative biomass. The lower nSO, the higher is the generative biomass. Parameter correlation was in an acceptable range and was highest between ncri0 and ϑm (r=−0.77). As for wheat, ncri0 was the highleverage parameter. Scaled sensitivities ranked in the following order: ncri0 (1.00), nSO (0.30), and ϑm (0.27). Fig. 6 shows the result of the model calibration for maize. For model performance measures, see Table 8. The overall model performance can be regarded as very good. The phenological development is well matched in both regions (EF = 0.95) although in the cold year 2013 the development is somewhat fast on SA. The observed LAI dynamics are fairly well reproduced by the model (EF = 0.68). Gecros tends to overestimate LAI (bias = 0.24) during the period of highest canopy coverage. On SA, the inter-annual LAI variability between 2012 and 2013 is well simulated. Due to the cold weather conditions in 2013 emergence is heavily delayed. In both years, the farmer drilled maize in late April (28 April in 2013; 26 April in 2012), but simulated emergence 332

Agricultural and Forest Meteorology 262 (2018) 322–339

J. Ingwersen et al.

Fig. 6. Calibration runs for maize.

dynamics are remarkably poorly reproduced in KR. Neither the timing of the LAI dynamics nor the absolute LAI level is matched. On SA, in contrast, the LAI dynamics are fairly well matched in 2010. The delayed development as well as the reduced LAI level during the main vegetation period is reproduced, although the simulated decline from mid- to late September does not agree with the measurements. In the validation run EC1-2010 (Fig. 8), from late April to mid-June the simulated ET is mostly within the uncertainty ranges. Later on, however, there is one longer period over which the simulated ET is distinctly higher than the observed one (June 23 to July 5). In September, during the maturity phase, the declining ET is reproduced but the decline is too strong, systematically underestimating ET. Over the whole season, only 59% of the simulated fluxes are within the uncertainty range, 18% are above and 23% are below PUB. Over the maize season in 2011, the simulated ET matches the measured ET at station EC5 on 73% of the days. Mismatches are solely related to underestimated fluxes. As in 2010, the longest period of systematic deviations between simulated and measured ET coincides with the maturity phase (mid August – mid September). The decline of ET is better reproduced than in 2010 at EC1, but over this period the simulated ET falls below the ET error bars on average every second day. In the validation run EC4-2014, from April 25 to late May the simulated ETs are located within the error bars. From early June on, the simulated ETs tend to be higher than the measured ones, but the simulated fluxes are mostly still within the PUB. In total, 88% of the simulated fluxes fall within uncertainty limits, 6% are overestimated, and 6% are below. As opposed to two previous seasons, the ET is well simulated over the maturity phase.

Fig. 7. Measured and simulated total leaf area index dynamics of maize in the validation runs.

needed 40 days in 2013 instead of 15 days in 2012. Consequently, the LAI increase starts later and the maximum LAI level remains below the level of the previous year. In KR, the delayed development in 2012 versus 2011 is mainly related to a later drilling date. In 2011, maize was drilled on 18 April, whereas in 2012 the farmer postponed drilling to 2 May due to the low temperatures in April. The simulated increase in canopy height is shifted by about 2–3 weeks ahead of the observed heights. On average, the models overestimate plant height by 0.3 m. With regard to the generative biomass, Gecros performs similarly well as for winter wheat (EF = 0.86). Beyond winter wheat, the vegetative biomass data and the analysed total nitrogen content in plants are also matched well. Fig. 7 shows the LAI dynamics for the four validation runs. In none of the validation runs the measured LAI data reach the maximum levels observed in KR in 2011 and 2012. Due to this fact and to the general trend of the model to overestimate LAI particularly during the secondhalf of the season, the simulated LAIs are clearly too high. In three out of the four runs they reach the maximum levels of KR in 2011 and 2012. In the EC1-2014 run the LAI dynamics are well reproduced over the period of the first three plant rating dates. Later, however, the simulated LAI is about two LAI units higher than measured. In 2010, the LAI

4.4. Model comparison: Noah-MP versus Noah-MP-Gecros The TLAI dynamics used in Noah-MP differs considerably from the simulated TLAI dynamics of Noah-MP-Gecros (Fig. 9). The difference is in both timing and absolute level. In Noah-MP, the peak value of 3.5 is reached mid of July. With Noah-MP-Gecros, winter wheat has its maximum TLAI of 5.7 already end of May, and maize reaches its maximum TLAI of 6.5 about four weeks later than with Noah-MP (mid of August). Due to these pronounced differences in the vegetation dynamics, both models show also strong differences in the transpired fraction of ET (fT) (Fig. 10). For wheat, the differences are strongest in May and July. In June both models simulate quite similar fractions, but Noah-MP-Gecros simulates higher fT due to higher TLAI values. For 333

Agricultural and Forest Meteorology 262 (2018) 322–339

J. Ingwersen et al.

Fig. 8. Measured and simulated ET in the maize validation runs. The grey band indicates the post-closure method uncertainty band (PUB).

the low-rainfall period in June 2014. While Noah-MP matches measured ET over this period well, Noah-MP-Gecros overestimates ET systematically. Periods over which Noah-MP and Noah-MP-Gecros perform quite similar are from April to the beginning of June 2013 and in April 2014. For maize, Noah-MP performs much better during the ripening phase than for winter wheat (Fig. 8). Both models reproduce the break down of ET fairly well in the final stage of the season. Over the growing season there is not a clear trend that one of the models delivers systematically higher or lower ET. In all three test years, Noah-MP delivers higher ET than Noah-MP-Gecros in June. In July, however, Noah-MPGecros tends to simulate higher ET than Noah-MP. Both models move clearly out of the PUB in the end of June 2010, while Noah-MP overestimates the measured ET much more strongly than Noah-MP-Gecros. A similar situation occurred in the beginning of June 2014. Periods over which Noah-MP and Noah-MP-Gecros deliver rather similar ET values are May and in EC5-2011 from mid of July until the end of the season. Overall, both models have a quite similar PUB statistics (Table 10). Only in the simulation EC3-2012, Noah-MP-Gecros clearly outperformed Noah-MP. The fraction of simulated fluxes within the PUB was higher for winter wheat than for maize. For winter wheat, NoahMP-Gecros simulates systematically higher mean daily ET than NoahMP. For maize, both models produce comparable mean daily ET rates.

Fig. 9. Mean dynamics of leaf area index used in Noah-MP and simulated with Noah-MP-Gecros for winter wheat and maize for the validation periods. Solid lines show the total leaf area index. Dashed lines display the green leaf area index.

maize the differences are strongest at the beginning of the growing season. The sudden drops of the fT e.g. on July 1 2012 or July 22, 2010 are related to rain events. The reason is that, as long as the canopy is wet, evaporation is taken predominantly from the canopy interception pool. In the winter wheat validation periods EC3-2012 and EC3-2014, before onset of ripening ET rates simulated with Noah-MP are located most of the time at the lower bound of the PUB, while those simulated with Noah-MP-Gecros is mostly in the mid to upper part of the PUB (Fig. 5). The picture changes with the onset of ripening (beginning of July in KR, end of July on SA). Noah-MP cannot reproduce the break down of ET in the senescing phase of the wheat stand. Instead of decreasing ET, Noah-MP simulates peak rates in July, which clearly move out of the PUB envelope. Noah-MP-Gecros shows some deficiencies in

4.5. Temperature sensitivity and bias adjustment To investigate the effect of temperature biases on Noah-MP-Gecros, we ran simulations for winter wheat and maize with air temperatures increased or decreased by 1.5 °C (Fig. 11). Firstly, winter wheat responds distinctly less sensitively to temperature changes than maize. In wheat, a temperature bias moves the graphs basically along the time axis to the left ( + 1.5 °C) or the right (1.5 °C). As expected, a positive bias accelerates plant development and a negative bias slows it down. A temperature biases of ± 1.5 °C shifts the harvest day forwards or backwards, respectively, by about 10 days. Maize responds very 334

Agricultural and Forest Meteorology 262 (2018) 322–339

J. Ingwersen et al.

Fig. 10. Time series of transpired fraction of evapotranspiration for Noah-MP and Noah-MP-Gecros. For winter wheat, the validation run EC3-2012 is shown. For maize, the validation run EC1-2010 is presented.

of parameters that performed slightly better than the final calibration (data not shown). In the validation phase, however, this set of parameters showed severe deficiencies. In case of winter wheat, for example, the crop development was considerably delayed in 2013 and 2014. While in 2013 the measured LAI was already 0.6 in mid-May, the modelled LAI was only a third of this value. In 2014 the situation was similar: while the measured LAI was already 0.45 in late March, the modelled LAI was only 0.1. As this parameter set failed in the validation phase, a second round of model optimization was required. In the previous parameterization, the crop developed too slowly. Accordingly, To and υcrit were manually changed by expert guess and trial and error from the previous settings of 25 °C and 0.25 to 22.5 °C and 0.225, respectively. With these new settings, the calibration was repeated. This time the obtained parameter set performed well both in the calibration and validation phase. The phenomenon of equifinality and the issue that we cannot be certain that we found the global minimum in the multi-dimensional parameter space needs to be investigated more closely in future, for example, by applying more advanced optimization algorithms such as the AMALGAM evolutionary search algorithm (e.g., Wöhling et al., 2013). These types of search algorithm are time demanding and require much more computer power than UCODE, because we finally found a robust parameter set, we refrained from using these routines and left their application to future work. Due to the fact that we manually adjusted To and υcrit after the first failed validation, one might raise the objection that the validation dataset is not fully independent anymore. And indeed, this circumstance weakens to some extent the evidentiary value of the validation. During

differently. An increase of 1.5 °C moves the harvest day from 4 October to 30 August. A decrease by 1.5 °C, however, leads to the situation that maize does not reach maturity. Also the overall response is very different between both crops. Wheat shows a more or less symmetrical response: increasing or decreasing the temperature by 1.5 °C moves the graphs along the time axis forwards or backwards, but only minimally affects the shape of the curves. Maize shows an asymmetrical response. In the beginning of the vegetation period, the curves are also shifted for- or backwards, but later on the curve shape also changes considerably (Fig. 10F and G). At a positive temperature bias the canopy is already fully senescent around mid-August. Conversely, with a negative bias, the canopy is still fully green during that period. In the simulation with + 1.5 °C, grain yield declined from 9.7 to 7.7 t ha−1, a decrease of 13.4% grain yield per °C (data not shown). The result of the proposed bias adjustment is shown in Fig. 12. After adjustment, maize develops very similarly in the three simulations (Fig. 12C). Its response to a temperature bias is now much like that wheat: the bias moves the graphs basically for- and backwards on the time axis. After this adjustment, moreover, grain yield at a + 1.5 °C temperature increase is at the same level as in the reference simulation.

5. Discussion During the calibration phase of wheat we observed the phenomenon of “equifinality”, that means there are more than one parameter sets that are capable of reproducing the behaviour of the model system under study (Beven, 2006). In a first calibration attempt, we found a set

Table 10 Comparison of evapotranspiration (ET) simulated with Noah-MP-Gecros and Noah-MP. For evaluating the performance the percentage of simulated daily ET rates within the post-closure method uncertainty band (PUB) was determined. Values in brackets: standard deviation. Noah-MP-Gecros Simulation

Noah-MP

Mean daily ET mm d−1

Above PUB %

Below lower error bar %

Inside PUB %

Mean daily ET mm d−1

Above PUB %

Below lower error bar %

Inside PUB %

Winter wheat EC3-2012 EC1-2013 EC3_2014 EC4-2014

3.89 3.37 3.61 3.19

(1.16) (1.57) (1.37) (1.34)

0 1 14 20

4 2 4 0

96 97 82 80

3.43 3.26 3.19 2.89

(0.90) (1.37) (1.00) (1.03)

9 3 13 18

15 4 9 0

76 93 78 82

Maize EC1-2010 EC5-2011 EC1-2014

2.90 (1.40) 2.64 (0.83) 3.09 (1.35)

18 0 6

23 28 6

59 72 88

2.85 (1.23) 2.87 (0.92) 2.93 (0.93)

21 4 7

19 26 4

60 70 89

335

Agricultural and Forest Meteorology 262 (2018) 322–339

J. Ingwersen et al.

Fig. 11. Effect of a temperature bias of ± 1.5 °C on the simulation of the vegetation dynamics of winter wheat (A–D) and maize (E–H).

dataset, that means four site years of field measurements, and to acquire a new fully independent dataset. In practice, however, this is difficult to achieve given the limited runtime and budget of research projects. Note, in this study we used for calibration and validation a dataset consisting of eight site years collected over five years. Such a

the parameter optimization with UCODE, however, the validation dataset was not used at all. Therefore, we are convinced that the validation that we performed in this study for winter wheat gives reliable evidence for the robustness of the derived set of parameters. The best solution of this issue indeed would be to fully neglect the validation

Fig. 12. Effect of a temperature bias of 1.5 °C on the dynamics of the development stage and green leaf area index: A and B with no adjustment of the phenological parameters mV and mR, C + D: after adjusting the phenological parameters for the temperature bias. 336

Agricultural and Forest Meteorology 262 (2018) 322–339

J. Ingwersen et al.

optimal manner. Under real field conditions, however, nitrogen supply varies from year to year due to differences in nitrogen fertilization, nitrogen mineralization or the pre-crop. Accordingly, it cannot be expected that the model reproduces the inter-annual variation of nitrogen content in plant biomass. In 2014, during a longer period of low rainfall, the winter wheat stand became water-stressed resulting in a distinct decline of observed ET (Fig. 5). This decline was not well reproduced by Noah-MP-Gecros but well described by Noah-MP. This shortcoming of Gecros can be explained by the simplistic approach that reduces the potential to actual transpiration under water stress (see Chapter 2.2). It must be fixed in future, for example, by the approach implemented in Noah-MP, where the stomatal resistance is a function of a soil moisture factor. Noah-MP-Gecros performed better than Noah-MP during the ripening phase of winter wheat and during some shorter periods of the maize validation runs. Both models use basically the same physics for computing the energy fluxes at the land surface. The major difference between the models is in describing the vegetation dynamics. Whereas Noah-MP has been designed to represent mean energy fluxes over cropland and is forced with static satellite data, Noah-MP-Gecros computes a crop-group-specific (early versus late covering crops) vegetation and energy flux dynamics in a weather driven way. Therefore, it is not surprising that the TLAI dynamics of Noah-MP is so much different from that of Noah-MP-Gecros (Fig. 9). Obviously, this difference must result in different energy fluxes. Over some periods, however, despite the profound differences in simulating TLAI dynamics, Noah-MP and Noah-MP-Gecros produce nearly identical ET values. What drastically differs over these periods, however, is the partitioning of ET into transpiration and evaporation. Consequently, these are periods when differences in vegetation dynamics mainly affect the soil water regime. Evaporation primarily affects the soil water in the surface layer while transpiration is taken from the root zone. The real added value of Noah-MP-Gecros can only be evaluated in high-resolution (< 3 km) coupled crop-climate simulations in future. Integrating NoahMP-Gecros in recent RCM with a resolution of 50 or 12 km is problematic, as in Western Europe, for example, there are no 12 km x 12 km areas that are covered exclusively with early or late covering crops. The use of Noah-MP-Gecros at such a resolution would probably introduce strong horizontal gradients of temperature and humidity in the atmosphere which in reality do not exist. Bassu et al. (2014) investigated the response of 19 maize CMs to climate change factors. These models were evaluated at four locations. In Lusignan (France), whose climate is most similar to that of KR, the median grain yield of the model ensemble declined by 4.5% per °C temperature increase. At an increase of 3 °C, the model with the highest temperature sensitivity simulated a decline of 16.3% grain yield per °C. In our sensitivity analysis, grain yield showed a sensitivity of 13.4% grain yield per °C. Hence, Noah-MP-Gecros’ reaction to temperature increase is in the range of other maize models, but has an aboveaverage temperature sensitivity. Our temperature sensitivity study demonstrates some of the difficulties that we face when implementing CMs into RCMs. The temperature bias of an RCM propagates to the CM which, depending on the temperature sensitivity of the crop (maize > wheat), can result in defective crop simulations. Similar problems were reported by Osborne et al. (2007). They integrated the General Large Area Model (GLAM) into the land surface component of a global climate model. A validation of the crop simulations could not be undertaken, because the coupling to the climate model resulted in biases in the climate simulation of weather which propagated into the crop simulations. The proposed approach to adjust the phenological parameters instead of the air temperature has the advantage that the simulated state variables and fluxes and the relationship between them remain untouched. It avoids the physical inconsistencies that come along with the typical bias correction (where the bias is simply subtracted from the air temperature). Fluxes depending on temperature (longwave outgoing radiation, LE etc.) stay consistent with the air

multi-year and comprehensive dataset is only seldom available. Many process descriptions in Gecros are rather mechanistic. Therefore, in principle many model input parameters, as they have a plant physiological meaning, can be directly measured (Yin and Struik, 2010). In the present study, however, we estimated selected crop parameters using automated calibration. In Gecros several crop parameters, the so-called genotype-specific parameters, are cultivar-dependent. In large scale applications, e.g., on national or continental scales, crop parameter sets need to be robust. On these scales, information on cultivars is not available. Therefore, we are looking for a crop parameterization that is representative for a wide variety of winter wheat or maize cultivars. Estimating crop parameters over a range of different cultivars and seasons is a feasible approach to achieve this. Furthermore, it is important to recognize that the behaviour of the model cannot be explained in terms of the behaviour of the parts in isolation (Yin and Struik, 2010). Take the parameter χvcn as an example. χvcn is the slope of the linear relationship between the maximum rate of rubisco-limited carboxylation of photosynthesis and leaf nitrogen (see Eq. (3a)). The estimated value of about 25 μmol CO2 s−1 g−1 N is distinctly lower than the default value of 62 μmol CO2 s−1 g−1 N. In our calibration, the parameter was lowered as the canopy ripened too fast and transpiration declined too early (see Chapter 4.2 and Fig. 2). As in the simulation the nitrogen plant content was systematically underestimated by 30.5 kg ha−1, leaf nitrogen decreased too fast, lowering photosynthesis and accelerated senescence. By lowering χvcn from 62 to 25 μmol CO2 s−1 g−1 N the decrease of photosynthesis was slowed down. This means that setting χvcn to 25 μmol CO2 s−1 g−1 N compensates the systematic negative bias in the simulated plant nitrogen content. Therefore, using a direct measurement of χvcn for parameterizing the model would not necessarily improve the model performance. An additional parameter which was clearly lower than the default value was ncri0 (default: 0.05 g g-1; calibrated: 0.036 g g-1). This default value, however, refers to spring wheat. Therefore, a direct comparison is difficult, but the very low standard error (Table 6) indicates that the parameter could be reliably estimated. All other nonphenotype-specific wheat parameters were adjusted by less than ± 20% of the default value and can therefore be regarded as being in a plausible range. In general, Noah-MP-Gecros performed well in simulating crop development and vegetation dynamics. Over some periods, however, the model showed pronounced deviations from the observations. In 2012, for example, the sudden drop of LAI in mid-June could not be reproduced. As confirmed by field inspection, this sudden drop in LAI was related to a fungal infestation. The lower levels of leaves exhibited brownish stains, typical for Septoria, and the leaves ultimately died off. Furthermore, the simulated development of the maize cultivar Cannavaro did not match the observations in 2010 well. In that year, other than in all the remaining years, maize in KR developed at a pace comparable to SA (Fig. 7). Maize was drilled on 17 April, but the first plant rating was performed more than five weeks later on 28 May at a BBCH of 13. This indicates that there must have been an event, not considered in the model, which delayed the development of maize. It is unlikely that night frost caused this delay. Night temperatures below 0 °C were measured five and seven days after drilling and hence before emergence. The measured soil water content in 0.05 m depth was 5–7 Vol.% lower than the five-year mean (data not shown). In 2011, the soil water conditions were comparable. Maize was drilled on 18 April and the first plant rating was performed about three weeks later on 6 May. Therefore, the most probable explanation also in this case is that a plant disease, for example a damping-off disease caused by soil-borne fungi such as Pythium or Fusarium, delayed plant development. That is the reason why this maize season was forced to be part of the validation dataset. Finally, the model did not well reproduce the inter-annual variation of the nitrogen content in vegetative and generative biomass. This is because the model does not simulate nitrogen turnover in soil but assumes that the farmers provide the plants with nitrogen in an 337

Agricultural and Forest Meteorology 262 (2018) 322–339

J. Ingwersen et al.

temperature used in the CM. The ultimately most satisfying approach, however, is to reduce the bias in RCMs by improving the models themselves, for example, by moving from parameterized to an explicit representation of deep convection (Ehret et al., 2010). In the future, we intend to integrate Noah-MP-Gecros with WRF to perform climate projections over the next decades. In the event of such a large-scale application of Noah-MP-Gecros, drilling and harvesting dates must be made dependent on the weather conditions and the expectations of the farmers. In years with a warm early summer, for example, maize should be drilled earlier than in years with a cold early summer. On larger time scale, drilling dates are expected to shift under global warming. Spring crops will be drilled earlier and winter crops later than today. Adaptations made by farmers can be incorporated by introducing learning algorithms. To compute the drilling date of spring crops, for example, Aurbacher et al. (2013) used a temperature threshold that refers to the average temperature of the upper 5 cm of topsoil during seven successive days. Furthermore, on large scales, where information on maize cultivars is not readily available, an approach is required that considers the pronounced differences in phenological development of maize cultivars in a practical manner. One possible approach would be to compute mV and mR as a function of annual mean temperature. The warmer KR and the colder SA cover a wide range of mean temperatures typical for Germany.

Bassu, S., Brisson, N., Durand, J.-, Boote, K., Lizaso, J., Jones, J.W., Rosenzweig, C., Ruane, A.C., Adam, M., Baron, C., et al., 2014. How do various maize crop models vary in their responses to climate change factors? Glob. Change Biol. 20, 2301–2320. Beven, K., 2006. A manifesto for the equifinality thesis. J. Hydrol. 320, 18–36. Boogaard, H.L., Van Diepen, C.A., Rötter, R.P., Cabrera, J.M.C.A., Van Laar, H.H., 1998. User’S Guide for the WOFOST 7.1 Crop Growth Simulation Model and WOFOST Control Center 1.5. Technical Document 52. Winand Staring Centre, Wageningen, the Netherlands. Brisson, N., Ruget, F., Gate, P., Lorgeou, J., Nicoullaud, B., Tayot, X., Plenet, D., Jeuffroy, M.-H., Bouthier, A., Ripoche, D., Mary, B., Justes, E., 2003. STICS: a generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and corn. Agronomie. 18, 311–346. Chen, F.F., Dudhia, J., 2001a. 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., Dudhia, J., 2001b. Coupling an advanced land surface-hydrology model with the Penn-State-NCAR MM5 modeling system. Part II: preliminary model validation. Mon. Weather Rev. 129, 587–604. Ehret, U., Zehe, E., Wulfmeyer, V., Warrach-Sagi, K., Liebert, J., 2010. Should we apply bias correction to global and regional climate model data? Hydrol. Earth Syst. Sci. 16, 3391–3404. Fan, J., McConkey, B., Wang, H., Janzen, H., 2016. Root distribution by depth for temperate agricultural crops. Field Crop Res. 189, 68–74. Farquhar, G.D., von Caemmerer, S., Berry, J.A., 1980. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90. Goudriaan, J., van Laar, H.H., 1994. Modelling Potential Crop Growth Processes. Kluwer Academic Publishers, Dodrecht 238 p. Hawkins, E., Osborne, T., Ho, C., Challinor, A., 2013. Calibration and bias correction of climate projections for crop modelling: an idealised case study over Europe. Agric. For. Meteorol. 170, 19–31. Högy, P., Wieser, H., Köhler, P., Schwadorf, K., Breuer, J., Franzaring, J., Muntifering, R., Fangmeier, A., 2009. Effects of elevated CO2 on grain yield and quality of wheat: results from a 3-year free-air CO2 enrichment experiment. Plant Biol. 11, 60–69. Imukova, K., Ingwersen, J., Streck, T., 2015. Determining the spatial and temporal dynamics of the green vegetation fraction of croplands using high-resolution RapidEye satellite images. Agric. For. Meterol. 206, 113–123. Ingwersen, J., Steffens, K., Högy, P., Warrach-Sagi, K., Zhunusbayeva, D., Poltoradnev, M., Gäbler, R., Wizemann, H.-, Fangmeier, A., Wulfmeyer, V., et al., 2011. Comparison of Noah simulations with eddy covariance and soil water measurements at a winter wheat stand. Agric. For. Meteorol. 151, 345–355. Ingwersen, J., Imukova, K., Högy, P., Streck, T., 2015. On the use of the post-closure methods uncertainty band to evaluate the performance of land surface models against eddy covariance flux data. Biogeosciences 12, 2311–2326. Jones, J.W., Hoogenboom, G., Porter, C.H., Boote, K.J., Batchelor, W.D., Hunt, L.A., Wilkens, P.W., Singh, U., Gijsman, A.J., Ritchie, J.T., 2003. The DSSAT cropping system model. Eur. J. Agron. 18, 235–265. Jones, C.A., Kiniry, J.R. (Eds.), 1986. CERES-Maize. A Simulation Model of Maizegrowth and Development. Texas A&M University Press, Texas, College Station. KA5, 2005. German Soil Survey Instructions, Bodenkundliche Kartieranleitung, 5th ed. Hannover, Germany. Kotlarski, S., Keuler, K., Christensen, O.B., Colette, A., Déqué, M., Gobiet, A., Goergen, K., Jacob, D., Lüthi, D., Van Meijgaard, E., et al., 2014. Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble. Geosci. Model Dev. 7, 1297–1333. Lenz, V.I.S., 2007. A Process-Based Crop Growth Model for Assessing Global Change Effects on Biomass Production and Water Demand. Liang, X., Xu, M., Gao, W., Raja Reddy, K., Kunkel, K., Schmoldt, D.L., Samel, A.N., 2012. A distributed cotton growth model developed from GOSSYM and its parameter determination. Agron. J. 104, 661–674. Liu, X., Chen, F., Barlage, M., Zhou, G., Niyogi, D., 2016. Noah-MP-crop: introducing dynamic crop growth in the Noah-MP land surface model. J. Geophys. Res. 121 (23), 953–972. 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. Biogeosci. Discuss. 6, 1903–1944. Lu, Y., Jin, J., Kueppers, L.M., 2015. Crop growth and irrigation interact to influence surface fluxes in a regional climate-cropland model (WRF3.3-CLM4crop). Clim. Dyn. 45, 3347–3363. McPherson, R.A., 2007. A review of vegetation-atmosphere interactions and their influences on mesoscale phenomena. Prog. Phys. Geog. 31, 261–285. Meier, U., 2001. Growth stages of mono-and dicotyledonous plants. Federal Biological Research Centre for Agriculture and Forestry. Blackwell, Oxford 158 p. Müller, C., 1999. Modeling Soil-Biosphere Interactions. CABI Publishing, Wallingford. UK. Niu, G., Yang, Z., Mitchell, K.E., Chen, F., Ek, M.B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., et al., 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: Atmos. 116. Osborne, T., Lawrence, D., Challinor, A., Slingo, J., Wheeler, T., 2007. Development and assessment of a coupled crop–climate model. Glob. Change Biol. 13, 169–183. Osborne, T., Slingo, J., Lawrence, D., Wheeler, T., 2009. Examining the interaction of growing crops with local climate using a coupled crop-climate model. J. Clim. 22, 1393–1411. Poeter, E.P., Hill, M.C., Banta, E.R., Mehl, S., Steen, C., 2005. UCODE_2005 and Six Other Computer Codes for Universal Sensitivity Analysis, Calibration, and Uncertainty

6. Summary and conclusions We successfully extended the LSM Noah-MP by a dynamic crop growth component (Gecros) for wheat and maize. The model performed well both in calibration and in validation runs over in total 16 seasons. Our study demonstrates that multiple-year data records are mandatory for a reliable and robust calibration of a model of this complexity. A two-year record is clearly insufficient. The results support the conclusion that we developed a robust parameterization for the model region. Maize simulations were not as good as winter wheat simulations. We forward two potential explanations: firstly, maize shows pronounced cultivar differences in phenological development and, secondly, its development seems to be more prone to plant diseases, which the implemented CM does not consider. The pronounced differences in the vegetation dynamics between Noah-MP and Noah-MP-Gecros strongly affect the partitioning of evapotranspiration into transpiration and soil evaporation. The new Noah-MP-Gecros enables simulating the development of early and late covering crops in a weather-driven way. It is intended for investigating two-way interactions in high-resolution, coupled crop-climate simulations. The added value of this improved description of the vegetation dynamics needs to be evaluated in future. Acknowledgements We gratefully acknowledge the financial support by the German Research Foundation (DFG) in the frame of the Research Unit 1695 “Agricultural Landscapes under Global Climate Change – Processes and Feedbacks on a Regional Scale”. We thank the farmers Mr. Bosch senior†, Mr. Bosch junior, Mr. Fink, Mr. Herrmann, and Mr. Reichart for their cooperation, and three anonymous reviewers for their helpful and stimulating comments. Thanks also to Michael Stachowitsch for providing language help and proof reading. References Abendroth, L.J., Elmore, R.W., Boyer, M.J., Marlay, S.K., 2011. Corn Growth and Development. PMR 1009. Iowa State University Extension, Ames, IA. 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. Agric. Syst. 119, 44–57. Bakker, A., Bessembinder, J., de Wit, A.J.W., van den Hurk, B., Hoek, S., 2014. Exploring the efficiency of bias corrections of regional climate model output for the assessment of future crop yields in Europe. Reg. Environ. Change 14, 865–877.

338

Agricultural and Forest Meteorology 262 (2018) 322–339

J. Ingwersen et al.

Wöhling, T., Gayler, S., Priesack, E., Ingwersen, J., Wizemann, H.-, 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. WRB, 2006. WRB and Food and Agricultural Organization of the United Nations. World Reference Base for Soil Resources. – World Resources Reports 103. Rome, Italy. . Wu, X., Vuichard, N., Ciais, P., Viovy, N., De Noblet-Ducoudré, N., Wang, X., Magliulo, V., Wattenbach, M., Vitale, L., Di Tommasi, P., et al., 2016. ORCHIDEE-CROP (v0), a new process-based agro-land surface model: model description and evaluation over Europe. Geosci. Model Dev. 9, 857–873. 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 - Wagen. J. Life Sc. 51, 407–425. Yin, X., Struik, P.C., 2010. Modelling the crop: from system dynamics to systems biology. J. Exp. Bot. 61 (8), 2171–2183. https://doi.org/10.1093/jxb/erp375. Yin, X., van Laar, H.H., 2005. Crop Systems Dynamics – an Ecophysiological Simulation Model for Genotype-by-Environment Interactions. Wageningen Academic Publishers, Wageningen 155 p. Yin, X., Goudriaan, J., Lantinga, E.A., Vos, J., Spiertz, H.J., 2003. A flexible sigmoid function of determinate growth. Ann. Bot. 91, 361–371.

Evaluation: U.S. Geological Survey Techniques and Methods. pp. 6–A11. Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X.Y., Wang, W., Powers, J.G., 2008. A Description of the Advanced Research WRF Version 3. NCAR Tech Notes-475+STR. Statistisches Bundesamt, 2017. International Statistics. (Accessed 6 September 2017). https://www.destatis.de/EN/FactsFigures/CountriesRegions/InternationalStatistics. Streck, N.A., Weiss, A., Baenziger, P.S., 2003. A generalized vernalization response function for winter wheat. Agron. J. 95, 155–159. Tsarouchi, G.M., Buytaert, W., Mijic, A., 2014. Coupling a land-surface model with a crop growth model to improve flux estimations in the Upper Ganges basin, India. Hydrol. Earth Syst. Sci. 18, 4223–4238. Tsvetsinskaya, E.A., Mearns, L.O., Easterling, W.E., 2001. Investigating the effect of seasonal plant growth and development in three-dimensional atmospheric simulations. Part I: simulation of surface fluxes over the growing season. J. Clim. 14, 692–709. Van den Hoof, C., Hanert, E., Vidale, P.L., 2010. Simulating dynamic crop growth with an adapted land surface model - JULES-SUCROS: model development and validation. Agric. For. Meteorol. 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. 24, 39–59.

339