Connection Between Root Zone Soil Moisture and Surface Energy

Journal of Geophysical Research: Biogeosciences RESEARCH ARTICLE 10.1029/2016JG003753 Key Points: • Connection between root zone soil moisture and surface energy flux partitioning • Evaluation of two different-generation land surface models over the Falkenberg grassland site • Model biases in third-generation land surface models identified via data assimilation with joint state parameter update Correspondence to: P. Shrestha, [email protected]

Connection Between Root Zone Soil Moisture and Surface Energy Flux Partitioning Using Modeling, Observations, and Data Assimilation for a Temperate Grassland Site in Germany P. Shrestha1 , W. Kurtz2 , G. Vogel3, J.-P. Schulz3, M. Sulis4 S. Kollet5,6 , and C. Simmer1,6

, H.-J. Hendricks Franssen5,6

,

1

Meteorological Institute, University of Bonn, Bonn, Germany, 2Environmental Computing Group, Leibniz Supercomputing Centre, Munich, Germany, 3Deutscher Wetterdienst, Offenbach am Main, Germany, 4Environmental Research and Innovation, Luxembourg Institute of Science and Technology (LIST), Esch-sur-Alzette, Luxembourg, 5Institute of Bio- and Geosciences IBG-3 (Agrosphere), Forschungszentrum Jülich GmbH, Jülich, Germany, 6Centre for High-Performance Scientific Computing in Terrestrial Systems, Geoverbund ABC/J, Jülich, Germany

Abstract Land surface models (LSMs) with different degrees of complexity are in use as lower boundary Citation: Shrestha, P., Kurtz, W., Vogel, G., Schulz, J.-P., Sulis, M., Hendricks Franssen, H.-J., et al. (2018). Connection between root zone soil moisture and surface energy flux partitioning using modeling, observations, and data assimilation for a temperate grassland site in Germany. Journal of Geophysical Research: Biogeosciences, 123. https://doi.org/ 10.1029/2016JG003753 Received 22 DEC 2016 Accepted 12 AUG 2018 Accepted article online 23 AUG 2018 Author Contributions: Conceptualization: P. Shrestha Data curation: P. Shrestha, G. Vogel Formal analysis: P. Shrestha, W. Kurtz, G. Vogel Funding acquisition: S. Kollet, C. Simmer Investigation: P. Shrestha, W. Kurtz, G. Vogel, J.-P. Schulz, M. Sulis Methodology: P. Shrestha Resources: G. Vogel, J.-P. Schulz Supervision: H.-J. Hendricks Franssen, S. Kollet Validation: P. Shrestha Visualization: P. Shrestha, W. Kurtz Writing - original draft: P. Shrestha Writing – review & editing: P. Shrestha, M. Sulis, H.-J. Hendricks Franssen, S. Kollet, C. Simmer

©2018. The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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conditions for atmospheric models with the simpler LSMs preferentially used in numerical weather forecasting. This study evaluates the second-generation TERRA Multi-Layer and the third-generation Community Land Model (CLM) to better understand the connection between root zone soil moisture and surface energy fluxes, which is important for predictions. Both LSMs were compared in multiyear, observation-driven simulations at the Falkenberg grassland site (Germany), and their results were compared to observations. With their default settings for the site, both LSMs tend to overestimate the Bowen ratio, while CLM additionally exhibited a wet bias and a too low soil moisture variance. With modified photosynthetic parameters in CLM, the Bowen ratio improved considerably, but the soil moisture bias and its too low variance remained. Joint data assimilation with soil parameter update significantly improved the soil moisture variance but degraded the Bowen ratio. We could identify the default shallow root fraction distribution to be responsible for the overestimated Bowen ratio, which could be largely reduced by increasing the root fractions in deeper layers. This study demonstrates how observations and data assimilation with joint state-parameter updating can be used to improve the realism of third-generation LSMs and thus our understanding of the connection between root zone soil moisture and surface energy flux partitioning.

1. Introduction In the terrestrial system, turbulent motions arising from solar heating of the land surface strongly control the diurnal cycle of the atmospheric boundary layer (ABL; Parlange et al., 1995). In mesoscale atmospheric models, these turbulent motions are subgrid scale and accommodated in turbulence parameterizations or ABL schemes. These schemes interact via surface layer parameterizations with land surface models (LSMs), which provide the lower boundary conditions for atmospheric models. While the ABL schemes strongly control the shape of the wind, humidity, and temperature profiles in the ABL, the LSMs govern the diurnal cycle of ABL mean quantities (Shin & Hong, 2011) through surface energy flux partitioning, which in turn influences the timing and intensity of convection (Avissar & Liu, 1996; Betts et al., 1996; Chen & Avissar, 1994; Kang & Bryan, 2011; Sun & Barros, 2015; Yin et al., 2015), the development of mesoscale secondary circulations (Baidya & Avissar, 2002; Grossman et al., 2005; LeMone et al., 2002; Poll et al., 2017), and carbon and nitrogen feedbacks (Randerson et al., 2009; Thornton et al., 2007, 2009). The realism of the links between albedo, aerodynamic roughness, transpiration, and photosynthesis with soil moisture and soil temperature controls the quality of surface energy flux partitioning simulated by LSMs (Bonan et al., 2014; Chen et al., 2013; Katul et al., 2012; Lawrence et al., 2007). Since the first LSM was implemented into an atmospheric model by Manabe (1969), LSMs have evolved considerably in their ability to represent the spatiotemporal variability of plants, soils, and related biogeophysical and biogeochemical processes for the simulation of the exchange of energy, water, and carbon with the atmosphere (Pitman, 2003; Sellers et al., 1997). LSM evolution has been classified by Sellers et al. (1997) into three generations. In firstgeneration LSMs (e.g., Manabe, 1969), the latent heat flux is regulated through a moisture availability 1

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function in a bucket model for hydrology, while an aerodynamic resistance term regulates the transfer of heat and moisture from the land surface to the lowest atmospheric model level. Second-generation LSMs (e.g., Dickinson, 1984; Sellers et al., 1986) separate vegetation canopy and soil and introduce a leaf resistance/conductance linked to photosynthetic active radiation. These resistance formulations control the exchange fluxes directly from the soil and via the plants to the atmosphere. Third-generation models additionally include carbon fluxes linked with photosynthesis and stomatal conductance (e.g., Bonan, 1995; Sellers et al., 1996) and thus enable the simulation of land-atmosphere interactions under changing atmospheric CO2 concentrations. Third-generation LSMs are increasingly used in coupled models, which encompass the exchange processes between subsurface, surface, and the atmosphere in order to monitor and predict the past and future of the terrestrial system. One of the most complete examples is the Terrestrial Systems Modeling Platform (TerrSysMP; Shrestha et al., 2014), which was developed within the TR32 project (Simmer et al., 2014, Vereecken et al., 2016). In this modeling platform, the second-generation LSM TERRA Multi-Layer (TERRAML; Doms et al., 2011) operationally coupled to the regional atmospheric model COSMO (Consortium for Small-Scale Modeling; Baldauf et al., 2011; Steppeler et al., 2003) was replaced with the third-generation LSM, the NCAR Community Land Model (CLM; version 3.5; Oleson et al., 2004, 2008) and extended with the 3D distributed hydrological model ParFlow (Ashby & Falgout, 1996; Jones & Woodward, 2001; Kollet & Maxwell, 2006). Thus, TerrSysMP allows for the simulation of water and energy transports from the active groundwater layer to the atmosphere; a recent extension also links the carbon cycle between soil, vegetation, and the atmosphere (Uebel et al., 2017). In the above studies and also the current study, CLM is used with a priori specified plant phenology, that is, without the carbon-nitrogen module. Despite such developments, many operational weather forecast models still mostly rely on secondgeneration LSMs due to their lower degree of complexity and thus also easier inclusion in data assimilation (DA). Their lesser physical constraints allow for an easier (mis)use of, for example, soil moisture as a sink variable to compensate for other model structural errors when predicting near-surface temperature—however at the expense that soil moisture observations cannot be assimilated directly. The inclusion of thirdgeneration LSMs in numerical weather prediction (NWP) models inevitably changes the model response and sensitivity to the soil moisture content. The higher complexity in the parameterized biogeophysical processes in these LSMs also comes with many additional parameters; thus considerable work is required before the quality of the simulated flux partitioning, which is important for the ABL evolution, at least matches and hopefully exceeds the quality provided by the simpler model. Few studies did already evaluate the transition from TERRA-ML to CLM. Davin et al. (2011) used both LSMs coupled with COSMO for climate simulations over Europe with grid resolutions of 50 km. They attributed the improvement in cloud cover, surface temperature, and precipitation—when using CLM—to a better surface energy flux partitioning produced by the detailed representation of the canopy layer and other structural differences compared to TERRA-ML. The coupled climate runs are sensitive to the choice of LSM and produce different soil moisture-precipitation feedbacks. Akkermans et al. (2012) compared and validated both LSMs in offline mode over four tropical sites in Africa; they stress the importance of flux measurements also outside temperate and boreal climate zones including regions with sparse observations for model validation and parameter tuning. Both studies focused more on TERRA-ML deficiencies, which they compensated based on sensitivity studies with varying root depth, leaf area index, albedo, hydraulic conductivity, stomata resistance, or soil resistance. The studies by Davin et al. (2011) and Akkermans et al. (2012) showed that already with the default parameters, CLM resulted in better surface energy flux partitioning than TERRA-ML for all time scales (hourly, monthly, and annual cycle). However, other studies show that the increase in the complexity of parameterizations, and thus the number of model parameters, requires specific tuning and extensive evaluations for all compartments to account for nonlinear feedbacks that might compensate also model structural errors, when applying the model at different regions and at different scales (e.g., Bonan et al., 2011, 2012; Fu et al., 2016; Lawrence et al., 2011; Li et al., 2013; Oleson et al., 2008; Post et al., 2017; Sulis et al., 2015; Wang et al., 2016; Yan & Dickinson, 2014; Zheng & Wang, 2007, and many others). For example, Lawrence et al. (2011) linked the wet bias observed in CLM (which tends to increase the available root zone soil moisture) to the groundwater table model parameters controlling surface runoff and subsurface drainage. These

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parameters are generally adjusted via sensitivity analyses and comparisons with observed runoff. Sulis et al. (2015) showed the importance of adjusting plant photosynthetic parameters in CLM, which regulate the transpiration capacity of plants with prescribed plant phenology, to correctly simulate the surface energy flux partitioning for two agricultural crops. By implementing a dynamic rooting scheme in CLM-CN (CLM with carbon-nitrogen module), Wang et al. (2016) showed that more allocated root carbon in deeper layers led to a better simulation of latent heat fluxes and gross primary productivity (GPP) in the Amazon catchment. Thus, the realism of links between transpiration, photosynthesis, and available root zone soil moisture—the key processes controlling the quality of surface energy flux partitioning—still needs to be better understood for the transition from TERRA-ML to CLM and its use for operational weather forecasting (e.g., http://www.hpsc-terrsys.de). In this study, we build upon the earlier comparison studies between TERRA-ML and CLM and analyze the connection between available root zone soil moisture and the surface energy flux partitioning based on multiyear observations of meteorological variables, soil states, and surface energy fluxes at a temperate grassland site in Germany. Both LSMs are run in offline mode and use observed atmospheric forcing from in situ measurements of meteorological variables and a prescribed phenology. Thus, we analyze model biases originating from the feedbacks between the physical processes represented within the LSMs. We mainly address and correct remaining deficiencies in CLM (version 3.5 used in TerrSysMP) settings and make use of DA of soil moisture with joint state-parameter update. This leads to an improved simulation of the seasonal cycle of soil states, which also improves our understanding of the feedback processes between root zone soil moisture and surface energy flux partitioning. Further, we also make model comparison with the newly released CLM (version 5.0, hereafter referred as CLM5.0) to test whether it also exhibits similar deficiencies as in CLM (version 3.5) or most of the issues are resolved.

2. Model Description TERRA-ML (Doms et al., 2011; Grasselt et al., 2008; Schulz et al., 2016) is a second-generation LSM and also the default LSM for COSMO and its successor ICON (Zaengl et al., 2015), when run in numerical weather prediction mode. TERRA-ML simulates the energy and water balance at the land surface and in the shallow subsurface along with the soil states using the atmospheric forcing data as upper boundary condition. All processes in TERRA-ML are modeled only in the vertical direction (1D) with no lateral interactions between adjacent soil columns, which also holds for the third-generation NCAR CLM (version 3.5; Oleson et al., 2004, 2008). In addition to energy and water balance including soil states, CLM also directly simulates the carbon fluxes and vegetation states. 2.1. Differences Between TERRA-ML and CLM Key differences in the representation of soil-vegetation structure and the simulated biogeophysical processes between TERRA-ML and CLM (version 3.5) are presented in the following; we refer to Davin et al. (2011) and Akkermans et al. (2012) for additional discussions. 2.1.1. Soil-Vegetation Structure The surface heterogeneity within a grid cell in TERRA-ML is specified by the percentage of the land surface covered with plants, needle leaf, and deciduous trees. An explicit canopy layer is not considered. The hydrologic and thermal properties of the soil column including dry and wet albedo are specified using a lookup table for 10 possible soil types. Plant phenology is prescribed by vegetation fraction, evergreen and deciduous tree fractions, minimum and maximum LAI, a fixed roughness height, and a fixed root depth. TERRAML resolves the soil column with eight vertically stretched layers with lower boundaries at 0.01, 0.03, 0.09, 0.27, 0.81, 2.43, 7.29, and 21.87 m, which is also used in this study. The whole soil column belongs to one soil type. In CLM, surface heterogeneity can be represented using a subgrid tile approach at different hierarchy levels with five land units, multiple soil columns, and up to 17 plant functional types (PFTs), which includes bare soil (no vegetation). An explicit canopy layer represents the PFTs with specific photosynthetic, optical, aerodynamic, and vertical root distribution parameters. The hydrological and thermal properties of the vertical soil column are described by pedotransfer functions based on the profiles of sand and clay percentages. The surface soil color determines its dry and saturated albedo. For CLM without the carbon-nitrogen module, plant phenology is prescribed by the fractional cover of the PFTs in the vegetated portion along with monthly fixed SHRESTHA ET AL.

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leaf area index, stem area index, and canopy height. The latter is used to estimate the roughness height for momentum, which thus varies with season. CLM resolves the soil column into 10 vertically stretched layers with lower boundaries at 0.017, 0.045, 0.09, 0.16, 0.29, 0.49, 0.82, 1.38, 2.29, and 3.43 m, which is also used in this study. 2.1.2. Soil Temperature and Moisture TERRA-ML does not comprise an explicit canopy and assumes that the moisture flux between vegetation and the canopy air equals the flux between the air inside and above the canopy and that the vegetation temperature equals the temperature of the uppermost soil layer (Doms et al., 2011; Schulz et al., 2016). Soil temperature is calculated by the heat conduction equation with the lower boundary condition set to the climatological annual mean near-surface temperature. The temperature at the interface between surface and atmosphere (land surface temperature) is computed from the net radiation at the land surface and the sensible and latent heat fluxes via the surface energy balance equation. The resulting ground heat flux constitutes the upper boundary condition of the soil heat conduction equation. Soil water content is computed using the Richards equation (e.g., Hillel, 1980), with infiltration at the top soil layer as the upper boundary condition and gravitational drainage as the bottom boundary condition. The CLM vegetation/canopy temperature above the soil column is computed with the assumption that the air within the canopy has negligible capacity to store heat. The surface temperature of bare soil is taken as the temperature of the uppermost soil layer. The soil temperature is calculated via the heat conduction equation with the ground heat flux from the surface energy balance equation at the upper soil boundary and assuming zero heat flux at the bottom. Soil moisture is computed from the Richards equation with the infiltration flux after interception as top boundary condition and the exchange with an unconfined aquifer as bottom boundary condition using a simplified groundwater model (Niu et al., 2007). 2.1.3. Surface Fluxes The surface fluxes in TERRA-ML are computed using the Dyer-Businger relations (Businger, 1973) in their integrated form modified by Louis (1979). The BATS scheme (Dickinson, 1984) is used for computing the bare soil evaporation and canopy transpiration. The stomatal conductance model for canopy transpiration is based on the Jarvis-Stewart approach (Jarvis, 1976; Stewart, 1988) with empirical functions, which parameterize the influence of photosynthetically active radiation, soil water content, ambient specific humidity, and ambient temperature on stomatal resistance. The vegetation fraction determines the contributions of bare soil and plants to total evapotranspiration and moisture flux; carbon fluxes are not simulated. CLM computes the surface fluxes as the sum of vegetation and ground fluxes using the Monin-Obukhov Similarity Theory and thus depends on vegetation temperature, ground temperature in addition to surface temperature, and specific humidity (Oleson et al., 2004). CLM uses the coupled stomatal conductance and photosynthesis model following Collatz et al. (1991), which results in a nonlinear dependence of photosynthesis and transpiration on solar radiation via the stomata response to light. Hence, carbon fluxes are also simulated along with water and energy fluxes. CLM optionally includes dynamic vegetation and carbon/nitrogen cycles; this option is not invoked for this study, and a prescribed phenology is used instead. 2.1.4. Radiation Fluxes Radiation fluxes at the surface in TERRA-ML depend on grid cell albedo and ground temperature, while CLM explicitly solves for canopy radiative transfer using the two-stream approximation, which depends on the PFT optical parameters, phenology, and the presence of snow on the canopy (Oleson et al., 2004). Net radiation at the surface is the sum of net solar and longwave fluxes from vegetation and ground. 2.1.5. Bare Soil Evaporation Bare soil evaporation is parameterized in TERRA-ML via the BATS scheme (Dickinson, 1984) and estimated as the minimum between potential and maximum bare soil evaporation rate; soils much wetter than field capacity evaporate at the potential rate. The potential evaporation rate is computed using the aerodynamic resistance and the humidity gradient between ground and atmosphere, while the maximum bare soil evaporation depends on the average soil diffusivity, soil texture, relative saturation, and the depths of the surface soil layer and the active soil layer (Doms et al., 2011). In CLM, the latent heat flux from the ground over vegetated surfaces is computed using the aerodynamic resistance and the humidity gradient between ground and canopy. The specific humidity of the soil surface is computed by scaling the saturation-specific humidity with a factor αsoil, which is a weighted combination of values for snow (if existent) and soil. αsoil is computed as a function of the surface soil water matric SHRESTHA ET AL.

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potential and the ground temperature. To reduce excessive soil evaporation, an additional soil resistance term Rsoil is included based on the work by Sellers et al. (1992). For no-snow conditions, it is estimated via Rsoil ¼ e8:2064:255Sw

(1)

with Sw the relative saturation of the top soil layer. Hence, the resistance decreases exponentially with the increase in relative saturation of the top soil layer. 2.1.6. Root Water Uptake In TERRA-ML, the water removed from the soil layer i by transpiration via the roots ei depends on total tran spiration rate T tv , root zone integrated liquid water content (ηroot Þ, and the root depth rd: ei ¼

Δzroot;i ηi t T r d ηroot v

(2)

where Δzroot, i is the depth of layer i occupied by roots and ηi the water content of that layer. The root zone integrated liquid water content is estimated as ∑r d;i ηi ∑r d;i

ηroot ¼

(3)

where rd, i is the assumed root density profile, which can be either uniform or exponential. The latter is used in this study. The soil moisture limiting function (Fwat) affecting the stomatal resistance is formulated as F wat ¼

ηroot ηpwp ηtlp ηpwp

(4)

where ηpwp is the plant wilting point, which depends on soil texture, and ηtlp is the leaf turgor loss point. In CLM, the water removed by transpiration in a soil layer i is a function of total transpiration E tv and the effective root fraction (ei) of that layer: ei ¼ r e;i E tv

(5)

The effective root fraction for each layer is estimated as r e;i

¼

ri wi βtran

(6)

where ri is a PFT-dependent parameter for the root fraction in each layer, wi the plant wilting factor, and βtran the soil moisture limiting function. The plant wilting factor or soil water availability is assumed to be linearly proportional to the soil water matric potential (ψ i): wi

¼

ψ i ψ close ψ open ψ close

(7)

where the wilting point potential (ψ close) at which stomata fully closes and the potential at which the stomata is fully open (ψ open) are PFT-dependent parameters. Earlier studies with CLM by Li et al. (2013) and Jing et al. (2014) modified the soil water availability from a linear to an exponential function or replaced it with other root water uptake functions; they also used different root fractions to better simulate the partitioning of fluxes in arid ecosystems. The soil moisture limiting function is then computed as βtran ¼ ∑r i w i i

(8)

which ranges from 1 to near zero (wet to dry soil) and is used for the computation of the leaf-scale maximum carboxylation of Rubisco to simulate the effect of soil moisture stress on plant transpiration and photosynthesis.

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2.1.7. Stomatal Resistance Canopy transpiration in TERRA-ML is based on the BATS scheme (Dickinson, 1984), assuming the foliage temperature to be equal to the surface temperature; the flux between foliage and air inside the canopy equals the flux between the air inside and above the canopy (Doms et al., 2011). The functional form of the stomatal resistance (rs) includes the feedback with root water uptake via the soil moisture limiting function (Fwat) besides other limiting functions, which range from 0 to 1: 1 1 1 r 1 s ¼ r max þ r max þ r min ½F wat F rad F tem F hum

(9)

where rmax and rmin are fixed maximum and minimum stomatal resistances for vegetation cover. The F functions, Fwat, Frad, Ftem, and Fhum, describe the influence of soil moisture, radiation, temperature, and humidity on the stomatal resistance. In CLM, stomatal resistance is coupled to leaf photosynthesis, which is based on the models of Farquhar et al. (1980) and Collatz et al. (1991): A es r 1 Patm þ b s ¼ m c s ei

(10)

where m is a plant-specific empirical parameter, A is the leaf photosynthesis, cs is the CO2 concentration at leaf surface, cs is the atmospheric pressure, es is the vapor pressure at the leaf surface, ei is the saturation vapor pressure inside the leaf at vegetation temperature, and b is the minimum stomatal conductance. Leaf photosysnthesis is computed as A = min (wc, wj, we), where wc, wj, and we are respectively the Rubiscolimited rate of carboxylation, the light-limited rate of carboxylation, and the export-limited rate of carboxylation; they all depend on the maximum carboxylation rate (Vcmax), which varies between the different PFTs and depends on vegetation temperature and the nitrogen and soil moisture limiting functions, and thereby links root water uptake with stomatal resistance via

V cmax

T v 25 ¼ V cmax25 ðαvmax Þ 10 f ðT v Þβtran f ðNÞ

(11)

where f(N) is the PFT-dependent scale factor for nitrogen limitation derived from simulations with CLM coupled to the carbon/nitrogen cycle, βtran is again the soil moisture limiting function, f(Tv) mimics the thermal breakdown of metabolic process, Tv is the vegetation temperature, αvmax is the Q10 parameter, and Vcmax25 is the PFT-dependent maximum rate of carboxylation at 25°C. Vcmax25 is estimated as V cmax25 ¼ Na F lnr F nr act

(12)

where act is the specific activity of Rubisco at 25°C, Fnr is the ratio of molecular mass to nitrogen content of Rubisco, Flnr is the fraction of leaf nitrogen in Rubisco, and Na is the leaf nitrogen content per area. Na = 1/ (leafC : N * SLA) is estimated from the leaf carbon to nitrogen ratio (leafC : N) and specific leaf area at the top of canopy (SLA). Thus, Vcmax (equation (11)) depends besides on the variables Tv and βtran on timeinvariant and PFT-specific parameters.

3. Experiment Setup We set up offline simulations for both LSMs over the Falkenberg grassland site of the Meteorological Observatory Lindenberg—Richard-Aßmann Observatory (MOL-RAO), Germany, which is maintained by DWD (Deutscher Wetterdienst). Both LSMs were integrated at hourly time steps from 2008 to 2014 starting with spun-up initial conditions and forced with hourly atmospheric observations. All analyses are based on hourly averaged output. In the following section, we briefly describe the measurement site, available data, and the LSM setup in terms of vegetation and soil texture. 3.1. Falkenberg Grassland Site The site is located in a rural landscape 65 km southeast of the center of Berlin (52.17°N, 14.12°E, 73 m a.s.l.) and maintains long-term measurements of surface/subsurface and atmospheric states. Land cover varies

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from grassland (at 100 m spatial extent) to grassland/cropland (at 500 m spatial extent), and the soil texture is dominated by sandy soils. About 35 kg/ha of urea pellets (46% of nitrogen) is applied to the site once per year, which is mowed regularly up to six times per year in order to keep the mean vegetation height below 20 cm; this results in a roughness height (z0) of 0.01 m and below. For grasslands in Germany, Socher et al. (2012) showed that the intensity of mowing is strongly correlated with the intensity of fertilization, which enhances biomass and grass cover. The moderate mid-latitude climate is influenced by both marine and continental weather systems. The monthly average temperature for the study period (2008–2014) varies between 0.7 (January) and 19.5°C (July). The monthly average incoming shortwave radiation varies between 22 (December) and 239 W/m2 (June) and the incoming longwave radiation between 280 (February) and 361 W/m2 (July), respectively. The average annual precipitation was 584 mm for that period. The average annual total evapotranspiration (ground evaporation + evapotranspiration from plants, ET) is about 400 mm; thus, 68% of the precipitation is recycled by ET. More details about the site, instrumentation, and effects of surface heterogeneity on area-averaged ET from observations and models can be found in Neisser et al. (2002), Beyrich et al. (2002), Stiller et al. (2005), Mengelkamp et al. (2006), Ament and Simmer (2006), Beyrich and Mengelkamp (2006), and also at http://www.dwd.de/mol. 3.2. Data Gaps and Energy Balance Closure Data gaps are inevitable in measurements. For the 7 years of flux measurements from the Falkenberg grassland site, we analyzed the frequency of missing hourly fluxes per year. Observations for net shortwave (NSW) radiation were missing between 5.0% and 7.4% (mean 5.9%), for net longwave (NLW) radiation between 4.8% and 6% (mean 5.3%), and for ground heat flux (G) between 3.2% and 32.0% (mean 19.2%). For the year 2013, sensible and latent heat fluxes were missing from September to December. For the other years, observations of sensible heat flux (H) were missing between 8.3% and 14.1% (mean 10.8%) and for latent heat flux (LE) between 24% and 33.2% (mean 28.7%). Most of the missing observations occurred during night and the early morning hours, which accounted for 64%, 77%, 78%, and 84% of missing measurements of LE, H, NSW, and NLW, respectively. Missing observations of G were equally distributed throughout the day. Different methodologies of gap filling exist, which are useful for the estimation of annual sums, but for comparison with numerical models, the data filling also requires the conservation of the response to meteorological drivers, which has its own degree of uncertainty (Falge et al., 2001). In this study, we use the observed data solely for model comparison, and the data gaps in the surface fluxes (dominant during nighttime) are assigned with missing values. We tested the data quality of the measured fluxes via the surface energy balance (Wilson et al., 2002) for hourly observations with nonmissing values for all fluxes and each year in terms of (1) ordinary least squares regression between (H + LE) and (NSW + NLW – G S); (2) energy balance ratio (EBR); and (3) the energy imbalance percentage (δ). The available hourly data for annual energy closure was on average 61% (between 37% and 69% for the 7-year period). The mean EBR was 0.80 (between 0.76 and 0.86 for individual years), and the mean energy imbalance was 26% (between 20% and 29%). The slopes from regression analysis varied between 0.67 and 0.76 (average 0.70) and the intercepts between 5.46 and 9.46 W/m2 (mean 7.65 W/m2). For FLUXNET sites, Wilson et al. (2002) found the mean EBR (annual ratio based on 50 years for different sites) to be 0.84 (between 0.34 and 1.69) and slopes from regression analysis between 0.53 and 0.99 (mean 0.79). The intercept ranged from 32.9 to 36.9 W/m2 (mean 3.7 W/m2). Thus, the data quality for the Falkenberg grassland site compares well to the average quality of the FLUXNET data. The lack of closure (between 20% and 29% for this site) could be due to an underestimation of the eddycovariance flux measurements related to secondary circulations induced by land surface heterogeneities as suggested by Foken et al. (2010), but also many other reasons may be responsible (e.g., Twine et al., 2000). Here we acknowledge that it is still debated how to partition the missing energy into the turbulent fluxes. Many studies use either the Bowen ratio (BR) method (Barr et al., 1994; Blanken et al., 1997; Ingwersen et al., 2015; Twine et al., 2000), the LE method, or the H method, or just use uncorrected fluxes to compare with model data (Ingwersen et al., 2015). Here we make use of a postclosure methods uncertainty band (PUB) to evaluate the LSMs (as suggested by Ingwersen et al., 2015). The difference between the corrected flux and the uncorrected observed fluxes forms the PUB. The lower bound of the PUB is formed by the observed fluxes (OBS). The upper bound is formed by the H method (OBS-H) and the LE method (OBSLE) for H and LE, respectively. In the H method, the adjusted LE fluxes are identical to the raw fluxes. SHRESTHA ET AL.

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Figure 1. (a) Model setup for Community Land Model (CLM) soil columns showing the vertical discretization, root fraction (red line), and canopy layer on top (upright green lines). The soil column is divided into four zones with different hydraulic conductivities (ϕ~porosity; ksat~hydraulic conductivity). (b) The monthly mean observed grass height for the Falkenberg site for different years.

Similarly, in the LE method, the H fluxes are identical to raw fluxes. Also, fluxes corrected using the Bowen ratio (OBS-BR) method are shown when applicable. For the Bowen ratio method, using either hourly, three-hourly, six-hourly, or 12-hourly BR for correction did not significantly change the monthly averaged diurnal cycle of fluxes, so the hourly BR is used to correct the fluxes. 3.3. Soil-Vegetation Structure in the LSMs The soil and vegetation parameters used for TERRA-ML in this study are based on earlier experiments over this grassland site (Vogel et al., 2015). Accordingly, a sandy soil type was selected. Since TERRA-ML does not account for vertical soil heterogeneity, the soil thermal and hydraulic parameters are constant for all layers. For vegetation, the plant cover fraction was varied between 0.55 and 0.80 over the applied years. The root depth was set to 60 cm and a fixed roughness height for momentum (z0 = 0.03 m) was used for the entire simulation. The leaf area index (LAI) varied approximately from 0.5 in winter to 2.5 in summer. For the CLM site-optimized model, parameters do not exist yet. For the soil column, the sand and clay percentage (74% and 26%, respectively) including the saturated hydraulic conductivities were based on site observations (Figure 1a). For vegetation, we used the c3 nonarctic grass as the most appropriate PFT with its default parameters, including the root fraction profile (Zeng, 2001). A representative vegetation top height based on the 2008 monthly mean grassland height was used for all years, given the similarity between the different years (Figure 1b). The LAI varied approximately from 0.5 in winter to 2.5 in summer. The effect of grass mowing on LAI was not detectable and thus not included in this study. 3.4. CLM5.0 Setup The CLM5.0 compset I1Pt-CLM50SpGs was used for the offline simulation. In this compset, the Van Genuchten parameterization (Van Genuchten, 1980) is used for soil hydrology. The bed rock started at 28.29 m (based on the CLM global database), and the zero flux lower (bottom) boundary condition was used. For soil evaporation, the lee-pielke beta factor (Lee & Pielke, 1992) applicable to LSMs with a shallow top layer (~ 2 cm) was used to scale the potential soil evaporation to obtain the actual soil evaporation. For stomatal conductance, the unified stomatal model of Medlyn et al. (2011) was used along with light inhibition of the leaf maintenance respiration rate and the rooting profile derived from Jackson et al. (1996). The soil column is

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Figure 2. Annual variation of monthly averaged mean daytime Bowen ratio (BR) at the Falkenberg grassland site in Germany. The daytime BR covers the time interval 0600–1700 LT. For Community Land Model (CLM; version 3.5), the results with the default leafC : N = 25 g C/g N are shown, while the gray envelope indicates the range spanned by six runs with leafC : N of 10, 15, 20, 25, 30, and 35 g C/g N. The inset shows the same values for April to October as a scatter diagram of the simulated values (ordinate) against the observations (abscissa) and the modeled BR regression with observations. The regression analysis gives for CLM [y = 0.72(x 0.34) + 0.72], for TERRA-ML [y = 3.18(x 0.34) + 0.90]), and for CLM5.0 [y = 0.74(x 0.34) + 0.80].

discretized into 25 layers with linearly increasing soil depths. The soil and vegetation input parameters were kept similar to the settings used for the CLM (version 3.5) simulations.

4. Model Evaluation and Comparison Multiyear observations of energy, water, and carbon fluxes along with soil states are crucial for evaluating LSMs and understanding the simulated physical processes. In this section, we first evaluate and compare the fluxes and soil states simulated by TERRA-ML and CLM (version 3.5) using their hourly averaged output data for multiple years. 4.1. Partitioning of Sensible and Latent Heat Flux We first compare the monthly means of the mean daytime (0600–1700 LT) observed and simulated Bowen H Ratio BR ¼ LE for all years (Figure 2). The observed BR for the site ranges from 0.15 to 0.79 between April and October and 0.17 to 0.73 for June to August, which is lower than the range reported by Wilson et al. (2002) for grassland site observations (BR = 0.34 to 1.91) from 27 FLUXNET sites for June to August, over 66 years. Compared to the observations for April to October, TERRA simulates slightly higher net radiation

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Figure 3. Taylor diagrams for daytime (0600–1700 LT) hourly sensible heat flux (H), latent heat flux (LE), soil temperature (Tsoil), and volumetric soil moisture (θvol) at the Falkenberg grassland site in Germany for April to October for the years 2008 to 2014. The gray and blue markers in the H and LE diagrams refer to the observed fluxes corrected for energy balance closure (OBS-H/OBS-LE and OBS-BR, respectively). Soil temperature and moisture values are at 6-cm depth. For Community Land Model (CLM; version 3.5), the fluxes and soil states are examined for the default leafC : N = 25 g C/g N. The centered RMSD between the simulated and observed time series is proportional to the distance to the point on the x axis identified as “REF.”

(6 ± 4 W/m2) in (mean ± 1 s. d.) than observed, while CLM simulates lower net radiation (22 ± 6 W/m2) than observed. Both LSMs simulate overall a higher BR during these months for all years (Figure 2); CLM has an almost constant bias, while TERRA-ML exhibits a higher variance with sometimes also lower values and isolated positive peaks for some months. This is also clearly visible from the modeled BR (y) regression with observation (x) for the same months. 4.2. Surface Fluxes and Soil States The model performance in terms of sensible heat flux (H), latent heat flux (LE), soil temperature (Tsoil), and soil moisture (θvol) is illustrated with Taylor diagrams (Figure 3, Taylor, 2001), which summarize how well the simulated yearly time series match the observations in terms of correlation (r), root mean squared difference (RMSD), and standardized deviation (σ m/σ o). The uncertainty in observed fluxes due to the missing energy balance closure in the observations is addressed by showing model performance with respect to uncorrected and corrected observations. CLM consistently simulates higher correlations for sensible heat flux for all years (0.84 < r < 0.92) compared to TERRA-ML (0.76 < r < 0.82). But both CLM and TERRA-ML have higher (>1.0) SHRESTHA ET AL.

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standardized deviations (1.30 < σ m/σ o < 1.58) and (2.20 < σ m/σ o < 2.50)], respectively. The larger amplitude in standardized deviations and RMSD for simulated H in TERRA-ML compared to CLM is consistent with the peaks in the monthly averaged BR for all years (see Figure 2). LE simulated by CLM again better performs in terms of correlation (0.93 < r < 0.95) compared to TERRA-ML (0.79 < r < 0.86), while the standardized deviation is better captured by TERRA-ML (0.93 < σ m/σ o < 1.21) and underestimated by CLM (0.79 < σ m/σ o < 0.87). According to the Taylor diagrams, both models overestimate BR for different reasons: TERRA-ML simulates much higher H, and CLM simulates higher H and lower LE. For both models, the use of corrected surface fluxes as reference reduces the correlation and increases RMSD. In terms of standardized deviation, the sensible heat flux simulated by CLM improves with reference to the H and BR corrected fluxes compared to uncorrected fluxes, while the latent heat flux is further underestimated. For TERRA-ML, the standardized deviation improves significantly with flux corrected using the H method only, which is however still overestimated and also degrades the simulated latent heat flux. The simulated Tsoil at 6 cm highly correlates with the observations for both models; CLM performs better than TERRA-ML. Correlation and standardized deviation are 0.98 < r < 0.99, 1.11 < σ m/σ o < 1.20, and 0.93 < r < 0.96, 1.11 < σ m/σ o < 1.30,for CLM and TERRA-ML, respectively. For θvol at 6 cm depth, both models have lower standardized deviations ( 0.22 m3/m3) as expected, while the BR gradually increases for the default root distribution (OL (DA_1)) when 〈θ〉 drops below 0.19 m3/m3. A detailed examination of the days with BR higher than observed values (Figure 9b) shows that not only 〈θ〉 controls the BR, but the nonlinear feedbacks due to available soil moisture for ground evaporation can also affect the stomatal resistance and photosynthesis, and hence LE. For example, the decrease in the upper-layer soil moisture θvol, 1 during this period increases the resistance Rsoil (see equation (1)) and reduces αsoil, which lowers ground evaporation and increases daytime ground temperature, which leads to higher canopy air temperatures and a decrease in canopy air specific humidity. The latter increases stomatal resistance during midday and reduces photosynthesis and transpiration. This results in a higher daytime BR, even though the root zone soil moisture is not the limiting factor. This behavior is only observed for experiments with a shallow root distribution (OL (DA_1) and SMT (DA_1)). For the 24-day dry period in July, the experiments with the deeper root distribution have more water available for near-surface ground evaporation because the root water uptake from this layer is reduced. This inhibits the drying out of top soil layer during long recession period as observed over the grassland site. The simulations with different leafC : N values demonstrate the regulation of stomatal resistance, transpiration, LE, BR, and also the water demand by plant photosynthesis (see the time evolution of 〈θ〉 and βtran in Figure 10a). Although these runs are with the default root distribution and soil texture and without the assimilation of soil moisture, they allow to analyze the feedback between photosynthesis-regulated transpiration and the soil moisture limiting function (βtran). During dry periods, higher leaf photosynthesis lowers stomatal resistance and increases root water uptake and intensifies drying. The soil moisture limiting function βtran also decreases with a steeper slope. Different photosynthesis levels induced by varying leafC : N result in a phase lag of βtran evolution. Thus, higher root water uptake may trigger an earlier response of plants to water stress. The response of surface energy flux partitioning to leaf photosynthesis (Figure 10b) is similar to the curve for OL (DA_1; Figure 9a). If leafC:N is higher, root zone soil moisture is higher due to the decreasing root water uptake. Again even for 0.14 < 〈θ〉 > 0.22 m3/m3, BR can be large because of the nonlinear feedbacks between available soil moisture for ground evaporation and plant transpiration as discussed above (see Figure 9b). This nonlinear feedback increases stomatal resistance during midday, which reduces LE and increases H. Overall, the results shown in Figure 10 underline the importance of tuning the photosynthetic parameters to correctly model the photosynthesis and transpiration demand.

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Figure 10. (a) Time series of simulated soil moisture limiting function (dashed lines) and the weighted root zone volumetric soil moisture (solid lines) for different values of the photosynthetic parameter (leafC : N) for July and August 2013. (b) Relationship between weighted root zone volumetric soil moisture and daily averaged Bowen ratio (BR) for July and August 2013.

9. Discussion The observed monthly averaged BR from April to October ranged from 0.15 to 0.79 for this temperate grassland site in Germany. Both LSMs in their default settings simulate higher BR for different reasons: TERRA-ML simulates a much higher H and CLM simulates a higher H and a lower LE. In the default configurations for the site, TERRA-ML better captures the variance of LE and soil moisture at 6 cm, while CLM better reproduces the correlation between soil states and fluxes. The BR bias for CLM could be corrected by increasing the maximum rate of carboxylation Vcmax, which was achieved with a lower value of leafC : N than the default for a c3 nonarctic grassland PFT. This adjustment could however also compensate for other nonoptimal PFT settings for, for example, SLA, f(N), and Flnr, which also impact Vcmax. These parameters together influence photosynthesis and transpiration (e.g., Bonan et al., 2011, 2012, 2014; Chen et al., 2013; Post et al., 2017; Stöckli et al., 2008; Sulis et al., 2015; White et al., 2000). White et al. (2000) showed that leafC : N controls net primary productivity for all biomes. Stöckli et al. (2008) showed that f(N) is important to correctly partition turbulent fluxes in environments with high GPP. Bonan et al. (2011, 2012) pointed out that the relationship of Vcmax with Na controls the effect of nitrogen availability on GPP, but the prescribed time-invariant Vcmax25 precludes a fundamental feedback between GPP and nitrogen availability. In fact, Na, which depends on SLA and leafC : N, determines the nitrogen required to construct leaves (LAI), the amount of nitrogen available for investments in the photosynthetic machinery (also including Flnr), and leaf respiration rates (White et al., 2000). Recently, Ghimire et al. (2016) implemented a prognostic Na (based on carbon allocation and a flexible leafC : N) allowing Vcmax25 to vary, which allows for feedback between GPP and nitrogen availability in the CLM with the CN module, as pointed out by Bonan et al. (2011). For simulations with fixed plant phenology (LAI), the effect of this feedback on the surface flux partitioning has yet to be examined. While an accurate specification of LAI and other processes like irrigation are also imporatnt for improving surface energy fluxes for agricultural and grassland sites (e.g., Lu & Kueppers, 2012), our results show that the photosynthetic parameters create additional uncertainties, which challenge the use of third-generation models for operational weather forecasting. However, the availability of CO2, heat energy, and water flux observations are essential to evaluate and tune these parameters. The modified photosynthetic parameter leafC : N improved the simulated GPP and surface energy flux partitioning considerably in CLM. Thus, the increased LE also led to a slight improvement (reduction) in the simulated soil moisture via a higher root water uptake, but its variance was still underestimated indicating a wet bias in the model. The observations indicated that the grass at the site maintained transpiration during dry periods even when the upper soil layer dried out. For LSMs, an accurate simulation of the root zone soil moisture (〈θ〉) is thus very important for transpiration and consequently LE and BR. This could be achieved by either “bringing the water to the roots” or “bringing the roots to water,” while the latter is better supported by observations (Akkermans et al., 2012). The initial simulations of CLM with the wet bias brought the water to the shallow roots during dry

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periods. The initial assimilation of the observed (dry) soil moisture cut this connection and even further reduced transpiration. This could only be remedied by an update of soil texture (to observed characteristics) and a modification of the root distribution (deeper roots) so that plants could extract water from deeper layers during dry periods, which underlines the importance of the root distribution and root water uptake parameterization along with soil texture for surface energy flux partitioning (Feddes et al., 2001; Jackson et al., 2000; Schenk & Jackson, 2002). Of course, the shift of the root distribution toward deeper levels could also compensate other model structural errors like a missing root water uptake parameterizations that simulate hydraulic lift by roots (HR; e.g., Fu et al., 2016; Li et al., 2012; Yan & Dickinson, 2014; Zheng & Wang, 2007). However, Katul et al. (2012) indicate that the HR effectiveness as a mechanism to cope with water stress is also controlled by root distribution and soil type, while Neumann and Cardon (2012) suggest that the magnitude of HR is low for sandy soils as found for the Falkenberg site. The deeper roots also reduced the extraction of water from the uppermost soil layer, making more water available for ground evaporation, which leads to cooler ground temperatures during the dry periods. This mechanism could explain why the surface energy flux partitioning was improved with respect to the observations, when deeper rooting was included. Shortcomings in the representation of root distributions in LSMs are well known and often accounted for, but weather forecast models still use constant root profiles due to the lack of adequate measurements, and probably their effect is also masked by other model structural errors. We used the CLM (version 3.5) in this study while newer versions (up to 5.0) have been developed. Although CLM5.0 was found to better capture the standardized deviations of latent heat flux and soil moisture compared to CLM (version 3.5 used in TerrSysMP), the wet bias for the shallow layers persisted. Thus, the results found here will be largely applicable to CLM5.0 as well.

10. Conclusions The vertical root distribution strongly controls the competition between root water uptake and ground evaporation from the upper soil layer, which was found to indirectly control stomatal conductance via soil temperature and thus surface energy flux partitioning. We showed that a third-generation LSM like CLM is able to consistently simulate better surface energy flux partitioning than a second-generation LSM (here TERRA-ML) and contributes to a better process understanding. Observations and DA with joint state-parameter updates are crucial to identify model structural errors and uncertainties in estimating the physical parameters for the soil-vegetation structure (e.g., photosynthetic parameters, root distribution, and soil texture) in such LSMs. These parameters can potentially be included—along with soil texture values—in the state-parameter vector ψ for the EnKF (see Appendix A). This joint state-parameter update may be more important during periods of recession, when surface energy flux partitioning exhibits a higher sensitivity to changes in root zone soil moisture. As LSM parameters tend to be tuned with the help of eddy covariance sites measuring carbon, water, and energy fluxes, it is important that also soil moisture profiles are observed, including plant properties and states like rooting depth, root distribution, leaf area index, and photosynthetic parameters. The joint collection of this information at a large number of eddy covariance sites will lead to further model improvements that better capture the link between root zone soil moisture and surface energy flux partitioning.

Appendix A We use TerrSysMP-PDAF (Kurtz et al., 2016) to assimilate available soil moisture observations in CLM (version 3.5) at three soil depths (6, 18, and 54 cm). An ensemble with 256 members is generated by perturbing precipitation with multiplicative noise drawn from a uniform distribution U(0.5, 0.5) and a vertically homogeneous profile of perturbed sand and clay contents with additive noise drawn from a uniform distribution U(10.0, 10.0). The assimilation study was carried out for the year 2013, where Net Ecosystem Exchange (NEE) measurements were also available for validation. The ensemble members were initialized using spun-up initial conditions from the year 2012. The soil moisture observation error was set as 0.02 m3/m3 for all experiments, and soil moisture data were assimilated at 1-day intervals with the ensemble Kalman filter (Evensen, 1994). The joint state-parameter update of soil moisture and soil texture in CLM with the EnKF can be summarized as follows:

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The predicted soil moisture values at time t for realization i are derived from the dynamic model M using the forcing data q, which are in part uncertain, and the model parameters p, which are also in part uncertain: θtvol;i ðt Þ ¼ M θt1 vol;i ; pi ; qi

(A1)

The model M is CLM in our case, and the model parameters p include the uncertain soil texture (besides many vegetation parameters, for example), and the model forcings q include the uncertain precipitation (other model forcings are deterministic). The state-parameter vector ψ as input for the EnKF is formed by appending the soil texture values (sand and clay percentage) of CLM to the predicted soil moisture vector θvol: 0

θvol;i

1

B C C ψi ¼ B @ %Sandi A

(A2)

%Clayi

Acknowledgments This study was conducted with support from SFB/TR32 (www.tr32.de) “Patterns in Soil-Vegetation-Atmosphere Systems: Monitoring, Modeling, and Data-Assimilation” funded by the Deutsche Forschungsgemeinschaft (DFG). We thank the Deutscher Wetterdienst (DWD, in particular U. Rummel and J. Leps from the Lindenberg Meteorological Observatory —Richard-Aßmann-Observatory) for kindly providing the Falkenberg grassland data used in this study. The data analysis including the preprocessing and postprocessing of input data for TerrSysMP was done using the NCAR Command language (version 6.2.0). The source codes of the TerrSysMP interface including the test case used in the study are available from the https://git.meteo.uni-bonn.de. The COSMO and TERRA-ML offline source code and the Falkenberg grassland site measurements are available on request from DWD MOLRAO ([email protected]), Germany. The CLM (version 3.5) source code is available from http://www.cgd.ucar.edu/tss/clm/ distribution/clm3.5, and the OASIS3/OASIS3-MCT source codes are available from http://oasis3mct.cerfacs. fr/svn/branches/OASIS3-MCT_2.0_ branch/oasis3-mct. The CLM (version 5.0) source code is available from http:// www.cesm.ucar.edu/models/cesm2/ land/, and the scripts for the CLM5.0 simulation setup in JURECA machine at Jülich Supercomputing Centre (JSC) is available from https://github.com/ prabshr/iclm.git. We also gratefully acknowledge the computing time (project HBN33) granted by the John von Neumann Institute for Computing (NIC) provided on the supercomputer JURECA at JSC. Finally, we also thank JGR-B editorial team and the anonymous reviewers for their constructive comments and suggestions, which have tremendously improved the quality of the manuscript.

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In case of a pure state update, ψ only consists of soil moisture values θvol. The update of the state-parameter vector with soil moisture measurements y is then performed according to ψai ¼ ψti þ K y þ εi Hψti

(A3)

where ψa is the updated state-parameter vector, ψt is the predicted state-parameter vector, K is the Kalman gain matrix, y is the measurement vector, ε is a perturbation vector of the measurements with values drawn from a normal distribution with a mean of zero and a standard deviation corresponding to the assigned measurement error of 0.02 m3/m3 for soil moisture, and H is the measurement operator that maps the predicted soil moisture values θvol onto the measurement locations. The Kalman gain matrix K is calculated from the covariance matrix C of the different state-parameter vectors (for each ensemble member) and the measurement error covariance matrix R: 1 K ¼ CHT HCHT þ R

(A4)

Error correlations between different measurement locations are ignored in our case, so R only contains the assigned measurement errors on the diagonal. For the soil moisture as well as the soil texture update, several constraints were applied to assure that the updated values stay in plausible physical ranges. The updated soil moisture was restricted to values between zero and saturated soil water content. Values falling out of this range were corrected to these upper or lower boundaries. For the soil texture update, updated sand or clay percentages lower than zero were set to a value of 1%. In the case that the updated sum of sand and clay percentages exceeded 100%, the sand and clay percentages were normalized to the sum of updated sand and clay percentage. After each soil texture update, the soil hydraulic and thermal parameters were adjusted to the updated texture values with the in-built pedtransfer functions.

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