Incorporating dynamic root growth enhances the performance of Noah-MP at two contrasting winter wheat field sites

Gayler, Sebastian; Wöhling, Thomas; Grzeschik, Matthias; Ingwersen, Joachim; Wizemann, Hans-Dieter; Warrach‐Sagi, Kirsten; Högy, Petra; Attinger, Sabine; Streck, Thilo; Wulfmeyer, Volker

doi:10.1002/2013wr014634

Cited by 54 publications

(64 citation statements)

References 62 publications

(76 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Nevertheless, this tiny storage amount exerts first-order control on the partitioning of net radiation energy in latent and sensible heat flux (Kleidon and Renner, 2013a, b;Gayler et al, 2014;Turner et al, 2014) -possibly the key process in land surface atmosphere exchange. Crucially, soil moisture crucially controls CO 2 emissions of forest soils (Koehler et al, 2010) denitrification and related trace gas emissions into the atmosphere (Koehler et al, 2012) as well as metabolic transformations of pesticides (e.g.…”

Section: Introductionmentioning

confidence: 99%

A Lagrangian model for soil water dynamics during rainfall-driven conditions

Zehe

Jackisch

2016

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Within this study we propose a stochastic approach to simulate soil water dynamics in the unsaturated zone by using a non-linear, space domain random walk of water particles. Soil water is represented by particles of constant mass, which travel according to the Itô form of the Fokker-Planck equation. The model concept builds on established soil physics by estimating the drift velocity and the diffusion term based on the soil water characteristics. A naive random walk, which assumes all water particles to move at the same drift velocity and diffusivity, overestimated depletion of soil moisture gradients compared to a Richards solver. This is because soil water and hence the corresponding water particles in smaller pore size fractions are, due to the non-linear decrease in soil hydraulic conductivity with decreasing soil moisture, much less mobile. After accounting for this subscale variability in particle mobility, the particle model and a Richards solver performed highly similarly during simulated wetting and drying circles in three distinctly different soils. Both models were in very good accordance during rainfall-driven conditions, regardless of the intensity and type of the rainfall forcing and the shape of the initial state. Within subsequent drying cycles the particle model was typically slightly slower in depleting soil moisture gradients than the Richards model.Within a real-world benchmark, the particle model and the Richards solver showed the same deficiencies in matching observed reactions of topsoil moisture to a natural rainfall event. The particle model performance, however, clearly improved after a straightforward implementation of rapid nonequilibrium infiltration, which treats event water as different types of particles, which travel initially in the largest pore fraction at maximum velocity and experience a slow diffusive mixing with the pre-event water particles. The proposed Lagrangian approach is hence a promising, easy-to-implement alternative to the Richards equation for simulating rainfalldriven soil moisture dynamics, which offers straightforward opportunities to account for preferential, non-equilibrium flow.

show abstract

Section: Introductionmentioning

confidence: 99%

A Lagrangian model for soil water dynamics during rainfall-driven conditions

Zehe

Jackisch

2016

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…The rooting depth of the simulated grasslands was assumed to be constant throughout the simulation period. Dynamic root growth affects energy fluxes due to plant water stress (Gayler et al, 2014), but the root distribution of all species is not known for this site.…”

Section: Plantmentioning

confidence: 99%

Vegetation Growth Models Improve Surface Layer Flux Simulations of a Temperate Grassland

Klein

Biernath

Heinlein

et al. 2017

Vadose Zone Journal

View full text Add to dashboard Cite

Grassland models represent interactions of plant growth with soil and agricultural management based on underlying processes in different degrees of detail. To better understand the impact of these differences on the simulation of energy and matter exchange at the land-surface layer, we compared the ability of five land-surface models with different degrees of complexity to simulate energy fluxes in an intensively managed grassland in Switzerland. The aim was to evaluate the impacts of biomass growth, biomass harvest, soil profile characterization, and rooting depth on the dynamics of simulated near-surface soil moisture contents and energy fluxes. The case study included a comparison of model results with continuous observations of latent heat, sensible heat, and net radiation for a site-year. Energy fluxes were simulated more accurately by including a biomass growth model, encompassing the abrupt decline in leaf area caused by harvest. Sitespecific soil parametrization in combination with the absence of restrictions on rooting depth also improved the simulation results. The simulated energy fluxes of the five models differed significantly in the hot, dry month of July 2010 but were negligible under moist conditions in May. We conclude that the application of dynamic vegetation growth models improves energy flux simulations at the field scale in intensively managed grasslands during summer if biomass harvest dates and site-specific soil profile descriptions are considered. Our results imply that regional-scale simulations of grasslands will benefit significantly from high-resolution input information on soil properties, land use, and management.Abbreviations: LAI, leaf area index; LSM, land surface model; HPM, Hurley Pasture Model; WFPS, water-filled pore space.Grasslands cover >25% of the Earth's surface area and about 70% of the agricultural land area (FAO, 2015, p. 50). Therefore, a good representation of grassland systems in models plays a crucial role in reliably estimating energy and matter exchange between the land surface and the atmosphere at regional and global scales. During the last decades, increasingly improved terrestrial ecosystem models have been developed and applied to simulate energy, water and C fluxes between biosphere and atmosphere (Ma et al., 2015;Ershadi et al., 2014) and to estimate crop production under expected climate change (Asseng et al., 2013).However, compared with the numerous accessible forest and crop system models and in spite of the widespread surface area occupied by grassland, only a few grassland-specific models are available (Chang et al., 2013;Ma et al., 2015;Senapati et al., 2016), which for their part additionally address specific research questions. Among others, these are the CENTURY model built to quantify C, N, P, and S turnover on a monthly basis (Parton et al., 1998) and rebuilt for daily time steps under the name DAYCENT, the grassland model GEM (Chen et al., 1996a) focusing on natural grasslands, the LINGRA model (Schapendonk et al., 1998) to particularly pre...

show abstract

“…7) would most probably bring the observed and simulated fluxes into closer agreement. A further option is to search for multi-physics combinations that, with their default parameterization, lead to the best match of simulated and measured fluxes (Gayler et al, 2014).…”

Section: Energy Balance Closure (%)mentioning

confidence: 99%

On the use of the post-closure methods uncertainty band to evaluate the performance of land surface models against eddy covariance flux data

et al. 2015

View full text Add to dashboard Cite

Abstract. The energy balance of eddy covariance (EC) flux data is normally not closed. Therefore, at least if used for modelling, EC flux data are usually post-closed, i.e. the measured turbulent fluxes are adjusted so as to close the energy balance. At the current state of knowledge, however, it is not clear how to partition the missing energy in the right way. 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 propose to construct the post-closure methods uncertainty band (PUB), which essentially designates the differences between non-adjusted flux data and flux data adjusted with the three post-closure methods (Bowen ratio, latent heat flux (LE) and sensible heat flux (H ) method). To demonstrate this approach, simulations with the NOAH-MP land surface model were evaluated based on EC measurements conducted at a winter wheat stand in southwest Germany in 2011, and the performance of the Jarvis and Ball-Berry stomatal resistance scheme was compared. The width of the PUB of the LE was up to 110 W m −2 (21 % of net radiation). Our study shows that it is crucial to account for the uncertainty in EC flux data originating from lacking energy balance closure. Working with only a single post-closing method might result in severe misinterpretations in model-data comparisons.

show abstract

Incorporating dynamic root growth enhances the performance of Noah-MP at two contrasting winter wheat field sites

Cited by 54 publications

References 62 publications

A Lagrangian model for soil water dynamics during rainfall-driven conditions

A Lagrangian model for soil water dynamics during rainfall-driven conditions

Vegetation Growth Models Improve Surface Layer Flux Simulations of a Temperate Grassland

On the use of the post-closure methods uncertainty band to evaluate the performance of land surface models against eddy covariance flux data

Contact Info

Product

Resources

About