TY  - JOUR
AB  - Alternaria is a fungal phytopathogen affecting over 4,000 species and causing 20% of agricultural production losses. About 9% of people in Europe are sensitized to its allergens. Understanding spore concentration variability under different environmental conditions can optimize fungicide use and improve allergy diagnosis and treatment. This study examines the spatio-temporal abundance of airborne Alternaria spores across varying climate and pollution regimes, hypothesizing that regional land cover is the main predictor of spore concentrations. In 2015, airborne Alternaria spores were monitored at 23 sites in Bavaria using Hirst-type spore traps. Concentrations were assessed on a bihourly scale and differences between bioclimatic zones were analysed using regression (GLM, GLZ), variance (ANOVA, ANCOVA) and cluster analyses, controlling for meteorology, air quality and land use. Machine learning techniques, including random forest, regression tree and XGBoost, were also implemented to detect complex, non-linear patterns, while stepwise regression was used to identify the most influential predictors. The seasonal fungal index (SFI) of Alternaria spores varied considerably between locations. Cluster analysis identified five main groups based on the maximum concentration and monthly distribution. The highest SFI values were in the north, including Bayreuth, Bamberg and Hof, but with shorter season. SFI decreased toward the south with lower temperatures, but seasons lengthened. One-third of spores appeared after 6 pm, with half of daily peaks post-8 pm. At higher altitudes, spore circulation was more variable, with peaks mostly at night. NO₂ and air temperature had a greater impact on spore levels than land use. Our results indicate that in a world with warmer nights and higher pollution fungal spores may enhance growth and sporulation, increasing the risk of exposure to both human health and agricultural productivity, highlighting the need for monitoring and potential mitigation of fungal pathogens.
AU  - Plaza, M.P.
AU  - Oteros, J.*
AU  - Leier-Wirtz, V.*
AU  - Kolek, F.*
AU  - Menzel, A.*
AU  - Buters, J.T.M.*
AU  - Traidl-Hoffmann, C.
AU  - Damialis, A.*
C1  - 75114
C2  - 57758
CY  - Radarweg 29, 1043 Nx Amsterdam, Netherlands
TI  - A multi-scale analysis of airborne Alternaria spore dispersal: Influence of meteorology, land cover and air pollution.
JO  - Agric. For. Meteorol.
VL  - 372
PB  - Elsevier
PY  - 2025
SN  - 0168-1923
ER  - 

TY  - JOUR
AB  - Accurate simulation of soil temperature can help improve the accuracy of crop growth models by improving the predictions of soil processes like seed germination, decomposition, nitrification, evaporation, and carbon sequestration. To assess how well such models can simulate soil temperature, herein we present results of an inter-comparison study of 33 maize (Zea mays L.) growth models. Among the 33 models, four of the modeling groups contributed results using differing algorithms or “flavors” to simulate evapotranspiration within the same overall model family. The study used comprehensive datasets from two sites - Mead, Nebraska, USA and Bushland, Texas, USA wherein soil temperature was measured continually at several depths. The range of simulated soil temperatures was large (about 10–15 °C) from the coolest to warmest models across whole growing seasons from bare soil to full canopy and at both shallow and deeper depths. Within model families, there were no significant differences among their simulations of soil temperature due to their differing evapotranspiration method “flavors”, so root-mean-square-errors (RMSE) were averaged within families, which reduced the number of soil temperature model families to 13. The model family RMSEs averaged over all 20 treatment-years and 2 depths ranged from about 1.5 to 5.1 °C. The six models with the lowest RMSEs were APSIM, ecosys, JULES, Expert-N, SLFT, and MaizSim. Five of these best models used a numerical iterative approach to simulate soil temperature, which entailed using an energy balance on each soil layer. whereby the change in heat storage during a time step equals the difference between the heat flow into and that out of the layer. Further improvements in the best models for simulating soil temperature might be possible with the incorporation of more recently improved routines for simulating soil thermal conductivity than the older routines now in use by the models.
AU  - Kimball, B.A.*
AU  - Thorp, K.R.*
AU  - Boote, K.J.*
AU  - Stockle, C.*
AU  - Suyker, A.E.*
AU  - Evett, S.R.*
AU  - Brauer, D.K.*
AU  - Coyle, G.G.*
AU  - Copeland, K.S.*
AU  - Marek, G.W.*
AU  - Colaizzi, P.D.*
AU  - Acutis, M.*
AU  - Archontoulis, S.*
AU  - Babacar, F.*
AU  - Barcza, Z.*
AU  - Basso, B.*
AU  - Bertuzzi, P.*
AU  - Migliorati, M.D.A.*
AU  - Dumont, B.*
AU  - Durand, J.L.*
AU  - Fodor, N.*
AU  - Gaiser, T.*
AU  - Gayler, S.*
AU  - Grant, R.*
AU  - Guan, K.*
AU  - Hoogenboom, G.*
AU  - Jiang, Q.*
AU  - Kim, S.H.*
AU  - Kisekka, I.*
AU  - Lizaso, J.*
AU  - Perego, A.*
AU  - Peng, B.*
AU  - Priesack, E.
AU  - Qi, Z.*
AU  - Shelia, V.*
AU  - Srivastava, A.K.*
AU  - Timlin, D.*
AU  - Webber, H.*
AU  - Weber, T.*
AU  - Williams, K.*
AU  - Viswanathan, M.*
AU  - Zhou, W.*
C1  - 70580
C2  - 55583
CY  - Radarweg 29, 1043 Nx Amsterdam, Netherlands
TI  - Simulation of soil temperature under maize: An inter-comparison among 33 maize models.
JO  - Agric. For. Meteorol.
VL  - 351
PB  - Elsevier
PY  - 2024
SN  - 0168-1923
ER  - 

TY  - JOUR
AB  - Accurate simulation of crop water use (evapotranspiration, ET) can help crop growth models to assess the likely effects of climate change on future crop productivity, as well as being an aid for irrigation scheduling for today's growers. To determine how well maize (Zea mays L.) growth models can simulate ET, an initial inter-comparison study was conducted in 2019 under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). Herein, we present results of a second inter-comparison study of 41 maize models that was conducted using more comprehensive datasets from two additional sites - Mead, Nebraska, USA and Bushland, Texas, USA. There were 20 treatment-years with varying irrigation levels over multiple seasons at both sites. ET was measured using eddy covariance at Mead and using large weighing lysimeters at Bushland. A wide range in ET rates was simulated among the models, yet several generally were able to simulate ET rates adequately. The ensemble median values were generally close to the observations, but a few of the models sometimes performed better than the median. Many of the models that did well at simulating ET for the Mead site did poorly for drier, windy days at the Bushland site, suggesting they need to improve how they handle humidity and wind. Additional variability came from the approaches used to simulate soil water evaporation. Fortunately, several models were identified that did well at simulating soil water evaporation, canopy transpiration, biomass accumulation, and grain yield. These models were older and have been widely used, which suggests that a larger number of users have tested these models over a wider range of conditions leading to their improvement. These revelations of the better approaches are leading to model improvements and more accurate simulations of ET.
AU  - Kimball, B.A.*
AU  - Thorp, K.R.*
AU  - Boote, K.J.*
AU  - Stöckle, C.*
AU  - Suyker, A.E.*
AU  - Evett, S.R.*
AU  - Brauer, D.K.*
AU  - Coyle, G.G.*
AU  - Copeland, K.S.*
AU  - Marek, G.W.*
AU  - Colaizzi, P.D.*
AU  - Acutis, M.*
AU  - Alimagham, S.*
AU  - Archontoulis, S.*
AU  - Babacar, F.*
AU  - Barcza, Z.*
AU  - Basso, B.*
AU  - Bertuzzi, P.*
AU  - Constantin, J.*
AU  - De Antoni Migliorati, M.*
AU  - Dumont, B.*
AU  - Durand, J.L.*
AU  - Fodor, N.*
AU  - Gaiser, T.*
AU  - Garofalo, P.*
AU  - Gayler, S.*
AU  - Giglio, L.*
AU  - Grant, R.*
AU  - Guan, K.*
AU  - Hoogenboom, G.*
AU  - Jiang, Q.*
AU  - Kim, S.H.*
AU  - Kisekka, I.*
AU  - Lizaso, J.*
AU  - Masia, S.*
AU  - Meng, H.*
AU  - Mereu, V.*
AU  - Mukhtar, A.*
AU  - Perego, A.*
AU  - Peng, B.*
AU  - Priesack, E.
AU  - Qi, Z.*
AU  - Shelia, V.*
AU  - Snyder, R.*
AU  - Soltani, A.*
AU  - Spano, D.*
AU  - Srivastava, A.*
AU  - Thomson, A.*
AU  - Timlin, D.*
AU  - Trabucco, A.*
AU  - Webber, H.*
AU  - Weber, T.*
AU  - Willaume, M.*
AU  - Williams, K.*
AU  - van der Laan, M.*
AU  - Ventrella, D.*
AU  - Viswanathan, M.*
AU  - Xu, X.*
AU  - Zhou, W.*
C1  - 68163
C2  - 53609
CY  - Radarweg 29, 1043 Nx Amsterdam, Netherlands
TI  - Simulation of evapotranspiration and yield of maize: An Inter-comparison among 41 maize models.
JO  - Agric. For. Meteorol.
VL  - 333
PB  - Elsevier
PY  - 2023
SN  - 0168-1923
ER  - 

TY  - JOUR
AB  - Predicting wheat phenology is important for cultivar selection, for effective crop management and provides a baseline for evaluating the effects of global change. Evaluating how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat modeling groups participated in this evaluation. Our target population was wheat fields in the major wheat growing regions of Australia under current climatic conditions and with current local management practices. The environments used for calibration and for evaluation were both sampled from this same target population. The calibration and evaluation environments had neither sites nor years in common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology for new sites and weather conditions. Mean absolute error (MAE) for the evaluation environments, averaged over predictions of three phenological stages and over modeling groups, was 9 days, with a range from 6 to 20 days. Predictions using the multi-modeling group mean and median had prediction errors nearly as small as the best modeling group. About two thirds of the modeling groups performed better than a simple but relevant benchmark, which predicts phenology by assuming a constant temperature sum for each development stage. The added complexity of crop models beyond just the effect of temperature was thus justified in most cases. There was substantial variability between modeling groups using the same model structure, which implies that model improvement could be achieved not only by improving model structure, but also by improving parameter values, and in particular by improving calibration techniques.
AU  - Wallach, D.*
AU  - Palosuo, T.*
AU  - Thorburn, P.*
AU  - Hochman, Z.*
AU  - Andrianasolo, F.*
AU  - Asseng, S.*
AU  - Basso, B.*
AU  - Buis, S.*
AU  - Crout, N.*
AU  - Dumont, B.*
AU  - Ferrise, R.*
AU  - Gaiser, T.*
AU  - Gayler, S.*
AU  - Hiremath, S.*
AU  - Hoek, S.*
AU  - Horan, H.*
AU  - Hoogenboom, G.*
AU  - Huang, M.*
AU  - Jabloun, M.*
AU  - Jansson, P.E.*
AU  - Jing, Q.*
AU  - Justes, É.*
AU  - Kersebaum, K.C.*
AU  - Launay, M.*
AU  - Lewan, E.*
AU  - Luo, Q.*
AU  - Maestrini, B.*
AU  - Moriondo, M.*
AU  - Olesen, J.E.*
AU  - Padovan, G.*
AU  - Poyda, A.*
AU  - Priesack, E.
AU  - Pullens, J.W.M.*
AU  - Qian, B.*
AU  - Schütze, N.*
AU  - Shelia, V.*
AU  - Souissi, A.*
AU  - Specka, X.*
AU  - Kumar Srivastava, A.*
AU  - Stella, T.*
AU  - Streck, T.*
AU  - Trombi, G.*
AU  - Wallor, E.*
AU  - Wang, J.*
AU  - Weber, T.K.D.*
AU  - Weihermüller, L.*
AU  - de Wit, A.*
AU  - Wöhling, T.*
AU  - Xiao, L.*
AU  - Zhao, C.*
AU  - Zhu, Y.*
AU  - Seidel, S.J.*
C1  - 60986
C2  - 50010
CY  - Radarweg 29, 1043 Nx Amsterdam, Netherlands
TI  - Multi-model evaluation of phenology prediction for wheat in Australia.
JO  - Agric. For. Meteorol.
VL  - 298-299
PB  - Elsevier
PY  - 2021
SN  - 0168-1923
ER  - 

TY  - JOUR
AB  - Crop yield can be affected by crop water use and vice versa, so when trying to simulate one or the other, it can be important that both are simulated well. In a prior inter-comparison among maize growth models, evapotranspiration (ET) predictions varied widely, but no observations of actual ET were available for comparison. Therefore, this follow-up study was initiated under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). Observations of daily ET using the eddy covariance technique from an 8-year-long (2006–2013) experiment conducted at Ames, IA were used as the standard for comparison among models. Simulation results from 29 models are reported herein. In the first “blind” phase for which only weather, soils, phenology, and management information were provided to the modelers, estimates of seasonal ET varied from about 200 to about 700 mm. Subsequent three phases provided (1) leaf area indices for all years, (2) all daily ET and agronomic data for a typical year (2011), and (3) all data for all years, thus allowing the modelers to progressively calibrate their models as more information was provided, but the range among ET estimates still varied by a factor of two or more. Much of the variability among the models was due to differing estimates of potential evapotranspiration, which suggests an avenue for substantial model improvement. Nevertheless, the ensemble median values were generally close to the observations, and the medians were best (had the lowest mean squared deviations from observations, MSD) for several ET categories for inter-comparison, but not all. Further, the medians were best when considering both ET and agronomic parameters together. The best six models with the lowest MSDs were identified for several ET and agronomic categories, and they proved to vary widely in complexity in spite of having similar prediction accuracies. At the same time, other models with apparently similar approaches were not as accurate. The models that are widely used tended to perform better, leading us speculate that a larger number of users testing these models over a wider range of conditions likely has led to improvement. User experience and skill at calibration and dealing with missing input data likely were also a factor in determining the accuracy of model predictions. In several cases different versions of a model within the same family of models were run, and these within-family inter-comparisons identified particular approaches that were better while other factors were held constant. Thus, improvement is needed in many of the models with regard to their ability to simulate ET over a wide range of conditions, and several aspects for progress have been identified, especially in their simulation of potential ET.
AU  - Kimball, B.A.*
AU  - Boote, K.J.*
AU  - Hatfield, J.L.*
AU  - Ahuja, L.R.*
AU  - Stockle, C.*
AU  - Archontoulis, S.*
AU  - Baron, C.*
AU  - Basso, B.*
AU  - Bertuzzi, P.*
AU  - Constantin, J.*
AU  - Deryng, D.*
AU  - Dumont, B.*
AU  - Durand, J.L.*
AU  - Ewert, F.*
AU  - Gaiser, T.*
AU  - Gayler, S.*
AU  - Hoffmann, M.P.*
AU  - Jiang, Q.*
AU  - Kim, S.-H.*
AU  - Lizaso, J.*
AU  - Moulin, S.*
AU  - Nendel, C.*
AU  - Parker, P.*
AU  - Palosuo, T.*
AU  - Priesack, E.
AU  - Qi, Z.*
AU  - Srivastava, A.*
AU  - Stella, T.*
AU  - Tao, F.*
AU  - Thorp, K.R.*
AU  - Timlin, D.*
AU  - Twine, T.E.*
AU  - Webber, H.*
AU  - Willaume, M.*
AU  - Williams, K.*
C1  - 55733
C2  - 46487
SP  - 264-284
TI  - Simulation of maize evapotranspiration: An inter-comparison among 29 maize models.
JO  - Agric. For. Meteorol.
VL  - 271
PY  - 2019
SN  - 0168-1923
ER  - 

TY  - JOUR
AB  - Field-scale crop models are increasingly applied at spatio-temporal scales that range from regions to the globe and from decades up to 100 years. Sufficiently detailed data to capture the prevailing spatio-temporal heterogeneity in weather, soil, and management conditions as needed by crop models are rarely available. Effective sampling may overcome the problem of missing data but has rarely been investigated. In this study the effect of sampling weather data has been evaluated for simulating yields of winter wheat in a region in Germany over a 30-year period (1982-2011) using 12 process-based crop models. A stratified sampling was applied to compare the effect of different sizes of spatially sampled weather data (10, 30, 50, 100, 500, 1000 and full coverage of 34,078 sampling points) on simulated wheat yields. Stratified sampling was further compared with random sampling. Possible interactions between sample size and crop model were evaluated. The results showed differences in simulated yields among crop models but all models reproduced well the pattern of the stratification. Importantly, the regional mean of simulated yields based on full coverage could already be reproduced by a small sample of 10 points. This was also true for reproducing the temporal variability in simulated yields but more sampling points (about 100) were required to accurately reproduce spatial yield variability. The number of sampling points can be smaller when a stratified sampling is applied as compared to a random sampling. However, differences between crop models were observed including some interaction between the effect of sampling on simulated yields and the model used. We concluded that stratified sampling can considerably reduce the number of required simulations. But, differences between crop models must be considered as the choice for a specific model can have larger effects on simulated yields than the sampling strategy. Assessing the impact of sampling soil and crop management data for regional simulations of crop yields is still needed.
AU  - van Bussel, L.G.J.*
AU  - Ewert, F.*
AU  - Zhao, G.*
AU  - Hoffmann, H.*
AU  - Enders, A.*
AU  - Wallach, D.*
AU  - Asseng, S.*
AU  - Baigorria, G.A.*
AU  - Basso, B.*
AU  - Biernath, C.J.
AU  - Cammarano, D.*
AU  - Chryssanthacopoulos, J.*
AU  - Constantin, J.*
AU  - Elliott, J.*
AU  - Glotter, M.*
AU  - Heinlein, F.
AU  - Kersebaum, K.C.*
AU  - Klein, C.
AU  - Nendel, C.*
AU  - Priesack, E.
AU  - Raynal, H.*
AU  - Romero, C.C.*
AU  - Rötter, R.P.*
AU  - Specka, X.*
AU  - Tao, F.*
C1  - 47784
C2  - 39527
CY  - Amsterdam
SP  - 101-115
TI  - Spatial sampling of weather data for regional crop yield simulations.
JO  - Agric. For. Meteorol.
VL  - 220
PB  - Elsevier Science Bv
PY  - 2016
SN  - 0168-1923
ER  - 

TY  - JOUR
AB  - Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2°C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2].
AU  - Makowski, D.*
AU  - Asseng, S.*
AU  - Ewert, F.*
AU  - Bassu, S.*
AU  - Durand, J.L.*
AU  - Li, T.*
AU  - Martre, P.*
AU  - Adam, M.*
AU  - Aggarwal, P.K.*
AU  - Angulo, C.*
AU  - Baron, C.*
AU  - Basso, B.*
AU  - Bertuzzi, P.*
AU  - Biernath, C.J.
AU  - Boogaard, H.*
AU  - Boote, K.J.*
AU  - Bouman, B.*
AU  - Bregaglio, S.*
AU  - Brisson, N.*
AU  - Buis, S.*
AU  - Cammarano, D.*
AU  - Challinor, A.J.*
AU  - Confalonieri, R.*
AU  - Conijn, J.G.*
AU  - Corbeels, M.*
AU  - Deryng, D.*
AU  - de Sanctis, G.*
AU  - Doltra, J.*
AU  - Fumoto, T.*
AU  - Gaydon, D.*
AU  - Gayler, S.*
AU  - Goldberg, R.*
AU  - Grant, R.F.*
AU  - Grassini, P.*
AU  - Hatfield, J.L.*
AU  - Hasegawa, T.*
AU  - Heng, L.*
AU  - Hoek, S.*
AU  - Hooker, J.*
AU  - Hunt, L.A.*
AU  - Ingwersen, J.*
AU  - Izaurralde, R.C.*
AU  - Jongschaap, R.E.E.*
AU  - Jones, J.W.*
AU  - Kemanian, R.A.*
AU  - Kersebaum, K.C.*
AU  - Kim, S.H.*
AU  - Lizaso, J.*
AU  - Marcaida, M.*
AU  - Müller, C.*
AU  - Nakagawa, H.*
AU  - Naresh Kumar, S.*
AU  - Nendel, C.*
AU  - O'Leary, G.J.*
AU  - Olesen, J.E.*
AU  - Oriol, P.*
AU  - Osborne, T.M.*
AU  - Palosuo, T.*
AU  - Pravia, M.V.*
AU  - Priesack, E.
AU  - Ripoche, D.*
AU  - Rosenzweig, C.*
AU  - Ruane, A.C.*
AU  - Ruget, F.*
AU  - Sau, F.*
AU  - Semenov, M.A.*
AU  - Shcherbak, I.*
AU  - Singh, B.*
AU  - Singh, U.*
AU  - Soo, H.K.*
AU  - Steduto, P.*
AU  - Stöckle, C.*
AU  - Stratonovitch, P.*
AU  - Streck,T.*
AU  - Supit, I.*
AU  - Tang, L.*
AU  - Tao, F.*
AU  - Teixeira, E.I.*
AU  - Thorburn, P.J.*
AU  - Timlin, D.*
AU  - Travasso, M.*
AU  - Rötter, R.P.*
AU  - Waha, K.*
AU  - Wallach, D.*
AU  - White, J.W.*
AU  - Wilkens, P.*
AU  - Williams, J.R.*
AU  - Wolf, J.*
AU  - Yin, X.*
AU  - Yoshida, H.*
C1  - 46967
C2  - 39188
SP  - 483-493
TI  - A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration.
JO  - Agric. For. Meteorol.
VL  - 214-215
PY  - 2015
SN  - 0168-1923
ER  - 

TY  - JOUR
AB  - Quantifying the water exchange between a forest stand and the atmosphere is of major interest for the prediction of future growth conditions and the planning of silvicultural treatments. In the present study, we address (i) the uncertainties of sap flow estimations at the tree level and (ii) the performance of the simulation of stand transpiration. Terrestrial laser scan images (. TLS) of a mature beech stand (. Fagus sylvatica L.) in Southwestern Germany serve as input data for a representation of the aboveground tree architecture of the study stand. In the single-tree xylem water flow model (. XWF) used here, 98 beech trees are represented by 3D graphs of connected cylinders with explicit orientation and size. Beech-specific hydraulic parameters and physical properties of individual trees determine the physiological response of the tree model to environmental conditions.The XWF simulations are performed without further calibration to sap flow measurements. The simulations reliably match up with sap flow estimates derived from sap flow density measurements. The density measurements strongly depend on individual sapwood area estimates and the characterization of radial sap flow density gradients with xylem depth. Although the observed pure beech stand is even-aged, we observe a high variability in sap flow rates among the individual trees. Simulations of the individual sap flow rates show a corresponding variability due to the distribution of the crown projection area in the canopy and the different proportions of sapwood area.Stand transpiration is obtained by taking the sum of 98 single-tree simulations and the corresponding sap flow estimations, which are then compared with the stand-level root water uptake model (. RWU model) simulation. Using the RWU model results in a 35% higher simulation of seasonal stand transpiration relative to the XWF model. These findings demonstrate the importance of individual tree dimensions and stand heterogeneity assessments in estimating stand water use. As a consequence of species-specific model parameterization and precise TLS-based stand characterization, the XWF model is applicable to various sites and tree species and is a promising tool for predicting the possible water supply limitations of pure and mixed forest stands.
AU  - Hentschel, R.
AU  - Bittner, S.
AU  - Janott, M.
AU  - Biernath, C.J.
AU  - Holst, J.*
AU  - Ferrio, J.P.*
AU  - Gessler, A.*
AU  - Priesack, E.
C1  - 27554
C2  - 32718
SP  - 31-42
TI  - Simulation of stand transpiration based on a xylem water flow model for individual trees.
JO  - Agric. For. Meteorol.
VL  - 182
PB  - Elsevier Science
PY  - 2013
SN  - 0168-1923
ER  - 

TY  - JOUR
AB  - A functional-structural (FS) model of tree water flow is applied for single trees in an old-growth temperate broad-leaved forest stand. Roots, stems and branches are represented by connected porous cylinder elements that are divided into the inner heartwood cylinders surrounded by xylem and phloem. Xylem water flow is simulated by applying a non-linear Darcy water flow in porous media driven by the water potential gradient according to the cohesion-tension theory. The flow model is based on physiological input parameters such as the hydraulic conductivity, stomata] response to leaf water potential and root water uptake capability and, thus, can reflect the different properties of the two diffuse-porous tree species Fagus sylvatica and Tilia cordata and the ring-porous species Fraxinus excelsior. The structure of the canopy is obtained by applying an automatic tree skeleton extraction algorithm from point clouds obtained by terrestrial laser scans allowing an explicit representation of the water flow path in the stem and branches. Supported by measurements of stem sap flow, the model reveals differences of the simulated stomatal closure due to low branch xylem water contents between the tree species. The diffuse-porous species reduced the transpiration by the stomatal closure only at hot days with a high potential transpiration. For the ring-porous ash the simulated reduction is much higher with a mean value of all trees over the observation period of 0.72. The model gives insights to the mechanism that lead to the stomatal closure and can spot the axial xylem hydraulic conductance along the flow pathway as the limiting factor of leaf water supply at days with moist soil water conditions.
AU  - Bittner, S.
AU  - Janott, M.
AU  - Ritter, D.*
AU  - Kocher, P.*
AU  - Beese, F.*
AU  - Priesack, E.
C1  - 7972
C2  - 29934
SP  - 80-89
TI  - Functional-structural water flow model reveals differences between diffuse- and ring-porous tree species.
JO  - Agric. For. Meteorol.
VL  - 158
PB  - Elsevier Science
PY  - 2012
SN  - 0168-1923
ER  - 

TY  - JOUR
AB  - Abstract: This modeling study used recent observations at a temperate broad-leaved forest in Central Germany to calculate water balances of a Fagus sylvatica monoculture and mixed stands of F. sylvatica, Tilia spp., Acer spp., Carpinus betulus, Fraxinus excelsior and Quercus robur. To simulate soil water flow the modeling framework Expert-N was applied which combines models that describe the physiological and hydrological processes of the plant-soil system including models of evapotranspiration (Penman-Monteith equation), interception (revised Gash model) and soil water flow (Richards equation). Measurements of rainfall partitioning, volumetric soil water content, evapotranspiration and tree transpiration provided reliable data for the parameterization and the calibration of the model for three stands of different diversity levels. They allowed to include species specific physiological (transpiration rates, response to dry soil water conditions) and structural (leaf area dynamics) characteristics. During the 3-year long observation period 2005-2007 the mean yearly precipitation was 652 mm, the simulated mean yearly interception loss of the three observed forest stands was between 219 and 272 mm, the transpiration accounted for 197-225 mm, the forest floor evaporation for 96-104 mm. the drainage for 16-60 mm and the runoff for 13-50 mm. The calculations of the water balance were sensitive to the species composition of the forest and showed differences of rainfall interception and root water uptake between the stands. The applied stand-level model was able to simulate the water dynamics of the monospecific and mixed forest stands. It was shown that differences in drought tolerance of tree species can have a strong impact on the simulated soil water extraction during periods when available soil water is low.
AU  - Bittner, S.
AU  - Talkner, U.*
AU  - Krämer, I.*
AU  - Beese, F.*
AU  - Hölscher, D.*
AU  - Priesack, E.
C1  - 5544
C2  - 27600
SP  - 1347-1357
TI  - Modeling stand water budgets of mixed temperate broad-leaved forest stands by considering variations in species specific drought response.
JO  - Agric. For. Meteorol.
VL  - 150
IS  - 10
PB  - Elsevier
PY  - 2010
SN  - 0168-1923
ER  -