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cm). 92 where Tz* is the temperature measured at the uppermost soil depth, z* (2 Root water uptake was simulated using the model of Feddes et al. (1978). According to the root measurements, the maximum rooting depth was considered to be 1 m, with the highest root density in the upper 30 cm. The rooting depth increased linearly from 0 cm at the beginning of simulation to a maximum value at the date of “full cover” or harvest. The critical pressure heads in the water stress response function of Feddes were adapted from grass (Wesseling, 1991), and adjusted for the local conditions with a value of -1500 kPa for the wilting point p3. Water and Heat Flow Simulations The coupled water and heat transport module implemented in HYDURS-1D was tested using measured depth-averaged soil moisture and temperature (soil temperature at 5 cm depth was derived from Eq. [7] using the measured value in 2 cm depth). According to phenological data from IMGERS (Chen and Wang, 2000), the growing season starts aro<strong>und</strong> late April and ends aro<strong>und</strong> early October. According to our measurements (Gao, 2007), LAI increased rapidly from early May to late July, reached a peak in late August, and then decreased precipitously. Thus we assigned the growing season, i.e., simulation time, to the period from 1 May to 30 September. The simulated soil profile was considered to be 100 cm deep with observation nodes located at 5, 20, and 40 cm depths. In this study, we used five model approaches to explore the influence of soil hydraulic parameter availability on the quality of the model results (Table 1). Simulation 1 was conducted as a direct approach, using laboratory-derived hydraulic parameters (LDP) fitted by RETC code without any optimization. In simulation 2, hydraulic parameters were derived by a neural network (NN) prediction tool ROSETTA (Schaap et al., 2001) based on data of soil texture and bulk density. In simulation 3, an inverse model (Inverse) was used to estimate hydraulic parameters using a Levenberg–Marquardt parameter optimization algorithm. A layered soil profile with different parameters for each layer is assumed. The hydraulic functions described in Eqs. [1] and [2] may contain up to
Chapter 5 Modeling Grazing Effects on Coupled Water and Heat Fluxes in Inner Mongolia Grassland six unknown parameters (θr, θs, α, n, Ks, and L). Hence, our three-layer soil profile (second and third depths were combined in one layer) may have as many as 18 unknown variables. To improve the optimization efficiency, the parameters θs, α, n, and Ks were simultaneously optimized in all layers with other parameters (i.e., θr and L) constant using time series of observations of θw for each depth. For a preliminary sensitive analysis, we also mixed the simulation 1 and simulation 2, i.e., combined θr, θs, α, and n from LDP but Ks from NN (LDP-NN; simulation 4) and combined Ks from LDP but θr, θs, α, and n from NN (LDP+NN; simulation 5), respectively. The model was calibrated in 2004 and validated in 2005 and 2006. Optimizing efficiency, as well as model efficiency was evaluated by the root mean square errors (RMSE): RMSE = N 1 2 N ∑ Pi − Oi ) i= 1 ( [8] where N is the number of observations and Pi and Oi are the simulated and measured values, respectively. Table 5.1. Summary of simulation approaches used in model. Simulation approach Abbreviation Description Simulation 1 LDP Laboratory-derived hydraulic parameter from soil core, fitted by RETC software Simulation 2 NN Neutral network values based on soil texture and bulk density analysis, predicted by ROSETTA Simulation 3 Inverse Simulation with inverse model Simulation 4 LDP-NN Simulation with Ks from NN and measured θr, θs, α, n Simulation 5 LDP+NN Simulation with θr, θs, α, n from NN and measured Ks RESULTS Soil Texture, Bulk Density and Carbon Content At all four sites soil texture is coarser in the subsoil (18-22 cm) than in the topmost soil layer (4-8 cm) (Table 2). Also bulk density increases from the first to the second depth, while total C-content generally decreases (except at the second layer of UG 99) with increasing soil depth. Sand content is the highest in HG and the lowest in WG. Compared to UG 99 and WG, a higher sand content in 93
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SCHRIFTENREIHE Institut für Pflanz
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Aus dem Institut für Pflanzenernä
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I Table of contents I Table of cont
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controlling soil moisture are of pa
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Zusammenfassung Überweidung wird a
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Die Beweidung beieinflusst besonder
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II List of figures Fig. 2.1. Locati
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measured soil moisture in time stab
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III List of tables Table 2.1. Descr
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1.1 Background Chapter 1 Introducti
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Chapter 1 Introduction is also unce
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Chapter 1 Introduction water in spr
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Chapter 1 Introduction requires the
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Chapter 1 Introduction Krümmelbein
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Chapter 2 Spatial variability of so
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Chapter 3 Spatio-temporal variabili
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Page 93 and 94: MRD(%) MRD(%) 100 80 60 40 20 0 -20
Page 101 and 102: Soil water content (%) 40 30 20 10
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Page 135 and 136: Chapter 6 Modeling of Coupled Water
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Chapter 7 General discussion and co
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References Chapter 7 General discus
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8. ACKNOWLEDGMENTS Firstly I would
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Curriculum Vitae M.Sc. Ying Zhao Of
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Schriftenreihe des Instituts Neuere
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