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compensate the error of small scale variability at single sampling location and lead to more reliable estimates. Our study shows that only one location is needed when estimates of field mean moisture with 1% accuracy are warranted at each plot (Fig. 7). For instance, the mean water content estimated from p72 (UG 79) corresponding to each sampling date is plotted against the field mean water content. A very good linear regression is established between them (r 2 =0.98**) with a relative precision of
Chapter 4 Temporal stability of soil moisture and its application in model result validation indicate that these additional sampling locations are also close to the field mean water content. Therefore, this suggests that the locations with sand contents representative of the field mean sand contents can be served as time stable locations for estimations of soil moisture beforehand. Table 4.3. Mean values of soil texture, bulk density (BD) and hydraulic conductivity (K) in time stability point (TSP) for four investigated sites. Parameter UG 79 UG 99 WG HG Point number 72 41 49 22 Sand (%) 49.0 43.2 50.0 67.8 Silt (%) 34.5 40.5 32.4 21.5 Clay (%) 16.5 16.3 17.2 10.7 BD (g cm -3 ) 0.92 0.94 1.11 1.19 K (cm d -1 ) 164.6 63.6 106.8 141.8 Application of the time stability concept to a hydraulic model In this part we explore whether time stable points (TSPs) could be used to identify a certain modeled location. The selected TSP in the previous section is used to test by the HYDRUS-1D model. This is exemplified by UG 79. Firstly, we derived the van Genuchten parameters of p72 by ROSETTA, that is, θr=6.3%, θs=52.3%, α=0.009 cm -1 , n=1.509, Ks=149.9 cm d -1 , and L=0.5, respectively. Secondly, the Ks from ROSETTA is optimized by the scaled-predictive method. Here the predictive unsaturated hydraulic conductivity is scaled using a single measurement at a given water suction, from which Ks is replaced by the matching water suction (Thomasson et al., 2006). As shown in Fig. 8, there is a very good match between the measured infiltration rates and the simulated values via a two-term numerical conic function. According to this, the Ks is scaled to 164.6 cm d -1 (Table 3). Furthermore, the hydraulic parameters in the subsoil layer of p72 are also optimized based on the field measured value (i.e. soil texture and bulk density) in the soil profile combined with the scaled-calibration method (Thomasson et al., 2006). Remarkably, the modeled results from TSP match well the measured soil surface moisture in the same point (Fig. 9). Furthermore, the simulated result from p72 also shows a good agreement with measured water dynamics in another long-term monitoring 79
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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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Page 47 and 48: Chapter 2 Spatial variability of so
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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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