SCHRIFTENREIHE Institut für Pflanzenernährung und Bodenkunde ...

Recommendations

Info

Soil water content (cm 3 cm -3 ) 0.4 0.3 0.2 0.1 0.4 0.3 0.2 0.1 0.4 0.3 0.2 0.1 5 cm M LDP NN Inverse LDP-NN LDP+NN 2 20 cm 4 6 8 10 12 2 40 cm 4 6 8 10 12 4-Aug 10-Aug 16-Aug 22-Aug 28-Aug Time (days) Fig. 5.5. Comparison of the five model parameterization procedures with measured soil moisture in 2004 for UG 79. M: measured, LDP: laboratory-derived hydraulic parameters NN: neural network, Inverse: inverse model, LDP-NN: simulation with Ks from NN and measured θr, θs, α, n, and LDP-NN: simulation with θr, θs, α, n from NN and measured Ks. Model validation is performed with measured soil moisture and temperature data during the growing period of 153 d from 1 May to 30 Sept. 2005 and 2006, respectively. In general, simulated and measured water contents are comparable during the entire simulation period (Fig. 6). The increases in water content after rainfall at 5 and 20 cm depths are predicted reasonably well. In contrast to this, the simulated water contents decreased quicker than the measured values during a prolonged drought period. However, except for the inverse model, simulated and measured water contents at 40 cm depth are not matching well. Consistent with the model calibration, the inverse model predicts water contents best with the lowest RMSE (Table 4). This is to be expected because it adjusts the hydraulic parameters to fit the observed data for each soil layer. The LDP does not yield satisfying predictions at the deeper soil mirroring transfer gaps from laboratory data to field conditions, such as the 100
Chapter 5 Modeling Grazing Effects on Coupled Water and Heat Fluxes in Inner Mongolia Grassland representativeness of sampling and errors of measurement. The weak prediction is obtained from the NN, which is likely due to the spatial variations of PTFs. Volumetric soil water content (cm 3 cm -3 ) 0.4 0.3 0.2 0.1 0.0 0.3 0.2 0.1 0.0 0.3 0.2 0.1 0.0 5 cm 18 Jun 05 07 Aug 05 26 Sep 05 05 Jun 06 25 Jul 06 13 Sep 06 18-Jun-05 20 cm 07-Aug-05 26-Sep-05 05-Jun-06 25-Jul-06 13-Sep-06 18-Jun-05 07-Aug-05 26-Sep-05 05-Jun-06 25-Jul-06 13-Sep-06 40 cm Measured LDP NN Inverse 18-Jun-05 07-Aug-05 26-Sep-05 05-Jun-06 25-Jul-06 13-Sep-06 Time (days) Fig. 5.6. Soil moisture comparison between measured and simulated results in UG 79 during the growing period in 2005-2006. LDP: laboratory-derived hydraulic parameters, NN: neural network, and Inverse: inverse model. Table 5.4. Root mean square errors (RMSE) of five simulation approaches for model calibration and validation in UG 79. Simulation time Depth (cm) 40 30 20 10 0 Rainfall (mm) LDP NN Inverse LDP-NN LDP+NN 1 Aug. - 5 0.025 0.071 0.003 0.018 0.075 31 Aug., 2004 20 0.011 0.041 0.003 0.032 0.045 (calibration) 40 0.072 0.098 0.006 0.118 0.087 1 May - 5 0.019 0.034 0.038 - - 31 Sept., 2005 20 0.012 0.008 0.005 - - (validation) 40 0.066 0.062 0.006 - - 1 May - 5 0.032 0.040 0.045 - - 31 Sept., 2006 20 0.028 0.024 0.022 - - (validation) 40 0.041 0.048 0.008 - - -: Data not shown. 101
Page 1 and 2:
SCHRIFTENREIHE Institut für Pflanz
Page 3 and 4:
Aus dem Institut für Pflanzenernä
Page 5:
I Table of contents I Table of cont
Page 8 and 9:
controlling soil moisture are of pa
Page 11 and 12:
Zusammenfassung Überweidung wird a
Page 13 and 14:
Die Beweidung beieinflusst besonder
Page 15 and 16:
II List of figures Fig. 2.1. Locati
Page 17 and 18:
measured soil moisture in time stab
Page 19 and 20:
III List of tables Table 2.1. Descr
Page 21 and 22:
1.1 Background Chapter 1 Introducti
Page 23 and 24:
Chapter 1 Introduction is also unce
Page 25 and 26:
Chapter 1 Introduction water in spr
Page 27 and 28:
Chapter 1 Introduction requires the
Page 29 and 30:
Chapter 1 Introduction Krümmelbein
Page 31 and 32:
Chapter 2 Spatial variability of so
Page 33 and 34:
Page 35 and 36:
Page 37 and 38:
Page 39 and 40:
Page 41 and 42:
Page 43 and 44:
Page 45 and 46:
Page 47 and 48:
Page 49 and 50:
Page 51 and 52:
Page 53 and 54:
Page 55 and 56:
Page 57 and 58:
Page 59 and 60:
Chapter 3 Spatio-temporal variabili
Page 61 and 62:
Page 63 and 64:
Page 65 and 66:
Page 67 and 68:
Page 69 and 70: Chapter 3 Spatio-temporal variabili
Page 71 and 72: Chapter 3 Spatio-temporal variabili
Page 73 and 74: Chapter 3 Factors controlling spati
Page 85 and 86: Chapter 4 Temporal stability of soi
Page 89 and 90: Y orientation Chapter 4 Temporal st
Page 93 and 94: MRD(%) MRD(%) 100 80 60 40 20 0 -20
Page 101 and 102: Soil water content (%) 40 30 20 10
Page 105 and 106: Chapter 5 Modeling Grazing Effects
Page 119: Chapter 5 Modeling Grazing Effects
Page 135 and 136: Chapter 6 Modeling of Coupled Water
Page 159 and 160: Chapter 7 General discussion and co
Page 167 and 168: References Chapter 7 General discus
Page 171 and 172:
8. ACKNOWLEDGMENTS Firstly I would
Page 173 and 174:
Curriculum Vitae M.Sc. Ying Zhao Of
Page 175:
Schriftenreihe des Instituts Neuere
show all

SCHRIFTENREIHE Institut für Pflanzenernährung und Bodenkunde ...

Create successful ePaper yourself

Delete template?

Save as template?