SCHRIFTENREIHE Institut für Pflanzenernährung und Bodenkunde ...

Recommendations

Info

was estimated from the soil heat flux G [J m -2 h -1 ] as follows (Chung and Horton, 1987): cm). G Ts = + T λ ( θ ) z * [12] 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). The maximum rooting depth was considered to be 100 cm, with the highest root density in the upper 30 cm. The critical pressure heads in the water-stress-response function of Feddes et al. (1978) were design to be similar to the ones for grass based on Wesseling (1991), and adjusted for the local conditions with a value of -1500 kPa for the permanent wilting point. Water and Heat Flow Simulations In Zhao et al. (2008), grazing effects on soil hydraulic and thermal properties have been parameterized by calibrating the model HYDURS-1D, based on data for evaporation ratio (weighing lysimeter experiments), water interception (obtained by the model SHAW), and hydraulic parameters (laboratory-derived hydraulic properties). However, this work was limited to show the effects of grazing on coupled water and heat fluxes during the growing period, i.e. unfrozen condition. Here we particularly attend to the snow melt and soil freezing and thawing processes. For this aim, the current HYDRUS-1D version including soil frozen module (denoted as freezing model) was modified to consider the phase changes between water and ice, as well as the surface energy and water balances. The algorithms in HYDRUS-1D for the description of soil temperature and water movement during the freezing and thawing process will be tested and adjusted to the local conditions. To verify the function of freezing model, the “normal” version without soil frozen module (snow routine) was also run as a reference. In the algorithm of snow hydrology it was assumed that all precipitation is in the form of snow when the air temperature (soil surface) is below -2°C, and in the form of liquid when the air temperature is above 2°C. There is a linear interpolation between -2 and 2°C, i.e. 50% of precipitation is 122
Chapter 6 Modeling of Coupled Water and Heat Transfer in Freezing and Thawing Soil snow and 50% water for temperature equal to zero (Jarvis, 1989). The soil profile was considered to be 100 cm deep, with observation nodes located at 0, 5, 20, 40, and 100 cm depths. Three soil layers were defined according to measured and calibrated layered-soil parameters (Zhao et al., 2008). Model efficiency is evaluated in terms of root mean square error (RMSE), which is defined as: RMSE = N 1 2 N ∑ Pi − Oi ) i= 1 ( [13] where N is the number of observations and Pi and Oi are the simulated and measured values, respectively. RESULTS AND DISCUSSION Soil Moisture and Temperature Regimes Based on in situ soil water measurements (down to1 m depth), soil water storage was calculated for the four sites from August 2005 to July 2006 (Fig. 1). Soil water storage (mm) 140 120 100 80 60 40 20 0 9-Sep 19-Oct 28-Nov 7-Jan 16-Feb 28-Mar 7-May 16-Jun 26-Jul 0 Rainfall UG 79 UG 99 WG HG 60 9-Sep 19-Oct 28-Nov 7-Jan 16-Feb 28-Mar 7-May 16-Jun 26-Jul Time (days) Fig. 6.1. Soil water storage as a function of grazing intensity from August 2005 to July 2006. Liquid water storage is high in summer but low in winter, which coincides with rainfall and temperature. Compared with the grazed sites, soil water storage is higher in the ungrazed sites, indicating grazing reduced the water stored in soil. Even <strong>und</strong>er soil frozen conditions, it is higher in the ungrazed sites than the grazed sites, indicating that the volume of unfrozen water is also affected by the 15 30 45 Rainfall (mm) 123
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:
Page 71 and 72:
Page 73 and 74:
Chapter 3 Factors controlling spati
Page 75 and 76:
Page 77 and 78:
Page 79 and 80:
Page 81 and 82:
Page 83 and 84:
Page 85 and 86:
Chapter 4 Temporal stability of soi
Page 87 and 88:
Chapter 4 Temporal stability of soi
Page 89 and 90:
Y orientation Chapter 4 Temporal st
Page 91 and 92: Chapter 4 Temporal stability of soi
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 135 and 136: Chapter 6 Modeling of Coupled Water
Page 141: 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 ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?