Modelling soil temperature & soil water availability in semi-arid Sudan : validation and testing
Abstract: The aim with this thesis was to investigate the land surface model used in DAYCENT. Through comparing the simulated soil moisture and soil temperature results with observed data, at the depth of 30 and 60 cm at the Demokeya site in semiarid Sudan, it was possible to evaluate the model. The ecosystem model uses soil texture data, land management information, and daily weather data to simulate plant growth, decomposition of dead plant material and soil organic matter, N cycling in soil, and trace gas exchanges between the soil and the atmosphere. The Soil temperature sub model and the Available soil water sub model were tested. The DAYCENT model simulates soil temperature well (r = 0.79), but the accuracy decreases with soil depth. This is also the case in the available soil water sub model (r = 0.64), but it underestimates the soil water content. The user must, though, be observant to irregularities in annual and monthly rainfall while it might influence daily correlation and thereby affect gas fluxes. Parameters like soil texture, root fraction and crop has no significant impact on the model but exists more as a noise. Either an increase or decrease in air temperature generates a significant influence on the soil temperature and moisture, which makes accurate air temperature data by far the most important factor. It was also found that the estimation of biomass is crucial, while it is the governing parameter determining the simulation result of the two sub models. If DAYCENT will be used to estimate e.g. carbon sequestration in the Sahel, proper satellite images and climate data are needed.
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