||Because of global warming and human over-development, now the gradually exhausted water resource in many region of the world is a serious problem we need to face. The simulated soil moisture and land surface temperature (LST) have being key points for studying and understanding this kind of problem. The first step of such a research, the global scale analysis (much larger spatial and temporal scale) is chosen as our study’s viewpoint, and we choose NOAH Land Surface Model (LSM) developed by NCEP to carry out five-year offline simulations subject to observed near-surface atmospheric forcing at Shih-Men reservoir watershed. Finally, we’ll investigate how climate change affects our water resource through discussing the variation of modeled LST and soil moisture. |
The atmosphere forcing data come from two sources: one is from local surface measurements like daily accumulative rainfall data measured by surface stations maintained by the Water Resources Agency; the other source is assimilated data produced by the GSFC GLDAS. Major variables in the GLDAS data set include near surface atmospheric parameters (air temperature, specific humidity, wind speed, and surface pressure), surface radiative fluxes (incident short-wave radiation, incident long-wave radiation) and surface rainfall rate. The GLDAS also provides land surface parameters. It includes land use type (vegetation type), soil type, land surface elevation, surface vegetation fraction, and albedo.
We perform two numerical experiences, E1 and E2, in the selected region of Shih-Men reservoir watershed. The two experiments are subject to the same forcing data (2001-2005) and land surface parameters, except surface rainfall. E1 is forced by GSFC GLDAS rainfall data, and E2 is forced by station rainfall data. Results show that the simulated runoff by E2 is better than that of E1.
At this stage, the evaluation of simulated LST in the current thesis is restricted by many factors, we can only investigate preliminary. We need to understand how important land surface parameters should be when simulate LST, however, since energy balance and water balance processes will be affected by land surface parameter in model calculation. For the next stage of the research, the spatial heterogeneous characteristic of land surface parameter should be considered to improve the evaluation approach. Besides, if more in situ and satellite observations data or high quality assimilation data are available, we can not only do more completed evaluation and refinement, but also apply it for regional climate modeling study.
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