Houston, TX 77005
2:00 p.m. Friday, Sept. 20, 2013
On Campus | Alumni
The rapid development of land surface models (LSMs) over the past three decades has reached a point that these LSMs can adequately represent the surface energy, water, and carbon balances spanning a wide range of space and time scales, as judged by comparison with a wealth of surface and remote sensing datasets. LSMs have been used in various weather forecasting and climate change studies, such as assessing the coupling strength between the land surface and the atmosphere, understanding climate and carbon interaction and feedbacks, and quantifying the impacts of land use and land cover change on climate change. Recently, LSMs are being merged with other types of models including surface hydrology (river flows with implications for flooding and drought, soil chemistry, nutrient transport, and freshwater inflow to coastal zones), groundwater (aquifers, irrigation, and human withdrawals), ecology (vegetation growth and health, crop yield, wetlands, and riverine ecosystems), and air quality (biogenic emissions, dust emissions, aerosols, urban canopy layer, and dry/wet deposition). New data assimilation methods are being explored to take advantage of remote sensing products, surface flux network measurements, and aircraft datasets to improve LSMs’ predictive skills. Multi-physics (or multi-parameterization) frameworks have been incorporated in LSMs to allow for multi-hypothesis testing and uncertainty quantification. Hyperresolution modeling at scales of O(100 m) is being proposed to take advantage of the emerging petascale computational resources. Therefore, next-generation LSMs are becoming more complex as we are facing unprecedented challenges to understand variability and change on all time and space scales, and to quantify the climatic impacts on energy and water resources, agriculture, ecosystems, and environmental conditions for decision-making. As a result, the new development of these LSMs demands much more coordinated and integrated efforts from multi-disciplinary groups.