CSDMS 2015 annual meeting poster YuZhang

Presentation provided during CSDMS annual meeting 2015

The harmony of data-model interaction in landscape evolution modeling, an example from the Shale Hills CZO of central Pennsylvania

Yu Zhang, Penn State University, Pennsylvania, United States. yzz130@psu.edu
Rudy Slingerland, Penn State University, Pennsylvania, United States.
Christopher Duffy, Penn State University, Pennsylvania, United States.


A 0.08 km2 first order catchment (Shale Hills Critical Zone Observatory, PA) installed hundreds of sensors and equipment for bedrock weathering, regolith movement, soil moisture, soil temperature, soil water flow path, solar radiation, plant growth, evaporation, surface water flow, sediment load, snow, etc. Previously, these data, plus the remote sensed data, only support a particular aspect of research. However, the increasing cooperation among different disciplines requires a higher level of collaboration among interdisciplinary observations and measurements. With the support of multi-spatial and temporal scale data for model parameterization, calibration and validation, this study utilizes a 3D hydrologic-morphodynamic model (LE-PIHM) which links bedrock, soil, surface and subsurface water flow, plant, energy, and seasonal climate to explore the possible factors that causes the topographic asymmetry on both sides of hillslopes of the Shale Hills CZO. The simulated results show obvious spatial variations of solar insolation, infiltration and groundwater flow which affect overland flow, freeze-thaw events, bedrock weathering, and morphodynamic feedbacks on both sides of hillslopes. Especially, the solar insolation is the major factor that affects evaporation and freeze-thaw frequency, thereby causing the asymmetric sediment diffusivity by freeze-thaw process on the hillslopes. The simulated hillslope diffusion flux indicates that the measurement overestimates the difference of sediment transport efficiency between the two hillslopes. The critical transition of diffusion flux by model simulation shows the limitation of current observations and measurements, and highlights the locations where additional measurement or observation should be conducted in order to support a better model simulation.

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