NASA Multi-sensor, multi-scale land use and hydrology project (2005-2011)
Laura Bowling, Agronomy, Purdue
Keith Cherkhauer, Agricultural and Biological Engineering, Purdue
Bryan Pijanowski, Forestry and Natural Resources, Purdue
Dev Niyogi, Agronomy and Earth and Atmospheric Sciences, Purdue
This project focuses on developing an integrated modeling tool with specific focus on the Upper Great Lakes region and will study the Monitoring, Impacts and Climate aspects of LCLUC of concern in the current solicitation. The study will investigate impacts of LCLUC and urbanization on atmospheric circulation and weather and the subsequent impacts on land surface hydrometeorology within the Upper Great Lakes region.
Our primary objective is to develop an integrated modeling tool, incorporating spatial data from multiple sensors, to study the interactions between land-use, weather, and surface hydrometeorology, with a specific focus on the potential impacts of increases in urbanization within Upper Great Lakes region.
Specific research questions that will be addressed are:
1) How has urbanization altered the orientation and/or composition of approaching thunderstorms, and in particular, the hydroclimatology of storm events?
2) How do different sources of data, which are at different resolutions and contain different numbers of land use classes and land cover details, affect the simulated hydrometerologic and energy fluxes?
3) How will different future potential land use change trajectories affect climate and water
fluxes across different spatial scales?
Our general approach for addressing these questions encompasses three fronts, including multisensor analysis, model development and integrative analysis and modeling. Datasets from multiple spaceborne sensors will be utilized to explore the role of resolution and spatial heterogeneity on observed and predicted hydrometeorology. Spatial patterns of future LCLUC will be predicted using the Land Transformation Model (LTM), enhanced to incorporate dynamic landcover, economic and policy using reduced-input node neural network version of the LTM as well as seasonal home components into its land use change predictions. Different land use scenarios predicted by the LTM will be represented through the Noah Land Surface Model (LSM), currently embedded in the Weather Research and Forecasting (WRF) model. The embedded Noah model will be enhanced to incorporate recent improvements to urban representations. The coupled WRF-Noah LSM model will be used to explore the connections between land-use, surface hydrology and the atmosphere, through analysis of water and energy balances over several urbanized watersheds within the Upper Great Lakes region, and with a specific focus on the effects of changing extents of urban and forested areas on episodic, hydrometeorological events.
Last updated March 7, 2011