SIMULATING FLUXES THROUGH LARGE WATERSHEDS USING REMOTELY SENSED AND GROUND BASED DATASETS WITH THE INTEGRATED LANDSCAPE HYDROLOGY MODEL ILHM

Changes in climate and land use are expected to have a significant effect on hydrologic fluxes in many parts of the world. A range of remote sensing datasets can be integrated with GIS and ground based data sources in process-based hydrologic models to forecast the influence of changes in landscape and climate factors on the hydrologic cycle of a watershed. We developed the Integrated Landscape Hydrology Model (ILHM) to simulate flows through watersheds given climate and landscape data. Our prediction of streamflows throughout the 7,500 square kilometer Muskegon River watershed over the period from 1986-2006 provides insight into the importance of various land use and climate variables on evapotranspiration, recharge, and streamflow. Remotely sensed products that are currently used in these simulations include 8 day Leaf Area Index, hourly NEXRAD rainfall estimates, Digital Elevation Models, and Land Cover Maps. However the approach can readily incorporate LIDAR and other data sources that have relevance to hydrologic processes. Geophysical measurements that provide insight into soil moisture, including Ground Penetrating Radar and Resistivity methods, can also be used to improve model domain characterization and parameterization. The regional scale ILHM results could also be directly compared with novel satellite-based gravity anomalies and estimates of soil moisture.