Abstract

Surface water, plant water, soil and groundwater, and the atmosphere are all linked components of the hydrologic continuum. Understanding and prediction of the interaction between these components requires an integrated approach. Here we present a framework to couple physics, numerics, data and computation, with the goal to simulate coupled hydrologic processes at multiple spatial and temporal scales. The framework is unique in its use of adaptive decomposition strategies, flexibility to use different approximations and number of processes, use of multi-processor clusters, and seamless flow of data between data-management systems and hydrologic models. The framework has been applied from hill-slope (10-100m) to catchment (100-1000m) to synoptic scales (>100km), to answer fundamental questions such as: a) What kind of process feedbacks exist between groundwater, soil moisture, overland flow and vegetation, b) What are the spatial and temporal scales of these feedbacks, and c) How are they altered by land use/cover, hydrogeology, topography and antecedent conditions. The results underscore the need for synergistic observation and modeling of hydrologic cycle to address mass, momentum and energy closure at multiple scales.

Citation

Kumar, M., Duffy, C., Bhatt, G. (2010): Understanding and Prediction: An Evolving Paradigm for Modeling Hydrologic Process Feedbacks at Multiple Scales. AGU Annual Fall Conference Proceedings.