Distributed hydrologic models supported by national data on soil survey, geology, topography and vegetation can provide valuable information about the watershed hydrologic cycle. However numerical simulation of the multistate, multi-process system is structurally complex and computationally intensive. This presents a major difficulty in model calibration using traditional techniques. Here we present an efficient calibration strategy for the physics-based, fully coupled, distributed hydrologic model Penn State Integrated Hydrologic Model (PIHM) with the support of national data products. PIHM uses a semi-discrete Finite Volume Method (FVM) formulation of the system of coupled ordinary differential equations (e.g. canopy interception, transpiration, soil evaporation) and partial differential equations (e.g.
groundwater-surface water, overland flow, infiltration, channel flow, etc.). The matrix of key parameters to be estimated in the optimization process was partitioned into two groups according to the difference in time scales. The first group of parameters is largely influenced by seasonal changes in energy (seasonal time scale group: SG), while the second group of parameters generally describes hydrologic processes influenced by hydrologic events (eventbased group: EG). The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is used to optimize the EG parameters, followed by the estimation of parameters in the SG. The calibration strategy was applied at 3 watersheds in central PA: a small upland catchment (8 ha), a watershed in the Appalachian Plateau (231 km2 ) and the Valley and Ridge of central PA (843 km2 ). A 2-partition 2-stage calibration enabled a fast and efficient estimation of parameters.
Yu, X., Bhatt, G., Duffy, C., Shi, Y. (2012): A Two-Scale Parameterization for Distributed Watershed Modeling Using National Data and Evolutionary Algorithm. AGU Annual Fall Conference Proceedings.
This Paper/Book acknowledges NSF CZO grant support.