Modeling Transition from Lumped to Distributed Systems in Hydrology

1. TRANSITION FROM LUMPED TO DISTRIBUTED SYSTEMS Victor Koren, Michael Smith, Seann Reed, Ziya Zhang NOAA/NWS/OHD/HL, Silver Spring, MD

2. Distributed and Lumped Modeling Dynamics

3. History Lessons • There is a similarity in dynamics of lumped and distributed model developments • There is a large delay between model development and application • There is no ‘unique best’ model. Selection for application is rather arbitrary process that depends on an expertise of the user and practical requirements • Most successful models in an operational use are models which have well developed parameterization tools

4. Distinguishing Features of Lumped and Distributed Models • Physics • Does point rainfall-runoff model represent well ‘field’ processes • Can hillslope/channel routing be represented well on ‘practically reasonable’ space/time scales • Does statistical approach solve a basin heterogeneity problem

5. Distinguishing Features of Lumped and Distributed Models (Continued) • Physics • Does statistical approach solve a basin heterogeneity problem Surface runoff simulated with and without use of rainfall distribution function at different scales

6. Distinguishing Features of Lumped and Distributed Models (Continued) • Space/Time Variability • Does accounting for the space/time variability of input data and parameters guarantee better results • Does scale effect significantly on the model structure • Is a lumped model a reasonable candidate in a distributed system Effect of noisy rainfall data on the peak volume at different simulation scales.

7. Distinguishing Features of Lumped and Distributed Models (Continued) • Parameterization/Calibration • Can distributed model parameters be measured on the grid scale • Are distributed model parameters identifiable enough from hydrograph analyses • How much does scale effect on model parameters Change an ‘effective’ parameter value at different scales as a function of rainfall variability

8. HL-Research Modeling System (HL-RMS) Modeling framework for testing lumped, semi-distributed, and fully distributed hydrologic modeling approaches

9. HL-RMS Structure • Uses channel connectivity matrix defined on the HRAP grid • Each computational element consists of a number of uniform hillslopes and ‘conceptual’ channels • Rainfall-runoff component (Sacramento model in the 1st version) generates ‘fast’ and ‘slow’ runoffs • Hillslope transforms (kinematic routing) ‘fast’ runoff into lateral channel inflow • Channel inflow combined with ‘slow’ runoff and upstream cell outflow is routed through a cell ‘conceptual’ channel • Ingests NEXRAD Stage III data • Includes features of lumping parameters/input data • Modular design to test other models

11. HL-RMS Structure • Conceptualization of a grid cell

12. HL-RMS Parameterization • A priori parameters: • Rainfall-runoff model parameter grids are estimated using soil/vegetation data • Hillslope/Channel routing parameter grids, slope, length, area above, are calculated based on DEM • Uniform channel shape and roughness coefficient is assumed at each grid cell • Parameter adjustment: • Scaling/Replacement based on lumped or semi-distributed calibration of rainfall-runoff model • Spatially variable channel shape and roughness parameters can be generated from discharge measurements at outlets and geomorphological properties at each grid cell