1. Hydrological Network Modelling GEOG1002 Dr P. Lewis

2. Interest in part of hydrological cycle Precipitation: clouds to ground Flow: vertical - infiltration horizontal - surface runoff

4. Components of river forecast model: Baseflow: –the amount of water coming from groundwater. Runoff: –the amount of water coming from surface runoff. Routed Flow: –the amount of water coming from an upstream point.

5. Examine here hydrological network model required output:required output: –flow at a point / time inputs:inputs: –hydrological network –runoff require for:require for: –flood modelling/prediction (water flow through network) –routing of water in Global Climate Models

6. GCMs Models of global energy –inputs (solar radiation) –outputs (longwave radiation) –transfers (e.g., atmosphere, ocean fluxes) –state (prediction)

7. GCMs Models concentrate on vertical fluxes poor modelling of hydrological routing –essentially dump excess in nearest ocean grid cell –doesn’t give time lag for travel cant easily relate to measurement

8. Model for hydrological routing in GCM: simple fast validated Describe model of Naden (1992) Information sources for networks

9. Naden (1992) Model grid to point via river channel network can validate at series of points Require data on river network –topology –cross-section –slope/speed

10. Network Response Function: define Network Width Function (NWF) –no. of 'links' upstream from a point DISTANCE LINKS

11. define Routing Function function of stream velocity (slope) and cross-section describes time lag due to friction in channel RF introduces delay into system through convolution with NWF

12. Routing Function response time

13. Routing Function

14. Network Response Function (NRF) 'flash' (impulse) of water onto system - measure NRF essentially same 'shape' and NWF, but smoother depends strongly on time lag in RF

15. CALCULATION OF NETWORK RESPONSE FUNCTION FROM NWF NRF calculated from NWF and RF using velocity (A m/s) and diffusion (D m 2 /s) coefficients Routing Function (parameters A and D) TIME RESPONSE = NRF * LINKS DISTANCE NWF

16. Convolution powerful mathematical tool for linear systems sum of weighted contributions over a moving window

17. convolve distance altitude with

18. distance altitude Has effect of SMOOTHING

19. CALCULATION OF PREDICTED RIVER FLOWS Disaggregated precipitation generated by a mesoscale model Hydrological model used to produce ‘generated runoff’ from precipitation, where ‘generated runoff’ is that portion of precipitation which enters the channel network * TIME RESPONSE NRF TIME FLOW PREDICTED FLOW = TIME RUNOFF

20. now have model of predicted flow based on: –network topology (NWF) –cross-section. speed/slope (RF) –runoff can 'validate' model by comparing predicted/modelled flows at point model is simple enough for GCMs requires data to define NRF

21. Consider data sources need to be global range of sources available: –digital network data ('blue line') topological structuring network links structured to flow downhill removal of braids and lakes –derive from DEMs models to derive networks from DEM e.g. GIS model used in ARC/INFO can use to define catchment

25. Data sets

26. Case studies Severn and Thames ~10 000 km 2 each

32. (Higher values are better, approaching 1.00) EFFICIENCY OF FIT UK Predicted Flows vs Observed Flows where:

33. Summary hydrological flow modelling important for routing of water –e.g. in GCM –simple, fast, validate –Naden 1992 flow to a point in network –define: NWF RF NRF = NWF * RF flow = runoff * NRF

34. Summary model requires network data –various available, DCW etc. –variable quality –can derive network from DEM result dependent on quality/resolution of DEM need accurate high resolution DEM globally (satellites)