hydrological network modelling geog1002 dr p. lewis
TRANSCRIPT
Hydrological Network Modelling
GEOG1002
Dr P. Lewis
Interest in part of hydrological cycle
Precipitation:clouds to ground
Flow: vertical - infiltrationhorizontal - surface runoff
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.
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
GCMs
• Models of global energy – inputs (solar radiation) – outputs (longwave radiation) – transfers (e.g., atmosphere, ocean fluxes) – state (prediction)
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
Model for hydrological routing in GCM:
• simple
• fast
• validated
• Describe model of Naden (1992)
• Information sources for networks
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
Network Response Function:
• define Network Width Function (NWF) – no. of 'links' upstream from a point
DISTANCE
LIN
KS
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
Routing Functionre
spon
se
time
Routing Function
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
CALCULATION OF NETWORK RESPONSE FUNCTION FROM NWF
• NRF calculated from NWF and RF using velocity (A m/s) and diffusion (D m2/s) coefficients
RoutingFunction
(parametersA and D)
TIME
RE
SP
ON
SE
=
NRF
*LIN
KS
DISTANCE
NWF
Convolution
• powerful mathematical tool for linear systems
• sum of weighted contributions over a moving window
convolve
distance
alti
tude
with
distance
alti
tude
Has
effect of
SMOOTHING
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
RE
SP
ON
SE
NRF
TIME
FL
OW
PREDICTEDFLOW
=
TIME
RUNOFF
RU
NO
FF
• 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
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
Data sets
DATA SET SCALE SOURCEDCW Blue Line 1:1 000 000 USDMA/ESRI1:50k Blue Line 1:50 000 Ordnance Survey/IHDCW DEM 1km UCL (ANUDEM)EDC DEM 1km EDCDTED DEM 1 1km Military (MCE Feltham)DTED DEM 2 100m Military (MCE Feltham)OS/IH DEM 50m Ordnance Survey/IHIFSAR DEM 30m UCL 3DIMGTOPO30 DEM 1km USGS EDCHYDRO1K Blue Line 1km USGS EDCRIVER REACH 1:250 000 EPA
Case studies
Severn and Thames ~10 000 km2 each
Data Set Scale /Resolution
Thames Severn
1:50k Blue Line 1: 50 000 0.89 0.87DCW Blue Line 1: 1 000 000 0.88 0.85DTED Level 1 100 m 0.88 0.83DTED Level 2 1 km 0.86 0.74EDC 1 km --- 0.74GTOPO30 1 km 0.86 0.68Summed Runoff (Not routed) 0.72 0.50
(Higher values are better, approaching 1.00)
EFFICIENCY OF FITUK Predicted Flows vs Observed Flows
Efficiency of Fit 1 QO QE 2
QO QO 2QO = Observed Flow
QE = Estimated Flowwhere:
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
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)