1. Distributed modelling
Today’s topics
Distributed modelling
08:45 – 09:30 Distributed catchment modelling
09:45 – 10:30 Choices in degree of distribution and data
? Means that I should ask you a question
July 21, 2018

2. Spatial representation of river basin models
Trade-off between data availability and model complexity E P P E
P, E
P, E E P
P, E
P, E
Lumped
Semi-distributed
Distributed
Water Resources Modelling – UNESCO IHE
July 21, 2018

3. Example: the Rhine upstream Lobith
Lumped
Semi-distributed
Lobith
July 21, 2018

4. Why distributed modelling?
Scientific reason
Known variability in hydrological processes
Distributed data available, so why not use it?
Engineering reasons
Evaluate water balance in specific sub-domains
We have a certain question that can only be answered with a distributed model …
July 21, 2018 ?

5. Lumped Advantages Fast Easy to implement Disadvantages
only the outlet (discharge) behaviour or lumped over whole catchment
No internal info  no coupling with e.g. soil erosion or vegetation models
Loss of information on spatial distribution
July 21, 2018

6. Distributed Advantages
Include distributed data sources (e.g. rainfall)
Include distributed scenarios (e.g. land use change)
Interpret distributed results (e.g. local erosion potential, vegetation changes, include local pollution sources)
Disadvantages
Slow
Equifinality (i.e. distribution of parameters often causes confusion and not necessarily a better model)
July 21, 2018 ?

7. A distributed catchment model
Consists of:
1. A (conceptual or physical) model for the water balance (input: rainfall, potential evaporation; output: evaporation, river discharge, and any other flux or store that a user may want to incorporate in the model.
2. Some form of river routing
July 21, 2018 ?

8. 1. Conceptual water balance model Water balance computation
Rainfall
Evaporation
River discharge
Storage dynamics
grid
cell
Runoff (according to flow direction)
July 21, 2018

9. 1. Conceptual WB model 1-α α Flux State Perception Model structure
Interception
Unsaturated zone
Groundwater
Rainfall
Radiation, humidity /etc.
Base flow
(Sub)surface flow
Transpiration
1-α α
Flux
State
Percolation
Perception
Model structure
River discharge
July 21, 2018

10. 2. Routing Each cell has: Input (e.g. rainfall)
Evaporation
River discharge
Storage dynamics
Each cell has:
Input (e.g. rainfall)
State variables (e.g. soil
moisture, groundwater level)
Output (e.g. evaporation, runoff)
Even groundwater outflow goes straight to the river
What to do with the runoff?
July 21, 2018

11. 2. Routing Runoff moves horizontally over the drainage network
Transport in a cell without storage consideration is:
where
n : location of upstream cell
i : location cell under computation
n = 0: the location of a water divide
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12. Example: Kabompo basin, Zambia
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13. 2. Routing Taking into account that there is channel storage
Kinematic wave approximations Q1 A Δx q P Q2
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14. Software environment Usually works with a GIS oriented environment
Often raster data but triangular shapes are also possible
Model consists of an excel-like script that combines dynamic input maps and parameter maps with arithmetic operators
July 21, 2018

15. Steps to the setup of a distributed model, catchment delineation
Step 1: obtain a digital elevation model (DEM) of area
Step 2: derive flow directions per cell
Step 3: Select outlet (and locations of intermediate flow stations)
Step 4: derive (sub)catchment boundaries (e.g. based on strahler stream orders)
Step 5: distribute parameters (?)

16. Strahler order
July 21, 2018

17. Example: Kabompo, strahler threshold ???
Catchment boundary
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18. Lumwana river (4th order stream)
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19. Catchment boundary
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20. Kabompo river (5th order stream)
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21. Catchment boundary
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22. Kabompo river (8th order stream)
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23. Hydrotopes Areas (classes) of assumed similar hydrological behaviour
Remotely sensing data is interesting
Expert judgment or field examinations are required
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24. Hydrotopes, Example: Volta basin
slopes
plateaus
wetlands
July 21, 2018

25. Hydrotopes LandSAT imagery
Use thresholds to derive the classes
Normalized Difference Vegetation Index (NDVI)
Logical expression:
If NDVI > 0.05, wetland
July 21, 2018

26. Hydrotopes DEM  slopes
Use thresholds to derive the classes
Slope of topography
Logical expression:
If slope > 0.04, hill
July 21, 2018

27. Hydrotopes Classification
1. Plains (DEM)
2. Wetlands (NDVI)
3. Rivers (runoff)
4. Slopes (DEM)
July 21, 2018

28. Hydrotopes only on elevation: Height-Above-Nearest-Drainage (HAND)
height almost equal to stream height
large height difference with stream

29. FLEX TOPO: topography driven distributed modelling