Soil mapping helps to improve catchment hydrological modelling. The water that ends up in streams passes through hillslopes encountering various controls in the process. One of these controls is the hydropedology type of the soils that govern among other things recharge or percolation, interflow or overland flow processes and how these contribute to streamflow.

1. DIGITAL SOIL
MAPPING FOR
HYDROLOGICAL
MODELLING George van Zijl
Johan van Tol
Eddie Riddell
Daniel Fundisi

2. Hard rock C horizon showing redox mottling
Prismatic impermeable horizon Permeable soil horizon
CONCEPTUAL HYDROLOGICAL RESPONSE MODEL

3. HYDROPEDOLOGY
?
?
Soil Map

4. AIMS
Create a soil map of area with DSM
Use soil map to create CHRM map
Use CHRM map to configure ACRU
Assess model outputs
HYPOTHESIS
Soil Information will improve model accuracy

6. STEVENSON HAMILTON RESEARCH SUPERSITE
• Geology: Granite
• MAP: 537 mm/a
• Vegetation: Savannah
• Land Use: Natural veld

7. MATERIAL AND METHODS
• Soil Map
– SoLIM rule based
– Expert knowledge approach
– Environmental covariates
• Satellite imagery (SPOT + Landsat)
• DEM - SUDEM (van Niekerk, 2012)
• Remotely sensed Biomass and ET (eLEAF)
– 119 Observations
– Functional soil class map
– 73% validation point accuracy

8. MATERIAL AND METHODS
• Soil Map

9. MATERIAL AND METHODS
• Soil Map
• Hillslopes

10. MATERIAL AND METHODS
• Soil Map
• Hillslopes
• CHRM

11. MODELLING

12. ACRU CONFIGURATIONS
• Lumped
– Average soil values for catchment
• ACRU 2000
– 2 Soil layers
– Groundwater store
• ACRUint
– 2 Soil Layers
– Int-ermediate vadoze zone
– Groundwater store

13. ACRU CONFIGURATION

14. THREE QUESTIONS
• Does the soil info improve the modelling?
– ACRU lumped vs ACRU 2000 and ACRUint
• Does the introduction of intermediate vadoze zone (IVZ)
improve modelling?
– ACRU 2000 vs ACRUint
• What is optimal scale for modelling?
– Stream Orders
– Time Series

15. STREAMFLOW MODEL OUTPUTS – 3RD ORDER
0
20
40
60
80
100
120
0
1
2
3
4
5
Rainfall
(mm)
Q
(mm
day
-1
)
Rainfall ACRU_lumped ACRU2000 ACRU_int Observed
0
100
200
300
400
500
600
0
20
40
60
80
100
120
140
160
2012/11/15
2012/11/22
2012/11/29
2012/12/06
2012/12/13
2012/12/20
2012/12/27
2013/01/03
2013/01/10
2013/01/17
2013/01/24
2013/01/31
2013/02/07
2013/02/14
2013/02/21
2013/02/28
2013/03/07
2013/03/14
Cum.
Rainfall
(mm)
Cum.
flow
(mm)

16. DOES SOIL INFO IMPROVE MODEL?
Catchment
Model run (level of
detail) R2 NS RMSE
1
st
order
ACRU_Lumped 0.49 -7.62 6.21
ACRU2000 0.57 -0.51 6.22
ACRU-Int 0.51 -0.71 6.62
2
nd
order
ACRU_Lumped 0.57 0.52 2.10
ACRU2000 0.83 0.72 1.55
ACRU-Int 0.87 0.79 1.36
3
rd
order
ACRU_Lumped 0.82 0.67 2.89
ACRU2000 0.90 0.72 2.68
ACRU-Int 0.91 0.73 2.63

17. DOES SOIL INFO IMPROVE MODEL?
Catchment
Model run (level of
detail) R2 NS RMSE
1
st
order
ACRU_Lumped 0.49 -7.62 6.21
ACRU2000 0.57 -0.51 6.22
ACRU-Int 0.51 -0.71 6.62
2
nd
order
ACRU_Lumped 0.57 0.52 2.10
ACRU2000 0.83 0.72 1.55
ACRU-Int 0.87 0.79 1.36
3
rd
order
ACRU_Lumped 0.82 0.67 2.89
ACRU2000 0.90 0.72 2.68
ACRU-Int 0.91 0.73 2.63

18. CONCLUSIONS
• Soil map for large area could be created with DSM
methods
• Soil map used to create hillslope based CHRM’s
• CHRM map could be used to configure ACRU

19. CONCLUSIONS
• Indications are:
• Soil info improved modelling
• Introduction of IVZ improved modelling
• 2nd / 3rd order best spatial scale to model at
• Inconclusive evidence for temporal scale

20. Water Research Commission
University of the Free State
SANPARKS
Faith Jumbi
Daniel Fundisi