1. Ali. M. Al-Turki Hesham M. Ibrahim
Prediction of water content at different potentials
from soil property data in Jazan region
Ali. M. Al-Turki
Hesham M. Ibrahim
Department of Soil Science
King Saud University
This research was funded by the National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia, Award Number 12-AGR

2. Introduction
In dry regions, effective irrigation management is crucial to maintain crop production and sustain limited water resources.
Effective irrigation requires good knowledge of soil water content in the root zone.
Measurements of SWRC are required to determine water availability to plants and to simulate the transport of water and solutes in the soil environment
Direct measurement of SWRC is laborious, time consuming, and expensive
Pedotransfer functions (PTFs) are widely used to determine SWRC from basic soil properties
Multiple linear regression (MLR) and artificial neural networks (ANN) are the two most common methods used to develop PTFs

3. Jazan region
Jazan region is about km2 (about 0.6% of the total area of KSA), and its population is approximately (about 5% of the total population of KSA). The region has two main watersheds: Baysh and Jazan

4. Study Location Watershed delineation was carried out in WMS 9.3
Jazan
Watershed delineation was
carried out in WMS 9.3
using 10 m DEM
Jazan watershed:
Total area km2
Average temp oC
Average rainfall 230 mm
Based on a 10 m DEM for the region, the delineation for the Jazan watershed was carried out using the Watershed Modeling System package. The main channel and streams were checked against a topographic map for the region to ensure high accuracy for the delineation process.

5. Mountains in the East

6. Jazan Dam

7. Doum Palm trees

8. Wadi Jazan, central region low flooding

9. Wadi Jazan, west region low flooding

10. Plain fields

11. Sorghum fields

12. Mango farm

13. Typical soil profiles West Middle East Soil profiles
F101: Loamy sand, light yellowish brown color, very week structure, medium drainage. F114: Loamy sand, dark grayish brown color, week structure, good drainage. F110: very gravel loamy sand, dark yellowish brown color, massive structure, good drainage.

14. Landforms and soil texture distribution
Wadi and alluvial plain are the main dominant landforms, with small areas of sabakhat and costal plain in the west, and lava and alluvial fan in the east. The dominant soil texture is sand and loamy sand, with very small areas of clay and silt loam textures.

15. Objectives
To predict water content at variable potentials (0, -10, -33, -60, -100, -300, -500, -800, -1000, and kPa) using three hierarchical approaches based on the Rosetta model:
Soil texture class (STC)
Percent of sand, silt, and clay (SSC)
Bulk density, percent of sand, silt, and clay, and water content at -33 and kPa (SSC+WC)
To determine the ability of the three approaches to predict available water content (AWC)

16. Sampling locations
Outlet
Jazan watershed:
43 measurement locations

17. Measurements and analysis
The following soil properties were measured in the collected soil samples:
Particle size distribution (PSD) (Sand, ; silt, ; clay, <0.002 mm)
Bulk density (BD)
Total Calcium carbonate (CaCO3)
Saturation percent (SP)
Electrical conductivity (EC)
Organic carbon (OC)

18. Measurements and analysis
In each soil sample, the water content at matric potentials of -10, -33, -60, -100, -300, -500, -800, -1000, and kPa was determined using pressure plate extractor. The SWRC was determined by fitting the retention data to the equation of van Genuchten (1980):
Where is the volumetric water content (cm3 cm-3),
and are the residual and saturated water content (cm3 cm-3),
(cm-1) and n (-) are shape parameters of the SWRC

19. Measurements and analysis
The following statistical indices were used to evaluate the
accuracy between measured and predicted WC:
Root mean square error (RMSE):
Mean relative error (MRE):

20. Measurements and analysis
D-index:
Nash-Sutcliffe coefficient of efficiency (NSCE):
Where and are the measured and predicted values
is the average measured value, n is the total observations

21. Results
SWRC
The van Genuchten equation adequately described the SWRC with an average R2 of The best fit was obtained with the third approach

22. Results Root mean square error
Model
Water potential (kPa)
-10
-33
-60
-100
-300
-500
-800
-1000
-1500
RMSE
STC
0.133
0.103
0.073
0.062
0.055
0.043
0.042
SSC
0.129
0.075
0.063
0.041
0.040
SSC+WC
0.119
0.053
0.039
0.032
0.026
0.021
0.024
0.025
The largest ( cm3 cm-3) RMSE was observed close to saturation
The third approach showed lower RMSE at all potentials

23. Results Mean relative error
Model
Water potential (kPa)
-10
-33
-60
-100
-300
-500
-800
-1000
-1500
MRE
STC
12.52
6.61
4.33
3.57
3.12
1.86
1.45
1.11
0.89
SSC
12.24
7.24
4.89
4.04
3.51
2.13
1.68
1.31
1.29
1.06
SSC+WC
10.89
2.42
0.64
0.56
0.61
-0.07
-0.38
-0.69
-0.91
The first and second approaches always overestimated WC
The third approach provided lower MRE, and showed slight underestimation of WC at water potentials <=300 kPa

24. Results D-index Smaller D-index was observed close to saturation
Model
Water potential (kPa)
-10
-33
-60
-100
-300
-500
-800
-1000
-1500
D-index
STC
0.355
0.685
0.780
0.773
0.750
0.722
0.683
0.662
0.651
0.625
SSC
0.369
0.673
0.777
0.763
0.751
0.719
0.698
0.686
0.654
SSC+WC
0.362
0.802
0.893
0.911
0.927
0.931
0.913
0.895
0.889
0.875
Smaller D-index was observed close to saturation
Larger D-index with the third approach

25. Results NSCE NSCE values are negative at water potentials >33 kPa
Larger NSCE with the third approach

26. Results Available water content
Maximum prediction accuracy with the third approach (R2=0.8)

27. Conclusions
The van Genuchten equation adequately described the SWRC in the Jazan region with an average R2 of 0.95
The three approaches failed to describe water content accurately at saturation conditions (>-10kPa)
The third approach gave the best prediction of WC as indicated by an average NSCE value of 0.75 as compared to 0.16 and 0.18 for the first and second approaches, respectively
The ability to predict the amount of available water in the soil profile will facilitate the accurate estimate of irrigation requirements and achieve effective irrigation scheduling

28. Thank You!