The SALTMED model is a tool for efficiently managing water, crops, and fertilizers in agriculture. It can be used to: 1. Predict the impacts of climate change and water quality on soils, vegetation, and food security. 2. Improve water use efficiency and increase crop yields with less water. 3. Guide users on suitable crop selection, irrigation systems, and strategies when using poor quality water. The model simulates processes like evapotranspiration, plant water uptake, soil nitrogen dynamics, drainage, and crop yields. It has been applied and tested in multiple countries to optimize irrigation practices and minimize environmental impacts.

1. Part I
SALTMED model as a tool for efficient use of
water, crop, and fertilizers:
Ragab Ragab
Centre for Ecology and Hydrology, CEH, Wallingford,
OX10 8BB, UK
Vice President H, the International Commission on Irrigation and
Drainage, ICID
Rag@ceh.ac.uk

2. Rational
Reliable and tested models can be useful tools
to manage the limited water resources more
efficiently and to study the long term impact
of using poor quality water on soil, crops and
the environment without the need to conduct
extensive and labour intensive field
experiments.

3. 1. Help to predict the impact of climate change (rainfall, temperature, CO2 ,
seawater intrusion , seawater inundation /tsunami) on soil, vegetation and
food security. Example : application on UK lowland coastal sites.
2. Improve water use efficiency: reduce water use in agriculture and
increase productivity to meet population needs, more crop per drop.
3. Help to select the best strategies to irrigate using less water and save
more. Example: irrigating half of the root zone in alternating system
(Partial Root Drying Method, PRD). This method saves up to 40% of
water thus optimising the water-food system.
4. Guide users to select the most suitable crop , irrigation system and
irrigation strategies when using poor quality water (saline water, brackish
groundwater, agriculture drainage water and treated waste water). Thus,
secure sustainable and healthy food supplies and increase water recycling
and re-use.
5. Predict the impact of using poor quality water on the environment and
guide the user to the best strategies to minimize the negative impact , less
water pollution and improve biodiversity.
Why use SALTMED model

4. SALTMED model application
• The model has been applied within four EU funded projects: SALTMED
(www.nwl.ac.uk/research/cairoworkshop/saltmedmodel.htm),
SAFIR (www.safir4eu.org) and SUP-MED (www.swup-med.dk) and
water4crops (www.water4crops.org).
• The countries involved in the EU projects are: UK, Egypt, Syria, Spain,
Denmark, Italy, France, Serbia, Greece, Portugal, China & Poland. All of
them are using the model and following up the new development.
• The model was developed as an activity to serve the three projects and has
been successfully tested against field experiments conducted in Egypt,
Syria, Turkey, Morocco, Spain, Portugal, Greece, Denmark, and Italy. In
addition, the model has been tested in Iran, USA and France.
• The model has recently been used to predict the impact of seawater rise
and inundation on soil and vegetation of 7 lowland coastal sites in the UK
using the climate change prediction up to 2099 (DEFRA funded).

5. Irrigation – a managed water cycle
Efficiencies
from storage to
the field
Dam to Farm
 Operating Spills
 Poor Measurement
 Leaks
 Seepage
 Evaporation
Use by Plant
 Imprecise Timing
 No Measurement of
Crop Needs
Supply to Crop
 Poor Service
 Slow Delivery
 Varying Flows
 Poor Control
Dam
Channel
Farm
Plant
Managing Water Demand: Improving Irrigation Efficiency

6. Crop water
productivity
Water productivity
Crops: “more crop per drop”
Improving Irrigation Efficiency and productivity

8. SALTMED Model for Agriculture water management

9. SALTMED Model Main Components
• Evapotranspiration
• Plant water uptake
• Water and solute transport under different
irrigation systems
• Leaching
• Soil Nitrogen dynamics
• Soil Temperature
• Drainage and Groundwater levels
• Crop yield - water use relationship
• Crop rotation, 20 multiple fields or several
treatments

10. Evapotranspiration
Penman - Monteith, FAO-56 (1998)Version
)34.01(
)(
273
900
)(408.0
2
2
U+
eeU
T
+GR
=ET
an
o
S
+∆
+
−∆ −
γ
γ
)( ecboc KKETET +=

11. Evaporation
In presence of stomata / canopy surface resistance data,
one could use the widely used equation Penman-
Monteith (1965) in the following form:
where rs and ra are the bulk surface and aerodynamic
resistances ( s m-1 ).
)1(
r
r+
r
e)-e(
C+R
=E
a
s
a
s
pn
p
+∆
∆
γ
ρ
λ

12. Calculating the stomata Conductance from regression Equation
gs = gsmax * f(VPD) * f(T) * F(SW)*f(PAR)
gsmax = Maximum Stomata conductance
f (VPD) is the relative effect of the VPD on stomata conductance
f(T) is the relative effect of the Temperature on stomata conductance
f (SW) is the relative effect of the soil water content on stomata conductance
f(PAR) is the relative effect of the radiation on stomata conductance

13. Sauter et al. 2001

14. The Stomata Conductance using the ABA
Tardieu, F, Zhang, J. and Gowing, D. J. G. 1993. Stomatal control by both [ABA]
in the xylem sap and leaf water status: a test of a model for droughted or ABA-
fed field-grown maize. Plant, Cell and environment .16:413-420.
gs = gs minimum + α * Exp (ABA * β* Exp (σ *Ψl ))
gs = Stomata conductance, mole/m2/sec
gs minimum = mimimum Stomata conductance (mole/m2/sec)
ABA = Absecic Acid concentration, daily values, (mmole/m3 )
Ψl = Leaf water potential in M pa, daily values, ( -1.3 Mpa)

15. Water uptake in presence of salts
The water uptake function accounts for water stress & osmotic stress
according to Van Genuchten (1987), where the water uptake S(z,t) is
estimated as:
S z t
S t
a t h
t
z t( , )
( )
( )
( )
( , )max
=
+
+



















1
50
3
π
π
λ

16. Water uptake in presence of salts
• where Smax (t) is the maximum potential root water uptake at
the time t,
• z is the vertical depth taken positive downwards,
• λ(z,t) is the depth-and time-dependent fraction of total root
mass,
• h is the matrix pressure head,
• π is the osmotic pressure head,
• π50 (t) is the time-dependent value of the osmotic pressure at
which Smax(t) is reduced by 50%,

17. Water uptake in presence of salts
• a(t) is a weighing coefficient accounts for the
differential response of a crop to matrix and
solute pressure.
a(t) = π50(t)/h50(t)
where h50(t) is the matrix pressure at which Smax(t)
is reduced by 50%.

18. Water Uptake
Smax(t) = ETo (t)* Kcb (t)
Root depth (t) = [Root depthmin + ( Root depthmax - Root depthmin )] * Kc (t)/Kcmax
Root width (t) = [Root width / Root depth] ratio * root depth(t)
λ (z) = 5/3L for z≤ 0.2L
= 25/12L * (1 - z/L) for 0.2L < z ≤ L
= 0.0 for z > L
where L is the maximum rooting depth

19. Crop Yield
∑
∑=
)(
)(
max tS
tS
RY
max*YRYAY =

20. Leaching Requirements
C
C
=LR
d
i
Where Ci is irrigation water Concentration & Cd is
drainage water concentration or mean salinity conc.
of the root zone.

21. Crop Growth and Biomass production
Eckersten, H and Jansson, P,.- E. 1991. Modelling water flow, nitrogen uptake and production for
wheat. Fertilizer Research 27: 313-329.
Increase in Biomass
Δ q, g/m2/day = Net Assimilation “NA”
Net Assimilation, “NA” =
Assimilation ”A ”– Respiration losses ”R”
Assimilation rate, ”A”per unit of area =
E* I* f(Temp)* f(T)*f(Leaf-N)
g/m2/day

22. Crop Growth and Biomass production
Assimilation rate per unit of area =
E* I* f(Temp)* f(T)*f(Leaf-N)
E = is the photosynthetic Efficiency, g dry matter / MJ
I : The radiation input: = Rs (1- e –k*LAI )
Rs is global Radiation, MJ/m2/day, k is extinction
LAI is the leaf area Index (m2/m2).

24. f(leaf-N) = Nitrogen stress effect on assimilation:
= [(Leaf N – LeafN min) / (Leaf N max – LeafN min)]
0 < f(leaf-N) < 1
Respiration losses , R = x* Y *Q10
((Ta – T base) / 10)
Y is yield, x is the fraction of yield that is lost by respiration
process.

25. Soil nitrogen cycle and processes according to Johnsson et al. (1987)
Processes
• Mineralization
• Immobilization
• Nitrification
• Denitrification
• Leaching
• Plant N Uptake

26. Water and solute transport under Trickle/Furrow
irrigation systems

27. Water and solute transport under Trickle/Furrow
irrigation systems

28. Water and solute flow
The vertical transient-state flow water in a stable and uniform
segment of the root zone can be described by a Richard's
type equation as:
( )
wS
z
z
K
zt
−




 +
−=
∂
ψ∂
θ
∂
∂
∂
∂θ
)(

29. Water and solute flow
If one takes the continuity equation into consideration,
one-dimensional transient movement of a non-
interacting solute in soil can be expressed as:
( ) ( )∂ θ
∂
∂
∂
∂
∂
∂
∂
c
t z
D
c
z
qc
z
Sa s=





 − −

30. Water and solute transport under Trickle/Furrow
irrigation systems
For a stable, isotropic and homogeneous porous, the two-
dim. flow of water in the soil can be describes as:
( ) ( ) wS
z
z
K
zx
K
xt
−



 +
+



=
∂
ψ∂
θ
∂
∂
∂
∂ψ
θ
∂
∂
∂
∂θ )(

31. Water and solute transport under Trickle/Furrow
irrigation systems
where λL is the longitudinal dispersivity of the medium; λT is the
transversal dispersivity of the medium; δij is Kronecker delta; Vi is the
i component of the average interstitial solution velocity V; and Ds (θ)
is the soil diffusion coefficient. If one considers only two dimensions
and substituting Dij , the salt flow equation becomes:
( ) szzxzzxxzxx SCq
x
C
D
z
C
D
z
Cq
z
C
D
x
C
D
xt
C
−





−++





−+=
∂
∂
∂
∂
∂
∂
∂
∂
∂
∂
∂
∂
∂
θ∂

32. • The soil Moisture content, θ
• The salt concentration, C (mg/litre soil solution)
• The salt content C*θ, (mg/litre bulk soil)
• Relative salt concentration, C – Cirr /Cini

34. Evolution of soil moisture profile over time under trickle line source

35. Evolution of soil salinity profile over time under trickle line source

37. Salinity profile under basin irrigation

38. Soil moisture and salinity distribution under basin irrigation

39. Evolution of soil moisture over time under trickle
line source

40. Soil moisture profile of multilayers

42. Partial Root Drying , PRD

43. Furrow irrigation

45. PRD- Soil moisture

48. Evolution of crop parameters Kc, Kcb and root depth over time

50. Irrigation + rainfall

51. Crop potential and actual water uptake and yield

60. The goodness of fit expressions are:
the root mean square error (RMSE), coefficient of determination (R2), and
coefficient of residual mass (CRM).
The CRM is a measure of the tendency of the model to over- or underestimate
the measurements. Positive values for CRM indicate that the model
underestimates the measurements and negative values for CRM indicate a
tendency to overestimate. For a perfect fit between observed and simulated data,
values of RMSE, CRM and R2 should equal 0.0, 0.0, and 1.0, respectively.