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Ragab R 2 - UEI Day 2 - Kochi Jan18

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Ragab R 2 - UEI Day 2 - Kochi Jan18

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Ragab R 2 - UEI Day 2 - Kochi Jan18

  1. 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. 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. 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. 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. 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. 6. Crop water productivity Water productivity Crops: “more crop per drop” Improving Irrigation Efficiency and productivity
  7. 7. SALTMED Model for Agriculture water management
  8. 8. 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
  9. 9. 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 +=
  10. 10. 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
  11. 11. 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
  12. 12. Sauter et al. 2001
  13. 13. 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)
  14. 14. 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
  15. 15. 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%,
  16. 16. 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%.
  17. 17. 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
  18. 18. Crop Yield ∑ ∑= )( )( max tS tS RY max*YRYAY =
  19. 19. 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.
  20. 20. 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
  21. 21. 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).
  22. 22. 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.
  23. 23. Soil nitrogen cycle and processes according to Johnsson et al. (1987) Processes • Mineralization • Immobilization • Nitrification • Denitrification • Leaching • Plant N Uptake
  24. 24. Water and solute transport under Trickle/Furrow irrigation systems
  25. 25. Water and solute transport under Trickle/Furrow irrigation systems
  26. 26. 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 )(
  27. 27. 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=       − −
  28. 28. 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 )(
  29. 29. 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∂
  30. 30. • 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
  31. 31. Evolution of soil moisture profile over time under trickle line source
  32. 32. Evolution of soil salinity profile over time under trickle line source
  33. 33. Salinity profile under basin irrigation
  34. 34. Soil moisture and salinity distribution under basin irrigation
  35. 35. Evolution of soil moisture over time under trickle line source
  36. 36. Soil moisture profile of multilayers
  37. 37. Partial Root Drying , PRD
  38. 38. Furrow irrigation
  39. 39. PRD- Soil moisture
  40. 40. Evolution of crop parameters Kc, Kcb and root depth over time
  41. 41. Irrigation + rainfall
  42. 42. Crop potential and actual water uptake and yield
  43. 43. 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.

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