Multicomponent Solute Transport in Two Multifactorial Experiments

Transcription

1 Objective Multicomponent Solute Transport in Two Multifactorial Experiments T. B. Ramos, M. C. Gonçalves, A. Prazeres & J. C. Martins Department of Soil Science,, Estação Agronómica Nacional, Oeiras, Portugal N. L. Castanheira Department of Rural Engineering, University of Évora, Évora,, Portugal To evaluate the effectiveness of HYDRUS-1D to predict: Water content and fluxes Concentration of individual cations (Na +, Ca 2+, Mg 2+ ) Overall salinity (Electrical conductivity EC) Sodium Adsorption Ratio (SAR) Exchangeable Sodium Percentage (ESP) The experiment Triple Emitter Source System Two locations Two different soils Fluvisol Medium texture Anthrosol Coarse texture Time period June 4 to February 7 3 irrigation cycles and 3 rainfall leaching cycles Culture Maize Trickle irrigation system A triple emitter source irrigation system was used for water, salt (Na + ), and fertilizer (N) application. Based on Israelian designs 1

2 Triple Emitter Source System Fresh water Triple Emitter Source System Saline Solution Fertilizer water Saline water Nitrogen Solution Salt Gradient Fresh Water Source Group I Group II Group III Group IV Nitrogen Gradient Monitoring irrigation and leaching cycles (,, and cm) Soil water content (TDR) Electrical conductivity from soil solution Soluble cations from soil solution Ceramic Cups Soil sampling before and after each irrigation cycle (-, -, and -) Electrical conductivity of soil saturation extract Soluble cations of soil saturation extract + Exchangeable cations ( Na ) SAR = ( Ca + Mg ) Cation exchange capacity (CEC) 2 Sodium Adsorption Ratio (SAR) + Na ESP = x1 Exchangeable Sodium Percentage (ESP) ( CEC) INPUT DATA 2

3 2 Soils Soil layers (-3, 3-75, 7-1 cm) (-3, 3-5, 5-9 cm) Time period June 4 February 7 Boundary Conditions Surface: Water fluxes Bottom: Free Drainage Soil initial conditions Soil hydraulic properties Solute transport parameters Gapon selectivity coefficients Ionic concentrations of irrigation waters Root distribution Soil Surface Rainfall (daily) / Irrigation water Soil Evaporation (E) = ET c - T Crop Transpiration (T) = ET c Soil Cover Factor (Leaf Area Index measured every week) ET c = ET K c K c for maize (FAO) Daily ET (Penman-Monteith Monteith) (mm) days Evaporation Transpiration Soil Initial Conditions Soil Hydraulic Properties Soil water content, TDR Dry bulk density Soluble cations of soil saturation extract Na +, Ca 2+, Mg 2+, K + Exchangeable cations Na +, Ca 2+, Mg 2+, K + Cation exchange capacity (CEC) Measured in the laboratory - Suction tables - Pressure plate - Evaporation method - Hot Air method Described with the Mualem-van equation using RETC code Genuchten 3

4 Soil Hydraulic Properties Solute Transport Parameters - Chloride breakthrough curves - CXTFIT 2.1 Gapon Selectivity Coefficients Ionic Composition of irrigation waters Determined from initial soil conditions (Exchangeable and Soluble cations) Ca Na K Ca / Na = + 2 1/ 2 Na ( Ca + ) meq/l 8 meq/l 8 c = T M z i = 1 c i Ca2+ Mg2+ Na+ K+ Fresh Water Saline Water (4-5) Ca2+ Mg2+ Na+ K+ Fresh Water Saline Water (4-6) Saline Water (6) 4

5 Root Distribution Monitored using a Minirhizotron technique associated with a video camera system Roots distributed over cm depth RESULTS Water content Electrical Conductivity (EC) 5

6 Na+ concentration Mg2+ concentration Ca2+ concentration Sodium Adsorption Ratio (SAR) 6

7 Exchangeable Sodium Percentage (ESP) Depth (cm) ESP (%) Depth (cm) ESP (%) Statistical Analysis θ CE Na+ Ca2+ Determination Coefficients n = 282 n = 379 n = 247 n = 363 n = 242 n = 356 n = 227 n = 345 b = 1. b =.86 b = 1.17 b = 1.16 b = 1.5 b =.91 b = 1.21 b = 1. R 2 Mg2+ n = 233 n = 343 b = 1.38 b = 1.61 GIA - Hydrus GIA - Observed GIVC - Hydrus GIVC - Measured SAR ESP n = 219 n = 327 n = 36 n = 36 b =.73 b =.61 b = 1.5 n = 1.44 Conclusions CONCLUSIONS HYDRUS-1D has successfully simulated water regime, and the effects of different irrigation waters on the geochemistry of the studied Fluvisol and Anthrosol. The best correspondence was obtained for water content, EC, and soluble sodium. Soluble magnesium and calcium, and SAR were predicted less well. HYDRUS-1D proved to be a powerful tool for evaluating irrigation management, and for predicting the effects of irrigation water quality on the soil and groundwater quality. Models, such as HYDRUS-1D, should be used to establish sound irrigation policies and to mitigate environmental risks. 7