December 2010

Oasification is a natural or anthropogenically induced process by which an area of desert or one with a (semi-)arid desert climate at risk of desertification is transformed into one with vegetation, thereby creating an oasis under controlled conditions. Oasification is a process opposite to desertification due to soil erosion. In layman’s terms an extreme case of oasification would be the process of greening the desert. To stimulate oasification a combination of methods are used to address various hydrological and edaphic issues including water harvesting mechanisms, soil preparation and preservation and the introduction of appropriate plant species. The term oasification was coined in 1999 by Martínez de Azagra, Professor of Hydraulics, Forestry and Hydrologic Models at Escuela Técnica Superior de Ingeniería Agrarias at the University of Valladolid in Palencia, Spain. Oasification: Application in November 2010 accepted and pending submission to a panel for review as a new word in the Oxford English Dictionary. Introducing the SWAP model Oasification – Case study: The Borkhar irrigation district, Iran Simulating oasification using SWAP {irrigation strategies} 2

SWAP is a computer model that uses mathematical equations based on theoretical concepts grounded in hydrology and soil physics. SWAP simulates the transport of water, solutes and heat in variably saturated top soils. The program is designed for integrated modelling of the Soil-Water- Atmosphere-Plant System. Transport processes at field scale level and during whole growing seasons are considered. The upper system boundaries are defined by the soil surface, with or without a crop and the atmospheric conditions. The lower boundary is located in the unsaturated zone or in the upper part of the groundwater and describes interaction with regional groundwater. The lateral boundary simulates interaction with surface water systems. (Kroes, J.G., J.C. van Dam, J. Huygen, and R.W. Vervoort, 1999) 3

(Gautier, 2008) 4

Integrated modelling of the Soil–Water-Atmosphere-Plant System 5

Integrated modelling of the Soil–Water-Atmosphere- Plant System {Processes} Impact on surface water (+/-) and direction of flows ( ). Precipitation /irrigation Interception Transpiration Soil evaporation Surface runoff/on Drainage / infiltration Deep percolation / Seepage 6

Integrated modelling of the Soil–Water-Atmosphere-Plant System 7

Integrated modelling of the Soil–Water-Atmosphere-Plant System {Equations} Soil water flow : Richards’ equation based on Darcy’s Law. Soil hydraulic function: Van Genuchten and Mualem. Hysteresis: Scott et al. Precipitation : Rain gauges or tipping buckets Interception: Von Hoyningen-Hune and Braden Transpiration: Penman-Monteith Soil evaporation: Penman-Monteith Surface runoff: Horton / Dunne - {Surface runoff is defined as a function of the ponding height} Deep percolation/seepage: {Soil water flow/soil hydraulic function} Drainage/infiltration: Hooghoudt and Ernst 8

If each of the above processes could be represented in a single equation the SWAP model would have been somewhat simplified. However, rather than each of the above processes being equated to a single equation which is fed into the SWAP model, the picture is a little more complex. An equation may represent more than one process and a single process may be calculated in the SWAP model by using more than one equation. Some equations compliment each other to derive a particular calculation. Interaction between water flow, solute transport, heat flow and crop growth. Soil water flow: Precipitation, interception, evapotranspiration, surface runoff, percolation and drainage are calculated as described earlier. Solute transport: Convection, diffusion and dispersion, non linear adsorption, first order decomposition and root uptake – This allows simulation of pesticide and salt transport as well as the effect of salinity on crop growth. For detailed pesticide transport or nitrate leaching, daily water fluxes can be generated as an input into the pesticide model PEARL (Leistra et al, 2000)/(Tiktak et al, 2001) or the nutrient model ANIMO (Groenendijk and Kroes, 1997). Crop growth: The simple calculation is prescribed as leaf area index or soil cover fraction, crop height and rooting depth as a function of development stage. The detailed calculation is prescribed as crop growth and is based on the crop growth model WOFOST. Soil heat flow: Solved either analytically, where uniform and constant thermal conductivity and soil heat capacity and at the soil surface a sinusoidal temperature wave is assumed or numerically where thermal conductivity and soil heat capacity are calculated from the soil texture and the volume fractions of water and air as described by De Vries (1975) and at the soil surface the daily average temperature is used as a boundary condition. (Kroes, J.G., 2004) 9

Sensitivity Analysis A sensitivity analysis was performed on the swap model by Wesseling and Kroes and the study published in Generation of parameter values and the analysis were carried out for different crop-soil combinations. Analysis was carried out with a range of meteorological years, including average and extreme data. Input parameters were selected associated with soil physics, evapotranspiration, drainage and regional hydrology. For each input parameter a distribution type, its average, variance, minimum and maximum value were selected using existing databases and expert judgment. 10

Conclusions from sensitivity analysis (1): Boundary conditions are of crucial importance. For all soil-crop combinations the soil and crop evaporation were strongly dependent on the function describing the Leaf Area Index (LAI). Drainage simulated as lateral discharge is very sensitive to surface water levels. High groundwater levels are strongly related to surface water levels; low groundwater levels depend on a combination of LAI, soil physical parameters and surface water levels. Evaluation of the effects of different drainage designs in relation to long term water and salinity stress. 11

Strengths of the SWAP model Conclusions from sensitivity analysis (2): Allows use by different types of user and different scales of application. GIS can be used to generate input data for field scale models, to run these models for fields with unique boundary conditions and physical properties and to compile regional results of viable management scenarios. Irrigation demand or potential can be estimated and used to conduct field sensors. Irrigation potential and strategies can be directly translated into spatial distributions. Optimise timing and amount of sprinkling or surface irrigation. 12

Strengths of the SWAP model Conclusions from sensitivity analysis (3): Simulation of water and solute balances for different land use options. Generate optimal surface water levels depending on the actual situation, desired groundwater levels and expected weather conditions. Long term prediction of water demand. Analyse field experiments, carry out methodological comparisons and generate input as a powerful preprocessor for solute modelling. Model recharge of aquifers, irrigation strategies and leaching of nutrients or pesticides. A list of examples is given at 13

Strengths of the SWAP model Simulation period up to 70 years. Three crops a year, simple and detailed crop model (WOFOST). Irrigation scheduling criteria. Actual rainfall intensities are used to generate surface runoff. Interaction between water flow, solute transport, heat flow and crop growth. Soil heterogeneity options: scaling of soil hydraulic functions, mobile/immobile concept, swelling and shrinking of clay soils. Multi-level drainage. Interaction with surface water management. Graphical user interface. Documentation. Structured Fortran code with program. (Reinder A.Feddes, 2005) 14

Shortcomings of the SWAP model Temporal restrictions as it was developed for calculations with daily meteorological input data. Horizontal and vertical space of application are limited. It is a one dimensional model designed for processes in the unsaturated zone. In the saturated zone a pseudo two dimensional approach allows interaction with a surface water system but this is very sensitive to the scale of the application. No simulation of regional groundwater Hydrology. No interaction of between crop growth and nutrient availability. No non equilibrium sorption of pesticides and no simulation of metabolites. Not compatible with all versions of Windows operating system. (Kroes, J.G., 2004) 15

The Borkhar irrigation district, Iran {Introduction} SWAP team contacted concerning data for an oasification/(semi-)arid case study. Dr. Jos van Dam of Wageningen UR/SWAP team suggested: Vazifedoust, M., J.C. van Dam, R.A. Feddes and M. Feizi, Increasing water productivity of irrigated crops under limited water supply at field scale. Agricultural Water Management, 95, Borkhar irrigation district is located north of the ancient town of Esfahan, Iran. The region regularly faces widespread drought. The district has a predominantly arid to semi-arid desert climate. Water scarcity means that limited available water should be used efficiently. In order to avoid desertification increasing water productivity (WP) of irrigated crops under limited water supply is of vital importance. SWAP was used to evaluate irrigation strategies. (Vazifedoust, M., J.C. van Dam, R.A. Feddes and M. Feizi, 2008) 16

(Vazifedoust, M., J.C. van Dam, R.A. Feddes and M. Feizi, 2008) Borkhar irrigation district, Iran {Introduction} 17

The Borkhar irrigation district, Iran {Introduction} The field (M2) in the Borkhar irrigation district were planted with maize during SWAP was calibrated using data from the two sites to simulate increasing water productivity of irrigated crops in the Borkhar region under conditions of water scarcity. Photosynthesis and Transpiration related through diffusion processes of CO2 and H2O. Water Use Efficiency= Dry Matter Growth Rate ÷ Transpiration Rate. In irrigation practices Water Productivity (WP) is a proxy for Water Use Efficiency. Integrating Dry Matter Yield and Transpiration over time (growing season) gives > Efficiency of water used by crop = WP T (Water Productivity with respect to Transpiration). At field scale it is difficult to distinguish Transpiration T (mm) from Soil Evaporation E (mm) hence instead of WP T we use WP ET (Water Productivity with respect to Evapotranspiration). Total Dry Matter Yield (Y) is converted into Marketable Yield (Y M ). WP values for the crop were simulated using Transpiration (T), Evapotranspiration (ET), Irrigation (I) and Marketable Yield (Y M ). If Irrigation + Precipitation is considered ‘water use of the crop’ then WP I+P may be used which under arid conditions tends towards WP I and WP$ as an expression in terms of money. (Vazifedoust, M., J.C. van Dam, R.A. Feddes and M. Feizi, 2008) 18

The Borkhar irrigation district, Iran {Introduction} WP indicators express the benefit derived from the consumption of water and assess strategies for scarce water usage. WP indicators provide a vision of where and when water could be saved. WP indicators are useful for looking at the potential increase in crop yield that may result from increased water availability. WP indicators are necessary to plan for efficient irrigation and water management under water scarcity. SWAP helps simulate the water balance components and therefore increase our ability to improve water productivity (WP) under water shortage. SWAP/ WP - Which irrigation strategies help us achieve more crop per drop? (Vazifedoust, M., J.C. van Dam, R.A. Feddes and M. Feizi, 2008) 19

The Borkhar irrigation district, Iran {Soil type} Maize 2 M2M2 20

The Borkhar irrigation district, Iran {Data collection} Data collected from the maize field: Meteorological data. Irrigation data. Soil layers, texture and bulk density. Crop parameters for the detailed crop growth module. Soil layers and derived soil hydraulic parameters. Root mean square error RMSE and number of observations for soil moisture content. SWAP water balance components for maize. (Vazifedoust, M., J.C. van Dam, R.A. Feddes and M. Feizi, 2008) 21

The Borkhar irrigation district, Iran {Data collection} ( Vazifedoust, M., J.C. van Dam, R.A. Feddes and M. Feizi, 2008) 22

The Borkhar irrigation district, Iran {Data collection} ( Vazifedoust, M., J.C. van Dam, R.A. Feddes and M. Feizi, 2008) 23

The Borkhar irrigation district, Iran {Data collection} ( Vazifedoust, M., J.C. van Dam, R.A. Feddes and M. Feizi, 2008) 24

The Borkhar irrigation district, Iran {Data collection} ( Vazifedoust, M., J.C. van Dam, R.A. Feddes and M. Feizi, 2008) 25

The Borkhar irrigation district, Iran {Data collection} ( Vazifedoust, M., J.C. van Dam, R.A. Feddes and M. Feizi, 2008) 26

The Borkhar irrigation district, Iran {SWAP simulation} Recorded data input into SWAP and simulation results used to calculate: Simulated total dry matter yield (Y), simulated dry matter of storage organ (YSO), simulated marketable yields (YM), root mean square error RMSE and number of observations for the maize crop. Prices ($ kg -1 ) and costs ($ kg -1 ) for the maize crop in the Borkhar irrigation district in 2005 applied to calculate net return and water productivity. Water productivity indicators (WP T ), (WP ET ) and (WP I ) in (kg m⁻ 3 ) and ($ m⁻ 3 ) for the field. Water productivity indicators (WP T ), (WP ET ) and (WP I ) in (kg m⁻ 3 ) and ($ m⁻ 3 ) for the maize crop. (Vazifedoust, M., J.C. van Dam, R.A. Feddes and M. Feizi, 2008) Oasification - Simulating irrigation strategies using SWAP 27

The Borkhar irrigation district, Iran {Data collection} ( Vazifedoust, M., J.C. van Dam, R.A. Feddes and M. Feizi, 2008) 28

Oasification - Simulating irrigation strategies using SWAP 29

The Borkhar irrigation district, Iran {SWAP simulation} ( Vazifedoust, M., J.C. van Dam, R.A. Feddes and M. Feizi, 2008) 30

Oasification - Simulating irrigation strategies using SWAP 31

The Borkhar irrigation district, Iran {SWAP simulation} (Vazifedoust, M., J.C. van Dam, R.A. Feddes and M. Feizi, 2008) 32

The Borkhar irrigation district, Iran {SWAP simulation} (Vazifedoust, M., J.C. van Dam, R.A. Feddes and M. Feizi, 2008) 33

The Borkhar irrigation district, Iran {SWAP simulation} ( Vazifedoust, M., J.C. van Dam, R.A. Feddes and M. Feizi, 2008) 34

The Borkhar irrigation district, Iran {Results} ( Vazifedoust, M., J.C. van Dam, R.A. Feddes and M. Feizi, 2008) 35

Oasification - Simulating irrigation strategies using SWAP 36

The Borkhar irrigation district, Iran {SWAP simulation} Conculsion - Borkhar - High irrigation requirement, high transpiration rates and negligible rainfall - The maize field (M2) suffers from water deficit and requires optimised irrigation - Over irrigation can lead to nutrient leaching and therefore decreased yield - The semi-arid climate of the region is not conducive to achieving high crop yields - Fodder maize provides the highest economic benefit in the Borkhar irrigation district relative to other crops Oasification - Simulating irrigation strategies using SWAP 37

Conclusion (2) Soil characteristics of the region have surprisingly high clay content. By reducing cultivated land we can actually increase economic gains Water balance can tell us a lot about soil profile and soil moisture content Relatively successful oasification strategy ‘More crop per drop’ 38

Further research into Oasification and SWAP : Oasification blog {Group website with links} United Nations Convention to Combat Desertification Food and Agriculture Organisation (UN) SWAP 39

Thank you