SahysMod is a computer program for the prediction of the salinity of soil moisture, groundwater and drainage water, the depth of the watertable, and the drain discharge in irrigated agricultural lands, using different hydrogeologic and aquifer conditions, varying water management options, including the use of ground water for irrigation, and several crop rotation schedules, whereby the spatial variations are accounted for through a network of polygons.
Rationale
There is a need for a computer program that is easier to operate and that requires a simpler data structure then most currently available models. Therefore, the SahysMod program was designed keeping in mind a relative simplicity of operation to facilitate the use by field technicians, engineers and project planners instead of specialized geo-hydrologists. It aims at using input data that are generally available, or that can be estimated with reasonable accuracy, or that can be measured with relative ease. Although the calculations are done numerically and have to be repeated many times, the final results can be checked by hand using the formulas in this manual.Fathi Zereini & Heinz Hötzl (Eds.), 2008, Climatic Changes and Water Resources in the Middle East and North Africa. In: Environmental Science and Engineering, Springer Verlag, Berlin, : Online
SahysMod's objective is to predict the long-term hydro-salinity in terms of general trends, not to arrive at exact predictions of how, for example, the situation would be on the first of April in ten years from now. Further, SahysMod gives the option of the re-use of drainage and well water (e.g. for irrigation) and it can account for farmers' responses to waterlogging, soil salinity, water scarcity and over-pumping from the aquifer. Also it offers the possibility to introduce subsurface drainage systems at varying depths and with varying capacities so that they can be optimized. Other features of SahysMod are found in the next section.
Methods
Calculation of aquifer conditions in polygons
The model calculates the ground water levels and the incoming and outgoing ground water flows between the polygons by a numerical solution of the well-known Boussinesq equation. The levels and flows influence each other mutually. The ground water situation is further determined by the vertical groundwater recharge that is calculated from the agronomic water balance. These depend again on the levels of the ground water.
When semi-confined aquifers are present, the resistance to vertical flow in the slowly permeable top-layer and the overpressure in the aquifer, if any, are taken into account.Sina Akram, Heydar Kashkouli, Ebrahim Pazira, 2008. Sensitive variables controlling salinity and water table in a bio-drainage system using SahysMod. Irrigation and Drainage Systems Volume 22, Numbers 3-4, December, 2008 pp. 271-285. Online: https://doi.org/10.1007%2Fs10795-008-9056-4
Hydraulic boundary conditions are given as hydraulic heads in the external nodes in combination with the hydraulic conductivity between internal and external nodes. If one wishes to impose a zero flow condition at the external nodes, the conductivity can be set at zero.
Further, aquifer flow conditions can be given for the internal nodes. These are required when a geological fault is present at the bottom of the aquifer or when flow occurs between the main aquifer and a deeper aquifer separated by a semi-confining layer.
The depth of the water table, the rainfall and salt concentrations of the deeper layers are assumed to be the same over the whole polygon. Other parameters can very within the polygons according to type of crops and cropping rotation schedule.
Seasonal approach
The model is based on seasonal input data and returns seasonal outputs. The number of seasons per year can be chosen between a minimum of one and a maximum of four. One can distinguish for example dry, wet, cold, hot, irrigation or fallow seasons. Reasons of not using smaller input/output periods are: short-term (e.g., daily) inputs would require much information, which, in large areas, may not be readily available; short-term outputs would lead to immense output files, which would be difficult to manage and interpret; this model is especially developed to predict long-term trends, and predictions for the future are more reliably made on a seasonal (long-term) than on a daily (short-term) basis, due to the high variability of short-term data; though the precision of the predictions for the future may be limited, a lot is gained when the trend is sufficiently clear. For example, it need not be a major constraint to the design of appropriate soil salinity control measures when a certain salinity level, predicted by SahysMod to occur after 20 years, will in reality occur after 15 or 25 years.Hosein Liaghat, M Mashal, 2008. Sustainability of Biodrainage Systems Considering Declining of Evapotranspiration Rate of Trees Due to Soil Salinization. Published by the American Society of Agricultural and Biological Engineers (ASABE), St. Joseph, Michigan . Citation: 9th International Drainage Symposium held jointly with CIGR and CSBE/SCGAB Proceedings, 13–16 June 2010 IDS-CSBE-100129. Online:
Computational time steps
Many water balance factors depend on the level of the water table, which again depends on some of the water-balance factors. Due to these mutual influences there can be non-linear changes throughout the season. Therefore, the computer program performs daily calculations. For this purpose, the seasonal water-balance factors given with the inpu] are reduced automatically to daily values. The calculated seasonal water-balance factors, as given in the output, are obtained by summations of the daily calculated values. Groundwater levels and soil salinity (the state variables) at the end of the season are found by accumulating the daily changes of water and salt storage.
In some cases the program may detect that the time step must be taken less than 1 day for better accuracy. The necessary adjustments are made automatically.Tsegay F. Desta, 2009. Spatial modelling and timely prediction of salinization processes using SahysMod in GIS environment. Thesis International Institute for Geo-information Science and Earth Observation (ITC), Enschede, The Netherlands. On line : http://www.itc.nl/library/papers_2009/msc/aes/desta.pdf
Data requirements
Polygonal network
The model permits a maximum of 240 internal and 120 external polygons with a minimum of 3 and a maximum of 6 sides each. The subdivision of the area into polygons, based on nodal points with known coordinates, should be governed by the characteristics of the distribution of the cropping, irrigation, drainage and groundwater characteristics over the study area.
The nodes must be numbered, which can be done at will. With an index one indicates whether the node is internal or external. Nodes can be added and removed at will or changed from internal to external or vice versa. Through another index one indicates whether the internal nodes have an unconfined or semi-confined aquifer. This can also be changed at will.
Nodal network relations are to be given indicating the neighboring polygon numbers of each node. The program then calculates the surface area of each polygon, the distance between the nodes and the length of the sides between them using the Thiessen principle.
The hydraulic conductivity can vary for each side of the polygons.
The depth of the water table, the rainfall and salt concentrations of the deeper layers are assumed to be the same over the whole polygon. Other parameters can very within the polygons according to type of crops and cropping rotation schedule.Sina Akram and Hossein Liaghat. (2010) Performance of biodrainage systems in arid and semiarid areas with salt accumulation in soils. 9th International Drainage Symposium held jointly with CIGR and CSBE/SCGAB Proceedings, 13–16 June 2010.
Hydrological data
The method uses seasonal water balance components as input data. These are related to the surface hydrology (like rainfall, potential evaporation, irrigation, use of drain and well water for irrigation, runoff), and the aquifer hydrology (e.g., pumping from wells). The other water balance components (like actual evaporation, downward percolation, upward capillary rise, subsurface drainage, groundwater flow) are given as output.
The quantity of drainage water, as output, is determined by two drainage intensity factors for drainage above and below drain level respectively (to be given with the input data) and the height of the water table above the given drain level. This height results from the computed water balance Further, a drainage reduction factor can be applied to simulate a limited operation of the drainage system. Variation of the drainage intensity factors and the drainage reduction factor gives the opportunity to simulate the effect of different drainage options.
To obtain accuracy in the computations of the ground water flow (sect. 2.8), the actual evaporation and the capillary rise, the computer calculations are done on a daily basis. For this purpose, the seasonal hydrological data are divided by the number of days per season to obtain daily values. The daily values are added to yield seasonal values.Ajay Singh, Sudhindra Nath Panda. (2012) Integrated Salt and Water Balance Modeling for the Management of Waterlogging and Salinization. I: Validation of SAHYSMOD. Journal of Irrigation and Drainage Engineering 138:11, 955-963 Abstract
Cropping patterns/rotations
The input data on irrigation, evaporation, and surface runoff are to be specified per season for three kinds of agricultural practices, which can be chosen at the discretion of the user:
A: irrigated land with crops of group A B: irrigated land with crops of group B U: non-irrigated land with rain-fed crops or fallow land
The groups, expressed in fractions of the total area, may consist of combinations of crops or just of a single kind of crop. For example, as the A-type crops one may specify the lightly irrigated cultures, and as the B type the more heavily irrigated ones, such as sugarcane and rice. But one can also take A as rice and B as sugar cane, or perhaps trees and orchards. A, B and/or U crops can be taken differently in different seasons, e.g. A=wheat plus barley in winter and A=maize in summer while B=vegetables in winter and B=cotton in summer. Non-irrigated land can be specified in two ways: (1) as U = 1−A−B and (2) as A and/or B with zero irrigation. A combination can also be made.
Further, a specification must be given of the seasonal rotation of the different land uses over the total area, e.g. full rotation, no rotation at all, or incomplete rotation. This occurs with a rotation index. The rotations are taken over the seasons within the year. To obtain rotations over the years it is advisable to introduce annual input changes as explained
When a fraction A1, B1 and/or U1 differs from the fraction A2, B2 and/or U2 in another season, because the irrigation regime changes in the different seasons, the program will detect that a certain rotation occurs. If one wishes to avoid this, one may specify the same fractions in all seasons (A2=A1, B2=B1, U2=U1) but the crops and irrigation quantities may be different and may need to be proportionally adjusted. One may even specify irrigated land (A or B) with zero irrigation, which is the same as un-irrigated land (U).
Cropping rotation schedules vary widely in different parts of the world. Creative combinations of area fractions, rotation indexes, irrigation quantities and annual input changes can accommodate many types of agricultural practices.Azhar Inam et al., 2017. Coupling of a distributed stakeholder-built system dynamics socio-economic model with SAHYSMOD for sustainable soil salinity management – Part 1: Model development. In Journal of Hydrology,