Irrigation and Drainage Systems 16: 213–231, 2002.© 2002 Kluwer Academic Publishers. Printed in the Netherlands.

Application of SALTMOD in coastal clay soil in India ∗

MAN SINGH∗, A.K. BHATTACHARYA, A.K. SINGH & A. SINGHWater Technology Centre, Indian Agricultural Research Institute, New Delhi 110012, India;∗E-mail: mansingh@iari.ernet.in

Accepted 27 July 2002

Abstract. SALTMOD is a simulation model which predicts root zone soil salinity, drainagewater quality and water table depth in agricultural land under different geo-hydrological con-ditions and varying water management scenarios. The model was applied to the data fromcoastal Andhra Pradesh of India where subsurface drainage system is laid out at several drainspacings at the experimental site. Field data for 1999, 2000 and 2001 were collected from 35and 55 m drain spacing plots for SALTMOD application. Modelling was done consideringtwo simulation approaches. The first approach (Simulation-I) used the same initial values forthe entire simulation period. In the second approach (Simulation-II), the computations wereperformed year-by-year, giving each year the current input values obtained from the simulationresults of the previous year. Results of these two approaches were different from each other.Simulation-II gave better predictions than that of Simulation–I in terms of closeness to theobserved values. Simulation results of soil salinity in the root zone, drainage water qualityand quantity and depth to water table revealed that the salinity of root zone was predictedmore accurately than that of drainage water quality and depth to water table. Also throughsimulation, it was found that the salinity of drainage water was relatively independent of theroot zone soil salinity. Model application study suggests that SALTMOD can be used withconfidence to evaluate various drain spacings of a subsurface drainage system and facilitatereasonable prediction of the reclamation period.

Key words: coastal clay, drainage water quality, SALTMOD, simulation, soil salinity, subsur-face drainage

Introduction

The importance of irrigation in the World’s agriculture is rapidly increas-ing. Although it is practised on a large scale mainly in arid and semi-aridregions, supplementary irrigation is becoming popular in sub-humid regionsas well. The impact of irrigation speaks for itself in terms of increased cropproduction. However, the question as to how sustainable the achievements arestill remains unanswered. Judging from history, it seems that large irrigation

∗ This article is part of Ph.D. thesis of Dr. Man Singh

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systems eventually failed in many regions because the knowledge and techno-logy available to the society at that time were incapable of coping up with theproblems of water logging and soil salinity (Smedema 2000). Undoubtedly,soil salinity and water logging are the two most prevalent and widespreadproblems limiting crop production in irrigated areas. This is the consequenceof giving scant attention to drainage while developing irrigation dependentagricultural production systems. The assessment of ameliorative managementstrategies requires the analysis of the existing irrigation and drainage systemsas well as prediction of the potential consequences of changes to varioushydrological factors and cropping system. To enable such an assessment ina quick and efficient way, computer aided analytical tools and models areneeded. These models help evaluation of different development strategies,to suggest solutions and to predict medium to long-term consequences ofadopting such strategies. Several models have been developed to describethe performance of artificial drainage systems, including predicting effectof system design on crop yield and hydrology (Feddes 1987; Lesaffre &Zimmer 1987, 1988; Skaggs 1978, 1991; Zimmer et al. 1995). SALTMOD(Oosterbaan 1998) is an extended version of a similar model by the samename developed by Oosterbaan (1989). It facilitates the computation of soilsalinity in the root zone, salinity of drainage effluent, drain flow rates, watertable and several water balance components for different water managementoptions over a long period of time. Some applications of the earlier versionof SALTMOD viz. Oosterbaan and Abu Senna (1989) in Nile delta of Egypt;Rao et al. (1992) in Tungabhandra Irrigation Project, Karnataka, India, andVanegas Chacon (1993) in the Leziria Grande Polder, Portugal are found inthe literature. In this study, the focus is on the application of the 1998 versionof SALTMOD in a coastal environment of India where subsurface drainagesystems of various drain spacings are installed in the farmers’ field to reclaimsaline sodic clay soils. Results of the model application and fine-tuning of thesensitive parameters of the model would be helpful in predicting long termsustainability of the coastal agriculture with subsurface drainage system.

Materials and methods

Description of the study area

The experimental site is located at Endakuduru village in Krishna district ofAndhra Pradesh in India. It lies between 15◦43′ and 17◦10′N latitude and80◦0′ and 81◦33′E longitude, situated 18 km to the west of Bay of Bengal atan elevation of 1.5 m above the mean sea level. The land is flat and is dykedin small units for rice cultivation. The groundwater is at shallow depth and

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highly saline due to seawater intrusion. The site is characterised by a moder-ate coastal climate. The mean annual maximum and minimum temperaturesare 36.6◦C and 19.3◦C, respectively. The mean annual rainfall is 975 mmof which about 60% occurs during the southwest monsoon from June toSeptember. A specific feature of the area is the occurrence of cyclonic storms,any time usually during September to November, causing torrential rains. Therainfall during September to November may be as high as 40 to 45% of theannual rainfall. The period from December to May is relatively dry and hotwith some scanty rains. The monthly data of a few selected climatologicalparameters for the experimental site are presented in Table 1. The area issubjected to inundation as a result of excess rainfall, runoff and overflowfrom the drain during kharif (June–October) season. This causes flooding ofthe fields for over 15 to 20 days resulting in loss of crop. Sometimes, thekharif crop is totally lost or a loss of 30 to 50% is expected if the period ofinundation is more than 7 to 8 days. A majority of the farmers of the studyarea are marginal landholders having less than 1 ha land on an average. Rice-rice cropping system is the usual practice followed by the local farmers. Thecrop yield in kharif is lower as compared to that in rabi (January–April )due to the several factors discussed above. The rabi rice crop is more pop-ular in and around the study area due to assured canal irrigation and giveshigher return to farmers. In the growing season of the rabi rice there were20 irrigation events at about 4 to 5 days interval and 4 to 6 cm of irrigationdepths each. Also, the operation of subsurface drainage system is not feasibleduring the month of July and August when the fields are flooded and drainageoutlet is submerged. The rabi season was chosen for the experiment for twospecific reasons. First, the farmers do not prefer to grow rice in the khariffearing its failure and secondly, it would be impossible to operate the systemand collect the soil and drainage effluent samples during the kharif season.The rice productivity of the land is less than one ton per hectare withoutsubsurface drainage system. The first crop of kharif rice was cultivated in1998 and the crop failed due to floods resulting from the heavy downpours inAugust and September.

Saturated hydraulic conductivity of the soil

The top one metre layer is dark and heavy textured. The soil is of swellingand shrinking type with a high (avg. 58%) clay content and deep withoutany rock formations. A sandy layer exists at depths varying between 1 to2 m from the surface. The hydraulic conductivity of the top layer is low. Thevalues range from 0.02 to 0.90 m d−1 with a geometric mean of 0.144 m d−1

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Table 1. Climatological parameters of experimental site.

Year Parameter Jan. Feb. Mar. Apr. May Jun. July Aug. Sept. Oct. Nov. Dec.

1997a Max. temp. (◦C) 27.9 31.3 33.5 34.5 38.3 39.2 34.9 34.2 32.7 31.6 30.2 29.2

Min. temp. (◦C) 19.7 20.8 22.9 25.2 27.8 28.6 26.6 26.6 26.0 24.9 24.3 23.5

Rainfall (mm) 9.4 0.0 9.6 7.7 1.2 56.9 247.9 135.0 491.6 125.1 194.1 87.1

1998a Max. temp. (◦C) 29.8 31.5 33.1 35.1 39.0 38.6 33.4 32.6 32.6 31.3 31.0 29.4

Min. temp. (◦C) 22.6 23.3 25.0 26.4 28.9 28.9 26.6 26.4 26.4 25.3 23.7 19.4

Rainfall (mm) 19.6 0.0 0.4 19.8 0.2 78.2 162.5 181.5 216.5 311.1 57.5 0.0

Normal Rainfallb (mm) 7.9 8.3 7.6 4.2 28.1 86.8 169.7 182.0 166.6 153.9 140.7 19.2

PETa (mm) 109 122 166 176 193 167 134 136 123 118 108 102

Daily

(mean)

PET (mm) 3.5 4.2 5.3 5.8 6.2 5.5 4.3 4.4 4.1 3.8 3.6 3.3

aIndia Meteorological Department (IMD 1999);bDevadattam & Ramesh Chandra (1995)PET: Potential Evapotranspiration.

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(32 observations by auger hole method – Devadattam & Ramesh Chandra1995). The bottom layer is sandy loam with an average of 48% sand content.

Subsurface drainage installation

Sub-surface tile drains were installed in farmers’ fields for a pilot study. Theobjectives were to reclaim the chemically degraded soil and to intercept thecapillary flux towards root zone from the brackish ground water below andalso the downward moving water, high in salt concentration. Initially, tiledrains were laid at a narrow spacing of 15 m in the summer of 1986 in 0.4 haat an average depth of 1.0 m. Another 3.2 ha adjacent area was put under sub-surface drainage in the summer of 1987 with a wider lateral drain spacing of25 m at the same depth. The performance evaluation in terms of the changesin physical and chemical properties of the soil and rice yield under the drainedconditions was continuously monitored for more than a decade (Devadattam& Ramesh Chandra 1995; Bhattacharya 1996; AICRPAD 1997–99). Fielddata suggested the possibility of adopting even wider spacing of tile drainsand thus, a subsurface drainage system with two more spacings of 35 m and55 m at 1 m depth in 4.0 ha area was commissioned in 1998.

The subsurface drainage system lay out consists of five laterals, the 1st,2nd and 3rd at 35 m spacing and 3rd, 4th and 5th at 55 m spacing. Theaverage depth of the lateral drains is one metre. The laterals were 120 mlong and made of 100 mm diameter clay tiles. Each lateral drain dischargesinto a 0.75 m diameter and 1.8 m deep inspection chamber. These inspectionchambers are connected through a collector line of 150 mm diameter, whichdischarges into a 1.5 m diameter and 2.7 m deep sump. In this study the 4.0ha area was considered as the experimental site.

Soil-water sampling, measurements and analysis

Field measurements of important parameters, namely, root zone salinity,drainage water quality and quantity and depths to water table were done atthe beginning and at the end of the rabi rice seasons of 1999, 2000 and 2001.In order to determine soil salinity distribution in the crop growth season, thesampling was done both under cropped and saturated as well as under dryfallow conditions. The sampling locations were along as well as across thelateral drains. Soil samples were collected from 0–15, 15–30, 30–60 and60–90 cm layers for salinity determination. When soil samples for salinitydetermination were taken from saturated rice field, 1:1 soil:water suspensionwas prepared. EC was determined on the filtrate of the suspension. When soilsamples for salinity determination were taken under dry fallow condition, 1:2soil: water suspension was prepared and EC was determined on the filtrate of

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this suspension. A digital conductivity-measuring instrument was used for thesalinity measurement. These measured soil salinities (EC) with 1:1 and 1:2soil: water ratios were transformed to the standard ECe by multiplying 1.5and 2.5, respectively. These multipliers were determined experimentally andare site specific The initial value of the parameters like depth to water table inthe beginning of the simulation period (Season 1), mean root zone salinity,salinity of ground water and other conditions were measured. Subsurfacedrainage water sampling from the central lateral of both 35 and 55 m drainspacing began in March 1999 and continued till March 2001 and its salinitywas measured. These measured data were used as input parameters to themodel.

Principles of SALTMOD

Seasonal approach

SALTMOD is based on seasonal water and salt balances of agricultural lands.Four seasons in one year can be distinguished from among the possibleseasons of dry, wet, cold, hot, irrigation or fallow seasons. Seasonal timestep is considered in the computation method depending upon the specificsituation of the study site. The number of seasons (Ns) is chosen between aminimum of one and a maximum of four. The higher the number of seasonsconsidered, the larger the number of input data required. The duration of eachseason (Ts) is given in number of months (0 ≤ Ts ≤ 12). Day to day waterand salt balances are not considered for several reasons. Firstly, daily inputswould require much information, which are not readily available. Secondly,the model is especially developed to predict long term, and not day-to-daytrends. The predictions are more reliably made on seasonal (long term) thanon a daily (short term) basis. Thirdly, even if the accuracy of the predictionsis not very high, it may be useful when the trend of the prediction is clear. Forexample, it would not be a major constraint to design appropriate salinitycontrol measures when a certain salinity level, predicted by the model tooccur after 10 years, will in reality occur a few years before or a few yearsafter.

Hydrological data

The model needs seasonal water balance components as input data. These arerelated to the surface hydrology (e.g. rainfall, evaporation, irrigation, reuseof drainage water and runoff) and to the groundwater hydrology (e.g. upwardseepage, natural drainage and pumping from wells). The other water balancecomponents (e.g. percolation, capillary flux and subsurface drainage) are ob-tained as output. The quantity of drainage water, as an output, is determined

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by two drainage intensity factors for drainage above and below the plane ofthe drain axis, respectively. A drainage reduction factor (to simulate a limitedoperation of the drainage system) is chosen for the site conditions dependingupon the operational period of the subsurface drainage system. The heightof the water table is the result of the computed water balance. Variation ofthe drainage intensity factors and of the drainage reduction factor gives theopportunity to simulate the impact of different drainage design options.

Soil strata

SALTMOD accepts four different reservoirs of which one is above the soilsurface and three are below. These are named as (i) surface reservoir, (ii)shallow soil reservoir or root zone reservoir, (iii) an intermediate soil reservoiror transition zone and (iv) deep ground water or aquifer reservoir. The lastthree are porous reservoirs. The shallow soil reservoir is defined by the soildepth from which water evaporates and/or is taken up by the plant roots. Itis considered to cover the root zone depth. This reservoir could be saturatedor unsaturated, depending upon the water balance. The transition zone couldalso be saturated or unsaturated. If a horizontal subsurface drainage systemis present, the drains are assumed to be placed in this zone only. Then, thetransition zone is divided into two parts: an upper transition zone above thedrain level and a lower transition zone below the drain. Water balances arecalculated for each reservoir separately with a seasonal time step. The threeporous reservoirs are assigned three different thicknesses and input data set.Further details of the water balance computation are presented by Oosterbaan(1998).

Salt balances

The salt balances are calculated for each of the three porous reservoirs, asdefined in the previous section, separately. They are based on water balancesand on the salt concentrations of the incoming and outgoing water. The initialsalt concentrations of the water in the different soil reservoirs, in the irrigationwater and in the incoming groundwater from the deep aquifer are requiredas input data to the model. Salt concentrations of the outgoing water, eitherfrom one reservoir into the other or the drainage effluent, are computed on thebasis of the salt balance, with different leaching efficiencies. Here also a sea-sonal time step was adopted. In the model, the salt concentration is expressedas ECe, the values for which were obtained as explained under Soil-watersampling, measurements and analysis section. The amount of salt removedvia drainage effluent during a season is based on the weighted average of saltconcentration in the drainage effluents sampled during the season. The model

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has an option of computing salt balance if the drainage effluent is to be usedfor irrigation. However, in the present study this option was not consideredas the salinity of the drainage effluent was as high as that of seawater andmoreover the site had adequate fresh canal water supply in the rabi season.

Scope, assumptions and limitations of SALTMOD

Scope

The output of SALTMOD consists of the following:

− salt concentration of different soil reservoirs at the end of each season− seasonal average salt concentration of the drainage water− seasonal average depth of water table and− seasonal volumes of drainage water.

The output of the model is given for each season of any year for any num-ber of years as specified in the input data. Within a year, the output of thepreceding season becomes the input to the succeeding season for the modeloperation. The model runs either with fixed input data, for the number of yearsdetermined by the user or with annually changed input values (e.g. rainfall,irrigation, saturated hydraulic conductivity etc.). The first option is used topredict future developments like changes in soil water quality based on longterm average input values. In the second option, computations are done yearby year. If this option is chosen, the model creates transfer files by which thefinal results (conditions) of the previous year (e.g. salinity, water table andwater quality of drainage effluent) are automatically used as initial conditionsfor the subsequent period of simulation. This facility makes it possible to usevarious rainfall sequences drawn randomly from a known rainfall probabilitydistribution and obtain a stochastic prediction of the resulting output para-meters. When the simulations are performed with annual changes, it is notnecessary to change all other input parameters. For example, in this study,saturated hydraulic conductivity that changed over the years, were used inthe scheme of Simulation-II described in the Results and discussion section.Whereas, the parameters such as irrigation water quality, drainable porosity,leaching efficiency and many other parameters were kept at the same valuesfor all the years of simulation. The model offers the possibility of developinga multitude of relations between varied input data, resulting outputs and time.

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Assumptions

The model assumes uniform distribution of the cropping, irrigation and drain-age characteristics over the 4.0 ha experimental site. The minimum andmaximum time step of computations is 1 and 12 months, respectively. Allwater movements in the various soil reservoirs are vertical; either upward ordownward except the flow to subsurface drains, if it exists. The deep groundwater reservoir has both horizontal and vertical flows. The model solves theHooghoudt’s steady state formula to obtain the flow components from abovethe drain and from below the drain when the water table is below the soilsurface. When the water table is above the soil surface, the model assumes theflow component as double of that obtained by using Hooghoudt’s equation.The model assumes the solute movement to take place as mass flow. It alsoassumes the location of the subsurface drain to be anywhere in the transitionzone. The overall functioning of the model is on the basis of the principle ofmass conservation.

Limitations

The effects of dissolution of solid soil minerals, macro and micronutrientsand the chemical precipitation of poorly soluble salts are not included in themodel. The model is interactive but lacks in standard graphics.

Calibration of the model

Calibration of the model was done using the data of first year (i.e. 1999).At the study site, most of the water and salt balance factors needed by themodel were measured, while some specific parameters were estimated. Somefactors, notably the leaching efficiency of the root zone and surface run offcould not be measured. Leaching efficiency in SALTMOD is defined as theratio of the salt concentration of the water percolating from the root zone tothe average salt concentration of the soil solution in the root zone at field sat-uration. Before application of SALTMOD, these factors were determined bytrial and error runs of the model, using different values of leaching efficiencyand surface runoff from the known boundary conditions of the study site. Thechosen values of leaching efficiency and surface runoff were those, whichproduced soil salinity and depths to water table that corresponded well withthe actually measured values. Such a trial and error procedure is referred toas the calibration of the model. The various input parameters with respect todifferent treatments of drain spacing are given in Table 2.

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Table 2. Summary of input parameters needed by SALTMOD.

1. Duration of season (month)

Season 1 (January to May) 5

Season 2 (June to December) 7

2. Soil properties

Fraction of irrigation or rain water stored in root zone 0.65

Total porosity of root zone 0.60

Total porosity of transition zone 0.45

Total porosity of aquifer (assumed) 0.35

Drainable porosity of root zone 0.05

Drainable porosity of transition zone 0.08

Drainable porosity of aquifer 0.25

Leaching efficiency of root zone (calibrated) 0.60

Leaching efficiency of transition zone (assumed) 0.80

Leaching efficiency of aquifer (assumed) 1.00

3. Water balance components

Irrigation in season 1 (m) 1.25

Irrigation season 2 (m) 0.00

Rainfall in season 1 (m) 0.04

Rainfall in season 2 (m) 1.007

Evapotranspiration in season 1 (m) 0.766

Evapotranspiration in season 2 (m) 0.888

Incoming groundwater flow through aquifer in both season (as-sumed) (m)

0.0

Outgoing groundwater flow through aquifer in both season (as-sumed) (m)

0.0

Surface runoff in season 1 – calibrated (m) 0.350

Surface runoff in season 2 – calibrated (m) 0.250

4. Drainage criteria and system parameters

Root zone thickness (m) 0.30

Depth of subsurface drains (m) 1.00

Drain spacings (m) 35 and 55

Thickness of transition zone between root zone and aquifer (m) 1.60

Thickness of aquifer – assumed (m) 5.00

Ratio of drain discharge and height of the water table above drain(m d−1 m−1)

0.0011–0.015

Rate of drain discharge and squared height of the water table abovedrain (m d−1 m−2)

0.00015–0.002

Drainage reduction factor in season 1 0.2

Drainage reduction factor in season 2 0.8

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Table 2. Continued.

5. Initial and boundary conditions

Depth of water table in the beginning of season 1 (m) 0.30

Initial salt concentration of soil moisture in root zone at fieldsaturation (dS m−1)

35.0

Initial salt concentration of the soil moisture in transition zone(dS m−1)

40.0

Average salt concentration of incoming irrigation water (dS m−1) 1.5

Average salt concentration of incoming groundwater (dS m−1) 50.0

Results and discussion

Simulation runs of the model

In order to obtain the initial values for model parameters the calibration ofSALTMOD was conducted on computer through simulation runs using theinput given in Table 2. Three factors were calibrated: the soil salinity in theroot zone, the salt concentration of the subsurface drainage water and thedepth to water table. From the calibration process, the leaching efficiency wasfound to be 0.60 and surface drainage of 0.350 and 0.250 m for the season1 (rabi) and season 2 (kharif), respectively and given in Table 2. Simulationmodelling was done considering two approaches namely, Simulation-I andSimulation-II and two drain spacings (35 and 55 m) assuming similar soiland water regimes at the beginning of the simulation. Simulation-I was adop-ted to predict the soil salinity in the root zone, the salt concentration of thesubsurface drainage water and the depth to water table on a long term basiswith a seasonal time step with the same initial values for the entire simulationperiod of three years. In Simulation-II, the computations were performed yearby year, giving in each year a separate input. Bhattacharya (1999) reportedthat the value of saturated hydraulic conductivity increased from 0.14 m d−1

to 1.5 m d−1 in subsurface drained coastal clay soils in 8 years from thesame site with 10, 15, 25 and 35 m drain spacing till 1995. The rate of annualincrement of saturated hydraulic conductivity was derived from the above ref-erence and revised values of saturated hydraulic conductivity were used everyyear in the simulation run. The results found in Simulation-I and Simulation-II were different from each other. Separate sections for each identified factorare devoted to discuss the results in detail and these are presented hereafter.

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Soil salinity in the root zone

The results of simulation of the root zone salinity by SALTMOD, using theparameters listed in Table 2 together with the observed data for 3 years (i.e.1999, 2000 and 2001) for the season 1 are presented in Figure 1. The resultsindicate that the model performed well and the deviation between the modelpredicted and the observed root zone salinity varied from 5.3 to 8.9% in35 m drain spacing and from 2.6 to 15.3% in 55 m drain spacing. The modeloverestimated the root zone salinity in both 35 m and 55 m drain spacing.However, just after one year of operation of the subsurface drainage system,the model under-estimated the root zone salinity in 55 m drain spacing. Abetter agreement between the observed and the Simulation-II values was no-ticed in both spacings (Figure 1). The simulation further suggested that theroot zone salinity was significantly higher in the 55 m spacing as comparedto the 35 m spacing. In both the cases the simulated values of root zonesalinity stabilized in a period of six years. Simulation-II indicates that theroot zone salinity got reduced to approximately 8 dS m−1 at field saturationin 4 and 6 years period from 35 to 55 m drain spacing, respectively. Thus,the predictions made by the model suggest that the land with 35 m and 55 mdrain spacing, for existing soil, water and climatic parameters, may be re-claimed for rice-rice cultivation within 4 to 6 years. The data presented bySingh (2000) suggest that there was faster removal of salts in 35 m spacingas compared to 55 m spacing. The same study further concluded that coastalclay soil with given initial conditions would be reclaimed in 3–4 and 6–7years with 35 and 55 m drain spacings respectively, provided no additionalsalts are added to the soil profile of top 1 m depth from external sources.The results of simulation runs by SALTMOD are in good agreement with thefield-estimated values of soil salinity in the root zone as reported by Singh(2000).

Subsurface drainage water quality

The observed and simulated salinity of subsurface drainage water from thelateral drains of 35 and 55 m spacings for 3 years are presented in Figure 2.Figure 2 shows that the agreement between the observed and the simulatedvalues is not as good as in the case of root zone soil salinity. The deviationranged from 21 to 27% in 35 m spacing and 1.5 to 25% in 55 m spacing.The best agreement was found in season 1 of the first year of operation ofdrainage system for 55 m spacing area with only 1.5% deviation. In general,Simulation-II offered better agreement for the wider spacing. Singh (2000)reported two distinct drainage rates in 35 and 55 m drain spacing areas. Itwas also observed that the drainage rate has increased significantly by over

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Figure 1. Observed and simulated root zone soil salinity (ECe) at the end of season 1(January–May).

20% and 10% in the 35 and 55 m drain spacing areas, respectively, after 2years of operation of subsurface drainage system. In the same study, it wasalso observed that the improvement in soil physical condition was faster in35 m spacing as compared to the 55 m spacing. From the field measurementsit was estimated that the top 1 m layer of soil contained 257 and 172 ton/hasalt in 35 and 55 m drain spacing area, respectively. The data presented inFigure 2 suggest that salt concentrations in effluents and drainage rate were

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always much higher in 35 m spacing as compared to 55 m spacing. All thesedata corroborate the higher pace of reclamation with 35 m drain spacing.Application of SALTMOD revealed that the land with 35 and 55 m drainspacing for the existing agro-climatic condition might be reclaimed for rice-rice crop rotation within 4 and 6 years, respectively. The inference drawnfrom the simulation is in good agreement with the finding based on fieldmonitoring. The salt balance in the soil profile would have the permissiblesalinity to raise rice crop after the reclamation period mentioned above.

An interesting conclusion may be drawn from the Figures 1 and 2 that thesalt concentration of the drainage water is relatively independent of the saltconcentration of the root zone soil salinity. The main reason for this is thatthe water percolating from the root zone does not go directly to the drains,but passes through the transition zone that has a considerably large bufferingeffect. The other probable reason could be the contribution of highly salineground water to the subsurface drainage water which otherwise is assumed tobe zero in input parameters (Water balance components, Table 2) which maynot be true in real situation. The latter is further confirmed by Figures 2a & b.In all cases the observed values were higher than the simulated ones. Also, theleaching efficiency may have improved over the years due to improvement ofthe physical and chemical properties of the soil, which was not considered tobe changing year after year in the model.

Depth to water table

The water table depths were observed to be 0.65 and 0.55 m below soil sur-face in the end of May, 1999 (end of season 1) i.e. after one year of operationof subsurface drainage systems, in 35 and 55 m drain spacing areas, respect-ively (Figures 3a & b). Simulated values of water table did not match withthe observed ones. Generally, the land with rice crop in both rabi and kharifseasons remains saturated either with irrigation water or with rain water tosustain the rice crop. Thus, the water table fluctuates from 5 to 6 cm above thesoil surface (when surface ponding is maintained) to 20 to 30 cm below soilsurface, when lateral drains discharge free flow during the active crop growthseason. Field observations show that although, the rice fields are saturated anda few centimeters of water stand on the soil surface, the piezometers indicatea lower hydraulic head. This implies that there may not be continuity betweenthe ponded water above soil surface and the ground water. Moreover, it wasobserved that there are always about 30 and 45 days period, left after season2 and season 1, respectively, during which the water table goes below theroot zone. Another reason for disagreement of water table values could be thefact that SALTMOD computation method does not account for ponded watercase. Rather, it always assumes the location of water table below the soil

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Figure 2. Observed and simulated drainage water quality for season 1 (January–May).

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surface and solves with Hooghondt’s steady state formula, which is not thecase in reality in wetland rice cultivation. Over and above, accurate predictionof water table is not relevant for rice cultivation. Moreover, the purpose ofsubsurface drainage system in this experimental area was not for water tablecontrol but for salinity control. Also, the topographic observation suggeststhat the area with 55 m drain spacing is 5–10 cm lower as compared to the areawith 35 m drain spacing. In case of 55 m drain spacing the seasonal averagesimulated depth to water table was found to be close to the soil surface inseason 1 (Figure 3b). This may not be true in the presence of a subsurfacedrainage system. It is further evident from Figures 3a & b that the water tablepredictions with the scheme of Simulation-II are in a better agreement withthe observed values than with Simulation-I.

The performance of the two approaches in simulating root zone salinity,drainage water quality and depth to water table was evaluated in terms ofcoefficient of determination, slope and intercept values. Of the three para-meters evaluated, SALTMOD predicted the root zone salinity satisfactorilyas compared to the other parameters.

In view of the results presented above, the calibrated SALTMOD canbe considered to be valid for estimating soil salinity and the salinity ofthe drainage effluent. These two together are good indicators of the re-clamation effect of subsurface drainage in the coastal clay soils with rice-rice cropping system. However, the validity of the SALTMOD appears tobe doubtful for estimating the ground water table scenario. As has beendiscussed earlier, ground water table is not a matter of concern for rice pro-duction as conventionally rice fields mostly remain under a few centimeter ofwater submergence for the most part of its growing period both in season 1and season 2.

Summary and conclusions

The water and salt balance model, SALTMOD, was applied to the data fromsubsurface drained rice fields in the coastal clay soils of India. Subsurfacedrainage system with various drain spacings has been functioning in thestudy area. Out of these, the data from the areas with 35 and 55 m drainspacings were used in the model. The modelling study adopted two sim-ulation approaches. The first approach (Simulation-I) used the same initialvalues of model parameters for the simulation period. In the second approach(Simulation-II), the computations were performed year-by-year, giving eachyear the current input values obtained from the simulation results of the previ-ous year. The model predictions were compared with the measured root zonesoil salinity, drainage water quality and depth to water table.

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Figure 3. Observed and simulated water table depth for season 1 (January–May).

The results of Simulation-I and Simulation-II were different from eachother. Simulation II, in which the input was updated annually, gave betterpredictions than that of Simulation-I. Model application for soil salinity inthe root zone, drainage water quality and quantity and depth to water tablereveals that the root zone salinity was predicted more accurately than thoseof drainage water quality and depth to water table. Simulation results revealedthat the salinity of drainage water was relatively independent of the root zone

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soil salinity. The results suggest that SALTMOD can be used with confid-ence to evaluate various design and management alternatives of a subsurfacedrainage system in the coastal clay soils of India. Also, the model can facil-itate reasonable prediction of the reclamation period, which is useful for theplanners, policy makers and project designers.

Acknowledgements

The authors highly appreciate the help of Mr. R.J. Oosterbaan of Interna-tional Institute for Land Reclamation and Improvement (ILRI), Wageningen,The Netherlands, in providing the latest version for SALTMOD free of costand advising promptly as and when needed. The assistance provided by Er.K.R.K. Prasad, Er. S. Ramesh Chandra and Mr. Shyam Sunder of AgriculturalResearch Station of A.N.G. Ranga Agricultural University, Machilipatnam,Andhra Pradesh, is duly acknowledged.

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