Application of the model MACRO to water movement and salt leaching in drained and irrigated marsh soils, Marismas, Spain

Agricultural water management E LS EV I ER

Agricultural Water Management 25 (1994) 71-88

Application of the model MACRO to water movement and salt leaching in drained and irrigated marsh soils, Marismas, Spain L. Andreu a'*, F. Moreno a, N.J. Jarvis b, G.

Vachaud

~

alnstituto de Recursos Naturales y Agrobiologfa de Sevilla (CS1C). P.O. Box 1052, 41080 Sevilla, Spain bDepartment of Soil Sciences, Swedish University of Agricultrual Sciences, Box 7072, 750 07 Uppsala, Sweden ~Lab. d'Etude des Transferts en Hydrologie et Environnement, Inst. de M6canique de Grenoble (U.J.F.-C.N.R.S.-I.N.P.G.), BP53X, 38041 Grenoble, France

(Accepted 28 June 1993 )

Abstract This paper presents an application of a two-domain model of water flow and solute transport in macroporous soil (MACRO) to field experiments in drained and irrigated, saline heavy clay soils under cotton. Model predictions are compared to detailed measurements of the soil water balance and leaching of chloride to field drains made during a 17-day period following two successive sprinkler irrigations. The model was able to reproduce the measured drain hydrograph and the observed response of the water table, providing calibrated, rather than directly measured, saturated hydraulic conductivity values were used. At the end of the first irrigation, the soil profile was fully recharged, with the water table only 10 cm below the surface. In the 2 weeks following irrigation, soil water extraction by the crop was largely restricted to the upper 30 cm of soil, presumably due to excessive salt concentrations in the subsoil. Indeed, the model simulations indicated a reduction in transpiration below the potential rate after only 1 week, with an accumulated water deficit of 60 to 70 mm. A strong dilution of the chloride concentrations during peak drain discharges was observed and was also predicted by the model. This dilution was related to the rapid infiltration of irrigation water of low salt concentration in the cracks. In two flow domains, the model precisely matched observed leaching losses of chloride during peak drain discharges, but underestimated by -- 25% the accumulated loss of chloride 1 week after the irrigation. This was entirely due to an underestimation of chloride concentrations in the outflow during the late stages of recession. During such recession flows, the chloride concentrations in the drain outflow ( -~ 20 g. 1 ~) were most likely influenced by inflows of saline shallow groundwater from surrounding areas. In one flow domain, the model predicted qualitatively similar patterns of drain outflow and chloride leaching, Nevertheless, the model performed less well when bypass flow was not taken into account, *Corresponding author. 0378-3774/94/$07.00 © 1994 Elsevier Science B.V. All rights reserved S S D I 0 3 7 8 - 3 7 7 4 ( 9 3 ) E0035-J

72

L. Andreu et al./ Agricultural Water Management 25 (1994) 71-88

with chloride leaching overestimated during peak discharges, despite a total drain outflow which was 10 mm less than in the two-domain case. Key words: Two-domain model; Macroporous soil; Water movement; Salt leaching

I. Introduction The presence of cracks and fissures in clay soils due to swelling and shrinking processes allows the rapid non-equilibrium flow of water, a process variously termed short-circuiting, by-passing, or simply channelling (Bouma, 1981; Beven and Germann, 1982). In saltaffected clay soils, by-pass flow in cracks may strongly affect the drain response and also the efficiency of salt leaching during irrigation (Tanton et al., 1988; Youngs and LeedsHarrison, 1990). The Marismas marshes in south-west Spain are formed by soils of alluvial origin from the ancient Guadalquivir river estuary. The most important characteristics of these soils are the high clay content and swell/shrink potential, high salinity (Gonzalez Garcia et al., 1956; Gir~ildez and Cruz Romero, 1975; Moreno et al., 198l ) and an extremely saline shallow water table. The most recently reclaimed areas have been under cultivation since 1978, with crop production sustained by both irrigation and field drainage systems. A fuller understanding of the nature of water movement and salt leaching in these saline heavy clay soils is critical in maintaining and improving soil quality and thus ensuring efficient crop production by, for example, optimizing irrigation applications to maximize water use efficiency and salt leaching. Modelling approaches offer a cost-effective, efficient means of testing and improving understanding of the complex interaction of processes governing water movement and salt leaching in field soils. However, most existing management-oriented models of the soilplant system are based on Richards' equation for water flow in homogeneous soil. Such models should not, therefore, be applied to sites where by-pass flow processes may occur. Jarvis ( 1991 ) has described a non-steady state model that explicitly accounts for water flow and solute transport processes in two flow domains (i.e. micropores and macropores, aggregates and cracks). In this paper, we present an application of this model, MACRO, to the reclaimed and cultivated soils of the Marismas marshes. Model performance is assessed by comparing simulations to detailed measurements of the water balance and leaching of chloride to the field drainage system in a 2-week period following sprinkler irrigation. The significance of by-pass flow is also demonstrated by comparing model simulations in both one and two flow domains.

2. Model description MACRO is a detailed model of water movement and solute transport and transformation processes in a cropped soil profile. One important feature of MACRO is that the model may be run in two flow domains. This means that non-equilibrium flow processes characteristic

L. Andreu et al. / Agricultural Water Management 25 (1994) 71-88

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of structured soils are explicitly considered. Only a brief description is given here, since the model has been described in full detail by Jarvis ( 1991 ). We will focus our attention mainly on those aspects of the model which are of special relevance when modelling the leaching of salts in irrigated, drained, saline cracking clay soils. 2.1. Driving variables Driving variables in the model consist of measured daily or hourly precipitation at a given solute concentration (constant for the simulation) and daily potential evaporation. Irrigation is considered separately from natural rainfall, with the user specifying the dates and start and stop times of each irrigation, the irrigated amount, salt concentration in the applied water and also the fraction of the application intercepted by the crop. This allows the model user flexibility when applying the model to different types of irrigation application (e.g. sprinkler, surface flooding). Interception of rainfall is calculated from a running water balance for the canopy. Water added to the canopy in excess of the combined sum of daily evaporation loss and the interception capacity becomes throughfall and is input to the soil. Evaporation of intercepted water is assumed to occur at a rate given as a constant ratio ( ~> 1 ) of the daily potential transpiration. 2.2. Simulating in one and two flow domains In MACRO, the total soil porosity is divided into two components (e.g. micropores and macropores, aggregates and cracks) at a boundary water content/pressure head. The twopore systems operate as individual (although interacting) flow domains, each being characterized by a degree of saturation, a conductivity and a flux. For comparative purposes, the model may also be run in only one flow domain, with no change in the soil hydraulic functions. In this case, the soil is characterized by single values of water content, conductivity and flux at any given depth and the model calculations reduce to standard solutions of the Richards' and convection-dispersion equations for water movement and solute transport, respectively. 2.3. Soil water flow One-dimensional (vertical) water flow in the micropores in the unsaturated zone is calculated using Richards' equation including a sink term Sw to account for root water uptake:

Ot

Oz \

k Oz

(1)

where 0 is the soil water content, ~ is the soil water potential, t is time, z is vertical distance and Kmi is the unsaturated hydraulic conductivity. The hydraulic properties ~p(0) and Kmi(~0) are described by the functions of Brooks and Corey (1964) and Mualem ( 1976):

74

L. Andreu et al. /Agricultural Water Management 25 (1994) 71-88

Ob -- Or,]

~[fi~o]

gm,= Kb(~)2 + (2 +n,a

(3)

where 0b and ~ are the soil water content and water potential, respectively, at the boundary between flow domains, Oris the residual water content, A is the pore size distribution index, is the tortuosilyfactor and Kb is the hydraulic conductivity at the boundary water content/ potential. Water flow in macropores is calculated with Darcy's law assuming a unit hydraulic gradient and a power law function to represent the unsaturated hydraulic conductivity: (4)

Kma = (Ks - Kb)S~*a

where K~ is the saturated hydraulic conductivity, Sma is the degree of saturation in the macropores and n* is an empirical exponent accounting for macropore size distribution and continuity. 2.4. Soil shrinkage

A simple sub-model of soil shrinkage is included in MACRO (Messing and Jarvis, 1990). However, this approach is not well suited to short-term simulations, since instantaneous swelling and shrinkage in response to wetting and drying is assumed. For this reason, the sub-model accounting for swelling and shrinkage was not used in this study. 2.5. Solute transport

Non-reactive solute transport is predicted using the convection-dispersion equation with sink terms U and D accounting for uptake by roots and drainage:

O(Oc) 0 , Ot = ~Z \

OZ

I[DoOC-qc]-U-D ]

(5)

where c is the solute concentration, q is the Darcy water flux density and where D, the dispersion coefficient, is given by: D = (Dvv) + (Do f * )

(6)

where Do is the diffusion coefficient in free water, Dv is the dispersivity,f * is the impedance factor and v is the pore water velocity given by q/O. Salt transport is treated in the same way in micropores and macropores, except that dispersion is neglected in the macropores where convection is assumed to dominate.

L. Andreu et al. /Agricultural Water Management 25 (1994) 71-88

75

2.6. Boundary conditions The surface boundary condition in MACRO partitions the net precipitation R into an amount taken up by micropores Imi and an excess amount flowing into macropores Ima: Imi = R lma = 0 Imi =/max l,na =R-l,,~i

; R ~ Imax

(7)

; R>lmax

where/max is the infiltration capacity of the micropores approximated by: 1. . . . = A z I ( 0 b -- 0) + Q I 2

8)

where Azj is the thickness of the surface soil layer and QI2 is the amount of water flowing out of the top layer in the time interval under consideration. The solute concentration in water routed into the macropores c* is calculated by assuming complete mixing of incoming net precipitation with water stored in a shallow "mixing" depth Zm:

C* = Osm(t- At) + RCr R + (Z~nO~)

(9)

where Q~r, is the total amount of solute stored in the mixing depth, cr is the concentration of solute in net precipitation and the subscript 1 refers to the top soil layer. Several different bottom boundary conditions are available as options in the model. In this study, we have assumed a zero vertical flux of water at the saturated base of the soil profile, due to low permeability and/or an absence of hydraulic gradient in the vertical direction. Flow of water to the drains may nevertheless occur at this depth, driven by significant horizontal hydraulic potential gradients in the saturated zone. 2.7. Mass transfer between domains Diffusive exchange of solute qsl between the two flow domains and the convective fluxes of water qw and solute q~2 into the micropores are given by empirical expressions: q~l = (aDo) (cm~ --Cmi) Oma

(lO)

q w = ( / 3 K b ) ( Ob--O]Oma \ Ob -- Or]

(11)

qs2 =qwCma

(12)

where a and/3 are mass transfer coefficients related to the aggregate size distribution and flowpath geometry and subscripts ma and mi refer to macropores and micropores, respectively. Eq. ( 11 ) represents uptake of water by aggregates in unsaturated soil. Mass flow in

L. Andreu et al. /Agricultural Water Management 25 (1994) 71~8

76

the reverse direction (from micropores to macropores) may be generated if the micropore region becomes over-saturated. This may occur at the soil surface if rainfall inputs exceed the infiltration capacity of the micropores (see above) and also deeper in the soil profile if a textural boundary is associated with a smaller micropore hydraulic conductivity. In the latter situation, the excess water (and solute) is instantaneously routed into the macropores.

2.8. Crop uptake Root water uptake in MACRO is predicted as a function of the evaporative demand using the simple empirical water uptake model described by Jarvis (1989). In this model, water uptake is calculated as a function of a weighted stress index w* which varies from zero to unity depending on the distribution of root length and water stress in the soil profile:

w* = ~r~o)~

(13)

i=l

where ri and wi are the proportion of roots and a dimensionless water stress index in layer i respectively (both varying from zero to unity) andj is the number of layers in the profile. A threshold response of the crop to water stress is assumed, such that no effect on transpiration occurs unless the water stress index is reduced below a critical value tocnt, here termed the adaptability factor:

Ea-~EP

" ('/) * ~-~ OJcrit

(14)

Ea =Ep~o*; w* < wc~i, where Ea and Ep are the actual and potential transpiration, respectively. This threshold response function allows the crop to adjust to water stress in one part of the root system by increasing uptake from other soil layers where conditions may be more favourable. It is further assumed that the roots are distributed logarithmically in the soil profile, while the water status in a given layer is at an optimum [i.e. wi in Eq. (13) equals unity] if the soil water pressure head and soil air content are above critical threshold values. At values below these critical thresholds, the water stress index is reduced towards zero. The calculated water uptake is distributed within the root depth according to the weighted stress in each layer, while the demand for water calculated for any given layer is assumed to be preferentially satisfied from the water stored in the macropores. Salt uptake is modelled as a function of the calculated water uptake, the concentration in the liquid phase and a "concentration factor" which varies from zero (i.e. complete exclusion) to unity (i.e. passive uptake).

2.9. Drainage Quasi two-dimensional saturated flow of water to the drainage system is calculated using the seepage potential theory (Youngs, 1980). For parallel drains overlain by fully pene-

L. Andreu et al. /Agricultural Water Management 25 (1994) 71-88

77

trating seepage surfaces (i.e. drain pipes with permeable backfill), the total drainage rate qdftot) is given by: 8E

qd~tot)

~ h L2

(15)

where L is the drain spacmg and E is the seepage potential given by: H

E= fK(h) ( H - h)dh

(16)

ho

where K(h) is the hydraulic conductivity varying with height h, ho is the height of the base of the saturated zone and H is the water table height. All heights are measured positive upwards with respect to a datum (h = 0) at the base of the profile. It is assumed that only those saturated layers above drain depth contribute to the total drain discharge, since seepage surfaces will not exist deeper than the drains. Discretized forms of Eqs. (15) and (16) applicable to layered soils are used in the model (see Leeds-Harrison et al., 1986). Thus, evaluating the integral in Eq. (16) gives the seepage potential e in each macropore and micropore domain within the saturated zone as:

(17) and the drainage rate q,~ as: 8e

qd =~-~

(18)

where hu and h I a r e the heights at the top and bottom of the layer [replacing the integration limits H and ho in Eq. (16) ] and where K is given by Ks - Kb in the macropores and Kb in the micropores. In the one-domain case, K is simply given by Ks. It should be noted here that in the layer in which the water table is located, hu in Eq. (17) is replaced by H. The water table height H is estimated assuming hydrostatic conditions in the capillary fringe. The loss of solute to tile drains is calculated from the known water flow rates qo from each layer in the saturated zone and the solute concentrations in each saturated domain, assuming complete mixing within the flow domains in the horizontal dimensions.

3. Materials and methods

3.1. Site details The experiments were carried out in 1989 on a 1 ha plot (40 × 250 m) situated in the area of the marshes on the left bank of the Guadalquivir river, near Lebrija, south-west

78

L. Andreu et al. / Agricultural Water Management 25 (1994) 71-88

Table 1 General characteristics and properties of the soil Depth (cm)

Particle size distribution (%w/w,/zm)

0-30 30-60 60-90

> 50

50-2

<2

1.0 1.0 1.0

32.0 30.0 30.0

67.0 69.0 69.0

CaCO 3 (%)

OM (%)

EC ( d S . m -~)

16.0 16.0 19.0

1.03 -

3.0 7.5 11.0

SAR

5.8 20.5 36.5

EC in saturated paste extract. EC and SAR are given as mean values for the experimental period. Cotton

metres

240-

A

220-

0 0

150-

zx u o x •

o

0×

Pie.zometer Neutron probe Tensio'neter Solution extractor

Salinity sensor • Drain pipe

90

4O

QD o 0 o

lO

-

o

- - "~/"

//

0

10

20

30 m

Collecting channel

Fig. I. Experimental layout at the field site

,Y J/

not to scale).

Spain (36 ° 56'N, 6 ° 7' W). The soil is of clayey texture ( = 70% clay content) with the general properties shown in Table 1. The experimental plot is situated within a larger area of 12.5 ha (slope < 1°), within which a drainage system was installed in 1979. This consists of ceramic sections, each 30 cm in length, forming pipes 250 m in length at an average depth of 0.85 m and at 10 m spacings. The pipes discharge into an open-collecting channel perpendicular to the drains, as shown in Fig. 1. The crop grown in the summer 1989 season was cotton (Gossypium hirsutum L.), one of the most important commercial crops in the region. Sprinkler irrigation was performed on nine occasions during 1989, with a total water application of 560 ram. 3.2. Field measurements In this paper, we report measurements made in the period 14 to 31 July, 1989. These measurements form part of a much wider study carried out since 1988 at the site, within the

L. Andreu et al. ~Agricultural Water Management 25 (1994) 71-88

79

framework of an EEC-funded research programme. Two successive sprinkler irrigation applications of 60 and 50 mm were performed on 15 and 16 July, corresponding to successive positions of a single sprinkler line covering the measured drain line. Each of the irrigations was 7 h in duration and the chloride concentration in the irrigation water was 0.172 g°l on both occasions. No rainfall was recorded during the 17-day experimental period. During the experimental period the potential evapotranspiration was estimated by Penman' s equation using meteorological data from a weather station located = 1 km from the plot. Actual evapotranspiration could be estimated from the water balance in the soil. Thus, at several measurement sites located across the experimental plot (Fig. 1 ), soil water content profiles were measured by neutron probe and the water table monitored by piezometers. Mercury tensiometers were also used to measure soil water pressure head at different depths, although this data is not presented in this paper. Drain water discharge from the central pipe of the three pipes draining the area of the experimental plot was measured by a limnigraph (Fig. 1). Drainage outflow from the surrounding pipes was also monitored to confirm that all pipes in the experimental plot produced similar discharges. Samples of drain water discharge were obtained at frequent intervals for analysis of chloride concentrations by the spectrophotometric method (Florence and Farrar, 1971 ). Chloride concentrations in the soil were measured in saturated soil pastes and 1:5 extracts of soil samples. This information is only used in this study to derive initial chloride concentrations for the upper part of the soil profile to 0.6 m depth (see Table 2). The initial chloride concentrations required by the model for the water-saturated subsoil below 0.6 m depth were obtained from chloride concentrations measured immediately the drains started discharging during the first irrigation on 15 July (20 g-1 ~, Table 2). 3.3. Input p a r a m e t e r estimation

Model input parameter values were estimated by a combination of some direct measurements, literature values and by model calibration. The physical and hydraulic parameter values input to the model are summarized in Table 3. A limited number of measurements of hydraulic conductivity at and close to saturation have been carried out at the site with disc permeameters using the dual-disc technique by Thony et al. ( 1991 ). They reported a mean Ks value of 18.4 m m . h - 1, whilst a K value of 0.4 mm° b - 1 was found by combining data obtained at supply potentials of - 2 and - 10 cm. In this study, we assume a boundary tension between the macropores and micropores of 10 cm and a conductivity at this tension, Kb, of 0.1 ram, h - 1 (see Table 3), which is in reasonable agreement with the unsaturated K measurements reported by Thony et al. ( 1991 ). In contrast, running the model with the Table 2 Initial chloride concentrationsin soil solution Depth (cm)

Chloride concentration (g.1-1 )

0-30 30-60 60-90

2.7 6.7 20.0

80

L. Andreu et al. / Agricultural Water Management 25 (1994) 71-88

Table 3 Soil physical and hydraulic parameters Parameter

Saturated hydraulic conductivity K~ (ram , h - ~ ) Boundary hydraulic conductivity Kb (mm" h - ~) Saturated water content 0s (m 3"rn 3) Boundary water content 0b (m 3° m - 3) Wilting point water content 0,~ (m 3° m - 3) Residual water content 03 ( m 3" m - 3) Boundary pressure head qtb (cm) Pore size distribution index (rnicropores) A Tortuosity factor (micropores) n Pore size distribution index (macropores) n *

Soil layer (cm) 0-30

30-60

60-90

300.0 0.1 0.55 0.43 0.26 0.20 10 0.04 O.5 4.0

75.0 0.1 0.54 0.51 0.43 0.38 10 0.04 0.5 4.0

2.0 0.1 0.53 0.52 0.46 0.42 10 0.04 O.5 4.0

directly measured saturated hydraulic conductivity as input gave very poor results, with the model underestimating the measured peak drain outflows by --~70%. This discrepancy is most likely due to the difficulties in measuring hydraulic properties in cracking clay soils. Firstly, the larger cracks which will dominate flow under saturated conditions in this soil may be widely spaced and may not have been represented in the field measurements owing to the relatively small size of the sample. Secondly, the field measurements reported by Thony et al. (1991) were made when the soil was initially wet, so that the effects of dessication cracks were probably minimized. Measurements made when the soil was drier showed K~ values of 27.1 mm- h - ~, whilst a Kvalue of 5.4 m m . h - ~was found by combining data obtained at supply potentials of - 2 and - 10 cm. In this study, the soil was initially quite dry and the shrinkage cracks in the topsoil most likely played a much more important role. For these reasons, the values of saturated hydraulic conductivity shown in Table 3 were derived by model calibration. The larger values in the topsoil (300 mm- h - ~) reflect the influence of the widely-spaced, surface-connected cracks at the site and are in agreement with values of K~ ( 100 to 300 mm h - l) calculated from the measured drainage outflows and water table heights. The Brooks-Corey model parameters describing the soil water release characteristic in the micropores (Table 3) were obtained from measurements made on small undisturbed core samples (8 cm in diameter, 2 cm in height) in standard pressure plate apparatus. Finally, the mass transfer coefficient/3 for water exchange between flow domains was set to zero. A negligibly small value of/3 seemed appropriate in this model application and is presumably related to a widely-spaced crack distribution at the site. Tables 4 and 5 give the solute transport and crop input parameters respectively. Most of these parameters were either estimated from a combination of general knowledge and/or literature values or known " a priori" (e.g. diffusion coefficient, cropping dates). The root depth of the cotton crop was estimated from experimental observations made in the plot using minirhizotrons. In contrast, the mass transfer coefficient o~ was estimated by model calibration against the measured chloride concentrations in the drain outflow. The "bestfit" value of o~ ( = 0.01 mm -2, see Table 4) may be converted to an approximate profileaverage diffusion path length by assuming a Fickian diffusion model for aggregates of

L. Andreu et al. /Agricultural Water Management 25 (1994) 71-88

81

Table 4 Solute transport parameters Parameter

Value

Mass transfer coefficient a (ram- 2) Dispersivity Dv (cm) Diffusion coefficient Do (rn2"s ~) Impedance factorf Mixing depth Zm (mm)

0.01 1.0 1.9× 10 9 0.5 10.0

known shape (e.g. van Genuchten and Dalton, 1986). Assuming spherical aggregates, together with profile-representative values of macropore and micropore water content of 0.1 and 0.4 m 3. m-3, respectively, gives an approximate effective diffusion path length of 60 ram.

4. Results and discussion 4.1. Water balance

A comparison of measured and predicted drain water outflows is shown in Fig. 2. The two hydrographs are very similar, showing a double peak due to the influence of the successive sprinkler irrigations. The model accurately reproduced both the time to start of drainage, occurring = 2 h after starting the irrigation, and the peak drainage rates = 1.4 ram. h t). However, discrepancies were found in the timing of peak discharges. The model predicted that maximum outflows coincided with the end of the irrigations, although observed peak discharges were maintained for = 6-8 h afterwards (see Fig. 2), This may perhaps be related to a temporary storage of water at the soil surface during irrigation, with subsequent infiltration of the ponded water occurring when the supply of irrigation water ceased. The model accurately predicted the drain hydrograph recession, which continued for = 1 week following the double irrigation event. The predicted total drainage outflow during the experimental period was 57 mm compared to the observed total of 64 mm. The evolution of water table heights observed in the field mirrored the measured drain hydrographs (Fig. 3), with the maximum water table height of -~ 0.1 m below the soil surface occurring at the end of the second irrigation. In general, the model successfully matched the observed water table heights. However, the model predicted that the water table actually reached the surface during the second irrigation, resulting in a predicted surface runoff which was not observed during the experiment. A comparison of measured and predicted soil water content profiles, on three dates following the irrigation are shown in Fig. 4. A reasonable agreement between the model and measurements was found in each case. Both the model and measurements show a near complete recharge of the soil profile immediately after the first irrigation. The measurements made on the two occasions following the second irrigation (18 and 27 July) suggest a shallow depth of water uptake by the roots (to = 0.3 m). The comparison of model simulations and measurements of drain outflow, water table

82

L. Andreu et al. / Agricultural Water Management 25 (1994) 71-88

E

,+_.

*5 o c-

d3 ~O0

15

20

.

.

.

.

.

.

.

25

30

July 1989 Fig. 2. Measured (O) and predicted (

-) drain outflow rates (14-31 July, 1989).

-0.2 r-

¢)

-0.4

_Q 4.--'

-0.6 o

o--

¸

~" -0.8 -1

,

15

,

,

I ~--20

,

25

,

,

,

,

30

July 1989 Fig. 3. Measured (O) and predicted (

) water table depths ( 14-31 July, 1989).

height and soil water recharge discussed above and presented in Figs. 2 to 4 suggest that the soil water balance predicted by the model generally reflects conditions observed in the field. Fig. 5 summarizes, therefore, the different components of the water balance predicted by the model. As mentioned above, predicted surface runoff reached 22 mm during the second sprinkler irrigation, although no surface runoff was observed during the experiment. Nevertheless, the measurements would suggest that = 10 to 15 mm of the applied irrigation water is unaccounted for. As surface runoff is discounted, this water may have been lost from the field as subsurface lateral flow towards the ditch, either deep in the subsoil or as interflow close to the soil surface. The latter is perhaps more likely since deep seepage may be limited by a smaller hydraulic conductivity in the soil below drain depth. Fig. 5 shows that the evapotranspiration predicted by the model was maintained at potential rates ( = 8

L. Andreu et al./ Agricultural Water Management 25 (1994) 71-88

83

0

o ", o o:

-20

oi ;o ,' o

15-7-89

[o o

-1 O0 0.1

0,2

0.3

0,4 0.5 (ms rn"3)

0.6

Water content 0

"',

o

-20 ] ~

-40

~

-60

"o'. o 18-7-89

:o

-80

9 o

-100 1 0.1

0.2

013 01,

Water content

015 0.6

(m~ m"3)

0 o

\

-20

•.•

-40

£

0

io

-60

27- 7-89

q

-80 o -100 0.1

0.2

0.3

Water content

Fig. 4. Measured ((3) and predicted

( -

-

-

)

0.4 0.5 (m3 m"3)

0.6

soil water contents on three dates following irrigation.

mm- d - l) for only 4 to 5 days following the irrigation, mainly due to the shallow rooting of the crop in this salt-affected soil (see Table 5 and Fig. 4). These model predictions are supported by our own unpublished measurements of the midday leaf water potential of the cotton crop in the plot. Leaf water potential changed from - 1.1 MPa one day after the irrigation to - 1.7 MPa 1 week later, reaching - 2.6 MPa at the end of the irrigation cycle on 31 July. For this complete period up to 31 July, the model predicted a total water uptake of 76 ram, compared to a potential transpiration loss of 136 ram. An actual evapotranspiration of about 50 mm was calculated from the water balance in the soil, which is lower than that predicted by the model. However, this value may be underestimated since some water may be supplied from the water table by capillary rise.

84

L. Andreu et al. /Agricultural Water Management 25 (1994) 71-88

150 E

g

(.2 t"

100

_Q

50 ©

>

E (,_)

~'~o_._~_~_ ~

~_~

-50 15

20

25

30

July 1989 Fig. 5. Predicted soil water balance, 14-31 July ( - • - irrigation, - o - drainage, - 4 - surface runoff, - [ ] - actual transpiration, - • - potential transpiration, - o - soil water storage).

Table 5 Crop parameters Parameter

Value

Root depth (maximum) (cm) Fraction of roots in top 15 cm of soil Root adaptability factor tOcrit Crop emergence Maximum leaf area Harvest Critical soil water pressure head (m) Criritcal soil air content (m3-m 3) Interception capacity (mm) Ratio wet canopy evaporation/transpiration Solute concentrationfactor

60.0 0.8 0.5 March 21st July 13th September 27th 10.0 0.01 2.0 1.5 0.0

4.2. Chloride leaching Fig. 6 shows that the model was able to reproduce the marked dilution effect observed in chloride concentrations measured during peak drainage discharges. However, the chloride concentrations measured during the drain recession were somewhat larger than those predicted, perhaps due to inflow of deep groundwater of higher salinity. Fig. 7 compares the measured and predicted chloride balance. The predicted loss of chloride to the drains reached 2550 kg. h a - 1 by 24 July, which is = 25% smaller than that measured in the drainage water during the same period (3510 kg. h a - l ) . The difference between measured and predicted losses of chloride was entirely due to the model underestimating chloride concentrations during the later stages of drain recession (see Fig. 6). Indeed, the model accurately reproduced the measured chloride leaching up to 18 July (see Fig. 7). As mentioned above, this

L. Andreu et al. / Agricultural Water Management 25 (1994) 71-88

85

25O

O

='2" 20"

o

C

._o (1) C) C 0

0

-C~ L.

o

t'--

10-

,S

5

c)

0

, i

.

.

15

.

.

.

.

.

.

.

.

2O

, , , ,

25

30

July 1989 Fig. 6. Measured (O) and predicted (

) chloride concentrations in the drain outflow ( 1 4 - 3 1 July, 1 9 8 9 ) .

0.4 E v

0.2

7D L_ O r,D 7D 03

0

E c) CA

-0.2 [ 15

20

25

30

July 1989 Fig. 7. Chloride mass balance. Measured ( ~ ) and predicted (-o-) chloride leaching, predicted change in soil storage ( - [ ] - ) and predicted loss of chloride in surface runoff (-~-). discrepancy at later dates may be due to chloride intrusion from the water table, perhaps resulting from irrigations in bordering fields. Fig. 7 shows that the predicted loss of chloride in the drainage water is mirrored by a reduction in the chloride stored in the soil profile. The model predicts some losses of chloride by surface runoff, although the amount ( 150 k g . h a - ~) is not large, since salt concentrations are small close to the soil surface.

4.3. Simulating in one flow domain The results of the model simulation run in one flow domain showed a similar pattern to that found in two domains, showing a rapid drain response to irrigation and a marked

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L. Andreu et al. / Agricultural Water Management 25 (1994) 71-88

dilution effect during peak discharges. However, in terms of the total water and chloride balances, the model performed less well when run in one domain. The total water discharge predicted in the one-domain case was 47 mm, which is 10 m m less than in the two-domain case and 17 m m less than that measured. Despite a smaller drain discharge, chloride leaching in the one-domain case was = 10% larger than in the two domain case (2830 k g . h a compared to 2550 k g . h a -1 on 24 July) and considerably overestimated the measured leaching losses during the peak discharge period (Fig. 8). The reason for this difference is illustrated in Fig. 9, which compares the predicted chloride concentrations at 0.6 to 0.7 m depth in the one- and two-domain simulations. In the two-domain case, the chloride con0.4 ¸

E

0.3

} o

0.2

© L..

o c'(D

0.1

15

20

25

30

July 1989 Fig. 8. Chloride mass balance. Measured leaching (-o-) and predicted leaching in two (-o-) and one (-[3-) domain simulations. 20 lg tO

18

',

. -- ;

C 0 LJ

f

/

17

~D 0 r*

16

o 14

16

18

20

22

24

July 1989 F i g . 9. P r e d i c t e d c h l o r i d e c o n c e n t r a t i o n s i n t h e s o i l p r o f i l e at 0 . 6 to 0 . 7 m d e p t h . O n e - d o m a i n domain case (- - - micropores, - - - - macropores).

case (

), tWO-

L. Andreu et al. / Agricultural Water Management 25 (1994) 71-88

87

centrations are markedly reduced in the macropores due to deep penetration of irrigation water of low salt concentration. Equilibration of concentrations between micro- and macropores is a relatively slow process and was still not completed 1 week after the irrigation. The drains respond primarily to saturation in the macropore system, since conductivities are assumed very small in the micropores (Table 3). A strong dilution effect in the macropores is therefore reflected in the predicted chloride concentrations in the drain outflow. As noted above, a dilution effect in the drain outflow is still predicted in the one-domain simulation. This is because the water table rises into surface soil layers with smaller chloride concentrations. However, this dilution effect is less pronounced in only one flow domain, since subsoil chloride concentrations are largely unaffected by the irrigation (Fig. 9) because by-pass flow is not simulated.

5. Conclusions

The model seriously underestimated observed drain outflows using directly measured saturated hydraulic conductivities. This highlights the near-impossible task of measuring representative soil hydraulic properties in swelling/shrinking clay soils, in which the soil hydrology at saturation is dominated by large, widely-spaced cracks and fissures. However, an excellent agreement between model predictions and measurements of the soil water balance could be obtained with saturated hydraulic conductivity determined by model calibration. The time to start of drainage (2 h), together with maximum drain discharges = 1.4 m m . h - 1) and the subsequent slow drain recession, were all then well predicted by the model. Inferred Ks values were 300 nun- h - ~in the cracked topsoil, falling to 2 m m . h in the water-saturated subsoil below 60 cm depth. Similarly, the model successfully reproduced the rapid response of the water table to irrigation, followed by the slow recession. At the end of the first irrigation, the soil profile was fully recharged, with the water table only 10 cm below the surface. Following the irrigation, water extraction by the crop was largely restricted to the upper 30 cm of soil, presumably due to excessive salt concentrations in the subsoil. Indeed, the model simulations indicated reductions in transpiration below the potential rate after only 1 week, with an accumulated soil water deficit of = 60 to 70 mm. A strong dilution of the chloride concentrations during peak drain discharges was observed and could also be predicted by the model. This dilution was related to the rapid infiltration of irrigation water in the cracks. In two flow domains, the model matched observed leaching losses of chloride during peak drain discharges. However, the accumulated loss of chloride 1 week after the irrigation was underestimated in the model by = 25%, due largely to an underestimate of measured chloride concentrations in the outflow during recession. It is possible that inflows of saline shallow groundwater from surrounding areas may have influenced these results. The model predicted qualitatively similar patterns of drain outflow and chloride leaching even in the absence of by-pass flow. Nevertheless, the model performed less well, since chloride leaching was overestimated during peak discharges, despite a total drain outflow which was 10 mm less than in the two-domain case. We may conclude from this study that the two-domain model MACRO is able to represent the most important features of the water and solute balance at Marismas. Extrapolation of

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L. Andreu et aL /Agricultural Water Management 25 (1994) 71-88

t h e s e results b y p e r f o r m i n g l o n g - t e r m (i.e. g r o w i n g s e a s o n ) s i m u l a t i o n s w o u l d n o w b e d e s i r a b l e , so that o p t i m u m m a n a g e m e n t strategies c a n b e d e v i s e d w i t h the o b j e c t i v e s to m a i n t a i n or i m p r o v e soil quality, w a t e r u s e e f f i c i e n c y a n d c r o p p r o d u c t i o n .

6. Acknowledgements T h e a u t h o r s w i s h to a c k n o w l e d g e the h e l p r e c e i v e d f r o m Dr. F. C a b r e r a a n d Mrs. R. V a z w i t h the c h e m i c a l a n a l y s i s , a n d Mr. J. R o d r i g u e z for h e l p in the field e x p e r i m e n t s . R e s e a r c h w a s c a r r i e d out w i t h i n the f r a m e w o r k o f c o n t r a c t no. E V 4 V - 0 0 9 9 - C ( A ) o f the C E C .

7. References Beven, K. and Germann, P. 1982. Macropores and water flow in soils. Water Res. Res., 18:1311-1325. Bouma, J. 1981. Soil morphology and preferential flow along macropores. Agric. Water Manage., 3: 235-250. Brooks, R.H. and Corey, A.T. 1964. Hydraulic properties of porous media. Hydrology paper no. 3, Colorado State University, Fort Collins, Colorado, 27 pp. Florence, T.M. and Farrar, Y.J. 1971. Spectrophotometric determination of chloride at the part- per-billion level by the mercury (II) thiocyanate method. Anal. Chim. Acta, 54: 373-377. Gir~ildez, J.V. and Cruz Romero, G. 1975. Salt movement in Guadalquivir marshy soils under field and laboratory conditions. Egyptian J. Soil Sci., 15: 79-93. Gonz~ilez Garcfa, F., Gonzttlez Garcfa, S. and Chaves S~inchez, M. 1956. The alkali soils of the lower valley of the Guadalquivir: physico-chemical properties and nature of their clay fraction. Proceedings of the VI International Congress Soil Science, B (1.26), pp. 185-191. Jarvis, N.J. 1989. A simple empirical model of root water uptake. J. Hydrol., 107: 57-72. Jarvis, N.J. 1991. MACRO - A model of water movement and solute transport in macroporous soils. Reps. and Diss., 9, Dept. Soil Sci., Swedish Univ. of Agr. Sci., Uppsala, 58 pp. Leeds-Harrison, P.B., Shipway, C.J.P., Jarvis, N.J. and Youngs, E.G. 1986. The influence of soil macroporosity on water retention, transmission and drainage in a clay soil. Soil Use and Manage., 2: 47-50. Messing, I. and Jarvis, N.J. 1990. Seasonal variation in field-saturated hydraulic conductivity in two swelling clay soils in Sweden. J. Soil Sci., 41: 229-237. Moreno, F., Martin, J. and Mudarra, J.L. 1981. A soil sequence in the natural and reclaimed marshes of the Guadalquivir river, Seville (Spain). Catena, 8:201-221. Mualem, Y., 1976. A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Res. Res., 12: 513-522. Tanton, T.W., Rycroft, D.W. and Wilkinson, F.M. 1988. The leaching of salts from saline heavy clay soils: factors affecting the leaching process. Soil Use and Manage., 4:133-139. Thony, J.-L., Vachaud, G., Clothier, B.E. and Angulo-Jaramillo, R. 1991. Field measurement of the hydraulic properties of soil. Soil Tech., 4:111-123. van Genuchten, M.T. and Dalton, F.N., 1986. Models for simulating salt movement in aggregated field soils. Geoderma, 38: 165-183. Youngs, E.G. 1980. The analysis of groundwater seepage in heterogenous aquifers. Hydrol. Sci. Bull., 25:155165. Youngs, E.G. and Leeds-Harrison, P.B. 1990. Aspects of transport processes in aggregated soils. J. Soil Sci., 41: 665~i75.