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Potential groundwater recharge from deep drainage of irrigation water
Journal Pre-proof Potential groundwater recharge from deep drainage of irrigation water
Majid Altafi Dadgar, Mohammad Nakhaei, Jahangir Porhemmat, Bijan Elyasi, Asim Biswas PII:
S0048-9697(20)30615-X
DOI:
https://doi.org/10.1016/j.scitotenv.2020.137105
Reference:
STOTEN 137105
To appear in:
Science of the Total Environment
Received date:
5 December 2019
Revised date:
2 February 2020
Accepted date:
2 February 2020
Please cite this article as: M.A. Dadgar, M. Nakhaei, J. Porhemmat, et al., Potential groundwater recharge from deep drainage of irrigation water, Science of the Total Environment (2018), https://doi.org/10.1016/j.scitotenv.2020.137105
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© 2018 Published by Elsevier.
Journal Pre-proof
Potential groundwater recharge from deep drainage of irrigation water Majid Altafi Dadgara, Mohammad Nakhaeib, Jahangir Porhemmatc, Bijan Elyasid, Asim Biswase* a
Department of Applied Geology, Faculty of Earth Sciences, Kharazmi University, P.O. Box:
31979-37551, Tehran, Iran, E-mail: [email protected] Department of Applied Geology, Faculty of Earth Sciences, Kharazmi University, P.O. Box:
Research,
Education
and
Extension
(AREEO),
Tehran,
Iran,
E-mail:
Department of Applied Geology, Faculty of Earth Sciences, Kharazmi University, Tehran, Iran,
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E-mail: [email protected]
School of Environmental Sciences, University of Guelph, 50 Stone Road East, Guelph, Ontario,
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e
Organization
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[email protected] d
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Soil Conservation and Watershed Management Research Institute (SCWMRI), Agricultural
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c
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31979-37551, Tehran, Iran, E-mail: [email protected]
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b
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N1G 2W1, Canada, E-mail: [email protected] *Corresponding author: E-mail- [email protected] (A. Biswas), Phone +15198244120 Extn. 54249
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Journal Pre-proof Abstract Knowledge of soil water dynamics in the deep vadose zone provides valuable information on the temporal and spatial variability of groundwater recharge. However, semi-arid climate can complicate how the input of water, such as irrigation, can contribute to potential groundwater recharge. This study assessed the recharge rates and their timing under irrigated cropland from a semi-arid region of northern Iran. A deep drainage (10 m) experiment was performed and in situ
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soil water content was measured to analyze the soil water dynamics and model hydraulic
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parameters using HYSDRUS-1D. The best parameters selected from inverse parameter
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optimization were used to calibrate model and estimate the long-term (20-year) average
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groundwater recharge and the influence of the root zone, unsaturated zone and the time scale on
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the recharge processes. The simulated annual flux ranged from 24 mm to 268 mm (mean of 110 mm) at 2-m depth and ranged between 26 mm to 207 mm (mean of 95 mm) at the 10-m depth.
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High fluxes, observed between December and April, may be the result of greater precipitation
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combined with the irrigation return flow. The May-October period showed a gradual decrease in flux at the depth of 2 m. At the depth of 10 m, the flux showed some continuity (base flux)
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during the long-term recharge simulation. In total, 12.7% of the input water contributed to the recharge of the groundwater. The annual soil water fluxes were almost similar irrespective of depth below the root zone and the flux rates did not show any clear relation between the different components of the water budget at any depth. This approach improved our understanding of the recharge process in the deep vadose zone in a semiarid region and can help for the development of effective management of groundwater resources. Keywords: Groundwater recharge; deep vadose zone; drainage experiment, HYDRUS-1D; Simulation 2
Journal Pre-proof 1. Introduction Groundwater recharge is a small but important component of water balance in arid and semiarid areas. In these areas, where irrigated agriculture is common, accurate calculation of aquifer recharge and evapotranspiration are essential for assessing scarce water resources and their sustainable management (Garatuza-Payan et al., 1998). Generally, recharge is defined as the downward flow of water reaching the water table (de Vries and Simmers, 2002). However,
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groundwater recharge over a certain area is normally considered to be equal to infiltration excess
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over the same area if deep drainage is not obstructed by impervious horizons in the deep
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unsaturated zone (de Vries and Simmers, 2002). In an area with a deep-water table, downward
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soil water flux from below the root zone (deep drainage) is often known as potential recharge
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(Min et al., 2015; Radford et al., 2009; Wohling et al., 2012). Soil water dynamics usually vary with depth and time from changes in input factors, such as precipitation and irrigation events and
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processes, such as evapotranspiration, and changes in deep soil water storage (Hubbell et al.,
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2004; Timms et al., 2012). Therefore, the deep vadose zone plays an important role in the
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groundwater recharge process.
Several methods have been applied to quantify groundwater recharge with various degrees of success (Scanlon et al., 2002). These methods can be loosely grouped into three categories depending on the targeted zones: surface water, unsaturated zone, and the saturated zone. In each of these cases, physical and tracer techniques as well as numerical modeling showed promise (Jiménez-Martínez et al., 2009). Despite the recent interest in quantifying soil water dynamics in the deep vadose zone (Hubbell et al., 2004; Kurtzman and Scanlon, 2011; Min et al., 2015; Turkeltaub et al., 2014), variations in the recharge fluxes with depth and time are less understood. 3
Journal Pre-proof Physically based models, such as those solving the Richards’ equation for water flow in the vadose zone and water balance of surficial sediments, often have been used to estimate groundwater recharge under various conditions (Kurtzman and Scanlon, 2011; Leterme et al., 2012; Turkeltaub et al., 2015). These models require the information and/or knowledge of soil water retention and unsaturated hydraulic conductivity for all soil horizons (Dafny and Šimůnek, 2016). Although laboratory experiments have been advantageous for quick and precise
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estimation of parameters for these functions, they often lead to soil hydraulic properties that are
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not representative of those in the field. Therefore, inverse models have gained popularity for
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estimating the soil hydraulic functions of the unsaturated zone. Among the benefits of inverse
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methods, its equal applicability to field experiments, even under nontrivial boundary conditions, has gained in popularity (Hopmans et al., 2002). On the other hand, one of the most popular
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experiments to determine soil water retention and hydraulic conductivity functions in the field
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(in situ) has been the field drainage experiment (Dane and Hruska, 1983; Hillel et al., 1972). This method has shown promise for hydraulic characterization of field soils with little horizon
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differentiation (Kabat et al., 1997). However, this approach has never been used to determine the
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soil hydraulic functions in the deep vadose zone. The Karaj region in Iran is a major industrial region and there are now many migrants from Tehran, the capital of Iran, living there. Population growth in the area has led to an increase in water demand and declining groundwater levels (Abkhan, 2013). In a semiarid region like Karaj the return flow from irrigation events plays a significant role in groundwater recharge (Porhemmat et al., 2018). Knowledge about the dynamics of flow through the unsaturated zone to the aquifer needs to consider residence time and changes in flux rates to ensure appropriate management of available water supplies. Groundwater recharge in this region is generally
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Journal Pre-proof estimated usinFg empirical equations, usually by taking a fraction of the precipitation rate and irrigation return flow, which often is used in groundwater flow models and water budget calculations (Dadgar et al., 2018). However, these approaches do not consider the effects of the unsaturated zone on the dynamics of the recharge. The main objective of this study was to investigate the groundwater recharge process in the deep vadose zone (~10 m) using in-situ field experiments and numerical simulations. We have considered the Karaj region as a case study site
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typical of the semiarid climate in Iran to carry out the field experiment; we then performed a
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numerical assessment of the deep drainage. To achieve this objective, a field drainage
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experiment was conducted to: 1) determine the soil hydraulic functions of the unsaturated zone,
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2) calibrate the transient unsaturated flow using in-situ monitoring data of the field drainage experiment, and 3) estimate the long-term average groundwater recharge using the calibrated
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model and long-term meteorological data.
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2.1. Study area
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2. Material and methods
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A field experiment was conducted on an agricultural farm (35°47ʹ45ʺN and 50°56ʹ35ʺE) of about 8.33 ha, within the agriculture experimental site of the Seed and Plant Improvement Institute (SPII) of the Karaj city, in the northern part of Iran (Fig. 1). The average annual rainfall, recorded at the Karaj meteorological station, is 248 mm (1997-2017), with slightly higher rainfall in winter (69.7%) and the area is dominated by a semiarid climate. There are relatively thick unsaturated materials between the ground surface and the water table (about 80 m). Soil formations of the site are related to alluvial processes and are deposited in the old streambeds and banks of prior streams. The most common cropping system in the area is winter wheat with a
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Journal Pre-proof furrow irrigation system, including 5-7 irrigation applications around ~70 mm, depending on the
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availability and variability of precipitation.
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Fig. 1 Location map of the experimental site in the Karaj region
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2.2. Field drainage experiment
An in-situ transient drainage experiment was carried out in a field representing the SPII fields and region. A 40-cm depth trench was excavated within the study area to conduct the drainage experiment on a leveled, vegetation-free plot with 5 m-by-3 m (or 15 m2) surface area. This was done to ensure that the lateral water movement at the plot boundary would not influence the water regime in the plot center. Additionally, the sloping edges were also covered by PVC sheets (Fig. 2a). A 10-m deep monitoring well was dug in the center of the trench and instrumented with TDR sensors at 16 depths (Fig. 2b). Soil water content was measured more frequently near the surface with high sensitivity to the hydraulic parameters. 6
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sensor locations and material distribution
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Fig. 2 Experimental site: (a) trench dimensions and monitoring well location, (b) measurement
Soil and sediment samples were taken from each measurement sensor depth in the monitoring
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well to determine soil bulk density (Grossman and Reinsch, 2002), soil water retention curve
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(Dane and Hopmans, 2002) and soil texture (Gee and Or, 2002). The soil water retention curves were extracted using five tension (i.e. 0.1, 0.33, 1, 3, 5 and 15 Bar) applied by pressure plate.
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The sample sizes for extracting soil water retention curve were 5 cm in diameter and 1 cm thick. Vertical saturated hydraulic conductivity, Ks, was measured on undistributed soil cores using falling head experiments on undisturbed soil cores (Fig. 3). Particle size distributions in soil samples indicated the prevalence of a sand-over-clay profile. A thick loamy sand layer (from 30 cm to 10 m of ground surface) was uniformly underlain with thin loam soil (from 0 to 30 cm) at the top. In December 2017, the experiment was started by ponding water over the plot area with a constant height of 15 cm for 9 days. During this time, water was allowed to penetrate through the column and the total consumption of water was about 48 m3. Water content measurements 7
Journal Pre-proof were taken in decreasing time frequencies during infiltration days (9 days) until water content values exhibited no changes with time at any depth. In other words, a time-invariant unit hydraulic gradient was reached at the bottom of the soil column. At that point, the water supply was stopped and the ponded water was allowed to infiltrate. The plot was then covered with a plastic sheet to remove evaporation and temperature variations and insulated to ensure a zeroflux top boundary condition. Subsequently, the soil was allowed to drain by gravity. The
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transient drainage process was monitored by simultaneous measurements of soil water content
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until the changes with time were minimal. The soil water flux through each depth was calculated
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by multiplying the water content variations over time and the thickness of the soil depth.
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Monitoring the drainage process continued for 37 days.
Fig. 3 Depth-wise soil particle deviations and hydraulic properties measured in laboratory
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Journal Pre-proof 2.3. Model setup To have an accurate tool for analyses of the recharge process, a calibrated unsaturated flow model was used for groundwater recharge simulations. Numerical simulations of the drainage experiment and groundwater recharge simulations were conducted using the HYDRUS-1D software package (Šimunek et al., 2005). The Richards’ equation (Eq. 1) was implemented to
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account for water flow in the vadose zone.
(1)
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h K( h ) 1 S( z,t ) t z z
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where θ was the volumetric water content [L3 L-3], h was the pressure head [L], z was vertical
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spatial coordinate [L] assumed to be 0 at the soil surface and directed upward, S was a sink term
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accounting for root water uptake [T-1], t was time [T], and K was the unsaturated hydraulic
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conductivity function [LT-1].
For solution of Equation 1, the van Genuchten-Mualem soil hydraulic functions were used
h 0
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r s nr m ( 1 h ) ( h ) s
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(Mualem, 1976; van Genuchten, 1980):
(2)
h 0
1 K ( h ) K s S 1 ( 1 S em ) m
2
l e
(3)
where θr and θs were the residual and saturated water contents (L3 L-3), respectively. Ks (L T-1) was the saturated hydraulic conductivity, α, (L-1) and n represented the empirical shape parameters, m = 1 - 1/n; l was the pore connectivity parameter. Se was the effective saturation.
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Journal Pre-proof The domain was set perpendicular to the trench, assuming that a 1D flow pattern can be approximated just below the center of the trench base. 2.3.1. Inverse modeling For the inverse solution, the time series of soil water content at each observation point were reconstructed from the drainage experiment data monitored over 37 days (between 14 December
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2017 and 20 January 2017). As mentioned previously, the drainage experiment was executed on
(z ) i q (z , t ) 0
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the following initial and upper boundary conditions:
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a vegetation-free plot. Equation (1) was solved using HYDRUS-1D (Šimůnek et al., 2005) for
t 0, 0 z L
(4)
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t 0, z 0
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where z = 0 denoted the soil surface (upper boundary), with L equal to the depth of the measured
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soil domain. The free drainage type boundary condition was used to simulate the lower boundary of the model as the groundwater table (80 m below ground surface) was far below the lower
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boundary of the model (Šimůnek et al., 2005). The initial conditions were measured using TDR
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sensors before starting the drainage process and linearly interpolated along the vertical axis. The inverse estimation was conducted by minimizing the objective function (Šimůnek et al., 2005): m
( ; b | c ) obs (t i ) sim (t i , b | c )
2
i 1
(5) where m was the number of observations, θobs and θsim were measured and simulated volumetric water contents, b|c was the vector b containing the unknown parameters estimated given the fixed and known parameters in vector c. The model was constructed of two layers with six van
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Journal Pre-proof Genuchten–Mualem parameters in each layer. The pore connectivity and tortuosity factor l, which was fixed at 0.5 for two layers in this study (e.g. (Mualem, 1976); Nakhaei and Šimůnek (2014; Turkeltaub et al. (2015). Four parameters including saturated hydraulic conductivity, residual water content, α, and n were marked to be optimized. Different initial estimates of the parameters were used repeatedly to test uniqueness and stability of the inverse solution. For this reason, the behavior of the inverse solution was evaluated by plotting the values of the objective
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function ( ),against pairs of optimized parameters, which named the response surface
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(Hopmans et al., 2002). Therefore, the inverse solution with four adjustable parameters needs six
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parameter pairs to be analyzed. Each response surface was obtained by taking many
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combinations of a selected pair of parameter values, while the boundary and initial conditions and the other parameters were kept constant. Shape of the response surface shows the occurrence
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of local minima and global minima of the objective function and the sensitivity of the
Model prediction
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2.3.2.
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parameters.
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The calibrated model was then used to assess recharge over a 20-year period (Nov 1997-June 2017) meteorological data. During the simulation, the upper boundary condition was set as the atmospheric condition. The reference evapotranspiration (ET0) and daily values of the potential evapotranspiration (ETp) were calculated using the Penman-Monteith equation following (Allen et al., 1998): ETp Kc ET0
(6)
where Kc was a dimensionless crop coefficient and was extracted following Allen et al. (1998. An actual irrigation schedule was not available during the simulation period which was estimated
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Journal Pre-proof following published works (Min et al., 2015; Porhemmat et al., 2018; Sun et al., 2010). For approximating the irrigation schedule, the annual rainfall events were ranked in descending order and the probability of exceedance of the events was calculated using Hazen method (Hazen, 1930). For the years with the events more than 25% probability of exceedance, only key irrigation periods were applied (i.e. 5 events). Otherwise, two extra irrigation events were
values during considered years.
2016-2017 40.37
50.73 21.41
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27.87
59.19 14.70
2015-2016
49.81 15.45
2014-2015
33.76 30.65
2013-2014
38.62 44.70
2012-2013
79.91 24.36
2011-2012
58.57 26.46
2010-2011
72.33 16.80
2009-2010
34.33 39.26
2008-2009
36.83 35.52
2007-2008
-p 39.85 30.06
2006-2007
2005-2006
2004-2005 42.16 27.01
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2003-2004 35.85 26.85
2002-2003
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54.83
2001-2002
26.22
24.02
59.62
44.57
18.20
17.36
56.60
26.53
2000-2001
42.50
Precipitation (cm)
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56.65
1999-2000
Irrigation (cm)
21.29
Year
1998-1999
1997-1998
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Table 1. Irrigation schedule and annual precipitation values
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included (i.e. 7 events). Table 1 shows the total amount of irrigation along with precipitation
S ( h ) ( h )S p
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For modeling the root water uptake, the function was used following (Feddes et al., 1978): (8)
where S was a sink term accounting for root water uptake [T-1]. Sp was the potential water uptake rate [T-1]. α(h) was a dimensionless water stress response (0 ≤ α ≤ 1) and was defined by four threshold values h1 = 0 cm, h2 = -1 cm, h3 = -900 cm, h4 = -16,000 cm for wheat (Wesseling et al., 1991).
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water fluxes during drainage test (c)
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3. Results and discussion
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Fig. 4 Soil water profiles for wetting front propagation (a), drainage front propagation (b), soil
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3.1. Drainage experiment results
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After a day of ponding, the wetting front reached a depth of 600 cm below the ground surface (Fig. 4a). Based on the equation 1, when infiltration started, the gradient in pressure head due to capillary was the main driving force for the rapid movement of water into the soil. By flowing deeper into the soil, the role of capillary force might be diminished and only gravity was responsible for movement. Under these conditions, a minimum infiltration rate reached and it is approximately the saturated hydraulic conductivity (Radcliffe and Simunek, 2010). Time-invariant hydraulic gradient was reached after five days of ponding, i.e. values of water content were unchanged at any depth (steady conditions). The drainage front propagation (14
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Journal Pre-proof December 2017 to 20 January 2017) is shown in Fig. 4b. The maximum flux of 37.5 cm d-1 was calculated a day after starting drainage, with the average flux over the measurement period of 4.9 cm d-1. The flux was decreased over time by moving the drainage front down. Two days after starting the drainage process, an increasing trend of flux was observed at depths of 300 and 700 cm (Fig. 4b). This may be due to the occurrence of the drainage with a delay from the top layer (loam) in which water was released slowly. The total calculated volume of water drained from
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the soil column was 115 cm over the drainage period (shaded area in Fig. 4b). Stable conditions
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reached on 20-Jan, which indicated water content values closed to field capacity values.
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Subsequently, the flux decreased to zero after 37 days, revealing that drainable water removed
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from soil profile. In other words, as the amount of water stored in the soil closed to the field capacity value, drainage flux tended to zero (Bethune et al., 2008). It noted that the initial soil-
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water conditions have a major influence on groundwater recharge fluxes.
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3.2. Inverse modeling
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The uniqueness of inversed solution was analyzed using response surface shape of the optimized
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parameters. Figure (5) shows response surfaces for (Ks,α), (n,α), (θr,α), (Ks,n), (θr,n) and (θr,Ks). The ideal response surface shows a narrow minimum area with circular shape, indicating no correlation between the fitting parameters (Hopmans et al., 2002). Paralleling shapes to one of the axes reveal insensitivity to that parameter and indicates high parameter uncertainty. As revealed in figure (5), the response surface of the parameters is almost favorable. There were no multiple minima with the same Ф values, and there were no extremely narrow valleys paralleling to the axis for parameter combinations. Figure (6) shows the relation between hydraulic conductivity function with water content. After evaluation of the uniqueness of the inverse
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Journal Pre-proof solution, simulated results were compared with the corresponding observations. The comparison
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showed that the general dynamics of the measurements matched all simulations (Fig. 7).
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Fig. 5 Response surfaces for six pairs of parameters
Fig. 6 Soil hydraulic parameters of the optimized model 15
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Fig. 7 Observed water contents obtained by TDR sensors (blue points) and simulated water contents obtained with the calibrated model (green line) 16
Journal Pre-proof Upon closer inspection, it was noticed that there were small systematic deviations between the measured and simulated values (Table 2). This indicated that errors may be due to the spatial heterogeneity and observation errors (Vazifedoust et al., 2008). The values of root mean square error (RMSE) and coefficient of determination (R2) for 375 observed data points were 0.014 cm and 0.94, respectively. Indicating that the HYDRUS-1D successfully simulated increases in the water content following delayed drainage at the lower depths with gradual decreases during
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drying periods. The calibrated model was subsequently used to predict the field water cycle with
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meteorological data.
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Table 2. Optimization results of Genuchten–Mualem parameters along with statistics on the
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Layer 3
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Alpha
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Ɵr
Textural
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performance of simulation
3
-3
RMSE*
Ks n
R2*
l
-1
-1
(cm cm )
(cm cm )
(cm )
1
Loam
0.078
0.43
0.036
1.567
0.5
25.15
2
Loamy sand
0.065
0.41
0.074
1.981
0.5
129.82
(cm d )
1
n
R square, R
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1 n RMSE ( xi yi ) 2 ; * Root mean square error, n i 1
2
i 1 n
-3
(cm cm ) 0.014
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name
3
0.94
( yi xi ) 2 _
( xi x) 2 i 1
3.3. Recharge estimation
3.3.1. Soil water flux-depth and time relationships Annual sums of irrigation and precipitation (I + P), deep drainage (at 2-m depth), and recharge fluxes (10-m depth) varied over the years (Fig. 8). Deep drainage under the root zone was more affected by irrigation events than precipitation events. For example, the annual deep drainage during the years 2006 and 2007 decreased as irrigation decreased. During this period, the amount
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Journal Pre-proof of precipitation was high, and the number of irrigation applications was reduced. In this situation, the applied irrigation water was mainly lost by evapotranspiration. However, these variations indicated the complexities of the flux processes through the root zone. Generally, there were no clear or meaningful trends between input water and fluxes. The simulated annual flux at a depth of 2 m ranged from 24 mm to 268 mm, with a mean value of 110 mm. The annual recharge at
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10-m depth ranged between 26 mm to 207 mm, with a mean value of 95 mm.
Fig. 8 Comparing long-term annual water input (a) with corresponding annual deep drainage at 2-m depth and recharge flux at 10-m depth (b) during the period from Nov 1977 to June 2017 For more considerations, daily variations of recharge fluxes were compared with the corresponding input of water (Fig. 9). Fluctuations of daily flux under the root zone (2-m depth) were sharp and were increased 40–70 days after the first major infiltration event (Fig. 9a). High fluxes were generally observed during the period from December to April from the response of
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May and October exhibited a gradual decrease in the deep drainage fluxes.
Fig. 9 Daily deep drainage and recharge flux compared with the water input (a), details on the wet period from Nov 2007 to Nov 2012 (b) The peak flux below the root zone exceeded 8 mm d-1 during the rainy season, whereas the peak flux at the depth of 10 m was only approximately 1 mm d-1. The recharge flux (10-m depth) fluctuations were much attenuated and tended to smooth out individual annual cycles. Individual flux peaks and dips appeared at longer time lags. For example, the peak that occurred at a depth of 2 m in April 2011 reached the depth of 10 m in Dec 2011 and the peak at the depth of 2 m in 19
Journal Pre-proof early Feb 2012 reached the depth of 10 m in late Nov 2012 (Fig. 9b). Details on the input water showed a relatively wet period with regular precipitation events among irrigation applications from Nov 2007 to Nov 2012 (Fig. 9b). Comparison of this period with the preceding or subsequent periods showed that the sharp fluctuations of the deep drainage became gentle and more prolonged, whereas recharge fluxes exhibited no significant changes (Fig. 9b). Additionally, recharge fluxes showed a small continuous flux (base flux) during the long-term
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recharge simulation, which was not removed even in dry months. This reveals that variations in
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input water did not greatly affect the flux at the deeper depths or the effects were dampened as
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the depth increased. This revealed important characteristics of soil water flux at different depths,
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Martínez et al., 2009; Min et al., 2015).
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through the root zone and in the deep vadose zone (Carrera-Hernández et al., 2012; Jiménez-
Fig. 10 Soil water flux variations with soil depth for selected year (a), deviation of annual soil water flux from long-term average value with time scale (b)
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Journal Pre-proof Comparing results of annual fluxes with the corresponding depth showed significant flux variations in the root zone (i.e. 0-2 m) during selected years (Fig. 10a). In contrast, the annual soil water fluxes were nearly uniform below the root zone for each year. Therefore, the depth below the root zone acted as an interface for estimating annual recharge rates. This also indicated that the recharge process estimations at depths smaller than the root zone can be misleading for cropping land. Min et al. (2015 also reported a similar steady flux below the 2-m depth in long-
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Temporal deviations of the annual fluxes were further compered with the 20-year average at
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different depths (Fig. 10b). The annual deviation of flux from long-term average was reduced
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with increased depth. Additionally, a similar decreasing trend was observed at longer time scales
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indicating the need for investigation of longer time scales, often more than 10 years, for temporal assessment of recharge processes. In general, the results showed the sensitivity of the recharge
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estimations with both time and depth variations.
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3.3.2. Vadose zone water budget and travel time
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Simulated water balance components over the long-term (1998–2017) showed in Fig. 11. The mean value of monthly water input (P + I) was 6.35 cm, with the maximum value of 26.31 (in March 2003). The maximum value of transpiration (T) was 24.22 cm, with a mean value of 4.31 cm and had a similar trend to that of the water input. It indicated that the rate of transpiration was increased with increased water input (Fig. 11b). The maximum and mean value of actual evaporation (E) was 5.7 and 1.7 cm, respectively. Simulated monthly recharge (R) reflected high variability of water input with a range of 0.1 to 3 cm and an average of 0.8 cm. The cumulative applied water and recharge over the period were 1505 and 191 cm, respectively. Therefore, the percentage of the applied water that contributed to recharge of the groundwater was 12.7% 21
Journal Pre-proof during the simulated period. Long-term recharge simulations helped to calculate the travel time of waterfront to groundwater level. The average flux between the root zone and the depth of 10 m was 10.37 cm year-1 with the average water storage of 106.4 cm. Assuming a piston flow through the matrix, a travel time of 10.25 years to a depth of 10 m was calculated. Following this, the travel time of water to groundwater was calculated. Assuming the absence of any impervious layer between 10 m and the water table at 80 m and relatively homogeneous
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hydrogeology units between 2 and 10 m layers and 10 and 80 m layers, travel time was estimated
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to be 102 years. This will have a significant impact on the sustainability of the groundwater
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within the region. Here in support of the hydrogeology, a well log drilled to the depth of 120 m
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(named Chaman piezometer), 2 km away from the experimental plot, indicated sandy clay loam (10-24 m), sandy clay (24-27 m), sandy clay loam (27-52 m) and loamy sand (52-80 m) soil
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textures within the 10 and 80 m layer. This indicated a relatively homogenous hydrogeology.
Fig. 11 Monthly sums of simulated water balance components (a), accumulation flux rates (b) during the simulated period
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Journal Pre-proof 3.4. Uncertainty and sensitivity assessment There may be several sources of uncertianly that exist in the numerical approach used in the present study. Uncertainty may exist in extracting crop coefficients Kc, computing the daily reference evapotranspiration rate ET0, soil hydraulic parameters in the model and the long-term amount of irrigation water input. The last one was not under control in this study. Due to a lack of irrigation monitoring data, the uncertainty remained uncontrollable. In order to increase
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confidence or the predicted recharge, the model parameter uncertainty was investigated using
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sensitivity analysis (Jiménez-Martínez et al., 2009; Lu et al., 2011; Min et al., 2015). In this
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approach, the soil hydraulic parameters in Eq. 2 and 3 were perturbed (increased or decreased)
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by 10, 20 and 30%, one at a time in two soil layers while other parameters remained unchanged
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(Fig. 12). Results showed that the order of sensitivity of the parameters for groundwater recharge was θs, n, α, Ks, θr and l. Except for the upper and lower bounds of θs, the changes were
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relatively small and somewhat expected for simulations in which rainfall, irrigation and evaporation rates were mostly imposed as boundary conditions (Lu et al., 2011). The most
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sensitive parameter, θs, with an increase of 30%, decreased the recharge by 14% because of the
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larger capacity to hold water in the soil layers. However, the soil hydraulic parameters of the model werre inversed by in situ data collection. The sensitivity of ET was individually increased or decreased by 10%. Decreasing or increasing ET by 10% caused the groundwater recharge to change by +8% and −10%, respectively. Additionally, the increase in irrigation application by 10% led to an increase in recharge by up to 20%, indicating that the uncertainty in irrigation input would cause greater uncertainty than the model parameters. In arid and semi-arid areas, the recharge rate is always small relative to other water budget components, which could result in estimated recharge rates with larger errors
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Journal Pre-proof (Scanlon et al., 2002). In addition, the in-situ drainage experiment can be applied in a field with
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little horizon differentiation.
Summary and Conclusions
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Fig. 12 The results of sensitivity analysis of the soil hydraulic parameters
The current study simulated the long-term recharge in the deep vadose zone (10 m) under an
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irrigated cropland from the Karaj region of northern Iran. A field drainage experiment was
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conducted to measure soil physical and hydraulic properties at specified depths and to monitor soil water movement using a monitoring well. The van Genuchten-Mualem model soil hydraulic parameters were quantified from the soil water content data collected during the drainage experiment for 37 days and the inverse module in the HYDRUS-1D. A strong agreement (R2 as high as 0.94) was observed between the measured and estimated data. The calibrated model was then used to simulate long term natural groundwater recharge between 1997 and 2017. The simulated annual flux ranged from 24 mm to 268 mm, with a mean value of 110 mm at a depth
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Journal Pre-proof of 2 m. The recharge at 10-m depth ranged between 26 mm and 207 mm, with a mean value of 95 mm. The main results of the study are described as follows: 1- The variations in daily and even the annual flux at the root zone were significant and the hydrological dynamics at the root zone was determined not to be a good indicator of the
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deep recharge estimation.
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2- The long-term recharge at the depth of 10 m, showed the variations in water input effects were dampened with increased depth and indicated a small continuous flux (base flux),
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which was not removed even in dry months.
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3- The depth-wise flux variations of the soil profile showed the annual soil water fluxes
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were nearly uniform below the root zone (2 m). This indicated that the depth of 2 m below the ground surface acted as an interface for estimating the recharge.
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4- The results of the annual deviation of flux from the long-term average suggested the
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necessity of investigating longer time scales, more than 10 years, for temporal assessment
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of the recharge process.
The water budget calculation over the period showed the percentage of the applied water contributed to recharging the groundwater was 12.7%, which clarified the imbalance between farming water consumption and recharge rates for groundwater sustainability. This study showed that the recharge process is more complicated in irrigated areas, especially in arid and semiarid regions. Taking a fraction of the precipitation/irrigation water as recharge can result in large errors in water budget calculations.
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Journal Pre-proof The approach used in this study showed promise for using the results of a drainage experiment in the deep vadose zone along with the numerical model. The findings of this work contribute to estimate groundwater recharge and could be useful for groundwater authorities and decision makers for improving strategies to manage and ensure the sustainability of groundwater. Acknowledgments This research was supported by a research fund of the Soil Conversation and Watershed
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Management Research Institute (SCWMRI) of Iran and Natural Sciences and engineering
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research Council of Canada (RGPIN-2016-04100). The authors are grateful to the organization
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for their logistic and other support.
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References
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Abkhan CE. Water budget of water resources in Tehran-Karaj provinces, Tehran, 2013, pp. 85. Allen RG, Allen RG, Food, Nations AOotU. Crop evapotranspiration: guidelines for computing crop water requirements: Food and Agriculture Organization of the United Nations, 1998. Bethune MG, Selle B, Wang QJ. Understanding and predicting deep percolation under surface irrigation. Water Resources Research 2008; 44: 1-16. Carrera-Hernández JJ, Smerdon BD, Mendoza CA. Estimating groundwater recharge through unsaturated flow modelling: Sensitivity to boundary conditions and vertical discretization. Journal of Hydrology 2012; 452-453: 90-101. Dadgar MA, Nakhaei M, Porhemmat J, Biswas A, Rostami M. Transient potential groundwater recharge under surface irrigation in semi-arid environment: An experimental and numerical study. Hydrological Processes 2018; 0. Dafny E, Šimůnek J. Infiltration in layered loessial deposits: Revised numerical simulations and recharge assessment. Journal of Hydrology 2016; 538: 339-354. Dane JH, Hopmans JW. Pressure plate extractor. In: Dane J, Topp, C, editor. Methods of Soil Analysis, Part 4, SSSA Book Series. 5. American Society of Agronomy, Madison, WI, 2002, pp. 688–690. Dane JH, Hruska S. In-Situ Determination of Soil Hydraulic Properties during Drainage1. Soil Science Society of America Journal 1983; 47: 619-624. de Vries JJ, Simmers I. Groundwater recharge: an overview of processes and challenges. Hydrogeology Journal 2002; 10: 5-7. Feddes RA, Kowalik PJ, Zaradny H. Simulation of Field Water Use and Crop Yield: Wiley, 1978. Garatuza-Payan J, Shuttleworth WJ, Encinas D, McNeil DD, Stewart JB, deBruin H, et al. Measurement and modelling evaporation for irrigated crops in north-west Mexico. Hydrological Processes 1998; 12: 1397-1418. Gee GW, Or D. Particle size analysis. In: Dane J, Topp, C, editor. Methods of Soil Analysis, Part 4, SSSA Book Series. 5. American Society of Agronomy, Madison, WI, 2002, pp. 255–294. Grossman RB, Reinsch TG. Bulk density and linear extensibility. In: Dane J, Topp, C, editor. Methods of Soil Analysis, Part 4, SSSA Book Series. 5. American Society of Agronomy, Madison, WI, 2002, pp. 201–228. 26
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Hazen A. Flood flows: a study of frequencies and magnitudes: J. Wiley & Sons, inc., 1930. Hillel D, Krentos VD, Stylianou Y. Procedure and test of an internal drainage method for measuring soil hydraulic characteristics in situ. Soil Science 1972; 114: 395-400. Hopmans JW, Šimůnek J, Romano N, Durner W. Inverse modeling of transient water flow. In: Dane J, Topp, C, editor. Methods of Soil Analysis, Part 4, Physical Methods, SSSA Book Series. 5. American Society of Agronomy, Madison, WI, 2002, pp. 201–228. Hubbell JM, Nicholl MJ, Sisson JB, McElroy DL. Application of a Darcian Approach to Estimate Liquid Flux in a Deep Vadose Zone. Vadose Zone Journal 2004; 3: 560-569. Jiménez-Martínez J, Skaggs TH, van Genuchten MT, Candela L. A root zone modelling approach to estimating groundwater recharge from irrigated areas. Journal of Hydrology 2009; 367: 138-149. Kabat P, Hutjes RWA, Feddes RA. The scaling characteristics of soil parameters: From plot scale heterogeneity to subgrid parameterization. Journal of Hydrology 1997; 190: 363-396. Kurtzman D, Scanlon BR. Groundwater Recharge through Vertisols: Irrigated Cropland vs. Natural Land, Israel Vadose Zone Journal 2011; 10: 662-674. Leterme B, Mallants D, Jacques D. Sensitivity of groundwater recharge using climatic analogues and HYDRUS-1D. Hydrol. Earth Syst. Sci. 2012; 16: 2485-2497. Lu X, Jin M, van Genuchten MT, Wang B. Groundwater Recharge at Five Representative Sites in the Hebei Plain, China. Ground Water 2011; 49: 286-294. Min L, Shen Y, Pei H. Estimating groundwater recharge using deep vadose zone data under typical irrigated cropland in the piedmont region of the North China Plain. Journal of Hydrology 2015; 527: 305-315. Mualem Y. A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resources Research 1976; 12: 513-522. Nakhaei M, Šimůnek J. Parameter estimation of soil hydraulic and thermal property functions for unsaturated porous media using the HYDRUS-2D code. Journal of Hydrology and Hydromechanics. 62, 2014, pp. 7. Porhemmat J, Nakhaei M, Altafi Dadgar M, Biswas A. Investigating the effects of irrigation methods on potential groundwater recharge: A case study of semiarid regions in Iran. Journal of Hydrology 2018; 565: 455-466. Radcliffe DE, Simunek J. Soil Physics with HYDRUS: Modeling and Applications: CRC Press, 2010. Radford BJ, Silburn DM, Forster BA. Soil chloride and deep drainage responses to land clearing for cropping at seven sites in central Queensland, northern Australia. Journal of Hydrology 2009; 379: 20-29. Scanlon BR, Healy RW, Cook PG. Choosing appropriate techniques for quantifying groundwater recharge. Hydrogeology Journal 2002; 10: 18-39. Šimůnek J, Th. van Genuchten M, Šejna M. The Hydrus-1D software package for simulating the onedimensional movement of water, heat, and multiple solutes in variably-saturated media. . HYDRUS Software Series 1, University of California, Riverside,, 2005. Šimunek J, van Genuchten MT, Šejna M. The HYDRUS-1D software package for simulating the onedimensional movement of water, heat, and multiple solutes in variably-saturated media, Version 4.0, IGWMC-TPS-70. 2005. Sun H, Shen Y, Yu Q, Flerchinger GN, Zhang Y, Liu C, et al. Effect of precipitation change on water balance and WUE of the winter wheat–summer maize rotation in the North China Plain. Agricultural Water Management 2010; 97: 1139-1145. Timms WA, Young RR, Huth N. Implications of deep drainage through saline clay for groundwater recharge and sustainable cropping in a semi-arid catchment, Australia. Hydrol. Earth Syst. Sci. 2012; 16: 1203-1219. Turkeltaub T, Dahan O, Kurtzman D. Investigation of Groundwater Recharge under Agricultural Fields Using Transient Deep Vadose Zone Data. Vadose Zone Journal 2014; 13.
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Turkeltaub T, Kurtzman D, Bel G, Dahan O. Examination of groundwater recharge with a calibrated/validated flow model of the deep vadose zone. Journal of Hydrology 2015; 522: 618627. van Genuchten MT. A Closed-form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils1. Soil Sci. Soc. Am. J. 1980; 44: 892-898. Vazifedoust M, van Dam JC, Feddes RA, Feizi M. Increasing water productivity of irrigated crops under limited water supply at field scale. Agricultural Water Management 2008; 95: 89-102. Wesseling JG, Elbers JA, Kabat P, van den Broek BJ. SWATRE: instructions for input (internal notes). In: Centre WS, editor, Wageningen, the Netherlands, 1991. Wohling DL, Leaney FW, Crosbie RS. Deep drainage estimates using multiple linear regression with percent clay content and rainfall. Hydrol. Earth Syst. Sci. 2012; 16: 563-572.
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Declaration of interests
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☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
Graphical abstract
Highlights
Groundwater recharge (GWR) is quantified from flux observations at different depths Combined field experiment and modelling to study GWR in semi-arid regions of Iran Fluxes at different depths varied with time with shallow layer being highly variable Flux inconsistency indicated insufficiency in recharge estimation by shallow layer GWR should be estimated using deep vadose zone observations 28
Majid Altafi Dadgar, Mohammad Nakhaei, Jahangir Porhemmat, Bijan Elyasi, Asim Biswas PII:
S0048-9697(20)30615-X
DOI:
https://doi.org/10.1016/j.scitotenv.2020.137105
Reference:
STOTEN 137105
To appear in:
Science of the Total Environment
Received date:
5 December 2019
Revised date:
2 February 2020
Accepted date:
2 February 2020
Please cite this article as: M.A. Dadgar, M. Nakhaei, J. Porhemmat, et al., Potential groundwater recharge from deep drainage of irrigation water, Science of the Total Environment (2018), https://doi.org/10.1016/j.scitotenv.2020.137105
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© 2018 Published by Elsevier.
Journal Pre-proof
Potential groundwater recharge from deep drainage of irrigation water Majid Altafi Dadgara, Mohammad Nakhaeib, Jahangir Porhemmatc, Bijan Elyasid, Asim Biswase* a
Department of Applied Geology, Faculty of Earth Sciences, Kharazmi University, P.O. Box:
31979-37551, Tehran, Iran, E-mail: [email protected] Department of Applied Geology, Faculty of Earth Sciences, Kharazmi University, P.O. Box:
Research,
Education
and
Extension
(AREEO),
Tehran,
Iran,
E-mail:
Department of Applied Geology, Faculty of Earth Sciences, Kharazmi University, Tehran, Iran,
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E-mail: [email protected]
School of Environmental Sciences, University of Guelph, 50 Stone Road East, Guelph, Ontario,
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e
Organization
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[email protected] d
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Soil Conservation and Watershed Management Research Institute (SCWMRI), Agricultural
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c
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31979-37551, Tehran, Iran, E-mail: [email protected]
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b
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N1G 2W1, Canada, E-mail: [email protected] *Corresponding author: E-mail- [email protected] (A. Biswas), Phone +15198244120 Extn. 54249
1
Journal Pre-proof Abstract Knowledge of soil water dynamics in the deep vadose zone provides valuable information on the temporal and spatial variability of groundwater recharge. However, semi-arid climate can complicate how the input of water, such as irrigation, can contribute to potential groundwater recharge. This study assessed the recharge rates and their timing under irrigated cropland from a semi-arid region of northern Iran. A deep drainage (10 m) experiment was performed and in situ
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soil water content was measured to analyze the soil water dynamics and model hydraulic
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parameters using HYSDRUS-1D. The best parameters selected from inverse parameter
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optimization were used to calibrate model and estimate the long-term (20-year) average
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groundwater recharge and the influence of the root zone, unsaturated zone and the time scale on
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the recharge processes. The simulated annual flux ranged from 24 mm to 268 mm (mean of 110 mm) at 2-m depth and ranged between 26 mm to 207 mm (mean of 95 mm) at the 10-m depth.
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High fluxes, observed between December and April, may be the result of greater precipitation
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combined with the irrigation return flow. The May-October period showed a gradual decrease in flux at the depth of 2 m. At the depth of 10 m, the flux showed some continuity (base flux)
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during the long-term recharge simulation. In total, 12.7% of the input water contributed to the recharge of the groundwater. The annual soil water fluxes were almost similar irrespective of depth below the root zone and the flux rates did not show any clear relation between the different components of the water budget at any depth. This approach improved our understanding of the recharge process in the deep vadose zone in a semiarid region and can help for the development of effective management of groundwater resources. Keywords: Groundwater recharge; deep vadose zone; drainage experiment, HYDRUS-1D; Simulation 2
Journal Pre-proof 1. Introduction Groundwater recharge is a small but important component of water balance in arid and semiarid areas. In these areas, where irrigated agriculture is common, accurate calculation of aquifer recharge and evapotranspiration are essential for assessing scarce water resources and their sustainable management (Garatuza-Payan et al., 1998). Generally, recharge is defined as the downward flow of water reaching the water table (de Vries and Simmers, 2002). However,
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groundwater recharge over a certain area is normally considered to be equal to infiltration excess
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over the same area if deep drainage is not obstructed by impervious horizons in the deep
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unsaturated zone (de Vries and Simmers, 2002). In an area with a deep-water table, downward
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soil water flux from below the root zone (deep drainage) is often known as potential recharge
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(Min et al., 2015; Radford et al., 2009; Wohling et al., 2012). Soil water dynamics usually vary with depth and time from changes in input factors, such as precipitation and irrigation events and
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processes, such as evapotranspiration, and changes in deep soil water storage (Hubbell et al.,
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2004; Timms et al., 2012). Therefore, the deep vadose zone plays an important role in the
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groundwater recharge process.
Several methods have been applied to quantify groundwater recharge with various degrees of success (Scanlon et al., 2002). These methods can be loosely grouped into three categories depending on the targeted zones: surface water, unsaturated zone, and the saturated zone. In each of these cases, physical and tracer techniques as well as numerical modeling showed promise (Jiménez-Martínez et al., 2009). Despite the recent interest in quantifying soil water dynamics in the deep vadose zone (Hubbell et al., 2004; Kurtzman and Scanlon, 2011; Min et al., 2015; Turkeltaub et al., 2014), variations in the recharge fluxes with depth and time are less understood. 3
Journal Pre-proof Physically based models, such as those solving the Richards’ equation for water flow in the vadose zone and water balance of surficial sediments, often have been used to estimate groundwater recharge under various conditions (Kurtzman and Scanlon, 2011; Leterme et al., 2012; Turkeltaub et al., 2015). These models require the information and/or knowledge of soil water retention and unsaturated hydraulic conductivity for all soil horizons (Dafny and Šimůnek, 2016). Although laboratory experiments have been advantageous for quick and precise
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estimation of parameters for these functions, they often lead to soil hydraulic properties that are
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not representative of those in the field. Therefore, inverse models have gained popularity for
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estimating the soil hydraulic functions of the unsaturated zone. Among the benefits of inverse
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methods, its equal applicability to field experiments, even under nontrivial boundary conditions, has gained in popularity (Hopmans et al., 2002). On the other hand, one of the most popular
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experiments to determine soil water retention and hydraulic conductivity functions in the field
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(in situ) has been the field drainage experiment (Dane and Hruska, 1983; Hillel et al., 1972). This method has shown promise for hydraulic characterization of field soils with little horizon
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differentiation (Kabat et al., 1997). However, this approach has never been used to determine the
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soil hydraulic functions in the deep vadose zone. The Karaj region in Iran is a major industrial region and there are now many migrants from Tehran, the capital of Iran, living there. Population growth in the area has led to an increase in water demand and declining groundwater levels (Abkhan, 2013). In a semiarid region like Karaj the return flow from irrigation events plays a significant role in groundwater recharge (Porhemmat et al., 2018). Knowledge about the dynamics of flow through the unsaturated zone to the aquifer needs to consider residence time and changes in flux rates to ensure appropriate management of available water supplies. Groundwater recharge in this region is generally
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Journal Pre-proof estimated usinFg empirical equations, usually by taking a fraction of the precipitation rate and irrigation return flow, which often is used in groundwater flow models and water budget calculations (Dadgar et al., 2018). However, these approaches do not consider the effects of the unsaturated zone on the dynamics of the recharge. The main objective of this study was to investigate the groundwater recharge process in the deep vadose zone (~10 m) using in-situ field experiments and numerical simulations. We have considered the Karaj region as a case study site
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typical of the semiarid climate in Iran to carry out the field experiment; we then performed a
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numerical assessment of the deep drainage. To achieve this objective, a field drainage
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experiment was conducted to: 1) determine the soil hydraulic functions of the unsaturated zone,
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2) calibrate the transient unsaturated flow using in-situ monitoring data of the field drainage experiment, and 3) estimate the long-term average groundwater recharge using the calibrated
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model and long-term meteorological data.
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2.1. Study area
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2. Material and methods
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A field experiment was conducted on an agricultural farm (35°47ʹ45ʺN and 50°56ʹ35ʺE) of about 8.33 ha, within the agriculture experimental site of the Seed and Plant Improvement Institute (SPII) of the Karaj city, in the northern part of Iran (Fig. 1). The average annual rainfall, recorded at the Karaj meteorological station, is 248 mm (1997-2017), with slightly higher rainfall in winter (69.7%) and the area is dominated by a semiarid climate. There are relatively thick unsaturated materials between the ground surface and the water table (about 80 m). Soil formations of the site are related to alluvial processes and are deposited in the old streambeds and banks of prior streams. The most common cropping system in the area is winter wheat with a
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Journal Pre-proof furrow irrigation system, including 5-7 irrigation applications around ~70 mm, depending on the
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availability and variability of precipitation.
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Fig. 1 Location map of the experimental site in the Karaj region
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2.2. Field drainage experiment
An in-situ transient drainage experiment was carried out in a field representing the SPII fields and region. A 40-cm depth trench was excavated within the study area to conduct the drainage experiment on a leveled, vegetation-free plot with 5 m-by-3 m (or 15 m2) surface area. This was done to ensure that the lateral water movement at the plot boundary would not influence the water regime in the plot center. Additionally, the sloping edges were also covered by PVC sheets (Fig. 2a). A 10-m deep monitoring well was dug in the center of the trench and instrumented with TDR sensors at 16 depths (Fig. 2b). Soil water content was measured more frequently near the surface with high sensitivity to the hydraulic parameters. 6
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sensor locations and material distribution
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Fig. 2 Experimental site: (a) trench dimensions and monitoring well location, (b) measurement
Soil and sediment samples were taken from each measurement sensor depth in the monitoring
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well to determine soil bulk density (Grossman and Reinsch, 2002), soil water retention curve
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(Dane and Hopmans, 2002) and soil texture (Gee and Or, 2002). The soil water retention curves were extracted using five tension (i.e. 0.1, 0.33, 1, 3, 5 and 15 Bar) applied by pressure plate.
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The sample sizes for extracting soil water retention curve were 5 cm in diameter and 1 cm thick. Vertical saturated hydraulic conductivity, Ks, was measured on undistributed soil cores using falling head experiments on undisturbed soil cores (Fig. 3). Particle size distributions in soil samples indicated the prevalence of a sand-over-clay profile. A thick loamy sand layer (from 30 cm to 10 m of ground surface) was uniformly underlain with thin loam soil (from 0 to 30 cm) at the top. In December 2017, the experiment was started by ponding water over the plot area with a constant height of 15 cm for 9 days. During this time, water was allowed to penetrate through the column and the total consumption of water was about 48 m3. Water content measurements 7
Journal Pre-proof were taken in decreasing time frequencies during infiltration days (9 days) until water content values exhibited no changes with time at any depth. In other words, a time-invariant unit hydraulic gradient was reached at the bottom of the soil column. At that point, the water supply was stopped and the ponded water was allowed to infiltrate. The plot was then covered with a plastic sheet to remove evaporation and temperature variations and insulated to ensure a zeroflux top boundary condition. Subsequently, the soil was allowed to drain by gravity. The
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transient drainage process was monitored by simultaneous measurements of soil water content
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until the changes with time were minimal. The soil water flux through each depth was calculated
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by multiplying the water content variations over time and the thickness of the soil depth.
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Monitoring the drainage process continued for 37 days.
Fig. 3 Depth-wise soil particle deviations and hydraulic properties measured in laboratory
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Journal Pre-proof 2.3. Model setup To have an accurate tool for analyses of the recharge process, a calibrated unsaturated flow model was used for groundwater recharge simulations. Numerical simulations of the drainage experiment and groundwater recharge simulations were conducted using the HYDRUS-1D software package (Šimunek et al., 2005). The Richards’ equation (Eq. 1) was implemented to
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account for water flow in the vadose zone.
(1)
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h K( h ) 1 S( z,t ) t z z
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where θ was the volumetric water content [L3 L-3], h was the pressure head [L], z was vertical
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spatial coordinate [L] assumed to be 0 at the soil surface and directed upward, S was a sink term
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accounting for root water uptake [T-1], t was time [T], and K was the unsaturated hydraulic
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conductivity function [LT-1].
For solution of Equation 1, the van Genuchten-Mualem soil hydraulic functions were used
h 0
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r s nr m ( 1 h ) ( h ) s
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(Mualem, 1976; van Genuchten, 1980):
(2)
h 0
1 K ( h ) K s S 1 ( 1 S em ) m
2
l e
(3)
where θr and θs were the residual and saturated water contents (L3 L-3), respectively. Ks (L T-1) was the saturated hydraulic conductivity, α, (L-1) and n represented the empirical shape parameters, m = 1 - 1/n; l was the pore connectivity parameter. Se was the effective saturation.
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Journal Pre-proof The domain was set perpendicular to the trench, assuming that a 1D flow pattern can be approximated just below the center of the trench base. 2.3.1. Inverse modeling For the inverse solution, the time series of soil water content at each observation point were reconstructed from the drainage experiment data monitored over 37 days (between 14 December
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2017 and 20 January 2017). As mentioned previously, the drainage experiment was executed on
(z ) i q (z , t ) 0
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the following initial and upper boundary conditions:
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a vegetation-free plot. Equation (1) was solved using HYDRUS-1D (Šimůnek et al., 2005) for
t 0, 0 z L
(4)
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t 0, z 0
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where z = 0 denoted the soil surface (upper boundary), with L equal to the depth of the measured
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soil domain. The free drainage type boundary condition was used to simulate the lower boundary of the model as the groundwater table (80 m below ground surface) was far below the lower
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boundary of the model (Šimůnek et al., 2005). The initial conditions were measured using TDR
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sensors before starting the drainage process and linearly interpolated along the vertical axis. The inverse estimation was conducted by minimizing the objective function (Šimůnek et al., 2005): m
( ; b | c ) obs (t i ) sim (t i , b | c )
2
i 1
(5) where m was the number of observations, θobs and θsim were measured and simulated volumetric water contents, b|c was the vector b containing the unknown parameters estimated given the fixed and known parameters in vector c. The model was constructed of two layers with six van
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Journal Pre-proof Genuchten–Mualem parameters in each layer. The pore connectivity and tortuosity factor l, which was fixed at 0.5 for two layers in this study (e.g. (Mualem, 1976); Nakhaei and Šimůnek (2014; Turkeltaub et al. (2015). Four parameters including saturated hydraulic conductivity, residual water content, α, and n were marked to be optimized. Different initial estimates of the parameters were used repeatedly to test uniqueness and stability of the inverse solution. For this reason, the behavior of the inverse solution was evaluated by plotting the values of the objective
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function ( ),against pairs of optimized parameters, which named the response surface
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(Hopmans et al., 2002). Therefore, the inverse solution with four adjustable parameters needs six
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parameter pairs to be analyzed. Each response surface was obtained by taking many
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combinations of a selected pair of parameter values, while the boundary and initial conditions and the other parameters were kept constant. Shape of the response surface shows the occurrence
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of local minima and global minima of the objective function and the sensitivity of the
Model prediction
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2.3.2.
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parameters.
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The calibrated model was then used to assess recharge over a 20-year period (Nov 1997-June 2017) meteorological data. During the simulation, the upper boundary condition was set as the atmospheric condition. The reference evapotranspiration (ET0) and daily values of the potential evapotranspiration (ETp) were calculated using the Penman-Monteith equation following (Allen et al., 1998): ETp Kc ET0
(6)
where Kc was a dimensionless crop coefficient and was extracted following Allen et al. (1998. An actual irrigation schedule was not available during the simulation period which was estimated
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Journal Pre-proof following published works (Min et al., 2015; Porhemmat et al., 2018; Sun et al., 2010). For approximating the irrigation schedule, the annual rainfall events were ranked in descending order and the probability of exceedance of the events was calculated using Hazen method (Hazen, 1930). For the years with the events more than 25% probability of exceedance, only key irrigation periods were applied (i.e. 5 events). Otherwise, two extra irrigation events were
values during considered years.
2016-2017 40.37
50.73 21.41
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27.87
59.19 14.70
2015-2016
49.81 15.45
2014-2015
33.76 30.65
2013-2014
38.62 44.70
2012-2013
79.91 24.36
2011-2012
58.57 26.46
2010-2011
72.33 16.80
2009-2010
34.33 39.26
2008-2009
36.83 35.52
2007-2008
-p 39.85 30.06
2006-2007
2005-2006
2004-2005 42.16 27.01
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2003-2004 35.85 26.85
2002-2003
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54.83
2001-2002
26.22
24.02
59.62
44.57
18.20
17.36
56.60
26.53
2000-2001
42.50
Precipitation (cm)
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56.65
1999-2000
Irrigation (cm)
21.29
Year
1998-1999
1997-1998
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Table 1. Irrigation schedule and annual precipitation values
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included (i.e. 7 events). Table 1 shows the total amount of irrigation along with precipitation
S ( h ) ( h )S p
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For modeling the root water uptake, the function was used following (Feddes et al., 1978): (8)
where S was a sink term accounting for root water uptake [T-1]. Sp was the potential water uptake rate [T-1]. α(h) was a dimensionless water stress response (0 ≤ α ≤ 1) and was defined by four threshold values h1 = 0 cm, h2 = -1 cm, h3 = -900 cm, h4 = -16,000 cm for wheat (Wesseling et al., 1991).
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water fluxes during drainage test (c)
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3. Results and discussion
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Fig. 4 Soil water profiles for wetting front propagation (a), drainage front propagation (b), soil
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3.1. Drainage experiment results
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After a day of ponding, the wetting front reached a depth of 600 cm below the ground surface (Fig. 4a). Based on the equation 1, when infiltration started, the gradient in pressure head due to capillary was the main driving force for the rapid movement of water into the soil. By flowing deeper into the soil, the role of capillary force might be diminished and only gravity was responsible for movement. Under these conditions, a minimum infiltration rate reached and it is approximately the saturated hydraulic conductivity (Radcliffe and Simunek, 2010). Time-invariant hydraulic gradient was reached after five days of ponding, i.e. values of water content were unchanged at any depth (steady conditions). The drainage front propagation (14
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Journal Pre-proof December 2017 to 20 January 2017) is shown in Fig. 4b. The maximum flux of 37.5 cm d-1 was calculated a day after starting drainage, with the average flux over the measurement period of 4.9 cm d-1. The flux was decreased over time by moving the drainage front down. Two days after starting the drainage process, an increasing trend of flux was observed at depths of 300 and 700 cm (Fig. 4b). This may be due to the occurrence of the drainage with a delay from the top layer (loam) in which water was released slowly. The total calculated volume of water drained from
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the soil column was 115 cm over the drainage period (shaded area in Fig. 4b). Stable conditions
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reached on 20-Jan, which indicated water content values closed to field capacity values.
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Subsequently, the flux decreased to zero after 37 days, revealing that drainable water removed
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from soil profile. In other words, as the amount of water stored in the soil closed to the field capacity value, drainage flux tended to zero (Bethune et al., 2008). It noted that the initial soil-
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water conditions have a major influence on groundwater recharge fluxes.
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3.2. Inverse modeling
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The uniqueness of inversed solution was analyzed using response surface shape of the optimized
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parameters. Figure (5) shows response surfaces for (Ks,α), (n,α), (θr,α), (Ks,n), (θr,n) and (θr,Ks). The ideal response surface shows a narrow minimum area with circular shape, indicating no correlation between the fitting parameters (Hopmans et al., 2002). Paralleling shapes to one of the axes reveal insensitivity to that parameter and indicates high parameter uncertainty. As revealed in figure (5), the response surface of the parameters is almost favorable. There were no multiple minima with the same Ф values, and there were no extremely narrow valleys paralleling to the axis for parameter combinations. Figure (6) shows the relation between hydraulic conductivity function with water content. After evaluation of the uniqueness of the inverse
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showed that the general dynamics of the measurements matched all simulations (Fig. 7).
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Fig. 5 Response surfaces for six pairs of parameters
Fig. 6 Soil hydraulic parameters of the optimized model 15
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Fig. 7 Observed water contents obtained by TDR sensors (blue points) and simulated water contents obtained with the calibrated model (green line) 16
Journal Pre-proof Upon closer inspection, it was noticed that there were small systematic deviations between the measured and simulated values (Table 2). This indicated that errors may be due to the spatial heterogeneity and observation errors (Vazifedoust et al., 2008). The values of root mean square error (RMSE) and coefficient of determination (R2) for 375 observed data points were 0.014 cm and 0.94, respectively. Indicating that the HYDRUS-1D successfully simulated increases in the water content following delayed drainage at the lower depths with gradual decreases during
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drying periods. The calibrated model was subsequently used to predict the field water cycle with
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meteorological data.
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Table 2. Optimization results of Genuchten–Mualem parameters along with statistics on the
Ɵs
Layer 3
-3
Alpha
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Ɵr
Textural
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performance of simulation
3
-3
RMSE*
Ks n
R2*
l
-1
-1
(cm cm )
(cm cm )
(cm )
1
Loam
0.078
0.43
0.036
1.567
0.5
25.15
2
Loamy sand
0.065
0.41
0.074
1.981
0.5
129.82
(cm d )
1
n
R square, R
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1 n RMSE ( xi yi ) 2 ; * Root mean square error, n i 1
2
i 1 n
-3
(cm cm ) 0.014
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3
0.94
( yi xi ) 2 _
( xi x) 2 i 1
3.3. Recharge estimation
3.3.1. Soil water flux-depth and time relationships Annual sums of irrigation and precipitation (I + P), deep drainage (at 2-m depth), and recharge fluxes (10-m depth) varied over the years (Fig. 8). Deep drainage under the root zone was more affected by irrigation events than precipitation events. For example, the annual deep drainage during the years 2006 and 2007 decreased as irrigation decreased. During this period, the amount
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Journal Pre-proof of precipitation was high, and the number of irrigation applications was reduced. In this situation, the applied irrigation water was mainly lost by evapotranspiration. However, these variations indicated the complexities of the flux processes through the root zone. Generally, there were no clear or meaningful trends between input water and fluxes. The simulated annual flux at a depth of 2 m ranged from 24 mm to 268 mm, with a mean value of 110 mm. The annual recharge at
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10-m depth ranged between 26 mm to 207 mm, with a mean value of 95 mm.
Fig. 8 Comparing long-term annual water input (a) with corresponding annual deep drainage at 2-m depth and recharge flux at 10-m depth (b) during the period from Nov 1977 to June 2017 For more considerations, daily variations of recharge fluxes were compared with the corresponding input of water (Fig. 9). Fluctuations of daily flux under the root zone (2-m depth) were sharp and were increased 40–70 days after the first major infiltration event (Fig. 9a). High fluxes were generally observed during the period from December to April from the response of
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May and October exhibited a gradual decrease in the deep drainage fluxes.
Fig. 9 Daily deep drainage and recharge flux compared with the water input (a), details on the wet period from Nov 2007 to Nov 2012 (b) The peak flux below the root zone exceeded 8 mm d-1 during the rainy season, whereas the peak flux at the depth of 10 m was only approximately 1 mm d-1. The recharge flux (10-m depth) fluctuations were much attenuated and tended to smooth out individual annual cycles. Individual flux peaks and dips appeared at longer time lags. For example, the peak that occurred at a depth of 2 m in April 2011 reached the depth of 10 m in Dec 2011 and the peak at the depth of 2 m in 19
Journal Pre-proof early Feb 2012 reached the depth of 10 m in late Nov 2012 (Fig. 9b). Details on the input water showed a relatively wet period with regular precipitation events among irrigation applications from Nov 2007 to Nov 2012 (Fig. 9b). Comparison of this period with the preceding or subsequent periods showed that the sharp fluctuations of the deep drainage became gentle and more prolonged, whereas recharge fluxes exhibited no significant changes (Fig. 9b). Additionally, recharge fluxes showed a small continuous flux (base flux) during the long-term
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recharge simulation, which was not removed even in dry months. This reveals that variations in
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input water did not greatly affect the flux at the deeper depths or the effects were dampened as
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the depth increased. This revealed important characteristics of soil water flux at different depths,
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Martínez et al., 2009; Min et al., 2015).
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through the root zone and in the deep vadose zone (Carrera-Hernández et al., 2012; Jiménez-
Fig. 10 Soil water flux variations with soil depth for selected year (a), deviation of annual soil water flux from long-term average value with time scale (b)
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Journal Pre-proof Comparing results of annual fluxes with the corresponding depth showed significant flux variations in the root zone (i.e. 0-2 m) during selected years (Fig. 10a). In contrast, the annual soil water fluxes were nearly uniform below the root zone for each year. Therefore, the depth below the root zone acted as an interface for estimating annual recharge rates. This also indicated that the recharge process estimations at depths smaller than the root zone can be misleading for cropping land. Min et al. (2015 also reported a similar steady flux below the 2-m depth in long-
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Temporal deviations of the annual fluxes were further compered with the 20-year average at
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different depths (Fig. 10b). The annual deviation of flux from long-term average was reduced
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with increased depth. Additionally, a similar decreasing trend was observed at longer time scales
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indicating the need for investigation of longer time scales, often more than 10 years, for temporal assessment of recharge processes. In general, the results showed the sensitivity of the recharge
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estimations with both time and depth variations.
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3.3.2. Vadose zone water budget and travel time
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Simulated water balance components over the long-term (1998–2017) showed in Fig. 11. The mean value of monthly water input (P + I) was 6.35 cm, with the maximum value of 26.31 (in March 2003). The maximum value of transpiration (T) was 24.22 cm, with a mean value of 4.31 cm and had a similar trend to that of the water input. It indicated that the rate of transpiration was increased with increased water input (Fig. 11b). The maximum and mean value of actual evaporation (E) was 5.7 and 1.7 cm, respectively. Simulated monthly recharge (R) reflected high variability of water input with a range of 0.1 to 3 cm and an average of 0.8 cm. The cumulative applied water and recharge over the period were 1505 and 191 cm, respectively. Therefore, the percentage of the applied water that contributed to recharge of the groundwater was 12.7% 21
Journal Pre-proof during the simulated period. Long-term recharge simulations helped to calculate the travel time of waterfront to groundwater level. The average flux between the root zone and the depth of 10 m was 10.37 cm year-1 with the average water storage of 106.4 cm. Assuming a piston flow through the matrix, a travel time of 10.25 years to a depth of 10 m was calculated. Following this, the travel time of water to groundwater was calculated. Assuming the absence of any impervious layer between 10 m and the water table at 80 m and relatively homogeneous
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hydrogeology units between 2 and 10 m layers and 10 and 80 m layers, travel time was estimated
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to be 102 years. This will have a significant impact on the sustainability of the groundwater
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within the region. Here in support of the hydrogeology, a well log drilled to the depth of 120 m
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(named Chaman piezometer), 2 km away from the experimental plot, indicated sandy clay loam (10-24 m), sandy clay (24-27 m), sandy clay loam (27-52 m) and loamy sand (52-80 m) soil
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textures within the 10 and 80 m layer. This indicated a relatively homogenous hydrogeology.
Fig. 11 Monthly sums of simulated water balance components (a), accumulation flux rates (b) during the simulated period
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confidence or the predicted recharge, the model parameter uncertainty was investigated using
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sensitivity analysis (Jiménez-Martínez et al., 2009; Lu et al., 2011; Min et al., 2015). In this
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approach, the soil hydraulic parameters in Eq. 2 and 3 were perturbed (increased or decreased)
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by 10, 20 and 30%, one at a time in two soil layers while other parameters remained unchanged
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(Fig. 12). Results showed that the order of sensitivity of the parameters for groundwater recharge was θs, n, α, Ks, θr and l. Except for the upper and lower bounds of θs, the changes were
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relatively small and somewhat expected for simulations in which rainfall, irrigation and evaporation rates were mostly imposed as boundary conditions (Lu et al., 2011). The most
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sensitive parameter, θs, with an increase of 30%, decreased the recharge by 14% because of the
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larger capacity to hold water in the soil layers. However, the soil hydraulic parameters of the model werre inversed by in situ data collection. The sensitivity of ET was individually increased or decreased by 10%. Decreasing or increasing ET by 10% caused the groundwater recharge to change by +8% and −10%, respectively. Additionally, the increase in irrigation application by 10% led to an increase in recharge by up to 20%, indicating that the uncertainty in irrigation input would cause greater uncertainty than the model parameters. In arid and semi-arid areas, the recharge rate is always small relative to other water budget components, which could result in estimated recharge rates with larger errors
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Summary and Conclusions
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4.
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Fig. 12 The results of sensitivity analysis of the soil hydraulic parameters
The current study simulated the long-term recharge in the deep vadose zone (10 m) under an
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irrigated cropland from the Karaj region of northern Iran. A field drainage experiment was
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conducted to measure soil physical and hydraulic properties at specified depths and to monitor soil water movement using a monitoring well. The van Genuchten-Mualem model soil hydraulic parameters were quantified from the soil water content data collected during the drainage experiment for 37 days and the inverse module in the HYDRUS-1D. A strong agreement (R2 as high as 0.94) was observed between the measured and estimated data. The calibrated model was then used to simulate long term natural groundwater recharge between 1997 and 2017. The simulated annual flux ranged from 24 mm to 268 mm, with a mean value of 110 mm at a depth
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Journal Pre-proof of 2 m. The recharge at 10-m depth ranged between 26 mm and 207 mm, with a mean value of 95 mm. The main results of the study are described as follows: 1- The variations in daily and even the annual flux at the root zone were significant and the hydrological dynamics at the root zone was determined not to be a good indicator of the
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deep recharge estimation.
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2- The long-term recharge at the depth of 10 m, showed the variations in water input effects were dampened with increased depth and indicated a small continuous flux (base flux),
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which was not removed even in dry months.
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3- The depth-wise flux variations of the soil profile showed the annual soil water fluxes
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were nearly uniform below the root zone (2 m). This indicated that the depth of 2 m below the ground surface acted as an interface for estimating the recharge.
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4- The results of the annual deviation of flux from the long-term average suggested the
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necessity of investigating longer time scales, more than 10 years, for temporal assessment
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of the recharge process.
The water budget calculation over the period showed the percentage of the applied water contributed to recharging the groundwater was 12.7%, which clarified the imbalance between farming water consumption and recharge rates for groundwater sustainability. This study showed that the recharge process is more complicated in irrigated areas, especially in arid and semiarid regions. Taking a fraction of the precipitation/irrigation water as recharge can result in large errors in water budget calculations.
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Journal Pre-proof The approach used in this study showed promise for using the results of a drainage experiment in the deep vadose zone along with the numerical model. The findings of this work contribute to estimate groundwater recharge and could be useful for groundwater authorities and decision makers for improving strategies to manage and ensure the sustainability of groundwater. Acknowledgments This research was supported by a research fund of the Soil Conversation and Watershed
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Management Research Institute (SCWMRI) of Iran and Natural Sciences and engineering
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research Council of Canada (RGPIN-2016-04100). The authors are grateful to the organization
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for their logistic and other support.
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References
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Abkhan CE. Water budget of water resources in Tehran-Karaj provinces, Tehran, 2013, pp. 85. Allen RG, Allen RG, Food, Nations AOotU. Crop evapotranspiration: guidelines for computing crop water requirements: Food and Agriculture Organization of the United Nations, 1998. Bethune MG, Selle B, Wang QJ. Understanding and predicting deep percolation under surface irrigation. Water Resources Research 2008; 44: 1-16. Carrera-Hernández JJ, Smerdon BD, Mendoza CA. Estimating groundwater recharge through unsaturated flow modelling: Sensitivity to boundary conditions and vertical discretization. Journal of Hydrology 2012; 452-453: 90-101. Dadgar MA, Nakhaei M, Porhemmat J, Biswas A, Rostami M. Transient potential groundwater recharge under surface irrigation in semi-arid environment: An experimental and numerical study. Hydrological Processes 2018; 0. Dafny E, Šimůnek J. Infiltration in layered loessial deposits: Revised numerical simulations and recharge assessment. Journal of Hydrology 2016; 538: 339-354. Dane JH, Hopmans JW. Pressure plate extractor. In: Dane J, Topp, C, editor. Methods of Soil Analysis, Part 4, SSSA Book Series. 5. American Society of Agronomy, Madison, WI, 2002, pp. 688–690. Dane JH, Hruska S. In-Situ Determination of Soil Hydraulic Properties during Drainage1. Soil Science Society of America Journal 1983; 47: 619-624. de Vries JJ, Simmers I. Groundwater recharge: an overview of processes and challenges. Hydrogeology Journal 2002; 10: 5-7. Feddes RA, Kowalik PJ, Zaradny H. Simulation of Field Water Use and Crop Yield: Wiley, 1978. Garatuza-Payan J, Shuttleworth WJ, Encinas D, McNeil DD, Stewart JB, deBruin H, et al. Measurement and modelling evaporation for irrigated crops in north-west Mexico. Hydrological Processes 1998; 12: 1397-1418. Gee GW, Or D. Particle size analysis. In: Dane J, Topp, C, editor. Methods of Soil Analysis, Part 4, SSSA Book Series. 5. American Society of Agronomy, Madison, WI, 2002, pp. 255–294. Grossman RB, Reinsch TG. Bulk density and linear extensibility. In: Dane J, Topp, C, editor. Methods of Soil Analysis, Part 4, SSSA Book Series. 5. American Society of Agronomy, Madison, WI, 2002, pp. 201–228. 26
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Turkeltaub T, Kurtzman D, Bel G, Dahan O. Examination of groundwater recharge with a calibrated/validated flow model of the deep vadose zone. Journal of Hydrology 2015; 522: 618627. van Genuchten MT. A Closed-form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils1. Soil Sci. Soc. Am. J. 1980; 44: 892-898. Vazifedoust M, van Dam JC, Feddes RA, Feizi M. Increasing water productivity of irrigated crops under limited water supply at field scale. Agricultural Water Management 2008; 95: 89-102. Wesseling JG, Elbers JA, Kabat P, van den Broek BJ. SWATRE: instructions for input (internal notes). In: Centre WS, editor, Wageningen, the Netherlands, 1991. Wohling DL, Leaney FW, Crosbie RS. Deep drainage estimates using multiple linear regression with percent clay content and rainfall. Hydrol. Earth Syst. Sci. 2012; 16: 563-572.
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Declaration of interests
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☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
Graphical abstract
Highlights
Groundwater recharge (GWR) is quantified from flux observations at different depths Combined field experiment and modelling to study GWR in semi-arid regions of Iran Fluxes at different depths varied with time with shallow layer being highly variable Flux inconsistency indicated insufficiency in recharge estimation by shallow layer GWR should be estimated using deep vadose zone observations 28