Please wait a minute...
Journal of Arid Land
Journal of Arid Land  2020, Vol. 12 Issue (3): 447-461    DOI: 10.1007/s40333-020-0002-0     CSTR: 32276.14.s40333-020-0002-0
Research article     
Application of SALTMED and HYDRUS-1D models for simulations of soil water content and soil salinity in controlled groundwater depth
Masoud NOSHADI*(), Saghar FAHANDEJ-SAADI, Ali R SEPASKHAH
Department of Irrigation, Shiraz University, Shiraz 71441-65186, Iran
Download: HTML     PDF(757KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

Salinization is a gradual process that should be monitored. Modelling is a suitable alternative technique that saves time and cost for the field monitoring. But the performance of the models should be evaluated using the measured data. Therefore, the aim of this study was to evaluate and compare the SALTMED and HYDRUS-1D models using the measured soil water content, soil salinity and wheat yield data under different levels of saline irrigation water and groundwater depth. The field experiment was conducted in 2013 and in this research three controlled groundwater depths, i.e., 60 (CD60), 80 (CD80) and 100 (CD100) cm and two salinity levels of irrigation water, i.e., 4 (EC4) and 8 (EC8) dS/m were used in a complete randomized design with three replications. Soil water content and soil salinity were measured in soil profile and compared with the predicted values by the SALTMED and HYDRUS-1D models. Calibrations of the SALTMED and HYDRUS-1D models were carried out using the measured data under EC4-CD100 treatment and the data of the other treatments were used for validation. The statistical parameters including normalized root mean square error (NRMSE) and degree of agreement (d) showed that the values for predicting soil water content and soil salinity were more accurate in the HYDRUS-1D model than in the SALTMED model. The NRMSE and d values of the HYDRUS-1D model were 9.6% and 0.64 for the predicted soil water content and 6.2% and 0.98 for the predicted soil salinity, respectively. These indices of the SALTMED model were 10.6% and 0.81 for the predicted soil water content and 11.0% and 0.97 for the predicted soil salinity, respectively. According to the NRMSE and d values for the predicted wheat yield (9.8% and 0.91, respectively) and dry matter (2.9% and 0.99, respectively), we concluded that the SALTMED model predicted the wheat yield and dry matter accurately.

Key words： wheat      yield      dry matter      simulation      normalized root mean square error     
Received: 04 May 2019      Published: 10 May 2020
Corresponding Authors:
About author: *Corresponding author: Masoud NOSHADI (E-mail: noshadi@shirazu.ac.ir)
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
NOSHADI Masoud
FAHANDEJ-SAADI Saghar
R SEPASKHAH Ali
Cite this article:

Masoud NOSHADI, Saghar FAHANDEJ-SAADI, Ali R SEPASKHAH. Application of SALTMED and HYDRUS-1D models for simulations of soil water content and soil salinity in controlled groundwater depth. Journal of Arid Land, 2020, 12(3): 447-461.

URL:

http://jal.xjegi.com/10.1007/s40333-020-0002-0     OR     http://jal.xjegi.com/Y2020/V12/I3/447

Depth (cm) Soil texture Clay Silt Sand pH BD
(g/cm3)		 FC
(cm3/cm3)		 PWP
(cm3/cm3)
(%)
0-15 Clay loam 30 35 35 8 1.25 0.32 0.11
15-30 Clay loam 30 35 35 8 1.32 0.36 0.12
30-50 Clay loam 39 38 23 8 1.36 0.36 0.14
50-70 Clay 40 39 21 8 1.42 0.39 0.16
70-100 Clay 40 39 21 8 1.42 0.39 0.16
Table 1 Soil physical characteristics in different soil depths
Irrigation water salinity
(dS/m)		 Controlled groundwater depth
CD100 CD80 CD60 Mean
Ig (mm) ECe (dS/m) Ig (mm) ECe (dS/m) Ig (mm) ECe (dS/m) Ig (mm) ECe (dS/m)
4 (EC4) 710a 4.83l 580c 9.87i 450e 8.18j 580A 7.63B
8 (EC8) 618b 7.50k 517d 16.63g 407f 14.65h 514B 12.93A
Mean 664A 6.17C 549B 13.25A 429C 11.42B
Table 2 Statistical analysis of irrigation water depth, Ig and saturated paste electrical conductivity (ECe)
Irrigation water salinity (dS/m) CD100 CD80 CD60
GC (mm) GC (%) GC (mm) GC (%) GC (mm) GC (%)
4 (EC4) 0a 0.0 130c 18.3 260e 36.3
8 (EC8) 92b 13.0 193d 27.2 303f 42.7
Table 3 Groundwater contribution (GC) to sub-irrigation in different treatments
Soil layer thickness (cm) n α (1/cm) Lambda
pore size distribution index*		 Bubbling pressure* (cm) Ksat (cm/d) θsat (cm3/cm3) θres (cm3/cm3)
0-30 1.478 0.011 0.478 90.9 188.6 0.462 0.082
30-50 1.413 0.012 0.413 83.3 121.6 0.465 0.090
50-100 1.399 0.012 0.399 83.3 85.8 0.450 0.090
Table 4 Parameters of the van Genuchten (1980) equation
HYDRUS-1D
Soil layer thickness (cm) Disp (cm) Diff-W (cm2/d) Diff-G (cm2/d) Sink watera (1/d)
0-30 20 2 0 0.096
30-50 20 2 0 0.076
50-100 20 2 0 0.067
SALTMED
Crop factor Calibrated value Crop growth factor Calibrated value
Kc Initial 0.38 Photosynthesis efficiency (g/MJ) 1.60
Kc Mid 1.25 Extinction coefficient 0.45
Kc End 0.25 PAR ratio 0.50
Kcb Initial 0.20 Tmax (oC) 40.00
Kcb Mid 1.00 TopT2b (oC) 28.00
Kcb End 0.15 TopT1c (oC) 25.00
π50 (dS/m) 10.50 Tmin (oC) 4.00
Table 5 Calibrated values of parameters in the HYDRUS-1D and SALTMED models
Fig. 1 Observed and simulated soil water contents using the SALTMED and HYDRUS-1D models in EC4-CD100 treatment (calibration step) at different days after planting. (a), 114 d; (b), 126 d; (c), 148 d; (d), 163 d; (e), 177 d; (f), 189 d; (g), 200 d; (h), 212 d.
Parameter Statistical parameter EC4-CD80 EC4-CD60 EC8-CD100 EC8-CD80 EC8-CD60
H S H S H S H S H S
Soil water content NRMSE (%) - - 0.07 0.12 0.12 0.10 0.10 0.10 - -
d - - 0.56 0.82 0.73 0.82 0.65 0.81 - -
Soil salinity NRMSE (%) 0.06 0.13 0.08 0.10 0.03 0.05 0.06 0.12 0.08 0.15
d 0.99 0.98 0.99 0.99 0.98 0.91 0.99 0.98 0.99 0.98
Table 6 NRMSE and d indices during validation process of the SALTMED and HYDRUS-1D models
Fig. 2 Observed and simulated soil water contents using the SALTMED and HYDRUS-1D models under EC8-CD100 treatment (validation step) at different days after planting. (a), 114 d; (b), 126 d; (c), 148 d; (d), 163 d; (e), 177 d; (f), 189 d; (g), 200 d; (h), 212 d.
Fig. 3 Observed and simulated soil water contents using the SALTMED and HYDRUS-1D models under EC8-CD80 treatment (validation step) at different days after planting. (a), 114 d; (b), 126 d; (c), 148 d; (d), 163 d; (e), 177 d; (f), 189 d; (g), 200 d; (h), 212 d.
Fig. 4 Comparison of the measured and predicted soil water contents under (a) EC4-CD100, (b) EC4-CD60, (c) EC8-CD100, (d) EC8-CD80 and (e) all treatments. Comparison of the measured and predicted soil salinity under all treatments (f). NRMSE, normalized root mean square error; d, degree of agreement. Subscripted S and H represent the SALTMED and HYDRUS-1D models, respectively.
Fig. 5 Observed and simulated saturated paste soil salinities (ECe) using the SALTMED and HYDRUS-1D models under (a) EC4-CD100 (calibration step), (b) EC4-CD80, (c) EC4-CD60, (d) EC8-CD100, (e) EC8-CD80, and (f) EC8-CD60 treatments.
Treatment Dry matter (Mg/hm2) Error percentage (%) Wheat yield (Mg/hm2) Error percentage (%)
Predicted Measured Predicted Measured
EC4 CD100 11.52 11.35 1.5 4.60 4.88 -5.7
CD80 12.19 11.72 4.0 4.87 5.80 -16.0
CD60 10.56 10.46 1.0 4.22 4.06 3.9
EC8 CD100 9.40 9.43 -0.3 3.78 3.62 4.4
CD80 9.68 9.76 -0.8 4.12 4.12 -6.1
CD60 8.11 8.63 -6.0 3.17 3.17 2.2
Table 7 Measured and predicted wheat yield and dry matter by the SALTMED model
Fig. 6 Relationships between the measured and predicted (a) wheat yield and (b) dry matter
[1]   Allen R G, Pereira L S, Raes D, et al.1998. Crop evapotranspiration-guidelines for computing crop water requirements. FAO Irrigation and Drainage paper 56. FAO, Rome, 300(9): D05109.
[2]   Asseng S, Fillery I R P, Dunin F X, et al.2001a. Potential deep drainage under wheat crops in a Mediterranean climate. I. Temporal and spatial variability. Crop and Pasture Science, 52(1): 45-56.
[3]   Asseng S, Dunin F X, Fillery I R P, et al.2001b. Potential deep drainage under wheat crops in a Mediterranean climate. II. Management opportunities to control drainage. Crop and Pasture Science, 52(1): 57-66.
[4]   Ayars J E, Christen E W, Hornbuckle J W.2006. Controlled drainage for improved water management in arid regions irrigated agriculture. Agricultural Water Management, 86(1-2): 128-139.
[5]   Borg H, Grimes D W.1986. Depth development of roots with time: An empirical description. Transactions of the American Society of Agricultural Engineers, 29(1): 194-197.
[6]   Brouwer C, Prins K, Heibloem M.1989. Irrigation Water Management: Irrigation Scheduling. Training Manual, 4. Rome: Food and Agriculture Organization of the United Nations. [2019-02-05]. .
[7]   El-Shafie A F, Osama M A, Hussein M M, et al.2017. Predicting soil moisture distribution, dry matter, water productivity and potato yield under a modified gated pipe irrigation system: SALTMED model application using field experimental data. Agricultural Water Management, 184: 221-233.
[8]   Fazli R A, Gholami A, Andarzian B, et al.2013. Investigating the effect of applying drainaged-water on wheat yield using SALTMED model. Journal of Novel Applied Science, 2(Suppl. 3): 1003-1011.
[9]   Feddes R A, Bresler E, Neuman S P.1974. Field test of a modified numerical model for water uptake by root system. Water Resource Research, 10(5): 1199-1206.
[10]   Golabi M, Naseri A A, Kashkuli H A.2009. Evaluation of SALTMED model performance in irrigation and drainage of sugarcane farms in Khuzestan province of Iran. Journal of Food, Agriculture and Environment, 7(2): 874-880.
[11]   Najib B H, Mania I J, Zaoui A, et al.2007. Irrigation water management using fresh and saline water: modelling and experimental study SALTMED model calibration and validation using fresh water. In: Proceeding of ICID (International Commission on Irrigation and Drainage) 22nd European Regional Conference, 2-7 September 2007, Pavia, Italy.
[12]   Hirich A, Choukr-Allah R, Ragab R, et al.2012. The SALTMED model calibration and validation using field data from Morocco. Journal of Materials and Environmental Science, 3(2): 342-359.
[13]   Jha R K, Sahoo B, Panda R K.2017. Modeling the water and nitrogen transports in a soil-paddy-atmosphere system using HYDRUS-1D and lysimeter experiment. Paddy and Water Environment, 15(4): 831-846.
[14]   Jiang J, Huo Z, Feng S, et al.2012. Effect of irrigation amount and water salinity on water consumption and water productivity of spring wheat in Northwest China. Field Crops Research, 137: 78-88.
[15]   Kaya Ç I, Yazar A, Sezen S M.2015. SALTMED model performance on simulation of soil moisture and crop yield for quinoa irrigated using different irrigation systems, irrigation strategies and water qualities in Turkey. Agriculture and Agricultural Science Procedia, 4: 108-118.
[16]   Khalil B M, Abdel-Gawad S T, Millette J A. 2004. Impact of controlled drainage on rice production, irrigation water requirement and soil salinity in Egypt. In: Cooke R. Drainage VIII Proceedings of the 8th International Symposium, 21-24 March 2004, Sacramento, CA, USA. Michigan: American Society of Agricultural and Biological Engineers, 443-452.
[17]   Liu X W, Feike T, CHEN S Y, et al.2016. Effects of saline irrigation on soil salt accumulation and grain yield in the winter wheat-summer maize double cropping system in the low plain of North China. Journal of Integrative Agriculture, 15(12): 2886-2898.
[18]   Loague K, Green R E.1991. Statistical and graphical methods for evaluating solute transport model: Overview and application. Journal of Contaminant Hydrology, 7(1-2): 15-73.
[19]   Luo Y, Sophocleous M.2010. Seasonal groundwater contribution to crop-water use assessed with lysimeter observations and model simulations. Journal of Hydrology, 389(3-4): 325-335.
[20]   Ma W J, Mao Z Q, Yu Z R, et al.2008. Effects of saline water irrigation on soil salinity and yield of winter wheat-maize in North China Plain. Irrigation and Drainage Systems, 22(1): 3-18.
[21]   Malash N M, Flowers T J, Ragab R.2011. Plant-water relations, growth and productivity of tomato irrigated by different methods with saline and non‐saline water. Irrigation and Drainage, 60(4): 446-453.
[22]   Noshadi M, Foroutani A, Sepaskhah A.2017. Analysis of clodinafop-propargyl herbicide transport in soil profile under vetiver cultivation using HYDRUS-1D and modified PRZM-3 models. Toxicology: Open Access, 3: doi: 10.4172/2476-2067.1000120.
[23]   Ragab R.2002a. A holistic generic integrated approach for irrigation, crop and field management: the SALTMED model. Environmental Modelling and Software, 17(4): 345-361.
[24]   Ragab R.2002b. Integrated modelling approach for irrigation water management using saline and non-saline water: the SALTMED model. Acta Horticulturae, 573: 129-138.
[25]   Ranjbar G H, Ghadiri H, Razzaghi F, et al.2015. Evaluation of the SALTMED model for sorghum under saline conditions in an arid region. International Journal of Plant Production, 9(3): 373-392.
[26]   Razzaghi F, Ghannadi T.2016. "Assessing SALTMED model for wheat experiments irrigated with basin and sprinkler systems". International Journal of Plant Production, 10(2): 233-250.
[27]   Shahrokhnia M H, Sepaskhah A R.2018. Water and nitrate dynamics in safflower field lysimeters under different irrigation strategies, planting methods and nitrogen fertilization and application of HYDRUS-1D model. Environmental Science and Pollution Research, 25: 8563-8580.
[28]   Silva L L, Baptista F J, Meneses J F, et al.2017. Evaluation of the SALTMED model for tomato crop production in unheated greenhouses. Acta Horticulturae, 1170: 441-446.
[29]   Šimůnek J, Köhne J M, Kodešová R, et al.2008. Simulating non equilibrium movement of water, solutes, and particles using HYDRUS: A review of recent applications. Soil and Water Research, 3(Special Issue 1): 42-51.
[30]   Steppuhn H.2016. Enhancing subsurface drainage to control salinity in dry land agriculture. In: 10th International Drainage Symposium Conference, 6-9 September 2016, Minneapolis, Minnesota. Michigan: American Society of Agricultural and Biological Engineers.
[31]   van Genuchten M T.1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal, 44(5): 892-898.
[32]   van Genuchten M T, Leij F J, Lund L J.1992. Indirect methods for estimating the hydraulic properties of unsaturated soils.. In: Proceedings of the International Workshop on Indirect Methods for Estimating the Hydraulic Properties of Unsaturated Soils, 11-13 October, 1992, Riverside, California, USA
[33]   Wang X P, Yang J S, Liu G M, et al.2015. Impact of irrigation volume and water salinity on winter wheat productivity and soil salinity distribution. Agricultural Water Management, 149: 44-54.
[34]   Willmott C J, Ackleson S G, Davis R E, et al.1985. Statistics for the evaluation and comparison of models. Journal of Geophysical Research: Oceans, 90(5): 8995-9005.
[35]   Zeng W Z, Xu C, Wu J W, et al.2014. Soil salt leaching under different irrigation regimes: HYDRUS-1D modelling and analysis. Journal of Arid Land, 6(1): 44-58.
[1] ZUBAIDA Muyibul. Trade-offs and synergies between ecosystem services in Yutian County along the Keriya River Basin, Northwest China[J]. Journal of Arid Land, 2024, 16(7): 943-962.
[2] ZHU Haiqiang, WANG Jinlong, TANG Junhu, DING Zhaolong, GONG Lu. Spatiotemporal variations of ecosystem services and driving factors in the Tianchi Bogda Peak Natural Reserve of Xinjiang, China[J]. Journal of Arid Land, 2024, 16(6): 816-833.
[3] CHEN Jiazhen, KASIMU Alimujiang, REHEMAN Rukeya, WEI Bohao, HAN Fuqiang, ZHANG Yan. Temporal and spatial variation and prediction of water yield and water conservation in the Bosten Lake Basin based on the PLUS-InVEST model[J]. Journal of Arid Land, 2024, 16(6): 852-875.
[4] XU Wenjie, DING Jianli, BAO Qingling, WANG Jinjie, XU Kun. Improving the accuracy of precipitation estimates in a typical inland arid area of China using a dynamic Bayesian model averaging approach[J]. Journal of Arid Land, 2024, 16(3): 331-354.
[5] CHEN Yiyang, ZHANG Li, YAN Min, WU Yin, DONG Yuqi, SHAO Wei, ZHANG Qinglan. Spatiotemporal evolution and future simulation of land use/land cover in the Turpan-Hami Basin, China[J]. Journal of Arid Land, 2024, 16(10): 1303-1326.
[6] LIANG Shen, WANG Shu, LIU Yabin, PANG Jinghao, ZHU Haili, LI Guorong, HU Xiasong. Mechanism underlying the uprooting of taproot-type shrub species in the loess area of northeastern Qinghai- Xizang Plateau, China[J]. Journal of Arid Land, 2024, 16(10): 1426-1443.
[7] ZHANG Lingkai, SUN Jin, SHI Chong. Numerical simulation on the influence of plant root morphology on shear strength in the sandy soil, Northwest China[J]. Journal of Arid Land, 2024, 16(10): 1444-1462.
[8] LIN Yanmin, HU Zhirui, LI Wenhui, CHEN Haonan, WANG Fang, NAN Xiongxiong, YANG Xuelong, ZHANG Wenjun. Response of ecosystem carbon storage to land use change from 1985 to 2050 in the Ningxia Section of Yellow River Basin, China[J]. Journal of Arid Land, 2024, 16(1): 110-130.
[9] GAO Yalin, QI Guangping, MA Yanlin, YIN Minhua, WANG Jinghai, WANG Chen, TIAN Rongrong, XIAO Feng, LU Qiang, WANG Jianjun. Regulation effects of water and nitrogen on yield, water, and nitrogen use efficiency of wolfberry[J]. Journal of Arid Land, 2024, 16(1): 29-45.
[10] LEI Jia, CHENG Jianjun, GAO Li, MA Benteng, AN Yuanfeng, DONG Hongguang. Characteristics of snow cover distribution along railway subgrade and the protective effect of snow fences[J]. Journal of Arid Land, 2023, 15(8): 901-919.
[11] Reza DEIHIMFARD, Sajjad RAHIMI-MOGHADDAM, Farshid JAVANSHIR, Alireza PAZOKI. Quantifying major sources of uncertainty in projecting the impact of climate change on wheat grain yield in dryland environments[J]. Journal of Arid Land, 2023, 15(5): 545-561.
[12] Mohamed K EL-GHANNAM, Fatma WASSAR, Sabah MORSY, Mohamed HAFEZ, Chiter M PARIHAR, Kent O BURKEY, Ahmed M ABDALLAH. Controlled drainage in the Nile River delta of Egypt: a promising approach for decreasing drainage off-site effects and enhancing yield and water use efficiency of wheat[J]. Journal of Arid Land, 2023, 15(4): 460-476.
[13] LI Hongfang, WANG Jian, LIU Hu, MIAO Henglu, LIU Jianfeng. Responses of vegetation yield to precipitation and reference evapotranspiration in a desert steppe in Inner Mongolia, China[J]. Journal of Arid Land, 2023, 15(4): 477-490.
[14] CAO Yijie, MA Yonggang, BAO Anming, CHANG Cun, LIU Tie. Evaluation of the water conservation function in the Ili River Delta of Central Asia based on the InVEST model[J]. Journal of Arid Land, 2023, 15(12): 1455-1473.
[15] YAN Xue, LI Lanhai. Spatiotemporal characteristics and influencing factors of ecosystem services in Central Asia[J]. Journal of Arid Land, 2023, 15(1): 1-19.