Agricultural Water Management 213 (2019) 486–498 Contents lists available at ScienceDirect Agricultural Water Management journal homepage: www.elsevier.com/locate/agwat Development and application of long-term root zone salt balance model for predicting soil salinity in arid shallow water table area T ⁎ Guanfang Suna, Yan Zhua, , Ming Yeb, Jinzhong Yanga, Zhongyi Quc, Wei Maoa, Jingwei Wua a State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, Hubei, 430072, China b Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, FL, 32306, USA c College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, 010018, China ARTICLE INFO ABSTRACT Keywords: A simple root zone salt balance model was developed for long-term soil salt prediction. A groundwater balance Soil salinity prediction module was integrated to obtain the water flux at the bottom of the root zone, since groundwater level is easily Soil salt balance and reliably measured data. An assumption that the net percolation at the bottom of root zone equals to the net Hetao Irrigation District recharge from root zone to unconfined groundwater system was used, which makes capillary rise and infiltration Autumn irrigation water leaching of root zone obtainable. The model accuracy and limitation were evaluated by comparing the Water table depth simulation results with those from HYDRUS-1D under various soil texture and bottom boundary conditions. The mean relative error (MRE) of soil salt content ranged from −6.25% to 9.95%, and the root mean square error (RMSE) from 0.02 kg/100 kg to 0.05 kg/100 kg, which suggest the model accuracy. The determination coeffi- cient (R2) of the cumulative percolation at the bottom of root zone and recharge to groundwater at the end of each calculation period was 0.99, which validates the model assumption. The model was then applied to predict soil salinity in the Longsheng irrigation district of Hetao Irrigation District, China. The soil salinity and water table depth data from 1999 to 2016 were used to calibrate and validate the model parameters. The MRE values of water table depth and average soil salt content were 10.08% and 10.84%, and RMSE values 0.37 m and 0.04 kg/ 100 kg in the validation period. Subsequently, the validated model was used to simulate the soil salinity in future 100 years under various water saving conditions. Water table depth and autumn irrigation for salt leaching were the two major factors to control soil salinity. The required autumn irrigation to keep root zone in mild salinity level (0.3 kg/100 kg) can decrease by 25.1 mm with 0.1 m water table depth increase, while it should increase by 64 mm with 1.0 kg/m3 groundwater salinity increase in the crop growing season. Moderate groundwater ex- ploitation to increase water table depth is recommended to save autumn irrigation water and control soil sali- nity. 1. Introduction characteristics, irrigation schedules, irrigation water salinity, and water table depth (Ritzema, 2006; Rozema and Flowers, 2008). This problem Salt-affected soils present in more than 100 countries (Letey et al., may be resolved by using efficient and reliable models that can predict 2011; Munns and Tester, 2008; Rengasamy, 2006). 4.5 × 1011 m2 long-term soil salinity at the regional scale. agricultural area are under the risk of salt content increasing in the root The site of this study is the Hetao Irrigation District, located in the zone (Rozema and Flowers, 2008). Many measurements are proposed to upper reaches of the Yellow River. The saline-cultivated land occupies control soil salinity, e.g., dry drainage, well-canal conjunctive irriga- more than 50% of the total irrigated cropland (5.7 × 109 m2)(Feng tion, improvement of irrigation efficiency and land retirement (Mao et al., 2005). To relieve soil salinization, autumn irrigation is taken et al., 2017; Schoups et al., 2005; Wu et al., 2009). The key to the after harvest for washing soil salt out, and it consumes more than 30% success of management strategies is to reduce input salt and to raise the of total irrigation water from the Yellow River (Hao, 2016). Given that outflow in the subsoil (Singh, 2015, 2017). Applying additional water regulation agencies decreased the diversion allocation from the Yellow to drive salt out of root zone has been proved as an effective way. River from 5.4 billion m3 to 4 billion m3 in recent years, water saving However, it is difficult to determine the amount of leaching water as it and salinity control have been the main concerns for sustainable agri- is influenced by various factors, e.g., climate conditions, soil culture development. It is necessary to estimate the amount of ⁎ Corresponding author. E-mail address: [email protected] (Y. Zhu). https://doi.org/10.1016/j.agwat.2018.10.043 Received 8 June 2018; Received in revised form 28 October 2018; Accepted 30 October 2018 0378-3774/ © 2018 Elsevier B.V. All rights reserved. G. Sun et al. Agricultural Water Management 213 (2019) 486–498 appropriated autumn irrigation and to optimize irrigation schedule so needed by the soil salt balance model to calculate root zone salt content that the salt accumulated in the crop growing season can be washed out while the water flux at the bottom of the root zone are available from thoroughly. Thus, it is a great challenge to investigate the soil salt dy- the groundwater balance module, which makes it suitable for real- namics under different irrigation management strategies. world application with limited data. The groundwater balance module Many soil salt models have been developed to calculate root zone was used to obtain the water flux at the bottom of the root zone, by salt dynamics, including numerical models and mass balance models. assuming that the net percolation at the bottom of root zone equals to The numerical models, including HYDRUS (Šimůnek et al., 2013), the net recharge from root zone to shallow unconfined aquifer. This SWAP (Kroes and Dam, 2003), ENVIRO-GRO (Pang and Letey, 1998), makes the model validation be easily carried out with the indicator of and Q3D (Zhu et al., 2012), have solid theoretical foundation and are groundwater level, which is very reliably measured data. The model widely used for simulating water flow and solute transport in the sub- accuracy and limitation were evaluated by comparing the results with surface systems. However, for the numerical models, fine spatial dis- those from HYDRUS-1D (Šimůnek et al., 2013). A real-world applica- cretization and small time step are always needed to obtain accurate tion of the developed model at the Longsheng irrigation district in the results, which may result in high computational cost especially for long- Hetao Irrigation District was presented. The major impact factors and term and large-scale modeling. Setting up the numerical models also measurements to control soil salinity were also discussed. requires intensive hydrogeological information, which is not always available in practice (Zhu et al., 2013). 2. Model description Soil salt mass balance models are not subject to the problems above for numerical models. There are two kinds of soil salt balance models. 2.1. Model development The first kind is multi-reservoir model with subsurface system divided into multiple reservoirs and each reservoir is a simulation layer, e.g., The concept and flow chart of the developed model are shown in SaltMod (Oosterbaan, 1998) and OASIS_MOD (Askri et al., 2010). An- Fig. 1(a) and (b), respectively. The model included a root zone salt other is simple root zone model, which considers water and salt balance balance module and a groundwater balance module as shown in in the divided layers of root zone, e.g., TETrans (Corwin and Waggoner, Fig. 1(a). The model was developed based on the salt mass balance 1990), SALTIRSOIL (Visconti et al., 2011), WATSUIT (Rhoades and considering three major sources of salt input (i.e., irrigation water, Merrill, 1976; Corwin et al., 2007), and Salt storage model (Ritzema, precipitation, and capillary rise) and three major sources of salt output 2006). The development and application of these salt mass balance (i.e., uptake by crop, leaching to subsoil by irrigation and precipita- models are listed in Table 1. The multi-reservoir models usually con- tion). The percolation from irrigation or precipitation and the capillary sider water flow and salt transport from the root zone to groundwater rise were considered in the groundwater balance module Those items system. They have solid foundation to calculate the percolation and were assumed to be equal to those at the bottom of root zone. The capillary rise. The root zone models focus on the major processes that model first ran the groundwater balance module to obtain the perco- impact water flow and salt dynamics in the root zone. For example, lation from irrigation or precipitation and the capillary rise. Then it TETrans and SALTIRSOIL contain two modules to represent soil water started running the salt balance module to calculate the salt con- balance and soil salt balance. The “tipping-bucket” method (Kendy centration at the bottom of the root zone and average salt content in the et al., 2003) is used to simulate infiltration, redistribution, and drainage root zone. The model flowchart can be found in Fig. 1(b). processes. However, water can only be drained downward or directly depleted in the soil water module, while ignoring the upward soil water 2.1.1. Groundwater balance module movement (Corwin et al., 2007; Mao et al., 2018b). It is thus difficult There were seven items considered in the developed model for si- for these models to predict salt accumulation in areas with strong mulating groundwater level, which were seepage from canals, perco- evapotranspiration under shallow water table depth due to capillary lation from irrigation and precipitation, lateral flow of groundwater, rise.