Prediction of salt transport in different soil textures under drip irrigation in an arid zone using the SWAGMAN Destiny model

Abstract

In recent years, Xinjiang Oasis has faced a major challenge of increasing risk of secondary salinization caused by drip irrigation under plastic mulch. Predicting the salt balance is therefore essential for understanding how to sustain the use of salinized land in this arid area. This research validated the SWAGMAN (Salt, Water And Groundwater MANagement) Destiny model to simulate and forecast the movement of salt in different soil textures based on field experiments. The results were verified with extensive field work in Shihutan, Xinjiang, China. They show that soil salinity decreases in the upper layers and increases in the bottom layers of the investigated soil profile. The desalinization rate in sand, which shows an overall steady trend throughout the soil profile, is generally higher than that in loam and clay. The depth of 60cm is critical for loam and clay; soil salinity decreases above it but increases below it. Model sensitivity analysis reveals the variation of soil salinity is independent of the initial electrical conductivity setting of SWAGMAN Destiny simulations. This study indicates that numerical modelling is a useful approach for efficiently estimating the salt balance under drip irrigation. The result provides a scientific basis for making adaptive strategies to manage salinised farmlands in arid zones.

Inversion of Soil Salinity Using Multisource Remote Sensing Data and Particle Swarm Machine Learning Models in Keriya Oasis, Northwestern China

“…Xinjiang is the largest province in China in terms of land area, and its saline soil area accounts for 60.6% of the total saline soil surface in the country. As a region with mainly agriculture and animal husbandry, salinization has seriously threatened the sustainable development of agriculture and ecological security in Xinjiang [2,8,15]. Therefore, real-time monitoring and scientific management of soil salinization are essential in Xinjiang.…”

Research on soil moisture content combination prediction model based on ARIMA and BP neural networks

“…Shaohui Yang used time series analysis method to build ARIMA model and forecast the soil moisture in Beijing, the maximum relative error of the model forecast was 7.2%. 32 Li established an ARIMA model for soil moisture in Guizhou Province by using the method of time series analysis, the maximum relative error of the model was 4.2%. 33 Hexi Zhang used ARIMA model to predict the trend of soil moisture time series, compared with the actual measured data, the maximum relative error of the established model was 12.185%.…”

“…Shaohui Yang used time series analysis method to build ARIMA model and forecast the soil moisture in Beijing, the maximum relative error of the model forecast was 7.2% 32 . Li established an ARIMA model for soil moisture in Guizhou Province by using the method of time series analysis, the maximum relative error of the model was 4.2% 33 .…”

Cropland Soil Salinization and Associated Hydrology: Trends, Processes and Examples

“…The term "salt transport" is widely used in the literature to describe translocation of salt from one point to another [54][55][56][57], but this is a misleading term as solute transport is the mechanism that translocates salt in porous media in general, and agricultural environments in particular. Transport of salt as a solid mineral is possible by the drifting of small salt particles with the wind [9] but this mechanism is meaningful only over long time scales and distances [58], much greater than those relevant for agricultural environments.…”