Soil water-salt dynamics state and associated sensitivity factors in an irrigation district of the loess area: a case study in the Luohui Canal Irrigation District, China

Abstract

Soil salinisation determines the distribution pattern of crop processes in irrigation districts. The research presented here was conducted in the Luohui Canal Irrigation District, which is located in the loess area of Shaanxi Province, China. A back-propagation artificial neural network (BPANN) for the soil water-salt state was established to predict soil salinity and alkalinity. The degree of influence of numerous factors on the dynamics was quantitatively determined using the default factor testing method and verified with grey relational analysis. The results show that the BPANN prediction accuracy is very high, and it can efficiently depict the comprehensive relationships between the influential factors and dynamic states. The influence of soil moisture, evaporation, groundwater salt and groundwater depth on the dynamics is significant in the region. The current irrigation method, used for many years, cannot meet the water necessities for the vegetation, causing the groundwater levels to decline and a lowering of the soil moisture zone, leading to the occurrence of serious soil salinisation. Under the action of evaporation, more salt accumulates in the upper part of the soil, resulting in extensive soil salinisation. The higher the groundwater salt content, the more salt is carried by rising capillary water, and the more the soil is salinised. If groundwater depth was to exceed the critical water table, then groundwater and salt would move to the soil surface by moisture evaporation, and salt would build up on the soil surface in this irrigation district. The interactions between each factor forms a complex coupling relationship state.