Spatial Variability Of Soil Properties And Spatial Allocation Of Salt Discharge Areas For Irrigation Districts

Soil salinization is increasingly severe in Yinchuan North Irrigation District due to lack of water resource. Although existing studies tried to alleviate the soil salinization by land consolidation engineering methods, chemical and biological methods, the problem of soil salinization has not yet been resolved fundamentally to date. This is because the multi-functional spatial allocation for water and soil resources, including cultivated land production, salt discharge reservation and ecology, was still unreasonable. Very few studies have considered soil water and salt constraints, and examined use of spatial allocation of cultivated land and salt discharge area (uncultivated land) of land consolidation to solve the effect of soil salinization. The objective of this study was thus to solve the soil salinization problem by comprehensively considering water and salt constraints and the spatial allocation of water and soil resources. Firstly, on the basis of geostatistics and artificial neural network, we proposed effective methods for predicting soil properties with a high accuracy, thus obtaining regional parameters of soil water and salt movement. Then, based on the optimal management of irrigation and drainage, we investigated the effects of different irrigation and drainage treatments on soil salinity. Finally, we set up scenarios of cultivated land and salt discharge reservation by SahysMod, and proposed the optimal allocations of cultivated land and salt discharge reservation of land consolidation. Results of this study provide a theoretical basis and technical guide for the spatial allocation of cultivated land and salt discharge reservation of land consolidation, and hence have important implications to preventing soil salinization in salt-affected farming areas. The main results and conclusions are in the following.Firstly, geostatistics (e.g. OK and RK) cannot capture regional changes in soil properties due to the nonlinear relationships between soil properties and environmental factors. In addition, artificial neural network only consider the variations of the soil properties caused by correlated influencing factors,regardless of spatial autocorrelation of the surrounding measured data; or it only consider the spatial auto-correlative information on coordinates (X, Y) of survey points as input, regardless of environment factors. Thus, this study eliminated the aforementioned limiations by combining ordinary kriging with back-propagation network (OK_BP) (i.e., considering the two aspects of spatial variation) and expected to improve the mapping accuracy of soil salinity. Our results showed that OK_BP provided the best mapping accuracy among the four methods (i.e., OK, BP, RK and OK BP). Futhermore, OK BP revealed more details of the spatial variation responding to influencing factors, and provided more flexibility for incorporating various factors in mapping. Therefore, OK BP can serve as an effective method for mapping soil salinity with a high accuracy.Second, due to nonlinear relationships between soil properties and their correlated factors, the relationships can change with location in the same region. This is particularly true in regions of highly heterogeneous landscapes. However, existing study frequently assumed the linear relationships between soil properties and the factors to improve the prediction accuracy, and neglected different effects of these factors on soil properties in localized regions. By considering the effects of topography, soil type,soil texture and land use on soil organic matter, our study used self-organizing map to cluster these factors into different regions and then used ordinary kriging combined with the clustering of a self-organizing feature maps neural network (KCSOM) to predict soil organic matter. Results showed that the use of KCSOM was effective to describe the nonlinear relationships between the soil organic matter and its correlated factors. KCSOM effectively avoided underestimation of the higher values of the interpolation surface and overestimation of the lower values that existed in the other methods.Therefore, our method improved the prediction accuracy compared the previous approaches. Moreover,our method can more accurately present the spatial variation of soil organic matter for different clusters and within clusters based on the difference effects of the factors on soil properties. These results clearly indicate that KCSOM can serve as an effective method for prediction soil properties with a high accuracy, especially in regions of highly heterogeneous landscapes. It is worth noting that the preliminary study (i.e., the study on spatial prediction of soil electric conductivity by KCSOM in XIDATAN) shows KCSOM is not improved the prediction accuracy in small regions of the homogenous landscapes.Third, traditional methods developed equilibrium equations to model the water and salt movement by considering a region as a holistic and did not consider spatial variability of soil properties in the region. Moreover, very little attention has been paid for dynamic changes of soil water and salt under different irrigation and drainage management. Our study considered the spatial variability of soil properties of cultivated land and salt discharge reservation and used SahysMod to simulate dynamic changes of soil water and salt under different irrigation and drainage management in the following ten years. Results show that increasing irrigation water is critical to solve soil salinization, which can delay the accumulation of soil salinity to the barrier level in the rootzone. If electric conductivity of irrigation water is 0.6 dS m-1, cultivated land will not be subjected to salt stress in the next ten years. However,under existing irrigation water treatments, cultivated land will continue to be subjected to salt stress until 2024. Cultivated land will be subjected to salt stress if increasing the electric conductivity of irrigation water. We found that deepening the depth of drainage ditches can relieve soil salinization. The results can help to prevent soil salinization.Finally, We have addressed both scientific problems such as the spatial allocation of cultivated land and salt discharge area, responding mechanism of soil water and salt movement, and realistic probems of severe soil salinization of cultivated land due to blind land consolidation in a way of high newly increased cultivated land rate in the Yinchuan North Irrigation District (i.e., the key land consolidation area under water and salt constraints). Specifically, we analyzed the soil water and salt movement on different spatial allocations between cultivated land and salt discharge of land consolidation by SahysMod in the typical salt-affected soil of Yinchuan North Irrigation District——XIDATAN. Results showed that optimal allocation of cultivated land and salt discharge reservation can decrease salt salinization and accordingly is valable to prevent soil salinization of cultivated land, especially in saline-alkali soil or area where secondary salinization area of land consolidation is prevalent. The salt-removing effectiveness of cultivated land was higher where the area ratio of the cultivated land to the salt discharge area within the same land consolidation was lower; the ratio of salt discharge area was too large to have effect on controlling soil salinization. Excavation of soil depth in the salt discharge area could decrease soil salinization of cultivated land. The effectiveness of removing salt from cultivated land was higher when the salt discharge area was scattered across cultivated land. The spatial allocation parameters of cultivated land and salt discharge area are closely related to the excavation of soil depth, the ratio of cultivated land and salt discharge area, and the distance of salt-removing of salt discharge area.