Relationship between Soil Salinization and Groundwater Hydration in Yaoba Oasis, Northwest China

The Chaobai River (CBR) basin in northern China is experiencing an unprecedented continuous inflow of external water via the South–North Water Diversion Project, which has channeled water from the southern part of the country to the north. Consequently, the steady rise of groundwater table in recent years is threatening soil salinity regulation. The purpose of this study was to describe the status of salinity of the surface soil in the CBR basin and to evaluate the impact of environmental factors including groundwater table on the spatial distribution of soil salinity using multivariate analysis (MVA) technique. In this study, 10 chemical variables of soil samples collected in 204 sites along CBR were analyzed, considering their interaction with three environmental factors: the density of irrigation canals, groundwater depth and topography. Statistical analysis mainly consisted of principal component analysis (PCA), redundancy analysis (RDA) and clustering analysis (CA). The results allow defining the surface soil in the CBR basin as a slightly saline and moderately alkaline media. The first two axes of multivariate model approximately explains 51% of the observed variability and allows distinguishing two main domains: the saline and the alkaline. The variability of the saline domain, defined by major cations and anions, is obviously controlled by macro environmental factors, of which the density of irrigation canals and groundwater depth contributes 71% and 28%, respectively, while that of the alkaline domain, related to pH and bicarbonate, mainly manifests as singular behaviors of soil groups like rice cultivation or sewage irrigation. The results suggests that more attention should be paid to the ongoing water table rise to help inform future land management decisions and to prevent a double threat of both groundwater and surface water on soil salinization. Meanwhile, this study shows the enormous potential of MVA technique, specifically the complementary duo of RDA and CA, for integrating both global and local information of soil salinity and environmental factors.

Section: General Features Of the Soil Ionic Compositionsupporting

confidence: 80%

Multivariate Analysis of Soil Salinity in a Semi-Humid Irrigated District of China: Concern about a Recent Water Project

Zhang

et al. 2020

“…According to previous research results, the intense evaporation in the arid region mostly exhausted the flood formed during the rainstorm season before it infiltrated into the groundwater [16,47]. In addition, water diversion and irrigation during the agricultural production increased the groundwater level and caused it to exceed the critical depth, resulting in continuous strong water evaporation in the shallow phreatic water area of the western part of study region, which worsened soil salinization [27,48,49].…”

Section: Impact Of Climatic Factors On Groundwater Levelmentioning

confidence: 73%

“…The variation curves of the other five monitoring wells are generally similar during the year and the monthly average groundwater level changes regularly as the seasonal exploitation amount varies in different seasons. As a result, the characteristic of annual groundwater level from 1982 to 2018 in the study area can be roughly summarized into four stages [27].…”

Section: Dynamic Characteristics Of Annual Groundwater Levelmentioning

confidence: 99%

Seasonal and Inter-Annual Variability of Groundwater and Their Responses to Climate Change and Human Activities in Arid and Desert Areas: A Case Study in Yaoba Oasis, Northwest China

Zheng

et al. 2020

Self Cite

Climate change and human activities have profound effects on the characteristics of groundwater in arid oases. Analyzing the change of groundwater level and quantifying the contributions of influencing factors are essential for mastering the groundwater dynamic variation and providing scientific guidance for the rational utilization and management of groundwater resources. In this study, the characteristics and causes of groundwater level in an arid oasis of Northwest China were explored using the Mann-Kendall trend test, Morlet wavelet analysis, and principal component analysis. Results showed that the groundwater level every year exhibited tremendous regular characteristics with the seasonal exploitation. Meanwhile, the inter-annual groundwater level dropped continuously from 1982 to 2018, with a cumulative decline depth that exceeded 12 m, thereby causing the cone of depression. In addition, the monthly groundwater level had an evident cyclical variation on the two time scales of 17-35 and 7-15 months, and the main periodicity of monthly level was 12 months. Analysis results of the climatic factors from 1954 to 2018 observed a significant warming trend in temperature, an indistinctive increase in rainfall, an inconspicuous decrease in evaporation, and an insignificant reduction in relative humidity. The human factors such as exploitation amount, irrigated area, and population quantity rose substantially since the development of the oasis in the 1970s. In accordance with the quantitative calculation, human activities were decisive factors on groundwater level reduction, accounting for 87.79%. However, climate change, including rainfall and evaporation, which contributed to 12.21%, still had the driving force to change the groundwater level in the study area. The groundwater level of Yaoba Oasis has been greatly diminished and the ecological environment has deteriorated further due to the combined effect of climate change and human activities.

“…researchers indicate that the total amount of groundwater recharge is maintained at 31 million m 3 /year, whereas the amount of groundwater exploitation is maintained at approximately 40 million m 3 /year in the study area [31]. Since the development of the oasis in the 1970s, overexploitation has been observed in the region [32]. Subsequently, groundwater levels have been dropping continuously with a cumulative depth of decline that exceeds 12 m [33].…”

Section: Study Areamentioning

confidence: 99%

Groundwater Level Prediction for the Arid Oasis of Northwest China Based on the Artificial Bee Colony Algorithm and a Back-propagation Neural Network with Double Hidden Layers

Zheng

et al. 2019

Self Cite

Groundwater is crucial for economic and agricultural development, particularly in arid areas where surface water resources are extremely scarce. The prediction of groundwater levels is essential for understanding groundwater dynamics and providing scientific guidance for the rational utilization of groundwater resources. A back propagation (BP) neural network based on the artificial bee colony (ABC) optimization algorithm was established in this study to accurately predict groundwater levels in the overexploited arid areas of Northwest China. Recharge, exploitation, rainfall, and evaporation were used as input factors, whereas groundwater level was used as the output factor. Results showed that the fitting accuracy, convergence rate, and stabilization of the ABC-BP model are better than those of the particle swarm optimization (PSO-BP), genetic algorithm (GA-BP), and BP models, thereby proving that the ABC-BP model can be a new method for predicting groundwater levels. The ABC-BP model with double hidden layers and a topology structure of 4-7-3-1, which overcame the overfitting problem, was developed to predict groundwater levels in Yaoba Oasis from 2019 to 2030. The prediction results of different mining regimes showed that the groundwater level in the study area will gradually decrease as exploitation quantity increases and then undergo a decline stage given the existing mining condition of 40 million m3/year. According to the simulation results under different scenarios, the most appropriate amount of groundwater exploitation should be maintained at 31 million m3/year to promote the sustainable development of groundwater resources in Yaoba Oasis.