Model Prediction of Secondary Soil Salinization in the Keriya Oasis, Northwest China

Abstract

Significant anthropogenic and biophysical changes have caused fluctuations in the soil salinization area of the Keriya Oasis in China. The Driver-Pressure-State-Impact-Response (DPSIR) sustainability framework and Bayesian networks (BNs) were used to integrate information from anthropogenic and natural systems to model the trend of secondary soil salinization. The developed model predicted that light salinization (vegetation coverage of around 15-20%, soil salt 5-10 g/kg) of the ecotone will increase in the near term but decelerate slightly in the future, and that farmland salinization will decrease in the near term. This trend is expected to accelerate in the future. Both trends are attributed to decreased water logging, increased groundwater exploitation, and decreased ratio of evaporation/precipitation. In contrast, severe salinization (vegetation coverage of around 2%, soil salt ≥20 g/kg) of the ecotone will increase in the near term. This trend will accelerate in the future because decreased river flow will reduce the flushing of severely salinized soil crust. Anthropogenic factors have negative impacts and natural causes have positive impacts on light salinization of ecotones. In situations involving severe farmland salinization, anthropogenic factors have persistent negative impacts.

Landscape Design as a Tool to Reduce Soil Salinization: The Study Case of Keriya Oasis (NW China)

“…The dynamics of Keriya Oasis's landscape indicated that the oasis ecosystem was fragile, and it had experienced expansion and shrinking in slightly salinized areas. Excessive water logging at the eco-tone area had caused farmland salinization and even abandonment [5,7,8,30].…”

“…The secondary soil salinization is a very complex problem [8,[13][14][15]31,32]. Firstly, in order to make clear the mechanism of salinity hazards [8,9], this study built the socio-ecological and bio-physical conceptual models (Supplementary Materials) in the DPSIR frame [33] by a selected 39 variables [7,8,10,18,19,31,[34][35][36]. Secondly, collected spatial and cross sectional hydro-geological data from various sources, and based on the built hydrological baseline map ( Figure 7) to desalinization design.…”

“…To produce this mapin ArcGIS10.1, theLandsat-8 ETM+, GTOP30/DEM (resolution 30 m, cloud 0%, download date:21 July 2015) [37] were used for determine the thematic map of drainage, water bodies, river, farmland, basic landscape, contours, etc. [8,9],then the thematic map of salinization risk spatial distribution was produced from Seydehmet's study [9],finally, fold above together form the spatial hydro-geological map. The groundwater depth of 24 groundwater observation wells (sampling range of about 4-8 km, drilled in April 2012) were measured by using a HOBO barometric sensor.…”

“…Generally, it creates a natural drainage ditch in the Gobi area by eroding the earth surface. Meanwhile, it leads to bed elevation of the pit area by depositing mud sedimentation in the lower reaches of its path [8]. According to stakeholders, once a natural drainage system has been created, a few villages will benefit from it, particularly in the reference landscape and in the southwest part of the oasis area (Figure 7).…”

Comprehensive Analysis of Nitrogen Deposition in Urban Ecosystem: A Case Study of Xiamen City, China

“…For water bodies, Men and Gebremedhin et al [43,44] used the PSR framework to assess the vulnerability of water resources and the sustainability of fish and fisheries, respectively. For soil, Lin [45] used the DPSR framework to investigate the environmental impacts of changes in the agricultural production systems, whereas Seydehmet et al [46] used it to predict light salinization of a soil salinization area in an oasis. For other systems, Sekovski [47] applied the DPSR framework to elaborate on the role of coastal megacities in environmental degradation and their contribution to global climate change, whereas Ingram et al [48] used the DPSR framework to study complex social-ecological interactions using participatory modeling.…”