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Soil salinization leads to a significant degradation for oasis land. Variations in soil salinity are controlled by geologic, geomorphic, climatic, and hydrologic factors that are scale dependent. Many factors characterize soil salinity and its variations. Empirical mode decomposition (EMD) and correlation analysis are usually applied to examine the variations of soil salinity at different scales. However, few researches have been conducted on the modeling of these variations among different scales that requires further development and refinement. This paper investigates the potential of assessing scale‐specific variations in soil salinity via EMD using 2 modeling approaches (random forest and linear models). The remotely sensed environmental factors including land surface temperature, evapotranspiration, Tropical Rainfall Measuring Mission precipitation, and digital elevation model products were used as inputs for the models. The Junggar Basin in Xinjiang Province of China was selected as study area because the oasis was quite typical in the whole country and even in mid‐Asia. Soil salinity data and environmental factors were first decomposed using the EMD algorithm. Then, the decomposed components of the remotely sensed environmental factors were evaluated, and the most important components of each factor were selected to model the salinity variations that were represented by the decomposed components of soil salinity. The salinity variations estimated from the environmental factors were favorably consistent with the decomposed components of the soil salinity data, with coefficients of determination (R2) ranging from 0.24–0.52 and 0.73–0.98 for the linear and random forest models, respectively. In addition, land surface temperature and salinity were coupled well at the 326‐ and 334‐km scale. Our results showed that reasonably accurate results can be obtained using the proposed approach.

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Qiandao Lake (Xin'an Jiang reservoir) plays a significant role in drinking water supply for eastern China, and it is an attractive tourist destination. Three multivariate statistical methods were comprehensively applied to assess the spatial and temporal variations in water quality as well as potential pollution sources in Qiandao Lake. Data sets of nine parameters from 12 monitoring sites during 2010-2013 were obtained for analysis. Cluster analysis (CA) was applied to classify the 12 sampling sites into three groups (Groups A, B and C) and the 12 monitoring months into two clusters (April-July, and the remaining months). Discriminant analysis (DA) identified Secchi disc depth, dissolved oxygen, permanganate index and total phosphorus as the significant variables for distinguishing variations of different years, with 79.9% correct assignments. Dissolved oxygen, pH and chlorophyll-a were determined to discriminate between the two sampling periods classified by CA, with 87.8% correct assignments. For spatial variation, DA identified Secchi disc depth and ammonia nitrogen as the significant discriminating parameters, with 81.6% correct assignments. Principal component analysis (PCA) identified organic pollution, nutrient pollution, domestic sewage, and agricultural and surface runoff as the primary pollution sources, explaining 84.58%, 81.61% and 78.68% of the total variance in Groups A, B and C, respectively. These results demonstrate the effectiveness of integrated use of CA, DA and PCA for reservoir water quality evaluation and could assist managers in improving water resources management.

Abstract:The eastern coastal region