The Badain Jaran desert in western Inner Mongolia in China has a unique landscape that contains 72 lakes, with a total water surface area of 23 km2, and the world's highest stationary sand dunes, which are up to 500 m tall--despite the prevailing dry and windy weather conditions. Here we present evidence of a major groundwater system that underpins the factors leading to this landscape. Our finding could transform plans for the region's water resources.

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Field observations demonstrate that calc-sinters occurred in the lakes of Badain Jaran Desert. 87 Sr/ 86 Sr ratios, 14 C, δ 13 C and mineral compositions of calc-sinters, and 3 He/ 4 He, 4 He/ 20 Ne, δ 18 O, δ D, pH and TDS of water from springs and lakes are analyzed in detail. The results indicate that the lake water is supplied through deep fault zone. The "kernel" of stabilized dunes in the Badain Jaran Desert perhaps consists of calc-sinters and calcareous cementation layers. Deep-seated groundwater effuses from this "kernel" and recharges to lakes in desert. Precipitation and snowmelt water from the Qinghai-Tibet Plateau is fed into the Badain Jaran Desert, Gurinai, Wentougaole and Ejinqi areas through the Xigaze-Langshan Fault zone. The isotopic compositions of groundwater in the Alax Plateau are abnormal due to the strong evaporation of the Gyaring and Ngoring lake water in the headstream of the Yellow River. Groundwater dissolves dissoluble fractions of rocks during its transportation through the fault zone and flows out of the mouth of spring in the Badan Jaran Desert. The dissoluble fractions are finally developed into calc-sinters and calcareous cementation layers around the spring. Calci-sinters are gradually largened and eventually emerge on the surface of lake water. Eolian sands accumulate on the surfaces of calc-sinters and calcareous cementation layers, and eventually develop into dunes. Invasion of magma causes an increase in the temperature of groundwater within the faults. Groundwater evaporation provides water vapor for the formation of humid stabilized dunes during its upwelling. Rhizoconcretions found in Yihejigede indicate that the dune was formed and remained immovable 4700 years ago. The height of the megadunes is proportional to thermal quantity carried by the groundwater.

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The main purpose of this paper is to use ensembles techniques of functional tree-based bagging, rotation forest, and dagging (functional trees (FT), bagging-functional trees (BFT), rotation forest-functional trees (RFFT), dagging-functional trees (DFT)) for landslide susceptibility modeling in Zichang County, China. Firstly, 263 landslides were identified, and the landslide inventory map was established, and the landslide locations were randomly divided into 70% (training data) and 30% (validation data). Then, 14 landslide conditioning factors were selected. Furthermore, the correlation analysis between conditioning factors and landslides was applied using the certainty factor method. Hereafter, four models were applied for landslide susceptibility modeling and zoning. Finally, the receiver operating characteristic (ROC) curve and statistical parameters were used to evaluate and compare the overall performance of the four models. The results showed that the area under the curve (AUC) for the four models was larger than 0.74. Among them, the BFT model is better than the other three models. In addition, this study also illustrated that the integrated model is not necessarily more effective than a single model. The ensemble data mining technology used in this study can be used as an effective tool for future land planning and monitoring.

This paper focuses on landslide susceptibility prediction in Nanchuan, a high-risk landslide disaster area. The evidential belief function (EBF)-based function tree (FT), logistic regression (LR), and logistic model tree (LMT) were applied to Nanchuan District, China. Firstly, an inventory with 298 landslides was compiled and separated into two parts (70%: 209; 30%: 89) as training and validation datasets. Then, based on the EBF method, the Bel values of 16 conditioning factors related to landslide occurrence were calculated, and these Bel values were used as input data for building other models. The receiver operating characteristic (ROC) curve and the values of the area under the ROC curve (AUC) were used to evaluate and compare the prediction ability of the four models. All the models achieved good results and performed well. In particular, the LMT model had the best performance (0.847 and 0.765, obtained from the training and validation datasets, respectively). This paper also demonstrates the superiority of integration and optimization of models in landslide susceptibility evaluation. Finally, the best classification method was selected to draw landslide susceptibility maps, which may be helpful for government administrators and engineers to carry out land design and planning.

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