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Ephedra species are beneficial for environmental protection in desert and grassland ecosystems. They have high ecological, medicinal, and economic value. To strengthen the protection of the sustainable development of Ephedra, we used occurrence records of Ephedra sinica Stapf., Ephedra intermedia Schrenk et C.A. Mey., and Ephedra equisetina Bge., combined with climate, soil, and topographic factors to simulate the suitable habitat of three Ephedra based on ensemble models on the Biomod2 platform. The results of the models were tested using AUC, TSS, and kappa coefficients. The results demonstrated that the ensemble model was able to accurately predict the potential distributions of E. sinica, E. intermedia, and E. equisetina. Eastern and central Inner Mongolia, middle and eastern Gansu, and northeastern Xinjiang were the optimum regions for the growth of E. sinica, E. intermedia, and E. equisetina, respectively. Additionally, several key environmental factors had a significant influence on the suitable habitats of the three Ephedra. The key factors affecting the distribution of E. sinica, E. intermedia, and E. equisetina were annual average precipitation, altitude, and vapor pressure, respectively. In conclusion, the results showed that the suitable ranges of the three Ephedra were mainly in Northwest China and that topography and climate were the primary influencing factors.

Ephedra sinica Stapf. is a shrubby plant widely used in traditional Chinese medicine due to its high level of medicinal value, thus, it is in high demand. Ephedrine (E) and pseudoephedrine (PE) are key medicinal components and quality indicators for E. sinica. These two ephedrine-type alkaloids are basic elements that exert the medicinal effect of E. sinica. Recently, indiscriminate destruction and grassland desertification have caused the quantity and quality of these pharmacological plants to degenerate. Predicting potentially suitable habitat for high-quality E. sinica is essential for its future conservation and domestication. In this study, MaxEnt software was utilized to map suitable habitats for E. sinica in Inner Mongolia based on occurrence data and a set of variables related to climate, soil, topography and human impact. The model parametrization was optimized by evaluating alternative combinations of feature classes and values of the regularization multiplier. Second, a geospatial quality model was fitted to relate E and PE contents to the same environmental variables and to predict their spatial patterns across the study area. Outputs from the two models were finally coupled to map areas predicted to have both suitable conditions for E. sinica and high alkaloid content. Our results indicate that E. sinica with high-quality E content was mainly distributed in the Horqin, Ulan Butong and Wulanchabu grasslands. E. sinica with high-quality PE content was primarily found in the Ordos, Wulanchabu and Ulan Butong grasslands. This study provides scientific information for the protection and sustainable utilization of E. sinica. It can also help to control and prevent desertification in Inner Mongolia.

Accurate spatial population distribution information, especially for metropolises, is of significant value and is fundamental to many application areas such as public health, urban development planning and disaster assessment management. Random forest is the most widely used model in population spatialization studies. However, a reliable model for accurately mapping the spatial distribution of metropolitan populations is still lacking due to the inherent limitations of the random forest model and the complexity of the population spatialization problem. In this study, we integrate gradient-boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient-boosting machine (LightGBM) and support vector regression (SVR) through ensemble-learning algorithm-stacking to construct a novel population-spatialization model we name GXLS-Stacking. We integrate socioeconomic data that enhance the characterization of the population’s spatial distribution (e.g., point-of-interest data, building outline data with height, artificial impervious surface data, etc.) and natural environmental data with a combination of census data to train the model to generate a high-precision gridded population density map with a 100 m spatial resolution for Beijing in 2020. Finally, the generated gridded population density map is validated at the pixel level using the highest resolution validation data (i.e., community household registration data) in the current study. The results show that the GXLS-Stacking model can predict the population’s spatial distribution with high precision (R2 = 0.8004, MAE = 34.67 persons/hectare, RMSE = 54.92 persons/hectare), and its overall performance is not only better than the four individual models but also better than the random forest model. Compared to the natural environmental features, a city’s socioeconomic features are more capable in characterizing the spatial distribution of the population and the intensity of human activities. In addition, the gridded population density map obtained by the GXLS-Stacking model can provide highly accurate information on the population’s spatial distribution and can be used to analyze the spatial patterns of metropolitan population density. Moreover, the GXLS-Stacking model has the ability to be generalized to metropolises with comprehensive and high-quality data, whether in China or in other countries. Furthermore, for small and medium-sized cities, our modeling process can still provide an effective reference for their population spatialization methods.

Groundwater-surface water interactions play a crucial role in hydrologic cycles, especially in arid and semi-arid basins. There is a growing interest in developing integrated hydrologic models to describe groundwater-surface water interactions and the associated processes. In this study, an integrated process-based hydrologic model, ParFlow, was tested and utilized to quantify the hydrologic responses, such as changes in surface runoff and surface/subsurface storage. We progressively conducted a complexity-increasing series of benchmarking cases to assess the performance of ParFlow in simulating overland flow and integrated groundwater-surface water exchange. Meanwhile, the overall performance and the computational efficiency were quantitatively assessed using modified Taylor diagrams. Based on the benchmarking cases, two case studies in the Heihe River Basin were performed for further validation and to diagnose the hydrologic responses under disturbance, named the Bajajihu (BJH) and Dayekou (DYK) cases, respectively. Both cases were 2D transects configured with in-situ measurements in the mid- and downstream of the Heihe River Basin. In the BJH case, simulated soil moisture by ParFlow was shown to be comparable with in-situ observations in general, with Pearson’s correlation coefficient (R) > 0.93 and root mean square difference (RMSD) < 0.007. In the DYK case, seven scenarios driven by remote sensing and reanalysis data were utilized to study hydrological responses influenced by natural physical processes (i.e., precipitation) and groundwater exploitations (i.e., pumping) that are critical to surface and subsurface storage. Results show that subsurface storage is sensitive to groundwater exploitation before an obvious stationary point. Moreover, a correlation analysis was additionally provided demonstrating the impacts of different factors on subsurface storage timeseries. It was found that pumping influences subsurface storage remarkably, especially under short-term but large-volume pumping rates. The study is expected to provide a powerful tool and insightful guidance in understanding hydrological processes’ effects in arid and semi-arid basins.