•Subsurface runoff dominates the runoff components in karst hillslope.•Thinner soils decrease surface runoff and increase subsurface runoff.•Bedrock topography controlled subsurface runoff generation ...in the thinner soils.•Thinner soils had a higher contribution of new water than that in the thick soils.
Hydrological processes in the critical zone are closely related to the soil–bedrock structures. However, the effect of soil thickness on the rainfall-runoff relationship on the hillslope with complex topography remains unclear. Surface runoff, lateral subsurface runoff from soil–epikarst interface, and epikarst runoff from the epikarst–bedrock interface were monitored on two adjacent plots with deep and shallow (66.0 vs. 35.4 cm) soil thicknesses from June 2019 to December 2020 in the karst region of southwest China. During the monitoring period, surface and subsurface runoff account for 20% and 37% of the total runoff in the deep-soil plot (DSP), and 3% and 43% in the shallow-soil plot (SSP). This demonstrates that runoff from the soil-epikarst system is predominant compared to the relatively small contribution of surface runoff. In the SSP, the surface topography wetness index (TWI) was highly coupled with bedrock TWI, and the bedrock TWI had a significant negative linear relationship (p < 0.01) with subsurface runoff. Moreover, isotope hydrogen-separation results showed that subsurface and epikarst runoff were dominated by pre-event water, but a higher contribution of event water was observed in the SSP than in the DSP. These findings supported the hypothesis that rainwater could infiltrate the epikarst more easily in shallow soil slopes. Rainfall and surface runoff exhibited a linear relationship in the dry season and a non-linear relationship in the rainy season, indicating the occurrence of threshold rainfall–runoff behavior. The rainfall amount threshold for surface runoff was higher in DSP (44.7 mm) than in SSP (39.5 mm), and the corresponding variation of rainfall intensity interpretation was greater (54% vs. 38%). For subsurface runoff, the rainfall amount threshold was higher in the DSP than in the SSP (91.0 vs 79.4 mm), and the corresponding variation of soil moisture interpretation was higher (56% vs. 20%). This demonstrated that runoff can be better predicted at deeper soil hillslopes by rainfall and antecedent soil moisture. Accordingly, this study emphasizes the importance of evaluating the spatial heterogeneity of soil thickness in hydrological process research.
•Subsurface runoff was generated by the “fill and spill” mechanism.•Overland flow generation via an “infiltration-excess and saturation-excess” mechanism.•Steady infiltration rate of the epikarst was ...determined (40 mm/h).•A conceptual model to describe the runoff generation on karst slope was developed.
Southwest China receives abundant rainfall with a mean annual precipitation of 1450 mm (1960–2013) but surface runoff is small, whereas subsurface runoff is relatively large on karst hillslopes. However, not enough studies have been done to investigate the mechanisms of surface and subsurface runoff generation in subtropical karst landscapes. Here we report the dynamics of soil water content (SWC), instantaneous water levels at the soil-epikarst interface (SEI), and runoff characteristics related to the mechanisms of near-surface runoff generation at the slope scale (5 m × 20 m). Four field rainfall simulation experiments were conducted with rainfall intensities ranging from 35 to 136 mm h-1. Subsurface saturation started first at the relatively flat lower slope, and then extended up slope. Subsurface runoff began after subsurface saturated areas connected to each other, representing a “fill-and-spill” mechanism. Surface runoff, which mainly developed after instantaneous water levels reached near the surface, represents an “infiltration-excess and saturation-excess” runoff mechanism, where two thresholds must be attained: rainfall amount and intensity. The rainfall amount threshold is dependent on soil water deficit, water capacity of the epikarst-surface depression at the SEI, and deep percolation from SEI. The rainfall intensity threshold must be larger than the steady infiltration rate of SEI, which is the prerequisite for the saturation of the epikarst-surface depression and soil layer. Steady SEI infiltration rate was estimated (40 mm h-1) according to the surface runoff generation mechanism. This parameter is important as it represents the lower boundary condition in modeling hillslope hydrological processes. Rainfall-runoff thresholds for surface and subsurface runoff decrease with increasing rainfall intensity. Overall, our results show that epikarst permeability along karst hillslopes is relatively high, being the main factor controlling surface and subsurface runoff generation. Therefore, epikarst permeability significantly affects near-surface hydrological processes in karst landscapes. Our data contribute to a more comprehensive understanding of runoff generation processes and water cycle in the critical zone.
Five large and many small landslides are developed in Jurassic strata along the lower reaches of Xiangxi River, where interbedded weak and hard bedrock layers foster the development of landslides ...with a “stair-step” sliding surface. The paper investigates the evolution characteristics of these landslides and presents a novel forecasting model for their displacements. The distribution characteristics and behavior of landslides developed along Xiangxi River is revealed by the database of landslides in the larger Zigui basin, of which this area is part. Most landslides occur at rather low elevations of <300 m and in areas of moderate rainfall. The geological evolution of landslides in the Xiangxi River valley can be divided into four stages, beginning with anticline formation, followed by valley incision, then by weathering and erosion, and culminating in formation of the colluvial landslides. The accumulative displacement curves of landslides with a stair-step sliding surface in Xiangxi River region also present obvious, step-like characteristics. A novel GA-CEEMD-RF algorithm was developed to predict the displacement of these stair-step landslides, which helps to define the combination of induced factors and weak stableness of prediction results using a single displacement prediction model and the multi-field monitoring data.
•The distribution characteristics of landslides developed along lower reaches of Xiangxi River was revealed.•Interbedded weak and hard bedrock layers foster the development of landslides with a “stair-step” sliding surface.•The geological evolution of landslides in the Xiangxi River valley can be divided into four stages.•A novel GA-CEEMD-RF algorithm was developed to predict the displacement of stair-step landslides.
This work aimed to explore the characteristics of surface electromyography (EMG) signal of golfers’ low back pain and the effect of rehabilitation. Based on the time-varying parameter autoregressive ...model and artificial neural network, ARAN algorithm was constructed, which was compared with the autoregressive moving average (ARMA) algorithm and the convolutional neural network (CNN) algorithm. Then, the established ARAN algorithm was employed to evaluate the characteristics of surface EMG signal of 106 golfers with low back pain. It was found that the accuracy, sensitivity, and specificity of the ARAN algorithm were superior to those of the CNN and ARMA algorithms. The golfer’s Roland-Morris Disability Questionnaire (RMDQ) score after treatment was less than that before treatment (
P
< 0.05). Moreover, there was significant negative correlation between RMDQ score and the mean values of time-varying parameters
a
1 and
a
3 (
P
< 0.05). The RMDQ score had a very obvious positive correlation with the mean values of a
2
,
a
4, and
a
6 (
P
< 0.001) and had a negative correlation with the mean value of
a
5 (
P
< 0.05). To sum up, the time-varying parameters of the surface EMG signal can effectively evaluate the golfer’s low back pain and the effect of treatment and rehabilitation.
Display omitted
•Three TrAdaBoost transfer learning models were constructed to carry out landslide susceptibility (LS) mapping.•Frequency ratio method was introduced to tackle the problem of feature ...dissimilarities.•The landslide inventory in the Wenchuan-Yingxiu area was used to improve the performance of the conventional LS models in the Zigui basin using the TrAdaboost model.•The performance of three TrAdaBoost models and three conventional LS models are compared.
The prediction performance of conventional landslide susceptibility (LS) models is generally limited by the size of samples in the landslide inventory, as unsatisfying performance may be yielded with landslide samples less than certain thresholds. Increasing landslide samples is the most reliable approach for solving this problem, but it can be expensive, time-consuming, and even unable to conduct. In this study, a novel method is presented that improves the performance of the LS models using the TrAdaBoost transfer learning algorithm. The proposed method transfers useful knowledge from one landslide inventory to another to improve the performance of LS models through which the effort to recollect landslide data is reduced. A database involving 373 historical landslide locations and 5 landslide influencing factors (LIFs, the slope angle, slope aspect, altitude, lithology, and curvature) in the study area (Zigui Basin, China) and 4,120 historical landslide locations and the corresponding LIFs in the source area (Wenchuan-Yingxiu area, China) were used for demonstration. The frequency ratio method was used to tackle problems of feature dissimilarities of LIFs between the study area and the source area. And then, with these quantified influencing factors as inputs, the three TrAdaBoost models (with decision trees (DT), support vector machine (SVM), and random forest (RF) as basic learners, namely TrAdaBoost-DT, TrAdaBoost-SVM, and TrAdaBoost-RF, respectively), and three conventional machine learning models (DT, SVM, RF) were adopted for showing the performance of the TrAdaBoost in improving the LS models. The area under the receiver operating characteristic curve (AUC) and the existing landslides were used to evaluate the performance of the LS models. The calculated results show that the AUC values of the DT, SVM, RF, TrAdaBoost-DT, TrAdaBoost-SVM, and TrAdaBoost-RF are 0.73, 0.82, 0.83, 0.80, 0.85, and 0.85, respectively; the landslide prediction accuracies of these models are 69%, 77%, 71%, 74%, 87% and 75%, respectively. Compared to the aforementioned results, when using the landslide inventory in the Wenchuan-Yingxiu area, the AUCs of the DT, SVM and RF increase by 0.07, 0.03 and 0.02, respectively, and the landslide prediction accuracies increase by 5%, 10% and 4%, respectively. The results of this study present that in the uncomplete landslide inventory environment, using the TrAdaBoost model to improve the performance of LS models is promising due to its low costs and the great improvement to LS models.
The construction of the huge Three Gorges reservoir affected a large region, and the resultant geological and environmental impacts have caused global concern. The remarkable, 30-m annual fluctuation ...in the reservoir water level poses a significant threat to slope stability in this area. Four hundred sixty-two landslides were identified in the Zigui basin using historical records, satellite images, field investigations and unmanned aerial vehicle (UAV) observations, enabling the construction of a complete landslide database and distribution map. Three failure modes of landslides in the Zigui basin are used to illustrate the major factors that govern reservoir-induced landslides. The results show that (1) > 99% of identified landslides occur on slopes angle <47°, while >80% occur at elevations below 600 m; (2) Jurassic Niejiashan Formation is highly prone to landslides; (3) Low reservoir levels of 145 m to 155 m greatly reduce slope stability. Based on the information entropy method that use the conditional probability of different influencing factors and principal component analysis as inputs, the relative contributions of various influencing factors are quantified and a landslide susceptibility map was drawn. This susceptibility map helps define the countermeasures that will best reduce fatalities and property losses for areas having different landslide conditions and susceptibilities.
•Database of 462 reservoir-induced landslides in Zigui basin, TGR was created.•Identifying failure modes and quantifying contributions of influential factors.•Conditional probability and information entropy model yields landslide susceptibility.•Susceptibility map, failure modes, and strategies suppressing the slope instability.
•Most of stream water were recharged by underground flow in dolomite catchment.•Epikarst water dominates the recharge of springs and stream in small watershed.•Rock outcrop ratio and karstic degree ...enhance spring’s sensitivity to rainfall.•Subsurface flow controlled the rapid fluctuating of stream discharge in wet season.
Quantifying and understanding recharge behavior of aquifers in complex hydrogeological systems is challenging, which limits our ability to manage water resources in karstic areas. In this study, we analyzed the seasonal recharge sources and processes of a stream, an intermittent spring, and a perennial spring in a small dolomitic catchment. Weekly monitoring of stable isotopes and chemical characteristics and daily hydrological data of these waters was performed in 2017 and 2018. There were broad seasonal variations in rainfall isotopes, with more negative values observed in the wet season and more positive values observed in the dry season, while narrow ranges were observed in spring and stream waters. Such values plotting on a LMWL represented a homogeneous mixing of rainfall without the effective evaporation effect. Hydrograph separation showed that the mean proportion of old water was approximately 94% for the springs and stream, which indicated a mixing mechanism in recharge processes. The mean residence time was approximately 23 weeks for spring 1, 201 weeks for spring 2, and 43 weeks for stream. The significant difference between springs was attributed to the combined effects of relatively higher proportions of rocky outcrops, thinner soil–epikarst, and better karstic development in the aquifer of spring 1, which enhanced the sensitivity to rainfall. The stream was recharged by waters from hillslopes, which mixed extensively in the depression, accompanied by soil–epikarst interaction. However, only approximately 1.5% of total stream flow was recharged by springs annually, and most of the stream was recharged by water through underground paths, based on the discharge analyses. Moreover, stream was recharged by subsurface flows, which were considerably affected by soil, leading to the fluctuating stream discharge characteristics during the wet season. The results suggest that greater attention should be paid to the roles of near-surface soil–epikarst architecture in hydrological processes.
Nitrogen-doped carbon nanotube (N-CNT) arrays were prepared by chemical vapor deposition, using ferrocene as the catalyst precursor and imidazole as the carbon and nitrogen precursor. For the ...reduction of oxygen, the N-CNTs showed excellent electrocatalytic activity in both acidic and alkaline media. The N-CNTs were characterized by scanning and transmission electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, and elemental analysis. The samples had a high nitrogen content (8.54at.%) and a bamboo-like structure, and their activity varied according to the amount of pyridinic nitrogen they contained.
In this work, the effects of the addition of transition metals (Mn, Fe, Co, Ni, Cu) on the structure and performance of the doped carbon catalysts M-PANI/C-Mela are investigated. The results show ...that the doping of various transition metals affected structures and performances of the catalysts significantly. Doping with Fe and Mn leads to a catalyst with a graphene-like structure, and doping with Co, Ni, and Cu leads to a disordered or nanosheet structure. The doping of transition metals can enhance the performance of the catalysts, and their ORR activity follows the order of Fe > Co > Cu > Mn > Ni, which is consistent with the order of their active N contents. We suggest that the various performance enhancements of the transition metals may be the result of the joint effect of the following three aspects: the N content/active N content, metal residue, and the surface area and pore structure, but not the effect of any single factor.
