Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title><jats:p>The infilling rate of hollows is one of the primary factors affecting the recurrence interval of shallow landslides. Although many studies have focused on colluvium in hollows under various regional settings, few have directly estimated the infilling rates from radiocarbon dating and airborne LIDAR digital elevation models (DEMs) in shallow landslide scars. In this work, we analysed the radiocarbon dates of charcoal in the colluvium in the granitic mountains of Hiroshima and Hofu, where heavy rainfall has caused multiple shallow landslides in recent years. A total of 27 samples were collected from landslide scars in seven hollows, and the infilling rates were inferred from the calibrated radiocarbon ages and pre‐landslide depth of the samples estimated from the DEMs before the landslide event. Topographic parameters including local slope gradient and size of the source area were also measured using the DEMs. Our findings showed that the calibrated radiocarbon ages of colluvium in hollows ranged from about 350 to 1700 cal BP, and the infilling rates ranged from about 0.2 ± 0.2 to 5.4 ± 0.9 mm/y. In the two hollows with limited human impacts, the infilling rates were relatively low, and there is a trend of increase with increasing source‐area size and topographic curvature. In contrast, infilling rates were very high at two hollows with intensive human impacts where active surface erosion occurred from the 17th to the early 20th century. Three hollows located around the ruins of a medieval castle near the drainage divides of the source area also had relatively high infilling rates. For the hollows with intensive human impacts, the infilling rates were not controlled by topographic attributes such as source‐area size and curvature.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Variations of water discharge and sediment load in the Red River basin have received considerable attention due to its drastic reduction during the past several decades. This paper presents a more specifically investigating of the seasonal variations in water discharge and sediment load from 1958 to 2021, both before and after the impoundment of all large dam‐reservoirs, using daily observations from the Son Tay hydrological gauging station, the outlet of the Red River system and entry to the delta. Sediment loads have decreased progressively since the early 1990s due to sediment yield reduction and dams in the upper basin, with a reduction of about 91% (from 116 × 10<jats:sup>6</jats:sup> to 11 × 10<jats:sup>6</jats:sup> t/year) over the 64‐year observation period. Prior to the impoundment of the Hoa Binh dam‐reservoir in 1988, the hydrological processes in the Red River system exhibited seasonal anomalies (clockwise mode on the hysteresis of rating curve), which implies that sediment load is highly proportional to water discharge and precipitation. The hysteresis loops between mean monthly water discharge and suspended sediment concentration after 1988 were altered by tributary dam‐reservoirs and a phenomenon known as ‘temporal monsoon moving’, which shifted the rating curve from clockwise to counterclockwise mode. Our long‐term analysis indicates that approximately 57.5% and 79% of sediments were trapped during the periods 1989–2008 (after Hoa Binh dam‐reservoir impoundment) and 2009–2021 (a series of new dam‐reservoirs went into operation), respectively, primarily during the high‐discharge months (June–October). Additionally, we concluded that the contribution of climate components (e.g., rainfall) to the dramatic decline in sediment load of the Red River system was less than the human impact.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Landslide susceptibility mapping (LSM) enables the prediction of landslide occurrences, thereby offering a scientific foundation for disaster prevention and control. In recent years, numerous studies have been conducted on LSM using machine learning techniques. However, the majority of machine learning models is considered “black box” models due to their lack of transparent explanations. In contrast, the QLattice model serves as a white box model, as it can elucidate the decision‐making mechanism while representing a novel approach to whitening machine learning models. QLattice possesses the capability to automatically select and scale data features. In this study, Fengjie County in China was selected as the research area, with slope units serving as evaluation units. A geospatial database was constructed using 12 conditioning factors, including elevation, slope, and annual average rainfall. LSM models were conducted using both the QLattice and random forest (RF) algorithms. The findings demonstrate that the QLattice model achieved an area under curve value of 0.868, while the RF model attained an area under curve value of 0.849 for the test datasets. These results highlight the superior predictive ability and stability of the QLattice model compared with RF. Furthermore, QLattice can explicate and clarify the change processes of conditioning factors, thereby revealing the internal decision‐making mechanism and causes behind the LSM model's decisions. The innovative QLattice‐based model provides new ideas and methodologies for LSM research.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Soil formation in Mediterranean periglacial karst landscapes remains poorly understood as the interplay between local and allochthonous sources of parent materials, and mineral alteration and pedogenesis, as dominant post‐depositional processes, depends on a variety of climatic and environmental factors. Herein, we investigate the balance between erosion and aeolian dust accretion in the formation of an alpine soil profile in the periglacial zone of Mount Olympus in Greece. We applied a wide range of analytical methods to 23 samples, from a soil profile developed in a glaciokarstic plateau, from colluvial sediment horizons interbedded in postglacial scree slopes of different maturity and formation age and from modern Sahara dust samples deposited on the snowpack. Colluvial sediment horizons exhibit high concentrations of carbonate gravel and calcite‐rich sand and represent the local erosion products. The soil B horizon developed on the glaciokarstic plateau, contains high amounts of fine earth and is rich in quartz, mica, plagioclase, clays and Fe–Ti oxides. Based on physical and textural characteristics, the soil profile is partitioned in a surficial weathered Bw and an illuvial Bt horizon that overlies the local regolith composed of fragmented glacial till and slope wash deposits. Radiogenic isotope systematics, textural and mineralogical analysis show that the contribution of aeolian (Sahara and locally sourced) dust to the development of the soil B horizon ranges between 50% and 65%. Cryoturbation results in fine earth translocation from Bw to the Bt horizon, whereas weak pedogenetic modifications of detrital (aeolian and bedrock‐derived) minerals result in magnetic mineral weathering and secondary clay formation. Our findings reveal that aeolian dust accretion from Saharan and lcal sources is the dominant process in providing alpine soil parent material and that cryoturbation, weak pedogenesis and clay mineral alteration occur within the Mediterranean periglacial zone of Mount Olympus.</jats:p>