Sen Zhang

Rock glacier inventories offer essential baseline information for assessing mountain permafrost, hydrological storage, and climate change impacts in high mountain regions. Here, an updated rock glacier inventory for the Eastern Himalaya is presented. Rock glacier outlines were identified using a deep learning segmentation model UNetFormer and subsequently refined through manual improvement. The resulting inventory is consistent with the baseline and practical concepts of the International Permafrost Association Action Group Rock Glacier Inventories and Kinematics guidelines and provides standardized attributes describing location and related metadata. In total, the inventory comprises 10,552 mapped rock glaciers covering 587.78 km² and is intended to support the assessment of mountain permafrost, as well as cryospheric and hydrological processes.

The Campania region (southern Italy) is characterized by a widespread hydrogeological risk due to the presence of many slopes covered by pyroclastic deposits derived from the activity of the Vesuvius and Campi Flegrei volcanic complexes. Indeed, numerous areas in the region are prone to rainfall-induced landslides—particularly shallow landslides and debris flows—which are often triggered by short-duration, high-intensity precipitation events. Over the years, the region has been affected by severe landslide events, causing loss of lives and economic damage. In Campania, a territorial landslide early warning system (Te-LEWS) has been operational since 2005 managed by the regional department for Civil Protection. For early warning purposes, the regional territory is divided into eight distinct warning zones according to the following factors: hydrography, morphology, climate, geology, land use and administrative boundaries. The predictions of a weather numerical model are used to evaluate the possible occurrence of rainfall-induced landslides within each warning zone. The daily assessment of the criticality is established by comparing the weather forecasts to a set of thresholds associated with rainfall precursors.In the scientific literature it is widely recognized that rainfall primarily acts as a triggering mechanism, while hydrological variables (e.g., soil moisture) control slope predisposition to failure. Therefore, an evaluation that neglects antecedent hydrological conditions may result in a high number of false alarms, limiting the reliability and credibility of rainfall-only warning models. In recent years, a growing number of weather and hydrological reanalysis products have been produced at fine temporal and spatial resolutions, allowing the potential use of soil moisture data into operational Te-LEWS. This study proposes a hydrometeorological approach integrating meteorological and hydrological information and testing its performance in a landslide-susceptible area of the Campania region, southern Italy. A two-dimensional Bayesian analysis is employed to quantify the conditional probability of landslide occurrence and to derive multiple hydro-meteorological thresholds associated with increasing warning levels. The performances of the warning models are assessed by means of statistical indicators to identify the best-performing combination of hydro-meteorological thresholds. Finally, the potential added value of incorporating soil moisture into territorial landslide warning models is assessed by comparing the hydro-meteorological model developed in this study with the current regional warning system in a real-case scenario.

The risk of rainfall-induced landslides is expected to rise as climate change intensifies and increases the frequency of extreme precipitation. In this context, territorial landslide early warning system (Te-LEWS) represent effective non-structural measures for landslide risk mitigation at regional scale. Currently, most operational territorial warning models worldwide are based on rainfall thresholds. Since a trigger-cause conceptual framework of hydro-meteorological thresholds was proposed, a growing number of studies report that such thresholds outperform conventional rainfall thresholds. Nevertheless, hydro-meteorological thresholds have rarely been implemented in operational Te-LEWSs, because real-time monitoring of hydrological variables require dense in-situ networks, whereas the use of satellite/reanalysis products is constrained by latency.Recently, the availability of weather and hydrological forecast products allows incorporating soil moisture information into an operational Te-LEWS. In this work, we present an operational hydro-meteorological warning model developed employing multiple hydro-meteorological thresholds derived from a probabilistic analysis, using soil saturation and precipitation data retrieved from the ERA5-Land product for one of the warning zones defined by Civil Protection for landslide risk management in Campania region, Italy. The performance of the developed model was demonstrated using the real-time forecasts from the Integrated Forecasting System High-Resolution (IFS-HRES) product and compared with the rainfall-only warning model currently operational in Campania in the period 2021–2024. The performance demonstration highlights that the hydro-meteorological model outperforms the regional model, reducing false alarms by 4.1% and shortening the duration of first warning levels not associated to landslides. In addition, the hydro-meteorological model decreases missed alarms by 1.2% and detects a large landslide event missed by the regional model.

Water depth in shallow marine environments is a fundamental parameter for oceanographic research and coastal engineering applications. High-resolution satellite imagery and long-term medium-resolution imagery offer significant potential for detailed bathymetric mapping and monitoring spatiotemporal variations in bathymetry. However, most of these images contain only three visible bands (blue, green, and red), making bathymetric mapping from such images challenging in practical applications. For the empirical approach, high-quality in situ depth calibration data, which are essential for establishing a reliable empirical bathymetric model, are either unavailable or excessively expensive. For the physics-based approach, images containing only three visible bands can be problematic in accurately deriving depths. To address this limitation, this study proposes a novel semi-analytical-empirical hybrid model for water depth retrieval. The core of the proposed method is the integration of a semi-analytical model with a physics-based dual-band model. This integration quantifies the relative depth relationships among pixels and uses them as a physical constraint. Through this constraint, the method identifies physically reliable depth estimates from the multiple numerical solutions of the semi-analytical model for a subset of shallow-water pixels, which then serve as an in situ–free calibration dataset. This dataset is subsequently used to determine the empirically based optimal retrieval model, which is finally applied to generate the complete bathymetric map. The results from four typical coral reef regions—Buck Island, Yongxing Island, Kaneohe Bay, and Yongle Atoll—demonstrated that the proposed model achieved root-mean-square errors (RMSE) of 0.98–1.62 m, mean absolute errors (MAE) of 0.73–1.13 m, and coefficients of determination (R²) of 0.91–0.95 in comparison to in situ measurements. Compared to both the physics-based dual-band model and the L-S model (i.e., the bathymetry mapping approach combining Log-ratio and Semi-analytical models), the proposed model reduced the RMSE by 9–55%, reduced the MAE by 4–56%, and improved the R² by 0.01–0.29. Additionally, the accuracy of the proposed model surpasses that of both the physics-based dual-band model and the L-S model across all depth intervals, particularly in deeper depth waters (>15 m). This study offers a robust solution for bathymetric mapping in areas lacking in situ depth data and contributes significantly to advancing optical remote sensing techniques for underwater topography detection.

The Lueyang and Xunyang counties in the Shaanxi province (China) are highly susceptible to rainfall-induced landslides. Rainfall thresholds are the most used tool to predict the occurrence of rainfall-induced landslides over large areas. However, the definition of robust thresholds may be difficult for unbalanced datasets, for which the number of non-landslide observations is much higher than the number of landslide observations. This study aims at defining adequate rainfall thresholds for the two study areas using landslide datasets that are strongly unbalanced in terms of occurrences vs. non-occurrences. Two types of rainfall thresholds are determined using a frequentist method at several non-exceedance and exceedance probabilities, separately considering rainfall events responsible for landslides (positive thresholds) and rainfall events not responsible for landslides (negative thresholds). The comparison between the two sets of thresholds shows that the method based on non-triggering events allows defining rainfall thresholds characterized by lower uncertainties and a better performance than the ones defined considering the triggering events, in both the study areas. In particular, the best-performing thresholds are the negative threshold defined at 15% exceedance probability for the Lueyang county and the negative threshold defined at 20% exceedance probability for the Xunyang county.

Earthquake disasters are frequent, and the seismic intensity is large in Northeast China. Earthquake activity research is an important aspect of earthquake disaster management. We chose some unconventional means to study fault activity, to find updated activity evidence. The Ms 5.3 earthquake occurred near the Fuyu North Fault (FNF) of China on May 27, 2018. Using the Sentinel-1B descending orbit data from 2016 to 2019, the line-of-sight (LOS) surface deformation in the study area was calculated by using the small baseline subset (SBAS) method. After transforming to the horizontal EW deformation, the variance component estimation method was used for fusion reconstruction with the EW data of the surrounding GNSS stations. The polynomial least square method is used to fit the fault slip rate of three EW data on the surface trace of the FNF. The fitting results of the three regions show that the horizontal eastward distribution rate of the upper plate is significantly greater than that of the lower plate, which is left-lateral clockwise torsion. The vertical structural deformation caused by the growth strata of the upper and lower plates of the upper SYT2 seismic profile of the FNF is quantitatively calculated, and the thrust rate of the upper plate is 0.2 mm/y relative to that of the lower plate. Based on the Li Siguang chessboard structure model, we found that the compression stress in the north-south direction is gradually weakened, and the compression stress in the east-west direction is gradually enhanced. Through the Coulomb stress analysis, the three events of CMT only induced the historical focal location of the surrounding part. The events of 2017 did not induce the events of 2018, but the events of 2019 were related to the induced effects of 2017 and 2018.

The Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model has been widely used to define rainfall thresholds for triggering shallow landslides. In this study, the rainfall intensity(I)-duration(D) thresholds for multiple slope units of an area in Pu’an County, Guizhou Province, China were defined based on TRIGRS. Given that TRIGRS is used to simulate the slope stability under the conditions of a given increasing sequence of I-D data, if the slope reaches instability at I = a, D = b, it will also become unstable in the case of I = a, D > b or I > a, D = b. To explore the effect of these I-D data with the same I or D values on the definition of I-D thresholds and the best method to exclude these data, two screening methods were used to exclude the I-D data that caused instability in the TRIGTS simulation. First, I-D data with the same I values when D values are greater than a certain limit value were excluded. Second, several D values were selected to exclude I-D data with the same I values for a slope unit. Then, an I value was selected to exclude I-D data with the same D values. After screening, two different I-D thresholds were defined. The comparison with the thresholds defined without screening shows that the I-D data with the same I or D values will reduce the accuracy of thresholds. Moreover, the second screening method can entirely exclude these data.

The development of advanced and efficient methods for mapping and monitoring wetland regions is essential for wetland resources conservation, management, and sustainable development. Although remote sensing technology has been widely used for detecting wetlands information, it remains a challenge for wetlands classification due to the extremely complex spatial patterns and fuzzy boundaries. This study aims to implement a comprehensive and effective classification scheme for wetland land covers. To achieve this goal, a novel object-based multigrained cascade forest (OGCF) method with multisensor data (including Sentinel-2 and Radarsat-2 remote sensing imagery) was proposed to classify the wetlands and their adjacent land cover classes in the wetland National Natural Reserve. Moreover, a hybrid selection method (ReliefF-RF) was proposed to optimize the feature set in which the spectral and polarimetric decomposition features are contained. We obtained six spectral features from visible and shortwave infrared bands and 10 polarimetric decomposition features from the H/A/Alpha, Pauli, and Krogager decomposition methods. The experimental results showed that the OGCF method with multisource features for land cover classification in wetland regions achieved the overall accuracy and kappa coefficient of 88.20% and 0.86, respectively, which outperformed the support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and deep neural network (DNN). The accuracy of the wetland classes ranged from 75.00% to 97.53%. The proposed OGCF method exhibits a good application potential for wetland land cover classification. The classification scheme in this study will make a positive contribution to wetland inventory and monitoring and be able to provide technical support for protecting and developing natural resources.

The attention towards links of atmospheric parameter variation and earthquakes has increased exponentially by utilizing new methods and more accurate observations. Persistent research makes it possible to gain insight into the precursor mechanism of earthquakes. In this paper, we studied the universality of detecting atmospheric anomalies associated with earthquakes based on tidal force fluctuation in China for earthquakes of Ms > 6.0, and explored the influence of tidal force on tectonic stress. The data of air temperature, geopotential height, ozone mixing ratio, and relative humidity from the National Center for Environmental Prediction (NCEP) were analyzed to reveal the spatiotemporal variation of atmospheric anomalies at multiple isobaric surfaces. Furthermore, the coupling of atmospheric parameters was investigated. The results showed that continuous solicitation exerted by tidal forces could change the strength of tectonic stress that causes earthquakes. The evolution pattern of air temperature, geopotential height, and relative humidity could be supported by atmospheric thermal vertical diffusion, while the anomalies of ozone mixing ratio was not evident. This verified the feasibility of detecting multi-parameter atmospheric anomalies associated with earthquakes based on tidal force fluctuation. Our results provide more evidence for understanding the atmospheric precursor characteristics of earthquakes.