Inventory maps of earthquake-triggered landslides can be constructed using several methods,which are often subject to obvious differences due to lack of commonly accepted criteria or principles,To ...solve this problem,the author describes the principles for preparing inventory maps of earthquake-triggered landslides,focusing on varied methods and their criteria,The principles include the following key points：all landslides should be mapped as long as they can be recognized from images;both the boundary and source area position of landslides should be mapped;spatial distribution pattern of earthquake-triggered landslides should be continuous;complex landslides should be divided into distinct groups;three types of errors such as precision of the location and boundary of landslides,false positive errors,and false negative errors of earthquake-triggered landslide inventories should be controlled and reduced;and inventories of co-seismic landslides should be constructed by the visual interpretation method rather than automatic extraction of satellite images or/and aerial photographs,In addition,selection of remote sensing images and creation of landslides attribute database are also discussed in this paper,Then the author applies these principles to produce inventory maps of four events：the 12 May 2008 Wenchuan,China M_w 7.9,14 April 2010 Yushu,China M_w 6,9,12 January 2010 Haiti M_w7.0,and 2007 Aysen Fjord,Chile M_w 6.2,The results show obvious differences in comparison with previous studies by other researchers,which again attest to the necessity of establishment of unified principles for preparation of inventory maps of earthquake-triggered landslides.
Climate change and human activities are two major driving forces affecting the hydrologic cycle, which further influence the stationarity of the hydrologic regime. Hydrological drought is a ...substantial negative deviation from the normal hydrologic conditions affected by these two phenomena. In this study, we propose a framework for quantifying the effects of climate change and human activities on hydrological drought. First, trend analysis and change‐point test are performed to determine variations of hydrological variables. After that, the fixed runoff threshold level method (TLM) and the standardized runoff index (SRI) are used to verify whether the traditional assessment methods for hydrological drought are applicable in a changing environment. Finally, two improved drought assessment methods, the variable TLM and the SRI based on parameter transplantation are employed to quantify the impacts of climate change and human activities on hydrological drought based on the reconstructed natural runoff series obtained using the variable infiltration capacity hydrologic model. The results of a case study on the typical semiarid Laohahe basin in North China show that the stationarity of the hydrological processes in the basin is destroyed by human activities (an obvious change‐point for runoff series is identified in 1979). The traditional hydrological drought assessment methods can no longer be applied to the period of 1980–2015. In contrast, the proposed separation framework is able to quantify the contributions of climate change and human activities to hydrological drought during the above period. Their ranges of contributions to hydrological drought calculated by the variable TLM method are 20.6–41.2% and 58.8–79.4%, and the results determined by the SRI based on parameter transplantation method are 15.3–45.3% and 54.7–84.7%, respectively. It is concluded that human activities have a dominant effect on hydrological drought in the study region. The novelty of the study is twofold. First, the proposed method is demonstrated to be efficient in quantifying the effects of climate change and human activities on hydrological drought. Second, the findings of this study can be used for hydrological drought assessment and water resource management in water‐stressed regions under nonstationary conditions.
The deadly threat that landslide has brought about is drawing more and more attention to analyze the mechanisms of landslides and the relationship between landslides and climate change. Due to the ...limited record of historical landslides in developing countries, relevant research is mostly conducted in developed countries. Owing to the publicly available global long time-series Landsat images, such unbalance can be avoided by proposing a practical landslide detection model, especially in terms of national scale. This paper takes the advantage of google earth engine platform to synthesize the annual Landsat images covering the national scale of Nepal into one image and builds an end-to-end contour-based landslide detection deep learning framework. The framework consists of two parts, one is potential landslide detection using vegetation index and degradation of DEM, the other is exact landslide detection using semantic segmentation deep learning model based on the contour regions extracted from the detected potential landslide. The proposed method is applied to detect landslides of Nepal in the year of 2015 and achieves a satisfactory performance with 65% recall and 55.35% precision. The performance is 44% higher accurate than similarly published works, validating its promising applicability in practical landslide detection for national cases.
•Resist the imbalanced distribution of landslides using contour-based method.•Avoid feature engineering by applying deep learning framework.•Enlarge the practical applicability by evaluation in Nepal.
Preparation of landslide susceptibility map is the first step for landslide hazard mitigation and risk assessment. The main aim of this study is to explore potential applications of two new models ...such as two-class Kernel Logistic Regression (KLR) and Alternating Decision Tree (ADT) for landslide susceptibility mapping at the Yihuang area (China). The ADT has not been used in landslide susceptibility modeling and this paper attempts a novel application of this technique. For the purpose of comparison, a conventional method of Support Vector Machines (SVM) which has been widely used in the literature was included and their results were assessed. At first, a landslide inventory map with 187 landslide locations for the study area was constructed from various sources. Landslide locations were then spatially randomly split in a ratio of 70/30 for building landslide models and for the model validation. Then a spatial database with a total of fourteen landslide conditioning factors was prepared, including slope, aspect, altitude, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), plan curvature, landuse, normalized difference vegetation index (NDVI), lithology, distance to faults, distance to rivers, distance to roads, and rainfall. Using the KLR, the SVM, and the ADT, three landslide susceptibility models were constructed using the training dataset. The three resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and five statistical evaluation measures. In addition, pairwise comparisons of the area under the ROC curve were carried out to assess if there are significant differences on the overall performance of the three models. The goodness-of-fits are 92.5% (the KLR model), 88.8% (the SVM model), and 95.7% (the ADT model). The prediction capabilities are 81.1%, 84.2%, and 93.3% for the KLR, the SVM, and the ADT models, respectively. The result shows that the ADT model yielded better overall performance and accurate results than the KLR and SVM models. The KLR model considered slightly better than SVM model in terms of the positive prediction values. The ADT and KLR are the two promising data mining techniques which might be considered to use in landslide susceptibility mapping. The results from this study may be useful for landuse planning and decision making in landslide prone areas.
•Spatial prediction of landslide hazards was carried out by using KLR, ADT, and SVM.•All landslide models have a good prediction capability.•ADT model has better prediction capabilities.
While reliable drought prediction is fundamental for drought mitigation and water resources management, it is still a challenge to develop robust drought prediction models due to complex local ...hydro‐climatic conditions and various predictors. Sea surface temperature (SST) is considered as the fundamental predictor to develop drought prediction models. However, traditional models usually extract SST signals from one or several specific sea zones within a given time span, which limits full use of SST signals for drought prediction. Here, we introduce a new meteorological drought prediction approach by using the antecedent SST fluctuation pattern (ASFP) and machine learning techniques (e.g., support vector regression (SVR), random forest (RF), and extreme learning machine (ELM)). Three models (i.e., ASFP‐SVR, ASFP‐ELM, and ASFP‐RF) are developed for ensemble, probability, and deterministic drought predictions. The Colorado, Danube, Orange, and Pearl River basins with frequent droughts over different continents are selected, as the cases, where standardized precipitation evapotranspiration index (SPEI) are predicted at the 1° × 1° resolution with 1‐ and 3‐month lead times. Results show that the ASFP‐ELM model can effectively predict space‐time evolutions of drought events with satisfactory skills, outperforming the ASFP‐SVR and ASFP‐RF models. Our study has potential to provide a reliable tool for drought prediction, which further supports the development of drought early warning systems.
Key Points
Antecedent sea surface temperature fluctuations are introduced as predictors for meteorological drought prediction
Extreme learning machine predicts droughts more effectively than support vector regression and random forest
The new approach allows for ensemble, probability, and deterministic drought predictions
Large earthquakes initiate chains of surface processes that last much longer than the brief moments of strong shaking. Most moderate‐ and large‐magnitude earthquakes trigger landslides, ranging from ...small failures in the soil cover to massive, devastating rock avalanches. Some landslides dam rivers and impound lakes, which can collapse days to centuries later, and flood mountain valleys for hundreds of kilometers downstream. Landslide deposits on slopes can remobilize during heavy rainfall and evolve into debris flows. Cracks and fractures can form and widen on mountain crests and flanks, promoting increased frequency of landslides that lasts for decades. More gradual impacts involve the flushing of excess debris downstream by rivers, which can generate bank erosion and floodplain accretion as well as channel avulsions that affect flooding frequency, settlements, ecosystems, and infrastructure. Ultimately, earthquake sequences and their geomorphic consequences alter mountain landscapes over both human and geologic time scales. Two recent events have attracted intense research into earthquake‐induced landslides and their consequences: the magnitude M 7.6 Chi‐Chi, Taiwan earthquake of 1999, and the M 7.9 Wenchuan, China earthquake of 2008. Using data and insights from these and several other earthquakes, we analyze how such events initiate processes that change mountain landscapes, highlight research gaps, and suggest pathways toward a more complete understanding of the seismic effects on the Earth's surface.
Plain Language Summary
Strong earthquakes in mountainous regions trigger chains of events that modify mountain landscapes over days, years, and millennia. Earthquake shaking can cause many tens of thousands of landslides on steep mountain slopes. Some of these sudden slope failures can block rivers and form temporary lakes that can later collapse and cause huge floods. Other landslides move more slowly, in some cases in a stop‐start fashion during heavy rains or earthquake aftershocks. Debris from these landslides can clog channels, and during heavy rainfall, the debris can be transported downstream for many kilometers with catastrophic consequences. New landslides tend to happen more frequently than usual for months to years following an earthquake because the strong ground shaking has fractured and weakened the slopes. Other effects of large earthquakes can last, in various forms, over geologic time scales. Over the past two decades, our understanding of these issues has advanced because of the detailed study of the 1999 Chi‐Chi earthquake in Taiwan and the 2008 Wenchuan earthquake in China. We compile and discuss the results of research on these and other earthquakes and explain what we have learned, what we still need to know, and where we should direct future studies.
Key Points
Coupled surface processes initiated by strong seismic shaking are important hazards in mountain landscapes
Earthquake‐induced landslides pose challenges to hazard and risk assessment, management, and mitigation
Multidisciplinary approaches further the understanding of the earthquake hazard cascade, yet challenges remain
Support vector machine (SVM) modeling is based on statistical learning theory. It involves a training phase with associated input and target output values. In recent years, the method has become ...increasingly popular. The main purpose of this study is to evaluate the mapping power of SVM modeling in earthquake triggered landslide-susceptibility mapping for a section of the Jianjiang River watershed using a Geographic Information System (GIS) software. The river was affected by the Wenchuan earthquake of May 12, 2008. Visual interpretation of colored aerial photographs of 1-m resolution and extensive field surveys provided a detailed landslide inventory map containing 3147 landslides related to the 2008 Wenchuan earthquake. Elevation, slope angle, slope aspect, distance from seismogenic faults, distance from drainages, and lithology were used as the controlling parameters. For modeling, three groups of positive and negative training samples were used in concert with four different kernel functions. Positive training samples include the centroids of 500 large landslides, those of all 3147 landslides, and 5000 randomly selected points in landslide polygons. Negative training samples include 500, 3147, and 5000 randomly selected points on slopes that remained stable during the Wenchuan earthquake. The four kernel functions are linear, polynomial, radial basis, and sigmoid. In total, 12 cases of landslide susceptibility were mapped. Comparative analyses of landslide-susceptibility probability and area relation curves show that both the polynomial and radial basis functions suitably classified the input data as either landslide positive or negative though the radial basis function was more successful. The 12 generated landslide-susceptibility maps were compared with known landslide centroid locations and landslide polygons to verify the success rate and predictive accuracy of each model. The 12 results were further validated using area-under-curve analysis. Group 3 with 5000 randomly selected points on the landslide polygons, and 5000 randomly selected points along stable slopes gave the best results with a success rate of 79.20% and predictive accuracy of 79.13% under the radial basis function. Of all the results, the sigmoid kernel function was the least skillful when used in concert with the centroid data of all 3147 landslides as positive training samples, and the negative training samples of 3147 randomly selected points in regions of stable slope (success rate=54.95%; predictive accuracy=61.85%). This paper also provides suggestions and reference data for selecting appropriate training samples and kernel function types for earthquake triggered landslide-susceptibility mapping using SVM modeling. Predictive landslide-susceptibility maps could be useful in hazard mitigation by helping planners understand the probability of landslides in different regions.
►Susceptibility mapping of landslides using support vector machine modeling. ►Provide suggestions for selecting appropriate training samples and kernel functions. ►Be helpful in seismic hazard mitigation and infrastructure planning.
The rise of extreme precipitation events has attracted wide attention throughout the world. However, the analysis of precipitation extremes is not sufficient in the short period of instrumental ...observation and it is complicated by their non‐stationary behaviours. As a period dominated by natural forcing, the last millennium is helpful for understanding long‐term changes in precipitation extremes. Therefore, this study aimed to (1) investigate changes in daily and subdaily precipitation extremes in China from the last millennium to the end of the twenty‐first century, (2) detect the non‐stationarity of precipitation extremes and (3) compare design storms by using stationary and non‐stationary distributions. The annual maximum 1‐day precipitation (RX1day) and annual maximum 3‐hour precipitation (RX3hour) are used to quantify variations of precipitation extremes. The results show that the trend of RX1day was insignificant for the Medieval Climate Anomaly (MCA, 950–1250) and the Little Ice Age (LIA, 1500–1800). For the historical period (1850–2014), the trend of RX1day and RX3hour was insignificant in most subregions. In addition, the differences for RX1day between the historical period and the MCA/LIA were mostly in the range of −5% to 5%. For the future period (2015–2100), RX1day and RX3hour are projected to increase in almost all land grids compared with the historical period, and they show greater increases in western China than eastern China. The non‐stationarity of RX1day and RX3hour is mainly found under the high‐emission scenario. Moreover, stationary generalized extreme value (GEV) distribution underestimates 50‐year return levels for RX1day and RX3hour in most areas compared with non‐stationary GEV distribution under the high‐emission scenario. Overall, this study suggests that daily and subdaily extreme precipitation will intensify over China in the future. For design storms, non‐stationary methods need to be used for risk management and engineering design under the high‐emission scenario.
The stationary generalized extreme value (GEV) distribution underestimates 50‐year return levels of daily and subdaily extreme precipitation in most areas of China compared with non‐stationary GEV distribution under the high‐emission scenarios.
Bias correction techniques are widely used to bridge the gap between climate model outputs and input requirements of hydrological models to assess the climate change impacts on hydrology. In addition ...to univariate bias correction methods, several multivariate bias correction methods were proposed recently, which can not only correct the biases in marginal distributions of individual climate variables but also properly adjust the biased intervariable correlations simulated by climate models. Due to the diversities of climate regime and climate model bias, hydrological simulation for watersheds under different climate conditions may show various sensitivities to the correction of intervariable correlations. Therefore, it is of great importance to investigate (1) whether the correction of intervariable correlations has impacts on the hydrological modeling and (2) how these impacts vary with watersheds under different climate conditions. To achieve these goals, this study evaluates behaviors and their spatial variability of multiple state‐of‐the‐art multivariate bias correction methods in hydrological modeling over 2,840 watersheds distributed in different climate regimes in North America. The results show that, compared to using a quantile mapping univariate bias correction method, applying multivariate methods can improve the simulation of snow proportion, snowmelt, evaporation, and several streamflow variables. In addition, this improvement is more clear for watersheds with arid and warm temperate climates in southern regions, while it is limited for northern snow‐characterized watersheds. Overall, this study demonstrates the importance of using multivariate bias correction methods instead of univariate methods in hydrological climate change impact studies, especially for watersheds with arid and warm temperate climates.
Key Points
The correction of modeled precipitation‐temperature correlations can improve the accuracy of hydrological simulations
The advantages of using multivariate bias correction methods in hydrological simulations are weakened when it comes to the validation period
The benefits of correcting precipitation‐temperature correlations in hydrological simulations are climate regime dependent
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