Influence of the sediment delivery ratio index on the analysis of silting and break risk in the Plasencia reservoir (Central System,Spain)

The random forest method was used to generate susceptibility maps for debris flows, rock slides, and active layer detachment slides in the Donjek River area within the Yukon Alaska Highway Corridor, based on an inventory of landslides compiled by the Geological Survey of Canada in collaboration with the Yukon Geological Survey. The aim of this study is to develop data-driven landslide susceptibility models which can provide information on risk assessment to existing and planned infrastructure. The factors contributing to slope failure used in the models include slope angle, slope aspect, plan and profile curvatures, bedrock geology, surficial geology, proximity to faults, permafrost distribution, vegetation distribution, wetness index, and proximity to drainage system. A total of 83 debris flow deposits, 181 active layer detachment slides, and 104 rock slides were compiled in the landslide inventory. The samples representing the landslide free zones were randomly selected. The ratio of landslide/landslide free zones was set to 1:1 and 1:2 to examine the results of different sample ratios on the classification. Two-thirds of the samples for each landslide type were used in the classification, and the remaining 1/3 were used to evaluate the results. In addition to the classification maps, probability maps were also created, which served as the susceptibility maps for debris flows, rock slides, and active layer detachment slides. Success and prediction rate curves created to evaluate the performance of the resulting models indicate a high performance of the random forest in landslide susceptibility modelling.     

Effects of different soil and water conservation measures on hydrological extremes and flood processes in the Yanhe River,Loess Plateau,China

The runoff and sediment load of the Loess Plateau have changed significantly due to the implementation of soil and water conservation measures since the 1970s. However, the effects of soil and water conservation measures on hydrological extremes have rarely been considered. In this study, we investigated the variations in hydrological extremes and flood processes during different periods in the Yanhe River Basin (a tributary of the Loess Plateau) based on the daily mean runoff and 117 flood event data from 1956 to 2013. The study periods were divided into reference period (1956–1969), engineering measures period (1970–1995), and biological control measures period (1996–2013) according to the change points of the annual streamflow and the actual human activity in the basin. The results of the hydrological high extremes (HF1_max, HF3_max, HF7_max) exhibit a decreasing trend (P?<?0.01), whereas the hydrological low extremes (HBF1_min, HBF3_min, HBF7_min) show an increasing trend during 1956–2013. Compared with the hydrological extremes during the reference period, the hydrological high extremes increased during the engineering measures period at low (<?15%) and high frequency (>?80%), whereas decreased during the biological control measures period at almost all frequencies. The hydrological low extremes generally increased during both the engineering measures and biological control measures periods, particularly during the latter period. At the flood event scale, most flood event indices in connection with the runoff and sediment during the engineering measures period were significantly higher than those during the biological control measures period. The above results indicate that the ability to withstand hydrological extremes for the biological control measures was greater than that for the engineering measures in the studied basin. This work reveals the effects of different soil and water conservation measures on hydrological extremes in a typical basin of the Loess Plateau and hence can provide a useful reference for regional soil erosion control and disaster prevention policy-making.

     

Statistical forecast of the marine surge

Natural Hazards - This paper studies different machine learning methods for solving the regression problem of estimating the marine surge value given meteorological data. The marine surge is...     

Three decades of landslide activity in western Nepal: new insights into trends and climate drivers

In recent decades, landslide disasters in the Himalayas, as in other mountain regions, are widely reported to have increased. While some studies have suggested a link to increasing heavy rainfall under a warmer climate, others pointed to anthropogenic influences on slope stability, and increasing exposure of people and assets located in harm’s way. A lack of sufficiently high-resolution regional landslide inventories, both spatially and temporally, has prevented any robust consensus so far. Focusing on Far-Western Nepal, we draw on remote sensing techniques to create a regional inventory of 26,350 single landslide events, of which 8778 date to the period 1992–2018. These events serve as a basis for the analyses of landslide frequency relationships and trends in relation to precipitation and temperature datasets. Results show a strong correlation between the annual number of shallow landslides and the accumulated monsoon precipitation (r = 0.74). Furthermore, warm and dry monsoons followed by especially rainy monsoons produce the highest incidence of shallow landslides (r = 0.77). However, we find strong spatial variability in the strength of these relationships, which is linked to recent demographic development in the region. This highlights the role of anthropogenic drivers, and in particular road cutting and land-use change, in amplifying the seasonal monsoon influence on slope stability. In parallel, the absence of any long-term trends in landslide activity, despite widely reported increase in landslide disasters, points strongly to increasing exposure of people and infrastructure as the main driver of landslide disasters in this region of Nepal. By contrast, no climate change signal is evident from the data.

     

Dynamics of coastline changes in Mexico

Journal of Geographical Sciences - The goal of our work was to locate and quantify changes that occurred in 66% of the Mexican coastline, based on four land cover maps generated by the Mexican...     

Dynamic metabolite alterations of Portunus trituberculatus during larval development

Journal of Oceanology and Limnology - A mass mortality often occurs from molting to the megalopa stage during the larval development of the swimming crab Portunus trituberculatus. Larvae with...     

A method for checking the quality of geographic metadata based on ISO 19157

With recent advances in remote sensing, location-based services and other related technologies, the production of geospatial information has exponentially increased in the last decades. Furthermore, to facilitate discovery and efficient access to such information, spatial data infrastructures were promoted and standardized, with a consideration that metadata are essential to describing data and services. Standardization bodies such as the International Organization for Standardization have defined well-known metadata models such as ISO 19115. However, current metadata assets exhibit heterogeneous quality levels because they are created by different producers with different perspectives. To address quality-related concerns, several initiatives attempted to define a common framework and test the suitability of metadata through automatic controls. Nevertheless, these controls are focused on interoperability by testing the format of metadata and a set of controlled elements. In this paper, we propose a methodology of testing the quality of metadata by considering aspects other than interoperability. The proposal adapts ISO 19157 to the metadata case and has been applied to a corpus of the Spanish Spatial Data Infrastructure. The results demonstrate that our quality check helps determine different types of errors for all metadata elements and can be almost completely automated to enhance the significance of metadata.     

Sea Level Prediction in the Yellow Sea From Satellite Altimetry With a Combined Least Squares-Neural Network Approach

Accessible high-quality observation datasets and proper modeling process are critically required to accurately predict sea level rise in coastal areas. This study focuses on developing and validating a combined least squares-neural network approach applicable to the short-term prediction of sea level variations in the Yellow Sea, where the periodic terms and linear trend of sea level change are fitted and extrapolated using the least squares model, while the prediction of the residual terms is performed by several different types of artificial neural networks. The input and output data used are the sea level anomalies (SLA) time series in the Yellow Sea from 1993 to 2016 derived from ERS-1/2, Topex/Poseidon, Jason-1/2, and Envisat satellite altimetry missions. Tests of different neural network architectures and learning algorithms are performed to assess their applicability for predicting the residuals of SLA time series. Different neural networks satisfactorily provide reliable results and the root mean square errors of the predictions from the proposed combined approach are less than 2?cm and correlation coefficients between the observed and predicted SLA are up to 0.87. Results prove the reliability of the combined least squares-neural network approach on the short-term prediction of sea level variability close to the coast.     

夏季通风气象指数预报

随着人们生活水平的提高,对室内污染越来越关注,应"中国室内环境监测委员会"提出的需求,通风气象指数于2006年6月首次向北京公众发布,文章就首先研发并投入业务使用的夏季通风指数给出了详细论述.通过对2005年夏季(6～8月)的各气象要素(气压、温、湿、风、能见度、天空状况等)资料共1790个数据做检验分析,最终选取体感温度为主要预报因子并综合考虑夏季特殊天气条件和风力以及污染的影响,得出夏季通风指数在一天中7个时段的预报公式,并划分出相应的指数等级.     

青海省玉树地区高精度航磁调查与应用研究

青海玉树地区位于我国重要的西南"三江"成矿带,具有高海拔、切割剧烈等特点,而高精度航磁调查是一种精度高、见效快、成本低的方法技术手段.在该区运用直升机长杆硬架技术并采取随地形缓起伏的方法,获取了高质量的原始数据.在高精度航磁图上,磁异常信息十分丰富,通过详细的物性资料分析和严谨的异常解释工作,摸清了区域磁场基本特征,推...