Spatial prediction of landslide susceptibility using GIS-based statistical and machine learning models in Wanzhou County,Three Gorges Reservoir,China

Landslide susceptibility mapping is vital for landslide risk management and urban planning. In this study, we used three statistical models [frequency ratio, certainty factor and index of entropy (IOE)] and a machine learning model [random forest (RF)] for landslide susceptibility mapping in Wanzhou County, China. First, a landslide inventory map was prepared using earlier geotechnical investigation reports, aerial images, and field surveys. Then, the redundant factors were excluded from the initial fourteen landslide causal factors via factor correlation analysis. To determine the most effective causal factors, landslide susceptibility evaluations were performed based on four cases with different combinations of factors (“cases”). In the analysis, 465 (70%) landslide locations were randomly selected for model training, and 200 (30%) landslide locations were selected for verification. The results showed that case 3 produced the best performance for the statistical models and that case 2 produced the best performance for the RF model. Finally, the receiver operating characteristic (ROC) curve was used to verify the accuracy of each model’s results for its respective optimal case. The ROC curve analysis showed that the machine learning model performed better than the other three models, and among the three statistical models, the IOE model with weight coefficients was superior.

     

基于H-P滤波法、ARIMA和VAR模型的库区滑坡位移综合预测

受库水位涨落及降雨等影响,库区滑坡位移表现出明显的周期性。基于位移时间序列分析,将滑坡监测位移分解为趋势项与周期项之和。趋势项反映滑坡变形的长期趋势,其主要受滑坡本身地质结构等因素影响。周期项反映滑坡变形的波动性,其主要受外部因素影响。以三峡库区巫山塔坪滑坡为例,考虑长江水位与降雨量影响,采用H-P滤波法从滑坡位移中分解出趋势项及周期项,利用差分自回归滑动平均模型（ARIMA）对趋势项进行平稳处理并计算趋势项预测值,利用向量自回归模型（VAR）计算周期项预测值。趋势项预测值与周期项预测值之和为滑坡位移预测值。与实际监测值及多种方法分析比较,表明综合预测所得结果能较好反映滑坡变形的趋势性和波动性,位移预测效果较好。     

Snowmelt runoff assessment and prediction under variable climate and glacier cover scenarios in Astore River Basin,Western Himalayas

Climate change is expected to have a significant impact on the Himalayan region, which may ultimately affect the water security and agriculture productivity in the region. Investigations of hydrologic regimes and their linkage to climatic trends are therefore gaining importance to reduce vulnerability of growing implications in the region. In the present study, the eWater source software implementation of GR4JSG snow melt model was used for snow melt runoff modeling of the Astore river basin, western Himalayas. The model calibration and validation indicated a close agreement between the simulated and observed discharge data. The scenario of 0.9 °C increase in temperature indicated 33% rise in the river discharge, while an increase of 10% in precipitation may exaggerate the river flows by 15%. The scenario of 100% increase in glaciated area showed 41% increase in the Astore river discharge. On the other hand, reduction of 50% glacier cover may result in 34% decline in the river discharge, while 0% glacier coverage may reduce the river discharges by 49% from that of the base year 2014. It is essential to develop a long-term water resource monitoring process and adapt water management systems taking into account the socio-economic and ecological complexities of the region.     

Modeling runoff and soil erosion in a catchment area, using the GIS, in the Himalayan region, India

Remote sensing data and GIS techniques have been used to compute runoff and soil erosion in the catchment area along the NH-1A between Udhampur and Kud covering an area of approximately 181 km². Different thematic layers, for example lithology, a landuse and landcover map, geomorphology, a slope map, and a soil-texture map, were generated from these input data. By use of the US Soil Conservation Service curve number method, estimated runoff potential was classified into five levels—very low, low, moderate, high, and very high. Data integration was performed by use of the weighting rating technique, a conventional qualitative method, to give a runoff potential index value. The runoff potential index values were used to delineate the runoff potential zones, namely low, moderate, high, and very high. Annual spatial soil loss estimation was computed using the Morgan–Morgan–Finney mathematical model in conjunction with remote sensing data and GIS techniques. Greater soil erosion was found to occur in the northwestern part of the catchment area. When average soil loss from the catchment area was calculated it was found that a maximum average soil loss of more than 20 t ha⁻¹ occurred in 31 km² of the catchment area.     

塔里木河源区托什干河流域积雪动态及融雪径流模拟与预估

利用已被广泛使用的MODIS积雪数据, 获得了塔里木河源区之一的托什干河流域积雪变化信息. 结果表明: 流域积雪覆盖率时空差异显著, 在积雪丰富的年份, 1月积雪覆盖率可达90%以上, 但在积雪少的年份, 则只有50%; 2000年以来流域积雪呈现微弱增加的趋势, 积雪变化趋势呈现明显的时空差异. 相对于其他季节, 流域冬季积雪增加更为明显; 与其他高度带相比, 作为主要积雪覆盖区的海拔3 000~4 000 m高度带积雪的增加趋势也更为明显. 以流域所在的气象格网数据和积雪覆盖率变化曲线作为输入数据, 应用融雪径流模型(SRM)模拟了流域春季融雪径流过程, 率定了模型主要参数, 获得了较好的结果. 以CMIP5的3种RCP情景为驱动数据, 应用模型预估了流域2021-2050年的融雪径流状况, 结果显示 4月之前径流变化不明显, 之后径流峰值增大显著, 不同气候情景对径流的影响不明显.     

Lipid,sterol and saccharide sources and dynamics in surface soils during an annual cycle in a temperate climate region

Multi-biomarkers were characterized in surface soils with different vegetation during an annual cycle in Oregon, U.S.A., to study the composition and dynamics of soil organic matter (SOM). The major compound classes identified include saccharides, steroids, terpenoids, and homologous series of aliphatic lipids (n-alkanoic acids, n-alkanols, and n-alkanes). Saccharides, n-alkanoic acids, and sterols were the most dominant compound groups identified in Ryegrass field soils, whereas n-alkanoic acids, n-alkanols, and sterols were dominant in soils under conifer and deciduous vegetation. Plant species, instead of microbial organisms, was found to be the primary source influencing the concentration and distribution of the major biomarker tracers in the studied surface soils. Over an annual cycle, concentrations of higher plant lipids such as monoacyl glycerides, sterols, n-alkanoic acids and total wax were higher during summer (especially June–August). During fall into winter, the concentrations of all compounds decreased to steady state levels due to cessation of de novo synthesis and concomitant biodegradation and remineralization of detritus. Sucrose and glucose reached maximum concentrations during spring (especially March–May), which could be related with plant growth, especially rootlets in the soils. Mycose, the microbial/fungal metabolite, maximized during late summer, suggesting the concomitant increase of microbial/fungal activity with the increasing primary production. The composition and variation of biomarkers observed over an annual cycle improved our understanding of SOM dynamics in temperate soils, which could also be linked to regional and global carbon cycles.     

Holocene climate dynamics in Latvia,eastern Baltic region: a pollen‐based summer temperature reconstruction and regional comparison

Heikkilä, M. & Seppä, H. 2010: Holocene climate dynamics in Latvia, eastern Baltic region: a pollen‐based summer temperature reconstruction and regional comparison. Boreas, Vol. 39, pp. 705–719. 10.1111/j.1502‐3885.2010.00164.x. ISSN 0300‐9483. A pollen‐based summer temperature (T_summer) reconstruction reveals the Holocene climate history in southeastern Latvia and contributes to the limited understanding of past climate behaviour in the eastern sector of northern Europe. Notably, steady climate warming of the early Holocene was interrupted c. 8350–8150 cal. yr BP by the well‐known 8.2 ka cold event, recorded as a decrease of 0.9 to 1.8 °C in T_summer. During the Holocene Thermal Maximum, c. 8000–4000 cal. yr BP, the reconstructed summer temperature was ～2.5–3.5 °C higher than the modern reconstructed value, and subsequently declined towards present‐day values. Comparison of the current reconstruction with other pollen‐based reconstructions in northern Europe shows that the 8.2 ka event is particularly clearly reflected in the Baltic region, possibly as a result of distinct climatic and ecological gradients and the sensitivity of the vegetation growth pattern to seasonal temperature change. The new reconstruction also reveals that the Holocene Thermal Maximum was warmer in Latvia than in central Europe and Fennoscandia. In fact, a gradient of increasing positive temperature anomalies is detected from northernmost Fennoscandia towards the south and from the Atlantic coast in Norway towards the continental East European Plain. The dynamics of the temperate broadleaved tree species Tilia and Quercus in Latvia and adjacent northern Europe during the mid‐Holocene give complementary information on the multifaceted climatic and environmental changes in the region.     

Traditional prediction of drought under weather and climate uncertainty: analyzing the challenges and opportunities for small-scale farmers in Gaza province,southern region of Mozambique

This paper explores the traditional indicators that small-scale farmers in Gaza province in southern Mozambique use to predict drought events on their rain-fed farms. It analyzes the contextual situation regarding the accuracy and reliability of the traditional prediction methods under the current weather and conditions of climate uncertainty and variability, and the opportunities that their prediction methods can bring to reduce their current and future exposure and vulnerabilities to drought. Farmers use a total of 11 traditional environmental indicators to predict drought, either individually or combined, as required to increase their prediction certainty. However, the farmers perceive that current unpredictability, variability, and changes in weather and climate have negatively affected the interpretation, accuracy, and reliability of most of their prediction indicators, and thus their farming activities and their ability to predict and respond to drought. This, associated with the reduced number of elders in the community, is causing a decline in the diversity, and complexity of interpretation of indicators. Nonetheless, these difficulties have not impeded farmers from continuing to use their preferred prediction methods, as on some occasions they continue to be useful for their farming-related decisions and are also the main, or sometimes only, source of forecast. Considering the role these methods play in farmers’ activities, and the limited access to meteorological forecasts in most rural areas of Mozambique, and the fact that the weather and climate is expected to continually change, this paper concludes that it is important to enhance the use of traditional prediction methods. However, the increase of the accuracy and reliability, and continued existence of the methods depends on the farmers’ own abilities to enhance, preserve, and validate them by tailoring the traditional methods used to work with the new environmental, weather, and climatic conditions, or through the development of new methods.

     

Determination of earthquake-risky areas in Yalova City Center (Marmara region,Turkey) using GIS-based multicriteria decision-making techniques (analytical hierarchy process and technique for order preference by similarity to ideal solution)

Disaster mitigation as a pre-disaster measure within the scope of disaster risk management is significant in the sense of reducing the adverse effects of earthquakes in the context of earthquake-sensitive risk planning. In the urban planning context, the existence of numerous decision makers and alternatives, which are depending on many criteria, makes decision-making process difficult. This difficulty was overcomed through geographical information systems (GIS). In the context of GIS-based multicriteria decision-making (MCDM) analysis, we used analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) to determine earthquake-risky areas in Yalova City Center. First, AHP analysis related to geological and superstructure/infrastructure criteria was conducted and two separate AHP maps were obtained. Then, we conducted TOPSIS analysis to consider both criteria in the sense of earthquake risk-sensitive planning. Then, overall earthquake risk map obtained which could be used as an input for disaster mitigation processes.

     

Exploration of gold mineralization in a tropical region using Earth Observing-1 (EO1) and JERS-1 SAR data: a case study from Bau gold field,Sarawak, Malaysia

Bau gold mining district, located near Kuching, Sarawak, Malaysia, is a Carlin style gold deposits. Geological analyses coupled with remote sensing data were used to detect hydrothermal alteration rocks and structure elements associated with this type of gold mineralization. Image processing techniques, including principal components analysis, linear spectral unmixing, and Laplacian algorithms, were employed to carry out spectrolithological–structural mapping of mineralized zones, using Advanced Land Imager, Hyperion, and JERS-1 synthetic aperture radar scenes covering the study area and surrounding terrain. Hydrothermally alteration mineral zones were detected along the SSW to NNE structural trend of the Tai Parit fault that corresponds to the areas of occurrence of the gold mineralization in the Bau limestone. The results show that potentially interesting areas are observable by the methods used, despite limited bedrock exposure in this region and the constraints imposed by the tropical environment.