2-D crustal Poisson’s ratio from seismic travel time inversion in Changbaishan Tianchi volcanic region

Based on the inversion method of 2D velocity structure and interface, the crustal velocity structures of P-wave and S-wave along the profile L ₁ are determined simultaneously with deep seismic sounding data in Changbaishan Tianchi volcanic region, and then its Poisson’s ratio is obtained. Calculated results show that this technique overcomes some defects of traditional forward calculation method, and it is also very effective to determine Poisson’s ratio distribution of deep seismic sounding profile, especially useful for study on volcanic magma and crustal fault zone. Study result indicates that there is an abnormally high Poisson’s ratio body that is about 30 km wide and 12 km high in the low velocity region under Tianchi crater. Its value of Poisson’s ratio is 8% higher than that of surrounding medium and it should be the magma chamber formed from melted rock with high temperature. There is a high Poisson’s ratio zone ranging from magma chamber to the top of crust, which may be the uprise passage of hot substance. The lower part with high Poisson’s ratio, which stretches downward to Moho, is possibly the extrusion way of hot substance from the uppermost mantle. The conclusions above are consistent with the study results of both tomographic determination of 3D crustal structure and magnetotelluric survey in this region. Foundation item: Key Project from China Earthquake Administration and the Project (95-11-02-01) from Ministry of Science and Technology (2001DIA10003). Contribution No. RCEG200401, Geophysical Exploration Center, China Earthquake Administration.     

Discrete element method modeling of inherently anisotropic rocks under uniaxial compression loading

A new numerical approach is proposed in this study to model the mechanical behaviors of inherently anisotropic rocks in which the rock matrix is represented as bonded particle model, and the intrinsic anisotropy is imposed by replacing any parallel bonds dipping within a certain angle range with smooth‐joint contacts. A series of numerical models with β = 0°, 15°, 30°, 45°, 60°, 75°, and 90° are constructed and tested (β is defined as the angle between the normal of weak layers and the maximum principal stress direction). The effect of smooth‐joint parameters on the uniaxial compression strength and Young's modulus is investigated systematically. The simulation results reveal that the normal strength of smooth‐joint mainly affects the behaviors at high anisotropy angles (β > 45°), while the shear strength plays an important role at medium anisotropy angles (30°–75°). The normal stiffness controls the mechanical behaviors at low anisotropy angles. The angle range of parallel bonds being replaced plays an important role on defining the degree of anisotropy. Step‐by‐step procedures for the calibration of micro parameters are recommended. The numerical model is calibrated to reproduce the behaviors of different anisotropic rocks. Detailed analyses are conducted to investigate the brittle failure process by looking at stress‐strain behaviors, increment of micro cracks, initiation and propagation of fractures. Most of these responses agree well with previous experimental findings and can provide new insights into the micro mechanisms related to the anisotropic deformation and failure behaviors. The numerical approach is then applied to simulate the stress‐induced borehole breakouts in anisotropic rock formations at reduced scale. The effect of rock anisotropy and stress anisotropy can be captured. Copyright © 2015 John Wiley & Sons, Ltd.     

Distinguishing streamflow trends caused by changes in climate,forest cover,and permafrost in a large watershed in northeastern China

Understanding how rivers respond to changes in land cover, climate, and subsurface conditions is critical for sustainably managing water resources and ecosystems. In this study, long‐term hydrologic, climate, and satellite data (1973–2012) from the Upper Tahe River watershed (2359 km²) in the Da Hinggan Mountains of northeast China were analysed to quantify the relative hydrologic effects of climate variability (system input) and the combined influences of forest cover change and permafrost thaw (system characteristics) on average annual streamflow (system response) using 2 methods: the sensitivity‐based method and the Kendall–Theil robust line method. The study period was subdivided into a forest harvesting period (1973–1987), a forest stability period (1988–2001), and a forest recovery period (2002–2012). The results indicated that the combined effects of forest harvesting and permafrost thaw on streamflow (+ 47.0 mm) from the forest harvesting period to the forest stability period was approximately twice as large as the effect associated with climate variability (+20.2 mm). Similarly, from the forest stability period to the forest recovery period, the decrease in average annual streamflow attributed to the combined effects of forest recovery and permafrost thaw (?38.0 mm) was much greater than the decrease due to climate variability (?22.2 mm). A simple method was used to separate the distinct impacts of forest cover change and permafrost thaw, but distinguishing these influences is difficult due to changes in surface and subsurface hydrologic connectivity associated with permafrost thaw. The results highlight the need to consider multiple streamflow drivers in future watershed and aquatic ecosystem management. Due to the ecological and hydrological susceptibility to disturbances in the Da Hinggan Mountains, forest harvesting will likely negatively impact ecohydrological processes in this region, and the effects of forest species transition in the forest recovery process should be further investigated.     

Trend and extreme occurrence of precipitation in a mid‐latitude Eurasian steppe watershed at various time scales

The confounding effects of step change invalidate the stationarity assumption of commonly used trend analysis methods such as the Mann–Kendall test technique, so previous studies have failed to explain inconsistencies between detected trends and observed large precipitation anomalies. The objectives of this study were to (1) formulate a trend analysis approach that considers nonstationarity due to step changes, (2) use this approach to detect trends and extreme occurrences of precipitation in a mid‐latitude Eurasian steppe watershed in North China, and (3) examine how runoff responds to precipitation trends in the study watershed. Our results indicate that annual precipitation underwent a marginal step jump around 1995. The significant annual downward trend after 1994 was primarily due to a decrease in summer rainfall; other seasons exhibited no significant precipitation trends. At a monthly scale, July rainfall after 1994 exhibited a significant downward trend, whereas precipitation in other months had no trend. The percentage of wet days also underwent a step jump around 1994 following a significant decreasing trend, although the precipitation intensity exhibited neither a step change nor any significant trend. However, both low‐frequency and high‐frequency precipitation events in the study watershed occurred more often after than before 1994; probably as either a result or an indicator of climate change. In response to these precipitation changes, the study watershed had distinctly different precipitation‐runoff relationships for observed annual precipitations of less than 300 mm, between 300 and 400 mm, and greater than 400 mm. Copyright © 2013 John Wiley & Sons, Ltd.     

Exploring the interaction of surface roughness and slope gradient in controlling rates of soil loss from sloping farmland on the Loess Plateau of China

Surface roughness and slope gradient are two important factors influencing soil erosion. The objective of this study was to investigate the interaction of surface roughness and slope gradient in controlling soil loss from sloping farmland due to water erosion on the Loess Plateau, China. Following the surface features of sloping farmland in the plateau region, we manually prepared rough surfaces using four tillage practices (contour drilling, artificial digging, manual hoeing, and contour plowing), with a smooth surface as the control measure. Five slope gradients (3°, 5°, 10°, 15°, and 20°) and two rainfall intensities (60 and 90 mm/hr) were considered in the artificial rainfall simulation experiment. The results showed that the runoff volume and sediment yield increased with increasing slope gradient under the same tillage treatment. At gentle slope gradients (e.g., 3° and 5°), the increase in surface roughness prevented the runoff and sediment production, that is, the surface roughness reduced the positive effect of slope gradient on the runoff volume and sediment yield to a certain extent. At steep slope gradients, however, the enhancing effect of slope gradient on soil erosion gradually increased and surpassed the reduction effect of surface roughness. This study reveals the existence of a critical slope gradient that influences the interaction of surface roughness and slope gradient in controlling soil erosion on sloping farmland. If the slope gradient is equal to or less than the critical value, an increase in surface roughness would decrease soil erosion. Otherwise, the increase in surface roughness would be ineffective for preventing soil erosion. The critical slope gradient would be smaller under higher rainfall intensity. These findings are helpful for us to understand the process of soil erosion and relevant for supporting soil and water conservation in the Loess Plateau region of China.     

Generation of continuous surface soil moisture dataset using combined optical and thermal infrared images

Surface soil moisture (SSM) is a critical variable for understanding water and energy flux between the atmosphere and the Earth's surface. An easy to apply algorithm for deriving SSM time series that primarily uses temporal parameters derived from simulated and in situ datasets has recently been reported. This algorithm must be assessed for different biophysical and atmospheric conditions by using actual geostationary satellite images. In this study, two currently available coarse‐scale SSM datasets (microwave and reanalysis product) and aggregated in situ SSM measurements were implemented to calibrate the time‐invariable coefficients of the SSM retrieval algorithm for conditions in which conventional observations are rare. These coefficients were subsequently used to obtain SSM time series directly from Meteosat Second Generation (MSG) images over the study area of a well‐organized soil moisture network named REMEDHUS in Spain. The results show a high degree of consistency between the estimated and actual SSM time series values when using the three SSM dataset‐calibrated time‐invariable coefficients to retrieve SSM, with coefficients of determination (R²) varying from 0.304 to 0.534 and root mean square errors ranging from 0.020 m³/m³ to 0.029 m³/m³. Further evaluation with different land use types results in acceptable debiased root mean square errors between 0.021 m³/m³ and 0.048 m³/m³ when comparing the estimated MSG pixel‐scale SSM with in situ measurements. These results indicate that the investigated method is practical for deriving time‐invariable coefficients when using publicly accessed coarse‐scale SSM datasets, which is beneficial for generating continuous SSM dataset at the MSG pixel scale.     

Operational climate prediction in the era of big data in China: Reviews and prospects

Big data has emerged as the next technological revolution in IT industry after cloud computing and the Internet of Things. With the development of climate observing systems, particularly satellite meteorological observation and high-resolution climate models, and the rapid growth in the volume of climate data, climate prediction is now entering the era of big data. The application of big data will provide new ideas and methods for the continuous development of climate prediction. The rapid integration, cloud storage, cloud computing, and full-sample analysis of massive climate data makes it possible to understand climate states and their evolution more objectively, thus predicting the future climate more accurately. This paper describes the application status of big data in operational climate prediction in China; it analyzes the key big data technologies, discusses the future development of climate prediction operations from the perspective of big data, speculates on the prospects for applying climatic big data in cloud computing and data assimilation, and puts forward the notion of big data-based super-ensemble climate prediction methods and computerbased deep learning climate prediction methods.     

A timescale decomposed threshold regression downscaling approach to forecasting South China early summer rainfall

A timescale decomposed threshold regression(TSDTR) downscaling approach to forecasting South China early summer rainfall(SCESR) is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR.The two models are developed based on the partial least squares(PLS) regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915–84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation(PDO). On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985–2006, compared to other simpler approaches. This study suggests that the TSDTR approach,considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.     

2014—2016年超强厄尔尼诺事件的气候影响

2014—2016年,赤道中东太平洋发生了一次超强厄尔尼诺事件,此次事件于2015年11月达到峰值,12月开始衰减。其峰值强度超过了1951年以来另外两次超强厄尔尼诺事件（1982/1983年和1997/1998年）的强度,成为了1951年以来最强的事件。截至2016年4月,事件已持续20个月,也成为了1951年以来持续时间最长的厄尔尼诺事件。在这次事件的发展过程中,热带太平洋至东亚副热带地区的大气环流表现出了显著的响应特征：赤道中东太平洋对流活动加强,异常上升运动发展,而赤道西太平洋对流活动受抑制,异常下沉运动控制;菲律宾附近异常反气旋生成并发展加强,西太平洋副热带高压强度偏强、西伸脊点异常偏西,尤其2015年冬季副热带高压强度为1980年以来最强。与此同时,2015年秋、冬季,我国长江以南大部降水偏多,尤其冬季华南地区（广东、广西、海南三省区）平均降水量达历史第一,较常年偏多1.6倍以上。近期,超强厄尔尼诺正处于衰减阶段,但是考虑到热带印度洋暖海温的“接力”作用,厄尔尼诺事件对2016年春、夏季我国气候异常的影响可能仍将持续。