天山及邻区Vening Meinesz均衡重力异常特征及其动力学意义

本文基于Vening Meinesz区域均衡模型,通过试验不同参数计算Vening Meinesz均衡补偿深度,将其与CRUST1.0模型给出的莫霍面深度进行拟合,得到适应于天山及邻区的平均补偿深度、"地区性指标"以及区域补偿半径.结合地球重力场模型EIGEN-6C4与地形数据,利用球冠体积分方法进行地形效应、沉积层效应计算和均衡校正,得到了研究区的Vening Meinesz均衡重力异常.结果显示天山及邻区的均衡重力异常幅值在-110~120 mGal之间,表明了天山及周边盆地岩石圈所处于的均衡状态,同时揭示了研究区的壳幔密度分布特征.天山、塔里木盆地、准噶尔盆地等块体的地壳垂向形变可能部分地由均衡调整引起,且均衡调整趋势与地面形变测量结果相契合.通过对均衡重力异常成因的解释,从地壳均衡角度分析了该地区复杂的构造背景及其新生代以来的演化历程.     

一种地方与区域地震震源机制反演技术：广义极性振幅技术（一）——原理与数值实验

基于数十年来已有的研究进展,提出了一种地方和区域地震震源机制反演技术——广义极性振幅技术（GPAT）,并通过一系列数值实验检验了这种技术的可行性和抗干扰能力.首先,从地震波场概念出发,利用P波初动极性与广义震相振幅构建矢量,建立反演系统,并给出求解技术;然后,考虑影响反演结果的各种因素,包括台站布局、台站数目、随机噪声、震中位置误差、震源深度误差和速度模型误差,分别进行了单一因素影响测试;最后,同时考虑各种因素进行了综合测试.实验结果表明,GPAT是可行的,具有良好的抗干扰能力.需要强调的是,在众多影响因素中,速度模型误差对反演结果的影响最大.     

Development of an operational rainfall data quality-control scheme based on radar-raingauge co-kriging analysis

Abstract

Automatic raingauge data often serve as an important input to hydrological and weather warning operations. They are not only fundamental in quantitative rainfall analysis, but also act as the ground truth in warning operation and forecast validation. Quality control is required before the data can be used quantitatively due to systematic and random errors. Extremely large random errors and unreasonably small or false zero values can hamper effective monitoring of heavy rain. Yet both are difficult to detect in real-time by objective means. In an attempt to address these problems, a rainfall data quality-control scheme based on radar-raingauge co-kriging analysis was developed. The important threshold values required in the data quality control of 60-min raingauge rainfall were determined from a detailed analysis of the distributions of rainfall residuals defined as the arithmetic difference and the logarithm of the ratio between a raingauge measurement and its co-kriging estimate. The scheme has been developed and is in real-time use in Hong Kong, a coastal city of about 1100 km² area with more than 150 raingauges installed. Geographically, it is located in the subtropics and dominated by heavy convective rainfall in the summer. As a basis of the quality-control scheme, the co-kriging rainfall analysis was shown through a verification exercise to be superior to those obtained by the Barnes analysis and ordinary kriging of raingauge data. The performance of the quality-control algorithm was assessed using selected cases and controlled tests, and was found to be satisfactory, with a high error detection rate for the two targeted types of error. Limitations and operational issues identified during a real-time trial of the quality-control scheme are also discussed.

Citation Yeung, H.Y., Man, C., Chan, S.T., and Seed, A., 2014. Development of an operational rainfall data quality-control scheme based on radar-raingauge co-kriging analysis. Hydrological Sciences Journal, 59 (7), 1285–1299. http://dx.doi.org/10.1080/02626667.2013.839873     

A comparative study of extensive machine learning models for predicting long-term monthly rainfall with an ensemble of climatic and meteorological predictors

Rainfall prediction is of vital importance in water resources management. Accurate long-term rainfall prediction remains an open and challenging problem. Machine learning techniques, as an increasingly popular approach, provide an attractive alternative to traditional methods. The main objective of this study was to improve the prediction accuracy of machine learning-based methods for monthly rainfall, and to improve the understanding of the role of large-scale climatic variables and local meteorological variables in rainfall prediction. One regression model autoregressive integrated moving average model (ARIMA) and five state-of-the-art machine learning algorithms, including artificial neural networks, support vector machine, random forest (RF), gradient boosting regression, and dual-stage attention-based recurrent neural network, were implemented for monthly rainfall prediction over 25 stations in the East China region. The results showed that the ML models outperformed ARIMA model, and RF relatively outperformed other models. Local meteorological variables, humidity, and sunshine duration, were the most important predictors in improving prediction accuracy. 4-month lagged Western North Pacific Monsoon had higher importance than other large-scale climatic variables. The overall output of rainfall prediction was scalable and could be readily generalized to other regions.     

Temporal and spatial rainfall analysis across a humid tropical catchment

Temporal and spatial rainfall patterns were analysed to describe the distribution of daily rainfall across a medium‐sized (379km²) tropical catchment. Investigations were carried out to assess whether a climatological variogram model was appropriate for mapping rainfall taking into consideration the changing rainfall characteristics through the wet season. Exploratory, frequency and moving average analyses of 30 years' daily precipitation data were used to describe the reliability and structure of the rainfall regime. Four phases in the wet season were distinguished, with the peak period (mid‐August to mid‐September) representing the wettest period. A low‐cost rain gauge network of 36 plastic gauges with overflow reservoirs was installed and monitored to obtain spatially distributed rainfall data. Geostatistical techniques were used to develop global and wet season phase climatological variograms. The unscaled climatological variograms were cross‐validated and compared using a range of rainfall events. Ordinary Kriging was used as the interpolation method. The global climatological variogram performed better, and was used to optimize the number and location of rain gauges in the network. The research showed that although distinct wet season phases could be established based on the temporal analysis of daily rainfall characteristics, the interpolation of daily rainfall across a medium‐sized catchment based on spatial analysis was better served by using the global rather than the wet season phase climatological variogram model. Copyright © 2001 John Wiley & Sons, Ltd.     

How to obtain alert velocity thresholds for large rockslides

A reliable forecast of the failure stage of large rockslides is difficult, because of non-linear time dependency of displacements and seasonal effects. Aim of this paper is to suggest a practical method to prepare alert thresholds for large rockslides, assessing critical values of velocity for carrying out civil protection actions using monitoring data. Adopted data concern the 20 Mm³ Ruinon rockslide (Valfurva, Central Alps, Italy), still evolving and suitable to originate a fast moving rock avalanche. Multitemporal analysis of aerial photos, LIDAR-ALTM laser topography, field survey and geomechanical analyses allowed to infer the rockslide kinematics and better understand data provided by a monitoring network including distometers, extensometers, GPS benchmarks and inclinometers. The analysis of displacement and rainfall data over five years (1997–2001) allowed to recognise three different evolutionary patterns of displacements, showing a continuously increasing rate since 1997. Data representing large-scale behaviour of the rock mass were fitted by power-law curves, according to the “accelerating creep” model by Voight, in order to evaluate a suitable failure time. This was hampered by the large seasonal deviations, which can significantly delay the occurrence of failure. Data were fitted using the Voight’s equation, expressed in terms of displacement, through non-linear estimation techniques, in order to find values of the controlling parameters (A, α and t_f) suitable to represent the mechanical behaviour of the rock mass approaching the failure. This allowed to compute velocity–time theoretical curves and to define different velocity threshold values (pre-alert, alert and emergency) to be used for emergency management.     

Vegetation influences on water yields from grassland and shrubland ecosystems in the Chihuahuan Desert

This study examines runoff generated under simulated rainfall on Summerford bajada in the Jornada Basin, New Mexico, USA. Forty‐five simulation experiments were conducted on 1 m² and 2 m² runoff plots on grassland, degraded grassland, shrub and intershrub environments located in grassland and shrubland communities. Average hydrographs generated for each environment show that runoff originates earlier on the vegetated plots than on the unvegetated plots. This early generation of runoff is attributed to soil infiltration rates being overwhelmed by the rapid concentration of water at the base of plants by stemflow. Hydrographs from the degraded grassland and intershrub plots rise continuously throughout the 30 min simulation events indicating that these plots do not achieve equilibrium runoff. This continuously rising form is attributed to the progressive development of raindrop‐induced surface seals. Most grassland and shrub plots level out after the initial early rise indicating equilibrium runoff is achieved. Some shrub plots, however, display a decline in discharge after the early rise. The delayed infiltration of water into macropores beneath shrubs with vegetation in their understories is proposed to explain this declining form. Water yields predicted at the community level indicate that the shrubland sheds 150 per cent more water for a given storm event than the grassland. Copyright © 2002 John Wiley & Sons, Ltd.     

Heating status of the Tibetan Plateau from April to June and rainfall and atmospheric circulation anomaly over East Asia in midsummer

Based on the 1958-1999 monthly averaged reanalysis data of the National Center for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) and the rainfall data of 160 Chinese surface stations, the relationship between rainfall and the atmospheric circulation anomaly over East Asia (EA) in July and the sensible heating (SH) over the Tibetan Plateau (TP) from April to June (AMJ) is investigated by using the rotational experimental orthogonal function (REOF) method. The results show that the TP is an isolated heating source in this period. The lagged correlation analysis between the first rotational principal component (RPC) of SH over the TP in May and rainfall of EA in July demonstrates that strong SH over the TP before July leads to a positive rainfall anomaly over the TP, the valley between the Yangtze River and Huaihe River, and the regions south and southeast of the TP, and the Sichuan Basin and Yunnan-Guizhou Plateau, but less rainfall anomaly over the regions north, northeas     

Study on snowmelt runoff simulation in the Kaidu River basin

Alpine snowmelt is an important generation mode for runoff in the source region of the Tarim River basin, which covers four subbasins characterized by large area, sparse gauge stations, mixed runoff supplied by snowmelt and rainfall, and remarkably spatially heterogeneous precipitation. Taking the Kaidu River basin as a research area, this study analyzes the influence of these characteristics on the variables and parameters of the Snow Runoff Model and discusses the corresponding determination strategy to improve the accuracy of snowmelt simulation and forecast. The results show that: (i) The temperature controls the overall tendency of simulated runoff and is dominant to simulation accuracy, as the measured daily mean temperature cannot represent the average level of the same elevation in the basin and that directly inputting it to model leads to inaccurate simulations. Based on the analysis of remote sensing snow maps and simulation results, it is reasonable to approximate the mean temperature with 0.5 time daily maximum temperature. (ii) For the conflict between the limited gauge sta-tion and remarkably spatial heterogeneity of rainfall, it is not realistic to compute rainfall for each elevation zone. After the measured rainfall is multiplied by a proper coefficient and adjusted with runoff coefficient for rainfall, the measured rainfall data can satisfy the model demands. (iii) Adjusting time lag according to the variation of snowmelt and rainfall position can improve the simulation precision of the flood peak process. (iv) Along with temperature, the rainfall increases but cannot be completely monitored by limited gauge stations, which results in precision deterioration.