水文模型参数区域化研究进展

参数区域化方法是解决资料缺乏地区水文模拟和预报的有效手段,主要包括回归法、空间邻近法和属性相似法三类方法,可将有资料流域的水文模型参数移用到资料缺乏流域。首先回顾了区域化方法的基本原理和应用方法,并分析了三类主要区域化方法的适用性。从流域特征因子、水文模型及参数、不确定性探讨三个方面综述了区域化方法的研究进展。分析发现,当前区域化方法缺乏完善的理论基础,流域特征因子选择存在主观性,水文模型及参数的适用性方面研究不足。最后展望了未来的研究重点：（1）多维度适用性比较;（2）水文过程和参数的空间分布规律;（3）参数的尺度问题;（4）参数区域化的不确定性问题。     

Numerical simulation of the Rongcharong paleolandslide river-blocking event: implication for the longevity of the landslide dam

Landslides - Numerous paleolandslide dams are distributed along the upper reaches of the Jinsha River under the special geological setting of the Tibetan Plateau. A field investigation revealed...     

Modeling of the Correlation Between Mineral Size and Shale Pore Structure at Meso- and Macroscales

Mathematical Geosciences - Inorganic pore structures are critical to understand the oil and gas transport and storage properties of unconventional reservoirs. However, it can be difficult to...     

常见稀有气体采样容器研究进展及意义

稀有气体是地学研究的重要手段之一,在研究成矿流体来源、壳幔相互作用过程中具有重要的研究意义,其组成及同位素比值是研究天体和地质体来源、成岩机理及各种地质和物理化学过程的关键,可作为地球化学示踪剂。如何有效地在大气圈、水圈和岩石圈进行稀有气体同位素样品的提取,是气体同位素研究急需解决的基础科学问题,所以气体采样容器和取样技术显得尤为重要。本文在文献调研的基础上,对常见的采样容器的优缺点进行对比,并总结了不同采样容器对稀有气体取样的优劣性。常见的气体采集容器包括不锈钢瓶、铜管、玻璃瓶、气体采样袋、注射器等,而稀有气体的采集容器常为不锈钢瓶和铜管等;通过对比表明不锈钢瓶具有耐高温、耐高压、抗强腐蚀、不易燃、不易爆等特点和优越性,在气体样品采集和运输过程中稳定性最好,实验效果也最好;铜管采样效果和密封性好,但操作较为复杂;玻璃采样容器效果次之;石英玻璃瓶虽然操作简便,但是运输保存不便;气体采样袋和注射器的采集和运输储存效果较差。因此建议稀有气体样品采集使用不锈钢瓶和铜管以及钠钙材质的玻璃瓶,不建议将石英材质玻璃容器以及注射器和气袋作为稀有气体的采样工具。该工作可为气体地球化学的研究提供新的参考。     

Numerical simulation of the Qulong Paleolandslide Dam event in the late pleistocene using the finite volume type shallow water model

Natural Hazards - The Qulong paleolandslide dam event lies in the Benzilan-Batang zone of the upper Jinsha River. The Jinsha River is one of the most extensive water resources in southwest China....     

Flood vulnerability and buildings’ flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach

Natural Hazards - Advances in the availability of multi-sensor, remote sensing-derived datasets, and machine learning algorithms can now provide an unprecedented possibility to predict flood events...     

Ensemble standardization constraints on the influence of the tree growth trends in dendroclimatology

Climate Dynamics - Tree growth trends can affect the interpretation of the response of tree-ring proxies (especially tree-ring width) to climate in the low-frequency band, which in turn may limit...     

ECMWF集合预报统计量在江西降水预报中的检验分析

利用江西省93个国家气象观测站降水量资料,对2014年ECMWF集合预报降水统计量进行逐6 h和24 h晴雨检验、降水分级检验及区域性暴雨检验。结果表明:1)10%、25%、Mode、融合、最小值在晴雨预报准确率方面较控制预报更有参考价值。2)对于全年降水分级检验,10%、25%、Mode、融合、最小值这5个统计量在小雨的预报方面较控制预报更有参考价值;中位数、概率对中雨的ETS评分要略高于控制预报;90%、75%、概率对大雨预报比控制预报好。对于暴雨预报,最大值、90%、融合比控制预报好;融合、最大值对大暴雨落区的指示意义不大,但对大暴雨量级降水的可能性可以供预报员参考。3)对于区域性暴雨预报,90%、融合、最大值的预报技巧比控制预报高,最大值虽然空报较严重,但对降水量级有一定的指示意义。集合预报各统计量对于强降水过程爆发或发展阶段的预报效果不如降水过程成熟期或末期好。     

Probabilistic estimates of future changes in California temperature and precipitation using statistical and dynamical downscaling

Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were downscaled with two statistical techniques and three nested dynamical regional climate models, although not all global models were downscaled with all techniques. Both monthly and daily timescale changes in T and P are addressed, the latter being important for a range of applications in energy use, water management, and agriculture. The T changes tend to agree more across downscaling techniques than the P changes. Year-to-year natural internal climate variability is roughly of similar magnitude to the projected T changes. In the monthly average, July temperatures shift enough that that the hottest July found in any simulation over the historical period becomes a modestly cool July in the future period. Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Annual and seasonal P changes are small compared to interannual or intermodel variability. However, the annual change is composed of seasonally varying changes that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly wetter conditions in the North of the state, while spring and autumn show less precipitation. The dynamical downscaling techniques project increasing precipitation in the Southeastern part of the state, which is influenced by the North American monsoon, a feature that is not captured by the statistical downscaling.