A review of the physics and ecological implications of the thermal bar circulation

Following recent applications of numerical modelling and remote sensing to the thermal bar phenomenon, this paper seeks to review the current state of knowledge on the effect of its circulation on lacustrine plankton ecosystems. After summarising the literature on thermal bar hydrodynamics, a thorough review is made of all plankton observations taken in the presence of a thermal bar. Two distinct plankton growth regimes are found, one with production favoured throughout the inshore region and another with a maximum in plankton biomass near the position of the thermal bar. Possible explanations for the observed distributions are then discussed, with reference to numerical modelling studies, and the scope for future study of this interdisciplinary topic is outlined.     

1990s长江流域降水趋势分析

依据国家气象局提供的实测月降水和日降水资料,运用Mann-Kendall(M-K)非参数检验法验证了降水趋势,并通过空间插补法,由点扩展到面,分析了1990s长江流域降水变化特征,发现1990s长江流域降水变化以降水在时间和空间分布上的集中度的增加为主要特点:时间上,年降水的增加趋势以冬季1月和夏季6月降水的集中增加为主;一日降水量大于等于50mm的暴雨日数和暴雨量在1990s也有了较明显的增加.空间上,年降水、夏季降水、冬季降水的增加都以中下游区的增加为主,尤其以鄱阳湖水系、洞庭湖水系的降水增加为主.1990s长江流域春季和秋季降水的减少以5月和9月两个汛期月份的降水减少为主,除金沙江水系和洞庭湖水系等少数地区外,流域大部分地区降水呈减少趋势.上述1990s出现的降水趋势明显与近年来全球变暖背景下长江流域各地区不同的温度及水循环变异有关.     

长江流域降水变化及其趋势演变

本文对中国长江流域降水趋势进行了分析.指出对月降水量而言,20世纪后50年不同区域出现1不胃的降水趋势变化特征.趋势插补法研究表明中国降水时空分布趋势十分明显.对长江流域长期降水资料分析研究指出夏季月份降水时间更集中,而对年降水而言在一些站则表现出明显的周期变化.     

1990s长江下游干流径流量演变趋势

利用M-K相关分析方法和大通站1950-2000年逐月径流资料,研究了长江下游干流径流的趋势变化.研究结果表明:1950s以来长江下游径流量呈增加趋势,1990s平均径流量(30415.3 m³/s)比所有其他年代平均径流都大,为近50a以来的最大值;就季节和月份而言,秋季径流明显减少;夏、冬两季径流量,增加的趋势明显,尤以冬季枯水季节径流增加最为突出.洪水、枯水季节径流增加明显,但以枯水季节径流增加占优势.冬季枯水径流的增加,可能在一定程度上能够缓解长江口生态环境的巨大压力.1990s径流量的增加与全球变暖、水循环加快、长江流域降水量增加密切相关.     

Testing a model for predicting the timing and location of shallow landslide initiation in soil‐mantled landscapes

The growing availability of digital topographic data and the increased reliability of precipitation forecasts invite modelling efforts to predict the timing and location of shallow landslides in hilly and mountainous areas in order to reduce risk to an ever‐expanding human population. Here, we exploit a rare data set to develop and test such a model. In a 1·7 km² catchment a near‐annual aerial photographic coverage records just three single storm events over a 45 year period that produced multiple landslides. Such data enable us to test model performance by running the entire rainfall time series and determine whether just those three storms are correctly detected. To do this, we link a dynamic and spatially distributed shallow subsurface runoff model (similar to TOPMODEL) to an in?nite slope model to predict the spatial distribution of shallow landsliding. The spatial distribution of soil depth, a strong control on local landsliding, is predicted from a process‐based model. Because of its common availability, daily rainfall data were used to drive the model. Topographic data were derived from digitized 1 : 24 000 US Geological Survey contour maps. Analysis of the landslides shows that 97 occurred in 1955, 37 in 1982 and ?ve in 1998, although the heaviest rainfall was in 1982. Furthermore, intensity–duration analysis of available daily and hourly rainfall from the closest raingauges does not discriminate those three storms from others that did not generate failures. We explore the question of whether a mechanistic modelling approach is better able to identify landslide‐producing storms. Landslide and soil production parameters were ?xed from studies elsewhere. Four hydrologic parameters characterizing the saturated hydraulic conductivity of the soil and underlying bedrock and its decline with depth were ?rst calibrated on the 1955 landslide record. Success was characterized as the most number of actual landslides predicted with the least amount of total area predicted to be unstable. Because landslide area was consistently overpredicted, a threshold catchment area of predicted slope instability was used to de?ne whether a rainstorm was a signi?cant landslide producer. Many combinations of the four hydrological parameters performed equally well for the 1955 event, but only one combination successfully identi?ed the 1982 storm as the only landslide‐producing storm during the period 1980–86. Application of this parameter combination to the entire 45 year record successfully identi?ed the three events, but also predicted that two other landslide‐producing events should have occurred. This performance is signi?cantly better than the empirical intensity–duration threshold approach, but requires considerable calibration effort. Overprediction of instability, both for storms that produced landslides and for non‐producing storms, appears to arise from at least four causes: (1) coarse rainfall data time scale and inability to document short rainfall bursts and predict pressure wave response; (2) absence of local rainfall data; (3) legacy effect of previous landslides; and (4) inaccurate topographic and soil property data. Greater resolution of spatial and rainfall data, as well as topographic data, coupled with systematic documentation of landslides to create time series to test models, should lead to signi?cant improvements in shallow landslides forecasting. Copyright © 2003 John Wiley & Sons, Ltd.     

Estimation of fundamental periods of shear‐wall dominant building structures

Shear‐wall dominant multistorey reinforced concrete structures, constructed by using a special tunnel form technique are commonly built in countries facing a substantial seismic risk, such as Chile, Japan, Italy and Turkey. In spite of their high resistance to earthquake excitations, current seismic code provisions including the Uniform Building Code (International Conference of Building Officials, Whittier, CA, 1997) and the Turkish Seismic Code (Specification for Structures to be Built in Disaster Areas, Ankara, Turkey, 1998) present limited information for their design criteria. In this study, consistency of equations in those seismic codes related to their dynamic properties are investigated and it is observed that the given empirical equations for prediction of fundamental periods of this specific type of structures yield inaccurate results. For that reason, a total of 80 different building configurations were analysed by using three‐dimensional finite‐element modelling and a set of new empirical equations was proposed. The results of the analyses demonstrate that given formulas including new parameters provide accurate predictions for the broad range of different architectural configurations, roof heights and shear‐wall distributions, and may be used as an efficient tool for the implicit design of these structures. Copyright © 2003 John Wiley & Sons, Ltd.     

Stochastic estimation of facies using ground penetrating radar data

Explicitly defining large-scale heterogeneity is a necessary step of groundwater model calibration if accurate estimates of flow and transport are to be made. In this work, neural networks are used to estimate radar facies probabilities from ground penetrating radar (GPR) images, yielding stochastic facies-based models that honour the large-scale architecture of the subsurface. For synthetic GPR images, a neural network was able to correctly identify radar facies with an accuracy of approximately 90%. Manual interpretation of a set of 450 MHz GPR field data from the Borden aquifer resulted in the identification of four radar facies. Of these, a neural network was able to identify two facies with an accuracy of near 80% and one with an accuracy of 44%. The neural network was not able to identify the fourth facies, likely due to the choice of defining facies characteristics. Sequential indicator simulation was used to generate facies realizations conditioned to the radar facies probabilities. Numerical simulations indicate that significant improvements in the prediction of solute transport are possible when GPR is used to constrain the facies model compared to using well data alone, especially when data are sparse.This work was supported by funding to R. Knight under Grant No. DE-FG07–00ER15118-A000, Environmental Management Science Program, Office of Science and Technology, Office of Environment Management, United States Department of Energy (DOE). However, any opinions, findings, conclusions, or recommendations expressed herein are those of the authors and do not necessarily reflect the views of DOE. Further support was provided by a Stanford Graduate Fellowship to S. Moysey. The authors would also like to thank James Irving for his assistance with processing of the radar data.     

On the Integrability of Limited-Area Numerical Weather Prediction Model ALADIN over Extended Time Periods

This note summarizes results of the first integration of regional numerical weather prediction model ALADIN in a climate mode. The ALADIN model, developed in an international cooperation led by Météo France, is operationally used for weather prediction. The grid step of the model is 12 km; the integration domain covers a major part of Europe. A one-month-long run has been performed with this model on observed boundary conditions (represented by assimilations by the global model ARPEGE). It is demonstrated that no excessive error is generated and accumulated in the model during the integration; hence the model is integrable for extended time periods and may serve a basis for a development towards a regional climate model.     

Large-scale floodplain modelling

A two-dimensional horizontal finite element numerical model (RMA-2) was applied to a 24 km river channel-floodplain reach in West Germany. Initial results indicate that finite element schemes may successfully estimate inundation in large-scale floodplain applications. Potentially, the resulting detailed velocity vector distributions and identification of inundation zones throughout storm events could provide an insight into the present day sedimentary environment on the floodplain.     

The new approach to global deep sounding

The new approach to global geomagnetic sounding is developed to overcome difficulties of spherical harmonical analysis and subsequent transfer function determination. The approach is based on minimizing the discrepancy between experimental and simulated magnetic fields. The discrepancy is considered as a function of the medium model parameters and the coefficients of external fields. The method can be used for laterally inhomogeneous as well as homogeneous earth models. An example of its application to a radially symmetric model is demonstrated.