Isotope hydrological study of mean transit time in the granitic Strengbach catchment (Vosges massif,France): application of the FlowPC model with modified input function

Measurements of ¹⁸O concentrations in precipitation, soil solution, spring and runoff are used to determine water transit time in the small granitic Strengbach catchment (0·8 km²; 883–1146 m above sea level) located in the Vosges Mountains of northeastern France. Water transit times were calculated by applying the exponential, exponential piston and dispersion models of the FlowPC program to isotopic input (rainfall) and output (spring and stream water) data sets during the period 1989–95. The input function of the model was modified compared with the former version of the model and estimated by a deterministic approach based on a simplified hydrological balance. The fit between observed and calculated output data showed marked improvements compared with results obtained using the initial version of the model. An exponential piston version of the model applied to spring water indicates a 38·5 month mean transit time, which suggests that the volume in the aquifer, expressed in water depth, is 2·4 m. A considerable thickness (>45 m) of fractured bedrock may be involved for such a volume of water to be stored in the aquifer. Copyright © 2005 John Wiley & Sons, Ltd.     

Nutrient transports in a Swedish estuary

Himmerfjärd is a Swedish estuary bordering on the Baltic. The estuary lacks astronomical tides and its circulation is driven by winds and freshwater runoff. Because of a tertiary sewage treatment plant located at its inner end, the estuary is becoming increasingly eutrophic. A field study was carried out for a 78-day period in late summer and early fall of 1977 to determine rates of nutrient transport and to construct nutrient budgets. Since physical parameters (current velocity, temperature, salinity, winds and water level changes) were measured more frequently than nutrients (phosphate, nitrate, nitrite and ammonium) it was necessary to develop a suitable method to calculate nutrient flux time series and net nutrient fluxes. Over the study period, Himmerfjärd imported phosphorus and exported nitrogen. Direction of nutrient fluxes and changes in flux direction were consistent with the structure of the baroclinic currents.     

Use of multi‐platform,multi‐temporal remote‐sensing data for calibration of a distributed hydrological model: an application in the Arno basin,Italy

Images from satellite platforms are a valid aid in order to obtain distributed information about hydrological surface states and parameters needed in calibration and validation of the water balance and flood forecasting. Remotely sensed data are easily available on large areas and with a frequency compatible with land cover changes. In this paper, remotely sensed images from different types of sensor have been utilized as a support to the calibration of the distributed hydrological model MOBIDIC, currently used in the experimental system of flood forecasting of the Arno River Basin Authority. Six radar images from ERS‐2 synthetic aperture radar (SAR) sensors (three for summer 2002 and three for spring–summer 2003) have been utilized and a relationship between soil saturation indexes and backscatter coefficient from SAR images has been investigated. Analysis has been performed only on pixels with meagre or no vegetation cover, in order to legitimize the assumption that water content of the soil is the main variable that influences the backscatter coefficient. Such pixels have been obtained by considering vegetation indexes (NDVI) and land cover maps produced by optical sensors (Landsat‐ETM). In order to calibrate the soil moisture model based on information provided by SAR images, an optimization algorithm has been utilized to minimize the regression error between saturation indexes from model and SAR data and error between measured and modelled discharge flows. Utilizing this procedure, model parameters that rule soil moisture fluxes have been calibrated, obtaining not only a good match with remotely sensed data, but also an enhancement of model performance in flow prediction with respect to a previous calibration with river discharge data only. Copyright © 2006 John Wiley & Sons, Ltd.     

SIMULATION AND PREDICTION OF DEBRIS FLOW USING ARTIFICIAL NEURAL NETWORK

Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural haz-ard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting de-bris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and use-fill in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time se-ries of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collect-ed in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed.     

HYDROLOGIC SERIES CHARACTERISTICSANALYSIS OF THE MAJOR RIVERS AROUNDTHE TAKLIMAKAN DESERT

I.IN~crIOXLocatedinthecoddlepatofTallmBasin,withanareaof33.76X104klnZ,theTaldirnakan~isinthehinterlandofEurasia.Blockedbythehighmountainsaround,vapourofoceancanhardlyreachthedesert.APartfromthis,asthedeSertisinthesinkingcompensationareaoftheascensionalaircurrentaamstheQinghal-XIZangPlateau,itSprecipitationisrareandtheevaporationcapacityisintensealltheyearround.Allthesecontributetotheformationsofthetypifydrydesertclimate.Theacidityindexofthedesertandthearoundregionisashighasmorethan50,w…     

REVIEWS OF GEOGRAPHIC SOFTWARE

Reviews of geographic software in this article: DEMO-GRAPHICS: WORLD POPULATIONS AND PROJECTIONS. ESP GAUSS. CEMODEL S. Damus LIMDEP. William H. Greene MICROSTAT 4.1 OTIS PCIPS. (Personal Computer Image Processing System) . H.J. Meyers and R. Bernstein. REGRESSION ANALYSIS OF TIME SERIES (RATS) SPSS/PC+ URBAN DATA MANAGEMENT SOFTWARE (UDMS)     

Migration of the Ganga river and its implication on hydro-geological potential of Varanasi area, U.P., India

Borehole data reveals that during Late Quaternary, the Ganga river was non-existent in its present location near Varanasi. Instead, it was flowing further south towards peripheral craton. Himalayan derived grey micaceous sands were being carried by southward flowing rivers beyond the present day water divide of Ganga and mixed with pink arkosic sand brought by northward flowing peninsular rivers. Subsequently, the Ganga shifted to its present position and got incised. Near Varanasi, the Ganga river is flowing along a NW-SE tectonic lineament. The migration of Ganga river is believed to have been in response to basin expansion caused due to Himalayan tectonics during Middle Pleistocene times. Multi-storied sand bodies generated as a result of channel migration provide excellent aquifers confined by a thick zone of muddy sediments near the surface. Good quality potable water is available at various levels below about 70 m depth in sandy aquifers. Craton derived gravelly coarse-to-medium grained sand forms the main aquifer zones of tens of meter thickness with enormous yield. In contrast, the shallow aquifers made up of recycled interfluve silt and sandy silt occur under unconfined conditions and show water-level fluctuation of a few meters during pre-and post-monsoon periods.     

PSO-RBFNN模型及其在岩土工程非线性时间序列预测中的应用

岩土工程受力变形演化是一个典型的非线性问题,其演化的高度非线性和复杂性,很难用简单的力学、数学模型描述,但可用粒子群优化径向基神经网络对岩土工程应力、位移非线性时间序列进行动态实时预测。网络径向基层的单元数通过均值聚类法确定后,所有其它参数：中心位置、形状参数、网络权值,均通过粒子群优化算法在全局空间优化确定。工程实例应用表明,该模型预测结果准确、精度高,有良好的应用前景。     

西天山特克斯达坂库勒萨依序列埃达克岩的确立及钼找矿意义

库勒萨依序列斑岩体δ平均3.05,属于钙碱性系列岛弧浅成岩。 SiO₂含量为57.06％~70.74％,高Al₂O₃、Na₂O、Sr和相对富集LREE,低MgO、Y、Yb,强烈亏损HREE。Na₂O/K₂O＞1,Sr/Y平均57.67,正Eu异常,因而具有典型O型埃达克岩特征。库勒萨依序列斑岩体与Mo异常套合好,已发现钼矿体,找矿前景良好。该埃达克岩的确立,对深化西天山区域成矿规律和指导今后找矿具有借鉴和指导意义。     

基于分布式水文模拟的三峡区间洪水预报(Ⅰ)——模型构建及验证

长江三峡区间因暴雨形成的洪水峰高量大,对三峡水库的防洪安全和运行调度的影响很大.本论文依据三峡地区的地形地貌特征,采用基于GIS的机理性分布式水文模型,来模拟三峡区间入库洪水,以尽量减少洪水预报中的不确定性.利用近期建成的78个自动雨量站网监测的小时降雨信息作为模型的输入,对模型参数进行了率定和验证,结果表明:大多数洪水过程的模拟精度较好,但也有的模拟结果较差,其中降雨信息缺失是洪水预报不确定性的主要来源.