COMPUTING FIELD STATICS WITH THE HELP OF SEISMIC TOMOGRAPHY*

Field static corrections in general need be applied to all onshore seismic reflection data to eliminate the disturbing effects a weathering layer or near-surface low velocity zone has on the continuity of deep seismic reflections. The traveltimes of waves refracted at the bottom of the low velocity zone (or intermediate refracting interfaces) can often be observed as first breaks on shot records and used to develop a laterally inhomogeneous velocity model for this layer, from which the field static corrections can then be obtained. A simple method is described for computing accurate field statics from first breaks. It is based on a linearization principal for traveltimes and leads to the algorithms that are widely and successfully applied within the framework of seismic tomography. We refine an initial model for the low velocity layer (estimated by a standard traveltime inversion technique) by minimizing the errors between the observed first arrivals on field records and those computed by ray theory through an initial model of the low velocity layer. Thus, one can include more lateral velocity variations within the low velocity layers, which are important to obtain good field static corrections. Traditional first break traveltime inversion methods cannot, in general, provide such refined velocity values. The technique is successfully applied to seismic data from the Amazon Basin. It is based on a simple model for the low velocity layer that consists of an undulating earth surface and one planar horizontal refractor overlain by a laterally changing velocity field.     

Mudslides Developed on Waitemata Group Rocks, Tawharanui Peninsula, North Auckland

On Tawharanui Peninsula mudslides occur in soils developed on Waitemata Group flysch similar to that underlying much of the Auckland metropolitan area. Mudslides are shallow soil failures with a bowlshaped source area, an elongated track, and an accumulation zone. Movement may continue for decades to centuries. Modelling suggests that failure of saturated soils occurs along a basal surface defined by the regolith / bedrock contact; rates of movement and time of initiation have not been established. Reactivation or increased mobility of mudslides due to climatic change represents an increased hazard for infrastructure in Auckland City.     

旋转流场中的格子波耳兹曼模型

在大尺度的旋转流场中,由于存在哥氏力,使流体的流动出现了一系列复杂的动力学现象.在原格子Boltzmann模型研究的基础上,引入了哥氏力效应,发展了一个旋转流场中的格子Boltzmann模型.从该模型出发可导出地球流体力学方程,用这一模型对理想边界条件下的北半球大气环流进行了数值计算.数值结果很好地再现了大尺度地转流的流动特征.从理论和数值实验上验证了该模型的适用性.     

INTERRELATIONSHIP BETWEEN WATER QUALITY AND GROUNDWATER FLOW DYNAMICS IN A SMALL WETLAND SYSTEM ALONG A SANDY HILL RIDGE

Detailed modelling of the hydrological setting of fen meadows appears to be possible provided that detailed information on geomorphology, hydrochemistry and piezometric heads is available for a number of years. In the Laegieskamp, a small wetland reserve located in the central part of The Netherlands, a piezometric monitoring network was sampled for water quality analysis and piezometric heads between 1986 and 1992. Average yearly discharge and recharge periods were used for FLOWNET calculations. First, the models were used to determine, with the help of information on water quality, the hydrological systems in the study area. Secondly, they were used to define the present and past hydrological setting of a fen meadow in the reserve. The hydrological systems and water quality in the study area have changed considerably over the past 65 years. At present the fen meadow is mainly fed by precipitation. The mineral-rich conditions favouring the fen meadow vegetation are thought to be maintained thanks to a clayey peat layer and an oscillating shallow water body that prevents rapid leaching of minerals. The sulphate content in the fen exhibits a pattern of temporal variation, which is related to the severity of the annual drought. Our study showed that groundwater flow is mainly lateral, instead of the assumed vertical infiltration of groundwater in previous regional studies. This led us to the conclusion that conservation and restoration perspectives are much better than previously expected. The polluted middle, deep groundwater is not a major threat to this fen at the moment. © 1997 John Wiley & Sons, Ltd.     

SURFACE ROUGHNESS EVOLUTION OF SOILS CONTAINING ROCK FRAGMENTS

Soil surface roughness is a dynamic property which determines, to a large extent, erosion and infiltration rates. Although soils containing rock fragments are widespread in the Mediterranean region, the effect of the latter on surface roughness evolution is yet poorly understood. Therefore, laboratory experiments were conducted in order to investigate the effect of rock fragment content, rock fragment size and initial moisture content of the fine earth on the evolution of interrill surface roughness during simulated rainfall. Surface elevations of simulated plough layers along transects of 50 cm length were measured before and after simulated rainfall (totalling 192.5 mm, I = 70 mm h⁻¹) with a laser microreliefmeter. The results were used to investigate whether systematic variations in interrill surface roughness along stony hillslopes in southeastern Spain could be attributed to rock fragment cover and rock fragment size. Soil surface elevations were measured along the contour lines (50 cm long transects) with a contact microreliefmeter. Roughness was expressed by two parameters related to the height and frequency of roughness elements, respectively: standard deviation of de-trended surface elevations (random roughness: RR), and correlation length (L) derived from exponential fits of the autocorrelation functions. The frequently used assumption that surface roughness (RR) of cultivated topsoils decreases exponentially with cumulative rain is not valid for soil surfaces covered by rock fragments. The RR of soils containing small rock fragments (1.7–2.7 cm) increased with cumulative rainfall after an initial decrease during the first 17.5 mm of rainfall. For soils containing large rock fragments (7.7 cm), RR increased with rainfall above a threshold rock fragment content by mass of 52 per cent. For a given rainfall application, RR increased non-linearly with rock fragment content. The correlation length for soils containing small rock fragments decreases with rock fragment content and is significantly lower than for soils with large rock fragments. Soils covered with small rock fragments (large RR and small L) are thus well protected against raindrop impact by a water film in the depressions between the rock fragments. On abandoned agricultural fields along hillslopes in southeastern Spain, rock fragments cover increases non-linearly with slope owing to selective erosion of finer particles on steep slopes. The increase of surface cover by large rock fragments (>25 mm) is even more pronounced. The simultaneous increase of rock fragment cover and rock fragment size with slope explains the non-linear increase of RR with slope. These relationships differ for soils covered by platy misaschists and those covered with cubic andesites. The variations in correlation length along the hillslopes are not clear, probably owing to a simultaneous increase in rock fragment cover and rock fragment size. These findings may provide a better prediction of soil surface roughness of interrill areas covered by rock fragments using slope angle and lithology.     

ON THE NECESSITY OF APPLYING A ROTATION TO INSTANTANEOUS VELOCITY MEASUREMENTS IN RIVER FLOWS

In studies on river channel flow turbulence, it is often the case that the measured mean vertical velocity is different from zero, indicating that the frame of reference of the current meter is not parallel to the flow streamline. This situation affects the estimate of Reynolds shear stress in the streamwise and vertical planes and consequently the analysis of the flow turbulent structure. One way to solve this problem is to correct data by applying a rotation and this is reviewed in the first part of the paper. However, in fluvial geomorphology, the studied flow is often complex and streamlines may exhibit significant changes from one point of measurement to the other. In this context, applying a rotation complicates the situation more than it simplifies it. The second part of this paper examines the question of velocity data correction in complex flows using a field example of the turbulent boundary layer over a very rough gravel bed and a laboratory example taken from flow at a river channel confluence. In both cases, velocity vectors are spatially variable. In the first case, errors in the Reynolds shear stress estimates are relatively low (ranging from −13 to 7 per cent/deg) while in the second case, they are much larger (−200 to 164 per cent/deg). The significance of these errors on the interpretation of turbulence statistics in river channel flows is discussed. We propose that corrections should be applied in all clear cases of sensor misalignment and when the frame of reference changes spatially and temporally. However, no corrections should be used where different flow velocity vector orientations, not sensor misalignment, are responsible for the mean vertical velocity differing from zero.     

LISEM: A SINGLE-EVENT,PHYSICALLY BASED HYDROLOGICAL AND SOIL EROSION MODEL FOR DRAINAGE BASINS. II: SENSITIVITY ANALYSIS,VALIDATION AND APPLICATION

A new hydrological and soil erosion model has been developed and tested: LISEM, the Limburg soil erosion model. The model uses physically based equations to describe interception, infiltration and soil water transport, storage in surface depressions, splash and flow detachment, transport capacity and overland and channel flow. From the validation results it is clear that, although the model has several advantages over other models, the results of LISEM 1.0 are far from perfect. Based on the sensitivity analysis and field observations, the main reasons for these differences seems to be the spatial and temporal variability of the soil hydraulic conductivity and the initial pressure head at the basin scale. Another reason for the differences between measured and simulated results is our lack or understanding of the theory of hydrological and soil erosion processes.     

SPATIAL AND TEMPORAL VARIABILITY OF SOIL SURFACE ROUGHNESS AND THE APPLICATION IN HYDROLOGICAL AND SOIL EROSION MODELLING

Accurate estimations of water retention and detention are needed to simulate surface runoff and soil erosion following a rainfall event in a catchment. Several equations to estimate the amount of surface depressional storage, the fraction of the soil surface covered by water and the amount of rainfall excess needed to start surface runoff have been developed by Onstad (1984). The random roughness and slope gradient are needed for those estimations. Surface micro-elevation data have been gathered by a photographic method. The random roughness was determined from those elevation measurements. Several factors which have an impact on the soil surface roughness were taken into account. The main sources of influence are the type of land use, the crop stage within the growing period and tillage direction. Analyses of variance indicated that the variation in the RR-index could be explained mainly by type of land use, orientation and field type. The temporal variation was relatively small. Gradient data have been determined from a digital elevation model, constructed by digitizing contours. Combining the random roughness and the steepness of slope, the amounts of surface water retention and detention could be estimated. Knowledge of water retention and detention will improve the estimations of runoff and soil erosion modelling in catchments, such as those made with the LISEM model. The agricultural systems examined in this study have similar random roughness values in summer. Different soil erosion rates for several types of land use can not therefore be explained by the random roughness.     

ESTIMATING INFILTRATION PARAMETERS FROM BASIC SOIL PROPERTIES

Infiltration data were collected on two rectangular grids with 25 sampling points each. Both experimental grids were located in tropical rain forest (Guyana), the first in an Arenosol area and the second in a Ferralsol field. Four different infiltration models were evaluated based on their performance in describing the infiltration data. The model parameters were estimated using non-linear optimization techniques. The infiltration behaviour in the Ferralsol was equally well described by the equations of Philip, Green–Ampt, Kostiakov and Horton. For the Arenosol, the equations of Philip, Green–Ampt and Horton were significantly better than the Kostiakov model. Basic soil properties such as textural composition (percentage sand, silt and clay), organic carbon content, dry bulk density, porosity, initial soil water content and root content were also determined for each sampling point of the two grids. The fitted infiltration parameters were then estimated based on other soil properties using multiple regression. Prior to the regression analysis, all predictor variables were transformed to normality. The regression analysis was performed using two information levels. The first information level contained only three texture fractions for the Ferralsol (sand, silt and clay) and four fractions for the Arenosol (coarse, medium and fine sand, and silt and clay). At the first information level the regression models explained up to 60% of the variability of some of the infiltration parameters for the Ferralsol field plot. At the second information level the complete textural analysis was used (nine fractions for the Ferralsol and six for the Arenosol). At the second information level a principal components analysis (PCA) was performed prior to the regression analysis to overcome the problem of multicollinearity among the predictor variables. Regression analysis was then carried out using the orthogonally transformed soil properties as the independent variables. Results for the Ferralsol data show that the parameters of the Green–Ampt and Kostiakov model were estimated relatively accurately (maximum R² = 0.76). For the Arenosol, use of the second information level together with PCA produced regression models with an R² value ranging from 0.38 to 0.68. For the Ferralsol, most of the variance was explained by the root content and organic matter content. In the Arenosol plot, the fractions medium and fine sand explained most of the observed variance.