Evaluation of rainfall-runoff performance using the stochastic integral equation method

The stochastic integral equation method (S.I.E.M.) is used to evaluate the relative performance of a set of both calibrated and uncalibrated rainfall-runoff models with respect to prediction errors. The S.I.E.M. is also used to estimate confidence (prediction) interval values of a runoff criterion variable, given a prescribed rainfall-runoff model, and a similarity measure used to condition the storms that are utilized for model calibration purposes.Because of the increasing attention given to the issue of uncertainty in rainfall-runoff modeling estimates, the S.I.E.M. provides a promising tool for the hydrologist to consider in both research and design.     

A formulation for diagnostic anomaly models

It is shown that the anomaly model governing the difference between two integrations of the equations of motion can be written in a form that is formally linear in the anomalies, by choosing the basic state to be the mean of the results of the two integrations.     

Vertical distribution features of atmospheric water vapour in the Po valley area

Two sets of radiosounding measurements, taken at different hours from some stations in the Po Valley area, were examined in order to obtain the values of precipitable waterw and determine the shapes of the vertical distribution curves of absolute humidity. From these curves, we calculated the bestfit values of scale heightsH ₁ andH ₂ in the 0÷5 km and 5÷10 km altitude intervals, respectively. The analysis of the radiosounding data clearly shows that parametersw andH ₁ are closely related to the evolutionary features of the meteorological conditions on synoptic scale and are influenced, to a lesser extent, by the diurnal variations in the atmospheric ground layer. Seasonal average curves of temperature and absolute humidity, as functions of altitude, were also defined from the radiosounding measurements taken at various hours of the day. Moreover, interpolation methods in time (applied to a 12-hour range) and in space (range of about 300 km) were proposed for determining parametersw andH ₁ from the radiosounding measurements taken at different hours from the same station or from different stations at the same hour. Reliability tests, made by comparing the same station or from different stations at the same hour. Reliability tests, made by comparing the values given by the interpolation methods with those directly obtained from the radiosounding measurements, show that these evaluations ofw andH ₁ are affected by standard errors of estimate, which are comparable to the errors usually made in analyzing the radiosounding data.     

A four-parameter,two degree-of-freedom block-spring model: Effect of the driver velocity

We analyze the effect of tectonic plate velocities in the earthquake pattern using a simple mass-spring model of the Burridge and Knopoff type with two blocks and a velocity-weakening friction law. Previous versions of the two-block model assume a steady driver during slip events (limit of zero driver velocity), which, in some cases makes necessary the introduction of artificial parameters to start the numerical integration of the equations of motion at impending slip of any block. Still maintaining the condition of zero driver velocity during slip, we shall introduce a procedure to start the numerical integration without introducing artificial parameters and this will be done by using a linearized version of the equations of motion valid for small velocities and considering nonzero driver velocity. We also introduce a four parameter model in which the driver velocity enters the equations during the whole simulation, and analyze the effect of the new parameter, the driver velocity, in the displacement and time patterns of blocks motion, directly related to earthquake statistics such as coseismic slips and average repeat times.     

Rangeland production: use of models incorporating aggregated knowledge and fuzzy construction

This work reports on a method using fuzzy membership functions to construct an aggregated interaction matrix in which the summation of variables is scaled according to the way rainfall and soil variables affect water availability to plants and hence influence rangeland productivity. Aggregation of the variables gives a comprehensive value which can be used to predict production. The model increases the predicability of production to 81% compared to models using rainfall alone and a multiplicative parametric one which give predictibilities of 61 and 76% respectively. The results showed that (1) the importance of rainfall in determining production was most important at lower rainfalls i.e. <350 mm; (2) soil texture and particularly slope were important through out the rainfall range (149–700 mm) investigated; and (3) soil depth was only important at the higher >350 mm rainfalls. The aggregated interaction matrix gives a measure of land productive capability.     

Structural Spatial Interaction*

This paper identifies a concept of structural spatial interaction. Although it is defined differently from its counterpart of structural unemployment in economics, it attempts to define a similar idea. In economics, the focus is on equilibrium in the supply and demand of labor. In this paper structural interaction is that which minimizes aggregate distance travelled, subject to constraints on origins and destinations. The structural interaction model produces an equilibrium, therefore, in the supply and demand of movers, while identifying a spatially efficient trip distribution. It is portrayed as a type of relaxed interaction model and is illustrated using an example of structural migration with Canadian interprovincial migration data.     

Study of the crust and mantle using magnetic surveys by Magsat and other satellites

Among the first measurements made from near-Earth orbiting satellites were measurements of the magnetic field. The sources of that field lie both within the Earth, in its core and crust, and in the surrounding ionosphere and magnetosphere. This article summarizes some of the methodology and results for studies of the Earth’s mantle and crust. Mantle conductivity studies can be made either by studying signals impressed on the Earth from outside, e.g., the ionosphere or magnetosphere, or by studying signals originating in the core and transmitted through the mantle. Crustal field studies begin with a careful selection of the data and subsequent removal of core and external fields by some sort of filtering. Average maps from different local times sometimes differ, presumably due to the remaining presence of fields of external origin. Several techniques for further filtering are discussed. Where large-area aeromagnetic maps are available, crustal maps derived from satellite data can be compared with upward continued data. In general, the comparisons show agreement, with some differences, particularly in and near the auroral belts. The satellite data are further reduced by various methods of inverse and forward modelling, sometimes including reduction to the pole (RTP). These techniques are generally unstable at the equator. Common methods of stabilizing the inversions include principle components analysis and ridge regression. Because of the presence of the core field, the entire crustal contribution from the field is not known. Also, there is a basic nonuniqueness to the inverse solutions. Nevertheless, magnetizations that are interpretable can be derived.     

The equivalence of predictions from universal kriging and intrinsic random-function kriging

A proof is provided that the predictions obtained from kriging based on intrinsic random functions of orderk are identical to those obtained from anappropriate universal kriging model. This is a theoretical result based on known variability measures. It does not imply that people performing traditional universal kriging will get the same predictions as those using intrinsic random functions, because traditionally these methods differ in how variability is modeled. For intrinsic random functions, the same proof shows that predictions do not depend on the specific choice of the generalized covariance function. It is argued that the choice between these methods is really one of modeling and estimating the variability in the data.