Robin T. Clarke

Measurement of river level variations with satellite altimetry

Quantitative assessment of water levels and river discharge is often made difficult by large distances, limited access, and low population densities in remote areas. Satellite altimetry provides a repetitive remote sensing approach to determining river levels at a number of locations within a river system, providing the orbital repeat cycle is short enough in time, the ground track maintains a stable repeat over previous locations, and the return power of the altimeter signal can be readily identified and located. The U.S. Navys Geosat radar altimeter mission between 1985 and 1989 provided the first altimeter measurements with sufficient precision and extended duration to examine the utility of such measurements for long-term monitoring of inland waters. These measurements have been examined over the Amazon basin. Satellite observations are retrieved at four locations that overlap with river gauge measurements. A technique is developed to isolate radar return signals from the river. Two years of satellite measurements are compared with the river gauge retrievals. The overall level of comparison is 0.7 m rms when the technique is applied manually, and 1.2 m rms when an automated version of the method is applied. At one location the average difference is 0.2 m rms. This level of accuracy may not be useful for routine hydrological measurements. However, there are a variety of difficulties that are specific to the Geosat altimeter measurement over rough terrain. Present altimeter satellites, ERS 1 (launched June 1991) and TOPEX/Poseidon (launched August 1992), correct many of these problems. This study suggests that the prospect for obtaining useful measurements of river level from space is promising.

Adaptive image denoising using scale and space consistency

This paper proposes a new method for image denoising with edge preservation, based on image multiresolution decomposition by a redundant wavelet transform. In our approach, edges are implicitly located and preserved in the wavelet domain, whilst image noise is filtered out. At each resolution level, the image edges are estimated by gradient magnitudes (obtained from the wavelet coefficients), which are modeled probabilistically, and a shrinkage function is assembled based on the model obtained. Joint use of space and scale consistency is applied for better preservation of edges. The shrinkage functions are combined to preserve edges that appear simultaneously at several resolutions, and geometric constraints are applied to preserve edges that are not isolated. The proposed technique produces a filtered version of the original image, where homogeneous regions appear separated by well-defined edges. Possible applications include image presegmentation, and image denoising.

Environmental Issues in the la Plata Basin

Abstract The la Plata River basin is one of the largest in the world, with an area of about 3 million km2. It includes parts of five countries (Argentina, Bolivia, Brazil, Paraguay and Uruguay) and the water resources of the la Plata basin are essential for their economic development. The main tributaries are the rivers Parana, Paraguay and Uruguay. Some of the main developments having environmental impacts that influence the basin have been the following: (1) developments of many hydropower reservoirs in the Upper Parana River, in Brazil, from 1960-90; (2) deforestation in the Parana, Uruguay and Paraguay basins from 1950-90; (3) introduction of intensive agricultural practice after 1970; (4) urban developments with change to flood regimes; and (5) navigation and conservation of the Upper Paraguay River. Since 1970, flow increases have been observed which may have been caused by changes in vegetation cover, or by climate variation. The changes have raised important issues for both water resource developm...

Time trends in rainfall records in Amazonia

Abstract This paper reports the results of statistical analyses for the detection of time trend in 48 rainfall records from sites in the Amazon Basin with more than 15 yr of record. Using a nonparametric test for trend in monthly rainfall, three results emerge: (a) irrespective of the statistical significance of time trends, positive and negative trends occur with approximately equal frequencies over the Brazilian Amazon hydrographic basin; (b) the number of statistically significant time trends, whether positive or negative of statistically significant time trends, whether positive or negative, is very much greater than can be ascribed to chance variation; (c) significantly negative time trends are more common than significantly positive time trends in monthly rainfall. Over the period of approximately 30 yr covered by the records, during which deforestation has been rapid, negative trends seem to have occurred more frequently in two regions of western and central Amazonia, and positive trends more frequ...

Digital soil mapping using multiple logistic regression on terrain parameters in southern Brazil

Soil surveys are necessary sources of information for land use planning, but they are not always available. This study proposes the use of multiple logistic regressions on the prediction of occurrence of soil types based on reference areas. From a digitalized soil map and terrain parameters derived from the digital elevation model in ArcView environment, several sets of multiple logistic regressions were defined using statistical software Minitab, establishing relationship between explanatory terrain variables and soil types, using either the original legend or a simplified legend, and using or not stratification of the study area by drainage classes. Terrain parameters, such as elevation, distance to stream, flow accumulation, and topographic wetness index, were the variables that best explained soil distribution. Stratification by drainage classes did not have significant effect. Simplification of the original legend increased the accuracy of the method on predicting soil distribution.

Use of classification and regression trees (CART) to classify remotely-sensed digital images

Binary tree-structured rules can be viewed in terms of repeated splits of subsets of the feature space into two descendant subsets, starting from the entire feature space and ending in a partition of the feature space associated with each class. This paper presents a brief introduction to binary decision trees and shows results obtained in the classifying Landsat-TM and AVIRIS digital images.

Rainfall-based real-time flood forecasting

Abstract The use of conceptual rainfall-runoff models in real-time flood forecasting still presents problems, some of which relate to the updating of the mathematical model and to uncertainties associated with future rainfall. Both topics are approached in this study, in which a conceptual rainfall-runoff model (IPH-II) for real-time flood forecasting and a simplified stochastic model to determine the value of including quantitative rainfall forecasts were used. The methods were tested using data from a small watershed (the River Ray at Grendon Underwood, UK), for which 17 years of records were available. The results show that a simple method used to forecast rain falling during the next few hours, may help to improve real-time discharge estimates.

Método "fingerprinting" para identificação de fontes de sedimentos em bacia hidrográfica rural

In this study, the fingerprinting method was used to identify sediment sources in a small rural watershed located in a mountainous region in southern Brazil. This watershed represents a typical agricultural ecosystem, in which smallhold farmers cultivate tobacco in marginal areas, with severe consequences to the natural resources. The identification of sediment sources, based on analysis of sediments transported in suspension, enables an evaluation of which areas are truly contributing to sediment production and, therefore, to make inferences about the polluting potential of these sediments. The study considered the main limitations of this method, as well as its potential in the identification of sediment sources. Through classification, it was possible to identify the relative contribution of these different sources, with fields and roads being predominant. Results showed that the contribution from these two sources varied over time and that the relative proportion of the contribution from each source varied according to management and soil cover in the fields and maintenance work performed in the roads. The average contribution from fields and roads was 64 and 36%, respectively.

Likelihood-based confidence intervals for estimating floods with given return periods

This paper discusses aspects of the calculation of likelihood-based confidence intervals for T-year floods, with particular reference to (1) the two-parameter gamma distribution; (2) the Gumbel distribution; (3) the two-parameter log-normal distribution, and other distributions related to the normal by Box-Cox transformations. Calculation of the confidence limits is straightforward using the Nelder-Mead algorithm with a constraint incorporated, although care is necessary to ensure convergence either of the Nelder-Mead algorithm, or of the Newton-Raphson calculation of maximum-likelihood estimates. Methods are illustrated using records from 18 gauging stations in the basin of the River Itajai-Acu, State of Santa Catarina, southern Brazil. A small and restricted simulation compared likelihood-based confidence limits with those given by use of the central limit theorem; for the same confidence probability, the confidence limits of the simulation were wider than those of the central limit theorem, which failed more frequently to contain the true quantile being estimated. The paper discusses possible applications of likelihood-based confidence intervals in other areas of hydrological analysis.

Simulating Effects of Fiber Crimp, Flocculation, Density, and Orientation on Structure Statistics of Stochastic Fiber Networks:

The Neyman-Scott process is adapted to the problem of simulating the statistical properties of stratified stochastic fibrous materials. The simulations suggest a relationship between the mean number of fibers per zone and mean voids, independent of the nature of the stochastic fibrous structure: the characteristic shape of the transfer function curve persists whether or not the structure contains crimped fibers or if it is random or flocculated, isotropic or anisotropic. This could be an important universal effect. The mean and standard deviation turn out to be positively related for some fiber network parameters, such as mean voids, fiber density, and mean number of fiber bounds. Also, the simulations suggest that fiber crimp has a higher impact on isotropic structures. As crimp is increased, isotropic structures tend to present smaller mean voids, higher mean number of fibers per zone, and higher total number of bonds per fiber than anisotropic structures.