Recognition and mapping of lacustrine relict coastal features using high resolution aerial photographs and LiDAR data

Shallow lakes in semiarid regions experience frequent water level fluctuations. Each long-lasting episode of water-level lowering leaves behind abandoned littoral forms and deposits whose identification and mapping is hampered by their smooth relief. Given the difficulty of recognising these possible relict forms using traditional geomorphological techniques, two sources of information were employed in the present work: high resolution (1:15,000) aerial photographs and a digital terrain model (DTM) generated from LiDAR data. The improved definition of surface elevation enhanced the quality of geomorphological mapping as well as the accurate delineation of subtle geoforms. The method was applied to Gallocanta Lake, a highly fluctuating shallow lake 14 km² in area and less than 3 m deep located in a mountainous semiarid area of NE Spain. As a result, a sequence of relict coastal features (RCF) with high lateral continuity has been identified around the lakebed. These include well-preserved spits with recurved hooks, counter-spits, bays closed by barrier islands, beach ridges, deltas and cliffs. The highly precise LiDAR-derived topographic maps suggest a much greater extension of the lacustrine environment during the Late Pleistocene, reaching at least 51 km² of water surface and about 13 m of depth above the present lake bottom. The method presented in this paper generates very detailed palaeogeographical maps that are particularly useful for reconstructing lake changes in semiarid environments as a function of climate change.     

Testing spatial heterogeneity in geographically weighted principal components analysis

We propose a method to evaluate the existence of spatial variability in the covariance structure in a geographically weighted principal components analysis (GWPCA). The method, that is extensive to locally weighted principal components analysis, is based on performing a statistical hypothesis test using the eigenvectors of the PCA scores covariance matrix. The application of the method to simulated data shows that it has a greater statistical power than the current statistical test that uses the eigenvalues of the raw data covariance matrix. Finally, the method was applied to a real problem whose objective is to find spatial distribution patterns in a set of soil pollutants. The results show the utility of GWPCA versus PCA.     

Statistical properties of 49 years of rainfall rate events

Summary The paper presents a detailed investigation of magnitudes and properties pertaining to the large population of rainfall events recorded during 49 years at the Fabra Observatory in Barcelona. The study includes a statistical analysis of event durationT and rainfall quantityQ together with the statistical rainfall rate parameters: , ²(R) and maximum Rm within an event. The decorrelation time is also analysed. It is found thatQ, T, and can be well modelled by a lognormal distribution, but , ² and Rm are only so for a limited range of precipitation ratesR. The regression analysis between pairs of logarithms of the magnitudes investigated is generally good and a regression coefficient is often better than 0.9. Comparison with published work is also carried out. An attempt is made to discriminate between heavy and non-heavy rainfall rates, and the 50 mm/h threshold is used for the study. The twelve-monthly running average indicates that the rainfall amount has a small increasing trend over the fifty year period. However, this trend is reversed when considering heavy rains. Finally, the return period in years to exceeding a thresholdR within an event is also investigated and the distribution of the population of annual extremes is found to be Gumbel II.With 6 Figures     

Porosity and size gradation of saturated gravel with percolated fines

Fine particles may infiltrate through coarse alluvial beds and eventually saturate the subsurface pore space. It is essential to understand the conditions that lead to bed saturation, and to forecast the packing characteristics of saturated beds to assess the effect of excess fine sediment supply on a number of processes that occur in the stream–sediment boundary. To address this problem, in this study, a new method is introduced to predict the grain‐size distribution for the saturated condition, and the resulting porosity decrease, given the characteristics of the bed and the supplied sediments. The new method consists of the numerical aggregation of infilling fines in a finite bed volume, during which the bed properties change to affect further infilling. An existing semi‐empirical, particle packing model is implemented to identify these properties. It is shown that these types of models are adequate to describe regimes of natural sediment fabric quantitatively, and are thus useful tools in the analysis of sediment infiltration processes. Unlike previous developments to quantify saturated bed conditions, which assume that the supplied material is uniform and finer than the bed pore openings, the method developed herein considers poorly sorted fines, and can identify size fractions that are able to ingress into the bed due to being smaller than the particles that form the bed structure. Application of the new method to published experimental data showed that the final content of infiltrated fines is strongly sensitive to the initial bed packing density, highlighting the need to measure and understand open‐work gravel deposits. In addition, the new method was shown to be suitable for assessing the degree of bed saturation, when it was applied to a published data set of field samples.