Independent measurement of biogenic silica in sediments by FTIR spectroscopy and PLS regression     

Carsten Meyer-Jacob  Hendrik Vogel  Florian Boxberg  Peter Rosén  Michael E. Weber  Richard Bindler 《Journal of Paleolimnology》2014,52(3):245-255

We present an independent calibration model for the determination of biogenic silica (BSi) in sediments, developed from analysis of synthetic sediment mixtures and application of Fourier transform infrared spectroscopy (FTIRS) and partial least squares regression (PLSR) modeling. In contrast to current FTIRS applications for quantifying BSi, this new calibration is independent from conventional wet-chemical techniques and their associated measurement uncertainties. This approach also removes the need for developing internal calibrations between the two methods for individual sediments records. For the independent calibration, we produced six series of different synthetic sediment mixtures using two purified diatom extracts, with one extract mixed with quartz sand, calcite, 60/40 quartz/calcite and two different natural sediments, and a second extract mixed with one of the natural sediments. A total of 306 samples—51 samples per series—yielded BSi contents ranging from 0 to 100 %. The resulting PLSR calibration model between the FTIR spectral information and the defined BSi concentration of the synthetic sediment mixtures exhibits a strong cross-validated correlation ( \( {\text{R}}^{ 2}_{\text{cv}} \)  = 0.97) and a low root-mean square error of cross-validation (RMSECV = 4.7 %). Application of the independent calibration to natural lacustrine and marine sediments yields robust BSi reconstructions. At present, the synthetic mixtures do not include the variation in organic matter that occurs in natural samples, which may explain the somewhat lower prediction accuracy of the calibration model for organic-rich samples.  相似文献   

Variability and alterations of water temperatures across the Elbe and Danube River Basins     

Danijela Markovic  Ulrike Scharfenberger  Stefan Schmutz  Florian Pletterbauer  Christian Wolter 《Climatic change》2013,119(2):375-389

Climate change is predicted to be a major threat to river ecosystems in the 21st century, but long-term records of water temperature in streams and rivers are rare. This study uses long-term water temperature series from the Elbe and the Danube River Basin to quantify the variability, magnitude, and extent of temperature alterations at different time scales. The observed patterns in monthly and daily water temperatures have been successfully described through statistical models based on air temperature, river discharge and the North Atlantic Oscillation Index. These models reveal that air temperature variability describes more than 80 % of the total water-temperature variability, linking anticipated changes in water temperature mainly to those in air temperature. The North Atlantic Oscillation effect deteriorates with decreasing latitude, while the discharge effect becomes more important and increases with the increase in discharge amount. The detected water temperature alterations include a phase shift in spring warming of almost 2 weeks, an increase in the number of days with temperatures above 25 °C and an increase in the duration of summer heat stress. These findings underline a significant risk for fundamental changes in river ecosystems, specifically in disruption of established patterns in food-web synchrony, and may lead to significant distortions in community structure and composition.  相似文献   

Probabilistic GIS-based method for delineation of urban flooding risk hotspots   总被引：3，自引：1，他引：2  

Fatemeh Jalayer  Raffaele De Risi  Francesco De Paola  Maurizio Giugni  Gaetano Manfredi  Paolo Gasparini  Maria Elena Topa  Nebyou Yonas  Kumelachew Yeshitela  Alemu Nebebe  Gina Cavan  Sarah Lindley  Andreas Printz  Florian Renner 《Natural Hazards》2014,73(2):975-1001

Identifying urban flooding risk hotspots is one of the first steps in an integrated methodology for urban flood risk assessment and mitigation. This work employs three GIS-based frameworks for identifying urban flooding risk hotspots for residential buildings and urban corridors. This is done by overlaying a map of potentially flood-prone areas [estimated through the topographic wetness index (TWI)], a map of residential areas and urban corridors [extracted from a city-wide assessment of urban morphology types (UMT)], and a geo-spatial census dataset. A maximum likelihood method (MLE) is employed for estimating the threshold used for identifying the flood-prone areas (the TWI threshold) based on the inundation profiles calculated for various return periods within a given spatial window. Furthermore, Bayesian parameter estimation is employed in order to estimate the TWI threshold based on inundation profiles calculated for more than one spatial window. For different statistics of the TWI threshold (e.g. MLE estimate, 16th percentile, 50th percentile), the map of the potentially flood-prone areas is overlaid with the map of urban morphology units, identified as residential and urban corridors, in order to delineate the urban hotspots for both UMT. Moreover, information related to population density is integrated by overlaying geo-spatial census datasets in order to estimate the number of people affected by flooding. Differences in exposure characteristics have been assessed for a range of different residential types. As a demonstration, urban flooding risk hotspots are delineated for different percentiles of the TWI value for the city of Addis Ababa, Ethiopia.  相似文献