116 resultados para runoff erosivity parameter


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Electron microprobe and thermomagnetic analyses of selected basalt samples from Hole 597C were performed. The main purpose of this work was to investigate and estimate the degree of oxidation of the samples using the ratios of Fe to Ti and the Curie temperatures obtained from thermomagnetic curves. The results show that the magnetic properties of samples from Hole 597C change at a sub-bottom depth of 100 m, and that low-temperature and high-temperature oxidation processes prevailed above and below 100 m, respectively.

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Clay minerals recovered from the Galapagos hydrothermal mounds (Holes 506C, 507D, and 509B) are mainly iron-rich nontronite-like minerals enriched in potassium. Nontronites from Hole 509B show a distinct tendency to become micaceous toward the lower beds of clay sediments. Mn-crusts consist mainly of todorokite or a mixture of todorokite and nontronite. Minerals of clay and Mnrich sediments in the mounds originated from hydrothermal solutions of uncertain origin. Pelagic oozes from hydrothermal mounds and from areas between mounds (Hole 506D) consist mainly of calcite. In the carbonate beds on or near the hydrothermal mounds an admixture of smectite is often found.

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Arctic lowland landscapes have been modified by thermokarst lake processes throughout the Holocene. Thermokarst lakes form as a result of ice-rich permafrost degradation and they may expand over time through thermal and mechanical shoreline erosion. We studied proximal and distal sedimentary records from a thermokarst lake located on the Arctic Coastal Plain of northern Alaska to reconstruct the impact of catchment dynamics and morphology on the lacustrine depositional environment and to quantify carbon accumulation in thermokarst lake sediments. Short cores were collected for analysis of pollen, sedimentological and geochemical proxies. Radiocarbon and Pb/Cs dating, as well as extrapolation of measured historic lake expansion rates, were applied to estimate a minimum lake age of ~ 1,400 calendar years BP. The pollen record is in agreement with the young lake age as it does not include evidence of the "alder high" that occurred in the region ~ 4.0 cal ka BP. The lake most likely initiated from a remnant pond in a drained thermokarst lake basin (DTLB) and deepened rapidly as evidenced by accumulation of laminated sediments. Increasing oxygenation of the water column as shown by higher Fe/Ti and Fe/S ratios in the sediment indicate shifts in ice regime with increasing water depth. More recently, the sediment source changed as the thermokarst lake expanded through lateral permafrost degradation, alternating from redeposited DTLB sediments, to increased amounts of sediment from eroding, older upland deposits, followed by a more balanced combination of both DTLB and upland sources. The characterizing shifts in sediment sources and depositional regimes in expanding thermokarst lakes were therefore archived in the thermokarst lake sedimentary record. This study also highlights the potential for Arctic lakes to recycle old carbon from thawing permafrost and thermokarst processes.

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River runoff is an essential climate variable as it is directly linked to the terrestrial water balance and controls a wide range of climatological and ecological processes. Despite its scientific and societal importance, there are to date no pan-European observation-based runoff estimates available. Here we employ a recently developed methodology to estimate monthly runoff rates on regular spatial grid in Europe. For this we first assemble an unprecedented collection of river flow observations, combining information from three distinct data bases. Observed monthly runoff rates are first tested for homogeneity and then related to gridded atmospheric variables (E-OBS version 12) using machine learning. The resulting statistical model is then used to estimate monthly runoff rates (December 1950 - December 2015) on a 0.5° x 0.5° grid. The performance of the newly derived runoff estimates is assessed in terms of cross validation. The paper closes with example applications, illustrating the potential of the new runoff estimates for climatological assessments and drought monitoring.

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River runoff is an essential climate variable as it is directly linked to the terrestrial water balance and controls a wide range of climatological and ecological processes. Despite its scientific and societal importance, there are to date no pan-European observation-based runoff estimates available. Here we employ a recently developed methodology to estimate monthly runoff rates on regular spatial grid in Europe. For this we first collect an unprecedented collection of river flow observations, combining information from three distinct data bases. Observed monthly runoff rates are first tested for homogeneity and then related to gridded atmospheric variables (E-OBS version 11) using machine learning. The resulting statistical model is then used to estimate monthly runoff rates (December 1950-December 2014) on a 0.5° × 0.5° grid. The performance of the newly derived runoff estimates is assessed in terms of cross validation. The paper closes with example applications, illustrating the potential of the new runoff estimates for climatological assessments and drought monitoring.