TY - JOUR A1 - Konrad-Schmolke, Matthias A1 - Halama, Ralf A1 - Manea, Vlad C. T1 - Slab mantle dehydrates beneath KamchatkaYet recycles water into the deep mantle JF - Geochemistry, geophysics, geosystems N2 - The subduction of hydrated slab mantle is the most important and yet weakly constrained factor in the quantification of the Earth's deep geologic water cycle. The most critical unknowns are the initial hydration state and the dehydration behavior of the subducted oceanic mantle. Here we present a combined thermomechanical, thermodynamic, and geochemical model of the Kamchatka subduction zone that indicates significant dehydration of subducted slab mantle beneath Kamchatka. Evidence for the subduction of hydrated oceanic mantle comes from across-arc trends of boron concentrations and isotopic compositions in arc volcanic rocks. Our thermodynamic-geochemical models successfully predict the complex geochemical patterns and the spatial distribution of arc volcanoes in Kamchatka assuming the subduction of hydrated oceanic mantle. Our results show that water content and dehydration behavior of the slab mantle beneath Kamchatka can be directly linked to compositional features in arc volcanic rocks. Depending on hydration depth of the slab mantle, our models yield water recycling rates between 1.1 × 103 and 7.4 × 103 Tg/Ma/km corresponding to values between 0.75 × 106 and 5.2 × 106 Tg/Ma for the entire Kamchatkan subduction zone. These values are up to one order of magnitude lower than previous estimates for Kamchatka, but clearly show that subducted hydrated slab mantle significantly contributes to the water budget in the Kamchatkan subduction zone. KW - rainfall-runoff KW - scaling KW - heterogeneity in soil hydraulic properties KW - surface sealing KW - run-on KW - patched vegetation cover Y1 - 2016 U6 - https://doi.org/10.1002/2016GC006335 SN - 1525-2027 VL - 17 SP - 2987 EP - 3007 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Noonan, Michael J. A1 - Fleming, Christen H. A1 - Tucker, Marlee A. A1 - Kays, Roland A1 - Harrison, Autumn-Lynn A1 - Crofoot, Margaret C. A1 - Abrahms, Briana A1 - Alberts, Susan C. A1 - Ali, Abdullahi H. A1 - Blaum, Niels T1 - Effects of body size on estimation of mammalian area requirements JF - Conservation Biology N2 - Accurately quantifying species' area requirements is a prerequisite for effective area-based conservation. This typically involves collecting tracking data on species of interest and then conducting home-range analyses. Problematically, autocorrelation in tracking data can result in space needs being severely underestimated. Based on the previous work, we hypothesized the magnitude of underestimation varies with body mass, a relationship that could have serious conservation implications. To evaluate this hypothesis for terrestrial mammals, we estimated home-range areas with global positioning system (GPS) locations from 757 individuals across 61 globally distributed mammalian species with body masses ranging from 0.4 to 4000 kg. We then applied block cross-validation to quantify bias in empirical home-range estimates. Area requirements of mammals <10 kg were underestimated by a mean approximately15%, and species weighing approximately100 kg were underestimated by approximately50% on average. Thus, we found area estimation was subject to autocorrelation-induced bias that was worse for large species. Combined with the fact that extinction risk increases as body mass increases, the allometric scaling of bias we observed suggests the most threatened species are also likely to be those with the least accurate home-range estimates. As a correction, we tested whether data thinning or autocorrelation-informed home-range estimation minimized the scaling effect of autocorrelation on area estimates. Data thinning required an approximately93% data loss to achieve statistical independence with 95% confidence and was, therefore, not a viable solution. In contrast, autocorrelation-informed home-range estimation resulted in consistently accurate estimates irrespective of mass. When relating body mass to home range size, we detected that correcting for autocorrelation resulted in a scaling exponent significantly >1, meaning the scaling of the relationship changed substantially at the upper end of the mass spectrum. KW - allometry KW - animal movement KW - area-based conservation KW - autocorrelation KW - home range KW - kernel density estimation KW - reserve design KW - scaling Y1 - 2019 VL - 34 IS - 4 PB - Wiley-Blackwell CY - Oxford ER - TY - JOUR A1 - Tranter, Morgan Alan A1 - De Lucia, Marco A1 - Wolfgramm, Markus A1 - Kühn, Michael T1 - Barite scale formation and injectivity loss models for geothermal systems JF - Water N2 - Barite scales in geothermal installations are a highly unwanted effect of circulating deep saline fluids. They build up in the reservoir if supersaturated fluids are re-injected, leading to irreversible loss of injectivity. A model is presented for calculating the total expected barite precipitation. To determine the related injectivity decline over time, the spatial precipitation distribution in the subsurface near the injection well is assessed by modelling barite growth kinetics in a radially diverging Darcy flow domain. Flow and reservoir properties as well as fluid chemistry are chosen to represent reservoirs subject to geothermal exploration located in the North German Basin (NGB) and the Upper Rhine Graben (URG) in Germany. Fluids encountered at similar depths are hotter in the URG, while they are more saline in the NGB. The associated scaling amount normalised to flow rate is similar for both regions. The predicted injectivity decline after 10 years, on the other hand, is far greater for the NGB (64%) compared to the URG (24%), due to the temperature- and salinity-dependent precipitation rate. The systems in the NGB are at higher risk. Finally, a lightweight score is developed for approximating the injectivity loss using the Damkohler number, flow rate and total barite scaling potential. This formula can be easily applied to geothermal installations without running complex reactive transport simulations. KW - reactive transport KW - radial flow KW - geothermal energy KW - scaling KW - phreeqc KW - formation damage Y1 - 2020 U6 - https://doi.org/10.3390/w12113078 SN - 2073-4441 VL - 12 IS - 11 PB - MDPI CY - Basel ER -