@article{TranterDeLuciaWolfgrammetal.2020, author = {Tranter, Morgan Alan and De Lucia, Marco and Wolfgramm, Markus and K{\"u}hn, Michael}, title = {Barite scale formation and injectivity loss models for geothermal systems}, series = {Water}, volume = {12}, journal = {Water}, number = {11}, publisher = {MDPI}, address = {Basel}, issn = {2073-4441}, doi = {10.3390/w12113078}, pages = {24}, year = {2020}, abstract = {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.}, language = {en} } @phdthesis{Leins2023, author = {Leins, Johannes A.}, title = {Combining model detail with large scales}, doi = {10.25932/publishup-58283}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-582837}, school = {Universit{\"a}t Potsdam}, pages = {xv, 168}, year = {2023}, abstract = {The global climate crisis is significantly contributing to changing ecosystems, loss of biodiversity and is putting numerous species on the verge of extinction. In principle, many species are able to adapt to changing conditions or shift their habitats to more suitable regions. However, change is progressing faster than some species can adjust, or potential adaptation is blocked and disrupted by direct and indirect human action. Unsustainable anthropogenic land use in particular is one of the driving factors, besides global heating, for these ecologically critical developments. Precisely because land use is anthropogenic, it is also a factor that could be quickly and immediately corrected by human action. In this thesis, I therefore assess the impact of three climate change scenarios of increasing intensity in combination with differently scheduled mowing regimes on the long-term development and dispersal success of insects in Northwest German grasslands. The large marsh grasshopper (LMG, Stethophyma grossum, Linn{\´e} 1758) is used as a species of reference for the analyses. It inhabits wet meadows and marshes and has a limited, yet fairly good ability to disperse. Mowing and climate conditions affect the development and mortality of the LMG differently depending on its life stage. The specifically developed simulation model HiLEG (High-resolution Large Environmental Gradient) serves as a tool for investigating and projecting viability and dispersal success under different climate conditions and land use scenarios. It is a spatially explicit, stage- and cohort-based model that can be individually configured to represent the life cycle and characteristics of terrestrial insect species, as well as high-resolution environmental data and the occurrence of external disturbances. HiLEG is a freely available and adjustable software that can be used to support conservation planning in cultivated grasslands. In the three case studies of this thesis, I explore various aspects related to the structure of simulation models per se, their importance in conservation planning in general, and insights regarding the LMG in particular. It became apparent that the detailed resolution of model processes and components is crucial to project the long-term effect of spatially and temporally confined events. Taking into account conservation measures at the regional level has further proven relevant, especially in light of the climate crisis. I found that the LMG is benefiting from global warming in principle, but continues to be constrained by harmful mowing regimes. Land use measures could, however, be adapted in such a way that they allow the expansion and establishment of the LMG without overly affecting agricultural yields. Overall, simulation models like HiLEG can make an important contribution and add value to conservation planning and policy-making. Properly used, simulation results shed light on aspects that might be overlooked by subjective judgment and the experience of individual stakeholders. Even though it is in the nature of models that they are subject to limitations and only represent fragments of reality, this should not keep stakeholders from using them, as long as these limitations are clearly communicated. Similar to HiLEG, models could further be designed in such a way that not only the parameterization can be adjusted as required, but also the implementation itself can be improved and changed as desired. This openness and flexibility should become more widespread in the development of simulation models.}, language = {en} } @article{NoonanFlemingTuckeretal.2020, author = {Noonan, Michael J. and Fleming, Christen H. and Tucker, Marlee A. and Kays, Roland and Harrison, Autumn-Lynn and Crofoot, Margaret C. and Abrahms, Briana and Alberts, Susan C. and Ali, Abdullahi H. and Blaum, Niels}, title = {Effects of body size on estimation of mammalian area requirements}, series = {Conservation Biology}, volume = {34}, journal = {Conservation Biology}, number = {4}, publisher = {Wiley-Blackwell}, address = {Oxford}, pages = {12}, year = {2020}, abstract = {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.}, language = {en} } @misc{NoonanFlemingTuckeretal.2020, author = {Noonan, Michael J. and Fleming, Christen H. and Tucker, Marlee A. and Kays, Roland and Harrison, Autumn-Lynn and Crofoot, Margaret C. and Abrahms, Briana and Alberts, Susan C. and Ali, Abdullahi H. and Blaum, Niels}, title = {Effects of body size on estimation of mammalian area requirements}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {4}, issn = {1866-8372}, doi = {10.25932/publishup-52682}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-526824}, pages = {14}, year = {2020}, abstract = {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.}, language = {en} } @article{KonradSchmolkeHalamaManea2016, author = {Konrad-Schmolke, Matthias and Halama, Ralf and Manea, Vlad C.}, title = {Slab mantle dehydrates beneath KamchatkaYet recycles water into the deep mantle}, series = {Geochemistry, geophysics, geosystems}, volume = {17}, journal = {Geochemistry, geophysics, geosystems}, publisher = {American Geophysical Union}, address = {Washington}, issn = {1525-2027}, doi = {10.1002/2016GC006335}, pages = {2987 -- 3007}, year = {2016}, abstract = {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.}, language = {en} }