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1. An improved method for atmospheric ¹⁴CO measurements

   https://doi.org/10.5194/amt-14-2055-2021  

   Petrenko, Vasilii V. ; Smith, Andrew M. ; Crosier, Edward M. ; Kazemi, Roxana ; Place, Philip ; Colton, Aidan ; Yang, Bin ; Hua, Quan ; Murray, Lee T. ( January 2021 , Atmospheric Measurement Techniques)

   null (Ed.)

   Abstract. Important uncertainties remain in our understanding of the spatial andtemporal variability of atmospheric hydroxyl radical concentration ([OH]).Carbon-14-containing carbon monoxide (14CO) is a useful tracer that canhelp in the characterization of [OH] variability. Prior measurements ofatmospheric 14CO concentration ([14CO] are limited in both theirspatial and temporal extent, partly due to the very large air sample volumes that have been required for measurements (500–1000 L at standardtemperature and pressure, L STP) and the difficulty and expense associatedwith the collection, shipment, and processing of such samples. Here wepresent a new method that reduces the air sample volume requirement to≈90 L STP while allowing for [14CO] measurement uncertainties that are on par with or better than prior work (≈3 % or better, 1σ). The method also for the first time includes accurate characterization of the overall procedural [14CO] blank associated with individual samples, which is a key improvement over prior atmospheric 14CO work. The method was used to make measurements of [14CO] at the NOAA Mauna Loa Observatory, Hawaii, USA, between November 2017 and November 2018. The measurements show the expected [14CO] seasonal cycle (lowest in summer)and are in good agreement with prior [14CO] results from anotherlow-latitude site in the Northern Hemisphere. The lowest overall [14CO]uncertainties (2.1 %, 1σ) are achieved for samples that aredirectly accompanied by procedural blanks and whose mass is increased to≈50 µgC (micrograms of carbon) prior to the 14Cmeasurement via dilution with a high-CO 14C-depleted gas. 

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2. Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed

   https://doi.org/10.1029/2021WR030993  

   Xu, Tianfang ; Longyang, Qianqiu ; Tyson, Conor ; Zeng, Ruijie ; Neilson, Bethany T. ( March 2022 , Water Resources Research)

   Snow dominated mountainous karst watersheds are the primary source of water supply in many areas in the western U.S. and worldwide. These watersheds are typically characterized by complex terrain, spatiotemporally varying snow accumulation and melt processes, and duality of flow and storage dynamics because of the juxtaposition of matrix (micropores and small fissures) and karst conduits. As a result, predicting streamflow from meteorological inputs has been challenging due to the inability of physically based or conceptual hydrologic models to represent these unique characteristics. We present a hybrid modeling approach that integrates a physically based, spatially distributed, snow model with a deep learning karst model. More specifically, the high‐resolution snow model captures spatiotemporal variability in snowmelt, and the deep learning model simulates the corresponding response of streamflow as influenced by complex surface and subsurface properties. The deep learning model is based on the Convolutional Long Short‐Term Memory (ConvLSTM) architecture capable of handling spatiotemporal recharge patterns and watershed storage dynamics. The hybrid modeling approach is tested on a watershed in northern Utah with seasonal snow cover and variably karstified carbonate bedrock. The hybrid models were able to simulate streamflow at the watershed outlet with high accuracy. The spatial and temporal recharge and discharge patterns learned by the ConvLSTM model were then examined and compared with known hydrogeologic information. Results suggest that ConvLSTM simulates streamflow with higher accuracy than reference models for the study area and provides insight into spatially influenced hydrologic responses that are unavailable within lumped modeling approaches.

    

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3. Making It Spatial Makes It Personal: Engaging Stakeholders with Geospatial Participatory Modeling

   https://doi.org/10.3390/land8020038  

   Vukomanovic, Jelena ; Skrip, Megan ; Meentemeyer, Ross ( February 2019 , Land)

   Participatory research methods are increasingly used to collectively understand complex social-environmental problems and to design solutions through diverse and inclusive stakeholder engagement. But participatory research rarely engages stakeholders to co-develop and co-interpret models that conceptualize and quantify system dynamics for comparing scenarios of alternate action. Even fewer participatory projects have engaged people using geospatial simulations of dynamic landscape processes and spatially explicit planning scenarios. We contend that geospatial participatory modeling (GPM) can confer multiple benefits over non-spatial approaches for participatory research processes, by (a) personalizing connections to problems and their solutions through visualizations of place, (b) resolving abstract notions of landscape connectivity, and (c) clarifying the spatial scales of drivers, data, and decision-making authority. We illustrate through a case study how GPM is bringing stakeholders together to balance population growth and conservation in a coastal region facing dramatic landscape change due to urbanization and sea level rise. We find that an adaptive, iterative process of model development, sharing, and revision drive innovation of methods and ultimately improve the realism of land change models. This co-production of knowledge enables all participants to fully understand problems, evaluate the acceptability of trade-offs, and build buy-in for management actions in the places where they live and work. 

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4. Mutant Evolution in Spatially Structured and Fragmented Expanding Populations

   https://doi.org/10.1534/genetics.120.303422  

   Wodarz, Dominik ; Komarova, Natalia L. ( July 2020 , Genetics)

   Mutant evolution in spatially structured systems is important for a range of biological systems, but aspects of it still require further elucidation. Adding to previous work, we provide a simple derivation of growth laws that characterize the number of mutants of different relative fitness in expanding populations in spatial models of different dimensionalities. These laws are universal and independent of "microscopic" modeling details. We further study the accumulation of mutants and find that with advantageous and neutral mutants, more of them are present in spatially structured, compared to well-mixed colonies of the same size. The behavior of disadvantageous mutants is subtle: if they are disadvantageous through a reduction in division rates, the result is the same, and it is the opposite if the disadvantage is due to a death rate increase. Finally, we show that in all cases, the same results are observed in fragmented, non-spatial patch models. This suggests that the patterns observed are the consequence of population fragmentation, and not spatial restrictions per se. We provide an intuitive explanation for the complex dependence of disadvantageous mutant evolution on spatial restriction, which relies on desynchronized dynamics in different locations/patches, and plays out differently depending on whether the disadvantage is due to a lower division rate or a higher death rate. Implications for specific biological systems, such as the evolution of drug-resistant cell mutants in cancer or bacterial biofilms, are discussed. 

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5. Forward modeling seismic anisotropy in the lower mantle through 3-phase aggregate deformation

   https://doi.org/doi.org/10.1093/gji/ggab278  

   Chandler, C.B. ( January 2021 , Geophysical journal international)

   In recent years there have been several attempts to make the link between mineral properties and seismic anisotropy in the D’’ region but have yet to reach consensus with regards to the dynamics in lower mantle minerals that could give rise to the observed seismic anisotropy. Here, we aim to provide further constraints on the observed long wavelength shear velocity patterns seen in seismic tomography studies. We introduce a forward model of deformation in a subducting slab as it impacts the core mantle boundary (D’’ layer) and proceeds to upwelling at the edge of a simulated LLSVP. By implementing the most recent results from atomistic modeling and high-pressure deformation experiments coupled with a 3-dimensional geodynamic model, we compare the microstructural evolution of an aggregate with a pyrolytic composition to the macroscopically observed seismic anisotropy of the lowermost mantle. We account for topotaxial relations in the forward and reverse phase transitions of MgSiO3-perovskite (Pv) to post-perovskite (pPv) within the slab as well as explore the effects introduced by partial melting near the CMB. Comparisons in the two leading candidate deformation mechanisms in the post-perovskite phase, (001) and (010), are compared. In this study we find that the reverse transition (pPv to Pv) occurs at a depth which is ~ 150 km deeper than that of the forward transition due to increasing temperature near the CMB providing a varying topography of the D’’ discontinuity. Our model also produces good fits with the isotropic velocities of PREM for the bulk lower mantle. When coupled with temperature and pressure dependent forward and reverse phase transitions, a pPv system with dominant (001) slip provides good correlation with the currently observed VSH fast horizontal (~ 1 – 6%) in D’’ and with VSV consistently fast in upwelling areas. Azimuthal variations along the streamline are also investigated showing a symmetry lower than that of the assumed VTI in D’’ introduced by ‘rolling’ effects near the slab’s edge. The addition of 1% partial melting at the CMB is shown to increase S and P wave anisotropy beneath the slab at the base of upwelling with up to ~2.5 & 4.0% P and S wave reductions respectively compared to the global reference. 

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