TY - JOUR A1 - Rheinwahlt, Aljoscha A1 - Goswami, Bedartha A1 - Bookhagen, Bodo T1 - A network-based flow accumulation algorithm for point clouds BT - Facet-Flow Networks (FFNs) JF - Journal of geophysical research : Earth surface N2 - Flow accumulation algorithms estimate the steady state of flow on real or modeled topographic surfaces and are crucial for hydrological and geomorphological assessments, including delineation of river networks, drainage basins, and sediment transport processes. Existing flow accumulation algorithms are typically designed to compute flows on regular grids and are not directly applicable to arbitrarily sampled topographic data such as lidar point clouds. In this study we present a random sampling scheme that generates homogeneous point densities, in combination with a novel flow path tracing approach-the Facet-Flow Network (FFN)-that estimates flow accumulation in terms of specific catchment area (SCA) on triangulated surfaces. The random sampling minimizes biases due to spatial sampling and the FFN allows for direct flow estimation from point clouds. We validate our approach on a Gaussian hill surface and study the convergence of its SCA compared to the analytical solution. Here, our algorithm outperforms the multiple flow direction algorithm, which is optimized for divergent surfaces. We also compute the SCA of a 6-km(2)-steep, vegetated catchment on Santa Cruz Island, California, based on airborne lidar point-cloud data. Point-cloud-based SCA values estimated by our method compare well with those estimated by the D-infinity or multiple flow direction algorithm on gridded data. The advantage of computing SCA from point clouds becomes relevant especially for divergent topography and for small drainage areas: These are depicted with much more detail due to the higher sampling density of point clouds. KW - point clouds KW - drainage networks KW - lidar KW - tin KW - surface runoff KW - spatial sampling Y1 - 2019 U6 - https://doi.org/10.1029/2018JF004827 SN - 2169-9003 SN - 2169-9011 VL - 124 IS - 7 SP - 2013 EP - 2033 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Heimann, Sebastian A1 - Vasyura-Bathke, Hannes A1 - Sudhaus, Henriette A1 - Isken, Marius Paul A1 - Kriegerowski, Marius A1 - Steinberg, Andreas A1 - Dahm, Torsten T1 - A Python framework for efficient use of pre-computed Green's functions in seismological and other physical forward and inverse source problems JF - Solid earth N2 - The computation of such synthetic GFs is computationally and operationally demanding. As a consequence, the onthe-fly recalculation of synthetic GFs in each iteration of an optimisation is time-consuming and impractical. Therefore, the pre-calculation and efficient storage of synthetic GFs on a dense grid of source to receiver combinations enables the efficient lookup and utilisation of GFs in time-critical scenarios. We present a Python-based framework and toolkit - Pyrocko-GF - that enables the pre-calculation of synthetic GF stores, which are independent of their numerical calculation method and GF transfer function. The framework aids in the creation of such GF stores by interfacing a suite of established numerical forward modelling codes in seismology (computational back ends). So far, interfaces to back ends for layered Earth model cases have been provided; however, the architecture of Pyrocko-GF is designed to cover back ends for other geometries (e.g. full 3-D heterogeneous media) and other physical quantities (e.g. gravity, pressure, tilt). Therefore, Pyrocko-GF defines an extensible GF storage format suitable for a wide range of GF types, especially handling elasticity and wave propagation problems. The framework assists with visualisations, quality control, and the exchange of GF stores, which is supported through an online platform that provides many pre-calculated GF stores for local, regional, and global studies. The Pyrocko-GF toolkit comes with a well-documented application programming interface (API) for the Python programming language to efficiently facilitate forward modelling of geophysical processes, e.g. synthetic waveforms or static displacements for a wide range of source models. Y1 - 2019 U6 - https://doi.org/10.5194/se-10-1921-2019 SN - 1869-9510 SN - 1869-9529 VL - 10 IS - 6 SP - 1921 EP - 1935 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Bayona Viveros, Jose Antonio A1 - von Specht, Sebastian A1 - Strader, Anne A1 - Hainzl, Sebastian A1 - Cotton, Fabrice A1 - Schorlemmer, Danijel T1 - A Regionalized Seismicity Model for Subduction Zones Based on Geodetic Strain Rates, Geomechanical Parameters, and Earthquake-Catalog Data JF - Bulletin of the Seismological Society of America N2 - The Seismic Hazard Inferred from Tectonics based on the Global Strain Rate Map (SHIFT_GSRM) earthquake forecast was designed to provide high-resolution estimates of global shallow seismicity to be used in seismic hazard assessment. This model combines geodetic strain rates with global earthquake parameters to characterize long-term rates of seismic moment and earthquake activity. Although SHIFT_GSRM properly computes seismicity rates in seismically active continental regions, it underestimates earthquake rates in subduction zones by an average factor of approximately 3. We present a complementary method to SHIFT_GSRM to more accurately forecast earthquake rates in 37 subduction segments, based on the conservation of moment principle and the use of regional interface seismicity parameters, such as subduction dip angles, corner magnitudes, and coupled seismogenic thicknesses. In seven progressive steps, we find that SHIFT_GSRM earthquake-rate underpredictions are mainly due to the utilization of a global probability function of seismic moment release that poorly captures the great variability among subduction megathrust interfaces. Retrospective test results show that the forecast is consistent with the observations during the 1 January 1977 to 31 December 2014 period. Moreover, successful pseudoprospective evaluations for the 1 January 2015 to 31 December 2018 period demonstrate the power of the regionalized earthquake model to properly estimate subduction-zone seismicity. Y1 - 2019 U6 - https://doi.org/10.1785/0120190034 SN - 0037-1106 SN - 1943-3573 VL - 109 IS - 5 SP - 2036 EP - 2049 PB - Seismological Society of America CY - Albany ER - TY - JOUR A1 - Gailleton, Boris A1 - Mudd, Simon M. A1 - Clubb, Fiona J. A1 - Peifer, Daniel A1 - Hurst, Martin D. T1 - A segmentation approach for the reproducible extraction and quantification of knickpoints from river long profiles JF - Earth surface dynamics N2 - Changes in the steepness of river profiles or abrupt vertical steps (i.e. waterfalls) are thought to be indicative of changes in erosion rates, lithology or other factors that affect landscape evolution. These changes are referred to as knickpoints or knickzones and are pervasive in bedrock river systems. Such features are thought to reveal information about landscape evolution and patterns of erosion, and therefore their locations are often reported in the geomorphic literature. It is imperative that studies reporting knickpoints and knickzones use a reproducible method of quantifying their locations, as their number and spatial distribution play an important role in interpreting tectonically active landscapes. In this contribution we introduce a reproducible knickpoint and knickzone extraction algorithm that uses river profiles transformed by integrating drainage area along channel length (the so-called integral or chi method). The profile is then statistically segmented and the differing slopes and step changes in the elevations of these segments are used to identify knickpoints, knickzones and their relative magnitudes. The output locations of identified knickpoints and knickzones compare favourably with human mapping: we test the method on Santa Cruz Island, CA, using previously reported knickzones and also test the method against a new dataset from the Quadrilatero Ferrifero in Brazil. The algorithm allows for the extraction of varying knickpoint morphologies, including stepped, positive slope-break (concave upward) and negative slope-break knickpoints. We identify parameters that most affect the resulting knickpoint and knickzone locations and provide guidance for both usage and outputs of the method to produce reproducible knickpoint datasets. Y1 - 2019 U6 - https://doi.org/10.5194/esurf-7-211-2019 SN - 2196-6311 SN - 2196-632X VL - 7 IS - 1 SP - 211 EP - 230 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Pick, Leonie A1 - Effenberger, Frederic A1 - Zhelavskaya, Irina A1 - Korte, Monika T1 - A Statistical Classifier for Historical Geomagnetic Storm Drivers Derived Solely From Ground-Based Magnetic Field Measurements JF - Earth and Space Science N2 - Solar wind observations show that geomagnetic storms are mainly driven by interplanetary coronal mass ejections (ICMEs) and corotating or stream interaction regions (C/SIRs). We present a binary classifier that assigns one of these drivers to 7,546 storms between 1930 and 2015 using ground‐based geomagnetic field observations only. The input data consists of the long‐term stable Hourly Magnetospheric Currents index alongside the corresponding midlatitude geomagnetic observatory time series. This data set provides comprehensive information on the global storm time magnetic disturbance field, particularly its spatial variability, over eight solar cycles. For the first time, we use this information statistically with regard to an automated storm driver identification. Our supervised classification model significantly outperforms unskilled baseline models (78% accuracy with 26[19]% misidentified interplanetary coronal mass ejections [corotating or stream interaction regions]) and delivers plausible driver occurrences with regard to storm intensity and solar cycle phase. Our results can readily be used to advance related studies fundamental to space weather research, for example, studies connecting galactic cosmic ray modulation and geomagnetic disturbances. They are fully reproducible by means of the underlying open‐source software (Pick, 2019, http://doi.org/10.5880/GFZ.2.3.2019.003) KW - geomagnetic observatory data KW - geomagnetic storm drivers KW - historical geomagnetic storms KW - supervised machine learning Y1 - 2019 U6 - https://doi.org/10.1029/2019EA000726 SN - 2333-5084 VL - 6 SP - 2000 EP - 2015 PB - American Geophysical Union CY - Malden, Mass. ER - TY - JOUR A1 - Cordonnier, Guillaume A1 - Bovy, Benoit A1 - Braun, Jean T1 - A versatile, linear complexity algorithm for flow routing in topographies with depressions JF - Earth surface dynamics N2 - We present a new algorithm for solving the common problem of flow trapped in closed depressions within digital elevation models, as encountered in many applications relying on flow routing. Unlike other approaches (e.g., the Priority-Flood depression filling algorithm), this solution is based on the explicit computation of the flow paths both within and across the depressions through the construction of a graph connecting together all adjacent drainage basins. Although this represents many operations, a linear time complexity can be reached for the whole computation, making it very efficient. Compared to the most optimized solutions proposed so far, we show that this algorithm of flow path enforcement yields the best performance when used in landscape evolution models. In addition to its efficiency, our proposed method also has the advantage of letting the user choose among different strategies of flow path enforcement within the depressions (i.e., filling vs. carving). Furthermore, the computed graph of basins is a generic structure that has the potential to be reused for solving other problems as well, such as the simulation of erosion. This sequential algorithm may be helpful for those who need to, e.g., process digital elevation models of moderate size on single computers or run batches of simulations as part of an inference study. Y1 - 2019 U6 - https://doi.org/10.5194/esurf-7-549-2019 SN - 2196-6311 SN - 2196-632X VL - 7 IS - 2 SP - 549 EP - 562 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Gefen-Treves, Shany A1 - Kedem, Isaac A1 - Weiss, Gad A1 - Wagner, Dirk A1 - Tchernov, Dan A1 - Kaplan, Aaron T1 - Acclimation of a rocky shore algal reef builder Neogoniolithon sp. to changing illuminations JF - Limnology and oceanography e-lectures / Association for the Sciences of Limnology and Oceanography N2 - Vermetid reefs and rocky shores are hot spots of biodiversity, often referred to as the subtropical equivalent of coral reefs. The development of the ecosystem depends on the activity of several reef builders, including red crustose coralline algae (CCA) such as Neogoniolithon brassica-florida. Despite its importance, little is known about Neogoniolithon sp. acclimation to rapid changes in light intensity and corresponding photosynthetic activity. To overcome the large spatial variability in the light field (due to location and the porous nature of the rocks) we grew Neogoniolithon sp. on glass slides and characterized its photosynthetic performance in response to various light intensities by following O-2 exchange and fluorescence parameters. This was also performed on rock-inhabiting thalli collected from the east Mediterranean basin. Generally, maximal photosynthetic rate was reached when Neogoniolithon sp. thalli grown under low illumination (such as in protected niches where the light intensity can be as low as 1% of surface illumination) were examined. When exposed to light intensities higher than those experienced during growth, Neogoniolithon sp. activates adaptive/protective mechanisms such as state transition and nonphotochemical fluorescence quenching and increases the dark respiration thereafter. We find that the Fv/Fm parameter (variable/maximal fluorescence) is not suitable to assess photosynthetic performance in Neogoniolithon sp. and propose using instead an alternative parameter recently developed. Our findings help to clarify why Neogoniolithon sp. is usually observed in shaded niches along the reef surfaces. Y1 - 2019 U6 - https://doi.org/10.1002/lno.11245 SN - 0024-3590 SN - 1939-5590 VL - 65 IS - 1 SP - 27 EP - 36 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Brieger, Frederic A1 - Herzschuh, Ulrike A1 - Pestryakova, Luidmila Agafyevna A1 - Bookhagen, Bodo A1 - Zakharov, Evgenii S. A1 - Kruse, Stefan T1 - Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds JF - Remote sensing N2 - Forest structure is a crucial component in the assessment of whether a forest is likely to act as a carbon sink under changing climate. Detailed 3D structural information about the tundra–taiga ecotone of Siberia is mostly missing and still underrepresented in current research due to the remoteness and restricted accessibility. Field based, high-resolution remote sensing can provide important knowledge for the understanding of vegetation properties and dynamics. In this study, we test the applicability of consumer-grade Unmanned Aerial Vehicles (UAVs) for rapid calculation of stand metrics in treeline forests. We reconstructed high-resolution photogrammetric point clouds and derived canopy height models for 10 study sites from NE Chukotka and SW Yakutia. Subsequently, we detected individual tree tops using a variable-window size local maximum filter and applied a marker-controlled watershed segmentation for the delineation of tree crowns. With this, we successfully detected 67.1% of the validation individuals. Simple linear regressions of observed and detected metrics show a better correlation (R2) and lower relative root mean square percentage error (RMSE%) for tree heights (mean R2 = 0.77, mean RMSE% = 18.46%) than for crown diameters (mean R2 = 0.46, mean RMSE% = 24.9%). The comparison between detected and observed tree height distributions revealed that our tree detection method was unable to representatively identify trees <2 m. Our results show that plot sizes for vegetation surveys in the tundra–taiga ecotone should be adapted to the forest structure and have a radius of >15–20 m to capture homogeneous and representative forest stands. Additionally, we identify sources of omission and commission errors and give recommendations for their mitigation. In summary, the efficiency of the used method depends on the complexity of the forest’s stand structure. KW - UAV KW - photogrammetry KW - remote sensing KW - structure from motion KW - tundra-taiga ecotone KW - point cloud KW - forest structure Y1 - 2019 U6 - https://doi.org/10.3390/rs11121447 SN - 2072-4292 VL - 11 IS - 12 PB - MDPI CY - Basel ER - TY - JOUR A1 - Platz, Anna A1 - Weckmann, Ute T1 - An automated new pre-selection tool for noisy Magnetotelluric data using the Mahalanobis distance and magnetic field constraints JF - Geophysical journal international N2 - In Magnetotellurics (MT) natural electromagnetic field variations are recorded to study the electrical conductivity structure of the subsurface. Thereby long time-series of electromagnetic data are subdivided into smaller segments, which are Fourier transformed and typically averaged in a statistically robust manner to obtain MT transfer functions. Unfortunately, nowadays the presence of man-made electromagnetic noise sources often deteriorates a significant fraction of the recorded time-series by overprinting the desired natural field variations. Available approaches to obtain undisturbed and high quality MT results include, for example robust statistics, remote reference or multi-station analyses which aim at the removal of outliers or uncorrelated noise. However, we have observed that intermittent noise often affects a certain time span resulting in a second cluster of transfer functions in addition to the expected true MT distribution. In this paper, we present a novel criterion for the detection and pre-selection of EM noise in form of outliers or additional clusters based on a distance measure of each data segment with regard to the centre of the data distribution. For this purpose, we utilize the Mahalanobis distance (MD) which computes the distance between two multivariate points considering the covariance matrix of the data that quantifies the shape and the size of multivariate data distributions. As the MD considers the covariance matrix, it corrects not only for different variances but also for any correlation between the data. The computation of both, the mean value and covariance matrix, is susceptible to ouliers (e.g. noise) and requires a statistically robust estimation. We tested several robust estimators, for example median absolute deviation or minimum covariance determinant algorithm and finally implemented an automatic criterion using a deterministic minimum covariance determinant algorithm. We will present results using MT data from various field experiments all over the world, which illustrate successfull data improvement. This approach is able to remove scattered data points as well as to reject complete data cluster originating from noise sources. However, like all purely statistical algorithms the criterion is limited to cases where the majority of the recorded data is well-behaved, that is noise content is below 50 per cent. If the majority of data points originates from noise sources, the new criterion will fail if used in an automatic way. In these cases, additional input by the user either manually or in an automated fashion can be utilized. We therefore suggest to use an add-on criterion to back the MD selection and subsequent robust stacking in form of a physically motivated constraint based on the magnetic incidence direction. This property indicates whether the magnetic field originates from various sources in the far field or from a strong and well defined source in the near field. KW - Magnetotellurics KW - Statistical methods KW - Time-series analysis Y1 - 2019 U6 - https://doi.org/10.1093/gji/ggz197 SN - 0956-540X SN - 1365-246X VL - 218 IS - 3 SP - 1853 EP - 1872 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Hudson, Paul A1 - Pham, My A1 - Bubeck, Philip T1 - An evaluation and monetary assessment of the impact of flooding on subjective well-being across genders in Vietnam JF - Climate & development N2 - The intangible impacts of floods on welfare are not well investigated, even though they are important aspects of welfare. Moreover, flooding has gender based impacts on welfare. These differing impacts create a gender based flood risk resilience gap. We study the intangible impacts of flood risk on the subjective well-being of residents in central Vietnam. The measurement of intangible impacts through subjective well-being is a growing field within flood risk research. We find an initial drop in welfare through subjective well-being across genders when a flood is experienced. Male respondents tended to recover their welfare losses by around 80% within 5 years while female respondents were associated with a welfare recovery of around 70%. A monetization of the impacts floods have on an individual’s subjective well-being shows that for the average female respondent, between 41% to 86% of annual income would be required to compensate subjective well-being losses after 5 years of experiencing a flood. The corresponding value for males is 30% to 57% of annual income. This shows that the intangible impacts of flood risk are important (across genders) and need to be integrated into flood (or climate) risk assessments to develop more socially appropriate risk management strategies. KW - Subjective well-being KW - flood risk KW - welfare KW - gender KW - resilience KW - intangible impacts Y1 - 2019 U6 - https://doi.org/10.1080/17565529.2019.1579698 SN - 1756-5529 SN - 1756-5537 VL - 11 IS - 7 SP - 623 EP - 637 PB - Routledge, Taylor & Francis Group CY - Abingdon ER -