Refine
Has Fulltext
- no (240) (remove)
Year of publication
- 2019 (240) (remove)
Document Type
- Article (216)
- Other (9)
- Review (7)
- Doctoral Thesis (6)
- Habilitation Thesis (2)
Is part of the Bibliography
- yes (240)
Keywords
- climate (4)
- Holocene (3)
- paleoclimate (3)
- remote sensing (3)
- AMOC (2)
- Asia (2)
- Central Andes (2)
- Climate (2)
- Climate change (2)
- Climate change adaptation (2)
Institute
- Institut für Geowissenschaften (240) (remove)
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.
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.
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.
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.
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)
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.
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.
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.
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.
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.
An Overview of Using Weather Radar for Climatological Studies: Successes, Challenges, and Potential
(2019)
Weather radars have been widely used to detect and quantify precipitation and nowcast severe weather for more than 50 years. Operational weather radars generate huge three-dimensional datasets that can accumulate to terabytes per day. So it is essential to review what can be done with existing vast amounts of data, and how we should manage the present datasets for the future climatologists. All weather radars provide the reflectivity factor, and this is the main parameter to be archived. Saving reflectivity as volumetric data in the original spherical coordinates allows for studies of the three-dimensional structure of precipitation, which can be applied to understand a number of processes, for example, analyzing hail or thunderstorm modes. Doppler velocity and polarimetric moments also have numerous applications for climate studies, for example, quality improvement of reflectivity and rain rate retrievals, and for interrogating microphysical and dynamical processes. However, observational data alone are not useful if they are not accompanied by sufficient metadata. Since the lifetime of a radar ranges between 10 and 20 years, instruments are typically replaced or upgraded during climatologically relevant time periods. As a result, present metadata often do not apply to past data. This paper outlines the work of the Radar Task Team set by the Atmospheric Observation Panel for Climate (AOPC) and summarizes results from a recent survey on the existence and availability of long time series. We also provide recommendations for archiving current and future data and examples of climatological studies in which radar data have already been used.
Preparatory mechanisms accompanying or leading to nucleation of larger earthquakes have been observed at both laboratory and field scales, but conditions favoring the occurrence of observable preparatory processes are still largely unknown. In particular, it remains a matter of debate why some earthquakes occur spontaneously without noticeable precursors as opposed to events that are preceded by an extended failure process. In this study, we have generated new high-resolution seismicity catalogs framing the occurrence of 20 M-L > 2.5 earthquakes at The Geysers geothermal field in California. To this end, a seismicity catalog of the 11 days framing each large event was created. We selected 20 sequences sampling different hypocentral depths and hydraulic conditions within the field. Seismic activity and magnitude frequency distributions displayed by the different earthquake sequences are correlated with their location within the reservoir. Sequences located in the northwestern part of the reservoir show overall increased seismic activity and low b values, while the southeastern part is dominated by decreased seismic activity and higher b values. Periods of high injection coincide with high b values and vice versa. These observations potentially reflect varying differential and mean stresses and damage of the reservoir rocks across the field. About 50% of analyzed sequences exhibit no change in seismicity rate in response to the large main event. However, we find complex waveforms at the onset of the main earthquake, suggesting that small ruptures spontaneously grow into or trigger larger events.
We investigate the inclusions hosted in peritectic garnet from metapelitic migmatites of the Kinzigite Formation (Ivrea Zone, NW Italy) to evaluate the starting composition of the anatectic melt and fluid regime during anatexis throughout the upper amphibolite facies, transition, and granulite facies zones. Inclusions have negative crystal shapes, sizes from 2 to 10 mu m and are regularly distributed in the core of the garnet. Microstructural and micro-Raman investigations indicate the presence of two types of inclusions: crystallized silicate melt inclusions (i.e., nanogranitoids, NI), and fluid inclusions (FI). Microstructural evidence suggests that FI and NI coexist in the same cluster and are primary (i.e., were trapped simultaneously during garnet growth). FI have similar compositions in the three zones and comprise variable proportions of CO2, CH4, and N-2, commonly with siderite, pyrophyllite, and kaolinite, suggesting a COHN composition of the trapped fluid. The mineral assemblage in the NI contains K-feldspar, plagioclase, quartz, biotite, muscovite, chlorite, graphite and, rarely, calcite. Polymorphs such as kumdykolite, cristobalite, tridymite, and less commonly kokchetavite, were also found. Rehomogenized NI from the different zones show that all the melts are leucogranitic but have slightly different compositions. In samples from the upper amphibolite facies, melts are less mafic (FeO + MgO = 2.0-3.4 wt%), contain 860-1700 ppm CO2 and reach the highest H2O contents (6.5-10 wt%). In the transition zone melts have intermediate H2O (4.8-8.5 wt%), CO2 (457-1534 ppm) and maficity (FeO + MgO = 2.3-3.9 wt%). In contrast, melts at granulite facies reach highest CaO, FeO + MgO (3.2-4.7 wt%), and CO2 (up to 2,400 ppm), with H2O contents comparable (5.4-8.3 wt%) to the other two zones. Our results represent the first clear evidence for carbonic fluid-present melting in the Ivrea Zone. Anatexis of metapelites occurred through muscovite and biotite breakdown melting in the presence of a COH fluid, in a situation of fluid-melt immiscibility. The fluid is assumed to have been internally derived, produced initially by devolatilization of hydrous silicates in the graphitic protolith, then as a result of oxidation of carbon by consumption of Fe3+-bearing biotite during melting. Variations in the compositions of the melts are interpreted to result from higher T of melting. The H2O contents of the melts throughout the three zones are higher than usually assumed for initial H2O contents of anatectic melts. The CO2 contents are highest at granulite facies, and show that carbon-contents of crustal magmas are not negligible at high T. The activity of H2O of the fluid dissolved in granitic melts decreases with increasing metamorphic grade. Carbonic fluid-present melting of the deep continental crust represents, together with hydrate-breakdown melting reactions, an important process in the origin of crustal anatectic granitoids.
Arboreal epiphytes (plants residing in forest canopies) are present across all major climate zones and play important roles in forest biogeochemistry. The substantial water storage capacity per unit area of the epiphyte "bucket" is a key attribute underlying their capability to influence forest hydrological processes and their related mass and energy flows. It is commonly assumed that the epiphyte bucket remains saturated, or near-saturated, most of the time; thus, epiphytes (particularly vascular epiphytes) can store little precipitation, limiting their impact on the forest canopy water budget. We present evidence that contradicts this common assumption from (i) an examination of past research; (ii) new datasets on vascular epiphyte and epi-soil water relations at a tropical montane cloud forest (Monteverde, Costa Rica); and (iii) a global evaluation of non-vascular epiphyte saturation state using a process-based vegetation model, LiBry. All analyses found that the external and internal water storage capacity of epiphyte communities is highly dynamic and frequently available to intercept precipitation. Globally, non-vascular epiphytes spend <20% of their time near saturation and regionally, including the humid tropics, model results found that non-vascular epiphytes spend similar to 1/3 of their time in the dry state (0-10% of water storage capacity). Even data from Costa Rican cloud forest sites found the epiphyte community was saturated only 1/3 of the time and that internal leaf water storage was temporally dynamic enough to aid in precipitation interception. Analysis of the epi-soils associated with epiphytes further revealed the extent to which the epiphyte bucket emptied-as even the canopy soils were often <50% saturated (29-53% of all days observed). Results clearly show that the epiphyte bucket is more dynamic than currently assumed, meriting further research on epiphyte roles in precipitation interception, redistribution to the surface and chemical composition of "net" precipitation waters reaching the surface.
The occurrence of mounds dominated by siliceous sponges and microbialites is often related to distal, deep settings of middle ramps and shelves. This paper presents evidence for Bajocian (Garanliana garantiana Zone) microbial-siliceous sponge mounds formed in open marine but relatively shallow settings of a ramp from the Iberian Basin of eastern Spain. Marked differences in mound spacing, morphology, and composition of the related intermound facies are observed from distal to more proximal settings. The distal (below storm wave base) settings are characterized by alternating tabular-bedded marls and limestones rich in pelagic fossils (ammonites, belemnites), open-marine thin-shelled bivalves (Bositra-like), as well as peloids, which include widely or randomly spaced isolated, small (up to 0.4 m high) and larger (up to 2.5 m high) mounds with upward accretion. The intermediate (near to above storm wave base) settings show tabular, thickened beds of peloidal and/or intraclastic limestones with closely spaced mounds (similar to 1 m high), which often coalesce laterally, forming extensive lenticular structures (up to 10 m wide). The proximal (above storm wave base) depositional settings consist of tabular to irregular beds of intraclastic limestones with widely spaced small (up to 0.4 m high) mounds with mainly tabular geometries. The mound framework contains variable proportions of microbialites (dense to clotted peloidal thrombolitic fabrics) and siliceous sponges (hexactinellids and lithistids in similar proportion) ranging from planar to conic shapes. These morphological and compositional changes allow characterizing three shallowing-upward sequences (sequences 1-3) developed in the overall regressive trend of a basin-wide, upper Bajocian T-R cycle. Episodic wave reworking of the early-cemented mounds resulted in the formation of peloids, small rounded intraclasts, and large, rounded or subangular intraclasts. These nonskeletal micritic grains show internal fabrics related to those of the mound and/or microbialites. A progressive textural gradation towards greater size and lesser roundness of the nonskeletal grains in the areas in the vicinity of the main mound factory is documented (i.e., from large, subangular intraclasts in the areas close to the main mound factory to peloids in the areas that are far from it). We discuss the alternative model of internal waves (instead of storm-induced waves) as the hydrodynamic agent providing the high-energy events needed to explain the origin of the peloidal-intraclastic intermound facies and, likely, also the nutrients needed by the microbialites and siliceous sponges to grow.
In Germany, the irrigation sector accounts for only 1% of water use. In recent years, however, this sector has attracted more attention due to the occurrence of severe drought periods. Irrigation scheduling systems could support adaptation strategies but little is known about current providers, performance and users. In this study we aimed to depict the current situation of the existence and functioning of irrigation scheduling systems available in Germany. Six methods were identified and assessed based on direct interviews with end-users and a comparative analysis. The results showed a positive feedback from the users. However, the recommendations were rarely implemented, while only the seasonal irrigation requirement was considered to support actual water abstraction. These results were corroborated by the comparative analysis. Five of the six irrigation scheduling systems estimated the seasonal irrigation amount consistently, while wider differences were found by looking at the irrigation season and at the number of irrigations. Overall, it is found that irrigation support systems are valuable tools for supporting adaptation strategies to fast changes in agro-environmental conditions. However, specific assessments based on real measurements should be considered in order to improve the performance of the systems and provide more consistent support to end-users. (c) 2019 John Wiley & Sons, Ltd.
High spectral resolution (hyperspectral) remote sensing has already demonstrated its capabilities for soil constituent mapping based on absorption feature parameters. This paper tests different parametrizations of the 1.75 μm gypsum feature for the determination of gypsum abundances, from the laboratory to remote sensing applications of recent as well as upcoming hyperspectral sensors. In particular, this study focuses on remote sensing imagery over the large body of the Omongwa pan located in the Namibian Kalahari. Four common absorption feature parameters are compared: band ratio through the introduction of the Normalized Differenced Gypsum Index (NDGI), the shape-based parameters Slope, and Half-Area, and the Continuum Removed Absorption Depth (CRAD). On laboratory soil samples from the pan, CRAD and NDGI approaches perform best to determine gypsum content tested in cross validated regression models with XRD mineralogical data (R² = 0.84 for NDGI and R² = 0.86 for CRAD). Subsequently the laboratory prediction functions are transferred to remote sensing imagery of spaceborne Hyperion, airborne HySpex and simulated spaceborne EnMAP sensor. Variable results were obtained depending on sensor characteristics, data quality, preprocessing and spectral parameters. Overall, the CRAD parameter in this wavelength region proved not to be robust for remote sensing applications, and the simple band ratio based parameter, the NDGI, proved robust and is recommended for future use for the determination of gypsum content in bare soils based on remote sensing hyperspectral imagery.
Water fluxes in highly impounded regions are heavily dependent on reservoir properties. However, for large and remote areas, this information is often unavailable. In this study, the geometry and volume of small surface reservoirs in the semi-arid region of Brazil were estimated using terrain and shape attributes extracted by remote sensing. Regression models and data classification were used to predict the volumes, at different water stages, of 312 reservoirs for which topographic information is available. The power function used to describe the reservoir shapes tends to overestimate the volumes; therefore, a modified shape equation was proposed. Among the methods tested, four were recommended based on performance and simplicity, for which the mean absolute percentage errors varied from 24 to 39%, in contrast to the 94% error achieved with the traditional method. Despite the challenge of precisely deriving the flooded areas of reservoirs, water management in highly reservoir-dense environments should benefit from volume prediction based on remote sensing.
As a consequence of the rapid growing worldwide seismic data set, a huge variety of automatized data-processing methods have been developed. To perform automatized waveform-based seismological studies aiming for magnitudes or source process inversion, it is crucial to identify network stations with erroneous transfer functions, gain factors, or component orientations. We developed a new tool dedicated to automated station quality control of dense seismic networks and arrays. The python-based AutoStatsQ toolbox uses the pyrocko seismic data-processing environment. The toolbox automatically downloads data and metadata for selected teleseismic events and performs different tests. As a result, relative gain factors, sensor orientation corrections, and reliable frequency bands are computed for all stations in a chosen time period. Relative gain factors are calculated for all stations and events in a time domain based on maximum P-phase amplitudes. A Rayleigh-wave polarization analysis is used to identify deviating sensor orientations. The power spectra of all stations in a given frequency range are compared with synthetic ones, accessing Global Centroid Moment Tensor (CMT) solutions. Frequency ranges of coinciding synthetic and recorded power spectral densities (PSDs) may serve as guidelines for choosing band-pass filters for moment tensor (MT) inversion and help confirm the corner frequency of the instrument. The toolbox was applied to the permanent and temporary AlpArray networks as well as to the denser SWATH-D network, a total of over 750 stations. Stations with significantly deviating gain factors were identified, as well as stations with inverse polarity and misorientations of the horizontal components. The tool can be used to quickly access network quality and to omit or correct stations before MT inversion. Electronic Supplement: List of teleseismic events and tables of median, mean, and standard deviation of relative gain factors, and figures of relative gain factors of all event-station pairs, waveform example showing inverse polarity of horizontal components on ZS.D125, histograms of median, mean, and standard deviation of the correction angles, examples of synthetic and recorded frequency spectra of ZS.D046 and NI.VINO.