55 Geowissenschaften, Geologie
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The climate signal imprinted in the snow isotopic composition allows to infer past climate variability from ice core stable water isotope records. The concurrent evolution of vapor and surface snow isotopic composition between precipitation events indicates that post-depositional atmosphere-snow humidity exchange influences the snow and hence the ice core isotope signal. To date, however, this is not accounted for in paeleoclimate reconstructions from isotope records. Here we show that vapor-snow exchange explains 36% of the summertime day-to-day delta O-18 variability of the surface snow between precipitation events, and 53% of the delta D variability. Through observations from the Greenland Ice Sheet and accompanying modeling we demonstrate that vapor-snow exchange introduces a warm bias on the summertime snow isotope value relevant for ice core records. In case of long-term variability in atmosphere-snow exchange the relevance for the ice core signal is also variable and thus paleoclimate reconstructions from isotope records should be revisited.
We construct and examine the prototype of a deep learning-based ground-motion model (GMM) that is both fully data driven and nonergodic. We formulate ground-motion modeling as an image processing task, in which a specific type of neural network, the U-Net, relates continuous, horizontal maps of earthquake predictive parameters to sparse observations of a ground-motion intensity measure (IM). The processing of map-shaped data allows the natural incorporation of absolute earthquake source and observation site coordinates, and is, therefore, well suited to include site-, source-, and path-specific amplification effects in a nonergodic GMM. Data-driven interpolation of the IM between observation points is an inherent feature of the U-Net and requires no a priori assumptions. We evaluate our model using both a synthetic dataset and a subset of observations from the KiK-net strong motion network in the Kanto basin in Japan. We find that the U-Net model is capable of learning the magnitude???distance scaling, as well as site-, source-, and path-specific amplification effects from a strong motion dataset. The interpolation scheme is evaluated using a fivefold cross validation and is found to provide on average unbiased predictions. The magnitude???distance scaling as well as the site amplification of response spectral acceleration at a period of 1 s obtained for the Kanto basin are comparable to previous regional studies.
We have developed a 1D laterally constrained inversion of surface-wave dispersion curves based on the minimum gradient support regularization, which allows solutions with tunable sharpness in the vertical and horizontal directions. The forward modeling consists of a finite-elements approach incorporated in a flexible nonparametric gradient-based inversion scheme, which has already demonstrated good stability and convergence capabilities when tested on other kinds of data. Our deterministic inversion procedure is performed in the shear-wave velocity log space as we noticed that the associated Jacobian indicates a reduced model dependency, and this, in turn, decreases the risks of local nonconvexity. We show several synthetics and one field example to demonstrate the effectiveness and the applicability of the proposed approach.
Study region:
Ca Mau Province (CMP), Mekong Delta (MD), Vietnam.
Study focus:
Groundwater from deep aquifers is the most reliable source of freshwater in the MD but extensive overexploitation in the last decades led to the drop of hydraulic heads and negative environmental impacts. Therefore, a comprehensive groundwater investigation was conducted to evaluate its composition in the context of Quaternary marine transgression and regression cycles, geochemical processes as well as groundwater extraction.
New hydrological insights for the region:
The abundance of groundwater of Na-HCO3 type and distinct ion ratios, such as Na+/Cl-, indicate extensive freshwater intrusion in an initially saline hydrogeological system, with decreasing intensity from upper Pleistocene to deeper Miocene aquifers, most likely during the last marine regression phase 60-12 ka BP. Deviations from the conservative mixing line between the two endmembers seawater and freshwater are attributed to ion-exchange processes on mineral surfaces, making ion ratios in combination with a customized water type analysis a useful tool to distinguish between salinization and freshening processes. Elevated salinity in some areas is attributed to HCO3- generation by organic matter decomposition in marine sediments rather than to seawater intrusion. Nevertheless, a few randomly distributed locations show strong evidence of recent salinization in an early stage, which may be caused by the downwards migration of saline Holocene groundwater through natural and anthropogenic pathways into deep aquifers.
The study examined the potential future changes of drought characteristics in the Greater Lake Malawi Basin in Southeast Africa. This region strongly depends on water resources to generate electricity and food. Future projections (considering both moderate and high emission scenarios) of temperature and precipitation from an ensemble of 16 bias-corrected climate model combinations were blended with a scenario-neutral response surface approach to analyses changes in: (i) the meteorological conditions, (ii) the meteorological water balance, and (iii) selected drought characteristics such as drought intensity, drought months, and drought events, which were derived from the Standardized Precipitation and Evapotranspiration Index. Changes were analyzed for a near-term (2021-2050) and far-term period (2071-2100) with reference to 1976-2005. The effect of bias-correction (i.e., empirical quantile mapping) on the ability of the climate model ensemble to reproduce observed drought characteristics as compared to raw climate projections was also investigated. Results suggest that the bias-correction improves the climate models in terms of reproducing temperature and precipitation statistics but not drought characteristics. Still, despite the differences in the internal structures and uncertainties that exist among the climate models, they all agree on an increase of meteorological droughts in the future in terms of higher drought intensity and longer events. Drought intensity is projected to increase between +25 and +50% during 2021-2050 and between +131 and +388% during 2071-2100. This translates into +3 to +5, and +7 to +8 more drought months per year during both periods, respectively. With longer lasting drought events, the number of drought events decreases. Projected droughts based on the high emission scenario are 1.7 times more severe than droughts based on the moderate scenario. That means that droughts in this region will likely become more severe in the coming decades. Despite the inherent high uncertainties of climate projections, the results provide a basis in planning and (water-)managing activities for climate change adaptation measures in Malawi. This is of particular relevance for water management issues referring hydro power generation and food production, both for rain-fed and irrigated agriculture.
Modelers can improve a model by addressing the causes for the model errors (data errors and structural errors). This leads to implementing model enhancements (MEs), for example, meteorological data based on more monitoring stations, improved calibration data, and/or modifications in process formulations. However, deciding on which MEs to implement remains a matter of expert knowledge. After implementing multiple MEs, any improvement in model performance is not easily attributed, especially when considering different objectives or aspects of this improvement (e.g., better dynamics vs. reduced bias). We present an approach for comparing the effect of multiple MEs based on real observations and considering multiple objectives (MMEMO). A stepwise selection approach and structured plots help to address the multidimensionality of the problem. Tailored analyses allow a differentiated view on the effect of MEs and their interactions. MMEMO is applied to a case study employing the mesoscale hydro-sedimentological model WASA-SED for the Mediterranean-mountainous Isabena catchment, northeast Spain. The investigated seven MEs show diverse effects: some MEs (e.g., rainfall data) cause improvements for most objectives, while other MEs (e.g., land use data) only affect a few objectives or even decrease model performance. Interaction of MEs was observed for roughly half of the MEs, confirming the need to address them in the analysis. Calibration and increasing the temporal resolution showed by far stronger impact than any of the other MEs. The proposed framework can be adopted in other studies to analyze the effect of MEs and, thus, facilitate the identification and implementation of the most promising MEs for comparable cases.
We develop an amplitude spectral ratio method for event couples from clustered earthquakes to estimate seismic wave attenuation (Q-1) in the source volume. The method allows to study attenuation within the source region of earthquake swarms or aftershocks at depth, independent of wave path and attenuation between source region and surface station. We exploit the high-frequency slope of phase spectra using multitaper spectral estimates. The method is tested using simulated full wave-field seismograms affected by recorded noise and finite source rupture. The synthetic tests verify the approach and show that solutions are independent of focal mechanisms but also show that seismic noise may broaden the scatter of results. We apply the event couple spectral ratio method to northwest Bohemia, Czech Republic, a region characterized by the persistent occurrence of earthquake swarms in a confined source region at mid-crustal depth. Our method indicates a strong anomaly of high attenuation in the source region of the swarm with an averaged attenuation factor of Qp < 100. The application to S phases fails due to scattered P-phase energy interfering with S phases. The Qp anomaly supports the common hypothesis of highly fractured and fluid saturated rocks in the source region of the swarms in northwest Bohemia. However, high temperatures in a small volume around the swarms cannot be excluded to explain our observations.
We develop an amplitude spectral ratio method for event couples from clustered earthquakes to estimate seismic wave attenuation (Q-1) in the source volume. The method allows to study attenuation within the source region of earthquake swarms or aftershocks at depth, independent of wave path and attenuation between source region and surface station. We exploit the high-frequency slope of phase spectra using multitaper spectral estimates. The method is tested using simulated full wave-field seismograms affected by recorded noise and finite source rupture. The synthetic tests verify the approach and show that solutions are independent of focal mechanisms but also show that seismic noise may broaden the scatter of results. We apply the event couple spectral ratio method to northwest Bohemia, Czech Republic, a region characterized by the persistent occurrence of earthquake swarms in a confined source region at mid-crustal depth. Our method indicates a strong anomaly of high attenuation in the source region of the swarm with an averaged attenuation factor of Qp < 100. The application to S phases fails due to scattered P-phase energy interfering with S phases. The Qp anomaly supports the common hypothesis of highly fractured and fluid saturated rocks in the source region of the swarms in northwest Bohemia. However, high temperatures in a small volume around the swarms cannot be excluded to explain our observations.
Trait-based approaches to investigate (short- and long-term) phytoplankton dynamics and community assembly have become increasingly popular in freshwater and marine science. Although the nature of the pelagic habitat and the main phytoplankton taxa and ecology are relatively similar in both marine and freshwater systems, the lines of research have evolved, at least in part, separately. We compare and contrast the approaches adopted in marine and freshwater ecosystems with respect to phytoplankton functional traits. We note differences in study goals relating to functional trait use that assess community assembly and those that relate to ecosystem processes and biogeochemical cycling that affect the type of characteristics assigned as traits to phytoplankton taxa. Specific phytoplankton traits relevant for ecological function are examined in relation to
herbivory, amplitude of environmental change and spatial and temporal scales of study. Major differences are identified, including the shorter time scale for regular environmental change in freshwater ecosystems compared to that in the open oceans as well as the
type of sampling done by researchers based on site-accessibility. Overall, we encourage researchers to better motivate why they apply trait-based analyses to their studies and to make use of process-driven approaches, which are more common in marine studies. We further propose fully comparative trait studies conducted along the habitat gradient spanning freshwater to brackish to marine systems, or along geographic gradients. Such studies will benefit from the combined strength of both fields.
When dealing with issues that are of high so-cietal relevance, Earth sciences still face a lack of accep-tance, which is partly rooted in insufficient communicationstrategies on the individual and local community level. Toincrease the efficiency of communication routines, sciencehas to transform its outreach concepts to become more awareof individual needs and demands. The “encoding/decoding”concept as well as critical intercultural communication stud-ies can offer pivotal approaches for this transformation.