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Electronic databases of landslides seldom include the triggering mechanisms, rendering these inventories unusable for landslide hazard modeling. We present a method for classifying the triggering mechanisms of landslides in existing inventories, thus, allowing these inventories to aid in landslide hazard modeling corresponding to the correct event chain. Our method uses various geometric characteristics of landslides as the feature space for the machine-learning classifier random forest, resulting in accurate and robust classifications of landslide triggers. We applied the method to six landslide inventories spread over the Japanese archipelago in several different tests and training configurations to demonstrate the effectiveness of our approach. We achieved mean accuracy ranging from 67% to 92%. We also provide an illustrative example of a real-world usage scenario for our method using an additional inventory with unknown ground truth. Furthermore, our feature importance analysis indicates that landslides having identical trigger mechanisms exhibit similar geometric properties.
Stoof-Leichsenring, Kathleen R. ; Huang, Sichao ; Liu, Sisi ; Jia, Weihan ; Li, Kai ; Liu, Xingqi ; Pestryakova, Luidmila A. ; Herzschuh, Ulrike
Arctic and alpine aquatic ecosystems are changing rapidly under recent global warming, threatening water resources by diminishing trophic status and changing biotic composition. Macrophytes play a key role in the ecology of freshwaters and we need to improve our understanding of long-term macrophytes diversity and environmental change so far limited by the sporadic presence of macrofossils in sediments.
In our study, we applied metabarcoding using the trnL P6 loop marker to retrieve macrophyte richness and composition from 179 surface-sediment samples from arctic Siberian and alpine Chinese lakes and three representative lake cores.
The surface-sediment dataset suggests that macrophyte richness and composition are mostly affected by temperature and conductivity, with highest richness when mean July temperatures are higher than 12 degrees C and conductivity ranges between 40 and 400 mu S cm(-1). Compositional turnover during the Late Pleistocene/Holocene is minor in Siberian cores and characterized by a less rich, but stable emergent macrophyte community. Richness decreases during the Last Glacial Maximum and rises during wetter and warmer climate in the Late-glacial and Mid-Holocene.
In contrast, we detect a pronounced change from emergent to submerged taxa at 14 ka in the Tibetan alpine core, which can be explained by increasing temperature and conductivity due to glacial runoff and evaporation.
Our study provides evidence for the suitability of the trnL marker to recover modern and past macrophyte diversity and its applicability for the response of macrophyte diversity to lake-hydrochemical and climate variability predicting contrasting macrophyte changes in arctic and alpine lakes under intensified warming and human impact.
Large-scale groundwater models are required to estimate groundwater availability and to inform water management strategies on the national scale.
However, parameterization of large-scale groundwater models covering areas of major river basins and more is challenging due to the lack of observational data and the mismatch between the scales of modeling and measurements.
In this work, we propose to bridge the scale gap and derive regional hydraulic parameters by spectral analysis of groundwater level fluctuations.
We hypothesize that specific locations in aquifers can reveal regional parameters of the hydraulic system.
We first generate ensembles of synthetic but realistic aquifers which systematically differ in complexity. Applying Liang and Zhang's (2013), , semi-analytical solution for the spectrum of hydraulic head time series, we identify for each ensemble member and at different locations representative aquifer parameters.
Next, we extend our study to investigate the use of spectral analysis in more complex numerical models and in real settings.
Our analyses indicate that the variance of inferred effective transmissivity and storativity values for stochastic aquifer ensembles is small for observation points which are far away from the Dirichlet boundary.
Moreover, the head time series has to cover a period which is roughly 10 times as long as the characteristic time of the aquifer. In deterministic aquifer models we infer equivalent, regionally valid parameters. A sensitivity analysis further reveals that as long as the aquifer length and the position of the groundwater measurement location is roughly known, the parameters can be robustly estimated.
Kong, Xiangzhen ; Ghaffar, Salman ; Determann, Maria ; Friese, Kurt ; Jomaa, Seifeddine ; Mi, Chenxi ; Shatwell, Tom ; Rinke, Karsten ; Rode, Michael
Deforestation is currently a widespread phenomenon and a growing environmental concern in the era of rapid climate change.
In temperate regions, it is challenging to quantify the impacts of deforestation on the catchment dynamics and downstream aquatic ecosystems such as reservoirs and disentangle these from direct climate change impacts, let alone project future changes to inform management.
Here, we tackled this issue by investigating a unique catchment-reservoir system with two reservoirs in distinct trophic states (meso- and eutrophic), both of which drain into the largest drinking water reservoir in Germany.
Due to the prolonged droughts in 2015-2018, the catchment of the mesotrophic reservoir lost an unprecedented area of forest (exponential increase since 2015 and ca. 17.1% loss in 2020 alone).
We coupled catchment nutrient exports (HYPE) and reservoir ecosystem dynamics (GOTM-WET) models using a process-based modeling approach. The coupled model was validated with datasets spanning periods of rapid deforestation, which makes our future projections highly robust.
Results show that in a short-term time scale (by 2035), increasing nutrient flux from the catchment due to vast deforestation (80% loss) can turn the mesotrophic reservoir into a eutrophic state as its counterpart.
Our results emphasize the more prominent impacts of deforestation than the direct impact of climate warming in impairment of water quality and ecological services to downstream aquatic ecosystems. Therefore, we propose to evaluate the impact of climate change on temperate reservoirs by incorporating a time scale-dependent context, highlighting the indirect impact of deforestation in the short-term scale. In the long-term scale (e.g. to 2100), a guiding hypothesis for future research may be that indirect effects (e.g., as mediated by catchment dynamics) are as important as the direct effects of climate warming on aquatic ecosystems.
Landslides in deglaciated and deglaciating mountains represent a major hazard, but their distribution at the spatial scale of entire mountain belts has rarely been studied. Traditional models of landslide distribution assume that landslides are concentrated in the steepest, wettest, and most tectonically active parts of the orogens, where glaciers reached their greatest thickness.
However, based on mapping large landslides (>0.9 km(2)) over an unprecedentedly large area of Southern Patagonia (similar to 305,000 km(2)), we show that the distribution of landslides can have the opposite trend.
We show that the largest landslides within the limits of the former Patagonian Ice Sheet (PIS) cluster along its eastern margins occupying lower, tectonically less active, and arid part of the Patagonian Andes. In contrast to the heavily glaciated, highest elevations of the mountain range, the peripheral regions have been glaciated only episodically, leaving a larger volume of unstable sedimentary and volcanic rocks that are subject to ongoing slope instability.
The benefits of counting butterflies: recommendations for a successful citizen science project
(2022)
Kühn, Elisabeth ; Becker, Marc ; Harpke, Alexander ; Kühn, Ingolf ; Kuhlicke, Christian ; Schmitt, Thomas ; Settele, Josef ; Musche, Martin
Citizen science (CS) projects, being popular across many fields of science, have recently also become a popular tool to collect biodiversity data. Although the benefits of such projects for science and policy making are well understood, relatively little is known about the benefits participants get from these projects as well as their personal backgrounds and motivations. Furthermore, very little is known about their expectations. We here examine these aspects, with the citizen science project "German Butterfly Monitoring" as an example. A questionnaire was sent to all participants of the project and the responses to the questionnaire indicated the following: center dot Most transect walkers do not have a professional background in this field, though they do have a high educational level, and are close to retirement, with a high number of females; center dot An important motivation to join the project is to preserve the natural environment and to contribute to scientific knowledge; center dot Participants benefit by enhancing their knowledge about butterflies and especially their ability to identify different species (taxonomic knowledge); center dot Participants do not have specific expectations regarding the project beyond proper management and coordination, but have an intrinsic sense of working for a greater good. The willingness to join a project is higher if the project contributes to the solution of a problem discussed in the media (here, insect decline). Based on our findings from the analysis of the questionnaire we can derive a set of recommendations for establishing a successful CS project. These include the importance of good communication, e.g., by explaining what the (scientific) purpose of the project is and what problems are to be solved with the help of the data collected in the project. The motivation to join a CS project is mostly intrinsic and CS is a good tool to engage people during difficult times such as the COVID-19 pandemic, giving participants the feeling of doing something useful.
So far, various studies have aimed at decomposing the integrated terrestrial water storage variations observed by satellite gravimetry (GRACE, GRACE-FO) with the help of large-scale hydrological models. While the results of the storage decomposition depend on model structure, little attention has been given to the impact of the way that vegetation is represented in these models. Although vegetation structure and activity represent the crucial link between water, carbon, and energy cycles, their representation in large-scale hydrological models remains a major source of uncertainty. At the same time, the increasing availability and quality of Earth-observation-based vegetation data provide valuable information with good prospects for improving model simulations and gaining better insights into the role of vegetation within the global water cycle. In this study, we use observation-based vegetation information such as vegetation indices and rooting depths for spatializing the parameters of a simple global hydrological model to define infiltration, root water uptake, and transpiration processes. The parameters are further constrained by considering observations of terrestrial water storage anomalies (TWS), soil moisture, evapotranspiration (ET) and gridded runoff ( Q) estimates in a multi-criteria calibration approach. We assess the implications of including varying vegetation characteristics on the simulation results, with a particular focus on the partitioning between water storage components. To isolate the effect of vegetation, we compare a model experiment in which vegetation parameters vary in space and time to a baseline experiment in which all parameters are calibrated as static, globally uniform values. Both experiments show good overall performance, but explicitly including varying vegetation data leads to even better performance and more physically plausible parameter values. The largest improvements regarding TWS and ET are seen in supply-limited (semi-arid) regions and in the tropics, whereas Q simulations improve mainly in northern latitudes. While the total fluxes and storages are similar, accounting for vegetation substantially changes the contributions of different soil water storage components to the TWS variations. This suggests an important role of the representation of vegetation in hydrological models for interpreting TWS variations. Our simulations further indicate a major effect of deeper moisture storages and groundwater-soil moisture-vegetation interactions as a key to understanding TWS variations. We highlight the need for further observations to identify the adequate model structure rather than only model parameters for a reasonable representation and interpretation of vegetation-water interactions.
Neutrons on rails
(2021)
Schrön, Martin ; Oswald, Sascha ; Zacharias, Steffen ; Kasner, Mandy ; Dietrich, Peter ; Attinger, Sabine
Large-scale measurements of the spatial distribution of water content in soils and snow are challenging for state-of-the-art hydrogeophysical methods. Cosmic-ray neutron sensing (CRNS) is a noninvasive technology that has the potential to bridge the scale gap between conventional in situ sensors and remote sensing products in both, horizontal and vertical domains. In this study, we explore the feasibility and potential of estimating water content in soils and snow with neutron detectors in moving trains. Theoretical considerations quantify the stochastic measurement uncertainty as a function of water content, altitude, resolution, and detector efficiency. Numerical experiments demonstrate that the sensitivity of measured water content is almost unperturbed by train materials. Finally, three distinct real-world experiments provide a proof of concept on short and long-range tracks. With our results a transregional observational soil moisture product becomes a realistic vision within the next years.
Quantifying root water uptake is essential to understanding plant water use and responses to different environmental conditions. However, non-destructive measurement of water transport and related hydraulics in the soil-root system remains a challenge.
Neutron imaging, with its high sensitivity to hydrogen, has become an unparalleled tool to visualize and quantify root water uptake in vivo. In combination with isotopes (e.g., deuterated water) and a diffusion-convection model, root water uptake and hydraulic redistribution in root and soil can be quantified.
Here, we review recent advances in utilizing neutron imaging to visualize and quantify root water uptake, hydraulic redistribution in roots and soil, and root hydraulic properties of different plant species.
Under uniform soil moisture distributions, neutron radiographic studies have shown that water uptake was not uniform along the root and depended on both root type and age. For both tap (e.g., lupine [Lupinus albus L.]) and fibrous (e.g., maize [Zea mays L.]) root systems, water was mainly taken up through lateral roots. In mature maize, the location of water uptake shifted from seminal roots and their laterals to crown/nodal roots and their laterals.
Under non-uniform soil moisture distributions, part of the water taken up during the daytime maintained the growth of crown/nodal roots in the upper, drier soil layers. Ultra-fast neutron tomography provides new insights into 3D water movement in soil and roots. We discuss the limitations of using neutron imaging and propose future directions to utilize neutron imaging to advance our understanding of root water uptake and soil-root interactions.
Glückler, Ramesh ; Herzschuh, Ulrike ; Kruse, Stefan ; Andreev, Andrei ; Vyse, Stuart Andrew ; Winkler, Bettina ; Biskaborn, Boris ; Pestryakova, Luidmila Agafyevna ; Dietze, Elisabeth
Wildfires, as a key disturbance in forest ecosystems, are shaping the world's boreal landscapes. Changes in fire regimes are closely linked to a wide array of environmental factors, such as vegetation composition, climate change, and human activity. Arctic and boreal regions and, in particular, Siberian boreal forests are experiencing rising air and ground temperatures with the subsequent degradation of permafrost soils leading to shifts in tree cover and species composition. Compared to the boreal zones of North America or Europe, little is known about how such environmental changes might influence long-term fire regimes in Russia. The larch-dominated eastern Siberian deciduous boreal forests differ markedly from the composition of other boreal forests, yet data about past fire regimes remain sparse. Here, we present a high-resolution macroscopic charcoal record from lacustrine sediments of Lake Khamra (southwest Yakutia, Siberia) spanning the last ca. 2200 years, including information about charcoal particle sizes and morphotypes. Our results reveal a phase of increased charcoal accumulation between 600 and 900 CE, indicative of relatively high amounts of burnt biomass and high fire frequencies. This is followed by an almost 900-year-long period of low charcoal accumulation without significant peaks likely corresponding to cooler climate conditions. After 1750 CE fire frequencies and the relative amount of biomass burnt start to increase again, coinciding with a warming climate and increased anthropogenic land development after Russian colonization. In the 20th century, total charcoal accumulation decreases again to very low levels despite higher fire frequency, potentially reflecting a change in fire management strategies and/or a shift of the fire regime towards more frequent but smaller fires. A similar pattern for different charcoal morphotypes and comparison to a pollen and non-pollen palynomorph (NPP) record from the same sediment core indicate that broad-scale changes in vegetation composition were probably not a major driver of recorded fire regime changes. Instead, the fire regime of the last two millennia at Lake Khamra seems to be controlled mainly by a combination of short-term climate variability and anthropogenic fire ignition and suppression.