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Although distally steepened carbonate ramps have been studied by numerous researchers, the processes that control the development of these carbonate systems, including tectonics, differential carbonate production along the ramp profile, or antecedent physiography of the slopes, are an ongoing discussion. We use a stratigraphic forward model to test different hypotheses to unravel controls over distally steepened ramp development, referenced to the well-known Upper Miocene Menorca carbonate ramp (Spain). Sensitivity tests show that distally steepened ramps develop under complex interaction among accommodation, carbonate production and sediment transport parameters. Ramp slope initiation is favoured by still stands and falls of sea-level, in a setting with high-frequency sea-level fluctuations with amplitude between 20 m and 40 m. Low-frequency and higher amplitude sea-level fluctuations of about 115 m tend to form models with no significant slope development. The impact of antecedent slope on the geometry of ramps is determined by the paleoslope inclination, with flat to subhorizontal paleosurfaces resulting in ramps that mirror the antecedent slope. In contrast, steeper paleosurfaces tend to result in ramps with well-defined slopes. Our models, therefore, show that the ramp profile becomes more influenced by the depth constraints on the carbonate sediment producers than by the geometry of the underlying topography as the inclination of the paleosurface increases. The presented models also show that seagrass-dominated shallow carbonate production tends to result in steep slopes due to the low-transport characteristic imposed by seagrass trapping. This steepness can, however, be altered by the introduction of high transport sediment grains from deeper carbonate producers, which fill the slopes and more distal sections of the ramp profile.
Describing the heterogeneous structure of forests is often challenging.
One possibility is to analyze forest biomass in different plots and to derive plot-based frequency distributions.
However, these frequency distributions depend on the plot size and thus are scale dependent.
This study provides insights about transferring them between scales. Understanding the effects of scale on distributions of biomass is particularly important for comparing information from different sources such as inventories, remote sensing and modeling, all of which can operate at different spatial resolutions. Reliable methods to compare results of vegetation models at a grid scale with field data collected at smaller scales are still missing.
The scaling of biomass and variables, which determine the forest biomass, was investigated for a tropical forest in Panama. Based on field inventory data from Barro Colorado Island, spanning 50 ha over 30 years, the distributions of aboveground biomass, biomass gain and mortality were derived at different spatial resolutions, ranging from 10 to 100 m. Methods for fitting parametric distribution functions were compared.
Further, it was tested under which assumptions about the distributions a simple stochastic simulation forest model could best reproduce observed biomass distributions at all scales. Also, an analytical forest model for calculating biomass distributions at equilibrium and assuming mortality as a white shot noise process was tested.
Scaling exponents of about 0.47 were found for the standard deviations of the biomass and gain distributions, while mortality showed a different scaling relationship with an exponent of 0.3. Lognormal and gamma distribution functions fitted with the moment matching estimation method allowed for consistent parameter transfers between scales. Both forest models (stochastic simulation and analytical solution) were able to reproduce observed biomass distributions across scales, when combined with the derived scaling relationships.
The study demonstrates a way of how to approach the scaling problem in model-data comparisons by providing a transfer relationship. Further research is needed for a better understanding of the mechanisms that shape the frequency distributions at the different scales.
Lakes act as important sinks for inorganic and organic sediment components. However, investigations of sedimentary carbon budgets within glacial lakes are currently absent from Arctic Siberia. The aim of this paper is to provide the first reconstruction of accumulation rates, sediment and carbon budgets from a lacustrine sediment core from Lake Rauchuagytgyn, Chukotka (Arctic Siberia). We combined multiple sediment biogeochemical and sedimentological parameters from a radiocarbon-dated 6.5m sediment core with lake basin hydroacoustic data to derive sediment stratigraphy, sediment volumes and infill budgets. Our results distinguished three principal sediment and carbon accumulation regimes that could be identified across all measured environmental proxies including early Marine Isotope Stage 2 (MIS2) (ca. 29-23.4 ka cal BP), mid-MIS2-early MIS1 (ca. 23.4-11.69 ka cal BP) and the Holocene (ca. 11.69-present). Estimated organic carbon accumulation rates (OCARs) were higher within Holocene sediments (average 3.53 gOCm(-2) a(-1)) than Pleistocene sediments (average 1.08 gOCm(-2) a(-1)) and are similar to those calculated for boreal lakes from Quebec and Finland and Lake Baikal but significantly lower than Siberian thermokarst lakes and Alberta glacial lakes. Using a bootstrapping approach, we estimated the total organic carbon pool to be 0.26 +/- 0.02 Mt and a total sediment pool of 25.7 +/- 1.71 Mt within a hydroacoustically derived sediment volume of ca. 32 990 557m(3). The total organic carbon pool is substantially smaller than Alaskan yedoma, thermokarst lake sediments and Alberta glacial lakes but shares similarities with Finnish boreal lakes. Temporal variability in sediment and carbon accumulation dynamics at Lake Rauchuagytgyn is controlled predominantly by palaeoclimate variation that regulates lake ice-cover dynamics and catchment glacial, fluvial and permafrost processes through time. These processes, in turn, affect catchment and within-lake primary productivity as well as catchment soil development. Spatial differences compared to other lake systems at a trans-regional scale likely relate to the high-latitude, mountainous location of Lake Rauchuagytgyn.
Large earthquakes can increase the amount of water feeding stream flows, raise groundwater levels, and thus grant plant roots more access to water in water-limited environments. We examine growth and photosynthetic responses of Pine plantations to the Maule M-w 8.8 earthquake in headwater catchments of Chile's Coastal Range. We combine high-resolution wood anatomic (lumen area) and biogeochemical (delta 13C of wood cellulose) proxies of daily to weekly tree growth sampled from trees on floodplains and close to ridge lines. We find that, immediately after the earthquake, at least two out of six tree trees on valley floors had increased lumen area and decreased delta 13C, while trees on hillslopes had a reverse trend. Our results indicate a control of soil water on this response, largely consistent with models that predict how enhanced postseismic vertical soil permeability causes groundwater levels to rise on valley floors, but fall along the ridges. Statistical analysis with boosted regression trees indicates that streamflow discharge gained predictive importance for photosynthetic activity on the ridges, but lost importance on the valley floor after the earthquake. We infer that earthquakes may stimulate ecohydrological conditions favoring tree growth over days to weeks by triggering stomatal opening. The weak and short-lived signals that we identified, however, show that such responses are only valid under water-limited, rather than energy-limited tree, growth. Hence, dendrochronological studies targeted at annual resolution may overlook some earthquake effects on tree vitality.
Antarctica is the last pristine environment on Earth, its biota being adapted to the harsh and extreme polar climate. Until now, soil formation and vegetation development in continental Antarctica were considered very slow due to the extreme conditions of this polar desert. Since the austral summer 2002/2003, a long-term monitoring network of the terrestrial ecosystems (soils, vegetation, active layer thickness) has been established at Victoria Land (VL) across a > 500 km latitudinal gradient of coastal sites (73 degrees -77 degrees S). In only one decade large ecosystem changes were detected. Climate was characterized by a significant increase of thawing degree days in northern VL and of autumn air temperature. No extreme climatic events (such as hot spells) where detected in the study period. Soil chemistry suffered large quantitative changes, clearly indicating rapid pedogenetic processes. In most soils the upper layers exhibited a strong alkalinization (pH increases up to 3 units) and increases in conductivity, anions and cations (in particular of SO4 and Na). The largest changes were observed in soils with low vegetation cover. Statistically significant differences in soil chemistry were detected between soils with high and low vegetation cover, the former showing lower pH, conductivity, Na and Cl. Most plots exhibited changes of total cover, species richness and floristic composition, with vegetation expansion in soils with low vegetation cover and the largest increase recorded at Apostrophe Island (northern VL). Principal Component Analysis (PCA) identified the main trend of vegetation change, with a shift from lower to higher cover and a secondary trend of change associated with a gradient of water availability, consistent with an increase in water instead of snow. Redundancy analysis (RDA) identified the trend of change in soil chemistry with increases in pH, conductivity, anions and cations associated with the concomitant decrease in C, N, NO3, PO4. The RDA confirmed that soil changes were associated with a gradient of vegetation change (from low to high cover) as well as of water availability, as already indirectly outlined by the PCA. Field manipulation experiments carried out at five locations of the network between 73 degrees S and 77 degrees S, simulating increases of precipitation from snow or water additions didn't induce changes in soil pH, indicating that pulse events of snow accumulation or melting could not trigger persistent soil pH changes. These data allow hypothesize the occurrence of a main ecosystem change occurring at regional scale at Victoria Land. The slight air warming and its consequences on soil chemistry and vegetation, further highlight the sensitivity of the fragile Antarctic ecosystems to the consequences of even small changes in climate.
Deriving soil moisture content (SMC) at the regional scale with different spatial and temporal land cover changes is still a challenge for active and passive remote sensing systems, often coped with machine learning methods.
So far, the reference measurements of the data-driven approaches are usually based on point data, which entails a scale gap to the resolution of the remote sensing data. Cosmic Ray Neutron Sensing (CRNS) indirectly provides SMC estimates of a soil volume covering more than 1 ha and vertical depth up to 80 cm and is thus able to narrow this scale gap.
So far, the CRNS-based SMC has only been used as validation source of remote sensing based SMC products. Its beneficial large sensing volume, especially in depth, has not been exploited yet.
However, the sensing volume of the CRNS, which is changing with hydrological conditions, bears challenges for the comparison with remote sensing observations. This study, for the fist time, aims to understand the direct linkage of optical (Sentinel 2) and SAR (Sentinel 1) data with CRNS-based SMC.
Thereby, the CRNS-based SMC is obtained by an experimental CRNS cluster that covers the high temporal and spatial SMC variability of an entire pre-alpine subcatchment. Using different Random Forest regressions, we analyze the potentials and limitations of both remote sensing sensors to follow the CRNS-based SMC signal.
Our results show that it is possible to link the CRNS-based SMC signal with SAR and optical remote sensing observations via Random Forest modelling.
We found that Sentinel 2 data is able to separate wet from dry periods with a R2 of 0.68.
It is less affected by the changing soil volume that contributes to the CRNS-based SMC signal and it is able to assign a land cover specific SMC distribution.
However, Sentinel 2 regression models are not accurate (R2 < 0.21) in mapping the CRNSbased SMC for the frequently mowed grassland areas of the study site. It requires soil type and topographical information to accurately follow the CRNS-based SMC signal with Random Forest regression.
Sentinel 1 data instead is affected by the changing soil volume that contributes to the CRNS-based SMC signal. It has reasonable model performance (R2 = 0.34) when the CRNS data correspond to surface SMC. Also for Sentinel 1 the retrieval is impacted by the mowing activities at the test site.
When separating the CRNS data set into dry and wet periods, soil properties and topography are the main drivers of SMC estimation. Sentinel 1 or Sentinel 2 data add the existing temporal variability to the regression models. The analysis underlines the need of combining optical and SAR observations (Sentinel 1, Sentinel 2) as well as soil property and topographical information to understand and follow the CRNS-based SMC signal for different hydrological conditions and land cover types.
We review properties and processes of earthquake rupture zones based on field studies, laboratory observations, theoretical models and simulations, with the goal of assessing the possible dominance of different processes in different parts of the rupture and validity of commonly used models. Rupture zones may be divided into front, intermediate, and tail regions that interact to different extents. The rupture front is dominated by fracturing and granulation processes and strong dilatation, producing faulting products that are reworked by subsequent sliding behind. The intermediate region sustains primarily frictional sliding with relatively high slip rates that produce appreciable stress transfer to the propagating front. The tail region further behind is characterized by low slip rates that effectively do not influence the propagating front, although it (and the intermediate region) can spawn small offspring rupture fronts. Wave-mediated stress transfer can also trigger failures ahead of the rupture front. Earthquake ruptures are often spatially discontinuous and intermittent with a hierarchy of asperity and segment sizes that radiate waves with different tensorial compositions and frequency bands. While different deformation processes dominating parts of the rupture zones can be treated effectively with existing constitutive relations, a more appropriate analysis of earthquake processes would require a model that combines aspects of fracture, damage-breakage, and frictional frameworks.
Like many other regions in central Europe, Germany experienced sequential summer droughts from 2015 to 2018. As one of the environmental consequences, river nitrate concentrations have exhibited significant changes in many catchments.
However, catchment nitrate responses to the changing weather conditions have not yet been mechanistically explored.
Thus, a fully distributed, process-based catchment Nitrate model (mHM-Nitrate) was used to reveal the causal relations in the Bode catchment, of which river nitrate concentrations have experienced contrasting trends from upstream to downstream reaches. The model was evaluated using data from six gauging stations, reflecting different levels of runoff components and their associated nitrate-mixing from upstream to downstream.
Results indicated that the mHM-Nitrate model reproduced dynamics of daily discharge and nitrate concentration well, with Nash-Sutcliffe Efficiency >= 0.73 for discharge and Kling-Gupta Efficiency >= 0.50 for nitrate concentration at most stations.
Particularly, the spatially contrasting trends of nitrate con-centration were successfully captured by the model.
The decrease of nitrate concentration in the lowland area in drought years (2015-2018) was presumably due to (1) limited terrestrial export loading (ca. 40 % lower than that of normal years 2004-2014), and (2) increased in-stream retention efficiency (20 % higher in summer within the whole river network).
From a mechanistic modelling perspective, this study provided insights into spatially heterogeneous flow and nitrate dynamics and effects of sequential droughts, which shed light on water -quality responses to future climate change, as droughts are projected to be more frequent.
The optical properties, chemical composition, and potential chromophores of brown carbon (BrC) aerosol particles were studied during typical summertime and wintertime at a kerbside in downtown Karl-sruhe, a city in central Europe.
The average absorption coefficient and mass absorption efficiency at 365 nm (Abs(365) and MAE(365)) of methanol-soluble BrC (MS-BrC) were lower in the summer period (1.6 +/- 0.5 Mm(-1), 0.5 +/- 0.2 m(2) g(-1)) than in the winter period (2.8 +/- 1.9 Mm(-1), 1.1 +/- 0.3 m(2) g(-1)). Using a parallel factor (PARAFAC) analysis to identify chromophores, two different groups of highly oxygenated humic-like substances (HO-HULIS) dominated in summer and contributed 96 +/- 6 % of the total fluorescence intensity.
In contrast, less-oxygenated HULIS (LO-HULIS) dominated the total fluorescence intensity in winter with 57 +/- 12 %, followed by HO-HULIS with 31 +/- 18 %. Positive matrix factorization (PMF) analysis of organic compounds detected in real time by an online aerosol mass spectrometer (AMS) led to five characteristic organic compound classes.
The statistical analysis of PARAFAC components and PMF factors showed that LO-HULIS chromophores were most likely emitted from biomass burning in winter. HO-HULIS chromophores could be low-volatility oxy-genated organic aerosol from regional transport and oxidation of biogenic volatile organic compounds (VOCs) in summer.
Five nitro-aromatic compounds (NACs) were identified by a chemical ionization mass spectrometer (C7H7O3N, C7H7O4N, C6H5O5N, C6H5O4N, and C6H5O3N), which contributed 0.03 +/- 0.01 % to the total organic mass but can explain 0.3 +/- 0.1 % of the total absorption of MS-BrC at 365 nm in winter.
Furthermore, we identified 316 potential brown carbon molecules which accounted for 2.5 +/- 0.6 % of the organic aerosol mass. Using an average mass absorption efficiency (MAE(365)) of 9.5 m(2)g(-1) for these compounds, we can es-timate their mean light absorption to be 1.2 +/- 0.2 Mm(-1), accounting for 32 +/- 15 % of the total absorption of MS-BrC at 365 nm.
This indicates that a small fraction of brown carbon molecules dominates the overall ab-sorption. The potential BrC molecules assigned to the LO-HULIS component had a higher average molecular weight (265 +/- 2 Da) and more nitrogen-containing molecules (62 +/- 1 %) than the molecules assigned to the HOHULIS components.
Our analysis shows that the LO-HULIS, with a high contribution of nitrogen-containing molecules originating from biomass burning, dominates aerosol fluorescence in winter, and HO-HULIS, with fewer nitrogen-containing molecules as low-volatility oxygenated organic aerosol from regional transport and oxidation of biogenic volatile organic compounds (VOC), dominates in summer.
Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-triggering events, which is a typical limitation in remote, sparsely populated regions. We evaluate 136 statistical models trained on a precipitation dataset with five landslide-triggering precipitation events recorded near Sitka, Alaska, USA, as well as 6000 d of non-triggering rainfall (2002–2020). We also conduct extensive statistical evaluation for three primary purposes: (1) to select the best-fitting models, (2) to evaluate performance of the preferred models, and (3) to select and evaluate warning thresholds. We use Akaike, Bayesian, and leave-one-out information criteria to compare the 136 models, which are trained on different cumulative precipitation variables at time intervals ranging from 1 h to 2 weeks, using both frequentist and Bayesian methods to estimate the daily probability and intensity of potential landslide occurrence (logistic regression and Poisson regression). We evaluate the best-fit models using leave-one-out validation as well as by testing a subset of the data. Despite this sparse landslide inventory, we find that probabilistic models can effectively distinguish days with landslides from days without slide activity. Our statistical analyses show that 3 h precipitation totals are the best predictor of elevated landslide hazard, and adding antecedent precipitation (days to weeks) did not improve model performance. This relatively short timescale of precipitation combined with the limited role of antecedent conditions likely reflects the rapid draining of porous colluvial soils on the very steep hillslopes around Sitka. Although frequentist and Bayesian inferences produce similar estimates of landslide hazard, they do have different implications for use and interpretation: frequentist models are familiar and easy to implement, but Bayesian models capture the rare-events problem more explicitly and allow for better understanding of parameter uncertainty given the available data. We use the resulting estimates of daily landslide probability to establish two decision boundaries that define three levels of warning. With these decision boundaries, the frequentist logistic regression model incorporates National Weather Service quantitative precipitation forecasts into a real-time landslide early warning “dashboard” system (https://sitkalandslide.org/, last access: 9 October 2023). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers.
Increased rates of glacier retreat and thinning need accurate local estimates of glacier elevation change to predict future changes in glacier runoff and their contribution to sea level rise. Glacier elevation change is typically derived from digital elevation models (DEMs) tied to surface change analysis from satellite imagery. Yet, the rugged topography in mountain regions can cast shadows onto glacier surfaces, making it difficult to detect local glacier elevation changes in remote areas. A rather untapped resource comprises precise, time-stamped metadata on the solar position and angle in satellite images. These data are useful for simulating shadows from a given DEM. Accordingly, any differences in shadow length between simulated and mapped shadows in satellite images could indicate a change in glacier elevation relative to the acquisition date of the DEM. We tested this hypothesis at five selected glaciers with long-term monitoring programmes. For each glacier, we projected cast shadows onto the glacier surface from freely available DEMs and compared simulated shadows to cast shadows mapped from ∼40 years of Landsat images. W validated the relative differences with geodetic measurements of glacier elevation change where these shadows occurred. We find that shadow-derived glacier elevation changes are consistent with independent photogrammetric and geodetic surveys in shaded areas. Accordingly, a shadow cast on Baltoro Glacier (the Karakoram, Pakistan) suggests no changes in elevation between 1987 and 2020, while shadows on Great Aletsch Glacier (Switzerland) point to negative thinning rates of about 1 m yr−1 in our sample. Our estimates of glacier elevation change are tied to occurrence of mountain shadows and may help complement field campaigns in regions that are difficult to access. This information can be vital to quantify possibly varying elevation-dependent changes in the accumulation or ablation zone of a given glacier. Shadow-based retrieval of glacier elevation changes hinges on the precision of the DEM as the geometry of ridges and peaks constrains the shadow that we cast on the glacier surface. Future generations of DEMs with higher resolution and accuracy will improve our method, enriching the toolbox for tracking historical glacier mass balances from satellite and aerial images.
Regional Flood Frequency Analysis (RFFA) is one of the widely used approaches for estimating design floods in the ungauged basins.
We developed an eXtreme Gradient Boost (XGB) machine learning model for RFFA and flood estimation.
Our approach relies on developing a regression model between flood quantiles and the commonly available catchment descriptors.
We used CAMELs data for 671 catchments from the USA to test the approach's efficacy. The results were compared with the traditional Multiple Linear Regression methods and Artificial Neural Networks.
Results revealed that the XGB-based approach estimated design flood with the highest accuracy during training and validation with minor mean absolute error, root mean square error values, and percentage bias ranging from -10 to + 10.
The importance of each catchment feature is visualized by three different approaches Gini Impurity, Permutation, and Dropout Loss Feature Ranking. We observed that the most dominating variables are rainfall intensity, slope, snow fraction, soil porosity, and temperature. It is observed that the importance of these variables is a function of the hydroclimatic regions and varies with space.
In contrast, mean annual areal potential evapotranspiration, mean annual rainfall, fraction forest area, and soil conductivity have low significance in estimating design flood for an ungauged catchment.
Indeed, the proposed XGB-based approach has broader applicability and replicability.
In this work, we present a comprehensive evaluation of a stochastic multi-site, multi-variate weather generator at the scale of entire Germany and parts of the neighbouring countries covering the major German river basins Elbe, Upper Danube, Rhine, Weser and Ems with a total area of approximately 580,000 km(2). The regional weather generator, which is based on a first-order multi-variate auto-regressive model, is setup using 53-year long daily observational data at 528 locations. The performance is evaluated by investigating the ability of the weather generator to replicate various important statistical properties of the observed variables including precipitation occurrence and dry/wet transition probabilities, mean daily and extreme precipitation, multi-day precipitation sums, spatial correlation structure, areal precipitation, mean daily and extreme temperature and solar radiation. We explore two marginal distributions for daily precipitation amount: mixed Gamma-Generalized Pareto and extended Generalized Pareto. Furthermore, we introduce a new procedure to estimate the spatial correlation matrix and model mean daily temperature and solar radiation. The extensive evaluation reveals that the weather generator is greatly capable of capturing most of the crucial properties of the weather variables, particularly of extreme precipitation at individual locations. Some deficiencies are detected in capturing spatial precipitation correlation structure that leads to an overestimation of areal precipitation extremes. Further improvement of the spatial correlation structure is envisaged for future research. The mixed marginal model found to outperform the extended Generalized Pareto in our case. The use of power transformation in combination with normal distribution significantly improves the performance for non-precipitation variables. The weather generator can be used to generate synthetic event footprints for large-scale trans-basin flood risk assessment.
Large agricultural streams receive excessive inputs of nitrogen.
However, quantifying the role of these streams in nitrogen processing remains limited because continuous direct measurements of the interacting and highly time-varying nitrogen processing pathways in larger streams and rivers are very complex.
Therefore, we employed a monitoring-driven modelling approach with high-frequency in situ data and the river water quality model Water Quality Analysis Simulation Program (WASP) 7.5.2 in the 27.4 km reach of the sixth-order agricultural stream called Lower Bode (central Germany) for a 5-year period (2014-2018).
Paired high-frequency sensor data (15 min interval) of discharge, nitrate, dissolved oxygen, and chlorophyll a at upstream and downstream stations were used as model boundaries and for setting model constraints.
The WASP model simulated 15 min intervals of discharge, nitrate, and dissolved oxygen with Nash-Sutcliffe efficiency values higher than 0.9 for calibration and validation, enabling the calculation of gross and net dissolved inorganic nitrogen uptake and pathway rates on a daily, seasonal, and multiannual scale.
Results showed daily net uptake rate of dissolved inorganic nitrogen ranged from -17.4 to 553.9 mgNm(-2)d(-1). The highest daily net uptake could reach almost 30 % of the total input loading, which occurred at extreme low flow in summer 2018.
The growing season (spring and summer) accounted for 91 % of the average net annual uptake of dissolved inorganic nitrogen in the measured period. In spring, both the DIN gross and net uptake were dominated by the phytoplankton uptake pathway. In summer, benthic algae assimilation dominated the gross uptake of dissolved inorganic nitrogen.
Conversely, the reach became a net source of dissolved inorganic nitrogen with negative daily net uptake values in autumn and winter, mainly because the release from benthic algae surpassed uptake processes.
Over the 5 years, average gross and net uptake rates of dissolved inorganic nitrogen were 124.1 and 56.8 mgNm(-2)d(-1), which accounted for only 2.7 % and 1.2 % of the total loadings in the Lower Bode, respectively. The 5-year average gross DIN uptake decreased from assimilation by benthic algae through assimilation by phytoplankton to denitrification.
Our study highlights the value of combining river water quality modelling with high-frequency data to obtain a reliable budget of instream dissolved inorganic nitrogen processing which facilitates our ability to manage nitrogen in aquatic systems.
This study provides a methodology that can be applied to any large stream to quantify nitrogen processing pathway dynamics and complete our understanding of nitrogen cycling.
Landslide hazard models aim at mitigating landslide impact by providing probabilistic forecasting, and the accuracy of these models hinges on landslide databases for model training and testing.
Landslide databases at times lack information on the underlying triggering mechanism, making these inventories almost unusable in hazard models.
We developed a Python-based unique library, Landsifier, that contains three different machine-Learning frameworks for assessing the likely triggering mechanisms of individual landslides or entire inventories based on landslide geometry.
Two of these methods only use the 2D landslide planforms, and the third utilizes the 3D shape of landslides relying on an underlying digital elevation model (DEM). The base method extracts geometric properties of landslide polygons as a feature space for the shallow learner - random forest (RF).
An alternative method relies on landslide planform images as an input for the deep learning algorithm - convolutional neural network (CNN).
The last framework extracts topological properties of 3D landslides through topological data analysis (TDA) and then feeds these properties as a feature space to the random forest classifier.
We tested all three interchangeable methods on several inventories with known triggers spread over the Japanese archipelago. To demonstrate the effectiveness of developed methods, we used two testing configurations.
The first configuration merges all the available data for the k-fold cross-validation, whereas the second configuration excludes one inventory during the training phase to use as the sole testing inventory.
Our geometric-feature-based method performs satisfactorily, with classification accuracies varying between 67 % and 92 %. We have introduced a more straightforward but data-intensive CNN alternative, as it inputs only landslide images without manual feature selection.
CNN eases the scripting process without losing classification accuracy. Using topological features from 3D landslides (extracted through TDA) in the RF classifier improves classification accuracy by 12 % on average.
TDA also requires less training data. However, the landscape autocorrelation could easily bias TDA-based classification. Finally, we implemented the three methods on an inventory without any triggering information to showcase a real-world application.
Faults and fractures can be permeable pathways for focused fluid flow in structurally controlled ore-forming hydrothermal systems.
However, quantifying their role in fluid flow on the scale of several kilometers with numerical models typically requires high-resolution meshes.
This study introduces a modified numerical representation of m-scale fault zones using lower-dimensional elements (here, one-dimensional [1D] elements in a 2D domain) to resolve structurally controlled fluid flow with coarser mesh resolutions and apply the method to magmatic-hydrothermal ore-forming systems.
We modeled horizontal and vertical structure-controlled magmatic-hydrothermal deposits to understand the role of permeability and structure connectivity on ore deposition.
The simulation results of vertically extended porphyry copper systems show that ore deposition can occur along permeable vertical structures where ascending, overpressured magmatic fluids are cooled by downflowing ambient fluids. Structure permeability and fault location control the distribution of ore grades.
In highly permeable structures, the mineralization can span up to 3 km vertically, resulting in heat-pipe mechanisms that promote the ascent of a magmatic vapor phase to an overlying structurally controlled epithermal system.
Simulations for the formation of subhorizontal vein-type deposits suggest that the major control on fluid flow and metal deposition along horizontal structures is the absence of vertical structures above the injection location but their presence at greater distances.
Using a dynamic permeability model mimicking crack-seal mechanisms within the structures leads to a pulsating behavior of fracture-controlled hydrothermal systems and prevents the inflow of ambient fluids under overpressured conditions.
Floods affect more people than any other natural hazard; thus flood warning and disaster management are of utmost importance.
However, the operational hydrological forecasts do not provide information about affected areas and impact but only discharge and water levels at gauges.
We show that a simple hydrodynamic model operating with readily available data is able to provide highly localized information on the expected flood extent and impacts, with simulation times enabling operational flood warning.
We demonstrate that such an impact forecast would have indicated the deadly potential of the 2021 flood in western Germany with sufficient lead time.
Climate change heavily threatens forest ecosystems worldwide and there is urgent need to understand what controls tree survival and forests stability.
There is evidence that biodiversity can enhance ecosystem stability (Loreau and de Mazancourt in Ecol Lett 16:106-115, 2013; McCann in Nature 405:228-233, 2000), however it remains largely unclear whether this also holds for climate change and what aspects of biodiversity might be most important.
Here we apply machine learning to outputs of a flexible-trait Dynamic Global Vegetation Model to unravel the effects of enhanced functional tree trait diversity and its sub-components on climate-change resistance of temperate forests (http://www.pik-potsdam.de/similar to billing/video/Forest_Resistance_LPJmLFIT.mp4).
We find that functional tree trait diversity enhances forest resistance. We explain this with 1. stronger complementarity effects (similar to 25% importance) especially improving the survival of trees in the understorey of up to +16.8% (+/- 1.6%) and 2. environmental and competitive filtering of trees better adapted to future climate (40-87% importance).
We conclude that forests containing functionally diverse trees better resist and adapt to future conditions.
In this context, we especially highlight the role of functionally diverse understorey trees as they provide the fundament for better survival of young trees and filtering of resistant tree individuals in the future.
Seasonal variations in landslide activity remain understudied compared to recent advances in landslide early warning at hourly to daily timescales.
Here, we learn the seasonal pattern of monthly landslide activity in the Pacific Northwest from five heterogeneous landslide inventories with differing spatial and temporal coverage and reporting protocols combined in a Bayesian multi-level model.
We find that landslide activity is distinctly seasonal, with credible increases in landslide intensity, inter-annual variability, and probability marking the onset of the landslide season in November.
Peaks in landslide probability in January and intensity in February lag the annual peak in mean monthly precipitation and landslide activity is more variable in winter than in summer, when landslides are rare.
For a given monthly rainfall, landslide intensity at the season peak in February is up to 10 times higher than at the onset in November, underlining the importance of antecedent seasonal hillslope conditions.