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Flood risk is impacted by a range of physical and socio-economic processes. Hence, the quantification of flood risk ideally considers the complete flood risk chain, from atmospheric processes through catchment and river system processes to damage mechanisms in the affected areas. Although it is generally accepted that a multitude of changes along the risk chain can occur and impact flood risk, there is a lack of knowledge of how and to what extent changes in influencing factors propagate through the chain and finally affect flood risk. To fill this gap, we present a comprehensive sensitivity analysis which considers changes in all risk components, i.e. changes in climate, catchment, river system, land use, assets, and vulnerability. The application of this framework to the mesoscale Mulde catchment in Germany shows that flood risk can vary dramatically as a consequence of plausible change scenarios. It further reveals that components that have not received much attention, such as changes in dike systems or in vulnerability, may outweigh changes in often investigated components, such as climate. Although the specific results are conditional on the case study area and the selected assumptions, they emphasize the need for a broader consideration of potential drivers of change in a comprehensive way. Hence, our approach contributes to a better understanding of how the different risk components influence the overall flood risk.
This study analyzes the influence of local and regional climatic factors on the stable isotopic composition of rainfall in the Vietnamese Mekong Delta (VMD) as part of the Asian monsoon region. It is based on 1.5 years of weekly rainfall samples. In the first step, the isotopic composition of the samples is analyzed by local meteoric water lines (LMWLs) and single-factor linear correlations. Additionally, the contribution of several regional and local factors is quantified by multiple linear regression (MLR) of all possible factor combinations and by relative importance analysis. This approach is novel for the interpretation of isotopic records and enables an objective quantification of the explained variance in isotopic records for individual factors. In this study, the local factors are extracted from local climate records, while the regional factors are derived from atmospheric backward trajectories of water particles. The regional factors, i.e., precipitation, temperature, relative humidity and the length of backward trajectories, are combined with equivalent local climatic parameters to explain the response variables delta O-18, delta H-2, and d-excess of precipitation at the station of measurement. The results indicate that (i) MLR can better explain the isotopic variation in precipitation (R-2 = 0.8) compared to single-factor linear regression (R-2 = 0.3); (ii) the isotopic variation in precipitation is controlled dominantly by regional moisture regimes (similar to 70 %) compared to local climatic conditions (similar to 30 %); (iii) the most important climatic parameter during the rainy season is the precipitation amount along the trajectories of air mass movement; (iv) the influence of local precipitation amount and temperature is not sig-nificant during the rainy season, unlike the regional precipitation amount effect; (v) secondary fractionation processes (e.g., sub-cloud evaporation) can be identified through the d-excess and take place mainly in the dry season, either locally for delta O-18 and delta H-2, or along the air mass trajectories for d-excess. The analysis shows that regional and local factors vary in importance over the seasons and that the source regions and transport pathways, and particularly the climatic conditions along the pathways, have a large influence on the isotopic composition of rainfall. Although the general results have been reported qualitatively in previous studies (proving the validity of the approach), the proposed method provides quantitative estimates of the controlling factors, both for the whole data set and for distinct seasons. Therefore, it is argued that the approach constitutes an advancement in the statistical analysis of isotopic records in rainfall that can supplement or precede more complex studies utilizing atmospheric models. Due to its relative simplicity, the method can be easily transferred to other regions, or extended with other factors. The results illustrate that the interpretation of the isotopic composition of precipitation as a recorder of local climatic conditions, as for example performed for paleorecords of water isotopes, may not be adequate in the southern part of the Indochinese Peninsula, and likely neither in other regions affected by monsoon processes. However, the presented approach could open a pathway towards better and seasonally differentiated reconstruction of paleoclimates based on isotopic records.
Landslide hazard motivates the need for a deeper understanding of the events that occur before, during, and after catastrophic slope failures. Due to the destructive nature of such events, in situ observation is often difficult or impossible. Here, we use data from a network of 58 seismic stations to characterise a large landslide at the Askja caldera, Iceland, on 21 July 2014. High data quality and extensive network coverage allow us to analyse both long- and short-period signals associated with the landslide, and thereby obtain information about its triggering, initiation, timing, and propagation. At long periods, a landslide force history inversion shows that the Askja landslide was a single, large event starting at the SE corner of the caldera lake at 23:24:05 UTC and propagating to the NW in the following 2 min The bulk sliding mass was 7-16 x 10(10) kg, equivalent to a collapsed volume of 35-80 x 10(6) m(3). The sliding mass was displaced downslope by 1260 +/- 250 m. At short periods, a seismic tremor was observed for 30 min before the landslide. The tremor is approximately harmonic with a fundamental frequency of 2.3 Hz and shows time-dependent changes of its frequency content. We attribute the seismic tremor to stick-slip motion along the landslide failure plane. Accelerating motion leading up to the catastrophic slope failure culminated in an aseismic quiescent period for 2 min before the landslide. We propose that precursory seismic signals may be useful in landslide early-warning systems. The 8 h after the main landslide failure are characterised by smaller slope failures originating from the destabilised caldera wall decaying in frequency and magnitude. We introduce the term "afterslides" for this subsequent, declining slope activity after a large landslide.
Proglacial environments are ideal for studying the development of soils through the changes of rocks exposed by glacier retreat to weathering and microbial processes. Carbon (C) and nitrogen (N) contents as well as soil pH and soil elemental compositions are thought to be dominant factors structuring the bacterial, archaeal and fungal communities in the early stages of soil ecosystem formation. However, the functional linkages between C and N contents, soil composition and microbial community structures remain poorly understood. Here, we describe a multivariate analysis of geochemical properties and associated microbial community structures between a moraine and a glaciofluvial outwash in the proglacial area of a High Arctic glacier (Longyearbreen, Svalbard). Our results reveal distinct differences in developmental stages and heterogeneity between the moraine and the glaciofluvial outwash. We observed significant relationships between C and N contents, delta C-13(org) and delta N-15 isotopic ratios, weathering and microbial abundance and community structures. We suggest that the observed differences in microbial and geochemical parameters between the moraine and the glaciofluvial outwash are primarily a result of geomorphological variations of the proglacial terrain.
More than 41% of the Earth’s land area is covered by permanent or seasonally arid dryland ecosystems. Global development and human activity have led to an increase in aridity, resulting in ecosystem degradation and desertification around the world. The objective of the present work was to investigate and compare the microbial community structure and geochemical characteristics of two geographically distinct saline pan sediments in the Kalahari Desert of southern Africa. Our data suggest that these microbial communities have been shaped by geochemical drivers, including water content, salinity, and the supply of organic matter. Using Illumina 16S rRNA gene sequencing, this study provides new insights into the diversity of bacteria and archaea in semi-arid, saline, and low-carbon environments. Many of the observed taxa are halophilic and adapted to water-limiting conditions. The analysis reveals a high relative abundance of halophilic archaea (primarily Halobacteria), and the bacterial diversity is marked by an abundance of Gemmatimonadetes and spore-forming Firmicutes. In the deeper, anoxic layers, candidate division MSBL1, and acetogenic bacteria (Acetothermia) are abundant. Together, the taxonomic information and geochemical data suggest that acetogenesis could be a prevalent form of metabolism in the deep layers of a saline pan.
Organic matter characteristics in yedoma and thermokarst deposits on Baldwin Peninsula, west Alaska
(2018)
As Arctic warming continues and permafrost thaws, more soil and sedimentary organic matter (OM) will be decomposed in northern high latitudes. Still, uncertainties remain in the quality of the OM and the size of the organic carbon (OC) pools stored in different deposit types of permafrost landscapes. This study presents OM data from deep permafrost and lake deposits on the Baldwin Peninsula which is located in the southern portion of the continuous permafrost zone in west Alaska. Sediment samples from yedoma and drained thermokarst lake basin (DTLB) deposits as well as thermokarst lake sediments were analyzed for cryostratigraphical and biogeochemical parameters and their lipid biomarker composition to identify the below-ground OC pool size and OM quality of ice-rich permafrost on the Baldwin Peninsula. We provide the first detailed characterization of yedoma deposits on Baldwin Peninsula. We show that three-quarters of soil OC in the frozen deposits of the study region (total of 68 Mt) is stored in DTLB deposits (52 Mt) and one-quarter in the frozen yedoma deposits (16 Mt). The lake sediments contain a relatively small OC pool (4 Mt), but have the highest volumetric OC content (93 kgm(-3)) compared to the DTLB (35 kgm(-3)) and yedoma deposits (8 kgm(-3)), largely due to differences in the ground ice content. The biomarker analysis indicates that the OM in both yedoma and DTLB deposits is mainly of terrestrial origin. Nevertheless, the relatively high carbon preference index of plant leaf waxes in combination with a lack of a degradation trend with depth in the yedoma deposits indi-cates that OM stored in yedoma is less degraded than that stored in DTLB deposits. This suggests that OM in yedoma has a higher potential for decomposition upon thaw, despite the relatively small size of this pool. These findings show that the use of lipid biomarker analysis is valuable in the assessment of the potential future greenhouse gas emissions from thawing permafrost, especially because this area, close to the discontinuous permafrost boundary, is projected to thaw substantially within the 21st century.
Maize (Zea mays L.) is a staple food whose production relies on seed stocks that largely comprise hybrid varieties. Therefore, knowledge about the molecular determinants of hybrid performance (HP) in the field can be used to devise better performing hybrids to address the demands for sustainable increase in yield. Here, we propose and test a classification-driven framework that uses metabolic profiles from in vitro grown young roots of parental lines from the Dent x Flint maize heterotic pattern to predict field HP. We identify parental analytes that best predict the metabolic inheritance patterns in 328 hybrids. We then demonstrate that these analytes are also predictive of field HP (0.64 >= r >= 0.79) and discriminate hybrids of good performance (accuracy of 87.50%). Therefore, our approach provides a cost-effective solution for hybrid selection programs.
Most large-scale hydrologic models fall short in reproducing groundwater head dynamics and simulating transport process due to their oversimplified representation of groundwater flow. In this study, we aim to extend the applicability of the mesoscale Hydrologic Model (mHM v5.7) to subsurface hydrology by coupling it with the porous media simulator OpenGeoSys (OGS). The two models are one-way coupled through model interfaces GIS2FEM and RIV2FEM, by which the grid-based fluxes of groundwater recharge and the river-groundwater exchange generated by mHM are converted to fixed-flux boundary conditions of the groundwater model OGS. Specifically, the grid-based vertical reservoirs in mHM are completely preserved for the estimation of land-surface fluxes, while OGS acts as a plug-in to the original mHM modeling framework for groundwater flow and transport modeling. The applicability of the coupled model (mHM-OGS v1.0) is evaluated by a case study in the central European mesoscale river basin - Nagelstedt. Different time steps, i.e., daily in mHM and monthly in OGS, are used to account for fast surface flow and slow groundwater flow. Model calibration is conducted following a two-step procedure using discharge for mHM and long-term mean of groundwater head measurements for OGS. Based on the model summary statistics, namely the Nash-Sutcliffe model efficiency (NSE), the mean absolute error (MAE), and the interquartile range error (QRE), the coupled model is able to satisfactorily represent the dynamics of discharge and groundwater heads at several locations across the study basin. Our exemplary calculations show that the one-way coupled model can take advantage of the spatially explicit modeling capabilities of surface and groundwater hydrologic models and provide an adequate representation of the spatiotemporal behaviors of groundwater storage and heads, thus making it a valuable tool for addressing water resources and management problems.
The availability of high-throughput data from transcriptomics and metabolomics technologies provides the opportunity to characterize the transcriptional effects on metabolism. Here we propose and evaluate two computational approaches rooted in data reduction techniques to identify and categorize transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approaches determine the partial correlation between two metabolite data profiles upon control of given principal components extracted from transcriptomics data profiles. Therefore, they allow us to investigate both data types with all features simultaneously without doing preselection of genes. The proposed approaches allow us to categorize the relation between pairs of metabolites as being under transcriptional or post-transcriptional regulation. The resulting classification is compared to existing literature and accumulated evidence about regulatory mechanism of reactions and pathways in the cases of Escherichia coil, Saccharomycies cerevisiae, and Arabidopsis thaliana.
Cells and organelles are not homogeneous but include microcompartments that alter the spatiotemporal characteristics of cellular processes. The effects of microcompartmentation on metabolic pathways are however difficult to study experimentally. The pyrenoid is a microcompartment that is essential for a carbon concentrating mechanism (CCM) that improves the photosynthetic performance of eukaryotic algae. Using Chlamydomonas reinhardtii, we obtained experimental data on photosynthesis, metabolites, and proteins in CCM-induced and CCM-suppressed cells. We then employed a computational strategy to estimate how fluxes through the Calvin-Benson cycle are compartmented between the pyrenoid and the stroma. Our model predicts that ribulose-1,5-bisphosphate (RuBP), the substrate of Rubisco, and 3-phosphoglycerate (3PGA), its product, diffuse in and out of the pyrenoid, respectively, with higher fluxes in CCM-induced cells. It also indicates that there is no major diffusional barrier to metabolic flux between the pyrenoid and stroma. Our computational approach represents a stepping stone to understanding microcompartmentalized CCM in other organisms.