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Year of publication
- 2016 (251) (remove)
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- Article (221)
- Other (14)
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- yes (251)
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- Pollen (4)
- Body waves (3)
- climate change (3)
- erosion (3)
- water balance (3)
- Anatolia (2)
- Arctic (2)
- August 2002 flood (2)
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Institute
- Institut für Geowissenschaften (251) (remove)
The determination of the total carbon storage of peatlands is of high relevance in the context of climate-change mitigation efforts. This determination relies on data about stratigraphy and peat properties, which are conventionally collected by coring. Ground-penetrating radar (GPR) and electrical resistivity imaging (ERI) can support these point data by providing subsoil information in two-dimensional cross-sections. In this study, GPR and ERI were conducted at two groundwater-fed fen sites located in the temperate zone in north-east Germany. The fens of this region are embedded in low conductive glacial sand and are characterised by thick layers of gyttja, which can be either mineral or organic. The two study sites are representative of this region with respect to stratigraphy (total thickness, peat and gyttja types) and ecological conditions (pH-value, trophic condition). The aim of this study is to assess the suitability of GPR and ERI to detect stratigraphy and peat properties under these characteristic site conditions. Results show that GPR clearly detects the interfaces between (i) Carex and brown-moss peat, (ii) brown-moss peat and organic gyttja, (iii) organic- and mineral gyttja, and (iv) mineral gyttja and the parent material (glacial sand). These layers differ in bulk density and the related organic matter content. ERI, however, does not delineate these layers; rather it delineates regions of varying properties. At our base-rich site, pore fluid conductivity and cation.exchange capacity are the main factors that determine peat electrical conductivity (reverse of resistivity), whereas organic matter and water content are most influential at the more acidic site. Thus the correlation between peat properties and electrical conductivity are driven by site-specific conditions, which are mainly determined by the solute load in the groundwater at fens. When the total organic deposits exceed a thickness of 5 m, the depth of investigation by GPR is limited due to increasing attenuation. This is not a limiting factor for ERI, where the transition from organic deposits to glacial sand is visible at both sites. Due to these specific sensitivities, a combined application of GPR and ERI meets the demand for up-to-date information on carbon storage of peatlands, which is, moreover, very site-specific because of the inherent variety of ecological conditions and stratigraphy between peatlands in general and between fens and bogs in particular. (C) 2016 Elsevier B.V. All rights reserved.
We present an algorithm that performs sequentially one-dimensional inversion of subsurface magnetic permeability and electrical conductivity by using multi-configuration electromagnetic induction sensor data. The presented method is based on the conversion of the in-phase and out-of-phase data into effective magnetic permeability and electrical conductivity of the equivalent homogeneous half-space. In the case of small-offset systems, such as portable electromagnetic induction sensors, for which in-phase and out-of-phase data are moderately coupled, the effective half-space magnetic permeability and electrical conductivity can be inverted sequentially within an iterative scheme. We test and evaluate the proposed inversion strategy using synthetic and field examples. First, we apply it to synthetic data for some highly magnetic environments. Then, the method is tested on real field data acquired in a basaltic environment to image a formation of archaeological interest. These examples demonstrate that a joint interpretation of in-phase and out-of-phase data leads to a better characterisation of the subsurface in magnetic environments such as volcanic areas.
The humid tropics are the region with the highest rate of land-cover change worldwide. Especially prevalent is the deforestation of old-growth tropical forests to create space for cattle pastures and soybean fields.
The regional water cycle is influenced by vegetation cover in various ways. Especially evapotranspiration considerably contributes to water vapor content in the lower atmosphere. Besides active transpiration by plants, evaporation from wetted plant surfaces further known as interception loss is an important supply of water vapor. Changes in interception loss due to change in land cover and the related consequences on the regional water cycle in the humid tropics of Latin America are the research focus of my thesis. (1) In an experimental setup I assess differences in interception loss between an old-growth tropical forest and a soybean plantation. (2) In a modeling study, I examine interception losses of these two vegetation types compared to a younger secondary forest with the use of the Gash interception model, including an uncertainty analysis for the estimation of the necessary model parameters. (3) Studying the water balance of a 192-km² catchment I disentangle the influences of changes in land cover and climatic factors on interception loss.
The three different research sites in my thesis represent a currently typical spectrum for land-cover changes in Latin America. In the first example I study the consequences of deforestation of transitional forest, which forms the transition from the Brazilian tree savanna (cerrado) to tropical rain forest, for the establishment of soybean fields in the southern Amazon basin. The second study site is a young secondary forest within the “Agua Salud” project area in Panama as an example of reforestation of former pastures. The third study site is the Cirí Grande river catchment which comprises a mixture of young and old forests as well as pastures, which is typical for the southern sub-catchments of the Panama Canal.
The experimental approach consists of the indirect estimation of interception loss by measuring throughfall and stem flow. For the first experimental study I measured throughfall as well as stem flow manually. Measurements of the leaf area index of the two land covers do not show distinct differences; hence it could not serve as an explanation for the differences in the measured interception loss. The considerably higher interception loss at the soybean field is attributed to a possible underestimation of stemflow but also to the stronger ventilation within the well-structured plant rows causing higher evaporation rates. This situation is valid only for two months of the rainy season, when soybean plants are fully developed. In the annual balance evapotranspiration at the soybean site is clearly less than at the forest site, accelerating the development of fast runoff components and consequently discharge. In the medium term, a reduction of water availability in the study area can be expected.
For the modeling study, throughfall in a young secondary forest is sampled automatically. The resulting temporally high-resolution dataset allows the distinction between different precipitation and interception events. The core of this study is the sensitivity and uncertainty analysis of the Gash interception model parameters and the consequences for its results. Canopy storage capacity plays a key role for the model and parameter uncertainty. With increasing storage capacity uncertainty in parameter delineation also increases. Evaporation rate as the driving component of the interception process incorporates in this context the largest parameter uncertainty. Depending on the selected method for parameter estimation, parameter values may vary tremendously.
In the third study, I analyze the influence of interception loss on the water balance of the Cirí Grande catchment, incorporating the interlinked effects of temperature, precipitation and changes of the land use mosaic using the SWAT (soil water assessment tool) model. Constructing several land-cover scenarios I assess their influence on the catchment’s discharge. The results show that land-cover change exerts only a small influence on annual discharge in the Cirí Grande catchment whereas an increase in temperature markedly influences evapotranspiration. The temperature-induced larger transpiration and interception loss balances the simultaneous increase in annual precipitation, such that the resulting changes in annual discharge are negligible.
The results of the three studies show the considerable effect of land cover on interception. However, the magnitude of this effect can be masked by changes in local conditions, especially by an increase in temperature. Hence, the results cannot be transferred easily between the different study sites. For modeling purposes, this means that measurements of vegetation characteristics as well as interception loss at the respective sites are indispensable.
Ecosystems' exposure to climate change - Modeling as support for nature conservation management
(2016)
The problem of estimating the maximum possible earthquake magnitude m(max) has attracted growing attention in recent years. Due to sparse data, the role of uncertainties becomes crucial. In this work, we determine the uncertainties related to the maximum magnitude in terms of confidence intervals. Using an earthquake catalog of Iran, m(max) is estimated for different predefined levels of confidence in six seismotectonic zones. Assuming the doubly truncated Gutenberg-Richter distribution as a statistical model for earthquake magnitudes, confidence intervals for the maximum possible magnitude of earthquakes are calculated in each zone. While the lower limit of the confidence interval is the magnitude of the maximum observed event, the upper limit is calculated from the catalog and the statistical model. For this aim, we use the original catalog which no declustering methods applied on as well as a declustered version of the catalog. Based on the study by Holschneider et al. (Bull Seismol Soc Am 101(4): 1649-1659, 2011), the confidence interval for m(max) is frequently unbounded, especially if high levels of confidence are required. In this case, no information is gained from the data. Therefore, we elaborate for which settings finite confidence levels are obtained. In this work, Iran is divided into six seismotectonic zones, namely Alborz, Azerbaijan, Zagros, Makran, Kopet Dagh, Central Iran. Although calculations of the confidence interval in Central Iran and Zagros seismotectonic zones are relatively acceptable for meaningful levels of confidence, results in Kopet Dagh, Alborz, Azerbaijan and Makran are not that much promising. The results indicate that estimating mmax from an earthquake catalog for reasonable levels of confidence alone is almost impossible.
Hundecha, Yeshewatesfa ; Sunyer, Maria A. ; Lawrence, Deborah ; Madsen, Henrik ; Willems, Patrick ; Bürger, Gerd ; Kriauciuniene, Jurate ; Loukas, Athanasios ; Martinkova, Marta ; Osuch, Marzena ; Vasiliades, Lampros ; von Christierson, Birgitte ; Vormoor, Klaus Josef ; Yuecel, Ismail
The effect of methods of statistical downscaling of daily precipitation on changes in extreme flow indices under a plausible future climate change scenario was investigated in 11 catchments selected from 9 countries in different parts of Europe. The catchments vary from 67 to 6171 km(2) in size and cover different climate zones. 15 regional climate model outputs and 8 different statistical downscaling methods, which are broadly categorized as change factor and bias correction based methods, were used for the comparative analyses. Different hydrological models were implemented in different catchments to simulate daily runoff. A set of flood indices were derived from daily flows and their changes have been evaluated by comparing their values derived from simulations corresponding to the current and future climate. Most of the implemented downscaling methods project an increase in the extreme flow indices in most of the catchments. The catchments where the extremes are expected to increase have a rainfall dominated flood regime. In these catchments, the downscaling methods also project an increase in the extreme precipitation in the seasons when the extreme flows occur. In catchments where the flooding is mainly caused by spring/summer snowmelt, the downscaling methods project a decrease in the extreme flows in three of the four catchments considered. A major portion of the variability in the projected changes in the extreme flow indices is attributable to the variability of the climate model ensemble, although the statistical downscaling methods contribute 35-60% of the total variance. (C) 2016 Elsevier B.V. All rights reserved.