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Geochemical processes change the microstructure of rocks and thereby affect their physical behaviour at the macro scale. A micro-computer tomography (micro-CT) scan of a typical reservoir sandstone is used to numerically examine the impact of three spatial alteration patterns on pore morphology, permeability and elastic moduli by correlating precipitation with the local flow velocity magnitude. The results demonstrate that the location of mineral growth strongly affects the permeability decrease with variations by up to four orders in magnitude. Precipitation in regions of high flow velocities is characterised by a predominant clogging of pore throats and a drastic permeability reduction, which can be roughly described by the power law relation with an exponent of 20. A continuous alteration of the pore structure by uniform mineral growth reduces the permeability comparable to the power law with an exponent of four or the Kozeny-Carman relation. Preferential precipitation in regions of low flow velocities predominantly affects smaller throats and pores with a minor impact on the flow regime, where the permeability decrease is considerably below that calculated by the power law with an exponent of two. Despite their complete distinctive impact on hydraulics, the spatial precipitation patterns only slightly affect the increase in elastic rock properties with differences by up to 6.3% between the investigated scenarios. Hence, an adequate characterisation of the spatial precipitation pattern is crucial to quantify changes in hydraulic rock properties, whereas the present study shows that its impact on elastic rock parameters is limited. The calculated relations between porosity and permeability, as well as elastic moduli can be applied for upscaling micro-scale findings to reservoir-scale models to improve their predictive capabilities, what is of paramount importance for a sustainable utilisation of the geological subsurface.
BACKROUND: Few studies have been carried out to connect nutrients recovery from wastewater and heavy metals immobilization in contaminated soil. To achieve the goal, ammonia nitrogen (AN) and phosphorus (P) were recovered from rare-earth wastewater by using the formation of struvite, which was used as the amendment with plant ash for copper, lead and chromium immobilization. RESULTS: AN removal efficiency and residual P reached 95.32 +/- 0.73% and 6.14 +/- 1.72mgL(-1) under optimal conditions: pH= 9.0, n(Mg): n(N): n(P)= 1.2: 1: 1.1, which were obtained using response surface methodology (RSM). The minimum available concentrations of Cu, Pb and Cr (CPC) separately reduced to 320.82 mg kg(-1), 190.77 mg kg(-1) and 121.46 mg kg(-1) with increasing immobilization time at the mass ratio of phosphate precipitate (PP)/plant ash (PA) of 1: 3. Humic acid (HA) and fulvic acid (FA) were beneficial to immobilize Cu, both of which showed no effect or even a negative effect on Pb and Cr immobilization.
Variation of deuterium excess in surface waters across a 5000-m elevation gradient in eastern Nepal
(2020)
The strong elevation gradient of the Himalaya allows for investigation of altitude and orographic impacts on surface water delta O-18 and delta D stable isotope values. This study differentiates the time- and altitude-variable contributions of source waters to the Arun River in eastern Nepal. It provides isotope data along a 5000-m gradient collected from tributaries as well as groundwater, snow, and glacial-sourced surface waters and time-series data from April to October 2016. We find nonlinear trends in delta O-18 and delta D lapse rates with high-elevation lapse rates (4000-6000 masl) 5-7 times more negative than low-elevation lapse rates (1000-3000 masl). A distinct seasonal signal in delta O-18 and delta D lapse rates indicates time-variable source-water contributions from glacial and snow meltwater as well as precipitation transitions between the Indian Summer Monsoon and Winter Westerly Disturbances. Deuterium excess correlates with the extent of snowpack and tracks melt events during the Indian Summer Monsoon season. Our analysis identifies the influence of snow and glacial melt waters on river composition during low-flow conditions before the monsoon (April/May 2016) followed by a 5-week transition to the Indian Summer Monsoon-sourced rainfall around mid-June 2016. In the post-monsoon season, we find continued influence from glacial melt waters as well as ISM-sourced groundwater.
The spatial pattern of extreme precipitation from 40 years of gauge data in the central Himalaya
(2022)
The topography of the Himalaya exerts a substantial control on the spatial distribution of monsoonal rainfall, which is a vital water source for the regional economy and population. But the occurrence of short-lived and high-intensity precipitation results in socio-economic losses. This study relies on 40 years of daily data from 204 ground stations in Nepal to derive extreme precipitation thresholds, amounts, and days at the 95th percentile. We additionally determine the precipitation magnitude-frequency relation. We observe that extreme precipitation amounts follow an almost uniform band parallel to topographic contour lines in the southern Himalaya mountains in central and eastern Nepal but not in western Nepal. The relationship of extreme precipitation indices with topographic relief shows that extreme precipitation thresholds decrease with increasing elevation, but extreme precipitation days increase in higher elevation areas. Furthermore, stations above 1 km elevation exhibit a power-law relation in the rainfall magnitude-frequency framework. Stations at higher elevations generally have lower values of power-law exponents than low elevation areas. This suggests a fundamentally different behaviour of the rainfall distribution and an increased occurrence of extreme rainfall storms in the high elevation areas of Nepal.
Trends in precipitation over Germany and the Rhine basin related to changes in weather patterns
(2017)
Precipitation as the central meteorological feature for agriculture, water security, and human well-being amongst others, has gained special attention ever since. Lack of precipitation may have devastating effects such as crop failure and water scarcity. Abundance of precipitation, on the other hand, may as well result in hazardous events such as flooding and again crop failure. Thus, great effort has been spent on tracking changes in precipitation and relating them to underlying processes. Particularly in the face of global warming and given the link between temperature and atmospheric water holding capacity, research is needed to understand the effect of climate change on precipitation.
The present work aims at understanding past changes in precipitation and other meteorological variables. Trends were detected for various time periods and related to associated changes in large-scale atmospheric circulation. The results derived in this thesis may be used as the foundation for attributing changes in floods to climate change. Assumptions needed for the downscaling of large-scale circulation model output to local climate stations are tested and verified here.
In a first step, changes in precipitation over Germany were detected, focussing not only on precipitation totals, but also on properties of the statistical distribution, transition probabilities as a measure for wet/dry spells, and extreme precipitation events.
Shifting the spatial focus to the Rhine catchment as one of the major water lifelines of Europe and the largest river basin in Germany, detected trends in precipitation and other meteorological variables were analysed in relation to states of an ``optimal'' weather pattern classification. The weather pattern classification was developed seeking the best skill in explaining the variance of local climate variables.
The last question addressed whether observed changes in local climate variables are attributable to changes in the frequency of weather patterns or rather to changes within the patterns itself. A common assumption for a downscaling approach using weather patterns and a stochastic weather generator is that climate change is expressed only as a changed occurrence of patterns with the pattern properties remaining constant. This assumption was validated and the ability of the latest generation of general circulation models to reproduce the weather patterns was evaluated.
% Paper 1
Precipitation changes in Germany in the period 1951-2006 can be summarised briefly as negative in summer and positive in all other seasons. Different precipitation characteristics confirm the trends in total precipitation: while winter mean and extreme precipitation have increased, wet spells tend to be longer as well (expressed as increased probability for a wet day followed by another wet day). For summer the opposite was observed: reduced total precipitation, supported by decreasing mean and extreme precipitation and reflected in an increasing length of dry spells.
Apart from this general summary for the whole of Germany, the spatial distribution within the country is much more differentiated. Increases in winter precipitation are most pronounced in the north-west and south-east of Germany, while precipitation increases are highest in the west for spring and in the south for autumn. Decreasing summer precipitation was observed in most regions of Germany, with particular focus on the south and west.
The seasonal picture, however, was again differently represented in the contributing months, e.g.\ increasing autumn precipitation in the south of Germany is formed by strong trends in the south-west in October and in the south-east in November. These results emphasise the high spatial and temporal organisation of precipitation changes.
% Paper 2
The next step towards attributing precipitation trends to changes in large-scale atmospheric patterns was the derivation of a weather pattern classification that sufficiently stratifies the local climate variables under investigation. Focussing on temperature, radiation, and humidity in addition to precipitation, a classification based on mean sea level pressure, near-surface temperature, and specific humidity was found to have the best skill in explaining the variance of the local variables. A rather high number of 40 patterns was selected, allowing typical pressure patterns being assigned to specific seasons by the associated temperature patterns. While the skill in explaining precipitation variance is rather low, better skill was achieved for radiation and, of course, temperature.
Most of the recent GCMs from the CMIP5 ensemble were found to reproduce these weather patterns sufficiently well in terms of frequency, seasonality, and persistence.
% Paper 3
Finally, the weather patterns were analysed for trends in pattern frequency, seasonality, persistence, and trends in pattern-specific precipitation and temperature. To overcome uncertainties in trend detection resulting from the selected time period, all possible periods in 1901-2010 with a minimum length of 31 years were considered. Thus, the assumption of a constant link between patterns and local weather was tested rigorously. This assumption was found to hold true only partly. While changes in temperature are mainly attributable to changes in pattern frequency, for precipitation a substantial amount of change was detected within individual patterns.
Magnitude and even sign of trends depend highly on the selected time period. The frequency of certain patterns is related to the long-term variability of large-scale circulation modes.
Changes in precipitation were found to be heterogeneous not only in space, but also in time - statements on trends are only valid for the specific time period under investigation. While some part of the trends can be attributed to changes in the large-scale circulation, distinct changes were found within single weather patterns as well.
The results emphasise the need to analyse multiple periods for thorough trend detection wherever possible and add some note of caution to the application of downscaling approaches based on weather patterns, as they might misinterpret the effect of climate change due to neglecting within-type trends.
In this study, we detect high percentile rainfall events in the eastern central Andes, based on Tropical Rainfall Measuring Mission (TRMM) with a spatial resolution of 0.25 × 0.25°, a temporal resolution of 3 h, and for the duration from 2001 to 2018. We identify three areas with high mean accumulated rainfall and analyze their atmospheric behaviour and rainfall characteristics with specific focus on extreme events. Extreme events are defined by events above the 95th percentile of their daily mean accumulated rainfall. Austral summer (DJF) is the period of the year presenting the most frequent extreme events over these three regions. Daily statistics show that the spatial maxima, as well as their associated extreme events, are produced during the night. For the considered period, ERA-Interim reanalysis data, provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) with 0.75° x0.75° spatial and 6-hourly temporal resolutions, were used for the analysis of the meso- and synoptic-scale atmospheric patterns. Night- and day-time differences indicate a nocturnal overload of northerly and northeasterly low-level humidity flows arriving from tropical South America. Under these conditions, cooling descending air from the mountains may find unstable air at the surface, giving place to the development of strong local convection. Another possible mechanism is presented here: a forced ascent of the low-level flow due to the mountains, disrupting the atmospheric stratification and generating vertical displacement of air trajectories. A Principal Component Analysis (PCA) in T-mode is applied to day- and night-time data during the maximum and extreme events. The results show strong correlation areas over each subregion under study during night-time, whereas during day-time no defined patterns are found. This confirms the observed nocturnal behavior of rainfall within these three hotspots.
Arboreal epiphytes (plants residing in forest canopies) are present across all major climate zones and play important roles in forest biogeochemistry. The substantial water storage capacity per unit area of the epiphyte "bucket" is a key attribute underlying their capability to influence forest hydrological processes and their related mass and energy flows. It is commonly assumed that the epiphyte bucket remains saturated, or near-saturated, most of the time; thus, epiphytes (particularly vascular epiphytes) can store little precipitation, limiting their impact on the forest canopy water budget. We present evidence that contradicts this common assumption from (i) an examination of past research; (ii) new datasets on vascular epiphyte and epi-soil water relations at a tropical montane cloud forest (Monteverde, Costa Rica); and (iii) a global evaluation of non-vascular epiphyte saturation state using a process-based vegetation model, LiBry. All analyses found that the external and internal water storage capacity of epiphyte communities is highly dynamic and frequently available to intercept precipitation. Globally, non-vascular epiphytes spend <20% of their time near saturation and regionally, including the humid tropics, model results found that non-vascular epiphytes spend similar to 1/3 of their time in the dry state (0-10% of water storage capacity). Even data from Costa Rican cloud forest sites found the epiphyte community was saturated only 1/3 of the time and that internal leaf water storage was temporally dynamic enough to aid in precipitation interception. Analysis of the epi-soils associated with epiphytes further revealed the extent to which the epiphyte bucket emptied-as even the canopy soils were often <50% saturated (29-53% of all days observed). Results clearly show that the epiphyte bucket is more dynamic than currently assumed, meriting further research on epiphyte roles in precipitation interception, redistribution to the surface and chemical composition of "net" precipitation waters reaching the surface.
Hydrometric networks play a vital role in providing information for decision-making in water resource management. They should be set up optimally to provide as much information as possible that is as accurate as possible and, at the same time, be cost-effective. Although the design of hydrometric networks is a well-identified problem in hydrometeorology and has received considerable attention, there is still scope for further advancement. In this study, we use complex network analysis, defined as a collection of nodes interconnected by links, to propose a new measure that identifies critical nodes of station networks. The approach can support the design and redesign of hydrometric station networks. The science of complex networks is a relatively young field and has gained significant momentum over the last few years in different areas such as brain networks, social networks, technological networks, or climate networks. The identification of influential nodes in complex networks is an important field of research. We propose a new node-ranking measure – the weighted degree–betweenness (WDB) measure – to evaluate the importance of nodes in a network. It is compared to previously proposed measures used on synthetic sample networks and then applied to a real-world rain gauge network comprising 1229 stations across Germany to demonstrate its applicability. The proposed measure is evaluated using the decline rate of the network efficiency and the kriging error. The results suggest that WDB effectively quantifies the importance of rain gauges, although the benefits of the method need to be investigated in more detail.
Hydrometric networks play a vital role in providing information for decision-making in water resource management. They should be set up optimally to provide as much information as possible that is as accurate as possible and, at the same time, be cost-effective. Although the design of hydrometric networks is a well-identified problem in hydrometeorology and has received considerable attention, there is still scope for further advancement. In this study, we use complex network analysis, defined as a collection of nodes interconnected by links, to propose a new measure that identifies critical nodes of station networks. The approach can support the design and redesign of hydrometric station networks. The science of complex networks is a relatively young field and has gained significant momentum over the last few years in different areas such as brain networks, social networks, technological networks, or climate networks. The identification of influential nodes in complex networks is an important field of research. We propose a new node-ranking measure – the weighted degree–betweenness (WDB) measure – to evaluate the importance of nodes in a network. It is compared to previously proposed measures used on synthetic sample networks and then applied to a real-world rain gauge network comprising 1229 stations across Germany to demonstrate its applicability. The proposed measure is evaluated using the decline rate of the network efficiency and the kriging error. The results suggest that WDB effectively quantifies the importance of rain gauges, although the benefits of the method need to be investigated in more detail.