Contribution of flow conditions and sand addition on hyporheic zone exchange in gravel beds

We conducted a series of tracer test experiments in 12 outdoor semi-natural flumes to assess the effects of variable flow conditions and sand addition on hyporheic zone conditions in gravel beds, mimicking conditions in headwater streams under sediment pressure. Two tracer methods were applied in each experiment: 2–5 tracer-pulse tests were conducted in all flumes and pulses were monitored at three distances downstream of the flume inlet (0 m, 5 m and 10 m, at bed surface), and in pipes installed into the gravel bed at 5 m and 10 m distances. The tracer breakthrough curves (total of 120 tracer injections) were then analysed with a one-dimensional solute transport model (OTIS) and compared with data from the gravel pipes in point-dilution pulse tests. Sand addition had a strong negative effect on horizontal fluxes (qh), whereas the fraction of the median travel time due to transient storage (F200) was determined more by flow conditions. These results suggest that even small additions of sand can modify the hyporheic zone exchange in gravel beds, thus making headwater streams with low sediment transport capacity particularly vulnerable to sediments transported into the stream from catchment land use activities

Derivation of lowland riparian wetland deposit architecture using geophysical image analysis and interface detection

For groundwater-surface water interactions to be understood in complex wetland settings, the architecture of the underlying deposits requires investigation at a spatial resolution sufficient to characterize significant hydraulic pathways. Discrete intrusive sampling using conventional approaches provides insufficient sample density and can be difficult to deploy on soft ground. Here a noninvasive geophysical imaging approach combining three-dimensional electrical resistivity tomography (ERT) and the novel application of gradient and isosurface-based edge detectors is considered as a means of illuminating wetland deposit architecture. The performance of three edge detectors were compared and evaluated against ground truth data, using a lowland riparian wetland demonstration site. Isosurface-based methods correlated well with intrusive data and were useful for defining the geometries of key geological interfaces (i.e., peat/gravels and gravels/Chalk). The use of gradient detectors approach was unsuccessful, indicating that the assumption that the steepest resistivity gradient coincides with the associated geological interface can be incorrect. These findings are relevant to the application of this approach in settings with a broadly layered geology with strata of contrasting resistivities. In addition, ERT revealed substantial structures in the gravels related to the depositional environment (i.e., braided fluvial system) and a complex distribution of low-permeability putty Chalk at the bedrock surface—with implications for preferential flow and variable exchange between river and groundwater systems. These results demonstrate that a combined approach using ERT and edge detectors can provide valuable information to support targeted monitoring and inform hydrological modeling of wetlands

Examining the effects of adjuvant chemotherapy on cognition and the impact of any cognitive impairment on quality of life in colorectal cancer patients: study protocol

Background: Research suggests that chemotherapy can cause deficits in both patients’ objectively measured and self-reported cognitive abilities which can in turn affect their quality of life (QoL). The majority of research studies have used post-treatment retrospective designs or have not included a control group in prospective cohorts. This has limited the conclusions that can be drawn from the results. There have also been a disproportionate number of studies focussed on women with breast cancer, which has limited the generalisability of the results to other cancer populations. Aim: This study aims to identify the extent and impact of chemotherapy-induced cognitive decline in colorectal cancer patients. Possible associations with poorer QoL will also be explored. Design: This will be a longitudinal controlled cohort study. Questionnaires measuring subjective cognitive functioning, QoL, fatigue and mood, and neuropsychological assessments of objective cognitive function will be collected pre-, mid- and post- chemotherapy treatment from a consecutive sample of 78 colorectal cancer patients from five London NHS Trusts. A further 78 colorectal cancer surgery only patients will be assessed at equivalent time points; this will allow the researchers to compare the results of patients undergoing surgery, but not chemotherapy against those receiving both treatments. Pre- and post-chemotherapy difference scores will be calculated to detect subtle changes in cognitive function as measured by the objective neuropsychological assessments and the self-reported questionnaires. A standardised zscore will be computed for every patient on each neuropsychological test, and for each test at each time point. The post-chemotherapy score will then be subtracted from the pre-chemotherapy score to produce a relative difference score for each patient. ANCOVA will be used to compare mean difference z-scores between the chemotherapy and surgery-only groups while controlling for the effects of gender, age, depression, anxiety, fatigue and education. Discussion: The result from this study will indicate whether a decline in cognitive functioning can be attributed to chemotherapy or to disease, surgical or some other confounding factor. Identification of risk factors for cognitive deficits may be used to inform targeted interventions, in order to improve QoL and help patients’ cope

Runoff generation in a plough-drained cutover fen in Central Finland

The hydrology of a cutover fen was studied from May to October in 1995 and 1996. Rainfall equivalent depths were measured every 15 min and stream runoff was continuously monitored. Water table depths were measured bi-weekly during 1995 and continuously during 1996. Storm runoff was separated into different components; rain falling directly into channels and rapid groundwater response based on a contributing area method and on different electrical conductivities of rain-, ground- and streamwater. The results show three sources of storm runoff from cutover fens. Frequent small runoff peaks were caused by rain falling directly into channels. The amount of quick runoff increased when rain fell on wet soil, resulting in intermediate sized runoff peaks generated by rapid groundwater response. The largest runoff values were observed after prolonged rainfall when water from the upper part of the catchment flooded the fen. (C) 1999 Elsevier Science B.V. All rights reserved

Kinematic model of solute transport in stream networks: example with phosphate retention in Morsa Watershed, Norway

A theoretical description of reactive solute transport in a network of stream channels is derived by convoluting unit solutions based on a physical representation of transport and topographical information of the distributions of solute load as well as pathways. The theory is applied to a generic analysis of the phosphate export in Morsa watershed due to the load from 620 individual households with a local wastewater treatment. Essential factors for the phosphate export is filtering of the water in stream-bed sediments through a distribution of hyporheic flow paths of various lengths. This generic study indicates that a significant portion of phosphate is retained in the hyporheic zones for a long time. The 90\% recovery time following a hypothetical remediation action in the households is expected to be in the order of one decade

Land use effects to surface water quality of some watersheds in north Finland and north Turkey

2nd International Conference on Sustainable Watershed Management, SUWAMA 2014 -- 13 October 2014 through 15 October 2014 -- Sarigerme 107301[No abstract available]2-s2.0-8491884636

Monitoring vegetation height using data acquisition from ubiquitous multi-sensor’s platform

Abstract Vegetation height plays a crucial role in various ecological and environmental applications, such as biodiversity assessment and monitoring, landscape characterization, conservation planning and disaster management. Its estimation is traditionally based on in situ measurements or airborne Light Detection and Ranging sensors. However, such methods are often proven insufficient in covering large area landscapes due to high demands in cost, labor and time. Since, the emergence of wearable technology, ubiquitous sensors and Internet of Things offers an appealing framework for monitoring environmental parameters at extremely low cost, which, in turn, contributes to the development of affordable real-time vegetation monitoring system. This is especially relevant to rural environments and underdeveloped countries. We proposed a methodology for data acquisition from a ubiquitous sensor wearable platform and developed a machine-learning model to learn vegetation height on the basis attribute associated with pressure sensor. The proposed methods are proven particularly effective in a region where the land has forestry structure. The results of linear regression model (r2 = 0.81 and RSME = 16.73 cm) and multi-regression model (r2= 0.83 and RSME = 15.73 cm), indicate a promising alternative in vegetation height estimation when in situ or Light Detection and Ranging data or wireless sensor network are not available or affordable, thus facilitating and reducing the cost of ecological monitoring and environmental sustainability planning tasks

Snow depth classification using MultiSensory ubiquitous platform and machine learning

Abstract In the drastic period of climate change the continuous data monitoring of snow characteristic is required. The immensely impact of snow on hydro production, water resource management and its inhabitants, drive to the need for the importance of snow information such as its extent, dynamics and water it holds at global and local scale. At present, there are various approaches such as traditional ground-based approach, optical satellite imaging and the radio, which are available for snow monitoring at global scale. However, the use of these approaches incurs from large labor and high monitoring cost. Since, the advance in sensor technologies and Internet of Things (Iot), provides an appealing possibility to develop a framework for monitoring snow parameters at enormously low cost. In this study. we implemented two machine learning classifiers model based on the input acquired from the low-cost wearable sensor platform. The results of Random forest classifier showed the accuracy of 88.8%, indicate a promising alternative in snow depth measurements with in-situ validation, when data or wireless sensor network are not available or affordable

Scope of the special issue

No abstract availabl