Using geophysical surveys to test tracer-based storage estimates in headwater catchments

Acknowledgements The authors are grateful to Stian Bradford, Chris Gabrielli, and Julie Timms for practical and logistical assistance. The provision of transport by Iain Malcolm and Ross Glover of Marine Scotland Science was greatly appreciated. We also thank the European Research Council ERC (project GA 335910 VEWA) for funding through the VeWa project and the Leverhulme Trust for funding through PLATO (RPG-2014-016).Peer reviewedPostprin

Free global DSM assessment on large scale areas exploiting the potentialities of the innovative google earth engine platform

The high-performance cloud-computing platform Google Earth Engine has been developed for global-scale analysis based on the Earth observation data. In particular, in this work, the geometric accuracy of the two most used nearly-global free DSMs (SRTM and ASTER) has been evaluated on the territories of four American States (Colorado, Michigan, Nevada, Utah) and one Italian Region (Trentino Alto-Adige, Northern Italy) exploiting the potentiality of this platform. These are large areas characterized by different terrain morphology, land covers and slopes. The assessment has been performed using two different reference DSMs: the USGS National Elevation Dataset (NED) and a LiDAR acquisition. The DSMs accuracy has been evaluated through computation of standard statistic parameters, both at global scale (considering the whole State/Region) and in function of the terrain morphology using several slope classes. The geometric accuracy in terms of Standard deviation and NMAD, for SRTM range from 2-3 meters in the first slope class to about 45 meters in the last one, whereas for ASTER, the values range from 5-6 to 30 meters. In general, the performed analysis shows a better accuracy for the SRTM in the flat areas whereas the ASTER GDEM is more reliable in the steep areas, where the slopes increase. These preliminary results highlight the GEE potentialities to perform DSM assessment on a global scale

FLIAT, an object-relational GIS tool for flood impact assessment in Flanders, Belgium

Floods can cause damage to transportation and energy infrastructure, disrupt the delivery of services, and take a toll on public health, sometimes even causing significant loss of life. Although scientists widely stress the compelling need for resilience against extreme events under a changing climate, tools for dealing with expected hazards lag behind. Not only does the socio-economic, ecologic and cultural impact of floods need to be considered, but the potential disruption of a society with regard to priority adaptation guidelines, measures, and policy recommendations need to be considered as well. The main downfall of current impact assessment tools is the raster approach that cannot effectively handle multiple metadata of vital infrastructures, crucial buildings, and vulnerable land use (among other challenges). We have developed a powerful cross-platform flood impact assessment tool (FLIAT) that uses a vector approach linked to a relational database using open source program languages, which can perform parallel computation. As a result, FLIAT can manage multiple detailed datasets, whereby there is no loss of geometrical information. This paper describes the development of FLIAT and the performance of this tool

A high-precision liDAR-based method for surveying and classifying coastal notches

Formation of notches is an important process in the erosion of seaside cliffs. Monitoring of coastal notch erosion rate and processes has become a prime research focus for many coastal geomorphologists. Observation of notch erosion rate considers a number of characteristics, including cliff collapse risk, distinction of historical sea levels, and recognition of ongoing erosional mechanisms. This study presents new approaches for surveying and classifying marine notches based on a high-precision light detection and ranging (LiDAR)-based experiment performed on a small region of a coastal cliff in southern Portugal. A terrestrial LiDAR scanner was used to measure geometrical parameters and surface roughness of selected notches, enabling their classification according to shape and origin. The implemented methodology proved to be a highly effective tool for providing an unbiased analysis of marine morphodynamic processes acting on the seaside cliffs. In the analyzed population of voids carved into Miocene calcarenites in a coastal cliff section, two types of notch morphology were distinguished, namely U-shaped and V-shaped. The method presented here provides valuable data for landscape evaluation, sea-level changes, and any other types of analyses that rely on the accurate interpretation of cliff morphological features.National Science Centre [UMO-2015/17/D/ST10/02191

The University as Publisher: Summary of a Meeting Held at UC Berkeley on November 1, 2007

With the advent of electronic publishing, the scholarly communication landscape at universities has become increasingly diverse. Multiple stakeholders including university presses, libraries, and central IT departments are challenged by the increasing volume and the rapidity of production of these new forms of publication in an environment of economic uncertainties. As a response to these increasing pressures, as well as the recent publication of important reports and papers on the topic, the Center for Studies in Higher Education (CSHE) convened a meeting of experts titled, The University as Publisher. The event was sponsored as part of the A.W. Mellon Foundation-funded Future of Scholarly Communication project at CSHE.Our goal was to explore among stakeholders -- faculty, publishers, CIOs, librarians, and researchers -- the implications of the academic community, in some structure, taking over many, if not all, aspects of scholarly publishing. Two themes were the focus of the public panels: Institutional Roles in Evaluation, Quality Assessment, and Selection and Structuring and Budgeting Models for Publishing within the University Community. Our discussions included the importance of distinguishing between informal dissemination and formal publishing and the challenges that each presents to the university community. The harsh economic realities of high-quality formal scholarly publication, not least of which are managing peer review and editorial processes, were emphasized. Understanding disciplinary needs was cited as paramount throughout the discussions; the needs and traditions of scholars in the sciences and humanities, as well as among myriad disciplines, will likely demand different dissemination and publishing models and solutions. An additional theme that emerged was acknowledging the diverse forms electronic dissemination takes in the academy and the need to foster a spectrum of alternatives in publication forms, business models, and the peer review process. Budgetary and academic freedom concerns were explored as well. Regarding the expensive infrastructure required for electronic dissemination and publishing, it was agreed that there is enormous duplication among the university press, IT, and the library

Accurate simulation of ice and snow runoff for the mountainous terrain of the Kunlun Mountains, China

While mountain runoff provides great potential for the development and life quality of downstream populations, it also frequently causes seasonal disasters. The accurate modeling of hydrological processes in mountainous areas, as well as the amount of meltwater from ice and snow, is of great significance for the local sustainable development, hydropower regulations, and disaster prevention. In this study, an improved model, the Soil Water Assessment Tool with added ice-melt module (SWATAI) was developed based on the Soil Water Assessment Tool (SWAT), a semi-distributed hydrological model, to simulate ice and snow runoff. A temperature condition used to determine precipitation types has been added in the SWATAI model, along with an elevation threshold and an accumulative daily temperature threshold for ice melt, making it more consistent with the runoff process of ice and snow. As a supplementary reference, the comparison between the normalized difference vegetation index (NDVI) and the quantity of meltwater were conducted to verify the simulation results and assess the impact of meltwater on the ecology. Through these modifications, the accuracy of the daily flow simulation results has been considerably improved, and the contribution rate of ice and snow melt to the river discharge calculated by the model increased by 18.73%. The simulation comparison of the flooding process revealed that the accuracy of the simulated peak flood value by the SWATAI was 77.65% higher than that of the SWAT, and the temporal accuracy was 82.93% higher. The correlation between the meltwater calculated by the SWATAI and the NDVI has also improved significantly. This improved model could simulate the flooding processes with high temporal resolution in alpine regions. The simulation results could provide technical support for economic benefits and reasonable reference for flood prevention

Integrating remote sensing datasets into ecological modelling: a Bayesian approach

Process-based models have been used to simulate 3-dimensional complexities of forest ecosystems and their temporal changes, but their extensive data requirement and complex parameterisation have often limited their use for practical management applications. Increasingly, information retrieved using remote sensing techniques can help in model parameterisation and data collection by providing spatially and temporally resolved forest information. In this paper, we illustrate the potential of Bayesian calibration for integrating such data sources to simulate forest production. As an example, we use the 3-PG model combined with hyperspectral, LiDAR, SAR and field-based data to simulate the growth of UK Corsican pine stands. Hyperspectral, LiDAR and SAR data are used to estimate LAI dynamics, tree height and above ground biomass, respectively, while the Bayesian calibration provides estimates of uncertainties to model parameters and outputs. The Bayesian calibration contrasts with goodness-of-fit approaches, which do not provide uncertainties to parameters and model outputs. Parameters and the data used in the calibration process are presented in the form of probability distributions, reflecting our degree of certainty about them. After the calibration, the distributions are updated. To approximate posterior distributions (of outputs and parameters), a Markov Chain Monte Carlo sampling approach is used (25 000 steps). A sensitivity analysis is also conducted between parameters and outputs. Overall, the results illustrate the potential of a Bayesian framework for truly integrative work, both in the consideration of field-based and remotely sensed datasets available and in estimating parameter and model output uncertainties

Model-based Range Prediction for Electric Cars and Trucks under Real-World Conditions

The further development of electric mobility requires major scientific efforts to obtain reliable data for vehicle and drive development. Practical experience has repeatedly shown that vehicle data sheets do not contain realistic consumption and range figures. Since the fear of low range is a significant obstacle to the acceptance of electric mobility, a reliable database can provide developers with additional insights and create confidence among vehicle users. This study presents a detailed, yet easy-to-implement and modular physical model for both passenger and commercial battery electric vehicles. The model takes consumption-relevant parameters, such as seasonal influences, terrain character, and driving behavior, into account. Without any a posteriori parameter adjustments, an excellent agreement with known field data and other experimental observations is achieved. This validation conveys much credibility to model predictions regarding the real-world impact on energy consumption and cruising range in standardized driving cycles. Some of the conclusions, almost impossible to obtain experimentally, are that winter conditions and a hilly terrain each reduce the range by 7–9%, and aggressive driving reduces the range by up to 20%. The quantitative results also reveal the important contributions of recuperation and rolling resistance towards the overall energy budget