Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2019
Authors
Knut ØistadAbstract
No abstract has been registered
Abstract
Bipolar surface EMG (sEMG) signals of the trapezius muscles bilaterally were recorded continuously with a frequency of 800 Hz during full-shift field-work by a four-channel portable data logger. After recordings of 60 forest machine operators in Finland, Norway and Sweden, we discovered erroneous data. In short of any available procedure to handle these data, a method was developed to automatically discard erroneous data in the raw data reading files (Discarding Erroneous EPOchs (DESEPO) method. The DESEPO method automatically identifies, discards and adjusts the use of signal disturbances in order to achieve the best possible data use. An epoch is a 0.1 s period of raw sEMG signals and makes the basis for the RMS calculations. If erroneous signals constitute more than 30% of the epoch signals, this classifies for discharge of the present epoch. Non-valid epochs have been discarded, as well as all the subsequent epochs. The valid data for further analyses using the automatic detection resulted in an increase of acceptable data from an average of 2.15–6.5 h per day. The combination of long-term full-shift recordings and automatic data reduction procedures made it possible to use large amount of data otherwise discarded for further analyses.
Authors
Jamal Zaherpour Nick Mount Simon N. Gosling Rutger Dankers Stephanie Eisner Dieter Gerten Xingcai Liu Yoshimitsu Masaki Hannes Müller Schmied Qiuhong Tang Yoshihide WadaAbstract
This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global catchments. The benchmarked, median performance gain of the MMC solutions is 45% compared to the best performing GHM and exceeds 100% when compared to the ensemble mean (EM). The performance gain offered by MMC suggests that future multi-model applications consider reporting MMCs, alongside the EM and intermodal range, to provide end-users of GHM ensembles with a better contextualised estimate of runoff. Importantly, the study highlights the difficulty of interpreting complex, non-linear MMC solutions in physical terms. This indicates that a pragmatic approach to future MMC studies based on machine learning methods is required, in which the allowable solution complexity is carefully constrained.
Abstract
A robust hydrological modeling at a fine spatial resolution is a vital tool for Norway to simulate river discharges and hydrological components for climate adaptation strategies. However, it requires improvements of modelling methods, detailed observational data as input and expensive computational resources. This work aims to set up a distributed version of the HBV model with a physically based evapotranspiration scheme at 1 km resolution for mainland Norway and to calibrate/validate the model for 124 catchments using regionalized parameterizations. The Penman-Monteith equation was implemented in the HBV model and vegetation characteristics were derived from the Norwegian forest inventory combined with multi-source remote sensing data at 16 m spatial resolution. The estimated potential evapotranspiration (Ep) was compared with pan measurements and estimates from the MODerate Resolution Imaging Spectrometer (MOD16) products, the Global Land Evaporation Amsterdam Model (GLEAM) and Variable Infiltration Capacity (VIC) hydrological model. There are 5 climatic zones in Norway classified based on 4 temperature and precipitation indices. For each zone, the model was calibrated separately by optimizing a multi-objective function including the Nash-Sutcliff efficiency (NSE) and biases of selected catchments. In total, there are 85 catchments for calibration and 39 for validation. The Ep estimates showed good agreement with the measurements, GLEAM and VIC outputs. However, the MOD16 product significantly overestimates Ep compared to the other products. The discharge was well reproduced with the median daily NSE of 0.68/0.67, bias of −3%/−1%, Kling-Gupta efficiency (KGE) of 0.70/0.69 and monthly NSE of 0.80/0.78 in the calibration/validation periods. Our results showed a significant improvement compared to the previous HBV application for all catchments, with an increase of 0.08–0.16 in the median values of the daily NSE, KGE and monthly NSE. Both the temporal and spatial transferability of model parameterizations were also enhanced compared to the previous application.
Authors
Jan Magnusson Stephanie Eisner Shaochun Huang Cristian Lussana Giulia Mazzotti Richard Essery Tuomo Saloranta Stein BeldringAbstract
Climate models show that global warming will disproportionately influence high‐latitude regions and indicate drastic changes in, among others, seasonal snow cover. However, current continental and global simulations covering these regions are often run at coarse grid resolutions, potentially introducing large errors in computed fluxes and states. To quantify some of these errors, we have assessed the sensitivity of an energy‐balance snow model to changes in grid resolution using a multiparametrization framework for the spatial domain of mainland Norway. The framework has allowed us to systematically test how different parametrizations, describing a set of processes, influence the discrepancy, here termed the scale error, between the coarser (5 to 50‐km) and finest (1‐km) resolution. The simulations were set up such that liquid and solid precipitation was identical between the different resolutions, and differences between the simulations arise mainly during the ablation period. The analysis presented in this study focuses on evaluating the scale error for several variables relevant for hydrological and land surface modelling, such as snow water equivalent and turbulent heat exchanges. The analysis reveals that the choice of method for routing liquid water through the snowpack influences the scale error most for snow water equivalent, followed by the type of parametrizations used for computing turbulent heat fluxes and albedo. For turbulent heat exchanges, the scale error is mainly influenced by model assumptions related to atmospheric stability. Finally, regions with strong meteorological and topographic variability show larger scale errors than more homogenous regions.
Authors
Stephanie EisnerAbstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
Purpose of Review The adoption of Structure from Motion photogrammetry (SfM) is transforming the acquisition of three-dimensional (3D) remote sensing (RS) data in forestry. SfM photogrammetry enables surveys with little cost and technical expertise. We present the theoretical principles and practical considerations of this technology and show opportunities that SfM photogrammetry offers for forest practitioners and researchers. Recent Findings Our examples of key research indicate the successful application of SfM photogrammetry in forestry, in an operational context and in research, delivering results that are comparable to LiDAR surveys. Reviewed studies have identified possibilities for the extraction of biophysical forest parameters from airborne and terrestrial SfM point clouds and derived 2D data in area-based approaches (ABA) and individual tree approaches. Additionally, increases in the spatial and spectral resolution of sensors available for SfM photogrammetry enable forest health assessment and monitoring. The presented research reveals that coherent 3D data and spectral information, as provided by the SfM workflow, promote opportunities to derive both structural and physiological attributes at the individual tree crown (ITC) as well as stand levels. Summary We highlight the potential of using unmanned aerial vehicles (UAVs) and consumer-grade cameras for terrestrial SfM-based surveys in forestry. Offering several spatial products from a single sensor, the SfM workflow enables foresters to collect their own fit-for-purpose RS data. With the broad availability of non-expert SfM software, we provide important practical considerations for the collection of quality input image data to enable successful photogrammetric surveys.
Authors
Amy Elizabeth Eycott Sylwia Wierzcholska Anna Lubek Martin Kukwa Wojciech Adamowski Fride Høistad Schei Bogdan JaroszewiczAbstract
No abstract has been registered