| 1. |
- Clemenzi, Ilaria, et al.
(författare)
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Impact of snow distribution modelling for runoff predictions
- 2023
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Ingår i: Nordic Hydrology. - : IWA Publishing. - 0029-1277 .- 1996-9694. ; 54:5, s. 633-647
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Tidskriftsartikel (refereegranskat)abstract
- Snow in the mountains is essential for the water cycle in cold regions. The complexity of the snow processes in such an environment makes it challenging for accurate snow and runoff predictions. Various snow modelling approaches have been developed, especially to improve snow predictions. In this study, we compared the ability to improve runoff predictions in the Överuman Catchment, Northern Sweden, using different parametric representations of snow distribution. They included a temperature-based method, a snowfall distribution (SF) function based on wind characteristics and a snow depletion curve (DC). Moreover, we assessed the benefit of using distributed snow observations in addition to runoff in the hydrological model calibration. We found that models with the SF function based on wind characteristics better predicted the snow water equivalent (SWE) close to the peak of accumulation than models without this function. For runoff predictions, models with the SF function and the DC showed good performances (median Nash–Sutcliffe efficiency equal to 0.71). Despite differences among the calibration criteria for the different snow process representations, snow observations in model calibration added values for SWE and runoff predictions.
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| 2. |
- Zhang, Jie, et al.
(författare)
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Improving the snowpack monitoring in the mountainous areas of Sweden from space : a machine learning approach
- 2021
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Ingår i: Environmental Research Letters. - : Institute of Physics Publishing (IOPP). - 1748-9326. ; 16:8
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Tidskriftsartikel (refereegranskat)abstract
- Under a warming climate, an improved understanding of the water stored in snowpacks is becoming increasingly important for hydropower planning, flood risk assessment and water resource management. Due to inaccessibility and a lack of ground measurement networks, accurate quantification of snow water storage in mountainous terrains still remains a major challenge. Remote sensing can provide dynamic observations with extensive spatial coverage, and has proved a useful means to characterize snow water equivalent (SWE) at a large scale. However, current SWE products show very low quality in the mountainous areas due to very coarse spatial resolution, complex terrain, large spatial heterogeneity and deep snow. With more high-quality satellite data becoming available from the development of satellite sensors and platforms, it provides more opportunities for better estimation of snow conditions. Meanwhile, machine learning provides an important technique for handling the big data offered from remote sensing. Using the overuman Catchment in Northern Sweden as a case study, this paper explores the potentials of machine learning for improving the estimation of mountain snow water storage using satellite observations, topographic factors, land cover information and ground SWE measurements from the spatially distributed snow survey. The results show that significantly improved SWE estimation close to the peak of snow accumulation can be achieved in the catchment using the random forest regression. This study demonstrates the potentials of machine learning for better understanding the snow water storage in mountainous areas.
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| 3. |
- Norell, Björn, et al.
(författare)
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Lesion characterization using spectral mammography
- 2012
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Ingår i: Medical Imaging 2012. - : SPIE - International Society for Optical Engineering.
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Konferensbidrag (refereegranskat)abstract
- We present a novel method for characterizing mammographic findings using spectral imaging without the use of contrast agent. Within a statistical framework, suspicious findings are analyzed to determine if they are likely to be benign cystic lesions or malignant tissue. To evaluate the method, we have designed a phantom where combinations of different tissue types are realized by decomposition into the material bases aluminum and polyethylene. The results indicate that the lesion size limit for reliable characterization is below 10 mm diameter, when quantum noise is the only considered source of uncertainty. Furthermore, preliminary results using clinical images are encouraging, but allow no conclusions with significance.
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| 4. |
- Norell, Björn, et al.
(författare)
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Approximate spectral factorization for design of efficient sub-filter sequences
- 2011
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- A well-known approach to the design of computationally efficient filters is to use spectral factorization, i.e. a decomposition of a filter into a sequence of sub-filters. Due to the sparsity of the sub-filters, the typical processing speedup factor is within the range 1-10 in 2D, and for 3D it achieves10-100. The design of such decompositions consists in choosing the proper number of sub-filters, their individual types and sparsity. We focus here on finding optimal values of coefficients for given sequences of sparse sub-filters. It is a non-convex large scale optimization problem. The existing approaches are characterized by a lack of robustness - a very slow convergence with no guarantee of success. They are typically based on generating random initial points for further refinement with the use of local search methods. To deal with the multi-extremal nature of the original problem, we introduce a new constrained optimization problem. Its solution is then used as an initial point in the original problem for further refinement. Our approach is applicable to designing multi-dimensional filters. Its efficiency and robustness is illustrated by designing sub-filter sequences for 2D low-pass, band-pass and high-pass filters of approximately the same quality as with the use of a standard approach, but with the overall design speedup factor of several hundred.
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