| 11. |
- Chen, D., et al.
(författare)
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A high-resolution, gridded dataset for monthly temperature normals (1971 – 2000) in Sweden
- 2007
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Ingår i: Geografiska Annaler. Series A, Physical Geography. - : Informa UK Limited. - 0435-3676 .- 1468-0459. ; 89:4, s. 249-261
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Tidskriftsartikel (refereegranskat)abstract
- A baseline climatology is required in evaluating climate variability and changes on regional and local scales. Gridded climate normals, i.e. averages over a 30-year period, are of special interest since they can be readily used for validation of climate models. This study is aimed at creating an updated gridded dataset for Swedish monthly temperature normals over the period 1971–2000, based on standard 2-m air temperature records at 510 stations in mainland Sweden. Spatial trends of the normal temperatures were modelled as functions of latitude, longitude and elevation by multiple linear regression. The study shows that the temperature normals are strongly correlated with latitude throughout the year and especially in cold months, while elevation was a more important factor in June and July. Longitude played a minor role and was only significant in April and May. Regression equations linking temperature to latitude, longitude and elevation were set up for each month. Monthly temperature normals were detrended by subtracting spatial trends given by the regressions. Ordinary kriging was then applied to both original data (simple method) and de-trended data (composite method) to model the spatial variability and to perform spatial gridding. The multiple regressions showed that between 82% (summer) and 96% (winter) of the variance in monthly temperature normals could be explained by latitude and elevation. Unexplained variances, i.e. the residuals, were modelled with ordinary kriging with exponential semivariograms. The composite grid estimates were calculated by adding the multiple linear trends back to the interpolated residuals at each grid point. Kriged original temperature normals provided a performance benchmark. The cross–validation shows that the interpolation errors of the normals are significantly reduced if the composite method rather than the simple one was used. A gridded monthly dataset with 30-arcsecond spacing was created using the established trends, the kriging model and a digital topographic dataset.
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| 12. |
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| 13. |
- Comley, Robert A., et al.
(författare)
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A Comparison of Gray Matter Density in Restless Legs Syndrome Patients and Matched Controls Using Voxel-Based Morphometry
- 2012
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Ingår i: Journal of Neuroimaging. - : WILEY-BLACKWELL. - 1051-2284 .- 1552-6569. ; 22:1, s. 28-32
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND Restless legs syndrome (RLS) is a common neurological disorder the pathophysiology of which is incompletely understood. Four studies have examined structural differences between the brains of RLS patients and healthy controls, using voxel-based morphometry (VBM). All 4 studies have provided different results. METHODS Optimized VBM was used to search for structural differences in gray matter density. Sixteen RLS patients naive to dopaminergic drugs and 16 age-and sex-matched controls received structural T1-weighted MR scans. Structural data were analyzed using FSL-VBM. RESULTS No difference in gray matter density was detected between the two groups (voxel-wise significance: no significant voxels at P = .89 (whole brain Family Wise Error (FWE) corrected); no significant voxels at P < .05 (whole brain False Discovery Rate (FDR) corrected; smallest achievable FDR threshold .99). CONCLUSION/DISCUSSION The present study did not replicate (confirm) previous findings of structural brain changes in RLS, but instead supported the findings of a recent study showing a lack of gray matter alteration in an elderly RLS population. More specifically, the results do not support neuronal loss as an underlying disease mechanism in RLS. Potential limitations in the application of VBM are also discussed.
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| 14. |
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| 15. |
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| 16. |
- Fuentes-Andino, Diana, 1984-, et al.
(författare)
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Event and model dependent rainfall adjustments to improve discharge predictions
- 2017
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Ingår i: Hydrological Sciences Journal. - : Informa UK Limited. - 0262-6667 .- 2150-3435. ; 62:2, s. 232-245
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Tidskriftsartikel (refereegranskat)abstract
- Most conceptual rainfall–runoff models use as input spatially averaged rainfall fields which are typically associated with significant errors that affect the model outcome. In this study, it was hypothesised that a simple spatially and temporally averaged event-dependent rainfall multiplier can account for errors in the rainfall input. The potentials and limitations of this lumped multiplier approach were explored by evaluating the effects of multipliers on the accuracy and precision of the predictive distributions. Parameter sets found to be behavioural across a range of different flood events were assumed to be a good representation of the catchment dynamics and were used to identify rainfall multipliers for each of the individual events. An effect of the parameter sets on identified multipliers was found; however, it was small compared to the differences between events. Accounting for event-dependent multipliers improved the reliability of the predictions. At the cost of a small decrease in precision, the distribution of identified multipliers for past events can be used to account for possible rainfall errors when predicting future events. By using behavioural parameter sets to identify rainfall multipliers, the method offers a simple and computationally efficient way to address rainfall errors in hydrological modelling.
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| 17. |
- Fuentes-Andino, Diana, 1984-
(författare)
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Flood Hazard Assessment in Data-Scarce Basins : Use of alternative data and modelling techniques
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Flooding is of great concern world-wide, causing damage to infrastructure, property and loss of life. Low-income countries, in particular, can be negatively affected by flood events due to their inherent vulnerabilities. Moreover, data to perform studies for flood risk management in low-income regions are often scarce or lacking sufficient quality.This thesis proposes new methodologies and explores the use of unconventional sources of information in flood hazard assessment in areas where the quantity or sufficient quality of traditional hydrometrical data are lacking. One method was developed to account for errors in spatially averaged rainfall, from a sparse rain-gauge network, used as input to a rainfall-runoff model. A spatially-averaged and event-dependent rainfall depth multiplier led to improvements of the hydrographs at calibration. And by using a distribution of the multiplier, identified from previous events in the catchment, improvement in predictions could also be obtained.A second method explored the possibility of reproducing an unmeasured extreme flood event using a combination of models, post-event data, precipitation and an uncertainty-analysis framework. This combination allowed the identification of likelihood-associated parameter sets from which the flood hazard map for the extreme event could be obtained.A third and fourth study made at the regional scale explored the value of catchment similarities, and the effects of climate on the hydrological response of catchments.Flood frequency curves were estimated for 36 basins, assumed ungauged, using regional information of short flow records, and local information about the frequency of the storm. In the second regional study, hydro-climatic information provided great value to constrain predictions of series of daily flow from a hydrological model.Previously described methods, used in combination with unconventional information within an uncertainty analysis, proven to be useful for flood hazard assessment at basins with data limitations. The explored data included: post-event measurements of an extreme flood event, hydro-climate regional information and local precipitation data. The methods presented in this thesis are expected to support development of hydrological studies underpinning flood-risk reduction in data-poor areas.
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| 18. |
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| 19. |
- Fuentes–Andino, Diana, 1984-, et al.
(författare)
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Reproducing an extreme flood with uncertain post-event information
- 2017
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Ingår i: Hydrology and Earth System Sciences Discussions. - : Copernicus GmbH. - 1812-2108 .- 1812-2116 .- 1607-7938. ; 21:7, s. 3597-3618
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Tidskriftsartikel (refereegranskat)abstract
- Studies for the prevention and mitigation of floods require information on discharge and extent of inundation, commonly unavailable or uncertain, especially during extreme events. This study was initiated by the devastating flood in Tegucigalpa, the capital of Honduras, when Hurricane Mitch struck the city. In this study we hypothesized that it is possible to estimate, in a trustworthy way considering large data uncertainties, this extreme 1998 flood discharge and the extent of the inundations that followed from a combination of models and post-event measured data. Postevent data collected in 2000 and 2001 were used to estimate discharge peaks, times of peak, and high-water marks. These data were used in combination with rain data from two gauges to drive and constrain a combination of well-known modelling tools: TOPMODEL, Muskingum-Cunge-Todini routing, and the LISFLOOD-FP hydraulic model. Simulations were performed within the generalized likelihood uncertainty estimation (GLUE) uncertainty-analysis framework. The model combination predicted peak discharge, times of peaks, and more than 90% of the observed highwater marks within the uncertainty bounds of the evaluation data. This allowed an inundation likelihood map to be produced. Observed high-water marks could not be reproduced at a few locations on the floodplain. Identifications of these locations are useful to improve model set-up, model structure, or post-event data-estimation methods. Rainfall data were of central importance in simulating the times of peak and results would be improved by a better spatial assessment of rainfall, e. g. from radar data or a denser rain-gauge net-work. Our study demonstrated that it was possible, considering the uncertainty in the post-event data, to reasonably reproduce the extreme Mitch flood in Tegucigalpa in spite of no hydrometric gauging during the event. The method proposed here can be part of a Bayesian framework in which more events can be added into the analysis as they become available.
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| 20. |
- Gebrehiwot, Solomon Gebreyohannis, et al.
(författare)
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Is observation uncertainty masking the signal of land use change impacts on hydrology?
- 2019
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Ingår i: Journal of Hydrology. - : Elsevier. - 0022-1694 .- 1879-2707. ; 570, s. 393-400
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Tidskriftsartikel (refereegranskat)abstract
- Analysis of hydrological impacts of land use change raises questions about whether, and how much, such impacts are misrepresented because of errors in river flow observations. In this paper, land use change impacts (represented by changes in watershed storage) and different ranges of discharge measurement error are compared to assess how errors in discharge measurement can potentially mask a land use change impact. Using a watershed from the Ethiopian highlands to exemplify this, we simulated five different levels of land use change impacts with five levels of watershed storage reductions (from 10% to 50% change) and the associated time series of runoff. Different levels of observation error were then introduced into these artificial time series. Comparison was made between every pair, i.e. a time series derived from a certain level of land use change (storage reduction) versus a time series corresponding to a given level of observation error, using a step-change t-test. Significant step-changes between pairs define the detectability of land use change impact. The analysis was made for the entire 30-year time series as well as for the most extreme annual weather conditions. The results showed that for the average year and wettest year, 75% or more error in observed discharge masks the maximum simulated land use change impact on hydrology. In dry years, a 50% error in discharge is enough to mask the same impact. Knowing (and improving) the level of data quality contributes to a better understanding of hydrological uncertainties and improves the precision in assessing land use change impacts. Both of these are essential elements in water resources development planning.
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