scholarly journals Historical Winter Storm Atlas for Germany (GeWiSA)

Atmosphere ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 387 ◽  
Author(s):  
Christopher Jung ◽  
Dirk Schindler
Keyword(s):  
Spline Interpolation ◽  
Coefficient Of Determination ◽  
Storm Damage ◽  
Winter Storm ◽  
Winter Storms ◽  
Damage Models ◽  
Step Procedure ◽  
Considerable Impact ◽  
Gust Speed

Long-term gust speed (GS) measurements were used to develop a winter storm atlas of the 98 most severe winter storms in Germany in the period 1981–2018 (GeWiSa). The 25 m × 25 m storm-related GS fields were reconstructed in a two-step procedure: Firstly, the median gust speed ( G S ˜ ) of all winter storms was modeled by a least-squares boosting (LSBoost) approach. Orographic features and surface roughness were used as predictor variables. Secondly, the quotient of GS related to each winter storm to G S ˜ , which was defined as storm field factor (STF), was calculated and mapped by a thin plate spline interpolation (TPS). It was found that the mean study area-wide GS associated with the 2007 storm Kyrill is highest (29.7 m/s). In Southern Germany, the 1999 storm Lothar, with STF being up to 2.2, was the most extreme winter storm in terms of STF and GS. The results demonstrate that the variability of STF has a considerable impact on the simulated GS fields. Event-related model validation yielded a coefficient of determination (R2) of 0.786 for the test dataset. The developed GS fields can be used as input to storm damage models representing storm hazard. With the knowledge of the storm hazard, factors describing the vulnerability of storm exposed objects and structures can be better estimated, resulting in improved risk management.

scholarly journals Wintersturmschäden im Schweizer Wald von 1865 bis 2014

2015 ◽  
Vol 166 (3) ◽  
pp. 184-190 ◽  
Author(s):  
Tilo Usbeck
Keyword(s):  
Present Article ◽  
Forest Area ◽  
Wind Gust ◽  
Turn Of The Millennium ◽  
Storm Events ◽  
Winter Storm ◽  
Winter Storms ◽  
Growing Stock ◽  
The Past ◽  
Gust Speed

Forest damages from winter storms in Switzerland from 1865 to 2014 Winter storms cause the most catastrophic damages in Swiss forests. The present article analyses how these storm damages correspond with wind gust speed, growing stock and forest area, in regard to the whole country and individual cantons, and from 1865 to 2014. During the study period, 26 storm events each totalling a volume of at least 70,000 m3 damaged wood were registered. Winter storm damages were highly variable regarding absolute numbers (volume) and portions per area (m3 per ha) and per growing stock (%). In the past 150 years, the cantons Nidwalden, Freiburg, Aargau, Zurich and Zug were hit most often by storm events, with damages ranging per event in average from 2.2 m3 per ha (Zurich) to 3.1 m3 per ha (Nidwalden). At the turn of the millennium, not only the greatest damages occurred but also growing stock peaked as well did the wind gust speeds.

Improving empirical storm damage models by coupling with high-resolution gust speed data

2019 ◽  
Vol 268 ◽  
pp. 23-31 ◽  
Author(s):  
Axel T. Albrecht ◽  
Christopher Jung ◽  
Dirk Schindler
Keyword(s):  
High Resolution ◽  
Storm Damage ◽  
Damage Models ◽  
Gust Speed

scholarly journals Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation

2021 ◽  
Vol 13 (3) ◽  
pp. 438
Author(s):  
Subrina Tahsin ◽  
Stephen C. Medeiros ◽  
Arvind Singh
Keyword(s):  
Vegetation Index ◽  
Coastal Wetland ◽  
Thermal Emission ◽  
Mean Squared Error ◽  
Wetland Vegetation ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Vegetation Monitoring ◽  
Time Space

Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R2) values of 0.0020 sr−1 and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects.

scholarly journals Satellite-Derived Protein Concentration of Phytoplankton in the Southwestern East/Japan Sea

2021 ◽  
Vol 9 (2) ◽  
pp. 189
Author(s):  
Hyeonji Bae ◽  
Dabin Lee ◽  
Jae Joong Kang ◽  
Jae Hyung Lee ◽  
Naeun Jo ◽  
...  
Keyword(s):  
Protein Concentration ◽  
Japan Sea ◽  
Biological Properties ◽  
Measured Data ◽  
Trophic Levels ◽  
Coefficient Of Determination ◽  
P Value ◽  
Chl A ◽  
Major Factors

The cellular macromolecular contents and energy value of phytoplankton as primary food source determine the growth of higher trophic levels, affecting the balance and sustainability of oceanic food webs. Especially, proteins are more directly linked with basic functions of phytoplankton biosynthesis and cell division and transferred through the food chains. In recent years, the East/Japan Sea (EJS) has been changed dramatically in environmental conditions, such as physical and chemical characteristics, as well as biological properties. Therefore, developing an algorithm to estimate the protein concentration of phytoplankton and monitor their spatiotemporal variations on a broad scale would be invaluable. To derive the protein concentration of phytoplankton in EJS, the new regional algorithm was developed by using multiple linear regression analyses based on field-measured data which were obtained from 2012 to 2018 in the southwestern EJS. The major factors for the protein concentration were identified as chlorophyll-a (Chl-a) and sea surface nitrate (SSN) in the southwestern EJS. The coefficient of determination (r2) between field-measured and algorithm-derived protein concentrations was 0.55, which is rather low but reliable. The satellite-derived estimation generally follows the 1:1 line with the field-measured data, with Pearson’s correlation coefficient, which was 0.40 (p-value < 0.01, n = 135). No remarkable trend in the long-term annual protein concentration of phytoplankton was found in the study area during our observation period. However, some seasonal difference was observed in winter protein concentration between the 2003–2005 and 2017–2019 periods. The algorithm is developed for the regional East/Japan Sea (EJS) and could contribute to long-term monitoring for climate-associated ecosystem changes. For a better understanding of spatiotemporal variation in the protein concentration of phytoplankton in the EJS, this algorithm should be further improved with continuous field surveys.

scholarly journals Development and Verification of the Available Number of Water Intake Days in Ungauged Local Water Source Using the SWAT Model and Flow Recession Curves

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1511
Author(s):  
Jung-Ryel Choi ◽  
Il-Moon Chung ◽  
Se-Jin Jeung ◽  
Kyung-Su Choo ◽  
Cheong-Hyeon Oh ◽  
...  
Keyword(s):  
Water Supply ◽  
Water Intake ◽  
Water Resource Management ◽  
Assessment Tool ◽  
Swat Model ◽  
High Water ◽  
Coefficient Of Determination ◽  
Severe Drought ◽  
Regional Government

Climate change significantly affects water supply availability due to changes in the magnitude and seasonality of runoff and severe drought events. In the case of Korea, despite high water supply ratio, more populations have continued to suffer from restricted regional water supplies. Though Korea enacted the Long-Term Comprehensive Water Resources Plan, a field survey revealed that the regional government organizations limitedly utilized their drought-related data. These limitations present a need for a system that provides a more intuitive drought review, enabling a more prompt response. Thus, this study presents a rating curve for the available number of water intake days per flow, and reviews and calibrates the Soil and Water Assessment Tool (SWAT) model mediators, and found that the coefficient of determination, Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS) from 2007 to 2011 were at 0.92, 0.84, and 7.2%, respectively, which were “very good” levels. The flow recession curve was proposed after calculating the daily long-term flow and extracted the flow recession trends during days without precipitation. In addition, the SWAT model’s flow data enables the quantitative evaluations of the number of available water intake days without precipitation because of the high hit rate when comparing the available number of water intake days with the limited water supply period near the study watershed. Thus, this study can improve drought response and water resource management plans.

Storm damage and long-term mortality in a semi-natural, temperate deciduous forest

2004 ◽  
Vol 188 (1-3) ◽  
pp. 197-210 ◽  
Author(s):  
Annett Wolf ◽  
Peter Friis Møller ◽  
Richard H.W. Bradshaw ◽  
Jaris Bigler
Keyword(s):  
Deciduous Forest ◽  
Storm Damage ◽  
Temperate Deciduous Forest ◽  
Temperate Deciduous ◽  
Long Term Mortality

scholarly journals Development of a predictive model for integrated medical and long-term care resource consumption based on health behaviour: application of healthcare big data of patients with circulatory diseases

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Tomoyuki Takura ◽  
Keiko Hirano Goto ◽  
Asao Honda
Keyword(s):  
Regression Analysis ◽  
Predictive Model ◽  
Clinical Outcomes ◽  
Long Term Care ◽  
Healthcare Resource ◽  
Resource Consumption ◽  
Coefficient Of Determination ◽  
Term Care ◽  
Adherence Score

Abstract Background Medical costs and the burden associated with cardiovascular disease are on the rise. Therefore, to improve the overall economy and quality assessment of the healthcare system, we developed a predictive model of integrated healthcare resource consumption (Adherence Score for Healthcare Resource Outcome, ASHRO) that incorporates patient health behaviours, and examined its association with clinical outcomes. Methods This study used information from a large-scale database on health insurance claims, long-term care insurance, and health check-ups. Participants comprised patients who received inpatient medical care for diseases of the circulatory system (ICD-10 codes I00-I99). The predictive model used broadly defined composite adherence as the explanatory variable and medical and long-term care costs as the objective variable. Predictive models used random forest learning (AI: artificial intelligence) to adjust for predictors, and multiple regression analysis to construct ASHRO scores. The ability of discrimination and calibration of the prediction model were evaluated using the area under the curve and the Hosmer-Lemeshow test. We compared the overall mortality of the two ASHRO 50% cut-off groups adjusted for clinical risk factors by propensity score matching over a 48-month follow-up period. Results Overall, 48,456 patients were discharged from the hospital with cardiovascular disease (mean age, 68.3 ± 9.9 years; male, 61.9%). The broad adherence score classification, adjusted as an index of the predictive model by machine learning, was an index of eight: secondary prevention, rehabilitation intensity, guidance, proportion of days covered, overlapping outpatient visits/clinical laboratory and physiological tests, medical attendance, and generic drug rate. Multiple regression analysis showed an overall coefficient of determination of 0.313 (p < 0.001). Logistic regression analysis with cut-off values of 50% and 25%/75% for medical and long-term care costs showed that the overall coefficient of determination was statistically significant (p < 0.001). The score of ASHRO was associated with the incidence of all deaths between the two 50% cut-off groups (2% vs. 7%; p < 0.001). Conclusions ASHRO accurately predicted future integrated healthcare resource consumption and was associated with clinical outcomes. It can be a valuable tool for evaluating the economic usefulness of individual adherence behaviours and optimising clinical outcomes.

scholarly journals Long-Term and Seasonal Trends in Global Wave Height Extremes Derived from ERA-5 Reanalysis Data

2020 ◽  
Vol 8 (12) ◽  
pp. 1015
Author(s):  
Alicia Takbash ◽  
Ian R. Young
Keyword(s):  
Southern Hemisphere ◽  
Wave Height ◽  
Significant Wave Height ◽  
Reanalysis Data ◽  
Extreme Value Analysis ◽  
Winter Storms ◽  
Value Analysis ◽  
Weather Forecasts ◽  
Significant Wave

A non-stationary extreme value analysis of 41 years (1979–2019) of global ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis) significant wave height data is undertaken to investigate trends in the values of 100-year significant wave height, Hs100. The analysis shows that there has been a statistically significant increase in the value of Hs100 over large regions of the Southern Hemisphere. There have also been smaller decreases in Hs100 in the Northern Hemisphere, although the related trends are generally not statistically significant. The increases in the Southern Hemisphere are a result of an increase in either the frequency or intensity of winter storms, particularly in the Southern Ocean.

scholarly journals COASTAL CHANGES AT BETHANY BEACH, DELAWARE

1984 ◽  
Vol 1 (19) ◽  
pp. 112 ◽  
Author(s):  
Jennifer E. Dick ◽  
Robert A. Dalrymple
Keyword(s):  
Coastal Erosion ◽  
Coastal Processes ◽  
Short Term ◽  
Winter Storms ◽  
Barrier Beach ◽  
Commercial Activities ◽  
Long Term Trends ◽  
Coastal Changes ◽  
Summer Homes

The coastal processes affecting Bethany Beach, Delaware were studied and the short-term and long-term trends in coastal changes were determined in order to develop recommendations for protecting Bethany against coastal erosion (Dick and Dalrymple, 1983). Bethany Beach is located on the Delaware Atlantic coastline which is a wide sandy baymouth barrier beach distinguished by highlands at Rehoboth Beach and Bethany Beach. The shoreline is straight, with only minor bulges and indentations (see Figure 1). Bethany Beach is a residential and resort community. Privatelyowned properties front the publicly-owned beach. Construction of new motels and summer homes is anticipated along with the continued growth of commercial activities to accommodate the increased number of visitors. Bethany is protected by a series of nine groins built between 1934 and 1945. Many of these groins have deteriorated, and are flanked at the landward end. Winter storms severely erode the beach and damage shorefront property. The beach is generally narrow (approximately 45 m wide), especially along the southern portion, and is backed by low dunes (about 15-45 m above NGVD). A timber bulkhead extends along most of the backshore.

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