scholarly journals Clustering Simultaneous Occurrences of the Extreme Floods in the Neckar Catchment

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 399
Author(s):  
Ehsan Modiri ◽  
András Bárdossy
Keyword(s):  
Agglomerative Hierarchical Clustering ◽  
Black Forest ◽  
Spatiotemporal Resolution ◽  
Extreme Floods ◽  
Verification Method ◽  
Daily Time Series ◽  
Kendall Correlation ◽  
Anthropogenic Alterations ◽  
Clustering Tree ◽  
Daily Time

Flood protection is crucial for making socioeconomic policies due to the high losses of extreme floods. So far, the synchronous occurrences of flood events have not been deeply investigated. In this paper, multivariate analysis was implemented to reveal the interconnection between these floods in spatiotemporal resolution. The discharge measurements of 46 gauges with a continuous daily time series for 55 years were taken over the Neckar catchment. Initially, the simultaneous floods were identified. The Kendall correlation between the pair sets of peaks was determined to scrutinize the similarities between the simultaneous events. Agglomerative hierarchical clustering tree (AHCT) and multidimensional scaling (MDS) were employed, and obtained clusters were compared and evaluated with the Silhouette verification method. AHCT shows that the Average and Ward algorithms are appropriate to detect reasonable clusters. The Neckar catchment has been divided into three major clusters: the first cluster mainly covers the western part and is bounded by the Black Forest and Swabian Alps. The second cluster is mostly located in the eastern part of the upper Neckar. The third cluster contains the remaining lowland areas of the Neckar basin. The results illustrate that the clusters act relatively as a function of topography, geology, and anthropogenic alterations of the catchment.

scholarly journals Assessing Early Heterogeneity in Doubling Times of the COVID-19 Epidemic across Prefectures in Mainland China, January–February, 2020

Epidemiologia ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 95-113 ◽  
Author(s):  
Isaac Chun-Hai Fung ◽  
Xiaolu Zhou ◽  
Chi-Ngai Cheung ◽  
Sylvia K. Ofori ◽  
Kamalich Muniz-Rodriguez ◽  
...  
Keyword(s):  
Regression Models ◽  
Doubling Time ◽  
Metropolitan Areas ◽  
Mainland China ◽  
Linear Regression Models ◽  
Case Count ◽  
Daily Time Series ◽  
The Mean ◽  
Doubling Times ◽  
Daily Time

To describe the geographical heterogeneity of COVID-19 across prefectures in mainland China, we estimated doubling times from daily time series of the cumulative case count between 24 January and 24 February 2020. We analyzed the prefecture-level COVID-19 case burden using linear regression models and used the local Moran’s I to test for spatial autocorrelation and clustering. Four hundred prefectures (~98% population) had at least one COVID-19 case and 39 prefectures had zero cases by 24 February 2020. Excluding Wuhan and those prefectures where there was only one case or none, 76 (17.3% of 439) prefectures had an arithmetic mean of the epidemic doubling time <2 d. Low-population prefectures had a higher per capita cumulative incidence than high-population prefectures during the study period. An increase in population size was associated with a very small reduction in the mean doubling time (−0.012, 95% CI, −0.017, −0.006) where the cumulative case count doubled ≥3 times. Spatial analysis revealed high case count clusters in Hubei and Heilongjiang and fast epidemic growth in several metropolitan areas by mid-February 2020. Prefectures in Hubei and neighboring provinces and several metropolitan areas in coastal and northeastern China experienced rapid growth with cumulative case count doubling multiple times with a small mean doubling time.

Homogenization of daily time series climatological data in the Eastern Nile basin, Ethiopia

2020 ◽  
Vol 143 (1-2) ◽  
pp. 737-760
Author(s):  
Sadame M. Yimer ◽  
Navneet Kumar ◽  
Abderrazak Bouanani ◽  
Bernhard Tischbein ◽  
Christian Borgemeister
Keyword(s):  
Time Series ◽  
Nile Basin ◽  
Climatological Data ◽  
Daily Time Series ◽  
Daily Time

scholarly journals CO and CO2 daily time series and time series of wind speed

Data in Brief ◽  
2019 ◽  
Vol 24 ◽  
pp. 103976
Author(s):  
Changgong Shan ◽  
Wei Wang
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Daily Time Series ◽  
Daily Time

scholarly journals Evaluation of multisite synthetic data generated by spatial weather generator and long climate data series

2018 ◽  
Vol 44 ◽  
pp. 00083 ◽  
Author(s):  
Leszek Kuchar ◽  
Slawomir Iwanski
Keyword(s):  
Synthetic Data ◽  
Weather Generator ◽  
Data Series ◽  
Climatic Data ◽  
Climate Data ◽  
Temperature And Precipitation ◽  
South West ◽  
Daily Time Series ◽  
Weather Parameters ◽  
Daily Time

In this paper a new validation test for the spatial weather generator SWGEN producing the multisite daily time series of solar radiation, temperature and precipitation is presented. The method was tested by comparing statistics of 1000 years of generated data with extra long series of 35 years of observed weather parameters and 24 sites of meteorological stations for south-west Poland. The method evaluation showed that the means (sums) and variances of generated data were comparable with observed climatic data aggregated for months, seasons and years.

scholarly journals Correlated daily time series and forecasting in the M4 competition

2020 ◽  
Vol 36 (1) ◽  
pp. 121-128 ◽  
Author(s):  
Anti Ingel ◽  
Novin Shahroudi ◽  
Markus Kängsepp ◽  
Andre Tättar ◽  
Viacheslav Komisarenko ◽  
...  
Keyword(s):  
Time Series ◽  
Daily Time Series ◽  
Daily Time
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