| dc.contributor.author |
Araujo, Vinicius |
|
| dc.contributor.author |
Abreu, Ana Elisa Silva de |
|
| dc.contributor.author |
Costa, Paula Dornhofer Paro |
|
| dc.contributor.author |
Falcetta, Filipe Antonio Marques |
|
| dc.contributor.author |
Perez, Gerald Augusto Corzo |
|
| dc.contributor.author |
Bitar, Omar Yazbek |
|
| dc.coverage.spatial |
Campinas, São Paulo, Brazil; 289.100, 293.000 , 7.467.700 , 7.462.900 |
|
| dc.date |
2022-12-05 |
|
| dc.date.accessioned |
2022-12-01 |
|
| dc.date.accessioned |
2022-12-06T12:11:14Z |
|
| dc.date.available |
2022-12-06T12:11:14Z |
|
| dc.identifier.uri |
https://doi.org/10.25824/redu/J2NLAY |
|
| dc.identifier.uri |
https://redu.unicamp.br/dataset.xhtml?persistentId=doi:10.25824/redu/J2NLAY |
|
| dc.description |
This dataset contains data acquired from P-band weather radar, river water levels, and water temperatures collected every five minutes for an urban watershed located in Campinas city, in the Southeast region of Brazil. The dataset has 12265 samples and gathers data on the 145 events of highest water levels from November 2014 to June 2019. It contains 15 variables, namelly: data, time_match, precipitation interpreted from data of 10 radar grids (which correspond to the watershed area), average precipitation, water level and water temperature in the watershed outlet. A baseline study using this dataset can be found in the Master Thesis entitled "Data-driven model for flood prediction in an urban watershed in Campinas, Brazil, aiming at building an early warning system" |
|
| dc.description.sponsorship |
Fundação de Amparo à Pesquisa do Estado de São Paulo |
|
| dc.description.sponsorship |
Fundação de Amparo à Pesquisa do Estado de São Paulo |
|
| dc.format |
text/csv |
|
| dc.publisher |
Abreu, Ana Elisa |
|
| dc.subject |
Earth and Environmental Sciences |
|
| dc.title |
Multimodal hydrological dataset for flash flood modeling in an urban watershed |
|
| dc.coverage.spatial.Lat |
289.100 |
|
| dc.coverage.spatial.Lat |
293.000 |
|
| dc.coverage.spatial.Long |
7.467.700 |
|
| dc.coverage.spatial.Long |
7.462.900 |
|
| dc.description.sponsorshipId |
FAPESP: 2020/00058-2 |
|
| dc.description.sponsorshipId |
FAPESP: 2017/50343-2 |
|