Cluster analysis and hydrological regionalization for Brazilian states

ABSTRACT Streamflow data from gauging stations are essential for effective water resources management. However, some regions in Brazil lack the necessary data. Hydrological regionalization is an alternative technique for obtaining data such regions. However, not all regions in Brazil have defined hydrological regionalization models, including the state of Goiás and the Brazilian Federal District. The objective of this study was to develop a hydrological regionalization methodology based on the separation of hydrologically homogeneous regions and multiple linear regression, using a Geographic Information System (GIS) program. Historical series data were used to calculate reference flows with 90 or 95% duration over time in the watercourse (Q90 and Q95) and the mean flow (Ǭ). Rain gauge station data were used to calculate the mean annual rainfall in each watershed through spatial interpolation by ordinary kriging. Subsequently, the physiographic characteristics of each watershed were calculated. The hydrologically homogeneous regions were delimited based on these data using cluster analysis, which identified seven hydrologically homogeneous regions in Goiás, two of them belonging to the Federal District. Multiple regression allowed the development of seven regionalization models. Models for regions 1, 3, 4, 5, and 7 showed better performance.

Authors

Institutions

Publication Details

Journal
Revista Brasileira de Engenharia Agrícola e Ambiental
Published
2024-08-12
DOI
https://doi.org/10.1590/1807-1929/agriambi.v28n11e277005
Primary Topic
Geography and Environmental Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Cluster analysis and hydrological regionalization for Brazilian states

Tárcio Rocha Lopes, Sérgio Nascimento Duarte, Hugo de Carvalho Ricardo, Jéssica Garcia Nascimento et al.
Revista Brasileira de Engenharia Agrícola e Ambiental
Geography and Environmental Studies
article

Cluster analysis and hydrological regionalization for Brazilian states

Tárcio Rocha Lopes, Sérgio Nascimento Duarte, Hugo de Carvalho Ricardo, Jéssica Garcia Nascimento, Fernando Campos Mendonça, Thaís da Silva Charles, Adriano Bicioni Pacheco
article en

Abstract

ABSTRACT Streamflow data from gauging stations are essential for effective water resources management. However, some regions in Brazil lack the necessary data. Hydrological regionalization is an alternative technique for obtaining data such regions. However, not all regions in Brazil have defined hydrological regionalization models, including the state of Goiás and the Brazilian Federal District. The objective of this study was to develop a hydrological regionalization methodology based on the separation of hydrologically homogeneous regions and multiple linear regression, using a Geographic Information System (GIS) program. Historical series data were used to calculate reference flows with 90 or 95% duration over time in the watercourse (Q90 and Q95) and the mean flow (Ǭ). Rain gauge station data were used to calculate the mean annual rainfall in each watershed through spatial interpolation by ordinary kriging. Subsequently, the physiographic characteristics of each watershed were calculated. The hydrologically homogeneous regions were delimited based on these data using cluster analysis, which identified seven hydrologically homogeneous regions in Goiás, two of them belonging to the Federal District. Multiple regression allowed the development of seven regionalization models. Models for regions 1, 3, 4, 5, and 7 showed better performance.

Revista Brasileira de Engenharia Agrícola e AmbientalVol. 28(11)
Universidade Estadual de Maringá (BR), Universidade de São Paulo (BR), Universidade Federal Rural da Amazônia (BR)
Clean water and sanitation
Openalex Percentile: Top 88%
Geography and Environmental Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.