Contribution to the classification of small catchments according to the drainage area

ABSTRACT

The adoption of an area value to define large, medium or small river basins should take into account the hydrological processes involved in the basin. Changes in runoff are identified with greater sensitivity in small catchments, as in large catchments it is not possible to disregard factors such as infiltration and evaporation, which directly interfere with the rainfall-runoff conversion phenomenon. Thus, the objective of this study is to classify the small catchments of the Amazon Region by drainage area using a simple linear model (SLM). The SLM is based on a linear and time-invariant system that establishes a cause-and-effect relationship between the data. The model was applied to rainfall and runoff data from test catchments to verify the linearity between these variables. The performance of the model was evaluated through the Root Mean Square Error (RMS) and metric distance $\left({\delta }_{Q_o{Q}_s}\right)$. The results indicated that in the catchments with drainage areas less than to 500 km², the adjustment between the observed and simulated flow was good, presenting RMS and metric distance values less than 3 m³/s and 2000, respectively. In basins of the dimensions above 500 km², the RMS and ${\delta }_{Q_o{Q}_s}$ results began to show nonlinearity from 4 m³/s and 3000, respectively, showing that SLM should not be applied to medium and large river basins. Thus, the small catchments of the Amazon were classified as drainage areas less than 500 km².

KEYWORDS:

• Cause–effect rainfall-runoff
• hydrological modelling
• RMS
• Amazon

Acknowledgments

The authors would like to thank the Coordination for the Improvement of Higher Education Personnel - Brasil (CAPES) - Finance Code 001. The second author thanks CNPq for the Research Productivity Grant process number 304936/2015-4. We would like to thank the office for research (PROPESP) and the Foundation for Research Development (FADESP) at the Federal University of Pará (UFPA) through grant number PAPQ 2018.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico [Grant number 304936/2015-4]; Coordination for the Improvement of Higher Education Personnel - Brasil (CAPES) [Grant number 001]; Office for research (PROPESP) and the Foundation for Research Development (FADESP) at the Federal University of Pará (UFPA) [Grant number PAPQ/2018].