The Drivers of Hydrologic Behavior in Brazil: Insights From a Catchment Classification

Author:

Almagro André1ORCID,Meira Neto Antônio Alves2ORCID,Vergopolan Noemi3ORCID,Roy Tirthankar4ORCID,Troch Peter A.5ORCID,Oliveira Paulo Tarso S.1ORCID

Affiliation:

1. Faculty of Engineering and Geography Federal University of Mato Grosso do Sul Campo Grande Brazil

2. Department of Civil and Environmental Engineering Colorado State University Fort Collins CO USA

3. Earth, Environmental and Planetary Sciences Rice University Houston TX USA

4. Department of Civil and Environmental Engineering University of Nebraska‐Lincoln Omaha NE USA

5. Department of Hydrology and Atmospheric Sciences University of Arizona Tucson AZ USA

Abstract

AbstractDespite hosting ∼16% of the global freshwater and almost 50% of water resources in South America, Brazilian catchment‐scale relationships between drivers and streamflow are still poorly understood. Here, we used streamflow signatures and attributes of 735 catchments from the Catchment Attributes for Brazil data set to investigate the dominant hydrological processes for the catchments. We also assess how catchments group based on hydrologic behavior similarities and analyze which climatic/landscape attributes control the streamflow variability. To classify and group the catchments, we used the k‐means method optimized by the Elbow approach, along with a Principal Component Analysis. Uncertainty on catchment grouping was checked by k‐fold cross‐validation. Then, we used a recursive feature elimination using the random forest technique to assess the most influential catchment attributes to the hydrological signatures. Our results revealed six similarity groups, which followed mainly an aridity gradient ranging from the wettest to the driest, but also seasonality. The climate is the primary driver of hydrological behavior for the water‐limited groups, highlighting the influence and importance of the atmospheric demand in several Brazilian catchments. High soil storage capacity in energy‐limited catchments associated with high precipitation led to high discharge all year due to the subsurface fluxes' contribution. Our findings may be useful to improve streamflow predictability and hydrological behavior identification by further understanding hydrological similarities and their signatures due to catchment landscape characteristics. Further, by employing an easily reproducible methodology and clear metrics to weigh uncertainty, our study provides a significant step toward establishing a catchment‐scale common classification system.

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Fundação de Apoio ao Desenvolvimento do Ensino, Ciência e Tecnologia do Estado de Mato Grosso do Sul

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

American Geophysical Union (AGU)

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3