Similar to most countries, the Brazilian water resources management considers topographically delineated catchment as a territorial unit for policy implementation. Yet, previous studies have shown that catchments are not hydrologically isolated, and topographic limits often neglect the groundwater boundaries. Thus, studies on effective catchment areas are promising for shedding light on inter‐catchment groundwater flow. Here, we investigated the deviation between the topographic and effective areas across Brazil. We applied the effective catchment area index (ECI) to 733 Brazilian catchments and identified the most influencing attributes on the ECI by using principal component and random forest analyses (PCA and RFA, respectively). Further analysis was carried out by contrasting the ECI values against the expected range of the Budyko curve, considering both topographic and effective catchment areas (classic and adjusted framework, respectively). We noted that nearly 32% of the Brazilian catchments presented more than 30% of difference between the effective area and its topographic boundaries. In general, the more arid biomes in Brazil—the Cerrado and Caatinga—are prone to have smaller effective areas while larger effective areas were mostly found in the Atlantic Forest biome, a humid tropical region with a higher mean elevation. Our findings indicate that the aridity index was the main driving factor and negatively correlated with ECI followed by mean slope, precipitation seasonality, and mean elevation. We highlight the potential of adopting a pooling of catchments based on their interconnectivity to minimize management costs while maximizing synergies and lessening trade‐offs between ecosystem functioning and water transfer processes. Our results contribute to a better country‐wide understanding of hydrological connectivity among catchments and highlight the need to consider the effective catchment area to overcome water‐food‐energy security challenges on multiple scales.
Similar to most countries, the Brazilian water resources management considers topographically delineated catchment as a territorial unit for policy implementation. Yet, previous studies have shown that catchments are not hydrologically isolated, and topographic limits often neglect the groundwater boundaries. Thus, studies on effective catchment area are promising for shedding light on inter-catchment groundwater flow. Here, we investigated the deviation between the topographic and effective areas across Brazil. We applied the Effective Catchment Area index (ECI) to 733 Brazilian catchments and identified the most influencing attributes on the ECI by using Principal Component and Random Forest Analyses (PCA and RFA, respectively). Further analysis of consistency was carried out by contrasting the ECI values against the expected range of the Budyko curve considering both topographic and effective catchment areas (classic and adjusted framework). Considering the studied catchments, 15% and 16% of their effective areas were respectively smaller than half (strong losing water condition) and larger than double (strong gaining water condition) of their corresponding topographic areas. The aridity index was the main driving factor and negatively correlated with ECI followed by mean slope, precipitation seasonality, and mean elevation. In general, the more arid biomes in Brazil — the Cerrado and Caatinga — are prone to have smaller effective areas while larger effective areas were mostly found in the Atlantic Forest biome, a humid tropical region with a higher mean elevation. We highlight the potential of adopting a pooling of catchments based on their interconnectivity to minimize management costs while maximizing synergies and lessening trade-offs of water transfer processes. Our results contribute to a better country-scale understanding of hydrological connectivity among catchments and highlight the need to consider the effective catchment area to overcome water-food-energy security challenges on multiple scales.
Curve Number (CN) values estimating from rainfall-runoff data is an attractive topic in hydrology. However, CN values are lacking for Interlocking Concrete Pavement (ICP) material, mainly when seated over bare soil (not over a permeable pavement structure). Here, we compute CN values for the ICP seated over clayey soil using measured rainfall and infiltration capacity data. We estimated runoff ( Q) using 32 events of 24-hour rainfall depth ( P 24) and an infiltration model, assuming a hortonian runoff process. To estimate the CN for each P 24 event, we used the rainfall-runoff incremental approach. Overall, we obtained CN values ranging from 52 to 63. The best CN values to estimate Q were equal to 52.2 ( R M S E = 9.09 mm and R 2 = 0.03) and 60.1 ( R M S E = 1.45 mm and R 2 = 0.97), considering natural- and rank-ordered P 24- Q data, respectively. Our results indicate that it is more suitable to use the initial abstraction ratio ( λ) equal to 0.20 for the ICP material. The findings provide a better understanding of the rainfall-runoff process in ICP and help improve the design of stormwater drainage systems.
Tropical regions are known for their complex ecosystems and biodiversity, which play a vital role in regulating the global climate. However, researching tropical cities can be challenging due to the need for multi-disciplinary and multi-dimensional approaches. In this study, we conducted a bibliometric analysis to gain a structured understanding of the developments and characteristics of tropical cities research in the last decade. We identified the fundamental influences in tropical cities research, based on four major sub-topics: climate change, sustainable urbanization, protecting biodiversity, and urban resource management. We examined the connections between these themes and performed a systematic literature review on each. Our analysis provides a comprehensive trend analysis of tropical cities, both quantitatively and qualitatively. Our findings aim to provide a solid foundation for bridging the gaps for future crosscutting research.
This study aims to present how continuous and systematic monitoring in representative and experimental watersheds can help form high-level professionals and researchers in water resources, based on a case study of the Onça Creek Watershed (OCW). Through a historical survey of the monitoring network and the scientific studies carried out in the area, we identified people and map their geographical and professional location, to analyze the impact and importance of this area for the water resources community. We identified 90 scientific studies already developed at the OCW, which resulted in 22 masters and 4 Ph.D. students formed, involving another 33 external collaborators. We observed that 85% of the trained professionals continue to exercise functions related to water resources, in public and private institutions, throughout Brazil and abroad. We highlight the importance of financial support from research and development agencies, both for the monitoring network expansion and the training students' scholarships. We believe that the involvement of water resources graduate programs can be a way to increase the number of experimental and representative watersheds monitored in Brazil.
Automated soil moisture systems are commonly used in precision agriculture. Using low-cost sensors, the spatial extension can be maximized, but the accuracy might be reduced. In this paper, we address the trade-off between cost and accuracy comparing low-cost and commercial soil moisture sensors. The analysis is based on the capacitive sensor SKU:SEN0193 tested under lab and field conditions. In addition to individual calibration, two simplified calibration techniques are proposed: universal calibration, based on all 63 sensors, and a single-point calibration using the sensor response in dry soil. During the second stage of testing, the sensors were coupled to a low-cost monitoring station and installed in the field. The sensors were capable of measuring daily and seasonal oscillations in soil moisture resulting from solar radiation and precipitation. The low-cost sensor performance was compared to commercial sensors based on five variables: (1) cost, (2) accuracy, (3) qualified labor demand, (4) sample volume, and (5) life expectancy. Commercial sensors provide single-point information with high reliability but at a high acquisition cost, while low-cost sensors can be acquired in larger numbers at a lower cost, allowing for more detailed spatial and temporal observations, but with medium accuracy. The use of SKU sensors is then indicated for short-term and limited-budget projects in which high accuracy of the collected data is not required.
Aos meus pais, Fernando e Márcia, que nunca mediram esforços para me apoiar durante esses anos.À minha companheira Lara, por ser meu porto seguro. Seu incentivo, compreensão e confiança tornaram os dias mais fáceis e agradáveis.Aos demais familiares, em especial meu irmão Fernando e minha cunhada Samantha, que, apesar da distância, sempre demonstraram incentivo.Ao professor Edson Wendland, pela orientação e oportunidade concedida de realizar esta pesquisa.Um agradecimento especial ao amigo Alan Reis, por sempre se mostrar disposto, paciente e atencioso durante as inúmeras horas de conversa que contribuíram com o amadurecimento desta pesquisa.Ao conterrâneo Érick, pelas sugestões que contribuíram com o aprimoramento desta dissertação, e também pela amizade e ótimas risadas do dia a dia.Agradeço também aos demais amigos do time de monitoramento da Bacia do Ribeirão da Onça, Rubens, Gescilam e Yuri, que tornaram os dias de campo mais leves.Ao Dimaghi, pela ajuda fornecida durante o manuseio dos sensores utilizados no trabalho.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.