Sediment flows in South America supported by daily hydrologic-hydrodynamic modeling

Fagundes, Hugo de Oliveira; Fan, Fernando Mainardi; Paiva, Rodrigo Cauduro Dias de; Siqueira, Vinícius Alencar; Buarque, Diogo Costa; Kornowski, Luísa Weizenmann; Laipelt, Leonardo; Collischonn, Walter

doi:10.1002/essoar.10503046.1

Cited by 5 publications

(5 citation statements)

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“…Examples include the introduction of basin‐scale inundation schemes that were later introduced to other river basins (Andriambeloson et al., 2020; Paris et al., 2020), at continental scale (Siqueira et al., 2018) and at the global‐scale (Alkama et al., 2010; Decharme et al., 2012; Yamazaki et al., 2011). Recent advances in large‐scale sediment transport using RS observations and modeling followed a similar path, with pioneering works in Amazon (Section 4.4) being followed by progress for all of South America (Fagundes et al., 2021).…”

Section: Synthesis Of Scientific Advances Future Challenges and Prioritiesmentioning

confidence: 93%

“…The move to hyper‐resolution models has been promoted at a global scale due to the development of new numerical techniques, equation sets, and software engineering, as well as increased computing power (Bates et al., 2018). Such modeling systems could then be coupled to models of other processes, as recently done by researchers aiming at understanding flooding impacts on photosynthesis and biosphere in general (Aderson de Castro et al., 2018), feedbacks between surface waters and atmosphere (Santos et al., 2019), sediment exports and floodplain trapping (Fagundes et al., 2021; Rudorff et al., 2018), carbon storage and emissions through wetlands and uplands (Hastie et al., 2019; Lauerwald et al., 2020), and dynamics of biogeochemistry cycles at the basin scale or over wetlands (Guilhen et al., 2020). All these efforts will require additional RS data and will move forward our predictability of the effects of ongoing environmental changes in the Amazon basin.…”

Section: Integrative and Interdisciplinary Studiesmentioning

confidence: 99%

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Amazon Hydrology From Space: Scientific Advances and Future Challenges

Fassoni‐Andrade

Fleischmann

Papa

et al. 2021

Reviews of Geophysics

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As the largest river basin on Earth, the Amazon is of major importance to the world's climate and water resources. Over the past decades, advances in satellite‐based remote sensing (RS) have brought our understanding of its terrestrial water cycle and the associated hydrological processes to a new era. Here, we review major studies and the various techniques using satellite RS in the Amazon. We show how RS played a major role in supporting new research and key findings regarding the Amazon water cycle, and how the region became a laboratory for groundbreaking investigations of new satellite retrievals and analyses. At the basin‐scale, the understanding of several hydrological processes was only possible with the advent of RS observations, such as the characterization of "rainfall hotspots" in the Andes‐Amazon transition, evapotranspiration rates, and variations of surface waters and groundwater storage. These results strongly contribute to the recent advances of hydrological models and to our new understanding of the Amazon water budget and aquatic environments. In the context of upcoming hydrology‐oriented satellite missions, which will offer the opportunity for new synergies and new observations with finer space‐time resolution, this review aims to guide future research agenda toward integrated monitoring and understanding of the Amazon water from space. Integrated multidisciplinary studies, fostered by international collaborations, set up future directions to tackle the great challenges the Amazon is currently facing, from climate change to increased anthropogenic pressure.

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Section: Synthesis Of Scientific Advances Future Challenges and Prioritiesmentioning

confidence: 93%

Section: Integrative and Interdisciplinary Studiesmentioning

confidence: 99%

Amazon Hydrology From Space: Scientific Advances and Future Challenges

Fassoni‐Andrade

Fleischmann

Papa

et al. 2021

Reviews of Geophysics

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“…These performances support the reliability of the model in simulating the environmental system. Thus, it has been used for several purposes, such as streamflow forecast (Siqueira et al ., 2020), climate change projections (Brêda et al ., 2020), and sediments production and transport (Fagundes et al ., 2020).…”

Section: Methodsmentioning

confidence: 99%

Assessing extreme precipitation from a regional climate model in different spatial–temporal scales: A hydrological perspective in South America

Brêda

Paiva

Chou

et al. 2022

Intl Journal of Climatology

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Global and regional climate models (GCM and RCM respectively) are the current mathematical tools used to project alterations on precipitation regimes given different greenhouse gases emissions scenarios. However, these models have specific resolutions, physical equations and numerical approaches that provide a diverse set of performances across different regions and spatialtemporal scales. In South America, most hydrological impact studies have used the Eta RCM to yield precipitation projections without a proper uncertainty analysis. It is important to acknowledge its uncertainties prior to any hydrological assessment to adequately support climate change investigations and related water decision making. Therefore, we aim to investigate how Eta extreme precipitation biases vary in different spatial-temporal scales from a hydrological perspective. Thus, we evaluate the extreme precipitation generated by the Eta RCM driven by four different GCMs. It is investigated Eta biases across different temporal (3 hr-5 days) and spatial scales (0.2-1.0 ) and how those errors affect river streamflow simulations. It is used local intensityduration-frequency (IDF) curves and gridded precipitation datasets (MSWEP and ERA5-Land) as references for Eta assessment. In general, Eta underestimates subdaily extreme precipitation across South America, regardless of the driven GCM. The median bias of a 10-year return period daily precipitation is −36 mm (1st and 3rd quantiles −58 and −17 mm) compared to MSWEP and −26 mm (1st and 3rd quantiles −45 and −11 mm) compared to ERA5-Land. However, the relative errors reduce with temporal and spatial aggregation. For example, the average bias of extreme precipitation decreases 8.4 and 5.4 percentage points from 1-to 5-day duration compared to MSWEP and ERA5-Land, respectively. The negative biases observed for precipitation ( ≈ 20%) are propagated to the flood discharges ( ≈ 40%), and these errors reduce with the drainage area. In general, there are greater biases in extreme discharges for small basins, but these errors considerably reduce for basins larger than 30,000 km 2 compared to MGB-MSWEP simulations. Compared to MGB-ERA5-Land simulations, MGB-Eta presents relatively similar errors for

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“…In the present work, a pre-analysis of field observation data inspires a single-parameter modelling approach, firstly, empirical mode decomposition (EMD) (Wu and Huang, 2004) is employed to decompose the measured SSC series into a few subseries, then spectral analysis (Sohn and Harris, 2021) demonstrates that each of the subseries well corresponds to a physical process that influences the variation of coastal SSC based on the unique periodicity information. This inspires an idea of modeling the coastal SSC with a decomposition-ensemble framework to realize single-parameter modeling which depends on historical SSC series only without considering additional environmental factors (Fagundes et al, 2021;Kim et al, 2021).…”

Section: Introductionmentioning

confidence: 99%