Spatial and seasonal patterns of flood change across Brazil

Bartiko, Daniel; Oliveira, Debora Yumi de; Bonumá, Nadia Bernardi; Chaffe, Pedro Luiz Borges

doi:10.1080/02626667.2019.1619081

Cited by 24 publications

(13 citation statements)

References 47 publications

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“…In the USA, mixed changes have been found for central and eastern regions; clearer patterns of decreasing floods have been detected in the western USA, particularly in California, in line with decreases in precipitation and catchment wetness [151][152][153][154][155][156] . In other regions of the world, decreasing trends in annual maxi mum flows have been found in northeast Brazil 157 and southeast Australia 158 as well as in China 159 and India 156 . There are also a number of regions with increasing trends such as the south of Brazil 157 and the north of Australia 158 .…”

Section: Observed Trendsmentioning

confidence: 99%

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Causes, impacts and patterns of disastrous river floods

Merz

Blöschl

Vorogushyn

et al. 2021

Nat Rev Earth Environ

282

137

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River floods caused about 7 million fatalities in the twentieth century 1 , and their direct global average annual loss (AAL) is estimated at US$ 104 billion (2015Exposure to floods is expected to grow by a factor of three by 2050 owing to increases in population and economic assets in flood-prone areas 3 . Depending on the socio-economic scenario, human losses from flooding are projected to rise by 70-83% and direct flood damage by 160-240% relative to 1976-2005, with a temperature increase of 1.5 °C (ref. 4 ). Understanding river flooding and its associated impacts are critical to effective risk reduction.River floods occur when a river overtops its banks and inundates adjacent areas. The expected impact floods have on society and the environment, often termed flood risk, results from the superposition of three components and the associated processes, which tend to be interlinked [5][6][7] , including over large distances 8 . These components are: hazards -the processes leading to high river flood levels; exposure -the elements at risk, such as population or infrastructure; and vulnerabilitythe susceptibility of the elements at risk when they are affected by a flood 2 . These components are, in turn, the compound effects of multiple processes (fig. 1).

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Section: Observed Trendsmentioning

confidence: 99%

“…In other regions of the world, decreasing trends in annual maxi mum flows have been found in northeast Brazil 157 and southeast Australia 158 as well as in China 159 and India 156 . There are also a number of regions with increasing trends such as the south of Brazil 157 and the north of Australia 158 .…”

Section: Observed Trendsmentioning

confidence: 99%

Causes, impacts and patterns of disastrous river floods

Merz

Blöschl

Vorogushyn

et al. 2021

Nat Rev Earth Environ

282

137

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“…In the case of the Paraguai River basin, Santos & Lima (2019) classified the flood producing storms and showed that a mixed distribution was useful for flood frequency analysis. A comparison of nonstationary and stationary frequency models for 275 gauge stations in Brazil showed a clear spatial pattern for the stations that had significant trends in annual maximum series -AMS (Bartiko et al, 2019). Regarding the Paraná River basin, Chan et al 2019showed that the majority of the AMS from hydropower reservoirs inflows were stationary.…”

Section: /28mentioning

confidence: 99%

Advances and challenges in the water sciences in Brazil: a community synthesis of the XXIII Brazilian Water Resources Symposium

Paiva

Chaffe

Anache

et al. 2020

RBRH

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In this paper we synthesize the special sessions of the XXIII Brazilian Water Resources Symposium 2019 in order to understand the major advances and challenges in the water sciences in Brazil. We analyzed more than 250 papers and presentations of 16 special sessions covering topics of Climate Variability and Change, Disasters, Modeling, Large Scale Hydrology, Remote Sensing, Education, and Water Resources Management. This exercise highlighted the unique diversity of natural and human water features in Brazil, that offers a great opportunity for understanding coupled hydrological and societal systems. Most contributions were related to methods and the quantification of water phenomena, therefore, there is a clear necessity for fostering more research on phenomena comprehension. There is a vast network of co-authorship among institutions but mostly from academia and with some degree of regional fragmentation. The ABRhidro community now has the challenge to enhance its collaboration network, the culture of synthesis analysis, and to build a common agenda for water resources research. It is also time for us to be aligned with the international water science community and to use our experiences to actively contribute to the tackling of global water issues.

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“…For example, multiple studies used large-sample datasets to investigate the drivers of hydrological change (e.g., Slater et al, 2015;Blöschl et al, 2019a;Gudmundsson et al, 2019), the impacts of anthropic activities on the water cycle (e.g., Milliman et al, 2008;Hoekstra and Mekonnen, 2012;Montanari et al, 2013), hydrological similarity and classification (e.g., Berghuijs et al, 2014;Sawicz et al, 2014;Knoben et al, 2018), predictions in ungauged basins (e.g., Yadav et al, 2007;Ehret et al, 2014;Singh et al, 2014), areas where ex-V. B. P. Chagas et al: CAMELS-BR treme events are a concern (e.g., Van Lanen et al, 2013;Villarini, 2016;Woldemeskel and Sharma, 2016), and the prediction of future hydrological change (e.g., Luke et al, 2017;Zscheischler et al, 2018). Moreover, large-sample hydrology is needed for evaluation of continental to global hydrological models; to identify limitations in model structure, parameterization, and forcing according to geographic and climatic regions (Haddeland et al, 2011;Gudmundsson et al, 2012;Beck et al, 2017a;Zhao et al, 2017;Siqueira et al, 2018;Veldkamp et al, 2018); to estimate uncertainty in model estimates (e.g., Müller Schmied et al, 2014;Beck et al, 2016;Hirpa et al, 2018); and to make use of data assimilation techniques (e.g., Wongchuig et al, 2019). Better predictions in such models allow for the quantification of water resources availability over large scales and are fundamental for nationwide water resources planning and management (Schewe et al, 2014;Döll et al, 2016;Alfieri et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Further, the difficulty of accessing national meteorological daily time series has led users to compute them from other gridded global databases (e.g., Xavier et al, 2016;Beck et al, 2017c;Sun et al, 2018). All these difficulties hinder large-sample hydrological studies in Brazil where, unsurprisingly, nationwide studies (e.g., Siqueira et al, 2018;Bartiko et al, 2019) are less common than in North America or Europe. Consequently, studies in Brazil generally include only a reduced number of stream gauges and catchment attributes and are restricted to specific regions, such as the Amazon (e.g., Tomasella et al, 2011;Paiva et al, 2013;Latrubesse et al, 2017;Levy et al, 2018) or the La Plata basin (e.g., Collischonn et al, 2001;Pasquini and Depetris, 2007;Melo et al, 2016;Lima et al, 2017;Chagas and Chaffe, 2018).…”

Section: Introductionmentioning

confidence: 99%

CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil

Chagas

Chaffe

Addor

et al. 2020

Earth Syst. Sci. Data

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Abstract. We introduce a new catchment dataset for large-sample hydrological studies in Brazil. This dataset encompasses daily time series of observed streamflow from 3679 gauges, as well as meteorological forcing (precipitation, evapotranspiration, and temperature) for 897 selected catchments. It also includes 65 attributes covering a range of topographic, climatic, hydrologic, land cover, geologic, soil, and human intervention variables, as well as data quality indicators. This paper describes how the hydrometeorological time series and attributes were produced, their primary limitations, and their main spatial features. To facilitate comparisons with catchments from other countries, the data follow the same standards as the previous CAMELS (Catchment Attributes and MEteorology for Large-sample Studies) datasets for the United States, Chile, and Great Britain. CAMELS-BR (Brazil) complements the other CAMELS datasets by providing data for hundreds of catchments in the tropics and the Amazon rainforest. Importantly, precipitation and evapotranspiration uncertainties are assessed using several gridded products, and quantitative estimates of water consumption are provided to characterize human impacts on water resources. By extracting and combining data from these different data products and making CAMELS-BR publicly available, we aim to create new opportunities for hydrological research in Brazil and facilitate the inclusion of Brazilian basins in continental to global large-sample studies. We envision that this dataset will enable the community to gain new insights into the drivers of hydrological behavior, better characterize extreme hydroclimatic events, and explore the impacts of climate change and human activities on water resources in Brazil. The CAMELS-BR dataset is freely available at https://doi.org/10.5281/zenodo.3709337 (Chagas et al., 2020).

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Spatial and seasonal patterns of flood change across Brazil

Cited by 24 publications

References 47 publications

Causes, impacts and patterns of disastrous river floods

Causes, impacts and patterns of disastrous river floods

Advances and challenges in the water sciences in Brazil: a community synthesis of the XXIII Brazilian Water Resources Symposium

CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil

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