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

Chagas, Vinícius B. P.; Chaffe, Pedro Luiz Borges; Addor, Nans; Fan, Fernando Mainardi; Fleischmann, Ayan Santos; Paiva, Rodrigo Cauduro Dias de; Siqueira, Vinícius Alencar

doi:10.5194/essd-2020-67

Cited by 18 publications

(27 citation statements)

References 46 publications

(66 reference statements)

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“…This 19-years period still allows for proper calibration and validation of the models. (Chagas et al, 2020). This product has shown good performance in several Brazilian regions (e.g., Paca et al, 2020;Paredes-Trejo et al, 2017), although it was also shown that CHIRPS has the tendency to underestimate extreme precipitation events across Brazil (Cavalcante et al, 2020).…”

Section: Study Area and Datamentioning

confidence: 98%

“…Our study includes (partly nested) catchments across Brazil, as shown in Figure 1. These are the catchments in the CAMELS-BR data set that have a reservoir capacity greater than zero (Chagas et al, 2020). Some cross-boundary catchments that only lay partly in Brazil are also included in this data set.…”

Section: Study Area and Datamentioning

confidence: 99%

“…The lake-like reservoirs require information about the weir coefficient (C; default 0.6), crest width (calibrated), maximum depth (h) and surface area (A). A (km 2 ) and h (m) can be calculated from the reservoir capacity (V, in 10 6 m 3 ) by reversing the equations given by Chagas et al (2020):…”

Section: Hbv-ec and Gr4j Models And Scenariosmentioning

confidence: 99%

“…In turn, these reservoirs have significant impacts on downstream hydrology (e.g., Almeida et al, 2020;Cavalcante et al, 2020;Dantas et al, 2020;Fantin-Cruz et al, 2015;Souza Filho, 2009) and ecology: they can lead to flooding of natural habitats, interfere with the migratory cycle of fish and alter the transport of sediments and nutrients (Best, 2019;Latrubesse et al, 2017). The recently released Catchment Attributes and MEteorology for Large-sample Studies -Brazil (CAMELS-BR) data set, introduced by Chagas et al (2020), contains both data on total reservoir capacity and hydrometeorological time series in Brazilian catchments. These data offer new opportunities to investigate how including reservoir dynamics in the hydrological model representation affects the model performance in a large-scale modeling exercise across Brazil.…”

Section: Introductionmentioning

confidence: 99%

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Accuracy vs Realism: Does including reservoirs improve hydrological models?

Langen¹,

Emmerik²,

Melsen³

et al. 2021

Preprint

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Brazil has invested considerably in the reservoir construction during the past decades, mainly for irrigation and hydro-power generation. Despite their large impact on catchment hydrology, reservoir dynamics are often not included in hydrological models due to their complexity. In this study, we investigated the effect of including reservoir dynamics (realism) in hydrological models on the model performance (accuracy). Combined, realism and accuracy form the model fidelity. We used the HBV-EC and GR4J models to simulate hydrological processes and daily streamflow of 403 catchments across Brazil in two scenarios, with and without reservoirs. The model performances were assessed with the Kling Gupta Efficiency (KGE) and its components, and were compared between the models and scenarios. We found a significant increase in the HBV-EC model performance when the reservoirs were taken into account, although the overall performance was relatively poor. The average KGE increased from 0.21 without the reservoirs to 0.40 with the reservoirs. The GR4J model, on the other hand, showed better overall performance, but without the improvement when including the reservoirs; the average KGE slightly decreased from 0.57 to 0.56. In the catchments with the largest reservoir capacity, HBV-EC in the scenario with reservoirs outperformed GR4J in both scenarios. We note that better model performance can still be obtained with a smaller spatial scale or other methods of including reservoirs, which require more data and detailed studies. With this paper, we demonstrate that model performance can improve when including reservoir dynamics, but this depends on model structure and does not always increase model fidelity.

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Section: Study Area and Datamentioning

confidence: 98%

Section: Study Area and Datamentioning

confidence: 99%

Section: Hbv-ec and Gr4j Models And Scenariosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Accuracy vs Realism: Does including reservoirs improve hydrological models?

Langen¹,

Emmerik²,

Melsen³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The CAMELS-BR dataset is freely available at https://doi.org/10.5281/zenodo.3709337 (Chagas et al, 2020). The files provided are (i) the 65 attributes in a zip file, (ii) the daily time series in zip files, (iii) the catchment boundaries used to compute the attributes and extract the time series computed by Do et al (2018) and Gudmundsson et al (2018), and (iv) a readme file.…”

Section: Data Availabilitymentioning

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

Self Cite

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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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Challenges in modeling and predicting floods and droughts: A review

et al. 2021

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Predictions of floods, droughts, and fast drought‐flood transitions are required at different time scales to develop management strategies targeted at minimizing negative societal and economic impacts. Forecasts at daily and seasonal scale are vital for early warning, estimation of event frequency for hydraulic design, and long‐term projections for developing adaptation strategies to future conditions. All three types of predictions—forecasts, frequency estimates, and projections—typically treat droughts and floods independently, even though both types of extremes can be studied using related approaches and have similar challenges. In this review, we (a) identify challenges common to drought and flood prediction and their joint assessment and (b) discuss tractable approaches to tackle these challenges. We group challenges related to flood and drought prediction into four interrelated categories: data, process understanding, modeling and prediction, and human–water interactions. Data‐related challenges include data availability and event definition. Process‐related challenges include the multivariate and spatial characteristics of extremes, non‐stationarities, and future changes in extremes. Modeling challenges arise in frequency analysis, stochastic, hydrological, earth system, and hydraulic modeling. Challenges with respect to human–water interactions lie in establishing links to impacts, representing human–water interactions, and science communication. We discuss potential ways of tackling these challenges including exploiting new data sources, studying droughts and floods in a joint framework, studying societal influences and compounding drivers, developing continuous stochastic models or non‐stationary models, and obtaining stakeholder feedback. Tackling one or several of these challenges will improve flood and drought predictions and help to minimize the negative impacts of extreme events. This article is categorized under: Science of Water > Science of Water

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

Cited by 18 publications

References 46 publications

Accuracy vs Realism: Does including reservoirs improve hydrological models?

Accuracy vs Realism: Does including reservoirs improve hydrological models?

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

Challenges in modeling and predicting floods and droughts: A review

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