ETo‐Brazil: A Daily Gridded Reference Evapotranspiration Data Set for Brazil (2000–2018)

Althoff, Daniel; Dias, Santos Henrique Brant; Filgueiras, Roberto; Rodrigues, Lineu Neiva

doi:10.1029/2020wr027562

Cited by 33 publications

(32 citation statements)

References 41 publications

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“…To overcome this limitation, we calculated the daily potential evapotranspiration (PET) using three different widely used methods based on energy balance and transfer mass, radiation, and temperature, using meteorological variables from the ERA5 and the ensemble products as inputs. These three newly products are, to the best of our knowledge, the most extent datasets of potential evapotranspiration for Brazil, covering a larger period than existent products, such as the one introduced in Althoff et al (2020) and Xavier et al (2016).…”

Section: Methodsmentioning

confidence: 99%

CABra: a novel large-sample dataset for Brazilian catchments

Almagro

Oliveira

Neto

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. In this paper, we present the Catchments Attributes for Brazil (CABra), which is a large-sample dataset for Brazilian catchments that includes long-term data (30 years) for 735 catchments in eight main catchment attribute classes (climate, streamflow, groundwater, geology, soil, topography, land cover, and hydrologic disturbance). We have collected and synthesized data from multiple sources (ground stations, remote sensing, and gridded datasets). To prepare the dataset, we delineated all the catchments using the Multi-Error-Removed Improved-Terrain Digital Elevation Model (MERIT DEM) and the coordinates of the streamflow stations provided by the Brazilian Water Agency, where only the stations with 30 years (1980–2010) of data and less than 10 % of missing records were included. Catchment areas range from 9 to 4 800 000 km2, and the mean daily streamflow varies from 0.02 to 9 mm d−1. Several signatures and indices were calculated based on the climate and streamflow data. Additionally, our dataset includes boundary shapefiles, geographic coordinates, and drainage area for each catchment, aside from more than 100 attributes within the attribute classes. The collection and processing methods are discussed, along with the limitations for each of our multiple data sources. CABra intends to improve the hydrology-related data collection in Brazil and pave the way for a better understanding of different hydrologic drivers related to climate, landscape, and hydrology, which is particularly important in Brazil, having continental-scale river basins and widely heterogeneous landscape characteristics. In addition to benefitting catchment hydrology investigations, CABra will expand the exploration of novel hydrologic hypotheses and thereby advance our understanding of Brazilian catchments' behavior. The dataset is freely available at https://doi.org/10.5281/zenodo.4070146 and https://thecabradataset.shinyapps.io/CABra/ (last access: 7 June 2021).

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Section: Methodsmentioning

confidence: 99%

CABra: a novel large-sample dataset for Brazilian catchments

Almagro

Oliveira

Neto

et al. 2021

Hydrol. Earth Syst. Sci.

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“…Cubist regression is chosen in this study for two reasons. First, it allowed other authors to achieve state-of-art performance within a range of studies (Noi et al 2017;Worland et al 2018;Filgueiras et al 2019;Althoff et al 2020b), and second, it provided a good trade-off between performance and computational time (Althoff et al 2020b), which was an important aspect when considering the optimization task. For more information on cubist regressions, refer to Quinlan (1993).…”

Section: The Hybrid Hydrological Modelmentioning

confidence: 99%

“…The meteorological forcings required by the models are precipitation (P) and potential evaporation (E). The datasets used to represent these variables were the GPM IMERG Final Precipitation V06 1 day (Huffman et al 2019) and the ETo-Brazil V3 (Althoff et al 2020a). The IMERG dataset has been favorably assessed across the Brazilian territory, while ETo-Brazil is a daily gridded Penman-Monteith evapotranspiration dataset developed for Brazil using machine learning models.…”

Section: Meteorological Forcing Datamentioning

confidence: 99%

Untangling hybrid hydrological models with explainable artificial intelligence

2021

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Hydrological models are valuable tools for developing streamflow predictions in unmonitored catchments to increase our understanding of hydrological processes. A recent effort has been made in the development of hybrid (conceptual/machine learning) models that can preserve some of the hydrological processes represented by conceptual models and can improve streamflow predictions. However, these studies have not explored how the data-driven component of hybrid models resolved runoff routing. In this study, explainable artificial intelligence (XAI) techniques are used to turn a ‘black-box’ model into a ‘glass box’ model. The hybrid models reduced the root-mean-square error of the simulated streamflow values by approximately 27, 50, and 24% for stations 17120000, 27380000, and 33680000, respectively, relative to the traditional method. XAI techniques helped unveil the importance of accounting for soil moisture in hydrological models. Differing from purely data-driven hydrological models, the inclusion of the production storage in the proposed hybrid model, which is responsible for estimating the water balance, reduced the short- and long-term dependencies of input variables for streamflow prediction. In addition, soil moisture controlled water percolation, which was the main predictor of streamflow. This finding is because soil moisture controls the underlying mechanisms of groundwater flow into river streams.

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“…The algorithm of GPM-IMERG aims to produce rainfall estimates every 30 min with a spatial resolution of 0.1 • between latitudes 60 • (version 5) and global (version 6), using all estimates of passive microwave precipitation (PMW), together with the infrared (IR) estimates available in the TRMM and GPM era (Huffman et al, 2019a,b;Huffman et al, 2020). The GPM-IMERG has been successfully used in the Amazon Basin (Oliveira et al, 2018;Althoff et al, 2020;da S. Freitas et al, 2020).…”

Section: Precipitation Datasetsmentioning

confidence: 99%

On the Hydroclimate-Vegetation Relationship in the Southwestern Amazon During the 2000–2019 Period

Gutierrez-Cori

Espinoza

et al. 2021

Front. Water

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The southern Amazonia is undergoing a major biophysical transition, involving changes in land use and regional climate. This study provides new insights on the relationship between hydroclimatic variables and vegetation conditions in the upper Madeira Basin (~1 × 106 km2). Vegetative dynamics are characterised using the normalized difference vegetation index (NDVI) while hydroclimatic variability is analysed using satellite-based precipitation, observed river discharge, satellite measurements of terrestrial water storage (TWS) and downward shortwave radiation (DSR). We show that the vegetation in this region varies from energy-limited to water-limited throughout the year. During the peak of the wet season (January-February), rainfall, discharge and TWS are negatively correlated with NDVI in February-April (r = −0.48 to −0.65; p < 0.05). In addition, DSR is positively correlated with NDVI (r = 0.47–0.54; p < 0.05), suggesting that the vegetation is mainly energy-limited during this period. Outside this period, these correlations are positive for rainfall, discharge and TWS (r = 0.55–0.88; p < 0.05), and negative for DSR (r = −0.47 to −0.54; p < 0.05), suggesting that vegetation depends mainly on water availability, particularly during the vegetation dry season (VDS; late June to late October). Accordantly, the total rainfall during the dry season explains around 80% of the VDS NDVI interannual variance. Considering the predominant land cover types, differences in the hydroclimate-NDVI relationship are observed. Evergreen forests (531,350 km2) remain energy-limited during the beginning of the dry season, but they become water-limited at the end of the VDS. In savannas and flooded savannas (162,850 km2), water dependence occurs months before the onset of the VDS. These differences are more evident during extreme drought years (2007, 2010, and 2011), where regional impacts on NDVI were stronger in savannas and flooded savannas (55% of the entire surface of savannas) than in evergreen forests (40%). A spatial analysis reveals that two specific areas do not show significant hydroclimatic-NDVI correlations during the dry season: (i) the eastern flank of the Andes, characterised by very wet conditions, therefore the vegetation is not water-limited, and (ii) recent deforested areas (~42,500 km2) that break the natural response in the hydroclimate-vegetation system. These findings are particularly relevant given the increasing rates of deforestation in this region.

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ETo‐Brazil: A Daily Gridded Reference Evapotranspiration Data Set for Brazil (2000–2018)

Cited by 33 publications

References 41 publications

CABra: a novel large-sample dataset for Brazilian catchments

CABra: a novel large-sample dataset for Brazilian catchments

Untangling hybrid hydrological models with explainable artificial intelligence

On the Hydroclimate-Vegetation Relationship in the Southwestern Amazon During the 2000–2019 Period

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