Solar Radiation Models and Gridded Databases to Fill Gaps in Weather Series and to Project Climate Change in Brazil

Bender, Fabiani Denise; Sentelhas, Paulo César

doi:10.1155/2018/6204382

Cited by 42 publications

(10 citation statements)

References 49 publications

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“…The Budyko hypothesis (Budyko, 1948(Budyko, , 1974 considers that the ratio between the long-term annual actual evapotranspiration (ET) and precipitation (P ) is a function of the ratio between the long-term potential evapotranspiration (PET) and precipitation (P ). The Budyko framework has been used to assess global impacts of climate change on water resources (Berghuijs et al, 2017;Roderick et al, 2014) and to gain further insight into the water balance controls at mean annual timescales (Donohue et al, 2007;Berghuijs et al, 2017;Meira Neto et al, 2020).…”

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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show abstract

“…Other uses of this dataset include the evaluation of precipitation from downscaled-global circulation models (Almagro et al, 2020), as well as other meteorological variables used in regional studies (Battisti et al, 2019;Bender and Sentelhas, 2018; https://doi.org/10.5194/hess-2020-521 Preprint. Discussion started: 14 October 2020 c Author(s) 2020.…”

Section: Comparison With the Camels-br And Broader Implications For Hmentioning

confidence: 99%

CABra: a novel large-sample dataset for Brazilian catchments

Almagro¹,

Oliveira²,

Neto³

et al. 2020

Preprint

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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 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. The 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.4070147.

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“…Daily air maximum and minimum temperatures, accumulated thermal time during the sugarcane cycle, and solar radiation (daily and accumulated) data from XAVIER and NASA presented a satisfactory performance compared to MWD at the four assessed sugarcane growing regions (Tables 1 and 3). The satisfactory quality of solar radiation and temperature data of GWD has been shown in many studies in Brazil (Battisti et al, 2019;Bender and Sentelhas, 2018;Duarte and Sentelhas, 2020;Monteiro et al, 2018;Valeriano et al, 2018) and around the world (Bai et al, 2010;Mourtzinis et al, 2017;Ojeda et al, 2017;Van Wart et al, 2013a;White et al, 2011). It is not surprising that key phenological stages of annual crops were well simulated with GWD in thermal time-dependent crop models (Battisti et al, 2019;Mourtzinis et al, 2017;van Bussel et al, 2011), similar to what was found in this study for the total number of sugarcane leaves, which is an important canopy development state variable of the APSIM-Sugar model (Table 4).…”

Section: Discussionsupporting

confidence: 87%

“…seasonal weather data and their impacts on crop yield simulations were assessed recently in Brazil (Battisti et al, 2019;Bender and Sentelhas, 2018;Duarte and Sentelhas, 2020;Monteiro et al, 2018;Valeriano et al, 2018). A common issue was the poor performance of rainfall predictions, even when data were aggregated at a 10-day scale, affecting negatively crop yield simulations in many sites assessed by Monteiro et al (2018) and Battisti et al (2019).…”

Section: Introductionmentioning

confidence: 99%

Sugarcane variety trait modelling: evaluating and improving the APSIM-Sugar model for simulating crop performance under current and future climates across Brazil

Dias¹

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Sugarcane variety trait modelling: evaluating and improving the APSIM-Sugar model for simulating crop performance under current and future climates across Brazil Brazil is the largest sugarcane producer in the world and the production systems across the country are very complex with large spatial and temporal variations in yields. This is a result of large Genotype × Environment × Management (G × E × M) interactions that ultimately affect crop performance. Moreover, likely changes in the climate conditions are expected in the future imposing additional challenges to sugarcane production. Process-based crop modelling is scientifically accepted as a way to understand those interactions. Upon that, this thesis aimed to integrate field experiments datasets with the APSIM-Sugar model for better understanding the G × E × M interactions in Brazilian cropping systems. The capability of the model was first evaluated with default settings and then modifications and traits were proposed to effectively predict G (varieties) differences. With APSIM-Sugar upgraded, an up-to-date projection of future climate impacts on sugarcane yields across Brazil was performed. The sugarcane dataset came from large field experiments carried out between 2012 and 2017 at two tropical sites in Brazil: Guadalupe, in the state of Piauí (PI), and São Romão, in the state of Minas Gerais (MG), where several varieties were cultivated under non-limiting (potential) conditions. These data were analysed and employed to characterise variety traits for use in APSIM-Sugar. Substantial changes were required to enable the model to simulate canopy development and stalk yield appropriately. The outcomes from the modelling studies showed that the model is now able, although yet empirically, to account for ageing processes, known as the reduced growth phenomenon, to get better yield predictions in high yielding environments, as well as the ability to distinguish between varieties when it comes to yield gains above ~ 150 t/ha (~ 40 t/ha in dry mass). The findings suggest it is reasonable to hypothesise that the APSIM-Sugar modifications and traits are plausible and are an important step for unravelling G × E × M interactions to improve the performance of the sugarcane industry. Another step of this thesis, before applying APSIM-Sugar, was to evaluate gridded weather datasets (NASA/POWER and other developed for Brazil referred to 'XAVIER') as alternative climate inputs in the model. The results indicated these data sources should be employed with caution when the purpose is to simulate sugarcane yield because of the poor performance of gridded rainfall, at least for Center-South Brazil. Lastly, a climate change impact assessment was performed with APSIM-Sugar considering new features and traits for 10 Brazilian sites, being six under rainfed and four under fully-irrigated conditions. Five global climate models were selected to represent basic classes of climate changes: relatively cool/wet; cool/dry; middle; hot/wet; and hot/dry, for four future scenarios. These c...

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Solar Radiation Models and Gridded Databases to Fill Gaps in Weather Series and to Project Climate Change in Brazil

Cited by 42 publications

References 49 publications

CABra: a novel large-sample dataset for Brazilian catchments

CABra: a novel large-sample dataset for Brazilian catchments

CABra: a novel large-sample dataset for Brazilian catchments

Sugarcane variety trait modelling: evaluating and improving the APSIM-Sugar model for simulating crop performance under current and future climates across Brazil

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