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Daniel Althoff
Department of Agricultural Engineering, Federal University of Viçosa (UFV), Av. Peter Henry Rolfs s.n, 36570-900 Viçosa, Minas Gerais, Brazil

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Journal article
Published: 19 July 2021 in Journal of South American Earth Sciences
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Rainfall is a major component of the hydrological cycle. The lack of basic information on rainfall spatial variability is a major source of uncertainty in many fields of studies, e.g., meteorology, hydrology, and agriculture. Satellite rainfall measurements are becoming increasingly popular for their extensive database and reliable estimates. The objective of this study was to use the Tropical Rainfall Measuring Mission (TRMM) dataset along with rain gauges to characterize aspects of rainfall spatial variability and discuss the possible impacts from recent trends in the Brazilian savannah biome (Cerrado). Gauge stations were used to assess TRMM bias error and calibrate data for further analyses. Rainfall rates and their spatial variability showed a strong relationship to the transition zones between different biomes. Rainfall showed a decreasing trend for the eastern region of the Cerrado biome, a region characterized by a recent and significant expansion of crop areas. These trends agree with results from different studies which highlight the current drawdown of groundwater levels and reduced discharge, and possible lengthening of the dry season in the long run. As many conflicts have already been documented for this region, these decreasing trends are alarming for urgent and consistent hydroclimatic monitoring, and improved water resources planning and management. Positive trends for rainfall in the central Cerrado are likely a momentary recovery of recent-period drought.

ACS Style

Daniel Althoff; Helizani Couto Bazame; Roberto Filgueiras; Lineu Neiva Rodrigues. Assessing rainfall spatial variability in the Brazilian savanna region with TMPA rainfall dataset. Journal of South American Earth Sciences 2021, 111, 103482 .

AMA Style

Daniel Althoff, Helizani Couto Bazame, Roberto Filgueiras, Lineu Neiva Rodrigues. Assessing rainfall spatial variability in the Brazilian savanna region with TMPA rainfall dataset. Journal of South American Earth Sciences. 2021; 111 ():103482.

Chicago/Turabian Style

Daniel Althoff; Helizani Couto Bazame; Roberto Filgueiras; Lineu Neiva Rodrigues. 2021. "Assessing rainfall spatial variability in the Brazilian savanna region with TMPA rainfall dataset." Journal of South American Earth Sciences 111, no. : 103482.

Journal article
Published: 15 July 2021 in Journal of Hydrology
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This study provides guidelines for the selection of proper goodness-of-fit criteria for the calibration and evaluation of hydrological models. Popular goodness-of-fit criteria and good practices for hydrological modeling are reviewed. The review discusses the advantages and disadvantages of several criteria and is followed by a case study that focuses on the review’s main findings. The main recommendation is for hydrologists to avoid using threshold values to assess model performance and preferably set a proper benchmark series. The case study was developed using the GR5J hydrological model and data from 179 watersheds in the Brazilian Cerrado biome. Several single- and multi-objective functions are used in optimization runs to assess the outcome for different goodness-of-fit criteria. The model performance is evaluated for each optimization run considering overall conditions, i.e., entire time series, and conditions under low- and peak-flow conditions. The study case reinforces that the popular Nash-Sutcliffe efficiency index should be avoided as an objective function. Alternatively, the Kling-Gupta efficiency index showed to be a more reliable criterion, resulting in lower bias for both calibration and validation, and balanced results for both low- and peak-flow conditions. Additionally, combining different criteria in multi-objective functions can result in robust trade-offs. General guidelines are summarized and additional emphasis is given to tropical watersheds where low flows deserve due attention.

ACS Style

Daniel Althoff; Lineu Neiva Rodrigues. Goodness-of-fit criteria for hydrological models: Model calibration and performance assessment. Journal of Hydrology 2021, 600, 126674 .

AMA Style

Daniel Althoff, Lineu Neiva Rodrigues. Goodness-of-fit criteria for hydrological models: Model calibration and performance assessment. Journal of Hydrology. 2021; 600 ():126674.

Chicago/Turabian Style

Daniel Althoff; Lineu Neiva Rodrigues. 2021. "Goodness-of-fit criteria for hydrological models: Model calibration and performance assessment." Journal of Hydrology 600, no. : 126674.

Journal article
Published: 01 July 2021 in Journal of Hydrologic Engineering
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ACS Style

Daniel Althoff; Rayssa Balieiro Ribeiro; Lineu Neiva Rodrigues. Erratum for “Gauging the Ungauged: Regionalization of Flow Indices at Grid Level” by Daniel Althoff, Rayssa Balieiro Ribeiro, and Line Neiva Rodrigues. Journal of Hydrologic Engineering 2021, 26, 08221001 .

AMA Style

Daniel Althoff, Rayssa Balieiro Ribeiro, Lineu Neiva Rodrigues. Erratum for “Gauging the Ungauged: Regionalization of Flow Indices at Grid Level” by Daniel Althoff, Rayssa Balieiro Ribeiro, and Line Neiva Rodrigues. Journal of Hydrologic Engineering. 2021; 26 (7):08221001.

Chicago/Turabian Style

Daniel Althoff; Rayssa Balieiro Ribeiro; Lineu Neiva Rodrigues. 2021. "Erratum for “Gauging the Ungauged: Regionalization of Flow Indices at Grid Level” by Daniel Althoff, Rayssa Balieiro Ribeiro, and Line Neiva Rodrigues." Journal of Hydrologic Engineering 26, no. 7: 08221001.

Journal article
Published: 26 May 2021 in Advances in Water Resources
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The impacts of land cover change have traditionally been assessed in hydrological modeling with a priori knowledge, e.g., using methods based on the curve number, or by calibrating hydrological models over different time periods. However, how hydrological processes respond to such changes is extremely context-dependent. Thus, there is an opportunity for the development of hydrological models that can learn from large hydrological data sets under the context of severe environmental changes. In this study, a single regional hydrological model is developed based on long short-term memory (LSTM) neural networks using different input configurations. One model considers only meteorological forcings as inputs (I1), another model considers meteorological forcings and static catchment attributes (I2), and a third model also considers meteorological forcings and catchment attributes but where the land cover characteristics are dynamic (I3). The models are trained using information from 411 catchments in the Brazilian Cerrado biome. The data set includes, for each catchment, the daily streamflow observations (target), daily precipitation and reference evapotranspiration (meteorological forcings), and 21 catchment attributes including topography, climate indices, soil characteristics, and land cover characteristics. Considering catchment attributes increases the performance of the LSTM model (I2 and I3 median KGE: 0.69). Considering the land use cover characteristics as dynamic improves the predictions under low-flow conditions (I3 median rNSE: 0.62) when compared to the model considering such characteristics as static (I2 median rNSE: 0.53). This study also uses the deep network with the integrated gradients technique to explore the contribution of the catchment characteristics to streamflow and the number of time steps of influence for the deep network in different regions.

ACS Style

Daniel Althoff; Lineu Neiva Rodrigues; Demetrius David da Silva. Addressing hydrological modeling in watersheds under land cover change with deep learning. Advances in Water Resources 2021, 154, 103965 .

AMA Style

Daniel Althoff, Lineu Neiva Rodrigues, Demetrius David da Silva. Addressing hydrological modeling in watersheds under land cover change with deep learning. Advances in Water Resources. 2021; 154 ():103965.

Chicago/Turabian Style

Daniel Althoff; Lineu Neiva Rodrigues; Demetrius David da Silva. 2021. "Addressing hydrological modeling in watersheds under land cover change with deep learning." Advances in Water Resources 154, no. : 103965.

Journal article
Published: 01 April 2021 in Journal of Hydrologic Engineering
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Despite advances in data collection and modeling, there still remains a significant gap in predicting flows in ungauged locations. This study presents an approach for using gridded data to regionalize flow indices (long-term average streamflows and flows that are equaled or exceeded for 95% of the time) along an entire streamflow network grid. The methodology is based on using the Terrain Analysis Using Digital Elevation Model (TauDEM) toolset to obtain input variables for the regionalization model as averaged to the catchment area of each pixel in the streamflow network grid. The variables used as input for the regionalization regression were the catchment area of each pixel in the streamflow network grid, average slope, annual rainfall, and annual evapotranspiration of the corresponding catchment area. These variables were downscaled to the resolution of the Multi-Error-Removed Improved-Terrain (MERIT) digital elevation model (DEM) (90×90 m). The result was a 90×90-m stream network grid with corresponding values for the modeled flow indices. Thus, the use of satellite-derived or gridded products improved the capacity of the grid to capture spatial patterns that fail to be captured in poorly gauged watersheds. The methodology can be adapted to other cases, for example, to model the parameters of hydrological models along the streamflow network grid. The prediction of the streamflow/flow indices over the entire basin can provide water managers with greater confidence in granting water rights and in sustainably managing the resources in ungauged catchments.

ACS Style

Daniel Althoff; Rayssa Balieiro Ribeiro; Line Neiva Rodrigues. Gauging the Ungauged: Regionalization of Flow Indices at Grid Level. Journal of Hydrologic Engineering 2021, 26, 04021008 .

AMA Style

Daniel Althoff, Rayssa Balieiro Ribeiro, Line Neiva Rodrigues. Gauging the Ungauged: Regionalization of Flow Indices at Grid Level. Journal of Hydrologic Engineering. 2021; 26 (4):04021008.

Chicago/Turabian Style

Daniel Althoff; Rayssa Balieiro Ribeiro; Line Neiva Rodrigues. 2021. "Gauging the Ungauged: Regionalization of Flow Indices at Grid Level." Journal of Hydrologic Engineering 26, no. 4: 04021008.

Journal article
Published: 04 March 2021 in Computers and Electronics in Agriculture
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In this study, an algorithm is implemented with a computer vision model to detect and classify coffee fruits and map the fruits maturation stage during harvest. The main contribution of this study is with respect to the assignment of geographic coordinates to each frame, which enables the mapping of detection summaries across coffee rows. The model used to detect and classify coffee fruits was implemented using the Darknet, an open source framework for neural networks written in C. The coffee fruits detection and classification were performed using the object detection system named YOLOv3-tiny. For this study, 90 videos were recorded at the end of the discharge conveyor of a coffee harvester during the 2020 harvest of arabica coffee (Catuaí 144) at a commercial area in the region of Patos de Minas, in the state of Minas Gerais, Brazil. The model performance peaked around the ~3300th iteration when considering an image input resolution of 800 × 800 pixels. The model presented an mAP of 84%, F1-Score of 82%, precision of 83%, and recall of 82% for the validation set. The average precision for the classes of unripe, ripe, and overripe coffee fruits was 86%, 85%, and 80%, respectively. As the algorithm enabled the detection and classification in videos collected during the harvest, it was possible to map the qualitative attributes regarding the coffee maturation stage along the crop lines. These attribute maps provide managers important spatial information for the application of precision agriculture techniques in crop management. Additionally, this study should incentive future research to customize the deep learning model for certain tasks in agriculture and precision agriculture.

ACS Style

Helizani Couto Bazame; José Paulo Molin; Daniel Althoff; Maurício Martello. Detection, classification, and mapping of coffee fruits during harvest with computer vision. Computers and Electronics in Agriculture 2021, 183, 106066 .

AMA Style

Helizani Couto Bazame, José Paulo Molin, Daniel Althoff, Maurício Martello. Detection, classification, and mapping of coffee fruits during harvest with computer vision. Computers and Electronics in Agriculture. 2021; 183 ():106066.

Chicago/Turabian Style

Helizani Couto Bazame; José Paulo Molin; Daniel Althoff; Maurício Martello. 2021. "Detection, classification, and mapping of coffee fruits during harvest with computer vision." Computers and Electronics in Agriculture 183, no. : 106066.

Journal article
Published: 28 February 2021 in Remote Sensing
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The lack of measurement of precipitation in large areas using fine-resolution data is a limitation in water management, particularly in developing countries. However, Version 6 of the Integrated Multi-satellitE Retrievals for GPM (IMERG) has provided a new source of precipitation information with high spatial and temporal resolution. In this study, the performance of the GPM products (Final run) in the state of Paraná, located in the southern region of Brazil, from June 2000 to December 2018 was evaluated. The daily and monthly products of IMERG were compared to the gauge data spatially distributed across the study area. Quantitative and qualitative metrics were used to analyze the performance of IMERG products to detect precipitation events and anomalies. In general, the products performed positively in the estimation of monthly rainfall events, both in volume and spatial distribution, and demonstrated limited performance for daily events and anomalies, mainly in mountainous regions (coast and southwest). This may be related to the orographic rainfall in these regions, associating the intensity of the rain, and the topography. IMERG products can be considered as a source of precipitation data, especially on a monthly scale. Product calibrations are suggested for use on a daily scale and for time-series analysis.

ACS Style

Jéssica G. Nascimento; Daniel Althoff; Helizani C. Bazame; Christopher M. U. Neale; Sergio N. Duarte; Anderson L. Ruhoff; Ivo Z. Gonçalves. Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Paraná State, Brazil. Remote Sensing 2021, 13, 906 .

AMA Style

Jéssica G. Nascimento, Daniel Althoff, Helizani C. Bazame, Christopher M. U. Neale, Sergio N. Duarte, Anderson L. Ruhoff, Ivo Z. Gonçalves. Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Paraná State, Brazil. Remote Sensing. 2021; 13 (5):906.

Chicago/Turabian Style

Jéssica G. Nascimento; Daniel Althoff; Helizani C. Bazame; Christopher M. U. Neale; Sergio N. Duarte; Anderson L. Ruhoff; Ivo Z. Gonçalves. 2021. "Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Paraná State, Brazil." Remote Sensing 13, no. 5: 906.

Original paper
Published: 04 February 2021 in Stochastic Environmental Research and Risk Assessment
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The use of neural networks in hydrology has been frequently undermined by limitations regarding the quantification of uncertainty in predictions. Many authors have proposed different methodologies to overcome these limitations, such as running Monte Carlo simulations, Bayesian approximations, and bootstrapping training samples, which come with computational limitations of their own, and two-step approaches, among others. One less-frequently explored alternative is to repurpose the dropout scheme during inference. Dropout is commonly used during training to avoid overfitting. However, it may also be activated during the testing period to effortlessly provide an ensemble of multiple “sister” predictions. This study explores the predictive uncertainty in hydrological models based on neural networks by comparing a multiparameter ensemble to a dropout ensemble. The dropout ensemble shows more reliable coverage of prediction intervals, while the multiparameter ensemble results in sharper prediction intervals. Moreover, for neural network structures with optimal lookback series, both ensemble strategies result in similar average interval scores. The dropout ensemble, however, benefits from requiring only a single calibration run, i.e., a single set of parameters. In addition, it delivers important insight for engineering design and decision-making with no increase in computational cost. Therefore, the dropout ensemble can be easily included in uncertainty analysis routines and even be combined with multiparameter or multimodel alternatives.

ACS Style

Daniel Althoff; Lineu Neiva Rodrigues; Helizani Couto Bazame. Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble. Stochastic Environmental Research and Risk Assessment 2021, 35, 1051 -1067.

AMA Style

Daniel Althoff, Lineu Neiva Rodrigues, Helizani Couto Bazame. Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble. Stochastic Environmental Research and Risk Assessment. 2021; 35 (5):1051-1067.

Chicago/Turabian Style

Daniel Althoff; Lineu Neiva Rodrigues; Helizani Couto Bazame. 2021. "Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble." Stochastic Environmental Research and Risk Assessment 35, no. 5: 1051-1067.

Original paper
Published: 07 January 2021 in Theoretical and Applied Climatology
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There is little information about the availability of water in the Cerrado biome, the main agricultural frontier in Brazil, especially about groundwater, and this has compromised the region’s economic and social development, as well as environmental sustainability. The reduction of rainfall in this region, indicated by numerous climate models, may reduce aquifer recharge and, consequently, groundwater availability and sustainable development of the Cerrado biome. This study aimed to evaluate the impact of global climate change on groundwater recharge in a Brazilian Savannah watershed. Rainfall and water table depth data were recorded between 2007 and 2015. Based on these data, equations were developed relating the average monthly depth of the water table with the accumulated average monthly rainfall. From these equations, monthly average recharges considering the future climate estimates made by climate models (Eta-HadGEM2-ES and Eta-MIROC5) and Representative Concentration Pathway (RCP) scenarios (4.5 and 8.5) were calculated. In a pessimistic scenario (RCP 8.5), the average monthly groundwater recharge is decreasing in the beginning and in the end of the rainy season, indicating that there may be an increase in the dry season and, consequently, a reduction in water availability in the Cerrado biome region.

ACS Style

Arnaldo José Cambraia Neto; Lineu Neiva Rodrigues; Demetrius David da Silva; Daniel Althoff. Impact of climate change on groundwater recharge in a Brazilian Savannah watershed. Theoretical and Applied Climatology 2021, 143, 1425 -1436.

AMA Style

Arnaldo José Cambraia Neto, Lineu Neiva Rodrigues, Demetrius David da Silva, Daniel Althoff. Impact of climate change on groundwater recharge in a Brazilian Savannah watershed. Theoretical and Applied Climatology. 2021; 143 (3-4):1425-1436.

Chicago/Turabian Style

Arnaldo José Cambraia Neto; Lineu Neiva Rodrigues; Demetrius David da Silva; Daniel Althoff. 2021. "Impact of climate change on groundwater recharge in a Brazilian Savannah watershed." Theoretical and Applied Climatology 143, no. 3-4: 1425-1436.

Journal article
Published: 01 January 2021 in H2Open Journal
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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.

ACS Style

Daniel Althoff; Helizani Couto Bazame; Jessica Garcia Nascimento. Untangling hybrid hydrological models with explainable artificial intelligence. H2Open Journal 2021, 4, 13 -28.

AMA Style

Daniel Althoff, Helizani Couto Bazame, Jessica Garcia Nascimento. Untangling hybrid hydrological models with explainable artificial intelligence. H2Open Journal. 2021; 4 (1):13-28.

Chicago/Turabian Style

Daniel Althoff; Helizani Couto Bazame; Jessica Garcia Nascimento. 2021. "Untangling hybrid hydrological models with explainable artificial intelligence." H2Open Journal 4, no. 1: 13-28.

Journal article
Published: 12 November 2020 in Scientific Reports
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Droughts are major natural disasters that affect many parts of the world all years and recently affected one of the major conilon coffee-producing regions of the world in state of Espírito Santo, which caused a huge crisis in the sector. Therefore, the objective of this study was to conduct an analysis with technical-scientific basis of the real impact of drought associated with high temperatures and irradiances on the conilon coffee (Coffea canephora Pierre ex Froehner) plantations located in the north, northwest, and northeast regions of the state of Espírito Santo, Brazil. Data from 2010 to 2016 of rainfall, air temperature, production, yield, planted area and surface remote sensing were obtained from different sources, statistically analyzed, and correlated. The 2015/2016 season was the most affected by the drought and high temperatures (mean annual above 26 °C) because, in addition to the adverse weather conditions, coffee plants were already damaged by the climatic conditions of the previous season. The increase in air temperature has higher impact (negative) on production than the decrease in annual precipitation. The average annual air temperatures in the two harvest seasons that stood out for the lowest yields (i.e. 2012/2013 and 2015/2016) were approximately 1 °C higher than in the previous seasons. In addition, in the 2015/2016 season, the average annual air temperature was the highest in the entire series. The spatial and temporal distribution of Enhanced Vegetation Index values enabled the detection and perception of droughts in the conilon coffee-producing regions of Espírito Santo. The rainfall volume accumulated in the periods from September to December and from April to August are the ones that most affect coffee yield. The conilon coffee plantations in these regions are susceptible to new climate extremes, as they continue to be managed under irrigation and full sun. The adoption of agroforestry systems and construction of small reservoirs can be useful to alleviate these climate effects, reducing the risk of coffee production losses and contributing to the sustainability of crops in Espírito Santo.

ACS Style

Luan Peroni Venancio; Roberto Filgueiras; Everardo Chartuni Mantovani; Cibele Hummel Do Amaral; Fernando França Da Cunha; Francisco Charles Dos Santos Silva; Daniel Althoff; Robson Argolo Dos Santos; Paulo Cezar Cavatte. Impact of drought associated with high temperatures on Coffea canephora plantations: a case study in Espírito Santo State, Brazil. Scientific Reports 2020, 10, 1 -21.

AMA Style

Luan Peroni Venancio, Roberto Filgueiras, Everardo Chartuni Mantovani, Cibele Hummel Do Amaral, Fernando França Da Cunha, Francisco Charles Dos Santos Silva, Daniel Althoff, Robson Argolo Dos Santos, Paulo Cezar Cavatte. Impact of drought associated with high temperatures on Coffea canephora plantations: a case study in Espírito Santo State, Brazil. Scientific Reports. 2020; 10 (1):1-21.

Chicago/Turabian Style

Luan Peroni Venancio; Roberto Filgueiras; Everardo Chartuni Mantovani; Cibele Hummel Do Amaral; Fernando França Da Cunha; Francisco Charles Dos Santos Silva; Daniel Althoff; Robson Argolo Dos Santos; Paulo Cezar Cavatte. 2020. "Impact of drought associated with high temperatures on Coffea canephora plantations: a case study in Espírito Santo State, Brazil." Scientific Reports 10, no. 1: 1-21.

Original paper
Published: 24 September 2020 in Theoretical and Applied Climatology
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The reference evapotranspiration (ET0) estimates is important for water resources and irrigation management. The Penman-Monteith equation is known for its accuracy but requires a high number of climatic parameters that are not always available. Thus, this study aimed to evaluate the performance of machine learning techniques (cubist regression, artificial neural network with Bayesian regularization, support vector machine with linear kernel function) and stepwise multiple linear regression method to estimate daily ET0 with limited weather data in a Brazilian agricultural frontier (MATOPIBA). Climatic data from 2000 to 2016 obtained from 23 weather stations were used. Five data scenarios were evaluated: (i) all variables, (ii) radiation and temperature, (iii) temperature and relative humidity, (iv) wind speed and temperature, and (v) temperature. The results showed that the machine learning methods are robust in estimating ET0, even in the absence of some variables. Among the methods evaluated using only temperature data, the cubist regression showed better performance. When estimating water demand for soybean and maize crops using only temperature, the cubist regression and calibrated Hargreaves-Samani equation showed the smallest errors.

ACS Style

Diego Bispo Dos Santos Farias; Daniel Althoff; Lineu Neiva Rodrigues; Roberto Filgueiras. Performance evaluation of numerical and machine learning methods in estimating reference evapotranspiration in a Brazilian agricultural frontier. Theoretical and Applied Climatology 2020, 142, 1 -12.

AMA Style

Diego Bispo Dos Santos Farias, Daniel Althoff, Lineu Neiva Rodrigues, Roberto Filgueiras. Performance evaluation of numerical and machine learning methods in estimating reference evapotranspiration in a Brazilian agricultural frontier. Theoretical and Applied Climatology. 2020; 142 (3-4):1-12.

Chicago/Turabian Style

Diego Bispo Dos Santos Farias; Daniel Althoff; Lineu Neiva Rodrigues; Roberto Filgueiras. 2020. "Performance evaluation of numerical and machine learning methods in estimating reference evapotranspiration in a Brazilian agricultural frontier." Theoretical and Applied Climatology 142, no. 3-4: 1-12.

Journal article
Published: 01 August 2020 in Journal of Hydrologic Engineering
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ACS Style

Daniel Althoff; Roberto Filgueiras; Lineu Neiva Rodrigues. Estimating Small Reservoir Evaporation Using Machine Learning Models for the Brazilian Savannah. Journal of Hydrologic Engineering 2020, 25, 05020019 .

AMA Style

Daniel Althoff, Roberto Filgueiras, Lineu Neiva Rodrigues. Estimating Small Reservoir Evaporation Using Machine Learning Models for the Brazilian Savannah. Journal of Hydrologic Engineering. 2020; 25 (8):05020019.

Chicago/Turabian Style

Daniel Althoff; Roberto Filgueiras; Lineu Neiva Rodrigues. 2020. "Estimating Small Reservoir Evaporation Using Machine Learning Models for the Brazilian Savannah." Journal of Hydrologic Engineering 25, no. 8: 05020019.

Journal article
Published: 20 July 2020 in Water Resources Research
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The reference evapotranspiration (ETo) has long been used as a climate parameter for many studies in climatology and hydrology. However, many regions suffer from shortage of both meteorological monitoring stations and historical information on ETo. Thus, the objective of this study was to develop a daily gridded reference evapotranspiration data set for Brazil that matches the period and grid cells of the Global Precipitation Measurement (GPM) data. ETo was calculated using data from 849 weather stations over the period from 1 June 2000 to 31 December 2018. The features used to model ETo were the GPM daily data set, WorldClim averages monthly, and two engineered features. Among the machine learning algorithms assessed, the Cubist presented the best performance‐computation cost trade‐off in a subset of the entire data and, therefore, was selected to model ETo daily. The developed data set presented root mean square error of 0.65 mm day−1, or 16% lower than previous ETo data set developed for Brazil using interpolation techniques. The GPM and engineered features showed higher importance for the models trained during the wet season, while the WorldClim maximum temperature averages monthly were more important during the dry and cold season. The new gridded reference evapotranspiration data set for Brazil (ETo‐Brazil) was made freely available to the community.

ACS Style

Daniel Althoff; Santos Henrique Brant Dias; Roberto Filgueiras; Lineu Neiva Rodrigues. ETo‐Brazil: A Daily Gridded Reference Evapotranspiration Data Set for Brazil (2000–2018). Water Resources Research 2020, 56, 1 .

AMA Style

Daniel Althoff, Santos Henrique Brant Dias, Roberto Filgueiras, Lineu Neiva Rodrigues. ETo‐Brazil: A Daily Gridded Reference Evapotranspiration Data Set for Brazil (2000–2018). Water Resources Research. 2020; 56 (7):1.

Chicago/Turabian Style

Daniel Althoff; Santos Henrique Brant Dias; Roberto Filgueiras; Lineu Neiva Rodrigues. 2020. "ETo‐Brazil: A Daily Gridded Reference Evapotranspiration Data Set for Brazil (2000–2018)." Water Resources Research 56, no. 7: 1.

Journal article
Published: 17 June 2020 in Semina: Ciências Agrárias
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Irrigation systems must be assessed periodically to verify equipment quality and the need for adjustments. For this, precipitation test kits are necessary. However, commercially available kits have as their main disadvantage the high cost. Therefore, this study aimed to develop an alternative low-cost precipitation kit and verify its efficiency compared to an available commercial brand. The validation test was carried out at the Laboratory of Hydraulics of the Federal University of Viçosa (UFV) using a conventional sprinkler system organized in a quadrangular arrangement. Water collections were carried out within two hours using a grid of plastic collectors spaced at 3 × 3 m and installed at 0.7 m above the ground. The coefficient of determination (R2), uniformity coefficients, application efficiency, and thematic maps of the spatial variability of the applied irrigation depth were compared between kits and used for the validation of measurements. The results showed a high agreement between the developed (GESAI) and a commercial kit (Trademark) (R2 = 0.9849), and a high spatial agreement between the collected water depths. Therefore, the GESAI kit is a low-cost alternative for the assessment of irrigation systems.

ACS Style

Roberto Filgueiras; Universidade Federal de Viçosa; Fernando França Da Cunha; Luan Peroni Venancio; Daniel Althoff; Robson Argolo Do Santos; Jannaylton Éverton Oliveira Santos; Carlos Augusto Brasileiro De Alencar. Alternative low-cost precipitation kit for assessing irrigation systems. Semina: Ciências Agrárias 2020, 42, 1783 -1798.

AMA Style

Roberto Filgueiras, Universidade Federal de Viçosa, Fernando França Da Cunha, Luan Peroni Venancio, Daniel Althoff, Robson Argolo Do Santos, Jannaylton Éverton Oliveira Santos, Carlos Augusto Brasileiro De Alencar. Alternative low-cost precipitation kit for assessing irrigation systems. Semina: Ciências Agrárias. 2020; 42 (5):1783-1798.

Chicago/Turabian Style

Roberto Filgueiras; Universidade Federal de Viçosa; Fernando França Da Cunha; Luan Peroni Venancio; Daniel Althoff; Robson Argolo Do Santos; Jannaylton Éverton Oliveira Santos; Carlos Augusto Brasileiro De Alencar. 2020. "Alternative low-cost precipitation kit for assessing irrigation systems." Semina: Ciências Agrárias 42, no. 5: 1783-1798.

Journal article
Published: 30 May 2020 in Tropical Grasslands-Forrajes Tropicales
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The objective of the present work was to evaluate the use of spectral sensors to determine nitrogen fertilizer requirements for pastures of Urochloa brizantha cv. Xaraés in Brazil. The experimental design was a randomized block design with 4 replications of 4 treatments: a control treatment (TT) without application of N; a reference treatment (TR) with N applied at a standard predetermined fixed rate (150 kg urea/ha/cycle); a treatment using GreenSeekerTM (TG) to determine N requirement by the canopy normalized difference vegetation index (NDVI); and a treatment using SPAD 502 (TS) to determine N requirement by foliar chlorophyll assessment. For treatments involving spectral sensors, N fertilizer was applied at half the rate of that in the reference treatment at the beginning of each cycle and further N was applied only when the nitrogen sufficiency index dropped below 0.85. The sensors used in the work indicated that no additional N fertilizer was required by these pastures above the half rates applied. Applying N at the reduced rates to the pastures was more efficient than the pre-determined fixed rate, as both sensor treatments and the fixed rate treatment produced similar total forage yields, with similar crude protein concentrations. All fertilized pastures supported similar stocking rates, while the sensor treatments used less N fertilizer, i.e. 75 kg urea/ha/cycle less than the reference plot. Longer-term studies to verify these findings are warranted followed by promotion of the technology to farmers to possibly reduce fertilizer application rates, improve profitability and provide environmental benefits.

ACS Style

Helizani C. Bazame; Francisco A.C. Pinto; Domingos S. Queiroz; Daniel M. De Queiroz; Daniel Althoff. Spectral sensors prove beneficial in determining nitrogen fertilizer needs of Urochloa brizantha cv. Xaraés grass in Brazil. Tropical Grasslands-Forrajes Tropicales 2020, 8, 60 -71.

AMA Style

Helizani C. Bazame, Francisco A.C. Pinto, Domingos S. Queiroz, Daniel M. De Queiroz, Daniel Althoff. Spectral sensors prove beneficial in determining nitrogen fertilizer needs of Urochloa brizantha cv. Xaraés grass in Brazil. Tropical Grasslands-Forrajes Tropicales. 2020; 8 (2):60-71.

Chicago/Turabian Style

Helizani C. Bazame; Francisco A.C. Pinto; Domingos S. Queiroz; Daniel M. De Queiroz; Daniel Althoff. 2020. "Spectral sensors prove beneficial in determining nitrogen fertilizer needs of Urochloa brizantha cv. Xaraés grass in Brazil." Tropical Grasslands-Forrajes Tropicales 8, no. 2: 60-71.

Journal article
Published: 20 April 2020 in Remote Sensing
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One of the obstacles in monitoring agricultural crops is the difficulty in understanding and mapping rapid changes of these crops. With the purpose of addressing this issue, this study aimed to model and fuse the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) using Landsat-like images to achieve daily high spatial resolution NDVI. The study was performed for the period of 2017 on a commercial farm of irrigated maize-soybean rotation in the western region of the state of Bahia, Brazil. To achieve the objective, the following procedures were performed: (i) Landsat-like images were upscaled to match the Landsat-8 spatial resolution (30 m); (ii) the reflectance of Landsat-like images was intercalibrated using the Landsat-8 as a reference; (iii) Landsat-like reflectance images were upscaled to match the MODIS sensor spatial resolution (250 m); (iv) regression models were trained daily to model MODIS NDVI using the upscaled Landsat-like reflectance images (250 m) of the closest day as the input; and (v) the intercalibrated version of the Landsat-like images (30 m) used in the previous step was used as the input for the trained model, resulting in a downscaled MODIS NDVI (30 m). To determine the best fitting model, we used the following statistical metrics: coefficient of determination (r2), root mean square error (RMSE), Nash–Sutcliffe efficiency index (NSE), mean bias error (MBE), and mean absolute error (MAE). Among the assessed regression models, the Cubist algorithm was sensitive to changes in agriculture and performed best in modeling of the Landsat-like MODIS NDVI. The results obtained in the present research are promising and can enable the monitoring of dynamic phenomena with images available free of charge, changing the way in which decisions are made using satellite images.

ACS Style

Roberto Filgueiras; Everardo Chartuni Mantovani; Elpídio Inácio Fernandes-Filho; Fernando França Da Cunha; Daniel Althoff; Santos Henrique Brant Dias. Fusion of MODIS and Landsat-Like Images for Daily High Spatial Resolution NDVI. Remote Sensing 2020, 12, 1297 .

AMA Style

Roberto Filgueiras, Everardo Chartuni Mantovani, Elpídio Inácio Fernandes-Filho, Fernando França Da Cunha, Daniel Althoff, Santos Henrique Brant Dias. Fusion of MODIS and Landsat-Like Images for Daily High Spatial Resolution NDVI. Remote Sensing. 2020; 12 (8):1297.

Chicago/Turabian Style

Roberto Filgueiras; Everardo Chartuni Mantovani; Elpídio Inácio Fernandes-Filho; Fernando França Da Cunha; Daniel Althoff; Santos Henrique Brant Dias. 2020. "Fusion of MODIS and Landsat-Like Images for Daily High Spatial Resolution NDVI." Remote Sensing 12, no. 8: 1297.

Research article
Published: 11 February 2020 in International Journal of Climatology
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This study describes the performance of five gridded datasets in reproducing precipitation and/or temperature over the complex terrain in the high Chilean Andes. The relationship of instrumental observations and the gridded datasets with climate modes of variability and the trends of indices of climate extremes are also explored between the period 1980‐2015. The mismatches between gridded datasets are larger in Northern and Southern regions in relation to precipitation, while for temperature, disagreement is higher in Central region. However, better results are delivered by the CRU and GPCC followed by ERA‐I. The El Niño Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) indices are well correlated with precipitation in North and South Chile. Additional, trend analyses reveal a significant downward (upward) tendency for precipitation (temperature), especially in Central region, delivered by observed and the majority of gridded datasets. Furthermore, the consecutive number of dry days is increasing in all regions at the annual scale. This study allows a better understanding of the capacity of global datasets and thus contributes to further climate research within this Andean region. This article is protected by copyright. All rights reserved.

ACS Style

Vanúcia Schumacher; Flávio Justino; Alfonso Fernández; Oliver Meseguer‐Ruiz; Pablo Sarricolea; Alcimoni Comin; Luan Peroni Venancio; Daniel Althoff. Comparison between observations and gridded data sets over complex terrain in the Chilean Andes: Precipitation and temperature. International Journal of Climatology 2020, 40, 5266 -5288.

AMA Style

Vanúcia Schumacher, Flávio Justino, Alfonso Fernández, Oliver Meseguer‐Ruiz, Pablo Sarricolea, Alcimoni Comin, Luan Peroni Venancio, Daniel Althoff. Comparison between observations and gridded data sets over complex terrain in the Chilean Andes: Precipitation and temperature. International Journal of Climatology. 2020; 40 (12):5266-5288.

Chicago/Turabian Style

Vanúcia Schumacher; Flávio Justino; Alfonso Fernández; Oliver Meseguer‐Ruiz; Pablo Sarricolea; Alcimoni Comin; Luan Peroni Venancio; Daniel Althoff. 2020. "Comparison between observations and gridded data sets over complex terrain in the Chilean Andes: Precipitation and temperature." International Journal of Climatology 40, no. 12: 5266-5288.

Article
Published: 22 January 2020 in Climatic Change
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The Cerrado (Brazilian savannah) is one of the few places in the world that has the potential to increase crop production to meet the projected food demand for 2050. However, for agriculture to be sustainable in this region, irrigation must be efficient. This depends on the water stored in small reservoirs, which play an important role in supporting the local economy. The increase in temperature and net radiation predicted by global climate models may increase the evaporation and reduce the availability of water in these small reservoirs. This work assesses the projected impact of climate change on small reservoir evaporation and water availability in the Brazilian savannah using data from the Eta-HadGEM2-ES and Eta-MIROC5 regional climate models under representative concentration pathways (RCP) 4.5 and 8.5. Evaporation increases of 7.3% (1.09 mm/year−1) and 18.4% (2.74 mm/year−1) are projected in RCP 4.5 and RCP 8.5, respectively, by the year 2100. The water stored in reservoirs is projected to decrease in the future, resulting in higher risks of failure in water supply, especially from the smaller reservoirs. Overall, evaporation increases are expected to reduce the availability of water in small reservoirs during the dry season by 5.5% in RCP 4.5 and 10.4% in RCP 8.5.

ACS Style

Daniel Althoff; Lineu Neiva Rodrigues; Demetrius David Da Silva. Impacts of climate change on the evaporation and availability of water in small reservoirs in the Brazilian savannah. Climatic Change 2020, 159, 215 -232.

AMA Style

Daniel Althoff, Lineu Neiva Rodrigues, Demetrius David Da Silva. Impacts of climate change on the evaporation and availability of water in small reservoirs in the Brazilian savannah. Climatic Change. 2020; 159 (2):215-232.

Chicago/Turabian Style

Daniel Althoff; Lineu Neiva Rodrigues; Demetrius David Da Silva. 2020. "Impacts of climate change on the evaporation and availability of water in small reservoirs in the Brazilian savannah." Climatic Change 159, no. 2: 215-232.

Journal article
Published: 30 October 2019 in IRRIGA
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DYNAMICS OF ACTUAL CROP EVAPOTRANSPIRATION BASED IN THE COMPARATIVE ANALYSIS OF SEBAL AND METRIC-EEFLUX ROBERTO FILGUEIRAS1; EVERARDO CHARTUNI MANTOVANI2; DANIEL ALTHOFF3; RAYSSA BALIEIRO RIBEIRO4; LUAN PERONI VENANCIO5 E ROBSON ARGOLO DOS SANTOS6 1 Department of Agricultural Engineering, Federal University of Viçosa, Peter Henry Rolfs avenue, s/n –University Campus, Viçosa - MG, 36570-900, Viçosa, MG, Brazil, [email protected] 2 Department of Agricultural Engineering, Federal University of Viçosa , Peter Henry Rolfs avenue, s/n –University Campus, Viçosa - MG, 36570-900, Viçosa, MG, Brazil, [email protected] 3 Department of Agricultural Engineering, Federal University of Viçosa , Peter Henry Rolfs avenue, s/n –University Campus, Viçosa - MG, 36570-900, Viçosa, MG, Brazil, [email protected] 4 Department of Agricultural Engineering, Federal University of Viçosa , Peter Henry Rolfs avenue, s/n –University Campus, Viçosa - MG, 36570-900, Viçosa, MG, Brazil, [email protected] 5 Department of Agricultural Engineering, Federal University of Viçosa , Peter Henry Rolfs avenue, s/n –University Campus, Viçosa - MG, 36570-900, Viçosa, MG, Brazil, [email protected] 6 Department of Agricultural Engineering, Federal University of Viçosa , Peter Henry Rolfs avenue, s/n –University Campus, Viçosa - MG, 36570-900, Viçosa, MG, Brazil, [email protected] 1 ABSTRACT Obtaining spatial evapotranspiration requires that the user has knowledge of the energy balance equation, as well as digital image processing. This fact has made researchers create and make available an actual evapotranspiration (ETa) product for scientific community, EEFLUX (Earth Engine Evapotranspiration Flux). Based on this, the present work aimed to compare ETa from the SEBAL (ETa-SEBAL) algorithm, with ETa based on the METRIC algorithm, which is available by EEFLUX (ETA-EEFLUX). For this, 14 Landsat images were used throughout the 2018 crop season, for maize crop irrigated by central pivot, in western Bahia, Brazil. The results showed that the product available by EEFLUX presents a higher estimate of evapotranspiration, when compared to SEBAL, for the areas with lower NDVI values and higher surface temperature, and the opposite was also observed. In addition, the SEBAL algorithm was more correlated with the NDVI variables and surface temperature. However, ET-EEFLUX showed agreement with the results obtained by the SEBAL algorithm, being an important information available to the scientific community and decision makers in the practice of irrigated agriculture, since it does not require in-depth technical knowledge. Keywords: water demand, crop monitoring, remote sensing FILGUEIRAS, R.; MANTOVANI, E. C.; ALTHOFF, D.; RIBEIRO, R. B.; VENANCIO, L. P.; SANTOS, R. A. DINÂMICA DA EVAPOTRANSPIRAÇÃO BASEADO NA ANÁLISE COMPARATIVA DO ALGORITMO SEBAL E DO METRIC-EEFLUX 2 RESUMO A obtenção da evapotranspiração espacializada requer que o usuário tenha conhecimento da equação do balanço de energia, bem como de processamento digital de imagens. Este fato fez com que pesquisadores criassem e disponibilizassem um produto de evapotranspiração real (ETa) para a comunidade científica, o EEFLUX (Earth Engine Evapotranspiration Flux). Baseado nisso, o presente trabalho teve como objetivo comparar a ETa proveniente do algoritmo SEBAL (ETa-SEBAL), com a ETa baseada no algoritmo METRIC, que está disponível pelo EEFLUX (ETa-EEFLUX). Para isso, utilizaram-se 14 imagens Landsat. Os resultados demonstraram que o produto disponível pelo EEFLUX apresenta uma estimativa maior de evapotranspiração, quando comparado com o SEBAL, para as áreas que apresentam valores de NDVI mais baixos e de temperatura da superfície mais elevada, sendo o contrário também observado. Além disso, o algoritmo SEBAL se mostrou mais correlacionado com as variáveis NDVI e temperatura de superfície. Entretanto, o ET-EEFLUX apresentou concordância com os resultados obtidos pelo algoritmo SEBAL, sendo uma importante informação disponível para comunidade científica e tomadores de decisão na prática da agricultura irrigada, visto que dispensa conhecimento técnico aprofundado. Palavras-chave: demanda hídrica, monitoramento de cultura, sensoriamento remoto.

ACS Style

Roberto Filgueiras; Everardo Chartuni Mantovani; Daniel Althoff; Rayssa Balieiro Ribeiro; Luan Peroni Venancio; Robson Argolo Dos Santos. DYNAMICS OF ACTUAL CROP EVAPOTRANSPIRATION BASED IN THE COMPARATIVE ANALYSIS OF SEBAL AND METRIC-EEFLUX. IRRIGA 2019, 1, 1 .

AMA Style

Roberto Filgueiras, Everardo Chartuni Mantovani, Daniel Althoff, Rayssa Balieiro Ribeiro, Luan Peroni Venancio, Robson Argolo Dos Santos. DYNAMICS OF ACTUAL CROP EVAPOTRANSPIRATION BASED IN THE COMPARATIVE ANALYSIS OF SEBAL AND METRIC-EEFLUX. IRRIGA. 2019; 1 (1):1.

Chicago/Turabian Style

Roberto Filgueiras; Everardo Chartuni Mantovani; Daniel Althoff; Rayssa Balieiro Ribeiro; Luan Peroni Venancio; Robson Argolo Dos Santos. 2019. "DYNAMICS OF ACTUAL CROP EVAPOTRANSPIRATION BASED IN THE COMPARATIVE ANALYSIS OF SEBAL AND METRIC-EEFLUX." IRRIGA 1, no. 1: 1.