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Drought events have increased in frequency and severity in recent years, and result in significant economic losses. Although the Brazilian semi-arid northeast has been historically associated with the impacts of drought, drought is of national concern, from 2011–2019, drought events were recorded in all Brazilian territories. Droughts can have major consequences for agricultural production, which is of particular concern given the importance of soybeans for socio-economic development. Due to its regional heterogeneity, it is important to develop accurate drought forecast and assessment tools for Brazil. We explore machine learning as a method to forecast the vegetation health index (VHI), for large scale monthly drought monitoring across agricultural land in Brazil. Furthermore, we also determine spatio-temporal drivers of VHI across the wide variation in climates, as well as evaluate machine learning performance for ENSO variation, forecasting of the onset of drought impact, and how the trade off between spatial variation and sample size affects model performance. We show that machine learning methods such as gradient boosting methods are able to more easily forecast vegetation health in the north and north east Brazil than south Brazil, and perform better during La Niña events than El Niño events. Drought impacts which reduce VHI below the commonly used 40 % threshold can be forecast across Brazil with similar model performance. SPEI is shown to be a useful indicator of drought impact, with 3 month accumulation periods preferred over 1 and 2 months. Results aim to inform future developments in operational drought monitoring at the National Center for Monitoring and Early Warning of Natural Disasters in Brazil (CEMADEN). Future work should build upon methods discussed here to improve drought forecasts for agricultural drought response and adaptation. [ABSTRACT FROM AUTHOR]

The study of rainfall trends is crucial for food security and water availability in Alagoas state, Northeast of Brazil. In this work, monthly, seasonal and annual rainfall trends have been studied (1960–2016) for homogeneous rainfall regions over the eastern part of the Northeast Brazil (ENEB) and later related to climate variability. Cluster analysis was applied to identify homogeneous rainfall regions while the Mann–Kendall (MK), modified Mann–Kendall (MMK) and Pettitt tests were used in the analysis and identification of trends on a spatial and temporal scale. To relate rainfall and climate variability modes, Spearman's correlation was used in each homogeneous region. The rainfall series provided evidence of a general decrease in rainfall in the rainy period and an increase in the dry period, mainly over the driest region. The break points of time series occurred mostly in periods of great variations in values of modes of climate variability, especially the Monthly Niño3.4 Index and the Southern Oscillation Index (SOI), both having a robust influence across the region. Moreover, the probable rainfall in the time series with trends was different in most months before and after the breakpoint. After the breakpoint, probable rainfall was lower, influenced by the breakpoint year (size of the series before and after the breakpoint), which mainly occurred in the 1980s and 1990s and presented a warm phase and a greater number of El Niño events. The MK and MMK trend tests showed the ability to detect trends, although there is no established standard on which test or version to use due to self‐correlated, nonhomogeneous series with nonrandom or nonindependent data. Rainfall is an important variable for water and food security and in the monitoring of natural disasters. The changes detected in this study can be used as reference information for public policies on water resources and future studies for Alagoas and similar regions. [ABSTRACT FROM AUTHOR]

Bioenergy from sugarcane production is considered a key mitigation strategy for global warming. Improving its representation in land surface models is important to further understand the interactions between climate and bioenergy production, supporting accurate climate projections and decision‐making. This study aimed to calibrate and evaluate the Joint UK Land Environment Simulator (JULES) for climate impact assessments in sugarcane. A dataset composed of soil moisture, water and carbon fluxes and biomass measurements from field experiments across Brazil was used to calibrate and evaluate JULES‐crop and JULES‐BE parametrizations. The ability to predict the spatiotemporal variability of sugarcane yields and the impact of climate change was also tested at five Brazilian microregions. Parameters related to sugarcane allometry, crop growth and development were derived and tested for JULES‐crop and JULES‐BE, together with the response to atmospheric carbon dioxide, temperature and low‐water availability (CTW‐response). Both parametrizations showed comparable performance to other sugarcane dynamic models, with a root mean squared error of 6.75 and 6.05 t ha−1 for stalk dry matter for JULES‐crop and JULES‐BE, respectively. The parametrizations were also able to replicate the average yield patterns observed in the five microregions over 30 years when the yield gap factors were taken into account, with the correlation (r) between simulated and observed interannual variability of yields ranging from 0.33 to 0.56. An overall small positive trend was found in future yield projections of sugarcane using the new calibrations, with exception of the Jataí mesoregion which showed a clear negative trend for the SSP5 scenario from the years 2070 to 2100. Our simulations showed that an abrupt negative impact on sugarcane yields is expected if daytime temperatures above 35°C become more frequent. The newly calibrated version of JULES can be applied regionally and globally to help understand the interactions between climate and bioenergy production. [ABSTRACT FROM AUTHOR]

The characterization of spatial and temporal patterns of wind is essential to several sectors, including energy, urban climate, and applied meteorology. However, few studies describe the regional characteristics of the wind regime over the Brazilian Southeast (SEB), the most developed and populated part of the country. The objectives of the current work were (a) to assess the spatial patterns of the wind regime using cluster analysis (CA) and (b) to apply principal components analysis (PCA) to investigate which meteorological systems influence the spatial and temporal patterns of the wind regime. The dataset consisted of wind speed and direction from 70 automatic weather stations with records from 2008 to 2019. According to the CA method, four groups of homogeneous wind speed (G1–G4) were identified; G4 presented the highest magnitudes of wind speed (wind speed >5 m·s−1, with maxima of 7.2 m·s−1). Seasonal well‐defined minima (March–June) and maxima (July–October) were observed only for G2 and G3. The systems South Atlantic Subtropical Anticyclone (SASA), Frontal Systems (FS), and the South Atlantic Convergence Zone (SACZ) influenced these groups. In addition, the mesoscale meteorological systems likely influence other groups such as breezes from land/sea (G1, G3, and G4), lakes (G2 and G3), and valley/mountain (G2 and G3). The topography had a strong influence on G3, mainly due to the mountain ranges of Mantiqueira and Serra do Mar and Paraíba River valley. Dominant wind directions were E, N/NE, and NW, associated with SASA, in addition to S and SE/SW, influenced by FS and SACZ. The wind speed range of 1.4–3.9 m·s−1 was dominant in all groups, except for G4 (range of 2.4–5.5 m·s−1). According to PCA analysis, two PCs are enough to explain the wind pattern in the SEB with 69.9% of the total variance explained. The surface roughness accounted for 29% of the total variance explained in PC1. The latitude (37.6%) and distance to the coast (30.8%) are the most important variables for PC2. Region's physiography and meteorological systems strongly influenced the wind regime over the Brazilian Southeast. The assessment of wind regime variability presented in this work is expected to support public policies on renewable energy applications, air pollution, and climate change mitigation. [ABSTRACT FROM AUTHOR]

Over the past decade, Brazil has experienced severe droughts across its territory, with important implications for soil moisture dynamics. Soil moisture variability has a direct impact on agriculture, water security and ecosystem services. Nevertheless, there is currently little information on how soil moisture across different biomes responds to drought. In this study, we used satellite soil moisture data from the European Space Agency, from 2009 to 2015, to analyze differences in soil moisture responses to drought for each biome of Brazil: Amazon, Atlantic Forest, Caatinga, Cerrado, Pampa and Pantanal. We found an overall soil moisture decline of -0.5 % yr -1 (p<0.01) at the national level. At the biome level, Caatinga presented the most severe soil moisture decline (-4.4 % yr -1), whereas the Atlantic Forest and Cerrado biomes showed no significant trend. The Amazon biome showed no trend but had a sharp reduction of soil moisture from 2013 to 2015. In contrast, the Pampa and Pantanal biomes presented a positive trend (1.6 % yr -1 and 4.3 % yr -1 , respectively). These trends are consistent with vegetation productivity trends across each biome. This information provides insights into drought risk reduction and soil conservation activities to minimize the impact of drought in the most vulnerable biomes. Furthermore, improving our understanding of soil moisture trends during periods of drought is crucial to enhance the national drought early warning system and develop customized strategies for adaptation to climate change in each biome. [ABSTRACT FROM AUTHOR]

Desertification is a serious threat to human development and sustainability. This process is characterized by land degradation over areas with dry sub‐humid, semi‐arid and arid climate. In this work, climate susceptibility to desertification was identified for the state of Rio de Janeiro (SRJ), Brazil, the third most populated state in the country. The state is characterized by complex topography, and diverse climate and land uses. Two formulations for the aridity index were used: Ia, defined by Thornthwaite (1948, Geographical Review, 38(1), 55–94) and Penman (1953, Horticultural Congress, 2, 913–924), and D, a modification of Ia by Hare (1983, Climate and Desertification: A Revised Analysis, Geneva: World Meteorological Organization). Both estimates of the aridity require annual rainfall and potential evapotranspiration, which were calculated from the climatological water balance using times series of rainfall and air temperature (1961–2010) from 98 stations over the SRJ. Annual rainfall, evapotranspiration, water deficit and the aridity indices were then spatially interpolated over a 5 × 5‐km grid using a spline tension method. Both indices agree on the driest regions of the SRJ: Norte and Baixada Litorânea, near the northern coast, both under sub‐humid and semi‐arid climate. The index Ia indicated susceptibility to desertification over 2,305 km2 on the Norte and Baixada Litorânea regions, which correspond to 5% of SRJ area; similarly, the index D classified a smaller area over the Norte region under desertification (431 km2). These subregions, among the others also considered in this study, are of great importance for the agricultural, forestry and tourism industries and contribute immensely to the economic development of the state. Hence, the evaluation of susceptibility to desertification in those areas is essential to help prevent further land degradation and support policies formulation on long‐term sustainable management of land and water resources, especially under regional and global climate change scenarios. The methodology used in this work can be applied to studies of desertification in other regions of the world. [ABSTRACT FROM AUTHOR]

Emissions of nitrous oxide (N2O) over croplands are a major source of greenhouse gases to the atmosphere. The precise accounting of sources of N2O is essential to national and global budgets, as well as the understanding of the spatial and temporal relationships with environmental variables such as rainfall, air and soil temperature, and soil moisture. The objective of this work was to investigate the temporal correlations of N2O fluxes with soil and air temperatures, as well as soil moisture. N2O fluxes were measured over four biofuel crops in Central Illinois during their establishment phase. Measurements were carried out from 2009 to 2011 using a trace gas analyzer (TGA) with tunable laser technology. Measurements of concentrations of N2O and CO2 were taken at the center of four plots of maize/soybean rotation, miscanthus (Miscanthus × giganteus), switchgrass (Panicum virgatum) and a mixture of native prairie plants. Cumulative fluxes indicate an average emission of nitrogen via N2O fluxes on the order of 1.5 kg N ha−1 year−1, in agreement with chamber measurements previously reported for the site. N2O fluxes were associated with peaks in soil and air temperature, and soil moisture, particularly during spring and winter thaws. Cross‐wavelet analysis was used to investigate the correlation between N2O fluxes and those variables. Results indicate that N2O fluxes and meteorological variables have significant covariance in time scales ranging from 4 to 32 days. In addition, temporal delays of 1–8 days were found in those relationships. Cross‐wavelet patterns were similar when relating N2O fluxes with soil temperature, air temperature and soil moisture. The temporal patterns of fluxes and environmental variables reported here support the modeling of emissions and highlight the importance of considering the timing of fluxes in relation to trends in meteorological variables. [ABSTRACT FROM AUTHOR]

Over the past decade, Brazil has experienced severe droughts across its territory, with important implications for soil moisture dynamics. Soil moisture variability has a direct impact on agriculture, water security, and ecosystem services. Nevertheless, there is currently little information on how soil moisture across different biomes respond to drought. In this study, we used satellite soil moisture data from the European Space Agency, from 2009 to 2015, to analyze differences in soil moisture responses to drought for each biome of Brazil: The Amazon, Atlantic Forest, Caatinga, Cerrado, Pampas and Pantanal. We found an overall soil moisture decline of -0.5 %/year (p < 0.01) at the national level. At the biome-level, Caatinga presented the most severe soil moisture decline (-4.4 % per year); whereas Atlantic Forest and Cerrado biomes showed no significant trend. The Amazon biome showed no trend but a sharp reduction of soil moisture from 2013 to 2015. In contrast, Pampas and Pantanal presented a positive trend (1.6 and 4.3 %/year, respectively). This information provides insights for drought risk reduction and soil conservation activities to minimize the impact of drought in the most vulnerable biomes. Furthermore, improving our understanding of soil moisture trends during periods of drought is crucial to enhance the national drought early warning system and develop customized strategies for adaptation to climate change in each biome. [ABSTRACT FROM AUTHOR]

It is generally accepted that El Niño–Southern Oscillation (ENSO) is the main modulator of rainfall variability over Northern South America in the interannual scale. Assuming that, an index is proposed to quantify this expected interannual variability in time series of rainfall. The result is the exposure assessment to the effects of droughts, measured with the Standardized Precipitation Index (SPI) for the monthly scale. The SPI is calculated from rainfall series, and wavelet analysis is used to estimate the variance for different frequencies present in the signal. The Wavelet Interannual Variability Index (WIVI) is calculated as the sum of the wavelet coefficients over a predetermined range of modes of variability (with periods longer than 2 year and shorter than 8 years). The index was tested using a dataset of rainfall records from Tocantins state, Central Brazil. Most of the series ranged from 1974 to 2012. On average, the series had 3.2% of gaps which were not filled to avoid the effect of artificial trends on the data. The state lies mostly over the Cerrado biome and is a new frontier of agricultural development in Brazil. According to the results, the Northern region is under higher exposure of interannual variability, with higher values of WIVI. The assessment is in agreement with large‐scale features of South American climate, specifically considering the influences of the Pacific and Atlantic Oceans and their patterns of sea surface temperature (SST). Interannual variability of rainfall was quantified by applying wavelet decomposition to time series of Standardized Precipitation Index (SPI). The wavelet variance for time scales of 2–8 years was quantified for 36 stations over Tocantins, Central Brazil. The variability within those scales was normalized as the Wavelet Index of Interannual Variability (WIVI). According to results, the Northern region is under higher exposure of interannual variability, with higher values of WIVI. The assessment is in agreement with large‐scale features of South American climate, specifically considering the influences of the Pacific and Atlantic Oceans and their patterns of sea surface temperature (SST). [ABSTRACT FROM AUTHOR]

The impact of extreme drought and heat stress that occurred in the Midwestern U.S. in 2012 on evapotranspiration ( ET), net ecosystem productivity ( NEP), and water-use efficiency ( WUE) of three perennial ecosystems (switchgrass, miscanthus, prairie) and a maize/soybean agroecosystem was studied as part of a long-term experiment. Miscanthus had a slower initial response but an eventually drastic ET as drought intensified, which resulted in the largest water deficit among the crops. The substantially higher ET at peak drought was likely supplied by access to deep soil water, but suggests that stomatal conductance of miscanthus during the drought may respond differently than the other ecosystems, consistent with an anisohydric strategy. While there was a discrepancy in the water consumption of maize and switchgrass/prairie in the early time of drought, all these ecosystems followed a water-saving strategy when drought intensified. The gross primary production ( GPP) of miscanthus dropped, but was reversible, when temperature reached 40 °C and still provided the largest total GPP among the ecosystems. Increased ET for miscanthus during 2012 resulted a large decline in ecosystem WUE compared to what was observed in other years. The biophysical responses of miscanthus measured during an extreme, historic drought suggest that this species can maintain high productivity longer than other ecosystems during a drought at the expense of water use. While miscanthus maintained productivity during drought, recovery lagged associated with depleted soil moisture. The enhanced ET of miscanthus may intensify droughts through increase supply of deep soil moisture to the atmosphere. [ABSTRACT FROM AUTHOR]

Perennial grasses are promising feedstocks for bioenergy production in the Midwestern USA. Few experiments have addressed how drought influences their carbon fluxes and storage. This study provides a direct comparison of ecosystem-scale measurements of carbon fluxes associated with miscanthus ( Miscanthus × giganteus), switchgrass ( Panicum virgatum), restored native prairie and maize ( Zea mays)/soybean ( Glycine max) ecosystems. The main objective of this study was to assess the influence of a naturally occurring drought during 2012 on key components of the carbon cycle and plant development relative to non-extreme years. The perennials reached full maturity 3-5 years after establishment. Miscanthus had the highest gross primary production (GPP) and lowest net ecosystem exchange (NEE) in 2012 followed by similar values for switchgrass and prairie, and the row crops had the lowest GPP and highest NEE. A post-drought effect was observed for miscanthus. Over the duration of the experiment, perennial ecosystems were carbon sinks, as indicated by negative net ecosystem carbon balance (NECB), while maize/soybean was a net carbon source. Our observations suggest that perennial ecosystems, and in particular miscanthus, can provide a high yield and a large potential for CO2 fixation even during drought, although drought may negatively influence carbon uptake in the following year, questioning the long-term consequence of its maintained productivity. [ABSTRACT FROM AUTHOR]

The contribution of coherent structures ( CSs) to daytime buoyancy heat flux was calculated for three forest sites in the Amazon region and a wetland site. Ejections and sweeps had similar contributions to fluxes at all sites, and when decomposing this contribution in scales, the resulting spectra were narrower for the forests. When accounting for times scales from 10 to 200s, CSs contributed to approximately 80% of fluxes for Caxiuanã forest site, with lower values for other locations. Among the forest, the contribution for the Jaru site was the lowest (≈66%) due to peak of contribution being close to 200s. [ABSTRACT FROM AUTHOR]

ABSTRACT Rainfall over the northeast part of Brazil is highly influenced by several weather systems such as trade winds, the Intertropical Convergence Zone, Easterly Wave Disturbances, Bolivia High and Atlantic Sub-tropical Anticyclone. The objectives of this work were to identify, using cluster analysis, spatial and temporal rainfall patterns for the northeastern Brazilian state of Alagoas, and to relate those patterns with the weather systems that occur over that region, which is the most populated area of semiarid climate in the world. The dataset consists of 30-years long time series of rainfall from 36 stations located on the state. Hierarchical clustering using Ward's algorithm was used to classify the sites into five groups. The stations were grouped according to the distance to the coast and orography. Rainfall is not uniformly distributed in space and time in all regions, such as in two groups within the arid zone, where more than 60% of annual precipitation occurs in a period of 5 months (March to July). Rainfall was highest in the coastal regions and over areas of the state with high altitude, due to orographic rainfall. Overall, rainfall over the state of Alagoas is characterized by strong gradients from the coast to the continent and from north to south due to the physiography of the region and the influence of weather systems with different time scales. [ABSTRACT FROM AUTHOR]

The carbon and water cycles for a southwestern Amazonian forest site were investigated using the longest time series of fluxes of CO2 and water vapor ever reported for this site. The period from 2004 to 2010 included two severe droughts (2005 and 2010) and a flooding year (2009). The effects of such climate extremes were detected in annual sums of fluxes as well as in other components of the carbon and water cycles, such as gross primary production and water use efficiency. Gap-filling and flux-partitioning were applied in order to fill gaps due to missing data, and errors analysis made it possible to infer the uncertainty on the carbon balance. Overall, the site was found to have a net carbon uptake of ≈5 t C ha−1 year−1, but the effects of the drought of 2005 were still noticed in 2006, when the climate disturbance caused the site to become a net source of carbon to the atmosphere. Different regions of the Amazon forest might respond differently to climate extremes due to differences in dry season length, annual precipitation, species compositions, albedo and soil type. Longer time series of fluxes measured over several locations are required to better characterize the effects of climate anomalies on the carbon and water balances for the whole Amazon region. Such valuable datasets can also be used to calibrate biogeochemical models and infer on future scenarios of the Amazon forest carbon balance under the influence of climate change. [ABSTRACT FROM AUTHOR]

Soil respiration ( Rsoil) is one of the largest CO2 fluxes in the global carbon (C) cycle. Estimation of annual Rsoil requires extrapolation of survey measurements or gap filling of automated records to produce a complete time series. Although many gap filling methodologies have been employed, there is no standardized procedure for producing defensible estimates of annual Rsoil. Here, we test the reliability of nine different gap filling techniques by inserting artificial gaps into 20 automated Rsoil records and comparing gap filling Rsoil estimates of each technique to measured values. We show that although the most commonly used techniques do not, on average, produce large systematic biases, gap filling accuracy may be significantly improved through application of the most reliable methods. All methods performed best at lower gap fractions and had relatively high, systematic errors for simulated survey measurements. Overall, the most accurate technique estimated Rsoil based on the soil temperature dependence of Rsoil by assuming constant temperature sensitivity and linearly interpolating reference respiration ( Rsoil at 10 °C) across gaps. The linear interpolation method was the second best-performing method. In contrast, estimating Rsoil based on a single annual Rsoil - Tsoil relationship, which is currently the most commonly used technique, was among the most poorly-performing methods. Thus, our analysis demonstrates that gap filling accuracy may be improved substantially without sacrificing computational simplicity. Improved and standardized techniques for estimation of annual Rsoil will be valuable for understanding the role of Rsoil in the global C cycle. [ABSTRACT FROM AUTHOR]

Soil moisture is being monitored over the Brazilian semiarid since 2014 using a network of 595 stations. The network was implemented by Cemaden, the National Center for Monitoring and Early Warning of Natural Disasters, with the objective of monitoring of spatial and temporal patterns of soil water and potential impacts on the region's agriculture activities. The Brazilian semiarid is the poorest in the country and hosts most of the family owned farms in Brazil. Crops such as maize and beans are mostly planted over small properties with no irrigation. In this work, agricultural risk is assessed using measurements of soil moisture which are presented in formats that highlight spatial, temporal and short-term variability. The Soil Moisture Index (SMI), based on a normalization of soil moisture by field capacity and wilting point, is used to characterize the current soil moisture conditions in a scale proportional to the water available to plants. The temporal evolution of SMI is used to visualize trends in short-term drought and response to rainfall events at daily time steps or recent weeks. A novel index based on continuous exposure to critical SMI is developed to help bring awareness of real time risk of water stress over the region: the Index of Stress in Agriculture (ISA). The index takes into account continuous exposure to a critical value of SMI (assumed as 0.4), below which water stress is expected to be triggered in plants. The index was tested during a short-term drought over the region and successfully identified locations under water stress for periods of three days or more. The monitoring tools presented here help to describe the real time conditions of drought over the region using daily observations. These tools are expected to be implemented over an online platform hosted at Cemaden's website. [ABSTRACT FROM AUTHOR]

Soil moisture over the Brazilian semiarid region is presented in different visualizations that highlight spatial, temporal and short-term agricultural risk. The analysis used the Soil Moisture Index (SMI), which is based on a normalization of soil moisture by field capacity and wilting point. The index was used to characterize the actual soil moisture conditions into categories from severe drought to very wet. In addition, the temporal evolution of SMI was implemented to visualize recent trends in short-term drought and response to rainfall events at daily time steps, as new data are available. Finally, a visualization of drought risk was developed by considering a critical value of SMI (assumed as 0.4), below which water stress is expected to be triggered in plants. A novel index based on continuous exposure to critical SMI was developed to help bring awareness of real time risk of water stress over the region: the Index of Stress in Agriculture (ISA). The index was tested during a drought over the region and successfully identified locations under water stress for periods of three days or more. The monitoring tools presented here help to describe the real time conditions of drought over the region using daily observations. The information from those tools support decisions on agricultural management such as planting dates, triggering of irrigation, or harvesting. [ABSTRACT FROM AUTHOR]