Seasonal variation of surface radiation and energy balances over two contrasting areas of the seasonally dry tropical forest (Caatinga) in the Brazilian semi-arid

Can changes in land use in a semi-arid region of Brazil cause seasonal variation in energy partitioning and evapotranspiration?

Changes to forests due to deforestation, or their replacement by agricultural areas, alter evapotranspiration and the partitioning of available energy. This study investigated seasonal variations in the energy balance and evapotranspiration in landscapes under different levels of anthropogenic intervention in the semi-arid region of Brazil. Micrometeorological data was obtained from September 2020 to October 2022 for three areas of the semi-arid region: preserved Caatinga (CAA, native vegetation), Caatinga under regeneration (REGE) and a deforested area (DEFA). Here, we use the Bowen ratio energy balance method. Measurements were taken of global solar radiation, air temperature, relative humidity, vapour pressure deficit, rainfall, net radiation, latent heat flux, sensible heat flux, soil heat flux, evapotranspiration, volumetric soil water content and Normalised Difference Vegetation Index. Sensible heat flux was the dominant flux in both areas with 66% for preserved Caatinga vegetation, 63% for Caatinga under regeneration and 62% deforested area. The latent heat flux was equivalent to 28% of the net radiation for preserved Caatinga vegetation, Caatinga under regeneration and deforested area. The evapotranspiration in turn responded as a function of water availability, being higher during the rainy seasons, with average values of 1.82 mm day-1 for preserved Caatinga vegetation, 2.26 mm day-1 for Caatinga under regeneration and 1.25 mm day-1 for deforested area. The Bowen ratio presented values > 1 in deforested area, preserved Caatinga vegetation and Caatinga under regeneration. Thus, it can be concluded that the change in land use alters the energy balance components, promoting reductions in available energy and latent and sensible heat fluxes during the rainy-dry transition in the deforested area. In addition, the seasonality of energy fluxes depends on water availability in the environment.

Remote sensing-based assessment of land degradation and drought impacts over terrestrial ecosystems in Northeastern Brazil

The spatio-temporal assessment of water and carbon fluxes in Brazil's Northeast region (NEB) allows for a better understanding of these surface flux patterns in areas with different vegetation physiognomies. The NEB is divided into four biomes: Amazon, Cerrado, Caatinga, and Atlantic Forest. Land degradation is a growing problem, particularly in susceptible areas of the Caatinga biome, such as the seasonally dry tropical forest. Furthermore, this region has experienced climatic impacts, such as severe droughts. Due to increasing human pressure, the Caatinga's natural land cover undergoes drastic changes, making it a region particularly vulnerable to desertification. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) estimates of evapotranspiration (ET) and gross primary production (GPP) were validated in two contrasting areas, dense Caatinga and sparse Caatinga, using eddy covariance (EC) data and then investigated their behavior over 21 years (2000-2021) for the NEB. MODIS products explained around 60% of the variations in ET and GPP, showing higher accuracy in dense Caatinga, while areas of sparse Caatinga presented the lowest GPP, indicating that land degradation has reduced the photosynthetic activity of the vegetation in this area. Based on the analysis of ET and GPP over 21 years, we observed a greater dependence of the sparse Caatinga on climate variability, demonstrating a stronger resilience of dense Caatinga to climate effects. In comparison with the other biomes of the NEB region, we found lower rates of ET and GPP in the Caatinga biome, with averages similar to the Sparse Caatinga. In comparison with the other biomes in the NEB region, we found the lowest averages of ET and GPP in the Caatinga biome, similar to values found in the sparse Caatinga. In forest areas, similar to the monitored DC, they allowed the Caatinga to behave closer to the other biomes present in the region.

Understanding interactive processes: a review of CO2 flux, evapotranspiration, and energy partitioning under stressful conditions in dry forest and agricultural environments

Arid and semiarid environments are characterized by low water availability (e.g., in soil and atmosphere), high air temperature, and irregularity in the spatio-temporal distribution of rainfall. In addition to the economic and environmental consequences, drought also causes physiological damage to crops and compromises their survival in ecosystems. The removal of vegetation is responsible for altering the energy exchange of heat and water in natural ecosystems and agricultural areas. The fluxes of CO₂ are also changed, and environments with characteristics of sinks, which can be sources of CO₂ after anthropic disturbances. These changes can be measured through methods such as sap flow, eddy covariance, remote sensing, and energy balance. Despite the relevance of each method mentioned above, there are limitations in their applications that must be respected. Thus, this review aims to quantify the processes and changes of energy fluxes, CO₂, and their interactions with the surfaces of terrestrial ecosystems in dry environments. Studies report that the use of methods that integrate data from climate monitoring towers and remote sensing products helps to improve the accuracy of the determination of energy fluxes on a global scale, also helping to reduce the dissimilarity of results obtained individually. Through the collection of works in the literature, it is reported that several areas of the Brazilian Caatinga biome, which is a Seasonally Dry Tropical Forest have been suffering from changes in land use and land cover. Similar fluxes of sensible heat in areas with cacti and Caatinga can be observed in studies. On the other hand, one of the variables influenced mainly by air temperature is net radiation. In dry forest areas, woody species can store large amounts of carbon in their biomass above and belowground. The use of cacti can modify the local carbon budget when using tree crops together. Therefore, the study highlights the complexity and severity of land degradation and changes in CO₂, water, and energy fluxes in dry environments with areas of forest, grassland, and cacti. Vegetation energy balance is also a critical factor, as these simulations are helpful for use in forecasting weather or climate change. We also highlight the need for more studies that address environmental conservation techniques and cactus in the conservation of degraded areas.

Using Remote Sensing to Quantify the Joint Effects of Climate and Land Use/Land Cover Changes on the Caatinga Biome of Northeast Brazilian

Caatinga biome, located in the Brazilian semi-arid region, is the most populous semi-arid region in the world, causing intensification in land degradation and loss of biodiversity over time. The main objective of this paper is to determine and analyze the changes in land cover and use, over time, on the biophysical parameters in the Caatinga biome in the semi-arid region of Brazil using remote sensing. Landsat-8 images were used, along with the Surface Energy Balance Algorithm for Land (SEBAL) in the Google Earth Engine platform, from 2013 to 2019, through spatiotemporal modeling of vegetation indices, i.e., leaf area index (LAI) and vegetation cover (VC). Moreover, land surface temperature (LST) and actual evapotranspiration (ETa) in Petrolina, the semi-arid region of Brazil, was used. The principal component analysis was used to select descriptive variables and multiple regression analysis to predict ETa. The results indicated significant effects of land use and land cover changes on energy balances over time. In 2013, 70.2% of the study area was composed of Caatinga, while the lowest percentages were identified in 2015 (67.8%) and 2017 (68.7%). Rainfall records in 2013 ranged from 270 to 480 mm, with values higher than 410 mm in 46.5% of the study area, concentrated in the northern part of the municipality. On the other hand, in 2017 the lowest annual rainfall values (from 200 to 340 mm) occurred. Low vegetation cover rate was observed by LAI and VC values, with a range of 0 to 25% vegetation cover in 52.3% of the area, which exposes the effects of the dry season on vegetation. The highest LST was mainly found in urban areas and/or exposed soil. In 2013, 40.5% of the region’s area had LST between 48.0 and 52.0 °C, raising ETa rates (~4.7 mm day−1). Our model has shown good outcomes in terms of accuracy and concordance (coefficient of determination = 0.98, root mean square error = 0.498, and Lin’s concordance correlation coefficient = 0.907). The significant increase in agricultural areas has resulted in the progressive reduction of the Caatinga biome. Therefore, mitigation and sustainable planning is vital to decrease the impacts of anthropic actions.

Effects of human-induced land degradation on water and carbon fluxes in two different Brazilian dryland soil covers

The Brazilian semiarid region presents a physical water scarcity and high seasonal and interannual irregularities of precipitation, known as a region with periodic droughts. This region is mainly covered by the Caatinga biome, recognized as a Seasonally Dry Tropical Forest (SDTF). Soil water availability directly impacts the ecosystem's functioning, characterized by low fertility and sparse vegetation cover during the dry season, making it a fragile ecosystem vulnerable to climatic variations. Additionally, this region has been suffering from several issues due to human activities over the centuries, which has resulted in extensive areas being severely degraded, which aggravates the impacts from climatic variations and the susceptibility to desertification. Thus, studying the soil-plant-atmosphere continuum in this region can help better understand the seasonal and annual behavior of the water and carbon fluxes. This study investigated the dynamics of water and carbon fluxes during four years (2013-2016) by using eddy covariance (EC) measurements within two areas of Caatinga (dense Caatinga (DC) and sparse Caatinga (SC)) that suffered anthropic pressures. The two study areas showed similar behavior in relation to physical parameters (air temperature, incoming radiation, vapor pressure deficit, and relative humidity), except for soil temperature. The SC area presented a surface temperature of 3 °C higher than the DC, related to their vegetation cover differences. The SC area had higher annual evapotranspiration, representing 74% of the precipitation for the DC area and 90% for the SC area. The two areas acted as a carbon sink during the study period, with the SC area showing a lower CO₂ absorption capacity. On average, the DC area absorbs 2.5 times more carbon than the SC area, indicating that Caatinga deforestation affects evaporative fluxes, reducing atmospheric carbon fixation and influencing the ability to mitigate the effects of increased greenhouse gas concentrations in the atmosphere.

Energy Balance, CO2 Balance, and Meteorological Aspects of Desertification Hotspots in Northeast Brazil

The main objective of this study was to evaluate meteorological variables and the simulated components of energy and CO2 balances in desertification hotspots in Northeast Brazil. Meteorological data were obtained from the National Institute of Meteorology measurement network for the Cabrobó and Ibimirim sites. Initially, hourly linear trends were calculated for the meteorological variables using the non-parametric Mann–Kendall test. Then, the seasonal variability in the components of energy and CO2 balances was assessed through simulations of the simple tropical ecosystem (SITE) model. Results showed evidence of increasing air temperature trends in the Cabrobó site in the first months of the year, which was not observed in the Ibimirim site. Regarding relative humidity, increasing trends were observed in a few months over the Cabrobó site, while decreasing trends were observed in the Ibimirim site. Opposite behaviors were also identified for the trends in wind speed in both sites. Gross primary production (GPP) and net ecosystem exchange (NEE) simulated values were higher in the first half of the year in both sites. GPP varied from 0.8 to 1.2 g C m−² h−¹, and NEE fluctuated around approximately −5 g C m−² h−¹. These results indicate that rainfall seasonality is a crucial factor for the modulation of CO2 and energy balance fluxes in the Caatinga biome.

Artificial Neural Network Model of Soil Heat Flux over Multiple Land Covers in South America

Soil heat flux (G) is an important component for the closure of the surface energy balance (SEB) and the estimation of evapotranspiration (ET) by remote sensing algorithms. Over the last decades, efforts have been focused on parameterizing empirical models for G prediction, based on biophysical parameters estimated by remote sensing. However, due to the existing models’ empirical nature and the restricted conditions in which they were developed, using these models in large-scale applications may lead to significant errors. Thus, the objective of this study was to assess the ability of the artificial neural network (ANN) to predict mid-morning G using extensive remote sensing and meteorological reanalysis data over a broad range of climates and land covers in South America. Surface temperature (Ts), albedo (α), and enhanced vegetation index (EVI), obtained from a moderate resolution imaging spectroradiometer (MODIS), and net radiation (Rn) from the global land data assimilation system 2.1 (GLDAS 2.1) product, were used as inputs. The ANN’s predictions were validated against measurements obtained by 23 flux towers over multiple land cover types in South America, and their performance was compared to that of existing and commonly used models. The Jackson et al. (1987) and Bastiaanssen (1995) G prediction models were calibrated using the flux tower data for quadratic errors minimization. The ANN outperformed existing models, with mean absolute error (MAE) reductions of 43% and 36%, respectively. Additionally, the inclusion of land cover information as an input in the ANN reduced MAE by 22%. This study indicates that the ANN’s structure is more suited for large-scale G prediction than existing models, which can potentially refine SEB fluxes and ET estimates in South America.