Droughts Over Amazonia in 2005, 2010, and 2015: A Cloud Cover Perspective

Anderson²,

et al. 2019

Front. Earth Sci.

Extreme droughts in Amazonia cause anomalous increase in fire occurrence, disrupting the stability of environmental, social, and economic systems. Thus, understanding how droughts affect fire patterns in this region is essential for anticipating and planning actions for remediation of possible impacts. Focused on the Brazilian Amazon biome, we investigated fire responses to the 2010 and 2015/2016 Amazonian droughts using remote sensing data. Our results revealed that the 2015/2016 drought surpassed the 2010 drought in intensity and extent. During the 2010 drought, we found a maximum area of 846,800 km 2 (24% of the Brazilian Amazon biome) with significant (p ≤ 0.05) rainfall decrease in the first trimester, while during the 2015/2016 the maximum area reached 1,702,800 km 2 (47% of the Brazilian Amazon biome) in the last trimester of 2015. On the other hand, the 2010 drought had a maximum area of 840,400 km 2 (23% of the Brazilian Amazon biome) with significant (p ≤ 0.05) land surface temperature increase in the first trimester, while during the 2015/2016 drought the maximum area was 2,188,800 km 2 (61% of the Brazilian Amazon biome) in the last trimester of 2015. Unlike the 2010 drought, during the 2015/2016 drought, significant positive anomalies of active fire and CO 2 emissions occurred mainly during the wet season, between October 2015 and March 2016. During the 2010 drought, positive active fire anomalies resulted from the simultaneous increase of burned forest, non-forest vegetation and productive lands. During the 2015/2016 drought, however, this increase was dominated by burned forests. The two analyzed droughts emitted together 0.47 Pg CO 2 , with 0.23 Pg CO 2 in 2010, 0.15 Pg CO 2 in 2015 and 0.09 Pg CO 2 in 2016, which represented, respectively, 209%, 136%, 82% of annual Brazil's national target for reducing carbon emissions from deforestation by 2017 (approximately 0.11 Pg CO 2 year −1 from 2006 to 2017). Finally, we anticipate that the increase of fires during the droughts showed here may intensify and can become more frequent in Amazonia due to changes in climatic variability if no regulations on fire use are implemented.

Section: Discussionmentioning

confidence: 62%

Fire Responses to the 2010 and 2015/2016 Amazonian Droughts

Anderson²,

et al. 2019

Front. Earth Sci.

“…It has been rarely considered as a variable, and few works have shown its importance in drought. As an example, Jimenez et al [58], in their study on droughts in the Amazonia region, analyzed its importance and found it to be a significant factor, owing to the long-term land use, land-cover change and forest loss in the region. Similarly, vapor pressure has shown a strong correlation with the water transport process in various vegetation types.…”

Section: Training Periodmentioning

confidence: 99%

Short-Term Spatio-Temporal Drought Forecasting Using Random Forests Model at New South Wales, Australia

2020

Droughts can cause significant damage to agriculture and water resources, leading to severe economic losses and loss of life. One of the most important aspect is to develop effective tools to forecast drought events that could be helpful in mitigation strategies. The understanding of droughts has become more challenging because of the effect of climate change, urbanization and water management; therefore, the present study aims to forecast droughts by determining an appropriate index and analyzing its changes, using climate variables. The work was conducted in three different phases, first being the determination of Standard Precipitation Evaporation Index (SPEI), using global climatic dataset of Climate Research Unit (CRU) from 1901–2018. The indices are calculated at different monthly intervals which could depict short-term or long-term changes, and the index value represents different drought classes, ranging from extremely dry to extremely wet. However, the present study was focused only on forecasting at short-term scales for New South Wales (NSW) region of Australia and was conducted at two different time scales, one month and three months. The second phase involved dividing the data into three sample sizes, training (1901–2010), testing (2011–2015) and validation (2016–2018). Finally, a machine learning approach, Random Forest (RF), was used to train and test the data, using various climatic variables, e.g., rainfall, potential evapotranspiration, cloud cover, vapor pressure and temperature (maximum, minimum and mean). The final phase was to analyze the performance of the model based on statistical metrics and drought classes. Regarding this, the performance of the testing period was conducted by using statistical metrics, Coefficient of Determination (R2) and Root-Mean-Square-Error (RMSE) method. The performance of the model showed a considerably higher value of R2 for both the time scales. However, statistical metrics analyzes the variation between the predicted and observed index values, and it does not consider the drought classes. Therefore, the variation in predicted and observed SPEI values were analyzed based on different drought classes, which were validated by using the Receiver Operating Characteristic (ROC)-based Area under the Curve (AUC) approach. The results reveal that the classification of drought classes during the validation period had an AUC of 0.82 for SPEI 1 case and 0.84 for SPEI 3 case. The study depicts that the Random Forest model can perform both regression and classification analysis for drought studies in NSW. The work also suggests that the performance of any model for drought forecasting should not be limited only through statistical metrics, but also by examining the variation in terms of drought characteristics.

“…The study area is a large area of the Amazon forest. The Amazon forest is a tropical ecosystem characterized by wet conditions; however, it has experienced recurrent and large-scale droughts over the last few years [44,45]. The extreme drought events occurring in 2005, 2010, and 2015 were induced by different large-scale atmospheric mechanisms associated with warm sea surface temperatures (SST).…”

Section: The Study Areamentioning

confidence: 99%

“…The 2005 and 2010 drought conditions are attributed to warm SSTs over the Atlantic (Atlantic Multidecadal Oscillation, AMO) inducing the contraction of the northeast trade winds and moisture flux from the warming Tropical North Atlantic (TNA) SST. On the other hand, the 2015 drought event is attributed to the warm SSTs over the tropical Pacific (to El Niño-Southern Oscillation, ENSO) [44,46,47].…”

Section: The Study Areamentioning

confidence: 99%

Recurrence Analysis of Vegetation Indices for Highlighting the Ecosystem Response to Drought Events: An Application to the Amazon Forest

Semeraro

Luvisi

Lillo³

et al. 2020

Remote Sensing

Forests are important in sequestering CO2 and therefore play a significant role in climate change. However, the CO2 cycle is conditioned by drought events that alter the rate of photosynthesis, which is the principal physiological action of plants in transforming CO2 into biological energy. This study applied recurrence quantification analysis (RQA) to describe the evolution of photosynthesis-related indices to highlight disturbance alterations produced by the Atlantic Multidecadal Oscillation (AMO, years 2005 and 2010) and the El Niño-Southern Oscillation (ENSO, year 2015) in the Amazon forest. The analysis was carried out using Moderate Resolution Imaging Spectroradiometer (MODIS) images to build time series of the enhanced vegetation index (EVI), the normalized difference water index (NDWI), and the land surface temperature (LST) covering the period 2001–2018. The results did not show significant variations produced by AMO throughout the study area, while a disruption due to the global warming phase linked to the extreme ENSO event occurred, and the forest was able to recover. In addition, spatial differences in the response of the forest to the ENSO event were found. These findings show that the application of RQA to the time series of vegetation indices supports the evaluation of the forest ecosystem response to disruptive events. This approach provides information on the capacity of the forest to recover after a disruptive event and, therefore is useful to estimate the resilience of this particular ecosystem.