Environmental and Political Factors Combine to Exacerbate Syrian Drought that Underpins Unrest

by Caroline Hays

Major climate events have social and political ramifications beyond their environmental impacts. In a recent study, Kelley et al. (2015) examine the extent of the drought in Syria that began in the winter of 2006/2007 and consider how it impacted the country socially and politically. The authors find that, although Syria has experienced several multiyear (three or more) droughts in the last 80 years, the most recent drought is the most extreme on record. Additionally, the authors note that three of the four most severe droughts recorded in Syria have taken place in the last 25 years. They connect the dots between anthropogenic climate effects, drought, agricultural collapse, and mass human migration, presenting a more comprehensive picture of a major climate event than is often shown. Continue reading

Drought-Fire Interactions in the Amazonian Rainforest Increase Tree Mortality

by Maithili Joshi

The relationship between fire-induced tree mortality and extreme weather remain poorly understood because it is restricted to post-fire observations of tree mortality. Studies done on the effects of forest fires and biodiversity remain understood on the patch scale, and do not consider the effects of fire on vegetation dynamics and structure. In the southeast Amazon forest, scientists established a large scale, and long term prescribed forest fire experiment in a transitional forest. Primarily, trying to determine if there are weather, and fuel, related thresholds in fire behavior associated with high levels of fire-induced tree mortality across two different fire regimes, and secondarily, what the effects of an intense forest fire are on forest structure, flammability, and aboveground live carbon stock. Continue reading

Could Climate Change Cause Famines?

by Caroline Chmiel

More than ever, the rapid growth of the world population is causing a heightened demand for food. Making this struggle infinitely worse is climate change. Per decade, food demand rises by 14%. Climate change reduces wheat yields by 2% compared to the amount without climate change, and corn yields by 1%. The demand for food causes worry and stress, so the idea that climate change worsens an already critical situation makes the fight to feed billions even harder. This is the bleak picture painted by Eduardo Porter writing in the New York Times. Food price spikes because of increased demand strongly correlate with urban unrest. From temperature changes due to global warming, production of crops can change. Less than expected production often causes producers to ban exports and importers to try to hoard the crop. Overall, commodity markets experience chaos and strain further than just feeding people. The culmination of climate change, increased population and demand for food leads to a serious question about the possibility of famine. More likely, though, is a volatile world full of wars over substances. The most highly affected population will be the poor, unable to afford increased food prices. Continue reading

The Anthropogenic Roots of Increased Flooding in Kano, Nigeria

by Dan McCabe

Intelligent planning for urban development requires an understanding of how different development paths can impact sustainability. In order to better understand what aspects of cities impact sustainability, Barau et al. (2015) investigated historical trends in the environmental resilience of Kano, Nigeria. Kano, northern Nigeria’s largest city with a population of over 2 million, has been a commercial center since the 10th century and has experienced extreme morphological changes in the centuries since then. Recently, the city has been subject to an increasing number of catastrophic flooding events that have caused deaths, exacerbated the spread of infectious diseases, and forced the relocation of hundreds of thousands of residents. As the frequency of extreme weather threatens to increase due to global climate change, Kano’s ability to respond to flooding is of great concern. Barau et al. therefore sought to determine how the city’s evolution has made it especially prone to severe floods. Continue reading

Just Released! “Energy, Biology, Climate Change”

FrontCover6x9 white border 72dpi EBCC2015

Our newest book, published on May 6, 2015 and available at Amazon.com for $19.95.

The focus of this book is the interactions between energy, ecology, and climate change, as well as a few of the responses of humanity to these interactions. It is not a textbook, but a series of chapters discussing subtopics in which the authors were interested and wished to write about. The basic material is cutting-edge science; technical journal articles published within the last year, selected for their relevance and interest. Each author selected eight or so technical papers representing his or her view of the most interesting current research in the field, and wrote summaries of them in a journalistic style that is free of scientific jargon and understandable by lay readers. This is the sort of science writing that you might encounter in the New York Times, but concentrated in a way intended to give as broad an overview of the chapter topics as possible. None of this research will appear in textbooks for a few years, so there are not many ways that readers without access to a university library can get access to this information.

This book is intended be browsed—choose a chapter topic you like and read the individual sections in any order; each is intended to be largely stand-alone. Reading all of them will give you considerable insight into what climate scientists concerned with energy, ecology, and human effects are up to, and the challenges they face in understanding one of the most disruptive—if not very rapid—event in human history; anthropogenic climate change. The Table of Contents follows: Continue reading

Effect of Climate Change on Australian and Global Food Production

by Shelby Long

Recent droughts associated with climate change have had immense negative effects on food production in Australia. Australia is an important producer and exporter of livestock, dairy, and wheat. Much of the wheat produced is exported to Indonesia, Japan, South Korea, Yemen, Vietnam, and China. The Murray-Darling Basin is one of the main agricultural areas in the country, contributing 40% of Australia’s gross value of agricultural production. Water scarcity is accompanied by a high demand for water for both agricultural irrigation and non-agricultural uses (Quiggin and Chambers 2004). Therefore, it is necessary for crop producers to adopt new strategies to mitigate the impacts of drought. Some of these strategies include land use changes and introducing drought tolerant crop varieties. Qureshi et al. (2013) aim to use the Australian Bureau of Statistics (ABS) data and modeling to explore the possible future effects of Australian water Continue reading

Lack of Sierra Snow

IMG_4409 SierrtanCrestAug1by Emil Morhardt

Another shot of the high Sierra above South Lake near Bishop, California, taken between rain events at the end of July, 2014. The snowy patches on the north-facing slopes are all that is left of the once much larger glaciers. Otherwise there’s no mountain snow left and precious little runoff at a time of year the runoff would normally have peaked a month ago and still be going strong. There’s a large fire in progress in Yosemite in the direction we’re looking, but so far the smoke is blowing the other way.

 

Climate on the Ground: Not Good for Hydro

SouthLakeLowIMG_4370by Emil Morhardt

This is South Lake, in the Eastern Sierra Nevada Mountains at 9500 ft. ASL above Bishop, California. Looks (and was) rainy at the end of July 2014, but this reservoir, one of the largest on the east side of the Sierra is at the lowest level I’ve seen it in many decades. It is the primary source of water for Southern California Edison’s (SCE) Bishop Creek Hydroelectric Project, usually a 50 MW source of renewable energy that won’t be producing much of anything this year. The problem this year is, of course, a severe drought that may or may not be attributable to anthropogenic climate change (I’m betting it is though.) But drought or not, most models predict that more and more Sierran precipitation will be coming down as rain going forward. So this time of year, South Lake will fill up during the autumn rains, but won’t continue to be refilled by snowmelt as summer approaches, sharply curtailing a major source of renewable energy.

 

Drought Modeling Consistency and Discrepancies in Predicting Drought in the Future

Many models of soil moisture, drought indices, and precipitation-minus evaporation predict increased drought in the twenty-first century. Furthermore, precipitation, stream flow, and drought indices have demonstrated increased aridity since the 1950s over land. Still, there are major differences between observed data and model predictions. Sea surface temperature has been shown to influence land precipitation. However, coupled models have not reproduced recent regional precipitation changes in their predictions, which may be due to a lack of observed sea surface temperature data in these model stimulations. Dai (2012) demonstrates that the models reproduce the effects of El Niño-Southern Oscillation and the observed data on global mean aridity for 1923 through 2010. According to Dai, natural variations in tropical sea surface temperature not accounted for in the models cause the regional differences in observed and model-simulated aridity changes. Furthermore, he concludes that the observed global aridity changes through 2010 are consistent with model predictions, thus validating predictions of increasing severity and frequency of droughts over the next century. —Hilary Haskell
                  Dai, A. 2012. Increasing drought under global warming in observations and models. Nature Climate Change 3, 52–58.

                  Dai reconciles historical data and model projections of increasing aridity and drought in order to gain a more comprehensive understanding of global climate change’s effects on drought patterns. A variety of different drought indices quantify drought, yet yield different results, especially on smaller geographical scales. Dai uses precipitation, stream flow, and soil moisture fields to quantify meteorological, hydrologic, and agricultural drought, respectively. For historical soil-moisture data, the author used the Palmer Drought Severity Index (PDSI) Penman–Monteith (sc_PDSI_pm) equation to calculate the potential evapotranspiration of water out of the leaves of vegetation, given there is enough water for evapotranspiration to occur. The PDSI calculation is based on water-balance models of soil moisture forced with observed precipitation and temperature. This index is widely used in monitoring drought and paleoclimate reconstructions. The PDSI_PM is regarded as more accurate in its analysis than the PDSI_th calculation, because it includes temperature, precipitation, radiation, wind-speed, and humidity data in its calculation of potential evapotranspiration. Therefore, it provides a more comprehensive analysis of global warming scenarios. The sc_PDSI_pm calculation is used to determine the relative impacts of different factors (humidity, precipitation, etc.) affecting drought. By comparing the results of forced sc_PDSI_pm calculations that include or exclude various drought factors, each factor’s impact on drought can be determined. The author used two coupled climate model simulations based on future GHG emission scenarios: Coupled Model Intercomparison Project phase 3 (CMIP3), which was used in the Intergovernmental Panel on Climate Change Fourth Assessment Report and the new phase 5 (CMIP5).
                  For 1950–2010, Dai found that observed annual precipitation data and sc_PDSI_pm data had similar linear trends. Furthermore, these trends were similar to stream flow trends since 1948 for the world’s main river basins. The author noted some regional and quantitative differences between observed annual precipitation, sc_PDSI_pm, and stream flow data. However, these variations are still closely related. The similar linear trends indicate increased aridity over most of Africa, Southeast Asia, Eastern Australia, and Southern Europe, while there is increased wetness over the central U.S., Argentina, and northern high-latitude areas. This consistency in linear trends across independent measurements of precipitation and stream flow data indicate that these trends are accurate reflections of projected hydroclimatic variations in the future. Furthermore, this comparison also verifies that the sc_PDSI_pm is a reliable method for monitoring changes in aridity.
                  Warming trends since the 1980s have globally impacted the upward trend in drought areas, increasing these areas by about eight percent. This drying is attributable to warming patterns that cause increased evapotranspiration, especially over northern mid-to-high latitudes. However, increased aridity in Africa, Southeast Asia, Eastern Australia, and Southern Europe is mainly due to precipitation decreases. This decrease in precipitation is mainly caused by variations in sea surface temperature. Dai used The Hadley Centre Sea Ice and Sea Surface Temperature data set (SST) over varying periods of time to study the effects of (SST) on global drought. SST trends over the long-run are included in global warming predictions. However, over the course of a few decades, SST variations are absent in greenhouse gas (GHG) and aerosol-coupled model simulations. The absence of SST variations means that these natural variations are excluded from model predictions, albeit their timing and spatial patterns may be dependent on the initial conditions of the other factors included in the models. The lack of SST trends in current models makes the effects of SST changes irreproducible in models’ future predictions.
                  There are consistencies across soil moisture predictions for the coupled models and the sc_PDSI_pm calculations. Fourteen of the CMIP5 models analyzed in this study demonstrated decreases in soil moisture content in the top 10 cm layer of soil for most of the Americas, Europe, Southern Africa, most of the Middle East, Southeast Asia and Australia during the twenty-first century. The multi-model average predicts further decreases of 5 to 15% by 2080–2099. The sc_PDSI_PM using the same multi-model data reproduced the same increased aridity in soil moisture. However, the sc_PDSI_pm yields larger increases in wetness over central and eastern Asia and northern North America. In comparison to the CMIP5 model, the CMIP3 models, with some regional differences, also predicts the same increasing soil aridity for all seasons.
                  According to Dai, SSTs have large influences on land precipitation and drought. To demonstrate this influence, the author used maximum covariance analysis (MCA) of global SSTs at latitudes (40–60° N) and sc_PDSI_pm calculations at latitudes (60–75°N) based on observations and the CMIP models. The author’s goal was to reproduce the observed relationships between SST and sc_PDSI_pm by using MCA modes, in order to conclude whether the models could stimulate the recent drying trend. MCA analysis uses a standard singular value, obtained through decomposition of variables or inputs into a model from two separate fields. By decomposing this singular value, comparisons can be made between the two fields. For this study, Dai used MCA to compare difference in the SSTs and sc_PDSI_pm from observations and the CMIP models. Dai’s comparison of leading MCA modes excluded some of the unforced, irreproducible natural variation in the modes.
The first leading MCA modes (MCA1) based on observations and models represent global warming in relation to sea surface temperature. The temporal coefficient in this analysis is strongly correlated (r=0.97) between the observed global mean surface temperature and the SST MCA1 patterns, which are similar to the observed warming patterns over the oceans. MCA1 from the models show similar nonlinear global warming trends, with widespread warming over the oceans. The sc_PDSI-PM demonstrates short-term variability in these models. Considering the models, the observed mean global warming mode is evident in the GHG-forced CMIP simulations for both SST and sc_PDSI_PM, with a correlation of r=0.86 and a regression coefficient of .09566 between global means of sc_PDSI_pm anomalies from MCA1 observations. This finding suggests that inclusion of GHG emissions as a factor in global aridity modeling is a valid methodology based on its demonstrated consistency across global aridity change models and historical observations.
The second MCA modes (MCA2) calculated from both observations and models demonstrate similarity across spatial patterns. Both the observations and the models represent El Niño-Southern Oscillation (ENSO) through SST anomaly patterns. Furthermore, the time-based coefficient is highly correlated to the ENSO index. El Niño modes vary considerably across decades. Since 1999, ENSO observations for the central and eastern Pacific have become cooler than the time period from 1977 to 1998.
The spatial patterns for the sc_PDSI_pm were from the various much different than the observations, trend maps, and long term MCA1 (1950–2009). This discrepancy is attributable to large intermodal variations in the MCA1 from 1923–2010 caused by unforced natural variations and weak GHG-forced indicators of precipitation. These results indicate that individual models and observed global warming modes have large natural variation patterns unrelated to previous calculations that did not include GHG data. The MCA does not completely separate GHG-caused changes in precipitation and the changes in the sc_PDSI_pm from other natural variations included in the predictions. This finding is supported by the fact that until the year 2010, GHG -attributable variation was not very strong (4–6 % of total variance), compared with the natural variations forced in the models.
                  Dai suggests that the large regional differences between observations and individual model runs, the differences over West Africa, the USA, Brazil, Southern Africa, and eastern Australia, are the result of sample errors between actual realization of hydroclimatic patterns and natural variation not seen in the CMIP models. The major differences between the U.S. and Sahel stand out. The Sahel’s drying trend since 1950 is mainly due to decreased summer rainfall caused by warming in the South Atlantic Ocean relative to the North Atlantic, along with the warming of the Indian Ocean. This warming, coupled with dynamic vegetation feedback (not included in the CMIP models) leads to the discrepancy between the U.S. and the Sahel. CMIP3 models would predict the opposite warming pattern in the Atlantic Ocean and increased precipitation over the Sahel in the twenty-first century in the face of GHG caused global warming. CMIP5 models reproduce the decline in rainfall over the Sahel from 1950s–1980s. However, the CMIP5 models predict the decline with decreased magnitude and consider sulphate aerosols as the main cause of the decline. Other CMIP5 models do not include the effect of sulphate aerosol in the twentieth century. However, for the twenty-first century, the GHG effect will dominate over the aerosol parameter, and therefore, the drought over the Sahel may not actually take place.
                  Projected increased wetness over the U.S. is the result of the upward trend from 1950–1990 in precipitation levels. After the 1990s, however, the U.S. has become drier. This variation across decades can be attributed to Interdecadal Pacific Oscillation (IPO), with the warm phase of above normal SSTs in the tropical Pacific occurring around the year 1977, and the cold period taking place around 1999. IPO greatly influences US precipitation and drought, especially in the southwest. Anthropogenic data forcing does not coincide with these cycles due to the fact that these natural cycles are not included in predictions that depend on initial conditions of the coupled models. Therefore, these cycles are not reproducible in climate change predictions.
                  Differences between temporal and spatial patterns of the MCA1 mode for SST and sc_PDSI_pm in observed data versus CMIP5 data are largely attributable to model deficiencies in representing the effect of sulphate aerosols in the twentieth century, natural SST variations not taken into account by the CMIP models, and sampling errors among different GHG induced changes in the sc_PDSI_pm that are still relatively weak. MCA1 and MCA2 demonstrate the only statistically significant resemblance. This finding indicates that the global warming mode from the observations will likely become part of the GHG-induced warming model. The models are able to reflect the GHG-induced trend mode (MCA1) seen in observations  and the main ENSO mode (MCA2), therefore increasing confidence in the model predications of increasing drought patterns for the Americas Southern Europe, Southern and Central Africa, Australia, and Southeast Asia as GHG emissions contribute to continuing warming in the twenty first century. Still, these models are not reliably able to stimulate the precipitation and PDSI changes for these regions with valid certainty. The MCA1 patterns for sc_PDSI_pm for the twenty-first century are stable due to the large forced trend in the context of natural variations in temperature, precipitation, and other variables. The MCA1 patterns for sc_PDS_pm predict severe drought conditions by the late half of the twenty-first century, especially for densely populated areas such as Europe, the eastern U.S., Southeast Asia, and Brazil. If the model’s regional predictions are correct, the effect on these populations will be drastic.

Recovery of the Amazonian rainforest canopy after increasingly severe droughts

The Amazon rainforest has recently experienced increasingly severe droughts. Currently, there is little known about the long-term effects of these droughts that result in tree dieback, altered rainforest canopy structure, and increased rainforest flammability in the Amazonia.  Saatchi et al. (2013) use satellite microwave observations of rainfall from the Tropical Rainfall Measuring Mission and canopy backscatter data from the SeaWinds Scatterometer on board QuickSCAT to demonstrate that western Amazonia experienced severe water deficit during the dry season in 2005, and a subsequent disruption in canopy structure and decrease in canopy moisture. Furthermore, even with an increase in precipitation in the years following the drought in 2005, the decrease in canopy backscatter and thus alteration in the rainforest canopy characteristics and water content remained until the next major drought occurred in 2010. If droughts occur more frequently and severely in the Amazon rainforest due to climate change, this drought disturbance may cause considerable changes in the Amazonian rainforest canopy.—Hilary Haskell
                  Saatchi, S., Asefi-Najafabady, S., Malhi, Y., Aragao, L., Anderson, L., Myneni, R., Nemani, R. 2013. Persistent effects of a severe drought on Amazonian forest canopy. Proceedings of the National Academy of Sciences of the United States of America 110, 565570.

                  Saatchi et al. conducted this study to understand the extent and severity of the long-term impacts of droughts on the Amazonian rainforest. Currently, only short-term effects of drought events have been studied through ground and satellite data. The long-term impacts of drought on Amazonian vegetation have only been studied on controlled, small-scale (1-ha plot) field experiments. Recent studies of rainforest structure and density show an increase in tree die off and a decrease in above ground biomass following drought periods. Saatchi et al. were able to conclude that the Amazon rainforest’s response to severe droughts includes the increased mortality of large trees’ leaves and branches in the upper canopy, when soil water availability declined below a critical threshold. This response has subsequent impacts on the Amazonian rainforest canopy
                  Two severe droughts have occurred in the Amazon over the past ten years, as demonstrated by the anomaly in the Rio Negro River’s water levels. These severe droughts are typically associated with El NinoSouthern Oscillation (ENSO) events, which result in decreased soil moisture. This decrease in soil moisture endangers Amazonian vegetation by crossing critical water availability thresholds for long periods of time. If water stress continues, it causes higher tree mortality rates and increased flammability across the Amazon rainforest. The two major Amazon droughts considered in this study occurred in 2005 and 2010. The 2005 drought was not considered an ENSO drought, due to its temporal and spatial extent. The peak of this drought hit during the dry season and mostly impacted south-western Amazonia, unlike droughts caused by ENSO events. River levels during the drought in 2005 were the lowest to date. The tropical North Atlantic Ocean’s sea surface temperature increase could be a major cause of the 2005 drought. Anomalies in precipitation leading up to the droughts were also major contributing factors to the severity of the droughts studied. In Southern Amazonia, rainfall decreased by almost 3.2% per year in 19701998.The region also experienced negative precipitation levels during the decade leading up to the severe drought in 2005. This pattern of decreased precipitation could worsen in the future based on climate model predictions of the effects of climate change. Therefore, if Amazonian droughts continue to be as frequent and severe as those considered in this study, the overall ecosystem function and health in the Amazon rainforest will be affected.
                  Satellite spectral observations are used to detect changes in the rainforest’s upper canopy characteristics, such as greenness and leaf area, and may be used to determine long-term impacts of drought. Due to clouds and atmospheric aerosols, after the drought in 2005, the data from these observations was largely contradictory. Therefore, Saatchi et al. used data from two microwave satellite sensors to measure precipitation and canopy water content, in order to quantify the severity of the Amazonian droughts in the years 2005 and 2010, and their subsequent impacts on canopy water content and structure. The authors analyzed Amazonian precipitation with three Tropical Rainfall Measuring Mission indices that measure monthly precipitation (TRMM; 19982010). These indices include the dry-season precipitation anomaly (DPA), dry season water deficit anomaly (DWDA), and maximum climatological water deficit (MCWD). For the DPA, DWDA and MCWD, more negative values indicate more severe water deficit. The information from these indices is complementary and provides spatial-specific indicators of the extent and severity of deficit in the Amazon rainforest.
                  To measure the impact of water deficit on the Amazon rainforest, the authors used observations from the SeaWinds Scatterometer onboard QuickSCAT (QSCAT: 20002009), which uses a microwave frequency to provide backscatter measurements that demonstrate temporal and spatial variations of water content and structure in the rainforest canopy. Backscatter in this study was the reflection of the microwave frequency back to the QSCAT Scatterometer. The QSCAT signal at 2.1 cm wavelength and incidence angles of 50° penetrates 15 m into the rainforest canopy, and then scatters from leaves and branches of the upper canopy of trees. Backscatter measurements demonstrate biophysical properties of forests, such as water content and canopy structure. Changes over time (diurnal and seasonal) of canopy water content and seasonal leaf phenology of the vegetation’s lifecycle events have the greatest impact on the radar backscatter. Large-scale rainforest degradation and deforestation result in fewer trees within the rainforest canopy. When fewer trees are present in the rainforest, this change affects the canopy by creating gaps in its structure and altering its water content or biomass. These structural and biophysical changes are thus reflected in the signal of the backscatter. To represent the upper-canopy rainforest structure and water content, the authors used QSCAT backscatter data from dawn orbits, which monitor vegetation at its least-water-stressed time of day. The time series for this study were on normalized monthly and seasonally, and demonstrated spatial variations in upper-canopy-forest structure over the Amazon.
                  The authors experienced failure in November of 2009 with the QSCAT sensor scanner. The scanner stopped collecting data globally, which made it impossible for them to analyze changes in the canopy during the 2010 drought. QSCAT was reliable and without bias or sensor degradation prior to this failure. In order to still analyze the 2010 drought, the authors used TRMM precipitation radar backscatter data, which responds to surface moisture deep in the canopy, and scatters across soil and understory vegetation below the canopy through gaps within the rainforest.
                  Based on three indices from the TRMM data, Saatchi et al. found a severe drought over southwestern Amazonia in the year 2005. Of the approximate 5.5 M/km2 of forested area in the Amazon basin, about 1.7 M/km2 experienced DWDA less than −1.0 σ in 2005. About 0.27 M/ km2 experienced severe drought with DWDA less than −2.0 σ. In 2010, as the drought persisted, the spatial extent and severity increased, resulting in 2.6 M/km2 of the area subject to DWDA less −1.0 σ and 1.1 M/km2of forest area with DWDA less than −2.0 σ. The southwestern part of the Amazon typically experiences a dry season in normal years, but with this drought, the severity of the water deficit, with MCWD less than −300 mm, became larger. The wetter forests in the central part of the Amazon that had the most negative DPA and DWDA experienced low to moderate deficit with MCWD less than −100 mm in 2005 and 2010. The time span of these decreased precipitation anomalies was fairly short over the past ten years. However, the spatial extent in the southwestern Amazon suggested a pattern consistent with the low water levels measured in the Rio Negro River and other rivers within the southwestern Amazon basin.
                  The impacts of the 2005 drought on the forest canopy were captured with QSCAT backscatter time series data. The authors found a strong spatial correlation with water deficit anomalies from droughts observed by TRMM data for the same period and the QSCAT backscatter data, indicating that drought caused the change in rainforest canopy properties. The dry season in 2005 demonstrated widespread decline in rainforest canopy backscatter of 2.1 M/km2with anomaly of less than −1.0 σ, in the southwest Amazon, and an area of 0.77 M/km2  experiencing a decline in backscatter with anomaly less than −2.0 σ. The areas affected by the QSCAT anomaly included old growth rainforests to terra firme(rainforests not inundated by flood waters), from south to north respectively.
                  The authors compared the QSCAT and TRMM anomalies, and found significant correlations, with a 1−3 month lag between the decreased precipitation anomaly and subsequent canopy characteristic changes. The patterns of these correlations depended on rainfall, and varied over the Amazon. The lag ranged from zero to three months. The longest lags occurred over the southwestern region, due to the coupled effects of the naturally-occurring dry season precipitation variation in this Amazon region and the 2005 drought. The spatial variations from QSCAT and TRMM in 2005 with very negative QSCAT values are larger than similar areas with less severe WDA. To explain this discrepancy, the authors considered areas with maximum water deficit MCWD in 2005 that exceeded 300 mm and whether there was severe water deficit during the entire dry quarter with DWDA less than −3.0 σ. QSCAT backscatter reduction in the southwest region could be associated with the WDA developing in the dry season in 2005.
                  In order to analyze the impacts of the drought in 2005 over time, the authors used time series data of TRMM and QSCAT anomalies for the drought-affected southwestern areas of Amazonia. They found that after the year 2005, even though the area recovered in total precipitation, WDA remained negative for the dry season in 2006 and 2007, before recovery from this water deficit started with an abnormally wet year in 2009 over the entire Amazon (except the northeastern region). The water deficit became most severe in the southwestern region in late 2009. Areas affected by water deficit in southwestern Amazonia had low values in QSCAT backscatter in 2005 through the end of November 2009. To analyze longer term data trends, the authors used autoregressive moving-average models, a statistical model used for predicting future patterns in time series, on the order of 5% of the data points. Saatchi et al. found that the anomalies in QSCAT over time imply that the 2005 drought caused a step change in the back-scatter properties of the canopy, and that there was little recovery in subsequent years following the drought. These step changes indicate abrupt changes in the mean level of a time series of data. The authors considered whether this response could be attributed to hypothetical changes in sensor performance such as backscatter signal and calibration, but found that the response was unique to certain regions in western Amazonia that had the greatest water deficit anomalies. The annual QSCAT spatial patterns for the dry season support the finding of long-term reduction in backscatter from 2005−2009, thus indicating a change in canopy structure with more gaps in the canopy.
                  The QSCAT anomaly was most negative at a value between −2 σ and 3.5 σ in 2005, and remained a negative anomaly compared to previous years. In the years before the 2005 drought, the most negative anomaly had been a value of 0 σ. (P 1.2 σ, and the most severe water deficit peaking in 2010 at −1.5 σ to 2.0 σ.
                  To quantify changes in QSCAT data in comparison to the TRMM water deficit data, the authors compared average monthly anomalies for the southwestern Amazonia, and calculated the difference between QSCAT backscatter anomaly and TRMM monthly WDA. Doing so indicated that the difference in anomalies was zero (slope of zero) before the drought in 2005, and had a negative slope after 2005. This further substantiates the finding that there is a lag in recovery of QSCAT anomaly relative to the TRMM WDA in southwestern Amazonia, meaning that the recovery of the rainforest canopy does not immediately respond to increased precipitation. The accumulation of negative anomalies during dry summer months resulted in the largest decline in QSCAT backscatter in September of 2005.
The authors were able to extrapolate their findings over the entire Amazon by mapping the spatial distribution of the pixels with negative anomalies (less than −1.0 σ) for both QSCAT and TRMM data. There were significantly (P < 0.01) negative slopes between the QSCAT anomaly and WDA after the 2005 drought. Larger negative slopes indicate areas with a longer time period between water deficit recovery and subsequent canopy recovery, which suggest that droughts have lasting effect on canopy characteristics. All of the regions studied had an abnormally high number of fires in 2005 and the years following, indicating that there was lower water content in the rainforest canopy and more flammable dead vegetation, arising from a lack of precipitation. The impacts of these fires and rainforest degradation over the years 2005−2009 did not affect the QSCAT negative anomaly. Still, over 35% of the fires between 2005 and 2009 took place in QSCAT pixels that demonstrated a strong negative anomaly of less than −1.0 σ. The relationship between fires and water deficit is further supported by the fact that more than 78% of the rainforest in 2010 occurred in pixels with large negative slopes less than −1.0 σ, indicating a very negative difference between QSCAT and monthly TRMM WDA data. The prevalence of fires and areas that recovered slowly from drought coincided with regions that had large seasonality of backscatter in QSCAT data, indicating that these events occurred in mostly transitional and seasonal rainforests in the Amazon.
From this study, Saatchi et al. concluded that the QSCAT anomalies in backscatter data for canopy structure demonstrate the effects of the 2005 drought on the Amazon rainforest, and that the QSCAT data patterns are consistent with areas that experienced the largest water deficit in the driest quarter. QSCAT backscatter variability is the result of changes in the top layer of the canopy, which are exposed to water-vapor pressure deficits, making them more sensitive to droughts. The anomalies in radar backscatter indicate a decrease in upper-canopy biomass, layering, water content, and change in canopy structure attributable to drought disturbance. The authors concluded that the severity of these declines in canopy characteristics and altered canopy structure in 2005 is significantly greater than natural cycles and normal seasonal declines in QSCAT backscatter resulting from phonological life cycles and canopy water content. The authors also concluded that because the QSCAT negative anomaly persisted over most of the western Amazon after the 2005 drought that droughts have lasting effects on the rainforest canopy. Due to the magnitude of the drought disturbance, there was a lag in recovery of the rainforest canopy in terms of biomass and roughness (canopy layering) after rainfall returned to normal levels.
Saatchi et al. discussed the need for more extensive surveys and airborne observations to better assess the decline of backscatter and its relation to rainforest disturbances evident in canopy biomass and layering. During the most severe periods of drought, tree leaves should wilt and shed, therefore causing a decline in net primary production of the rainforest canopy. Within a year of a drought event, recovery of the canopy to its original pre-drought status should occur. In central Amazonia, this recovery did occur where QSCAT backscatter anomalies recovered quickly after water deficits post-drought in 2005. However, the southwestern Amazon rainforest demonstrates a lasting decline in canopy structure, and a recovery time of three to four years, as indicated by delays in recovery of QSCAT backscatter data. Canopy structure in these instances is affected by branch dieback and tree fall creating holes in the canopy structure.
In order to verify the findings of this study, the authors attempted to find in situ observations over the region affected by the drought in 2005. From previous studies, there is consensus that large or emergent trees have higher mortality than small trees. The amount of light penetrating into the understory below the rainforest canopy is affected by the gaps in the canopy that occur from tree fall. After precipitation recovery occurs, tree fall from drought events can increase light availability, and therefore promote understory vegetation and pioneer species productivity. The authors found that in situmeasurements indicate that large trees die off more rapidly for a few years after drought. This finding suggests that canopy structure and biomass decline before recovering gradually with the emergence of more trees. However, this recovery process is slow, and may require a considerable amount of time to return to the pre-drought state. From their findings in this study, the authors suggest that the western Amazonia experienced a large-scale canopy disturbance due to the 2005 drought, resulting in the decline of and canopy structure and biomass that continued with slow recovery for the next few years. In the future, the authors suggest further research with direct examination of the effects of severe drought on forest plots in western Amazonia, to verify their conclusions.
Due to rainforest die-off from large-scale drought disturbance, there may be an impact on the rainforest’s carbon dioxide absorption and release processes. This impact is caused by the decay of wood from increased tree mortality. During the years 1995 through 2005, a decline in plant water availability created water stress before the onset of the 2005 drought, therefore playing a role in the canopy disturbance after the 2005 drought occurred. Because of the drought in 2005, and a local drought in 2007, much of the soil in the southwestern Amazonia may not have reached water content capacity that would encourage canopy recovery. Variability in the natural dry season over the southwestern Amazonia since the late 1980s and early 1990s may also have been contributing factors. Sea surface temperature is a major contributor to the negative anomalies and year-to-year variation.
                  Saatchi et al. concluded their study in 2009. From their findings, they suggest that the Amazon rainforest canopy response from the drought in 2005 was repeated in 2010. For the year 2010, there were no QSCAT data available. Therefore, further drought disturbances may have affected the rainforest canopy that was yet to recover from previous drought and decreased water availability. TRMM-PR backscatter anomalies indicate that surface moisture in southwestern Amazonia dropped significantly in 2010, and lasted longer than the dry season, further stressing the canopy that had not yet fully recovered. If this pattern of drought continues to occur on a 5–10 year time scale, or became even more frequent, the Amazonian rainforest canopy will be exposed to drought consistently, and the rainforest canopy will be slow to recover in structure and function. Southwestern Amazonia has been particularly subject to severe effects of rainfall variability during the last ten years, thus indicating that this region is perhaps the most vulnerable to large scale rainforest degradation based on climate change.