scholarly journals Amazon rainforest photosynthesis increases in response to atmospheric dryness

2020 ◽  
Vol 6 (47) ◽  
pp. eabb7232
Author(s):  
J. K. Green ◽  
J. Berry ◽  
P. Ciais ◽  
Y. Zhang ◽  
P. Gentine
Keyword(s):  
Carbon Sink ◽  
Amazon Rainforest ◽  
Machine Learning Techniques ◽  
Canopy Conductance ◽  
Learning Techniques ◽  
In Situ Observations ◽  
Soil Dryness ◽  
Earth System Models ◽  
Global Carbon

Earth system models predict that increases in atmospheric and soil dryness will reduce photosynthesis in the Amazon rainforest, with large implications for the global carbon cycle. Using in situ observations, solar-induced fluorescence, and nonlinear machine learning techniques, we show that, in reality, this is not necessarily the case: In many of the wettest parts of this region, photosynthesis and biomass tend to increase with increased atmospheric dryness, despite the associated reductions in canopy conductance to CO2. These results can be largely explained by changes in canopy properties, specifically, new leaves flushed during the dry season have higher photosynthetic capacity than the leaves they replace, compensating for the negative stomatal response to increased dryness. As atmospheric dryness will increase with climate change, our study highlights the importance of reframing how we represent the response of ecosystem photosynthesis to atmospheric dryness in very wet regions, to accurately quantify the land carbon sink.

scholarly journals Projected increases in intensity, frequency, and terrestrial carbon costs of compound drought and aridity events

2019 ◽  
Vol 5 (1) ◽  
pp. eaau5740 ◽  
Author(s):  
Sha Zhou ◽  
Yao Zhang ◽  
A. Park Williams ◽  
Pierre Gentine
Keyword(s):  
Vapor Pressure Deficit ◽  
Carbon Sink ◽  
Terrestrial Carbon ◽  
Negative Effects ◽  
Compound Events ◽  
High Vapor ◽  
In Situ Observations ◽  
Earth System Models ◽  
Very High

Drought and atmospheric aridity pose large risks to ecosystem services and agricultural production. However, these factors are seldom assessed together as compound events, although they often occur simultaneously. Drought stress on terrestrial carbon uptake is characterized by soil moisture (SM) deficit and high vapor pressure deficit (VPD). We used in situ observations and 15 Earth system models to show that compound events with very high VPD and low SM occur more frequently than expected if these events were independent. These compound events are projected to become more frequent and more extreme and exert increasingly negative effects on continental productivity. Models project intensified negative effects of high VPD and low SM on vegetation productivity, with the intensification of SM exceeding those of VPD in the Northern Hemisphere. These results highlight the importance of compound extreme events and their threats for the capability of continents to act as a carbon sink.

Amazon rainforest increases photosynthesis in reponse to atmospheric dryness

2020 ◽  
Author(s):  
Julia K. Green ◽  
Pierre Gentine ◽  
Yao Zhang ◽  
Joe Berry ◽  
Philippe Ciais
Keyword(s):  
Vapor Pressure ◽  
Vapor Pressure Deficit ◽  
Carbon Sink ◽  
Gross Primary Production ◽  
Remote Sensing Data ◽  
Amazon Rainforest ◽  
Machine Learning Techniques ◽  
Earth System ◽  
System Models ◽  
Earth System Models

<p>Earth system models predict that atmospheric dryness reduces photosynthesis due to its reductive effect on stomatal conductance. However, while this representation may be appropriate in many environments, in the wet Amazonian tropical rainforest, this is not the case. Using remote sensing data combined with machine learning techniques (k-means clustering and artificial neural networks), we show that in the wettest parts of the Amazon rainforest, gross primary production and evapotranspiration continue to increase alongside atmospheric dryness, i.e. vapor pressure deficit, despite reductions in ecosystem conductance. On the other hand, Earth system models have the opposite photosynthetic response to vapor pressure deficit in the wettest part of the Amazon, overestimating its reductive effect on tropical vegetation photosynthesis and evapotranspiration, leading to an exaggerated carbon source to the atmosphere. As vapor pressure deficit is expected to increase with climate change, our study highlights the importance of reframing how we understand and represent the response of ecosystem photosynthesis to atmospheric dryness in the wettest ecosystems, to accurately quantify the future land carbon sink and atmospheric CO2 growth rate.</p>

scholarly journals Predicting land deformation by integrating InSAR data and cone penetration testing through machine learning techniques

2020 ◽  
Vol 382 ◽  
pp. 525-529
Author(s):  
Melika Sajadian ◽  
Ana Teixeira ◽  
Faraz S. Tehrani ◽  
Mathias Lemmens
Keyword(s):  
Machine Learning ◽  
Soil Mechanics ◽  
Machine Learning Techniques ◽  
Cone Penetration ◽  
Penetration Testing ◽  
Cone Penetration Testing ◽  
Learning Techniques ◽  
Land Deformation ◽  
Spatio Temporal

Abstract. Built environments developed on compressible soils are susceptible to land deformation. The spatio-temporal monitoring and analysis of these deformations are necessary for sustainable development of cities. Techniques such as Interferometric Synthetic Aperture Radar (InSAR) or predictions based on soil mechanics using in situ characterization, such as Cone Penetration Testing (CPT) can be used for assessing such land deformations. Despite the combined advantages of these two methods, the relationship between them has not yet been investigated. Therefore, the major objective of this study is to reconcile InSAR measurements and CPT measurements using machine learning techniques in an attempt to better predict land deformation.

scholarly journals Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario, Canada

2020 ◽  
Vol 24 (10) ◽  
pp. 4887-4902
Author(s):  
Fraser King ◽  
Andre R. Erler ◽  
Steven K. Frey ◽  
Christopher G. Fletcher
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Water Resource Management ◽  
Snow Water Equivalent ◽  
Bias Reduction ◽  
Machine Learning Techniques ◽  
Spatial Coverage ◽  
Snow Water ◽  
In Situ Observations

Abstract. Snow is a critical contributor to Ontario's water-energy budget, with impacts on water resource management and flood forecasting. Snow water equivalent (SWE) describes the amount of water stored in a snowpack and is important in deriving estimates of snowmelt. However, only a limited number of sparsely distributed snow survey sites (n=383) exist throughout Ontario. The SNOw Data Assimilation System (SNODAS) is a daily, 1 km gridded SWE product that provides uniform spatial coverage across this region; however, we show here that SWE estimates from SNODAS display a strong positive mean bias of 50 % (16 mm SWE) when compared to in situ observations from 2011 to 2018. This study evaluates multiple statistical techniques of varying complexity, including simple subtraction, linear regression and machine learning methods to bias-correct SNODAS SWE estimates using absolute mean bias and RMSE as evaluation criteria. Results show that the random forest (RF) algorithm is most effective at reducing bias in SNODAS SWE, with an absolute mean bias of 0.2 mm and RMSE of 3.64 mm when compared with in situ observations. Other methods, such as mean bias subtraction and linear regression, are somewhat effective at bias reduction; however, only the RF method captures the nonlinearity in the bias and its interannual variability. Applying the RF model to the full spatio-temporal domain shows that the SWE bias is largest before 2015, during the spring melt period, north of 44.5∘ N and east (downwind) of the Great Lakes. As an independent validation, we also compare estimated snowmelt volumes with observed hydrographs and demonstrate that uncorrected SNODAS SWE is associated with unrealistically large volumes at the time of the spring freshet, while bias-corrected SWE values are highly consistent with observed discharge volumes.

scholarly journals Teaching robots social autonomy from in situ human guidance

2019 ◽  
Vol 4 (35) ◽  
pp. eaat1186 ◽  
Author(s):  
Emmanuel Senft ◽  
Séverin Lemaignan ◽  
Paul E. Baxter ◽  
Madeleine Bartlett ◽  
Tony Belpaeme
Keyword(s):  
Autonomous Systems ◽  
Human Robot Interaction ◽  
Machine Learning Techniques ◽  
Generic Properties ◽  
Domain Specific ◽  
Learning Techniques ◽  
The One ◽  
The Right ◽  
Social Autonomy

Striking the right balance between robot autonomy and human control is a core challenge in social robotics, in both technical and ethical terms. On the one hand, extended robot autonomy offers the potential for increased human productivity and for the off-loading of physical and cognitive tasks. On the other hand, making the most of human technical and social expertise, as well as maintaining accountability, is highly desirable. This is particularly relevant in domains such as medical therapy and education, where social robots hold substantial promise, but where there is a high cost to poorly performing autonomous systems, compounded by ethical concerns. We present a field study in which we evaluate SPARC (supervised progressively autonomous robot competencies), an innovative approach addressing this challenge whereby a robot progressively learns appropriate autonomous behavior from in situ human demonstrations and guidance. Using online machine learning techniques, we demonstrate that the robot could effectively acquire legible and congruent social policies in a high-dimensional child-tutoring situation needing only a limited number of demonstrations while preserving human supervision whenever desirable. By exploiting human expertise, our technique enables rapid learning of autonomous social and domain-specific policies in complex and nondeterministic environments. Last, we underline the generic properties of SPARC and discuss how this paradigm is relevant to a broad range of difficult human-robot interaction scenarios.

scholarly journals Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat

Climate ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 5 ◽  
Author(s):  
Vivek Shandas ◽  
Jackson Voelkel ◽  
Joseph Williams ◽  
Jeremy Hoffman
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Machine Learning Techniques ◽  
Metropolitan Region ◽  
Landscape Features ◽  
Ambient Temperatures ◽  
Air Temperatures ◽  
Learning Techniques ◽  
Urban Heat

The emergence of urban heat as a climate-induced health stressor is receiving increasing attention among researchers, practitioners, and climate educators. However, the measurement of urban heat poses several challenges with current methods leveraging either ground based, in situ observations, or satellite-derived surface temperatures estimated from land use emissivity. While both techniques contain inherent advantages and biases to predicting temperatures, their integration may offer an opportunity to improve the spatial resolution and global application of urban heat measurements. Using a combination of ground-based measurements, machine learning techniques, and spatial analysis, we addressed three research questions: (1) How much do ambient temperatures vary across time and space in a metropolitan region? (2) To what extent can the integration of ground-based measurements and satellite imagery help to predict temperatures? (3) What landscape features consistently amplify and temper heat? We applied our analysis to the cities of Baltimore, Maryland, and Richmond, Virginia, and the District of Columbia using geocomputational machine learning processes on data collected on days when maximum air temperatures were above the 90th percentile of historic averages. Our results suggest that the urban microclimate was highly variable across all of the cities—with differences of up to 10 °C between coolest and warmest locations at the same time—and that these air temperatures were primarily dependent on underlying landscape features. Additionally, we found that integrating satellite data with ground-based measures provided highly accurate and precise descriptions of temperatures in all three study regions. These results suggest that accurately identifying areas of extreme urban heat hazards for any region is possible through integrating ground-based temperature and satellite data.

scholarly journals The CarbonTracker Data Assimilation Shell (CTDAS) v1.0: implementation and global carbon balance 2001–2015

2017 ◽  
Author(s):  
Ingrid T. van der Laan-Luijkx ◽  
Ivar R. van der Velde ◽  
Emma van der Veen ◽  
Aki Tsuruta ◽  
Karolina Stanislawska ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Carbon Sink ◽  
Carbon Sources ◽  
Sources And Sinks ◽  
Wide Range ◽  
Abstract Data ◽  
Global Carbon ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. Data assimilation systems are used increasingly to constrain the budgets of reactive and long-lived gases measured in the atmosphere. Each trace gas has its own lifetime, dominant sources and sinks, and observational network (from flask sampling and in situ measurements to space-based remote sensing) and therefore comes with its own optimal configuration of the data assimilation. The CarbonTracker Europe data assimilation system for CO2 estimates global carbon sources and sinks, and updates are released annually and used in carbon cycle studies. CarbonTracker Europe simulations are performed using the new modular implementation of the data assimilation system which is called the CarbonTracker Data Assimilation Shell (CTDAS). Here, we present and document this redesign of the data assimilation code that forms the heart of CarbonTracker, specifically meant to enable easy extension and modification of the data assimilation system. This paper also presents the setup of the latest version of CarbonTracker Europe (CTE2016), including the use of the gridded state vector, and shows the resulting carbon flux estimates. We present the distribution of the carbon sinks over the hemispheres and between the land biosphere and the oceans. We show that with equal fossil fuel emissions, 2015 has a higher atmospheric CO2 growth rate compared to 2014, due to reduced land carbon uptake in later year. The European carbon sink is especially present in the forests, and is reduced during drought years. Finally, we also demonstrate the versatility of CTDAS by presenting an overview of the wide range of applications for which it has been used so far.

scholarly journals On the Data-Driven Modeling of Reactive Extrusion

Fluids ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 94 ◽  
Author(s):  
Ruben Ibañez ◽  
Fanny Casteran ◽  
Clara Argerich ◽  
Chady Ghnatios ◽  
Nicolas Hascoet ◽  
...  
Keyword(s):  
Reactive Extrusion ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Reactive Polymer ◽  
Complex Processes ◽  
Use Of Data ◽  
Learning Techniques ◽  
Twin Screw ◽  
Data Driven Modeling

This paper analyzes the ability of different machine learning techniques, able to operate in the low-data limit, for constructing the model linking material and process parameters with the properties and performances of parts obtained by reactive polymer extrusion. The use of data-driven approaches is justified by the absence of reliable modeling and simulation approaches able to predict induced properties in those complex processes. The experimental part of this work is based on the in situ synthesis of a thermoset (TS) phase during the mixing step with a thermoplastic polypropylene (PP) phase in a twin-screw extruder. Three reactive epoxy/amine systems have been considered and anhydride maleic grafted polypropylene (PP-g-MA) has been used as compatibilizer. The final objective is to define the appropriate processing conditions in terms of improving the mechanical properties of these new PP materials by reactive extrusion.

scholarly journals ON THE DEVELOPMENT OF A NOVEL APPROACH FOR IDENTIFYING PERENNIAL DRAINAGE IN SOUTHERN BRAZIL: A STUDY CASE INTEGRATING SENTINEL-2 AND HIGH-RESOLUTION DIGITAL ELEVATION MODELS WITH MACHINE LEARNING TECHNIQUES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 161-168
Author(s):  
A. Montibeller ◽  
M. Vilela ◽  
F. Hino ◽  
P. Mallmann ◽  
M. Nadas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Surface Water ◽  
High Resolution ◽  
Digital Elevation Models ◽  
Machine Learning Techniques ◽  
Water Runoff ◽  
Learning Techniques ◽  
Digital Elevation ◽  
Sentinel 2

Abstract. Riparian vegetation plays a key role in maintaining water quality and preserving the ecosystems along riverine systems, as they prevent soil erosion, retain water by increased infiltration, and act as a buffer zone between rivers and their surroundings. Within urban spaces, these areas have also an important role in preventing illegal occupation in areas of hydrologic risk, such as in floodplains. The goal of this research is to propose a framework for identifying areas of permanent protection associated with perennial drainage, utilizing satellite imagery and digital elevation models (DEM) in association with machine learning techniques. The specific objectives include the development of a decision tree to retrieve perennial drainage over high resolution, 1-meter DEM’s, and the development of digital image processing workflow to retrieve surface water bodies from Sentinel-2 imagery. In-situ information on perennial and ephemeral conditions of streams and rivers were obtained to validate our results, that happened in the first trimester of 2020. We propose a minimum of 7 days without precipitation prior to in-situ validation, for more accurate assessment of streamflow conditions, in order to minimize impacts of surface water runoff in flow regime. The proposed method will benefit decision makers by providing them with reliable information on drainage network and their buffer zones, as well as yield detailed mapping of the areas of permanent protection that are key to urban planning and management.

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