Advances in upscaling of eddy covariance measurements of carbon and water fluxes

Eddy covariance flux towers provide continuous measurements of ecosystem-level net exchange of carbon, water, energy, and other trace gases between land surface and the atmosphere. The upscaling of flux observations from towers to broad regions provides a new and independent approach for quantifying these fluxes over regions, continents, or the globe. The seven contributions of this special section reflect the most recent advances in the upscaling of fluxes from towers to these broad regions. The section mainly stems from presentations at the recent North American Carbon Program (NACP), FLUXNET, and AGU meetings. These studies focus on different aspects of upscaling: (1) assessing the representativeness of flux networks; (2) upscaling fluxes from towers to broad spatial scales; (3) examining the magnitude, distribution, and interannual variability of fluxes over regions, continents, or the globe; and (4) evaluating the impacts of spatial heterogeneity and parameter variability on flux estimates. Collectively, this special issue provides a timely update on upscaling science and also generates gridded flux data that can be used for model evaluations. Future upscaling studies are expected to advance toward incorporating the impacts of disturbance on ecosystem carbon dynamics, quantifying uncertainties associated with gridded flux estimates, and comparing various upscaling methods and the resulting gridded flux fields

Gap Filling in the Plant Kingdom---Trait Prediction Using Hierarchical Probabilistic Matrix Factorization

Plant traits are a key to understanding and predicting the adaptation of ecosystems to environmental changes, which motivates the TRY project aiming at constructing a global database for plant traits and becoming a standard resource for the ecological community. Despite its unprecedented coverage, a large percentage of missing data substantially constrains joint trait analysis. Meanwhile, the trait data is characterized by the hierarchical phylogenetic structure of the plant kingdom. While factorization based matrix completion techniques have been widely used to address the missing data problem, traditional matrix factorization methods are unable to leverage the phylogenetic structure. We propose hierarchical probabilistic matrix factorization (HPMF), which effectively uses hierarchical phylogenetic information for trait prediction. We demonstrate HPMF's high accuracy, effectiveness of incorporating hierarchical structure and ability to capture trait correlation through experiments.Comment: Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012

Maximally Divergent Intervals for Anomaly Detection

We present new methods for batch anomaly detection in multivariate time series. Our methods are based on maximizing the Kullback-Leibler divergence between the data distribution within and outside an interval of the time series. An empirical analysis shows the benefits of our algorithms compared to methods that treat each time step independently from each other without optimizing with respect to all possible intervals.Comment: ICML Workshop on Anomaly Detectio

A perspective on gaussian processes for earth observation

Earth observation (EO) by airborne and satellite remote sensing and in-situ observations play a fundamental role in monitoring our planet. In the last decade, machine learning and Gaussian processes (GPs) in particular has attained outstanding results in the estimation of bio-geo-physical variables from the acquired images at local and global scales in a time-resolved manner. GPs provide not only accurate estimates but also principled uncertainty estimates for the predictions, can easily accommodate multimodal data coming from different sensors and from multitemporal acquisitions, allow the introduction of physical knowledge, and a formal treatment of uncertainty quantification and error propagation. Despite great advances in forward and inverse modelling, GP models still have to face important challenges that are revised in this perspective paper. GP models should evolve towards data-driven physics-aware models that respect signal characteristics, be consistent with elementary laws of physics, and move from pure regression to observational causal inference.Comment: 1 figur

Indicators from diurnal FLUXNET patterns

Funding Information: Acknowledgements. This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, FluxnetCanada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, and USCCC. We acknowledge the financial support to the eddy covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, the Max Planck Institute for Biogeochemistry, the National Science Foundation, the University of Tuscia, Université Laval and Environment Canada, and the US Department of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, the University of California-Berkeley, and the University of Virginia. Publisher Copyright: © Author(s) 2018.Understanding of terrestrial carbon and water cycles is currently hampered by an uncertainty in how to capture the large variety of plant responses to drought. In FLUXNET, the global network of CO2 and H2O flux observations, many sites do not uniformly report the ancillary variables needed to study drought response physiology. To this end, we outline two data-driven indicators based on diurnal energy, water, and carbon flux patterns derived directly from the eddy covariance data and based on theorized physiological responses to hydraulic and non-stomatal limitations. Hydraulic limitations (i.e. intra-plant limitations on water movement) are proxied using the relative diurnal centroid (CET∗), which measures the degree to which the flux of evapotranspiration (ET) is shifted toward the morning. Non-stomatal limitations (e.g. inhibitions of biochemical reactions, RuBisCO activity, and/or mesophyll conductance) are characterized by the Diurnal Water-Carbon Index (DWCI), which measures the degree of coupling between ET and gross primary productivity (GPP) within each day. As a proof of concept we show the response of the metrics at six European sites during the 2003 heat wave event, showing a varied response of morning shifts and decoupling. Globally, we found indications of hydraulic limitations in the form of significantly high frequencies of morning-shifted days in dry/Mediterranean climates and savanna/evergreen plant functional types (PFTs), whereas high frequencies of decoupling were dominated by dry climates and grassland/savanna PFTs indicating a prevalence of non-stomatal limitations in these ecosystems. Overall, both the diurnal centroid and DWCI were associated with high net radiation and low latent energy typical of drought. Using three water use efficiency (WUE) models, we found the mean differences between expected and observed WUE to be -0.09 to 0.44 μmol mmol-1 and -0.29 to -0.40 μmol mmol-1 for decoupled and morning-shifted days, respectively, compared to mean differences -1.41 to -1.42 μmol mmol-1 in dry conditions, suggesting that morning shifts/hydraulic responses are associated with an increase in WUE, whereas decoupling/non-stomatal limitations are not.publishersversionpublishe

Observation-based assessment of secondary water effects on seasonal vegetation decay across Africa

Funding Information: ÇK acknowledges funding from the International Max Planck Research School for Global Biogeochemical Cycles. SK acknowledges the support of the Erdsystemforschung: Afrikanische Grundwasserressourcen im Zuge des globalen Wandels (Earth System Research: Groundwater Resources in Africa under Global Change) project of the Max Planck Society. DM acknowledges funding from the European Research Council (ERC) under grant agreement 715254 (DRY-2-DRY) and the European Union Horizon 2020 Programme project 869550 (DOWN2EARTH). MR acknowledges funding by the European Research Council (ERC) Synergy Grant Understanding and modeling the Earth System with Machine Learning (USMILE) under the Horizon 2020 research and innovation program (Grant Agreement No. 855187). Publisher Copyright: Copyright © 2022 Küçük, Koirala, Carvalhais, Miralles, Reichstein and Jung.Local studies and modeling experiments suggest that shallow groundwater and lateral redistribution of soil moisture, together with soil properties, can be highly important secondary water sources for vegetation in water-limited ecosystems. However, there is a lack of observation-based studies of these terrain-associated secondary water effects on vegetation over large spatial domains. Here, we quantify the role of terrain properties on the spatial variations of dry season vegetation decay rate across Africa obtained from geostationary satellite acquisitions to assess the large-scale relevance of secondary water effects. We use machine learning based attribution to identify where and under which conditions terrain properties related to topography, water table depth, and soil hydraulic properties influence the rate of vegetation decay. Over the study domain, the machine learning model attributes about one-third of the spatial variations of vegetation decay rates to terrain properties, which is roughly equally split between direct terrain effects and interaction effects with climate and vegetation variables. The importance of secondary water effects increases with increasing topographic variability, shallower groundwater levels, and the propensity to capillary rise given by soil properties. In regions with favorable terrain properties, more than 60% of the variations in the decay rate of vegetation are attributed to terrain properties, highlighting the importance of secondary water effects on vegetation in Africa. Our findings provide an empirical assessment of the importance of local-scale secondary water effects on vegetation over Africa and help to improve hydrological and vegetation models for the challenge of bridging processes across spatial scales.publishersversionpublishe

Characterizing the Response of Vegetation Cover to Water Limitation in Africa Using Geostationary Satellites

Publisher Copyright: © 2022 The Authors. Journal of Advances in Modeling Earth Systems published by Wiley Periodicals LLC on behalf of American Geophysical Union.Hydrological interactions between vegetation, soil, and topography are complex, and heterogeneous in semi-arid landscapes. This along with data scarcity poses challenges for large-scale modeling of vegetation-water interactions. Here, we exploit metrics derived from daily Meteosat data over Africa at ca. 5 km spatial resolution for ecohydrological analysis. Their spatial patterns are based on Fractional Vegetation Cover (FVC) time series and emphasize limiting conditions of the seasonal wet to dry transition: the minimum and maximum FVC of temporal record, the FVC decay rate and the FVC integral over the decay period. We investigate the relevance of these metrics for large scale ecohydrological studies by assessing their co-variation with soil moisture, and with topographic, soil, and vegetation factors. Consistent with our initial hypothesis, FVC minimum and maximum increase with soil moisture, while the FVC integral and decay rate peak at intermediate soil moisture. We find evidence for the relevance of topographic moisture variations in arid regions, which, counter-intuitively, is detectable in the maximum but not in the minimum FVC. We find no clear evidence for wide-spread occurrence of the “inverse texture effect” on FVC. The FVC integral over the decay period correlates with independent data sets of plant water storage capacity or rooting depth while correlations increase with aridity. In arid regions, the FVC decay rate decreases with canopy height and tree cover fraction as expected for ecosystems with a more conservative water-use strategy. Thus, our observation-based products have large potential for better understanding complex vegetation-water interactions from regional to continental scales.publishersversionpublishe