Multivariate hydrometeorological extreme events and their impacts on vegetation: potential methods and applications

Trockenheiten und Hitzewellen beeinflussen unsere Gesellschaft und die Vegetation. Insbesondere im Zusammenhang mit dem Klimawandel sind die Auswirkungen auf die Vegetation von besonderer Bedeutung. Im globalen Kohlenstoffkreislauf sind terrestrische Ökosysteme normalerweise Senken von Kohlenstoffdioxid, können sich aber während und nach Klimaextremereignissen in Kohlenstoffquellen verwandeln. Ein entscheidender Aspekt hierbei ist die Rolle verschiedener Pflanzenarten und Vegetationstypen auf verschiedenen Skalen, die die Auswirkungen auf den Kohlenstoffkreislauf beeinflussen. Obwohl durch physiologische Unterschiede zwischen verschiedenen Pflanzenarten unterschiedliche Reaktionen auf Extremereignisse naheliegen, sind diese Unterschiede auf globaler Ebene nicht systematisch ausgewertet und vollständig verstanden. Ein weiter Aspekt ist, dass Klimaextremereignissen von Natur aus multivariat sind. Beispielsweise kann heiße Luft mehr Wasser aufnehmen als kalte Luft. Extremereignisse mit starken Auswirkungen waren in der Vergangenheit häufig multivariat, wie beispielsweise in Europa 2003, Russland 2012, oder den USA 2012. Diese multivariate Natur von Klimaextremen erfordert eine multivariate Perspektive auf diese Ereignisse. Bisher werden meistens einzelne Variablen zu Detektion von Extremereignissen genutzt und keine Kovariation oder Nichtlinearitäten berücksichtigt. Neue generische Workflows, die solche multivariaten Strukturen berücksichtigen, müssen erst entwickelt oder aus anderen Disziplinen übertragen werden, um uns eine multivariate Perspektive auf Klimaextreme zu bieten. Das übergeordnete Ziel der Dissertation ist es, die Erkennung und das Verständnis von Klimaextremen und deren Auswirkungen auf die Vegetation zu verbessern, indem eine breitere multivariate Perspektive ermöglicht wird, die bisherige Ansätze zur Erkennung von Extremereignissen ergänzt

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

Drought, Heat, and the Carbon Cycle: a Review

Purpose of the Review Weather and climate extremes substantially affect global- and regional-scale carbon (C) cycling, and thus spatially or temporally extended climatic extreme events jeopardize terrestrial ecosystem carbon sequestration. We illustrate the relevance of drought and/or heat events (“DHE”) for the carbon cycle and highlight underlying concepts and complex impact mechanisms. We review recent results, discuss current research needs and emerging research topics. Recent Findings Our review covers topics critical to understanding, attributing and predicting the effects of DHE on the terrestrial carbon cycle: (1) ecophysiological impact mechanisms and mediating factors, (2) the role of timing, duration and dynamical effects through which DHE impacts on regional-scale carbon cycling are either attenuated or enhanced, and (3) large-scale atmospheric conditions under which DHE are likely to unfold and to affect the terrestrial carbon cycle. Recent research thus shows the need to view these events in a broader spatial and temporal perspective that extends assessments beyond local and concurrent C cycle impacts of DHE. Summary Novel data streams, model (ensemble) simulations, and analyses allow to better understand carbon cycle impacts not only in response to their proximate drivers (drought, heat, etc.) but also attributing them to underlying changes in drivers and large-scale atmospheric conditions. These attribution-type analyses increasingly address and disentangle various sequences or dynamical interactions of events and their impacts, including compensating or amplifying effects on terrestrial carbon cycling.publishedVersio

Contrasting biosphere responses to hydrometeorological extremes: revisiting the 2010 western Russian heatwave

Combined droughts and heatwaves are among those compound extreme events that induce severe impacts on the terrestrial biosphere and human health. A record breaking hot and dry compound event hit western Russia in summer 2010 (Russian heatwave, RHW). Events of this kind are relevant from a hydrometeorological perspective, but are also interesting from a biospheric point of view because of their impacts on ecosystems, e.g., reductions in the terrestrial carbon storage. Integrating both perspectives might facilitate our knowledge about the RHW. We revisit the RHW from both a biospheric and a hydrometeorological perspective. We apply a recently developed multivariate anomaly detection approach to a set of hydrometeorological variables, and then to multiple biospheric variables relevant to describe the RHW. One main finding is that the extreme event identified in the hydrometeorological variables leads to multidirectional responses in biospheric variables, e.g., positive and negative anomalies in gross primary production (GPP). In particular, the region of reduced summer ecosystem production does not match the area identified as extreme in the hydrometeorological variables. The reason is that forest-dominated ecosystems in the higher latitudes respond with unusually high productivity to the RHW. Furthermore, the RHW was preceded by an anomalously warm spring, which leads annually integrated to a partial compensation of 54% (36% in the preceding spring, 18% in summer) of the reduced GPP in southern agriculturally dominated ecosystems. Our results show that an ecosystem-specific and multivariate perspective on extreme events can reveal multiple facets of extreme events by simultaneously integrating several data streams irrespective of impact direction and the variables' domain. Our study exemplifies the need for robust multivariate analytic approaches to detect extreme events in both hydrometeorological conditions and associated biosphere responses to fully characterize the effects of extremes, including possible compensatory effects in space and time

Vegetation modulates the impact of climate extremes on gross primary production

Drought and heat events affect the uptake and sequestration of carbon in terrestrial ecosystems. Factors such as the duration, timing, and intensity of extreme events influence the magnitude of impacts on ecosystem processes such as gross primary production (GPP), i.e., the ecosystem uptake of CO2. Preceding soil moisture depletion may exacerbate these impacts. However, some vegetation types may be more resilient to climate extremes than others. This effect is insufficiently understood at the global scale and is the focus of this study. Using a global upscaled product of GPP that scales up in situ land CO2 flux observations with global satellite remote sensing, we study the impact of climate extremes at the global scale. We find that GPP in grasslands and agricultural areas is generally reduced during heat and drought events. However, we also find that forests, if considered globally, appear in general to not be particularly sensitive to droughts and heat events that occurred during the analyzed period or even show increased GPP values during these events. On the one hand, normal-to-increased GPP values are in many cases plausible, e.g., when conditions prior to the event have been particularly positive. On the other hand, however, normal-to-increased GPP values in forests may also reflect a lack of sensitivity in current remote-sensing-derived GPP products to the effects of droughts and heatwaves. The overall picture calls for a differentiated consideration of different land cover types in the assessments of risks of climate extremes for ecosystem functioning.ISSN:1726-4170ISSN:1726-417

Multivariate anomaly detection for Earth observations : A comparison of algorithms and feature extraction techniques

Today, many processes at the Earth's surface are constantly monitored by multiple data streams. These observations have become central to advancing our understanding of vegetation dynamics in response to climate or land use change. Another set of important applications is monitoring effects of extreme climatic events, other disturbances such as fires, or abrupt land transitions. One important methodological question is how to reliably detect anomalies in an automated and generic way within multivariate data streams, which typically vary seasonally and are interconnected across variables. Although many algorithms have been proposed for detecting anomalies in multivariate data, only a few have been investigated in the context of Earth system science applications. In this study, we systematically combine and compare feature extraction and anomaly detection algorithms for detecting anomalous events. Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams. We rely on artificial data that mimic typical properties and anomalies in multivariate spatiotemporal Earth observations like sudden changes in basic characteristics of time series such as the sample mean, the variance, changes in the cycle amplitude, and trends. This artificial experiment is needed as there is no "gold standard" for the identification of anomalies in real Earth observations. Our results show that a well-chosen feature extraction step (e.g., subtracting seasonal cycles, or dimensionality reduction) is more important than the choice of a particular anomaly detection algorithm. Nevertheless, we identify three detection algorithms (k-nearest neighbors mean distance, kernel density estimation, a recurrence approach) and their combinations (ensembles) that outperform other multivariate approaches as well as univariate extreme-event detection methods. Our results therefore provide an effective workflow to automatically detect anomalies in Earth system science data.</p