Spatial organisation of groundwater dynamics and streamflow response from different hydropedological units in a montane catchment

Funding was provided by the Leibniz Association (SAW-2012-IGB 4167) within the International Leibniz Graduate School: Aquatic boundaries and linkages- Aqualink. We would like to thank the NRI staff for their help during field work.Peer reviewedPostprin

Vegetation dynamics in northern south America on different time scales

The overarching goal of this doctoral thesis was to understand the dynamics of vegetation activity occurring across time scales globally and in a regional context. To achieve this, I took advantage of open data sets, novel mathematical approaches for time series analyses, and state-of-the-art technology to effectively manipulate and analyze time series data. Specifically, I disentangled the longest records of vegetation greenness (>30 years) in tandem with climate variables at 0.05° for a global scale analysis (Chapter 3). Later, I focused my analysis on a particular region, northern South America (NSA), to evaluate vegetation activity at seasonal (Chapter 4) and interannual scales (Chapter 5) using moderate spatial resolution (0.0083°). Two main approaches were used in this research; time series decomposition through the Fast Fourier Transformation (FFT), and dimensionality reduction analysis through Principal Component Analysis (PCA). Overall, assessing vegetation-climate dynamics at different temporal scales facilitates the observation and understanding of processes that are often obscured by one or few dominant processes. On the one hand, the global analysis showed the dominant seasonality of vegetation and temperature in northern latitudes in comparison with the heterogeneous patterns of the tropics, and the remarkable longer-term oscillations in the southern hemisphere. On the other hand, the regional analysis showed the complex and diverse land-atmosphere interactions in NSA when assessing seasonality and interannual variability of vegetation activity associated with ENSO. In conclusion, disentangling these processes and assessing them separately allows one to formulate new hypotheses of mechanisms in ecosystem functioning, reveal hidden patterns of climate-vegetation interactions, and inform about vegetation dynamics relevant for ecosystem conservation and management

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

Causal networks for climate model evaluation and constrained projections

Global climate models are central tools for understanding past and future climate change. The assessment of model skill, in turn, can benefit from modern data science approaches. Here we apply causal discovery algorithms to sea level pressure data from a large set of climate model simulations and, as a proxy for observations, meteorological reanalyses. We demonstrate how the resulting causal networks (fingerprints) offer an objective pathway for process-oriented model evaluation. Models with fingerprints closer to observations better reproduce important precipitation patterns over highly populated areas such as the Indian subcontinent, Africa, East Asia, Europe and North America. We further identify expected model interdependencies due to shared development backgrounds. Finally, our network metrics provide stronger relationships for constraining precipitation projections under climate change as compared to traditional evaluation metrics for storm tracks or precipitation itself. Such emergent relationships highlight the potential of causal networks to constrain longstanding uncertainties in climate change projections. Algorithms to assess causal relationships in data sets have seen increasing applications in climate science in recent years. Here, the authors show that these techniques can help to systematically evaluate the performance of climate models and, as a result, to constrain uncertainties in future climate change projections

Initial effects of post-harvest ditch cleaning on greenhouse gas fluxes in a hemiboreal peatland forest

Ditch cleaning (DC) is a well-established forestry practice across Fennoscandia to lower water table levels (WTL) and thereby facilitate the establishment of tree seedlings following clear-cutting. However, the implications from these activities for ecosystem-atmosphere greenhouse gas (GHG) exchanges are poorly understood at present. We assessed the initial DC effects on the GHG fluxes in a forest clear-cut on a drained fertile peatland in hemiboreal Sweden, by comparing chamber measurements of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) fluxes from soil and ditches in DC and uncleaned (UC) areas over the first two post-harvest years. We also evaluated spatial effects by comparing fluxes at 4 m and 40 m from ditches. We found that 2 years after DC, mean (+/- standard error) WTL of-65 +/- 2 cm was significantly lower in the DC area compared to-56 +/- 2 cm in the UC area. We further observed lower gross primary production and ecosystem respiration in the first year after DC which coincided with delayed development of herbaceous ground vegetation. We also found higher CH4 uptake but no difference in N2O fluxes after DC. Greater CH4 uptake occurred at 4 m compared to 40 m away from both cleaned and uncleaned ditches. Model extrapolation suggests that total annual GHG emissions in the second year were reduced from 49.4 +/- 17.0 t-CO2-eq-ha(-1) -year(-1) in the UC area to 27.8 +/- 10.3 t-CO2-eq-ha(-1) -year(-1) in the DC area. A flux partitioning approach suggested that this was likely caused by decreased heterotrophic respiration, possibly because of enhanced soil dryness following DC during the dry meteorological conditions. CH(4 )and N2O fluxes from clear-cut areas contributed < 2 % to the total (soil, ditches) GHG budget. Similarly the area -weighted contributions by CO2 and CH4 emissions from both cleaned and uncleaned ditches were < 2 %. Thus, our study highlights that DC may considerably alter the post-harvest GHG fluxes of drained peatland forests. However, long-term observations under various site conditions and forest rotation stages are warranted to better understand DC effects on the forest GHG balance

Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices

Accurate crop yield forecasting is essential in the food industry’s decision-making process, where vegetation condition index (VCI) and thermal condition index (TCI) coupled with machine learning (ML) algorithms play crucial roles. The drawback, however, is that a one-fits-all prediction model is often employed over an entire region without considering subregional VCI and TCI’s spatial variability resulting from environmental and climatic factors. Furthermore, when using nonlinear ML, redundant VCI/TCI data present additional challenges that adversely affect the models’ output. This study proposes a framework that (i) employs higher-order spatial independent component analysis (sICA), and (ii), exploits a combination of the principal component analysis (PCA) and ML (i.e., PCA-ML combination) to deal with the two challenges in order to enhance crop yield prediction accuracy. The proposed framework consolidates common VCI/TCI spatial variability into their respective subregions, using Vietnam as an example. Compared to the one-fits-all approach, subregional rice yield forecasting models over Vietnam improved by an average level of 20% up to 60%. PCA-ML combination outperformed ML-only by an average of 18.5% up to 45%. The framework generates rice yield predictions 1 to 2 months ahead of the harvest with an average of 5% error, displaying its reliability