Search Results
Detecting impacts of extreme events with ecological in situ monitoring networks
Extreme hydrometeorological conditions typically impact ecophysiological processes on land. Satellite-based observations of the terrestrial biosphere provide an important reference for detecting and describing the spatiotemporal development of such events. However, in-depth investigations of ecological processes during extreme events require additional in situ observations. The question is whether the density of existing ecological in situ networks is sufficient for analysing the impact of extreme events, and what are expected event detection rates of ecological in situ networks of a given size. To assess these issues, we build a baseline of extreme reductions in the fraction of absorbed photosynthetically active radiation (FAPAR), identified by a new event detection method tailored to identify extremes of regional relevance. We then investigate the event detection success rates of hypothetical networks of varying sizes. Our results show that large extremes can be reliably detected with relatively small networks, but also reveal a linear decay of detection probabilities towards smaller extreme events in log–log space. For instance, networks with ≈ 100 randomly placed sites in Europe yield a ≥ 90 % chance of detecting the eight largest (typically very large) extreme events; but only a ≥ 50 % chance of capturing the 39 largest events. These findings are consistent with probability-theoretic considerations, but the slopes of the decay rates deviate due to temporal autocorrelation and the exact implementation of the extreme event detection algorithm. Using the examples of AmeriFlux and NEON, we then investigate to what degree ecological in situ networks can capture extreme events of a given size. Consistent with our theoretical considerations, we find that today's systematically designed networks (i.e. NEON) reliably detect the largest extremes, but that the extreme event detection rates are not higher than would be achieved by randomly designed networks. Spatio-temporal expansions of ecological in situ monitoring networks should carefully consider the size distribution characteristics of extreme events if the aim is also to monitor the impacts of such events in the terrestrial biosphere.
Structurally coupled cooperative inversion of magnetic resonance with resistivity soundings
Hydrologic parameters, such as porosity, salinity, and hydraulic conductivity are keys for understanding the subsurface. Hydrogeophysical investigations can lead to ambiguous results, particularly in the presence of clay and saltwater. A combination of magnetic resonance sounding and vertical electrical sounding is known to provide insight into these properties. Structural coupling increases the model resolution and reduces the ambiguity for both methods. Inversion schemes using block models exist, but they have trouble resolving smooth or complex parameter distributions. We have developed a structurally coupled cooperative inversion (SCCI) that works with smooth parameter distributions and is able to introduce blocky features through the exchange of structural information. The coupling adapts the smoothness constraint locally in connection to the model roughness to allow for sharper model boundaries. We investigate the performance of the SCCI using blocky and smooth synthetic models that depend on two controlling coupling parameters. A well-known field case is used to verify the results with drilling core and well logs. Varying the coupling parameters results in equivalent models covering the bandwidth from smooth to blocky, while providing a similar data fit. The SCCI results are more consistent with the synthetic models. Structural coupling improves the resolution of the single methods and can be used to describe hydrogeophysical targets in more detail and less ambiguously.
Towards global empirical upscaling of FLUXNET eddy covariance observations: Validation of a model tree ensemble approach using a biosphere model
Global, spatially and temporally explicit estimates of carbon and water fluxes derived from empirical up-scaling eddy covariance measurements would constitute a new and possibly powerful data stream to study the variability of the global terrestrial carbon and water cycle. This paper introduces and validates a machine learning approach dedicated to the upscaling of observations from the current global network of eddy covariance towers (FLUXNET). We present a new model TRee Induction ALgorithm (TRIAL) that performs hierarchical stratification of the data set into units where particular multiple regressions for a target variable hold. We propose an ensemble approach (Evolving tRees with RandOm gRowth, ERROR) where the base learning algorithm is perturbed in order to gain a diverse sequence of different model trees which evolves over time. We evaluate the efficiency of the model tree ensemble (MTE) approach using an artificial data set derived from the Lund-Potsdam-Jena managed Land (LPJmL) biosphere model. We aim at reproducing global monthly gross primary production as simulated by LPJmL from 1998–2005 using only locations and months where high quality FLUXNET data exist for the training of the model trees. The model trees are trained with the LPJmL land cover and meteorological input data, climate data, and the fraction of absorbed photosynthetic active radiation simulated by LPJmL. Given that we know the "true result" in the form of global LPJmL simulations we can effectively study the performance of the MTE upscaling and associated problems of extrapolation capacity. We show that MTE is able to explain 92% of the variability of the global LPJmL GPP simulations. The mean spatial pattern and the seasonal variability of GPP that constitute the largest sources of variance are very well reproduced (96% and 94% of variance explained respectively) while the monthly interannual anomalies which occupy much less variance are less well matched (41% of variance explained). We demonstrate the substantially improved accuracy of MTE over individual model trees in particular for the monthly anomalies and for situations of extrapolation. We estimate that roughly one fifth of the domain is subject to extrapolation while MTE is still able to reproduce 73% of the LPJmL GPP variability here. This paper presents for the first time a benchmark for a global FLUXNET upscaling approach that will be employed in future studies. Although the real world FLUXNET upscaling is more complicated than for a noise free and reduced complexity biosphere model as presented here, our results show that an empirical upscaling from the current FLUXNET network with MTE is feasible and able to extract global patterns of carbon flux variability.
Graphical Rainflow Counting - Complete Algorithm for single courses (SCRF)
Rainflow counting can be considered the most important method in counting random stress- or strain histories with respect to time in the field of fatigue strength. The method is often explained by rules of a rain flow which gave the name to the method. The algorithmic implementation nevertheless is usually done in three-point or four-point algorithms which work by comparing the differences between the adjacent reversals and then delete the points of the identified intermediate cycle from the time history. The SCRF-algorithm presented in this contribution determines the courses of the rain flow in terms of their start und stop classes as half-cycles. In a subsequent step the half-cycles are matched together to form a closed loop resp. kept as a residue if the counter-course is missing. The algorithm is explained in general und by means of an example. For verification the SCRF-algorithm is compared to a three-point and a four-point algorithm. For periodic signals, there is complete agreement between the three methods. For non-periodic signals, there are small differences: The SCRF can combine some more half-cycles into closed cycles which remain as residues in the other methods. Furthermore the sense of direction of the hysteresis loops is captured more precisely with the SCRF due to its chronological approach.
A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)
Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. However, the climatic, environmental, and socioeconomic factors that control global fire activity in vegetation are only poorly understood, and in various complexities and formulations are represented in global process-oriented vegetation-fire models. Data-driven model approaches such as machine learning algorithms have successfully been used to identify and better understand controlling factors for fire activity. However, such machine learning models cannot be easily adapted or even implemented within process-oriented global vegetation-fire models. To overcome this gap between machine learning-based approaches and process-oriented global fire models, we introduce a new flexible data-driven fire modelling approach here (Satellite Observations to predict FIre Activity, SOFIA approach version 1). SOFIA models can use several predictor variables and functional relationships to estimate burned area that can be easily adapted with more complex process-oriented vegetation-fire models. We created an ensemble of SOFIA models to test the importance of several predictor variables. SOFIA models result in the highest performance in predicting burned area if they account for a direct restriction of fire activity under wet conditions and if they include a land cover-dependent restriction or allowance of fire activity by vegetation density and biomass. The use of vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. We further analyse spatial patterns of the sensitivity between anthropogenic, climate, and vegetation predictor variables and burned area. We finally discuss how multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with data-driven modelling and model–data integration approaches can guide the future development of global process-oriented vegetation-fire models.