@misc{LatrubesseParkKaestner, author = {Latrubesse, Edgardo and Park, Edward and K{\"a}stner, Karl}, title = {The Ayeyarwady River (Myanmar): Washload transport and its global role among rivers in the Anthropocene}, series = {PLOS One}, volume = {2021}, journal = {PLOS One}, doi = {10.1371/journal.pone.0251156}, pages = {18}, abstract = {The Ayeyarwady (Irrawaddy) is the second largest river of Southeast Asia and one of the rivers with the highest load of suspended sediment delivered to the sea in the world. The Ayeyarwady is the lifeline of Myanmar which concentrates the majority of the population and GDP of the country. It is the main way of transport, a source of fluvial aggregates for development projects, hydropower, and the basin plays a major role in food supply and irrigation. Despite the Ayeyarwady ranking amongst the world's largest rivers and its vital importance to Myanmar, scarce research has been undertaken to understand its morphodynamics and sediment transport regime. Current load estimates still heavily rely on the only systematic study of sediment transport dating back to the 19th century. Here, we provide a novel estimate for the recent washload sediment transport based on a field calibrated remote sensing model of surface suspended sediments concentrations. We show that the Ayeyarwady has likely become the river with the second or third largest delivery of washload to the sea in the world since it has so far been much less affected by damming compared to the vast majority of other rivers.}, language = {en} } @misc{KaestnerCaviedesVoulliemeFrechenetal., author = {K{\"a}stner, Karl and Caviedes-Voullieme, Daniel and Frechen, Tobias Nanu and Hinz, Christoph}, title = {Theory and empirical evidence for the irregularity of self-organized vegetation patterns}, series = {EGU General Assembly 2022, Vienna, Austria, 23-27 May 2022}, journal = {EGU General Assembly 2022, Vienna, Austria, 23-27 May 2022}, doi = {10.5194/egusphere-egu22-11905}, abstract = {In arid environments, vegetation tends to self-organize into patches separated by bare soil. This is necessitated by the lack of water for sustaining a continuous vegetation cover and facilitated by the attraction of water from barren interpatch areas by the vegetation. This process is a positive feedback which introduces spatially heterogeneity into otherwise homogeneous environments, characterised by regular patterns. These patterns are typically considered to be periodic and distinguished on hand of their wavelength. Such patterns have so far been studied with numerical models which generate periodic patterns in homogeneous environments. However, environments are rarely homogeneous, as topography and soil-hydraulic properties vary in space. This raises the questions to which degree heterogeneity of vegetation is self-organized or imposed by the environment, and how environmental heterogeneity interacts with the self-organization process. In contrast to the persisting conceptual model of periodic patterns, natural vegetation exhibit a high degree of irregularity. Several studies have linked this irregularity to heterogeneity in the environment, but a comprehensive theory for analysing the irregularity has not yet been established. Furthermore remains the extend of irregularity unexplored on a global scale. To fill this gap, we, demonstrate empirically the global prevalence of irregularity in vegetation patterns and find that natural vegetation patterns are stochastic, rather than periodic. We then propose a stochastic framework to conceptually describe and measure the regularity, based on the spectral density of the patterns. In addition to the dominant wavelength, measuring the spatial scale, it reveals a novel parameter, measuring the regularity. The parameter is determined by the correlation structure and discriminates gradually between the limit cases of periodicity and white noise. Applied to natural and computer-generated patterns, we find that the former are highly irregular, while the latter are close to periodic. We reproduce the stochasticity of patterns with numerical models by introducing spatial heterogeneity of the model coefficients. We provide a fresh look at the nature of vegetations patterns and present a comprehensive theory for a more holistic understanding of self-organized systems.}, language = {en} } @misc{KaestnerHinzCaviedesVoulliemeetal., author = {K{\"a}stner, Karl and Hinz, Christoph and Caviedes-Voulli{\`e}me, Daniel and Frechen, Tobias Nanu and Vijsel, Roeland C. van de}, title = {A metaanalysis of the regularity of environmental spatialpatterns and a theory relating them to stochastic processes}, series = {EGU General Assembly 2023, Vienna, Austria, 24-28 Apr 2023}, journal = {EGU General Assembly 2023, Vienna, Austria, 24-28 Apr 2023}, doi = {10.5194/egusphere-egu23-5817}, language = {en} } @misc{ShlewetCaviedesVoulliemeKaestneretal., author = {Shlewet, Marlin and Caviedes-Voulli{\`e}me, Daniel and K{\"a}stner, Karl and Hinz, Christoph}, title = {Effects of urban structures on spatial and temporal flood distribution}, series = {EGU General Assembly 2023, Vienna, Austria, 24-28 Apr 2023}, journal = {EGU General Assembly 2023, Vienna, Austria, 24-28 Apr 2023}, doi = {10.5194/egusphere-egu23-9498}, language = {en} } @misc{ShlewetKaestnerCaviedesVoulliemeetal., author = {Shlewet, Marlin and K{\"a}stner, Karl and Caviedes-Voulli{\`e}me, Daniel and Hinz, Christoph}, title = {Einfluss urbaner Strukturen auf die r{\"a}umliche und zeitliche Dynamik pluvialer Fluten}, series = {Abstract-Band, Tag der Hydrologie 2023, Nachhaltiges Wassermanagement - Regionale und Globale Strategien, 22. \& 23.03.2023, Ruhr-Universit{\"a}t Bochum \& Hochschule Bochum}, journal = {Abstract-Band, Tag der Hydrologie 2023, Nachhaltiges Wassermanagement - Regionale und Globale Strategien, 22. \& 23.03.2023, Ruhr-Universit{\"a}t Bochum \& Hochschule Bochum}, language = {de} } @misc{KaestnerVijselCaviedesVoulliemeetal., author = {K{\"a}stner, Karl and Vijsel, Roeland C. van de and Caviedes-Voulli{\`e}me, Daniel and Hinz, Christoph}, title = {Unravelling the spatial structure of regular environmental spatial patterns}, series = {EGU General Assembly 2024, Vienna, Austria \& Online, 14-19 April 2024}, journal = {EGU General Assembly 2024, Vienna, Austria \& Online, 14-19 April 2024}, publisher = {Copernicus GmbH}, doi = {10.5194/egusphere-egu24-3412}, abstract = {Spatial patterns where patches of high biomass alternate with bare ground occur in many resource-limited ecosystems. Especially fascinating are regular patterns, which are self-similar at a lag distance corresponding to the typical distance between patches. Regular patterns are understood to form autogenously through self-organization, which can be generated with deterministic reaction-diffusion models. Such models generate highly regular patterns, which repeat at the characteristic wavelength and are therefore periodic. Natural patterns do not repeat, as they are noisy and as the patch size and spacing vary. Natural patterns are therefore usually perceived as perturbed periodic patterns. However, the self-similarity of natural patterns decreases at longer lag distances, which indicates that their spatial structure is not a perturbed periodic structure originating through deterministic processes. Here, we provide an overview of our recent work on the spatial structure and formation of natural environmental spatial patterns as a basis for discussion: First, we develop a statistical periodicity test and compile a large dataset of more than 10,000 regular environmental spatial patterns. We find that neither isotropic (spotted) nor anisotropic (banded) patterns are periodic. Instead, we find that their spatial structure can be well described as random fields originating through stochastic processes. Second, we recognize the regularity as a gradually varying property, rather than a dichotomous property of being periodic or not. We develop a method for quantifying the regularity and apply it in a metastudy to a set of natural and model-generated patterns found in the literature. We find that patterns generated with deterministic reaction-diffusion models do not well reproduce the spatial structure of environmental spatial structure, as they are too regular. Third, we develop an understanding of pattern formation through stochastic reaction-diffusion processes, which incorporate random environmental heterogeneities. We find that regular patterns form through filtering of the environmental heterogeneities and identify stochastic processes which reproduce both isotropic and anisotropic patterns.}, language = {en} } @misc{KaestnerVijselCaviedesVoulliemeetal., author = {K{\"a}stner, Karl and Vijsel, Roeland C. van de and Caviedes-Voulli{\`e}me, Daniel and Hinz, Christoph}, title = {A scale-invariant method for quantifying the regularity of environmental spatial patterns}, series = {Ecological Complexity}, volume = {60}, journal = {Ecological Complexity}, publisher = {Elsevier BV}, issn = {1476-945X}, doi = {10.1016/j.ecocom.2024.101104}, pages = {13}, abstract = {Spatial patterns of alternating high and low biomass occur in a wide range of ecosystems. Patterns can improve ecosystem productivity and resilience, but the particular effects of patterning depend on their spatial structure. The spatial structure is conventionally classified as either regular, when the patches of biomass are of similar size and are spaced in similar intervals, or irregular. The formation of regular patterns is driven by scale-dependent feedbacks. Models incorporating those feedbacks generate highly regular patterns, while natural patterns appear less regular. This calls for a more nuanced quantification beyond a binary classification. Here, we propose measuring the degree of regularity by the maximum of a pattern's spectral density, based on the observation that the density of highly regular patterns consists of a narrow and high peak, while the density of highly irregular patterns consists of a low and wide lobe. We rescale the density to make the measure invariant with respect to the characteristic length-scale of a pattern, facilitating the comparison of patterns observed or modelled under different conditions. We demonstrate our method in a metastudy determining the regularity of natural and model-generated patterns depicted in previous studies. We find that natural patterns have an intermediate degree of regularity, resembling random surfaces generated by stochastic processes. We find that conventional deterministic models do not reproduce the intermediate regularity of natural patterns, as they generate patterns which are much more regular and similar to periodic surfaces. We call for appreciating the stochasticity of natural patterns in systems with scale-dependent feedbacks.}, language = {en} } @misc{KaestnerVijselCaviedesVoulliemeetal., author = {K{\"a}stner, Karl and Vijsel, Roeland C. van de and Caviedes-Voulli{\`e}me, Daniel and Frechen, Nanu T. and Hinz, Christoph}, title = {Unravelling the spatial structure of regular dryland vegetation patterns}, series = {CATENA}, volume = {247}, journal = {CATENA}, publisher = {Elsevier BV}, issn = {0341-8162}, doi = {10.1016/j.catena.2024.108442}, pages = {13}, abstract = {Many resource-limited ecosystems exhibit spatial patterns where patches of biomass alternate with bare ground. Patterns can enhance ecosystem functioning and resilience, depending on their spatial structure. Particularly conspicuous are regular patterns, where patches are of similar size and spaced in similar intervals. The spatial structure of regular patterns is often described to be periodic. This has been corroborated by statistical testing of natural patterns and generation of periodic patterns with deterministic reaction-diffusion models. Yet, natural regular patterns appear conspicuously erratic compared to periodic patterns. So far, this has been attributed to perturbations by noise, varying patch size and spacing. First, we illustrate by means of an example that the spatial structure of regular vegetation patterns cannot be reproduced by perturbing periodic patterns. We then compile a large dataset of regular dryland patterns and find that their spatial structure systematically differs from periodic patterns. We further reveal that previous studies testing for periodicity overlook two aspects which dramatically inflate the number of false positives and result in the misclassification of patterns as periodic. We amend the test procedure by accounting for both aspects, finding that regular natural patterns have no significant periodic components. Lastly, we demonstrate that stochastic processes can generate regular patterns with similar visual appearance, spatial structure and frequency spectra as natural regular patterns. We conclude that new methods are required for quantifying the regularity of spatial patterns beyond a binary classification and to further investigate the difference between natural and model generated patterns.}, language = {en} } @misc{KaestnerCaviedesVoulliemeHinz, author = {K{\"a}stner, Karl and Caviedes-Voulli{\`e}me, Daniel and Hinz, Christoph}, title = {Formation of spatial vegetation patterns in heterogeneous environments}, series = {PLOS One}, volume = {20}, journal = {PLOS One}, number = {5}, editor = {Li, Pan}, publisher = {Public Library of Science (PLoS)}, address = {San Francisco, California}, issn = {1932-6203}, doi = {10.1371/journal.pone.0324181}, pages = {1 -- 38}, abstract = {Functioning of many resource-limited ecosystems is facilitated through spatial patterns. Patterns can indicate ecosystems productivity and resilience, but the interpretation of a pattern requires good understanding of its structure and underlying biophysical processes. Regular patterns are understood to form autogenously through self-organization, for which exogenous heterogeneities are negligible. This has been corroborated by reaction-diffusion models which generate highly regular patterns in idealized homogeneous environments. However, such model-generated patterns are considerably more regular than natural patterns, which indicates that the concept of autogenous pattern formation is incomplete. Models can generate patterns which appear more natural when they incorporate exogenous random spatial heterogeneities (noise), such as microtopography or spatially varying soil properties. However, the mechanism through which noise influences the pattern formation has not been explained so far. Recalling that irregular patterns can form through stochastic processes, we propose that regular patterns can form through stochastic processes as well, where spatial noise is filtered through scale-dependent biophysical feedbacks. First, we demonstrate that the pattern formation in nonlinear reaction-diffusion models is highly sensitive to noise. We then propose simple stochastic processes which can explain why and how random exogenous heterogeneity influences the formation of regular and irregular patterns. Finally, we derive linear filters which reproduce the spatial structure and visual appearance of natural patterns well. Our work contributes to a more holistic understanding of spatial pattern formation in self-organizing ecosystems.}, language = {en} }