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- ItemUnderstanding the transgression of global and regional freshwater planetary boundaries(London : Royal Society, 2022) Pastor, A.V.; Biemans, H.; Franssen, W.; Gerten, D.; Hoff, H.; Ludwig, F.; Kabat, P.Freshwater ecosystems have been degraded due to intensive freshwater abstraction. Therefore, environmental flow requirements (EFRs) methods have been proposed to maintain healthy rivers and/or restore river flows. In this study, we used the Variable Monthly Flow (VMF) method to calculate the transgression of freshwater planetary boundaries: (1) natural deficits in which flow does not meet EFRs due to climate variability, and (2) anthropogenic deficits caused by water abstractions. The novelty is that we calculated spatially and cumulative monthly water deficits by river types including the frequency, magnitude and causes of environmental flow (EF) deficits (climatic and/or anthropogenic). Water deficit was found to be a regional rather than a global concern (less than 5% of total discharge). The results show that, from 1960 to 2000, perennial rivers with low flow alteration, such as the Amazon, had an EF deficit of 2–12% of the total discharge, and that the climate deficit was responsible for up to 75% of the total deficit. In rivers with high seasonality and high water abstractions such as the Indus, the total deficit represents up to 130% of its total discharge, 85% of which is due to withdrawals. We highlight the need to allocate water to humans and ecosystems sustainably. This article is part of the Royal Society Science+ meeting issue ‘Drought risk in the Anthropocene’.
- ItemTopology identification of complex network via chaotic ant swarm algorithm(New York, NY : Hindawi Publishing Corporation, 2013) Peng, H.; Li, L.; Kurths, J.; Li, S.; Yang, Y.Nowadays, the topology of complex networks is essential in various fields as engineering, biology, physics, and other scientific fields. We know in some general cases that there may be some unknown structure parameters in a complex network. In order to identify those unknown structure parameters, a topology identification method is proposed based on a chaotic ant swarm algorithm in this paper. The problem of topology identification is converted into that of parameter optimization which can be solved by a chaotic ant algorithm. The proposed method enables us to identify the topology of the synchronization network effectively. Numerical simulations are also provided to show the effectiveness and feasibility of the proposed method.
- ItemHow Price-Based Frequency Regulation Impacts Stability in Power Grids: A Complex Network Perspective(London : Hindawi, 2020) Ji, Peng; Zhu, Lipeng; Lu, Chao; Lin, Wei; Kurths, JürgenWith the deregulation of modern power grids, electricity markets are playing a more and more important role in power grid operation and control. However, it is still questionable how the real-time electricity price-based operation affects power grid stability. From a complex network perspective, here we investigate the dynamical interactions between price-based frequency regulations and physical networks, which results in an interesting finding that a local minimum of network stability occurs when the response strength of generators/consumers to the varying price increases. A case study of the real world-based China Southern Power Grid demonstrates the finding and exhibits a feasible approach to network stability enhancement in smart grids. This also provides guidance for potential upgrade and expansion of the current power grids in a cleaner and safer way. © 2020 Peng Ji et al.
- ItemTaxing interacting externalities of ocean acidification, global warming, and eutrophication(Malden, Mass. : Wiley-Blackwell, 2021) Hänsel, Martin C.; Bergh, Jeroen C. J. M. van denWe model a stylized economy dependent on agriculture and fisheries to study optimal environmental policy in the face of interacting external effects of ocean acidification, global warming, and eutrophication. This allows us to capture some of the latest insights from research on ocean acidification. Using a static two-sector general equilibrium model we derive optimal rules for national taxes on (Formula presented.) emissions and agricultural run-off and show how they depend on both isolated and interacting damage effects. In addition, we derive a second-best rule for a tax on agricultural run-off of fertilizers for the realistic case that effective internalization of (Formula presented.) externalities is lacking. The results contribute to a better understanding of the social costs of ocean acidification in coastal economies when there is interaction with other environmental stressors. Recommendations for Resource Managers: Marginal environmental damages from (Formula presented.) emissions should be internalized by a tax on (Formula presented.) emissions that is high enough to not only reflect marginal damages from temperature increases, but also marginal damages from ocean acidification and the interaction of both with regional sources of acidification like nutrient run-off from agriculture. In the absence of serious national policies that fully internalize externalities, a sufficiently high tax on regional nutrient run-off of fertilizers used in agricultural production can limit not only marginal environmental damages from nutrient run-off but also account for unregulated carbon emissions. Putting such regional policies in place that consider multiple important drivers of environmental change will be of particular importance for developing coastal economies that are likely to suffer the most from ocean acidification. © 2021 The Authors. Natural Resource Modeling published by Wiley Periodicals LLC.
- ItemPredicting the data structure prior to extreme events from passive observables using echo state network(Lausanne : Frontiers Media, 2022) Banerjee, Abhirup; Mishra, Arindam; Dana, Syamal K.; Hens, Chittaranjan; Kapitaniak, Tomasz; Kurths, Jürgen; Marwan, NorbertExtreme events are defined as events that largely deviate from the nominal state of the system as observed in a time series. Due to the rarity and uncertainty of their occurrence, predicting extreme events has been challenging. In real life, some variables (passive variables) often encode significant information about the occurrence of extreme events manifested in another variable (active variable). For example, observables such as temperature, pressure, etc., act as passive variables in case of extreme precipitation events. These passive variables do not show any large excursion from the nominal condition yet carry the fingerprint of the extreme events. In this study, we propose a reservoir computation-based framework that can predict the preceding structure or pattern in the time evolution of the active variable that leads to an extreme event using information from the passive variable. An appropriate threshold height of events is a prerequisite for detecting extreme events and improving the skill of their prediction. We demonstrate that the magnitude of extreme events and the appearance of a coherent pattern before the arrival of the extreme event in a time series affect the prediction skill. Quantitatively, we confirm this using a metric describing the mean phase difference between the input time signals, which decreases when the magnitude of the extreme event is relatively higher, thereby increasing the predictability skill.
- ItemGeneralized Synchronization Between ENSO and Hydrological Variables in Colombia: A Recurrence Quantification Approach(Lausanne : Frontiers Media, 2020) Salas, Hernán D.; Poveda, Germán; Mesa, Óscar J.; Marwan, NorbertWe use Recurrence Quantification Analysis (RQA) to study features of Generalized Synchronization (GS) between El Niño-Southern Oscillation (ENSO) and monthly hydrological anomalies (HyAns) of rainfall and streamflows in Colombia. To that end, we check the sensitivity of the RQA concerning diverse HyAns estimation methods, which constitutes a fundamental procedure for any climatological analysis at inter-annual timescales. In general, the GS and its sensitivity to HyAns methods are quantified by means of time-lagged joint recurrence analysis. Then, we link the GS results with the dynamics of major physical mechanisms that modulate Colombia's hydroclimatology, including the Caribbean, the CHOCO and the Orinoco Low-Level Jets (LLJs), and the Cross-Equatorial Flow (CEF) over northwestern Amazonia (southern Colombia). Our findings show that RQA exhibits significant differences depending on the HyAns methods. GS results are similar for the HyAns methods with variable annual cycle but the time-lags seem to be sensitive. On the other hand, our results make evident that HyAns in the Pacific, Caribbean, and Andean regions of Colombia exhibit strong (weak) GS with the ENSO signal during La Niña (El Niño), when hydrological anomalies are positive (negative). Results from the GS analysis allow us to identify spatial patterns of non-linear dependence between ENSO and the Colombian's climatology. The mentioned moisture transport sources constitute the interdependence mechanism and contribute to explain hydrological anomalies in Colombia during the phases of ENSO. During La Niña (El Niño), GS is strong (weak) for the Caribbean and the CHOCO LLJs whereas GS is moderate (strong) for the Orinoco LLJ. Moreover, moisture advection by the Caribbean and CHOCO LLJs exhibit synchrony with HyAns at 0–2 (2–4) months-lags over north-western Colombia and the Orinoco LLJ moisture advection synchronizes with HyAns at similar month-lags over the Amazon region of Colombia. Furthermore, our results suggest a strong (weak) GS between negative (positive) Sea Surface Temperatures (SST) anomalies in the Eastern Pacific and rainfall anomalies in Colombia. In contrast, GS is strong (weak) for positive (negative) SST anomalies in the Central Pacific. Our GS results contribute to advance our understanding on the regional effects of both phases of ENSO in Colombia, whose socio-economical, environmental and ecological impacts cannot be overstated. This work provides a novel approach that reveals new insights into the impact of ENSO on northern South America. © Copyright © 2020 Salas, Poveda, Mesa and Marwan.
- ItemThe Switch in a Genetic Toggle System with Lévy Noise(London : Nature Publishing Group, 2016) Xu, Y.; Li, Y.; Zhang, H.; Li, X.; Kurths, J.
- ItemSystems Medicine of Cancer: Bringing Together Clinical Data and Nonlinear Dynamics of Genetic Networks(London : Hindawi, 2016) Blyuss, Konstantin B.; Manchanda, Ranjit; Kurths, Jürgen; Alsaedi, Ahmed; Zaikin, AlexeyEditorial
- ItemSequential decision problems, dependent types and generic solutions(Braunschweig : Department of Theoretical Computer Science, Technical University of Braunschweig, 2017) Botta, N.; Jansson, P.; Ionescu, C.; Christiansen, D.R.; Brady, E.We present a computer-checked generic implementation for solving finite-horizon sequential decision problems. This is a wide class of problems, including inter-temporal optimizations, knapsack, optimal bracketing, scheduling, etc. The implementation can handle time-step dependent control and state spaces, and monadic representations of uncertainty (such as stochastic, non-deterministic, fuzzy, or combinations thereof). This level of genericity is achievable in a programming language with dependent types (we have used both Idris and Agda). Dependent types are also the means that allow us to obtain a formalization and computer-checked proof of the central component of our implementation: Bellman’s principle of optimality and the associated backwards induction algorithm. The formalization clarifies certain aspects of backwards induction and, by making explicit notions such as viability and reachability, can serve as a starting point for a theory of controllability of monadic dynamical systems, commonly encountered in, e.g., climate impact research.
- ItemCausal coupling inference from multivariate time series based on ordinal partition transition networks(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2021) Subramaniyam, Narayan Puthanmadam; Donner, Reik V.; Caron, Davide; Panuccio, Gabriella; Hyttinen, JariIdentifying causal relationships is a challenging yet crucial problem in many fields of science like epidemiology, climatology, ecology, genomics, economics and neuroscience, to mention only a few. Recent studies have demonstrated that ordinal partition transition networks (OPTNs) allow inferring the coupling direction between two dynamical systems. In this work, we generalize this concept to the study of the interactions among multiple dynamical systems and we propose a new method to detect causality in multivariate observational data. By applying this method to numerical simulations of coupled linear stochastic processes as well as two examples of interacting nonlinear dynamical systems (coupled Lorenz systems and a network of neural mass models), we demonstrate that our approach can reliably identify the direction of interactions and the associated coupling delays. Finally, we study real-world observational microelectrode array electrophysiology data from rodent brain slices to identify the causal coupling structures underlying epileptiform activity. Our results, both from simulations and real-world data, suggest that OPTNs can provide a complementary and robust approach to infer causal effect networks from multivariate observational data.
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