Preprints

Filtering by Subject: Dynamic Systems

Linear analysis of ice-shelf topography response to basal melting and freezing

Aaron Grey Stubblefield, Colin R. Meyer, Martin Wearing

Published: 2023-04-28
Subjects: Applied Mathematics, Dynamic Systems, Earth Sciences, Fluid Dynamics, Glaciology, Physical Sciences and Mathematics, Physics

Floating ice shelves in Antarctica and Greenland limit land-ice contributions to sea level rise by resisting the flow of grounded ice. Melting at the surface and base of ice shelves can lead to destabilisation by promoting thinning and fracturing. Basal melting often results in channelised features that manifest as surface topography due to buoyancy. The assumption of hydrostatic flotation [...]

An Artificial Neural Network Emulator of the Rangeland Hydrology and Erosion Model

Mahmoud Saeedimoghaddam, Grey Nearing, Mariano Hernandez, et al.

Published: 2022-06-17
Subjects: Artificial Intelligence and Robotics, Dynamic Systems, Hydrology, Natural Resources and Conservation

Machine learning (ML) is becoming an ever more important tool in hydrologic modeling. Many studies have shown the higher prediction accuracy of the ML models over traditional process-based ones. However, there is another advantage of ML which is its lower computer time of execution. This is important for the applications such as hydraulic soil erosion estimation over a large area and at a finer [...]

A review of model-based scenario analysis of poverty for informing sustainability

QI LIU, Zhaoxia Guo, Lei Gao, et al.

Published: 2021-11-22
Subjects: Applied Mathematics, Dynamic Systems, Environmental Sciences, Natural Resources Management and Policy, Physical Sciences and Mathematics, Sustainability

Ending poverty in all its forms everywhere is the first goal being targeted by the United Nations 2030 Agenda for Sustainable Development. Poverty eradication is a long-term process that faces the challenges of many uncertainties and complex interactions with other Sustainable Development Goals (SDGs). In order to better understand poverty and contribute to addressing poverty in a sustainable [...]

Nonlinear time series analysis of palaeoclimate proxy records

Norbert Marwan, Jonathan F. Donges, Reik V. Donner, et al.

Published: 2021-11-08
Subjects: Applied Statistics, Climate, Dynamic Systems, Earth Sciences, Geology, Longitudinal Data Analysis and Time Series, Multivariate Analysis, Non-linear Dynamics, Physical Sciences and Mathematics, Sedimentology, Statistical, Nonlinear, and Soft Matter Physics

Identifying and characterising dynamical regime shifts, critical transitions or potential tipping points in palaeoclimate time series is relevant for improving the understanding of often highly nonlinear Earth system dynamics. Beyond linear changes in time series properties such as mean, variance, or trend, these nonlinear regime shifts can manifest as changes in signal predictability, [...]

Dynamical Systems Theory Sheds New Light on Compound Climate Extremes in Europe and Eastern North America

paolo de luca, Gabriele Messori, Flavio M. E. Pons, et al.

Published: 2019-06-27
Subjects: Applied Mathematics, Atmospheric Sciences, Climate, Dynamic Systems, Earth Sciences, Meteorology, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Physics, Statistical, Nonlinear, and Soft Matter Physics

We propose a novel approach to the study of compound extremes, grounded in dynamical systems theory. Specifically, we present the co-recurrence ratio (α), which elucidates the dependence structure between variables by quantifying their joint recurrences. This approach is applied to daily climate extremes, derived from the ERA-Interim reanalysis over the 1979-2018 period. The analysis focuses on [...]

Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM

Ashesh Chattopadhyay, Pedram Hassanzadeh, Devika Subramanian

Published: 2019-06-20
Subjects: Applied Mathematics, Artificial Intelligence and Robotics, Atmospheric Sciences, Climate, Computer Sciences, Dynamic Systems, Earth Sciences, Fluid Dynamics, Non-linear Dynamics, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Physics

In this paper, the performance of three deep learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multi-scale spatio-temporal Lorenz 96 system is examined. The methods are: echo state network (a type of reservoir computing, RC-ESN), deep feed-forward artificial neural network (ANN), and recurrent neural network with long short-term memory [...]

River deltas as Multiplex networks: A framework for studying multi-process multi-scale connectivity via coupled-network theory

Alejandro Tejedor, Anthony Longjas, Paola Passalacqua, et al.

Published: 2018-04-13
Subjects: Applied Mathematics, Dynamic Systems, Earth Sciences, Environmental Sciences, Geomorphology, Hydrology, Mathematics, Non-linear Dynamics, Physical Sciences and Mathematics, Physics, Statistical, Nonlinear, and Soft Matter Physics

Transport of water, nutrients or energy fluxes in many natural or coupled human-natural systems occurs along different pathways that often have a wide range of transport timescales and might exchange fluxes with each other dynamically (e.g., surface-subsurface). Understanding this type of transport is key to predicting how landscapes will change under changing forcing. Here, we present a general [...]

A linear dynamical systems approach to streamflow reconstruction reveals history of regime shifts in northern Thailand

Hung Tan Thai Nguyen, Stefano Galelli

Published: 2017-11-01
Subjects: Applied Mathematics, Dynamic Systems, Earth Sciences, Hydrology, Physical Sciences and Mathematics

Catchment dynamics is not often modeled in streamflow reconstruction studies; yet, the streamflow generation process depends on both catchment state and climatic inputs. To explicitly account for this interaction, we contribute a linear dynamic model, in which streamflow is a function of both catchment state (i.e., wet/dry) and paleo-climatic proxies. The model is learned using a novel variant of [...]

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