Nonlinear translational symmetric equilibria relevant to the L-H transition

Nonlinear z-independent solutions to a generalized Grad-Shafranov equation (GSE) with up to quartic flux terms in the free functions and incompressible plasma flow non parallel to the magnetic field are constructed quasi-analytically. Through an ansatz the GSE is transformed to a set of three ordinary differential equations and a constraint for three functions of the coordinate x, in cartesian coordinates (x,y), which then are solved numerically. Equilibrium configurations for certain values of the integration constants are displayed. Examination of their characteristics in connection with the impact of nonlinearity and sheared flow indicates that these equilibria are consistent with the L-H transition phenomenology. For flows parallel to the magnetic field one equilibrium corresponding to the H-state is potentially stable in the sense that a sufficient condition for linear stability is satisfied in an appreciable part of the plasma while another solution corresponding to the L-state does not satisfy the condition. The results indicate that the sheared flow in conjunction with the equilibrium nonlinearity play a stabilizing role.Comment: 26 pages, 16 figure

Flood risk, climate change and settlement development: a micro-scale assessment of Austrian municipalities

Abstract in dt. Sprache nicht verfügbarThis paper analyses the influence of climate change and land development on future flood risk for selected Austrian flood-prone municipalities. As part of an anticipatory micro-scale risk assessment we simulated four different inundation scenarios for current and future 100- and 300-year floods (which included a climate change allowance), developed scenarios of future settlement growth in floodplains and evaluated changes in flood damage potentials and flood risk until the year 2030. Findings show that both climate change and settlement development significantly increase future levels of flood risk. However, the respective impacts vary strongly across the different cases. The analysis indicates that local conditions, such as the topography of the floodplain, the spatial allocation of vulnerable land uses or the type of land development (e.g. residential, commercial or industrial) in the floodplain are the key determinants of the respective effects of climate change and land development on future levels of flood risk. The case study analysis highlights the general need for a more comprehensive consideration of the local determinants of flood risk in order to increase the effectiveness of an adaptive management of flood risk dynamics.(VLID)164915

NeuralHydrology -- Interpreting LSTMs in Hydrology

Despite the huge success of Long Short-Term Memory networks, their applications in environmental sciences are scarce. We argue that one reason is the difficulty to interpret the internals of trained networks. In this study, we look at the application of LSTMs for rainfall-runoff forecasting, one of the central tasks in the field of hydrology, in which the river discharge has to be predicted from meteorological observations. LSTMs are particularly well-suited for this problem since memory cells can represent dynamic reservoirs and storages, which are essential components in state-space modelling approaches of the hydrological system. On basis of two different catchments, one with snow influence and one without, we demonstrate how the trained model can be analyzed and interpreted. In the process, we show that the network internally learns to represent patterns that are consistent with our qualitative understanding of the hydrological system.Comment: Pre-print of published book chapter. See journal reference and DOI for more inf

Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks

Rainfall–runoff modelling is one of the key challenges in the field of hydrology. Various approaches exist, ranging from physically based over conceptual to fully data-driven models. In this paper, we propose a novel data-driven approach, using the Long Short-Term Memory (LSTM) network, a special type of recurrent neural network. The advantage of the LSTM is its ability to learn long-term dependencies between the provided input and output of the network, which are essential for modelling storage effects in e.g. catchments with snow influence. We use 241 catchments of the freely available CAMELS data set to test our approach and also compare the results to the well-known Sacramento Soil Moisture Accounting Model (SAC-SMA) coupled with the Snow-17 snow routine. We also show the potential of the LSTM as a regional hydrological model in which one model predicts the discharge for a variety of catchments. In our last experiment, we show the possibility to transfer process understanding, learned at regional scale, to individual catchments and thereby increasing model performance when compared to a LSTM trained only on the data of single catchments. Using this approach, we were able to achieve better model performance as the SAC-SMA&thinsp;+&thinsp;Snow-17, which underlines the potential of the LSTM for hydrological modelling applications.</p