Uncertainty in river discharge observations: a quantitative analysis

Abstract. This study proposes a framework for analysing and quantifying the uncertainty of river flow data. Such uncertainty is often considered to be negligible with respect to other approximations affecting hydrological studies. Actually, given that river discharge data are usually obtained by means of the so-called rating curve method, a number of different sources of error affect the derived observations. These include: errors in measurements of river stage and discharge utilised to parameterise the rating curve, interpolation and extrapolation error of the rating curve, presence of unsteady flow conditions, and seasonal variations of the state of the vegetation (i.e. roughness). This study aims at analysing these sources of uncertainty using an original methodology. The novelty of the proposed framework lies in the estimation of rating curve uncertainty, which is based on hydraulic simulations. These latter are carried out on a reach of the Po River (Italy) by means of a one-dimensional (1-D) hydraulic model code (HEC-RAS). The results of the study show that errors in river flow data are indeed far from negligible

Cumulative learning through intrinsic reinforcements

Building artificial agents able to autonomously learn new skills and to easily adapt in different and complex environments is an important goal for robotics and machine learning. We propose that providing reinforcement learning artificial agents with a learning signal that resembles the charac- teristic of the phasic activations of dopaminergic neurons would be an advancement in the development of more autonomous and versatile systems. In particular, we suggest that the particular composition of such a signal, determined by both extrinsic and intrinsic reinforcements, would be suitable to improve the implementation of cumulative learning in artificial agents. To validate our hypothesis we performed experiments with a simulated robotic system that has to learn different skills to obtain extrinsic rewards. We compare different versions of the system varying the composition of the learning signal and we show that the only system able to reach high performance in the task is the one that implements the learning signal suggested by our hypothesis

Phasic dopamine as a prediction error of intrinsic and extrinsic reinforcement driving both action acquisition and reward maximization: A simulated robotic study

An important issue of recent neuroscientific research is to understand the functional role of the phasic release of dopamine in the striatum, and in particular its relation to reinforcement learning. The literature is split between two alternative hypotheses: one considers phasic dopamine as a reward prediction error similar to the computational TD-error, whose function is to guide an animal to maximize future rewards; the other holds that phasic dopamine is a sensory prediction error signal that lets the animal discover and acquire novel actions. In this paper we propose an original hypothesis that integrates these two contrasting positions: according to our view phasic dopamine represents a TD-like reinforcement prediction error learning signal determined by both unexpected changes in the environment (temporary, intrinsic reinforcements) and biological rewards (permanent, extrinsic reinforcements). Accordingly, dopamine plays the functional role of driving both the discovery and acquisition of novel actions and the maximization of future rewards. To validate our hypothesis we perform a series of experiments with a simulated robotic system that has to learn different skills in order to get rewards. We compare different versions of the system in which we vary the composition of the learning signal. The results show that only the system reinforced by both extrinsic and intrinsic reinforcements is able to reach high performance in sufficiently complex conditions

Increasing flood risk under climate change: a pan-European assessment of the benefits of four adaptation strategies

Future flood risk in Europe is likely to increase due to a combination of climatic and socio-economic drivers. Effective adaptation strategies need to be implemented to limit the impact of river flooding on population and assets. This research builds upon a recently developed flood risk assessment framework at European scale to explore the benefits of adaptation against extreme floods. Four different adaptation measures are simulated in a physically based modeling framework, including the rise of flood protections, reduction of the peak flows through water retention, reduction of vulnerability and relocation to safer areas. Their sensitivity is assessed in several configurations under a high-end global warming scenario over the time range 1976-2100. Results suggest that the future increase in expected damage and population affected by river floods can be compensated by a combination of different adaptation measures. The adaptation efforts should favor measures targeted at reducing the impacts of floods, rather than trying to avoid them. Conversely, adaptation plans only based on rising flood protections have the effect of reducing the frequency of small floods and exposing the society to less-frequent but catastrophic floods and potentially long recovery processes.JRC.H.7-Climate Risk Managemen

Water management for irrigation, crop yield and social attitudes: a socio-agricultural agent-based model to explore a collective action problem

When rainfall does not meet crop water requirements, supplemental irrigation is needed to maintain productivity. On-farm ponds can prevent excessive groundwater exploitation - to the benefit of the whole community - but they reduce the cultivated area and require investments by each farmer. Thus, choosing the source of water for irrigation (groundwatervson-farm pond) is a problem of collective action. An agent-based model is developed to simulate a smallholder farming system; the farmers' long-/short-view orientation determines the choice of the water source. We identify the most beneficial water source for economic gain and its stability, and how it can change across communities and under future climate scenarios. By using on-farm ponds, long-view-oriented farmers provide collective advantages but have individual advantages only under extreme climates; a tragedy of the commons is always possible. Changes in farmers' attitudes (and hence sources of water) based on previous experiences can worsen the economic outcome

The seventh facet of uncertainty:wrong assumptions, unknowns and surprises in the dynamics of human–water systems

The scientific literature has focused on uncertainty as randomness, while limited credit has been given to what we call here the “seventh facet of uncertainty”, i.e. lack of knowledge. This paper identifies three types of lack of understanding: (i) known unknowns, which are things we know we don’t know; (ii) unknown unknowns, which are things we don’t know we don’t know; and (iii) wrong assumptions, things we think we know, but we actually don’t know. Here we discuss each of these with reference to the study of the dynamics of human–water systems, which is one of the main topics of Panta Rhei, the current scientific decade of the International Association of Hydrological Sciences (IAHS), focusing on changes in hydrology and society. In the paper, we argue that interdisciplinary studies of socio-hydrological dynamics leading to a better understanding of human–water interactions can help in coping with wrong assumptions and known unknowns. Also, being aware of the existence of unknown unknowns, and their potential capability to generate surprises or black swans, suggests the need to complement top-down approaches, based on quantitative predictions of water-related hazards, with bottom-up approaches, based on societal vulnerabilities and possibilities of failure

Which is the best intrinsic motivation signal for learning multiple skills?

Humans and other biological agents are able to autonomously learn and cache different skills in the absence of any biological pressure or any assigned task. In this respect, Intrinsic Motivations (i.e., motivations not connected to reward-related stimuli) play a cardinal role in animal learning, and can be considered as a fundamental tool for developing more autonomous and more adaptive artificial agents. In this work, we provide an exhaustive analysis of a scarcely investigated problem: which kind of IM reinforcement signal is the most suitable for driving the acquisition of multiple skills in the shortest time? To this purpose we implemented an artificial agent with a hierarchical architecture that allows to learn and cache different skills. We tested the system in a setup with continuous states and actions, in particular, with a kinematic robotic arm that has to learn different reaching tasks. We compare the results of different versions of the system driven by several different intrinsic motivation signals. The results show (a) that intrinsic reinforcements purely based on the knowledge of the system are not appropriate to guide the acquisition of multiple skills, and (b) that the stronger the link between the IM signal and the competence of the system, the better the performance