Hydroinformatics has emerged over the last decade to become a recognised and established field of independent research within the hydrological sciences. Hydroinformatics is concerned with the development and hydrological application of mathematical modelling, information technology, high-performance computing, systems science and computational intelligence tools. It provides the computer-based decision-support systems that are now entering more and more into the offices of consulting engineers, water authorities and government agencies. Tools for capturing data, on both a mega-scale and a milli-scale, are immense and still emerging. As a result we have to face the challenges of Big Data: large data sets, both in size and complexity. Methods and technologies for data handling, visualization and knowledge acquisition are more and more often referred to as Data Science.

The aim of this session is to provide an active forum in which to demonstrate and discuss the integration and appropriate application of emergent computational technologies in a hydrological modelling context. Topics of interest are expected to cover a broad spectrum of theoretical and practical activities that would be of interest to hydro-scientists and water-engineers. The main topics will address the following classes of methods and technologies:

* Predictive and analytical models based on the methods of statistics, computational intelligence and data science: neural networks, fuzzy systems, support vector machines, genetic programming, cellular automata, chaos theory, etc.
* Methods for the analysis of complex data sets, including remote sensing data: principal and independent component analysis, feature extraction, time series analysis, data-infilling, information theory, etc.
* Specific concepts and methods of Big Data and Data Science such as data thinning, data fusion, information integration
* Optimisation methods associated with heuristic search procedures: various types of genetic and evolutionary algorithms, randomised and adaptive search, ant colony, particle swarm optimisation, etc.
* Applications of systems analysis and optimisation in water resources
* Hybrid modelling involving different types of models both process-based and data-driven, combination of models (multi-models), etc.
* Data assimilation and model reduction in integrated modelling
* Novel methods of analysing model uncertainty and sensitivity
* Appropriate software architectures for linking different types of models and data sources
* Opportunities and challenges in using high-performance computing for terrestrial systems modelling.

Applications could belong to any area of hydrology or water resources: rainfall-runoff modelling, flow forecasting, sedimentation modelling, analysis of meteorological and hydrologic data sets, linkages between numerical weather prediction and hydrologic models, model calibration, model uncertainty, optimisation of water resources, etc.

Many environmental and hydrological problems are spatial or temporal, or both in nature. Spatio-temporal analysis allows identifying and explaining large-scale anomalies which are useful for understanding hydrological characteristics and subsequently predicting hydrological events. Temporal information is sometimes limited; spatial information, on the other hand has increased in recent years due technological advances including the availability of remote sensing data. This development has motivated new research efforts to include data in model representation and analysis.

Geostatistics is the discipline that investigates the statistics of spatially extended variables. Spatio-temporal analysis is at the forefront of geostatistical research these days, and its impact is expected to increase in the future. This trend will be driven by increasing needs to advance risk assessment and management strategies for extreme events such as floods and droughts, and to support both short and long-term water management planning. Current trends and variability of hydrological extremes call for novel approaches of spatio-temporal and/or geostatistical analysis to assess, predict, and manage water related and/or interlinked hazards including the assessment of uncertainties.

The aim of this session is to provide a platform and an opportunity to demonstrate and discuss innovative applications and methodologies of spatio-temporal and/or geostatistical analysis in a hydrological context. The session is targeted at both hydrologists and statisticians interested in the spatial and temporal analysis of hydrological events, extremes, and related hazards, and it aims to provide a forum for researchers from a variety of fields to effectively communicate their research.

Given the broad scope of this session, the topics of interest include the following non-exclusive list of subjects:

1. Spatio-temporal methods for the analysis of hydrological, environmental and climate anomalies and/or related hazards.
2. New and innovative geostatistical applications in spatial modeling, spatio-temporal modeling, spatial reasoning and data mining.
3. Spatio-temporal and/or geostatistical methods with reduced computational complexity suitable for large-size hydrological problems.
4. Spatio-temporal dynamics of natural events (e.g. morphological changes, spatial displacement phenomena, other).
5. Generalization and optimization of spatial models including monitoring networks optimization.
6. Applications of copulas on the identification of spatio-temporal relationships.
7. Spatial switching and/or ensemble of models.
8. Spatial analysis and predictions using Gaussian and non-Gaussian models.
9. Spatial and spatio-temporal covariance application revealing links between hydrological variables and extremes.
10. Prediction on regions of unobserved or limited data where gridded and point simulated data from physical-based models is available.
11. Generalized extreme value distributions used to model extremes for spatial event analysis.
12. (Geostatistical) characterization of uncertainties.
13. Bayesian Geostatistical Analysis.

Citizen Observatories, crowdsourcing, and innovative sensing techniques are used increasingly in water resources monitoring, especially when dealing with natural hazards. These innovative opportunities allow scientists to benefit from citizens’ involvement, by providing key local information for the identification of natural phenomena. In this way new knowledge for monitoring, modelling, and management of water resources and their related hazards is obtained.

This session is dedicated to multidisciplinary contributions, especially those that are focused on the demonstration of the benefit of the use of Citizen Observatories, crowdsourcing, and innovative sensing techniques for monitoring, modelling, and management of water resources.

The research presented might focus on, but not limited to, innovative applications of Citizen Observatories, crowdsourcing, innovative and remote sensing techniques for (i) water resources monitoring; (ii) hazard, exposure, vulnerability and risk mapping; (iii) development of disaster management and risk reduction strategies. Research studies might also focus on the development of technology, modelling tools, and digital platforms within research projects.

The session aims to serve a diverse community of research scientists, practitioners, end users, and decision makers. Submissions that look into issues related to the benefits and impacts of innovative sensing on studies of climate change, anthropogenic pressure, as well as ecological and social interactions are highly desired. Early stage researchers are strongly encouraged to present their research.

This session aims to bring together researchers working with big data sets generated from monitoring networks, extensive observational campaigns and detailed modeling efforts across various fields of geosciences. Topics of this session will include the identification and handling of specific problems arising from the need to analyze such large-scale data sets, together with methodological approaches towards semi or fully automated inference of relevant patterns in time and space aided by computer science-inspired techniques. Among others, this session shall address approaches from the following fields:
• Dimensionality and complexity of big data sets
• Data mining in Earth sciences
• Machine learning, including deep learning and other advanced approaches
• Visualization and visual analytics of big data
• Informatics and data science
• Emerging big data paradigms, such as datacubes

Many situations occur in Geosciences where one wants to obtain an accurate description of the present, past or future state of a particular system. Examples are prediction of weather and climate, assimilation of observations, or inversion of seismic signals for probing the interior of the planet. One important aspect is the identification of the errors affecting the various sources of information used in the estimation process, and the quantification of the ensuing uncertainty on the final estimate.

The session is devoted to the theoretical and numerical aspects of that broad class of problems. A large number of topics are dealt with in the various papers to be presented: algorithms for assimilation of observations, and associated mathematical aspects (particularly, but not only, in the context of the atmosphere and the ocean), predictability of geophysical flows, with stress on the impact of initial and model errors, inverse problems of different kinds, and also new aspects at the crossing between data assimilation and data-driven methods. Applications to specific physical problems are presented.


Solicited Speakers
Olivier Pannekoucke (Météo-France, Toulouse)
Manuel Pulido (University of Reading)

This interdisciplinary session welcomes contributions on novel conceptual approaches and methods for the analysis of observational as well as model time series and associated uncertainties from all geoscientific disciplines.

Methods to be discussed include, but are not limited to:
- linear and nonlinear methods of time series analysis
- time-frequency methods
- predictive approaches
- statistical inference for nonlinear time series
- nonlinear statistical decomposition and related techniques for multivariate and spatio-temporal data
- nonlinear correlation analysis and synchronisation
- surrogate data techniques
- filtering approaches and nonlinear methods of noise reduction

We particularly encourage submissions addressing the problem of uncertainty of geoscientific time series and its treatment in the context of statistical and dynamical analysis, including:
- representation of time series with uncertain dating (in particular paleoclimatic records from ice cores, sediments, speleothems etc.)
- uncertainties in change point / transition detection
- uncertainty propagation in time series methods like correlation, synchronization, spectral analysis, PCA, networks, and similar techniques
- uncertainty propagation in empirical (i.e., data-derived) inverse models