Hydroinformatics has emerged over the last decades 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, systems science and computational intelligence tools. We also 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, machine learning and data science: neural networks, fuzzy systems, genetic programming, cellular automata, chaos theory, etc.
* Methods for the analysis of complex data sets, including remote sensing data: principal and independent component analysis, time series analysis, information theory, etc.
* Specific concepts and methods of Big Data and Data Science
* Optimisation methods associated with heuristic search procedures: various types of genetic and evolutionary algorithms, randomised and adaptive search, 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
* Software architectures for linking different types of models and data sources

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.

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

Society today demands sustainable technical solutions that reconcile the needs of society with those of nature . These solutions must coordinate between different and often competing demands within a sub-system (irrigation, ecological flow, power generation) and the variety of different uses of environmental resources across systems (e.g., power from water, wind, sun, or waves). The short term variability of precipitation, wind speed, sunshine, and other for environmental resources create a need for complex decisions to be taken in real time. Advances in real-time automatic control will play an essential role in making this possible. Moreover, while one might debate whether or not stationarity is dead, it is clear that fully deterministic models cannot cope with the connected world of today. The complex interactions of the randomness in the availability and quality of different resources calls out for an at least partially stochastic modelling approach.
We particularly invite contributions on:
• Stochastic modelling and control;
• Real-time control of environmental systems;
• Real-time monitoring and control of water quality;
• Real-time control of rural water systems;
• Real-time control of urban water systems.

The session is associated with Panta Rhei working group ``Natural and man-made control systems in water resources''.

Machine learning (ML) is now widely used across the Earth Sciences and especially its subfield deep learning (DL) has recently enjoyed increased attention in the context of Hydrology. The goal of this session is to highlight the continued integration of ML, and DL in particular, into traditional and emerging Hydrology-related workflows. Abstracts are solicited related to novel theory development, novel methodology, or practical applications of ML and DL in Hydrology. This might include, but is not limited to, the following:

(1) Identifying novel ways for DL in hydrological modelling.
(2) Testing and examining the usability of DL based approaches in hydrology.
(3) Improving understanding of the (internal) states/representations of DL models.
(4) Integrating DL with traditional hydrological models.
(5) Creating an improved understanding of the conditions for which DL provides reliable simulations. Including quantifying uncertainty in DL models.
(6) Clustering and/or classifying hydrologic systems, events and regimes.
(7) Using DL for detecting, quantifying or cope with nonstationarity in hydrological systems and modeling.
(8) Deriving scaling relationships or process-related insights directly from DL.
(8) Using DL to model or anticipate human behavior or human impacts on hydrological systems.
(10) DL based hazard analysis, detection/mitigation, event detection, etc.
(11) Natural Language Processing to analyze, interpret, or condense hydrologically-relevant peer-reviewed literature or social media data or to assess trends within the discipline.

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.

Statistics are in wide use in hydrology for example to estimate design events, forecast the risk and hazard of flood events, detect spatial or temporal clusters, model non-stationarity and changes and many more. Statistics are useful in the case when only few data are available but information for very rare events (extremes) or long time periods are needed. They are also helpful to detect changes and inconsistencies in the data and give a reliable statement on the significance. Moreover, temporal and spatial changes often lead to the violation of stationarity, a key assumption of many standard statistical approaches. This makes hydrological statistics interesting and challenging for so many researchers.

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 spatio-temporal and/or geostatistical analysis to assess, predict, and manage water related and/or interlinked hazards.

The aim of this session is to provide a platform and an opportunity to demonstrate and discuss innovative applications and methodologies of spatio-temporal analysis in a hydrological (hydrometeorological) 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.
This session is co-sponsered by ICSH-STAHY (IAHS).

Geostatistics is commonly applied in the Water, Earth and Environmental sciences to quantify spatial variation, produce interpolated maps with quantified uncertainty and optimize spatial sampling designs. Extensions to the space-time domain are also a topic of current interest. Due to technological advances and abundance of new data sources from remote and proximal sensing and a multitude of environmental sensor networks, big data analysis and data fusion techniques have become a major topic of research. Furthermore, methodological advances, such as hierarchical Bayesian modeling and machine learning, have enriched the modelling approaches typically used in geostatistics.

Earth-science data have spatial and temporal features that contain important information about the underlying processes. The development and application of innovative space-time geostatistical methods helps to better understand and quantify the relationship between the magnitude and the probability of occurrence of these events.

This session aims to provide a platform for geostatisticians, soil scientists, hydrologists, earth and environmental scientists to present and discuss innovative geostatistical methods to study and solve major problems in the Water, Earth and Environmental sciences. In addition to methodological innovations, we also encourage contributions on real-world applications of state-of-the-art geostatistical methods.

Given the broad scope of this session, the topics of interest include the following non-exclusive list of subjects:
1. Advanced parametric and non-parametric spatial estimation and prediction techniques
2. Big spatial data: analysis and visualization
3. Optimisation of spatial sampling frameworks and space-time monitoring designs
4. Algorithms and applications on Earth Observation Systems
5. Data Fusion, mining and information analysis
6. Integration of geostatistics with optimization and machine learning approaches
7. Application of covariance functions and copulas in the identification of spatio-temporal relationships
8. Geostatistical characterization of uncertainties and error propagation
9. Bayesian geostatistical analysis and hierarchical modelling
10. Functional data analysis approaches to geostatistics
11. Geostatistical analysis of spatial compositional data
12. Multiple point geostatistics
13. Upscaling and downscaling techniques
14. Ontological framework for characterizing environmental processes

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, deep learning and Artificial Intelligence applications in geosciences
• Visualization and visual analytics of big and high-dimensional data
• Informatics and data science
• Emerging big data paradigms, such as datacubes

This interdisciplinary session welcomes contributions on novel conceptual approaches and methods for the analysis of observational as well as model time series 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
- artificial intelligence and machine learning based analysis and prediction for univariate and multivariate time series

Contributions on methodological developments and applications to problems across all geoscientific disciplines are equally encouraged.