HS1.1 – Innovative sensors and monitoring in hydrology
The MacGyver session for innovative and/or self made tools to observe the geosphere
The MacGyver session focuses on novel sensors made, or data sources unlocked, by scientists. All geoscientists are invited to present:
- new sensor systems, using technologies in novel or unintended ways,
- new data storage or transmission solutions sending data from the field with LoRa, WIFI, GSM, or any other nifty approach,
- started initiatives (e.g., Open-Sensing.org) that facilitate the creation and sharing of novel sensors, data acquisition and transmission systems.
Connected a sensor for iPhone to an Arduino or Raspberri Pi? 3D printed an automated water quality sampler? Or build a Cloud Storage system from Open Source Components? Show it!
New methods in hydrology, plant physiology, seismology, remote sensing, ecology, etc. are all welcome. Bring prototypes and demonstrations to make this the most exciting Poster Only (!) session of the General Assembly.
This session is co-sponsered by MOXXI, the working group on novel observational methods of the IAHS.
Advances in river monitoring and modelling for a climate emergency: data-scarce environments, real-time approaches, inter-comparison of innovative and classical frameworks, uncertainties, harmonisation of methods and good practices
Water is our planet’s most vital resource, and the primary agent in some of the biggest hazards facing society and nature. The twin pressures of population growth and a rapidly changing global climate act as multipliers of water’s value and of water-related hazards.
River streamflow is one of the most crucial hydrological variables for ecology, for people and industry, for flood risk management and for understanding long term changes to the hydrological regime. However, despite significant efforts, long-term, spatially dense monitoring networks remain scarce, and even the best monitoring networks can fail to perform when faced with extreme conditions, and lack the precision and spatial coverage to fully represent crucial aspects of the hydrological cycle.
Happily, a number of new technologies and techniques are emerging which show great potential to meet these challenges. In this context, this session focuses on:
1) Innovative methodologies for measuring/modelling/estimating river stream flows;
2) Real-time acquisition of hydrological variables;
3) Remote sensing for hydrological & morphological monitoring;
4) Measuring extreme conditions associated with a changing climate;
5) Measurement of sudden-onset extreme flows associated with catastrophic events;
6) Strategies to quantify and describe hydro-morphological evolution of rivers;
7) New methods to cope with data-scarce environments;
8) Inter-comparison of innovative & classical models and approaches;
9) Evolution and refinement of existing methods;
10) Guidelines and standards for hydro-morphological streamflow monitoring;
11) Quantification of uncertainties;
12) Development of expert networks to advance methods.
Contributions are welcome with an emphasis on innovation, efficiency, operator safety, and meeting the growing challenges associated with the changing climate, and with natural and anthropogenically driven disasters such as dam failures and flash floods.
Additionally, presentations will be welcomed which explore options for greater collaboration in advancing riverflow methods and which link innovative research to operational monitoring.
This session is sponsored by the COST Action CA16219, Harmonisation of UAS techniques for agricultural and natural ecosystems monitoring (HARMONIOUS).
Combining sensors, complementary observation methods and models for monitoring flow and transport in soils
Flow and transport processes in soils are characterized by high heterogeneity, non-linearity, and complexity at different scales. At the same time, soil functions are critical for key aspects of hydrology, such as runoff generation, percolation, groundwater recharge, and transport of solutes and pollutants in soils. Processes of water flow and transport in soils can, therefore, only be monitored, analyzed, and predicted reliably with a smart and efficient combination of sensors, different and complementary observation methods, and close integration with modeling techniques. Several independent monitoring methods for studying flow and transport in soils exist and provide data and results on hydrological processes in soils. Soil moisture and suction data are collected to drive models based on the continuum equation. Several geophysical methods exist to provide measurements of state variables for water flow and transport at different scales. At the same time, tracer and isotope monitoring techniques have been developed to estimate water flow and transport. The combination and integration of these experimental methods and monitoring approaches remain a challenge.
The conveners invite contributions presenting advances in combining sensors and multi-sensor ensembles with new modeling techniques to improve the observation and monitoring of flow processes in soils. We invite contributions on new sensors and observation methods, such as instrumentation to monitor environmental tracers, especially when combined with modeling tools to integrate data into information on soil water status, soil functions, and flow processes.
Data challenges in machine learning for hydrometeorological modeling
The application of machine learning/deep learning methods and techniques in hydrometeorological modeling has increased rapidly. Such data-driven modeling approaches seek to uncover complex relationships among the system variables from training data, without prior explicit knowledge of the system processes. Therefore, the accuracy of the training data and the diversity of conditions covered are key to successful data-driven modeling. However, high-quality data collection and management continues to be one of the main barriers to the use of machine learning/deep learning in hydrometeorological studies.
This session seeks contributions which highlight and address current challenges related to training datasets, both in terms of quantity and quality, to facilitate the use of machine learning in hydrometeorology. We welcome contributions including, but not limited to, the following topics:
1) New and existing publicly-available datasets that enable modeling, including unconventional sources such as crowd-sourcing or social media data.
2) Best practices for collecting, pre-processing/gap-filling, labeling, and structuring data.
3) Input variable (feature) selection and importance.
4) The role of training data quality, quantity, and diversity in data-driven modeling.
5) Adequate handling of data and their uncertainty for data-driven modeling; quantifying or reducing data uncertainties or confounding factors.
6) Other issues & limitations related to training data in hydrometeorological data-driven applications.
Experimental hydrology and hydraulics in Geosciences
Within the water cycle, physical and chemical interactions between water, air and land shape the Earth’s surface. Human activity also induces major changes to natural systems at a wide range of temporal and spatial scales. Experimental methods have played – and still play – a fundamental role in Hydrology and Hydraulics. Laboratory- and field-based experiments allow physical systems to be analysed under semi-controlled conditions to understand process-form interactions. As such, experimental studies provide an effective platform for investigating physical processes under controlled hydrometeorological or physical conditions, and improve understanding of the Earth systems.
This session aims to provide with a discussion platform to exchange experiences on the design, methodologies and application of physical experiments in hydrology and hydraulics, both in the laboratory and the field. We welcome experimental research contributions across a series of disciplines with a hydrological, hydraulic and geomorphological focus across a wide range of spatiotemporal scales.
We invite contributions directing on (but not restricted to):
- The use of laboratory- and field-based experiments to understand real-world physical systems with a hydrological, geomorphological or hydraulic focus;
- Fundamental science and practical applications of physical and experimental models such as flumes, lysimeters, soil columns, rainfall simulators or scaled physical systems;
- The application of novel and innovative instrumentation, measurement and visualisation techniques;
- Experimental adaptations to well-established monitoring or data analysis techniques;
- Development and application of hybrid or composite (numerical-physical) models to contribute to numerical modelling frameworks;
- The use of experimental methods and models for science communication and as demonstrative teaching tools.
Geophysical and in-situ methods for snow and ice studies
Geophysical and in-situ measurements of the cryosphere offer important baseline datasets, as well as validation for modelling and remote sensing products. In this session we welcome contributions related to a wide spectrum of methods, including, but not limited to radioglaciology, active and passive seismology, acoustic sounding, Global Navigation Satellite System (GNSS) reflectometry or time delay techniques, cosmic ray neutron sensing, remotely operated vehicle (ROV) or drone applications, geoelectrics, nuclear magnetic resonance (NMR) and methods in radiative transfer (i.e. infrared photography, thermal sounding...).
Contributions could be related to field applications, new approaches in geophysical or in-situ survey techniques, or theoretical advances in data analysis processing or inversion. Case studies from all parts of the cryosphere such as snow and firn, alpine glaciers, ice sheets, glacial and periglacial environments, permafrost, or sea ice, are highly welcome. The focus of the session is to compare experiences in the application, processing, analysis and interpretation of different geophysical and in-situ techniques in these highly complex environments.
This session is offered as a hybrid PICO session, meaning it will allow physical and remote contributions - which we hope will increase the accessibility of this session to a wider range of presenters. The PICO format has proved to be an engaging presentation format for this session in previous years. The session begins with each presenter giving a “quick fire” overview of their research orally, followed by time to discuss and further present their research using interactive screens. This results in rich scientific feedback and is an effective tool for communicating science with high visibility.
From historical images to modern high resolution topography: methods and applications in geosciences
Recent advances in image collection, e.g. using unmanned aerial vehicles (UAVs), and topographic measurements, e.g. using terrestrial or airborne LiDAR, are providing an unprecedented insight into landscape and process characterization in geosciences. In parallel, historical data including terrestrial, aerial, and satellite photos as well as historical digital elevation models (DEMs), can extend high-resolution time series and offer exciting potential to distinguish anthropogenic from natural causes of environmental change and to reconstruct the long-term evolution of the surface from local to landscape scale.
For both historic and contemporary scenarios, the rise of techniques with ‘structure from motion’ (SfM) processing has democratized data access and offers a new measurement paradigm to geoscientists. Photogrammetric and remote sensing data are now available on spatial scales from millimetres to kilometres and over durations of single events to lasting time series (e.g. from sub-second to decadal-duration time-lapse), allowing the evaluation of event magnitude and frequency interrelationships.
The session welcomes contributions from a broad range of geoscience disciplines such as geomorphology, cryosphere, volcanology, hydrology, bio-geosciences, and geology, addressing methodological and applied studies.
Our goal is to create a diversified and interdisciplinary session to explore the potential, limitations, and challenges of topographic datasets for the reconstruction and interpretation of past and present 2D and 3D changes in different environments and processes. We further encourage contributions describing workflows that optimize data acquisition and processing to guarantee acceptable accuracies and to automate data application (e.g. geomorphic feature detection and tracking), and field-based experimental studies using novel multi-instrument and multi-scale methodologies. This session invites contributions on the state of the art and the latest developments in i) modern photogrammetric and topographic measurements, ii) remote sensing techniques as well as applications, iii) modelling technologies, iv) data processing tools, for instance, using machine learning approaches.
The Science-policy interface in hydrology – essentials for more impactful science
Liaising with stakeholders and policy-makers is becoming increasingly important for scientists to turn research into impactful action. In hydrological sciences, this is needed when implementing innovative solutions in areas such as river basin management, water allocation, impact-based hydrological forecasting, flood protection, drought risk management, climate change mitigation, ecohydrology and sustainable environmental solutions, among others.
The science-policy interface presents several challenges to both scientists and policy-makers. They include understanding the different steps in the policy cycle: from setting the agenda to formulating, adopting, implementing, monitoring and evaluating polices. It is also crucial to know which facts and evidences are most needed at each step, so scientists can provide the best information at the right time and in the best way.
This session provides the opportunity for discussing with policy makers and addressing the necessary skills to facilitate the uptake of science in policy formulation and implementation: how science influences policy and policies impact science? How scientists can provide easily digestible pieces of evidence to policy-makers? What are the key gaps in joining science to feasible policy solutions in the water sector? How can we use knowledge to improve policy, and vice-versa?
We invite contributions that reflect on the needs of scientists and policy makers at different levels, from local, regional to EU and international levels. Hydrologists have long contributed to produce policy briefs and provide government advice on water-related issues. This session focuses on sharing these experiences (successes or failures), case studies, narratives and best practices at different phases of the policy-making process.
Hydrology has shaped societies’ co-evolution with Earth since early civilizations. The 20th century saw a massive progress in hydrological sciences. Knowledge on the physics behind why & how water moves through the atmosphere & the critical zone informed the management of available water resources, giving the humankind some ability to control & predict water behaviour from local to global scales. In the 21st century, hydrology has transformed into a fundamental branch of geosciences addressing the need for multidisciplinary research that has both theoretical and/or operational value. As 2020 is soon ending, hydrologists are still trying to tackle water-related challenges of the near future. What about the far future? Let’s focus on the 2100s, or even the 2200s, for a change. What’s awaiting the planet Earth & humankind on its everlasting journey? What will hydrology look like in the 22nd century?
This PICO session will give EGU’s fantastic hydrology community an opportunity to share their vision of hydrology in the 2100s and beyond. Pushing the limits of our thinking already is a worthwhile effort for shaping the not-so-imminent future of hydrology —at least in our minds— starting here and today.
Please note that embracing holistic advances in technology, spirituality, and humanity with a scientific mind requires deep imagination and absolute creativity. We invite contributions from the members of the growing hydrology community who dare to imagine beyond the limits of their mind and with a touch from their heart. Some questions for inspiration are:
• What emerging technologies might we find in the 22nd century?
• Will we be able to measure & observe the unobservable of today with high spatial & temporal accuracy?
• Can we measure all water-related variables everywhere & build a smart database accessible by every human freely & instantly?
• Climate change projections usually end in 2100, but this is only a lifetime away. How can we prepare for climate impacts on hydrology out to 2120 or 2150 already?
• Will it be possible to have an Artificial Intelligence-led system that models the Earth system every second?
• What will be the definition of predictability?
• Could there be a catchment-book (like Facebook) to help identify & manage water resources for as far as 200 years ahead?
• Will there be a textbook on the Hydrology of Mars and beyond?
• Where is the brain of mother Earth? What is the role of catchments and/or rivers within the Cosmos?
Pathways towards solving the Unsolved Problems in Hydrology (UPH)
The International Association of Hydrological Sciences (IAHS), in collaboration with the Hydrology Divisions of EGU and AGU as well as the IAH, launched in 2017 a public consultation process for compiling a list of unsolved scientific problems in hydrology which resulted, in 2019, in a set of 23 Unsolved Problems in Hydrology (UPH) (see https://doi.org/10.1080/02626667.2019.1620507).
The UPH are articulated around 7 themes: Time variability and change, Space variability and scaling, Variability of extremes, Interfaces in hydrology, Measurements and data, Modelling methods, and Interfaces with society. Some of the UPH may have already been partially studied. Recent research may shed light on how to move forward in a more holistic way. A crucial issue is to put together fragmented knowledge to address the questions raised and enhance coherence in hydrological sciences.
The purpose of this session is to discuss progress in any of the 23 UPH. Contributions are encouraged to either:
- present research results that advance the understanding of any of the 23 UPH,
- review (or present a contribution to review) the state of the art of one (or more) of the UPH, pointing towards directions where progress is most promising.
Authors are asked to clearly state the UPH their work refers to or could contribute to solve. The authors may also reflect on how the community could evaluate if an UPH can be considered solved or not.
Panta Rhei: hydrology, society & environmental change
This session is organized as part of the IAHS Panta Rhei hydrological decade 2013- 2022 focusing on gains in our understanding of water cycle processes by focusing on their changing dynamics in respect of interactions and feedbacks with human systems. Approaching the end of this Panta Rhei decade (2013-2022), it is time to synthesize the achievements of this decade. The main focus of this grand synthesis, which will be published in an IAHS book, is on coevolution and prediction of coupled human-water systems, including understanding of emergent phenomena, mechanisms, and implications for predictions and practices.
This session welcomes contributions that contribute to and critically reflect the following synthesis topics:
1) Theoretical/conceptual framework for understanding changes in hydrology and society;
2) Coevolution and emergent phenomena;
3) dynamic models;
4) Data needs and acquisition;
5) Benchmark datasets in various context and scales, including human-flood, human-drought, agricultural, transboundary and global systems;
6) Case studies from Panta Rhei working groups, IAHS Commissions and beyond.
Hydrology in society: approaches for fostering collaborations across disciplines and beyond scientists
Inter- and transdisciplinary research deals with socially relevant problems, with a clear urgency for decision-making. The aim of this session is to discuss approaches that foster interdisciplinary and transdisciplinary research in human-water interaction with the objective to provide novel findings which would otherwise remain unknown.
Contributions are invited, but are not limited, to the following themes:
1. Co-production of knowledge and policy. Who are the users of our knowledge, how useful is our knowledge for those societal users, how useful are our tools, models and methods? What approaches are available to support a fruitful collaboration between hydrological science and practitioners? What novel findings have been revealed through collaborative research that would otherwise have remained hidden? How do we deal with uncertainty, adaptation, path dependencies but also with aspects of power, inequality and vested interests in these co-production processes?
2. Interdisciplinary collaborations. Transdisciplinary research requires an interdisciplinary collaboration that accounts not only for the physical processes of water systems, but also the interaction between physical and societal components of these systems. How do we create the interdisciplinary knowledge needed to address the questions faced by decision-makers and societal stakeholders? How have new, interdisciplinary, science questions been generated in response to existing and emerging research problems? How can individual disciplinary perspectives come together in interdisciplinary studies and experiments?
3. Hydrology as practiced within society. Scientists are not removed from the things they study. How has hydrology been shaped by the historical interplay of cultural, political and economic factors? And how does hydrological knowledge intersect with societal discourses as it is applied in water resources management? What are the opportunities and challenges that this science/society nexus creates for producing scientific knowledge?
We welcome traditionally researched contributions on inter and transdisciplinary processes, methods and case studies, but also practical experiences with collaborative research that are not usually reported in mainstream hydrology journals, but which are important to discuss to improve our capacity to engage in research beyond disciplines and scientists.
The coupled terrestrial-atmospheric water cycle: model development, cross-compartment observations and data assimilation
Understanding the complex interactions of the coupled terrestrial-atmospheric water cycle requires cross-compartment strategies encompassing coupled modeling from the bedrock to the top of the atmosphere, integrated hydro-meteorological observations and datasets, novel data assimilation schemes and multivariable validation approaches. The objective of the session is to create opportunities for interdisciplinary exchange of ideas and experiences among members of the Earth System and hydrology communities. Contributions are invited dealing with the complex interactions between groundwater, surface water, land surface and atmospheric processes with a specific focus on the development, application and validation of novel one-way (both deterministic and ensemble) or fully-coupled hydrometeorological modeling systems for process understanding and predictions and projections across various space- and time scales. This includes also combined dynamical-statistical approaches and studies addressing data assimilation in coupled models. An additional focus is placed on the use of field experiments and testbeds equipped with complex sensors and measurement systems allowing cross-compartment and multivariable validation of these modeling systems.
Hydrologic dynamics, analytics and predictability: physical and data-based approaches for improving hydrologic understanding and prediction
Hydrology is a rich multidisciplinary field encompassing a complex process network involving interactions of diverse nature and scales. Still, it abides to core dynamical principles regulating individual and cooperative processes and interactions, ultimately relating to the overall Earth System dynamics.
This session focuses on advances in theoretical and applied studies in hydrologic dynamics, regimes, transitions and extremes along with their physical understanding, predictability and uncertainty. Moreover, it welcomes research on dynamical co-evolution, feedbacks and synergies among hydrologic and other earth system processes at multiple spatiotemporal scales.
The session further encourages discussion on physical and analytical approaches to hydrologic dynamics ranging from stochastic, computational and system dynamic analysis, to more general frameworks addressing non-ergodic and thermodynamically unstable processes and interactions.
Contributions are welcome from a diverse community in hydrology and the broader physical geosciences, working with diverse approaches ranging from dynamical modelling to data mining, machine learning and analysis with physical understanding in mind.
The threshold values of the hydrological variables are an important tool for making decisions in the hydrological field where the stochastic nature of the processes makes impossible a deterministic forecast of both the magnitude of the processes and their effects. Thresholds are widely used in hydrology to identify where, when and how the effects of the hydrological processes are tolerable (around normal conditions) and self-organized (and remain tolerable), and under what changes or circumstances (e.g., influence of external events, such as rainfall) they reach critical conditions. Thresholds can be simple (e.g., the threshold of rainfall intensity that might separate stratiform from convective rainfall) or complex and multi-variate (e.g., the threshold for damaging snow-melt flooding, or the threshold for intense hillslope erosion in an agricultural field).
There are many fields of hydrology in which thresholds are of great importance and usefulness. They can be useful for real-time forecasts based on simple thresholds on rainfall data (e.g., activation of mass movements such as landslides, debris flow, rill and inter-rill erosion, etc.), for the adoption of satellite data in the management of ground actions (e.g., values of the satellite indexes to be used in irrigation management), for distinguishing among water flow regimes, and many other possible applications.
We invite contributions that:
(1) discuss the effect of the threshold selection in the interpretation of the hydrological processes,
(2) demonstrate the application of threshold analysis for increasing our understanding of hydrological processes,
(3) promote the use of the threshold methodology in the management of the hydrologic processes
(4) consider what kinds of hydrological threshold shifts might arise in coming decades as climate change progresses.
Open Hydrology: Advances towards fully reproducible, re-usable and collaborative research methods in Hydrology
Good scientific practice requires research results to be reproducible, experiments to be repeatable and methods to be reusable. This is a particular challenge for hydrological research, as scientific insights are often drawn from analysis of heterogeneous data sets comprising many different sources and based on a large variety of numerical models. The available data sets are becoming more complex and constantly superseded by new, improved releases. Similarly, new models and computational tools keep emerging and many are available in different versions and programming languages, with a large variability in the quality of the documentation. Moreover, how data and models are linked together towards scientific output is very rarely documented in a reproducible way. As a result, very few published results in hydrology are reproducible for the general reader.
A debate on good scientific practice is underway, while technological developments accelerate progress towards open and reproducible science. This session aims to advance this debate on open science, collect innovative ways of engaging in open science and showcase examples. It will include new scientific insights enabled by open science and new (combinations of) open science approaches with a documented potential to make hydrological research more open, accessible, reproducible and reusable.
This session should advance the discussion on open and reproducible science, highlight its advantages and also provide the means to bring this into practice. We strongly believe we should focus on the entire scientific process, instead of the results alone, obtained in a currently still rather fragmented way.
HS2.1 – Catchment hydrology in diverse climates and environments
Changes in the Mediterranean hydrology: observation and modeling
Water is a strategic issue in the Mediterranean region, mainly because of the scarcity of the available resources, in quantity and/or quality. The Mediterranean climate and the surface hydrology are characterized by a strong variability in time and space and the importance of extreme events, droughts and floods. This irregularity is also met at a lower level in aquifers dynamics. During the last century, modifications of all kinds and intensities have affected surface conditions and water uses. The Mediterranean hydrology is then continuously evolving.
This session intends to identify and analyse the changes in the Mediterranean hydrology, in terms of processes, fluxes, location. It will gather specialists in observation and modeling of the various water fluxes and redistribution processes within the catchments.
Contributions addressing the following topics are welcome:
• Spectacular case studies of rapid changes in water resources;
• Using various sources of information for comparing past and present conditions;
• Differentiating climatic and anthropogenic drivers (including GCM reanalysis);
• Modelling hydrological changes (in surface and/or ground water);
• Impacts of extreme events on water systems.
Advances in African hydrology and climate: modelling, water management, environmental and food security
Annually, various parts of Africa are affected by climate related impacts, such as droughts, flooding etc., to varying degrees of severity. Global and regional hydrological models have recently seen tremendous advances in improved representations of physical processes underpinning these impacts, resulting in better reproductions of observed variables such as streamflow and water extent. As a result, they are increasingly used for predicting socio-economic risks of floods, droughts and water stress in regions around the globe. However, the use of hydroclimatic models for disaster risk reductions in data-sparse regions, while gradually improving, is still limited in comparison.
This session aims to bring together communities working on different strands of African hydrology, climate and other water-related topics, including environmental and food security. We welcome both fundamental and applied research in the areas of hydrological process understanding, flood forecasting and mapping, seasonal forecasting, water resources management, climate impact assessment and societal impacts. Interdisciplinary studies aiming at increasing our understanding of the physical drivers of water-related risks and their impacts in Africa are encouraged. Case studies showcasing practical experiments and innovative solutions in decision making under large uncertainty are welcomed.
Zero flow: hydrology and biogeochemistry of intermittent and ephemeral streams
A large proportion of the global stream network comprises channels that cease to flow or dry periodically. These systems range from near-perennial rivers with infrequent, short periods of zero flow to rivers experiencing flow only episodically following large rainfall events. Intermittent and ephemeral rivers support a unique high-biodiversity because they are coupled aquatic-terrestrial systems that accommodate a wide range of aquatic, semi-aquatic and terrestrial flora and fauna. Extension and connection of the flowing stream network can affect the quantity and quality of water in downstream perennial rivers. In many arid conditions, they are the main source of fresh water for consumptive use. However, in many places intermittent and ephemeral rivers lack protection and adequate management. There is a clear need to study the hydrology, ecology and biogeochemistry of natural intermittent and ephemeral streams to characterize their flow regimes, to understand the main origins of flow intermittence and how this affects their biodiversity, and to assess the consequences of altered flow intermittency (both increased and decreased) in river systems.
This session welcomes all contributions on the science and management of intermittent and ephemeral streams, and particularly those illustrating:
• current advances and approaches in characterizing and modelling flow intermittency,
• the effects of flow in intermittent streams on downstream perennial streams,
• the factors that affect flowing stream network dynamics
• land use and climate change impacts on flow intermittency,
• links between flow intermittency and biogeochemistry and/or ecology.
Hydrological processes in agricultural lands under changing environments
Agriculture plays a vital role in the socio-economic development. For agricultural production, whether rainfed or using irrigation, water is a key requirement. Therefore, a thorough understanding of the hydrological processes in agricultural lands is essential to address a wide range of issues, including soil moisture condition, crop water requirement, agricultural productivity, water efficiency, soil erosion, and solute transport.
This session is intended to address and advance our understanding of the role of hydrological processes in agricultural lands. Some of the topics and questions of interest are: (1) modelling the impacts of climate change on water balance and agricultural productivity at watershed scale; (2) identification of dominant hydrological factors and how they can be measured locally for improving water supply to crops; (3) effects of irrigation schemes on regional evapotranspiration and soil moisture content; (4) effects of artificial drainage on water regime and solute transport at different spatial scales; (5) aquifer vulnerability to high rates of fertilizer and pesticide applications; (6) multi-process and multi-scale water and energy transitions in agricultural lands; (7) water and energy responses of water-saving practice; and (8) linking hydrological issues with other environmental issues, including removal of natural vegetation, drought or flood events, and soil erosion. We welcome abstracts addressing the above topics or other topics related to hydrological processes in agricultural lands.
Forests are recognized as prime regulators of the hydrological cycle. Their change has effects on the ecosystem services they provide via their water and biochemical cycles. The traditional idea that forest hydrology emphasizes the role of forests and forest management practices on runoff generation and water quality has been broadened in the light of rapid global change. Some of the largest pristine forested areas are in the tropics and have suffered drastic land-use changes during recent decades. These tropical systems are still markedly underrepresented in hydrological studies compared to temperate regions, especially concerning long-term experimental setups and monitoring networks.
Anthropogenic intervention is exerting enormous pressure on natural ecosystems, affecting water quantity and quality, and, consequently, threatening socio-economic and human development as described by the UN Sustainable Development Goals. Yet, we lack a proper understanding of how catchments respond to changing environmental conditions and disturbances. To answer these open questions, it requires interdisciplinary approaches in combination with novel monitoring methods and modelling efforts. This session brings together studies that will enhance our understanding and stimulate discussions on the impact of global change on hydrological processes in forest systems at different scales.
We invite field experimentalists and modelers to submit contributions that investigate hydrological processes in forests from boreal to tropical regions, including water quality, the carbon cycle, or ecohydrological aspects.
This session welcomes studies that:
1) Improves our understanding of forested hydrological processes using an experimental or modelling approach or a combination of both;
2) Assesses the hydrological-related impacts of land use/cover change in forested systems;
3) Presents new methods (e.g. remote sensing techniques) or tools that unveils new perspectives or data sources in forest hydrology;
4) Includes interdisciplinary research that supports the consideration of overlooked soil-plant-atmosphere components in hydrological studies.
Mountain hydrology under global change: monitoring, modelling and adaptation
Despite only representing about 25% of continental land, mountains are an essential part of the global ecosystem and are recognised to be the source of much of the world’s surfaces water supply apart from important sources of other commodities like energy, minerals, forest, and agricultural products, and recreation areas. In addition, mountains represent a storehouse for biodiversity and ecosystem services. People residing within mountains or in their foothills represent approximately 26% of the world’s population, and this percentage increases to nearly 40% when considering those who live within watersheds of rivers originated in a mountain range. This makes mountains particularly sensitive to climate variability, but also unique areas for identifying and monitoring the effects of global change thanks to the rapid dynamics of their physical and biological systems.
This session aims to bring together the scientific community doing hydrology research on mountain ranges across the globe to share results and experiences. Therefore, this session invites contributions addressing past, present, and future changes in mountain hydrology due to changes in either climate and/or land use, how these changes affect local and downstream territories, and adaptation strategies to ensure the long-term sustainability of mountain ecosystem services, with a special focus on water cycle regulation and water resources generation. Example topics of interest for this session are:
• Sources of information for evaluating past and present conditions (in either surface and/or groundwater systems).
• Methods for differentiating climatic and anthropogenic drivers of hydrological change.
• Modelling approaches to assess hydrological change.
• Evolution, forecasting, and impacts of extreme events.
• Case studies on adaptation to changing water resources availability.
Snow and ice accumulation, melt, and runoff generation in catchment hydrology: monitoring and modelling
By accumulating precipitation at high elevations, snow and ice change the hydrologic response of a watershed. Water stored in the snow pack and in glaciers thus represents an important component of the hydrological budget in many regions of the world and a sustainment to life during dry seasons. Predicted impacts of climate change in headwater catchments (including a shift from snow to rain, earlier snowmelt and a decrease in peak snow accumulation) will affect both water resources distribution and water uses at multiple scales, with potential implications for energy and food production.
Our knowledge about snow/ice accumulation and melt patterns is highly uncertain, because of both limited availability and inherently large spatial variability of hydrological and weather data in remote areas at high elevations. This translates into limited process understanding, especially in a warming climate. The objective of this session is to integrate specialists focusing on snow accumulation and melt within the context of catchment hydrology and snow as a source for glacier ice and melt, hence streamflow. The aim is to integrate and share knowledge and experiences about experimental research, remote sensing and modelling.
Contributions addressing the following topics are welcome:
- experimental research on snowmelt runoff processes and potential implementation in hydrological models;
- development of novel strategies for snowmelt runoff modelling in various (or changing) climatic and land-cover conditions;
- evaluation of remote-sensing (time-lapse imagery, laser scanners, radar, optical photography, thermal and hyperspectral technologies) or in-situ snow products (albedo, snow cover or depth, snow water equivalent) and application for snowmelt runoff calibration, data assimilation, streamflow forecasting or snow and ice physical properties quantification;
- observational and modelling studies that shed new light on hydrological processes in glacier-covered catchments, e.g., impacts of glacier retreat on water resources and water storage dynamic or the application of techniques for tracing water flow paths;
- studies on cryosphere-influenced mountain hydrology, such as landforms at high elevation and their relationship with streamflow, water balance of snow/ice-dominated, mountain regions.
Modelling and measuring snow processes across scales
Snow cover characteristics (e.g. spatial distribution, surface and internal physical properties) are continuously evolving over a wide range of scales due to meteorological conditions, such as precipitation, wind and radiation.
Most processes occurring in the snow cover depend on the vertical and horizontal distribution of its physical properties, which are primarily controlled by the microstructure of snow (e.g. density, specific surface area). In turn, snow metamorphism changes the microstructure, leading to feedback loops that affect the snow cover on coarser scales. This can have far-reaching implications for a wide range of applications, including snow hydrology, weather forecasting, climate modelling, and avalanche hazard forecasting or remote sensing of snow. The characterization of snow thus demands synergetic investigations of the hierarchy of processes across the scales ranging from explicit microstructure-based studies to sub-grid parameterizations for unresolved processes in large-scale phenomena (e.g. albedo, drifting snow).
This session is therefore devoted to modelling and measuring snow processes across scales. The aim is to gather researchers from various disciplines to share their expertise on snow processes in seasonal and perennial snowpacks. We invite contributions ranging from “small” scales, as encountered in microstructure studies, over “intermediate” scales typically relevant for 1D snowpack models, up to “coarse” scales, that typically emerge for spatially distributed modelling over mountainous or polar snow- and ice-covered terrain. Specifically, we welcome contributions reporting results from field, laboratory and numerical studies of the physical and chemical evolution of snowpacks, statistical or dynamic downscaling methods of atmospheric driving data, assimilation of in-situ and remotely sensed observations, representation of sub-grid processes in coarse-scale models, and evaluation of model performance and associated uncertainties.
HS2.2 – From observations to concepts to models (in catchment hydrology)
Understanding hydrological processes across spatio-temporal scales: from data to model
Understanding and representing hydrological processes is the basis for developing and improving hydrological and Earth system models. Modeling and learning is a symbiotic and continuous process through which our understanding of human-natural systems is formulated and tested constantly. As a result, a variety of models are developed and trained by quantitative and qualitative data across desired temporal and spatial scales.
In this session, we welcome contributions on the development of novel data sets and frameworks for model development and evaluation across spatio-temporal scales from catchment to continental scale hydrology. The vision of our session, following the initiative of 23 Unsolved Problems in Hydrology (UPH, https://doi.org/10.1080/02626667.2019.1620507), is to address three questions: What are the hydrologic laws at the catchment scale and how do they change with scale? How can hydrological models be adapted to be able to extrapolate to changing conditions, including changing vegetation dynamics? How can we disentangle and reduce model structural/parameter/input uncertainty in hydrological prediction?
We welcome contributions that (but not limited to):
(1) introduce new global and regional data products into the modeling process;
(2) introduce new approaches for model calibration and evaluation, especially to improve process representation, and/or to improve model predictions under changing conditions;
(3) improve model structure by representing often neglected processes in hydrological models such as human impacts, river regulations, irrigation, as well as vegetation dynamics;
(4) provide novel concepts for improving the characterization of internal and external model fluxes and their spatio-temporal dynamics;
(5) upscale experimentalists' knowledge from smaller to larger scale by identifying driving forces for spatial patterns for a better usage of them in models;
(6) suggest more effective monitoring and seamless modeling of spatial patterns in hydrology and land models using distributed earth observations;
(7) develop novel approaches and performance metrics for evaluating and constraining models in space and time; and
(8) identify model limitations and conceptual improvements that are of general relevance to the geosciences modeling community.
This session is organized as part of the grass-root modelling initiative on "Improving the Theoretical Underpinnings of Hydrologic Models" launched in 2016.
Isotope and tracer methods: flow paths characterization, catchment response and transformation processes
Stable and radioactive isotopes as well as other natural and artificial tracers are useful tools to fingerprint the source of water and solutes in catchments, to trace their flow pathways or to quantify exchanges of water, solutes and particulates between hydrological compartments. Papers are invited that demonstrate the application and recent developments of isotope and other tracer techniques in field studies or modelling in the areas of surface / groundwater interactions, unsaturated and saturated zone, rainfall-runoff processes, nutrient or contaminant export, ecohydrology or other catchment processes.
Subsurface runoff in catchment hydrology: from innovative experimental approaches to process modelling
In catchment hydrology, subsurface runoff is a well-recognized process, which is still challenging to capture. Different terms exist to characterize subsurface runoff such as shallow subsurface runoff, interflow, subsurface stormflow, lateral flow or soil water flow exist. Subsurface runoff is responsible for the transport of nutrients and pollutants from the terrestrial into the aquatic ecosystems, which underlines its importance for the adjacent surface water bodies. Significant knowledge has been gained from experimental studies at the point and hillslope scale which identified controlling factors for subsurface runoff (e.g., initial soil moisture, preferential flow paths, drainable porosity, precipitation inputs, soil properties, bedrock topography or stratification of soils). However, the importance at the catchment scale, and how these findings can be implemented in catchment rainfall-runoff models, remain poorly understood. This is mostly due to lacks of knowledge in understanding where subsurface runoff is generated within a catchment and when. Furthermore, the accuracy of the simulated subsurface runoff in catchment rainfall-runoff models is mainly calibrated and validated by single rainfall-runoff events. However, such often isolated events, assuming steady state conditions are not sufficient to capture the whole range of initial conditions and especially the thresholds for generating subsurface runoff. Thus, continuously measured proxies to assess the accuracy of the simulated subsurface runoff are needed. New, in-situ, high-frequency and high-temporal measurements of tracers can help to bridge the gap between hillslope and point scale versus catchment scale, necessary for modelling purposes of such fundamental processes.
This session aims to address the current state of the art for measurements, assessment and modeling of subsurface runoff processes. We welcome experimental and modeling studies on the following topics: (i) (Non-)Invasive methods for investigation and monitoring subsurface runoff in space and time and its interconnection to the stream network; (ii) linking spatial patterns of subsurface runoff with soil and lithological heterogeneity including stratification of soils; (iii) assessing the role of subsurface runoff for catchment response; and (iv) validation approaches to assess continuously the accuracy of the simulated subsurface runoff by using biogeochemical proxies (e.g. stable isotopes, dissolved silica, nitrate, DOC, eDNA).
Drivers and impacts of freshwater salinisation: from data to modelling approaches across spatio-temporal scales
Salinisation of both groundwater and surface water resources is a growing problem, threatening freshwater security for agricultural, domestic and industrial purposes, as well as biodiversity, in many regions of the world. Although the problem of freshwater salinisation is increasingly recognised, there are major research gaps in terms of its impacts, extent and magnitude, particularly at cross-regional to global scales. Both observational, remote sensing and model-driven approaches are needed to improve our understanding of salinisation processes, drivers and impacts across different scales, and to ensure sustainable water resources management today and in the future.
This session aims to bring together scientists working on salinity monitoring (in-situ or remote sensing) data, as well as model-driven studies related to quantifying and predicting historic to future salinisation patterns, drivers and impacts at catchment to global scales. Contributions including - but not limited to - any of the following topics are of particular interest for this session:
- Surface water and groundwater interactions and its effects on salinity dynamics
- Impacts of hydrological extremes and seasonality on salinity levels of freshwater resources
- Human and hydro-climatic drivers of freshwater salinisation across different spatial and temporal scales
- Implications of inland salinity for ecosystem health and sectoral water use
- Applications of surface and/or groundwater in-situ and remote sensing data, and/or data-driven models to determine salinity concentrations across multiple scales
- Global change (e.g. climate change, land use change) impacts on future freshwater salinisation
- Assessment of management and adaptation measures to salinity changes
Water quality at the catchment scale: measuring and modelling of nutrients, sediment and eutrophication impacts
Land use and climate change as well as legal requirements (e.g. the EU Water Framework Directive) pose challenges for the assessment and sustainable management of surface water quality at the catchment scale. Sources and pathways of nutrients and other pollutants as well as nutrient interactions have to be characterized to understand and manage the impacts in river systems. Additionally, water quality assessment needs to cover the chemical and ecological status to link the hydrological view to aquatic ecology.
Models can help to optimize monitoring schemes and provide assessments of future change and management options. However, insufficient temporal and/or spatial resolution, a short duration of observations and the widespread use of different analytical methods restrict the data base for model application. Moreover, model-based water quality calculations are affected by errors in input data, model errors, inappropriate model complexity and insufficient process knowledge or implementation. Additionally, models should be capable of representing changing land use and climate conditions, which is a prerequisite to meet the increasing needs for decision making. The strong need for advances in water quality models remains.
This session aims to bring scientist together who work on experimental as well as on modelling studies to improve the prediction and management of water quality constituents (nutrients, organic matter, algae, or sediment) at the catchment scale. Contributions are welcome that cover the following issues:
- Experimental and modelling studies on the identification of sources, hot spots, pathways and interactions of nutrients and other, related pollutants at the catchment scale
- New approaches to develop efficient water quality monitoring schemes
- Innovative monitoring strategies that support both process investigation and model performance
- Advanced modelling tools integrating catchment as well as in-stream processes
- Observational and modelling studies at catchment scale that relate and quantify water quality changes to changes in land use and climate
- Measurements and modelling of abiotic and biotic interaction and feedback involved in the transport and fate of nutrients and other pollutants at the catchment scale
- Catchment management: pollution reduction measures, stakeholder involvement, scenario analysis for catchment management
Data-driven analysis of water quality to understand solute and particulate export mechanisms in catchments
Surface water quality is typically assessed and managed at the catchment scale. Management decisions needs a sound process knowledge and understanding of underlying cause-effect relationships to be effective. However, the dynamics of solute and particulate concentrations integrate a multitude of hydrological and biogeochemical processes interacting at different temporal and spatial scales, which are difficult to assess using local field experiments. Hence, time series of water quality observed at the outlet of catchments can be highly beneficial to understand these processes. Long-term, high-frequency as well as multiple-site datasets can be used to inform experimental and modelling studies and formulate hypotheses on dominant ecohydrological and geochemical processes moving “from pattern to process”. Recent advances in this field have used concentration-discharge relationships to infer the interplay between hydrological and biogeochemical controls, both in the terrestrial part of catchments and in the river network. Long-term time series of nutrient input-output relationships help understand nutrients legacy effects and catchments response times. High-frequency observations allow understanding the fine structure of concentration dynamics, including flowpaths and their age distribution during runoff events and ecological controls on diel cycles. When multiple catchments are monitored, it is possible to relate water quality metrics to catchment properties to conclude on dominant processes.
This session aims to bring together studies using data-driven analysis of river concentration time series to infer solute and particulate mobilization, retention and export mechanisms. We strongly encourage studies that use findings from data-driven analysis to build conceptual and process-based models. Presentations of the following topics are invited:
- Interpretation of C-Q relationships from storm events to long-term shifts
- Utilization of high-frequency observations of water quality
- Long-term changes of nutrient inputs, outputs and nutrient stoichiometry
- Role of hydrological extremes such as the recent mid-European droughts in long-term trajectories of nutrient exports
- Instream, network and lake effects on nutrient load and concentration dynamics
- Utilizing time series of compound-specific isotopic fingerprints
- Linkage of water travel time distribution and water quality dynamics
Micropollutants and pathogens in the soil-groundwater-river continuum: modeling and monitoring
A large number of pathogens, micropollutants and their transformation products (veterinary and human pharmaceuticals, personal care products, pesticides and biocides, chlorinated compounds, heavy metals) pose a risk for soil, groundwater and surface water. The large diversity of compounds and of their sources makes the quantification of their occurrence in the terrestrial and aquatic environment across space and time a challenging task. Regulatory monitoring programmes cover a small selection out of the compound diversity and quantify these selected compounds only at coarse temporal and spatial resolution. Carefully designed monitoring however allows to detect and elucidate processes and to estimate parameters in the aquatic environment. Modeling is a complementary tool to generalize measured data and extrapolate in time and space, which is needed as a basis for scenario analysis and decision making.
This session invites contributions that improve our quantitative understanding of the sources and pathways, mass fluxes, the fate and transport of micropollutants and pathogens in the soil-groundwater-river continuum. Topics cover:
- Novel sampling and monitoring concepts and devices
- New analytical methods, new detection methods for DNA, pathogens, micropollutants, non-target screening
- Experimental studies and modelling approaches to quantify diffuse and point source inputs
- Novel monitoring approaches such as non-target screening as tools for improving processes understanding and source identification such as industries
- Comparative fate studies on parent compounds and transformation products
- Diffuse sources and (re-)emerging chemicals
- Biogeochemical interactions and impact on micropollutant behaviour
Plastic pollution in freshwater systems is a widely recognized global problem with potential environmental risks to water and sediment quality. Furthermore, freshwater plastic pollution is also considered the dominant source of plastic input to the oceans. Despite this, research on plastic pollution has only recently expanded from the marine environment to freshwater systems. Therefore data and knowledge from field studies are still limited in regard to freshwater environments. Sources, quantities, distribution across environmental matrices and ecosystem compartments, and transport mechanisms remain mostly unknown at catchment scale. These knowledge gaps must be addressed to understand the dispersal and eventual fate of plastics in the environment, enabling a better assessment of potential risks as well as development of effective mitigation measures.
In this session, we explore the current state of knowledge and activities on macro-, micro- and nanoplastics in freshwater systems, including aspects such as:
• Plastics in rivers, lakes, urban water systems, floodplains, estuaries, freshwater biota;
• Monitoring and analysis techniques;
• Source to sink investigations, considering quantities and distribution across environmental matrices (water and sediment) and compartments (water surface layer, water column, ice, riverbed, and riverbanks);
• Transport processes of plastics at catchment scale;
• The role of river regulation structures, e.g. dams, navigation, flood control, etc., in plastic retention and transport
• Effects of hydrological extremes, e.g. accumulation of plastics during droughts, and short-term export during floods in the catchment;
• Degradation and fragmentation processes, e.g. from macro- to micro- and nanoplastics;
• Modelling approaches for local and/or global river output estimations;
• Legislative/regulatory efforts, such as monitoring programs and measures against plastic pollution in freshwater systems.
(Contributions with a strong focus on remote sensing of plastics are encouraged to be submitted to the session “Detecting and Monitoring Plastic Pollution in Rivers, Lakes, and Oceans”)
Fate and transport processes of pathogens and emerging contaminants at multiple scales
The occurrence of pathogens and an exponentially increasing number of contaminants in freshwater and estuary environments pose a serious problem to public health. This problem is likely to increase in the future due to more frequent and intense storm events, the intensification of agriculture, population growth and urbanization. Pathogens (e.g., pathogenic bacteria and viruses) are introduced into surface water through the direct discharge of wastewater, or by the release from animal manure or animal waste via overland flow or groundwater, which subsequently presents potential risks of infection when used for drinking, recreation or irrigation. Contaminants of emerging concern are released as diffuse sources from anthropogenic activities or as discharges from wastewater treatment plants (e.g., trace organic contaminants). So far, the sources, pathways and transport mechanisms of fecal indicators, pathogens and emerging contaminants in water environments are poorly understood, and thus we lack a solid basis for quantitative risk assessment and selection of best mitigation measures. Innovative, interdisciplinary approaches are needed to advance this field of research. In particular, there is a need to better understand the dominant processes controlling fecal indicator, pathogen and contaminant fate and transport at larger scales. Consequently, we welcome contributions that aim to close these knowledge gaps and include both small and large-scale experimental and modelling studies with a focus on:
- The development and application of novel experimental and analytical methods to investigate fate and transport of fecal indicators, pathogens and emerging contaminants in rivers, groundwater and estuaries
- Hydrological, physically based modelling approaches
- Methods for identifying the dominant processes and for transferring fecal indicator, pathogen and contaminant transport parameters from the laboratory to the field or catchment scale
- Investigations of the implications of contamination of water resources for water safety management planning and risk assessment frameworks
The application of Bayesian approaches in water quality modelling
Bayesian approaches have become increasingly popular in water quality modelling, thanks to their ability to handle uncertainty comprehensively (data, model structure and parameter uncertainty) and as flexible statistical and data mining tools. Furthermore, graphical Bayesian Belief Networks can be powerful decision support tools that make it relatively easy for stakeholders to engage in the model building process. The aim of this session is to review the state-of-the-art in this field and compare software and procedural choices in order to consolidate and set new directions for the emerging community of Bayesian water quality modellers.
In particular, we seek contributions from water quality research that use Bayesian approaches to, for example but not exclusively:
• quantify the uncertainty of model predictions
• quantify especially model structural error through, for example, Bayesian Model Averaging or structural error terms
• address the problem of scaling (e.g. disparity of scales between processes, observations, model resolution and predictions) through hierarchical models
• model water quality in data sparse environments
• compare models with different levels of complexity and process representation
• use statistical emulators to allow probabilistic predictions of complex modelled systems
• integrate prior knowledge, especially problematizing the choice of Bayesian priors
• produce user-friendly decision support tools using graphical Bayesian Belief Networks
• involve stakeholders in model development and maximise the use of expert knowledge
• use machine-learning and data mining approaches to learn from large, possibly high-resolution data sets.
Multi-dataset, multi-variable and multi-objective techniques to improve prediction of hydrological and water quality models
The application of multi-datasets, multi-variable and multi-objective functions has proven to improve the performance of hydrologic and water quality models by extracting complementary information from multiple data sources or multiple features of modelled variables. This is particularly useful if more than one variable (e.g., runoff and snow cover or runoff and sediment concentration) or more than one characteristic of the same variable (e.g., flood peaks and recession curves) are of interest and thus should be all simulated similarly well.
Similarly, a multi-model approach can overcome shortcomings of individual models, while testing a model at multi-scales helps improving our understanding of the model functioning in relation to catchment processes. Incorporating (one of) multi-aspects into uncertainty quantification has an additional advantage of providing more reliable and more accurate prediction uncertainty.
This session welcomes contributions that apply one or more of the multi-aspects in hydrologic and water quality studies. Abstracts covering the following issues are welcome (but are not limited to):
• Applications of multi-datasets to improve the calibration of hydrologic or water quality models;
• Multi-aspect approaches to improve the identification and prediction of hydrologic or water quality models;
• Examples of multi-model approaches of hydrologic and water quality models;
• Use of temporal or spatial multi-scales or multiple catchments to improve understanding on catchment processes;
• One of the above aspect in combination with uncertainty analysis;
• Monte Carlo or Bayesian approaches in combination with multi-aspects of the model identification;
• Hypothesis testing with one of the multi-aspects;
• Techniques to optimize one of the multi-aspects in model calibration and uncertainty quantification;
• Development of new model calibration approaches that use one (or more) of the multi-aspects;
• New techniques to minimize computational efforts connected with the use of multi-aspect approaches.
HS2.4 – Hydrologic variability and change at multiple scales
Catchment organisation, similarity, and evolution
Catchments are always evolving complex systems, supporting and undergoing climate, landscape, and human changes. Their great variety in characteristics and behaviors requires innovative research at scales from the laboratory to the global modeling of atmospheric-land systems to address environmental challenges at the catchment scale.
This session welcomes abstracts over a broad range of spatial and temporal scales and experimental approaches (laboratory, field, and/or model experiments) that can be applied to gain insights into catchment behavior, response, organization, similarity, and/or evolution.
Example topics can include (but are not limited to):
- laboratory or field experiments that illuminate the organizing principles that shape catchments;
- multi-catchment analysis of the degree of similarity in climate, landscape, and hydrology;
- the effects of climate, landscape, and human interventions on catchment response;
- the uncertainties involved in the identification of dominating processes and hydrologic response;
- the use of coupled atmosphere and land surface models to address the future evolution of catchments dynamics
Hydrological change: regional hydrological behaviour under transient climate and land use conditions
Estimates of water availability and flooding risks remain one of the central scientific and societal challenges of the 21st century. The complexity of this challenge arises particularly from transient boundary conditions: Increasing atmospheric greenhouse gas concentrations lead to global warming and an intensification of the water cycle and finally to shifts in the temporal and spatial distribution of precipitation and terrestrial water availability. Likewise, large-scale land use changes impact and alter regional atmospheric circulation, thereby local precipitation characteristics and again terrestrial water availability. Also the feedbacks between the interlinked terrestrial and atmospheric processes on different spatial and temporal scales are still poorly understood.
This session therefore invites contributions addressing past, present and prospective changes in regional hydrological behaviour due to either (or joint) climate- and/or land use changes. We especially welcome contributions on the development of novel methods and methodologies to quantify hydrological change. Further aspects of this topic comprise particularly:
- Robustness of hydrological impact assessments based on scenarios using downscaled climate model – hydrology model modelling chains.
- Quantification of regional land use change predictions and impact of past, present and future land use changes on water and energy fluxes in meso- to large-scale catchments.
- Joint or coupled modelling of water and energy fluxes between the atmosphere and the land surface/subsurface and analyses of feedback mechanisms.
- Climate change/land use change signal separation techniques and quantification of future land use change vs. climate change induced hydrological change.
- Adequate handling of climate change and land use change data and their uncertainty for the forcing of hydrological models.
- Case studies of regional hydrological behaviour in climate sensitive and flood or drought prone regions worldwide.
Understanding the links between hydrological variability and internal/natural climate variability
In the current context of global change, assessing the impact of climate variability and changes on hydrological systems and water resources is increasingly crucial for society to better-adapt to future shifts in water resources as well as extreme conditions (floods and droughts). However, hitherto, important sources of uncertainties have often been neglected in projecting climate impacts on hydrological systems, especially uncertainties associated with internal/natural climate variability, whose contribution to near-future changes could be as important as forced anthropogenic climate changes at the regional scales. Internal climate modes of variability (e.g. ENSO, NAO, AMO) and their impact on the continent are not always properly reproduced in the current global climate models, leading to large underestimations of decadal climate and hydroclimatic variability at the global scale. At the same time, hydrological response strongly depends on catchment properties, whose interactions with climate variability are little understood at the decadal timescales. These factors altogether reduce significantly our ability to understand long-term hydrological variability and to improve projection and reconstruction of future and past hydrological changes on which improvement of adaption scenarios depends.
We welcome abstracts capturing recent insights for understanding past or future impacts of large-scale climate variability on hydrological systems and water resources as well as newly developed projection and reconstruction scenarios. Results from model intercomparison studies are encouraged.
Hydrological extremes (floods and droughts) have major impacts on society and ecosystems and are expected to increase in frequency and severity with climate change. Although both at the extreme ends of the hydrological spectrum, floods and droughts are governed by different processes, which means that they operate on different spatial and temporal scales and that different approaches and indices are needed to characterise them. However, there are also many similarities and links between the two extremes that are increasingly being studied.
This session on hydrological extremes aims to bring together the two communities in order to learn from the similarities and differences between flood and drought research. We aim to increase the understanding of the governing processes of both hydrological extremes, find robust ways of modelling and analysing floods and droughts, assess the influence of global change on hydroclimatic extremes, and study the socio-economic and environmental impacts of both extremes.
We welcome submissions that present innovative flood and/or drought research, including insightful case studies, large-sample studies, statistical hydrology, and analysis of flood or drought nonstationarity under the effects of climate change, land cover change, and other anthropogenic influences.
This session is jointly organised by the Panta Rhei Working Groups “Understanding Flood Changes”, “Changes in Flood Risk”, and “Drought in the Anthropocene” and will further stimulate scientific discussion on change detection, attribution, and the feedbacks between hydrological extremes and society. The session is linked to the European Low Flow and Drought Group of UNESCO´s IHP-VIII FRIEND-Water Program, which aims to promote international drought research. Submissions from early-career researchers are especially encouraged.
Floods, Droughts, or both
Large-sample hydrology or insightful case studies
Flood and drought nonstationarity
New approaches for analysis of extremes
Spatial and temporal variability
Space-time dynamics of floods: processes, controls, and risk
The space-time dynamics of floods are controlled by atmospheric, catchment, river system and anthropogenic processes and their interactions. The natural oscillatory behaviour of floods (between flood-rich and flood-poor periods) superimpose with anthropogenic climate change and human interventions in river morphology and land uses. In addition, flood risk is further shaped by continuous changes in exposure and vulnerability. Despite more frequent exploratory analyses of the changes in spatio-temporal dynamics of flood hazard and risk, it remains unclear how and why these changes are occurring. The scope of this session is to report when, where, how (detection) and why (attribution) changes in the space-time dynamics of floods occur. Of particular interest is what drivers are responsible for observed changes. Presentations on the impact of climate variability and change, land use changes and morphologic changes in streams, as well as on the role of pre-flood catchment conditions in shaping flood hazard and risk are welcome. Furthermore, contributions on the impact of socio-economic and structural factors on past and future risk changes are invited. This session is jointly organised by the Panta Rhei Working Groups “Understanding Flood Changes” and “Changes in Flood Risk”. The session will further stimulate scientific discussion on flood change detection and attribution. Specifically, the following topics are of interest for this session:
- Decadal oscillations in rainfall and floods
- Process-informed extreme value statistics
- Interactions between spatial rainfall and catchment conditions shaping flood patterns
- Detection and attribution of flood hazard changes: atmospheric drivers, land use controls and river training, among others
- Changes in flood risk: urbanisation of flood prone areas; implementation of risk mitigation measures, such as natural water retention measures; changes of economic, societal and technological drivers; flood damages; flood vulnerability; among others.
- Future flood risk changes and adaptation and mitigation strategies
Flash drought: definition, dynamics, detection, and prediction
Flash droughts (FDs) are distinguished from slower-developing droughts by their rapid rate of intensification. They may occur during the initial stage of a long-term drought, represent a period of rapid intensification within a longer-term drought, or terminate after a relatively short, yet impactful, event. Due to their rapid development, FDs are difficult to manage and can be particularly devastating for agriculture. They can occur with little or no warning due to limitations in monitoring capabilities, prediction skill of relevant environmental variables, and understanding of key physical mechanisms. Efforts to create working definitions of FD have been hindered by these limitations and a lack of data to quantify the many impacts associated with FD. This session welcomes abstracts relating to:
1) proposed FD definitions,
2) regionality and seasonality of FD physical mechanisms,
3) advances in FD detection and monitoring,
4) predictability and prediction of FDs,
5) quantification of impacts of FD, and
6) the changes in FD frequency and intensity in response to human-induced climate change.
We also encourage contributions that benefit from multivariate analysis, model-observation comparison, uncertainty quantification, or machine-learning predictions.
Extreme meteorological and hydrological events induced by severe weather and climate change
With global climate change affecting the frequency and severity of extreme meteorological and hydrological events, it is particularly necessary to develop models and methodologies for a better understanding and forecasting of present day weather induced hazards. Future changes in the event characteristics as well as changes in vulnerability and exposure are among the further factors for determining risks for infrastructure and society, and for the development of suitable adaptation measures. This session considers extreme events that lead to disastrous hazards induced by severe weather and climate change. These can, e.g., be tropical or extratropical rain- and wind-storms, hail, tornadoes or lightning events, but also floods, long-lasting periods of drought, periods of extremely high or of extremely low temperatures, etc. Papers are sought which contribute to the understanding of their occurrence (conditions and meteorological development), to assessment of their risk and their future changes, to the ability of models to reproduce them and methods to forecast them or produce early warnings, to proactive planning focusing to damage prevention and damage reduction. Papers are also encouraged that look at complex extreme events produced by combinations or sequences of factors that are not extreme by themselves. The session serves as a forum for the interdisciplinary exchange of research approaches and results, involving meteorology, hydrology, hazard management and applications like insurance issues.
Global, continental, and other large-scale hydrological research is very important in many different contexts. Examples include; increasing understanding of the climate system and water cycle, assessment of water resources in a changing environment, hydrological forecasting, and water resource management.
We invite contributions from across the atmospheric, meteorological and hydrological communities. In particular, we welcome abstracts that address advances in:
(i) understanding and predicting the current and future state of our global and large scale water resources;
(ii) use of global earth observations and in-situ datasets for large scale hydrology and data assimilation techniques for large scale hydrological models;
(iii) understanding and modelling of extremes: like droughts and floods.
(iv) representing and evaluating different components of the terrestrial water cycle fluxes and storages (e.g. soil moisture, snow, groundwater, lakes, floodplains, evaporation, river discharge) and their impact on current and future water resources and atmospheric modelling.
(v) synthesis studies assembling knowledge gained from smaller scales (e.g. catchments or hillslope) to advance our knowledge on process understanding needed for the further development of large scale models and to identify large scale patterns and trends.
Recent advancement in estimating global, continental and regional scale water balance components
Since early work on the assessment of global, continental and regional-scale water balance components, many studies use different approaches including global models, as well as data-driven approaches that ingest in-situ or remotely sensed observations or combination of these. They attempted to quantify water fluxes (e.g. evapotranspiration, runoff/discharge, groundwater recharge) and water storages on the terrestrial part of the Earth, either as total estimates (e.g. from GRACE satellites) or in separate compartments (e.g. water bodies, snow, soil, groundwater). In addition, more and more attention is given to uncertainties that stem from forcing datasets, model structure, parameters and combinations of these. Current estimates in literature show that flux and storage calculations differ considerably due to the methodology and datasets used such that a robust assessment of global, continental and regional water balance components is challenging.
This session is seeking for contributions that are focusing on the:
i. past/future assessment of water balance components (fluxes and storages) such as precipitation, river discharge to the oceans (and/or inland sinks), evapotranspiration, groundwater recharge, water use, changes in terrestrial water storage or individual components at global, continental and regional scales,
ii. application of innovative explorative approaches undertaking such assessments – through better use of advanced data driven, statistical approaches and approaches to assimilate (or accommodate) remote sensing datasets for improved estimation of terrestrial water storages/fluxes,
iii. analysis of different sources of uncertainties in estimated water balance components,
iv. examination and attribution of systematic differences in storages/flux estimates between different methodologies, and/or
v. applications/consequences of those findings such as sea level rise and water scarcity.
We encourage submissions using different methodological approaches. Contributions could focus on any of the water balance components or in an integrative manner with focus on global, continental or regional scale applications. Assessments of uncertainty in past/future estimates of water balance components and their implications are highly welcome.
Large-sample hydrology: characterizing and understanding hydrologic diversity
Large-sample studies lead to generalizable insights about hydrologic similarity, understanding of dominant hydrologic processes and our current ability to model the wide variety of hydrologic conditions that exist worldwide. Studies that use a diverse set of catchments and models can provide a testing ground for hydrologic theories based on smaller numbers of well-monitored experimental catchments. Large-sample studies can also provide insights unavailable to local studies, for example about hydrological variability across large spatial scales or varied hydroclimatic conditions
This session provides the opportunity for researchers to highlight recent data and model-based efforts on large-sample hydrology. We welcome abstracts relevant to all aspects of Large Sample Hydrology, and specifically encourage studies that seek to advance understanding of the following topics:
1. Data mobilization for hydrologic similarity and regionalization:
Can currently available global datasets of hydrologically relevant information (e.g. soil properties, land use, soil moisture estimates, meteorological re-analysis) effectively be used to define hydrologic similarity and thus improve the prediction in ungauged or scarcely gauged basins?
2. Testing of hydrologic theories:
To what extent can hydrologic theory developed in well-monitored experimental catchments be transferred to larger samples of relatively data-scarce catchments?
3. Modelling capabilities:
What can large sample hydrology reveal about the strengths and weaknesses of current modelling capabilities and how can large sample approaches be used to improve and constrain modelling efforts?
4. Explaining water use dynamics:
How can we use large sample hydrology to better understand water resource use, allocation and future availability, and inform sustainable management of these resources?
5. Development and improvement of large-sample data sets:
How can we overcome current challenges on unequal geographical representation of catchments, quantification of uncertainty, catchment heterogeneity and inclusion of human interaction with the global water cycle?
A splinter meeting is planned to discuss development and improvement of large-sample data sets, titled “Large sample hydrology: facilitating the production and exchange of data sets worldwide”. See the final program for location and timing.
The session and splinter meeting are organized as part of the Panta Rhei Working Group on large-sample hydrology.
Surface−subsurface water exchange at the large scale
Surface−subsurface water exchange is of great importance for water balance at different scales. It affects not only water but also nutrients, pollutants and bio-organisms in streams and groundwater. Many studies have focused on interactions between streamflow and riverbed for water and solute transport, e.g. hyporheic exchange. However, not much attention has been given to surface−subsurface water exchange at larger scales. During droughts, streams can be mainly fed by groundwater via the regional groundwater system. Streams can also feed groundwater for the long-distance transport typically in the arid and semi-arid regions. Inter-catchment groundwater flow affects the water balance of streamflow that is commonly defined by topography. It demonstrates the necessity of quantifying the flux between surface and subsurface. Apart from water and substance exchange, ecosystems are consequently influenced by surface−subsurface water exchange. Additionally, human activities (e.g. pumping/irrigation) could alter the natural conditions for the exchange between surface and subsurface. At the larger scale, it is challenging to properly and adequately handle the surface−subsurface water exchange, in particular for the large scale hydrological modeling.
The session aims to understand surface−subsurface water exchange at the large scale, and its consequences on hydrological study and practice. Therefore, the session seeks contributions addressing:
• surface−subsurface water exchange at the catchment to global scales from both observational and modeling aspects.
• development of new methods and models that represent surface−subsurface water exchange.
• effects of surface−subsurface water exchange on hydrological extremes (drought/flood), water availability, and solute and pollutant transport under climate change.
• implications of surface−subsurface water exchange on monitoring design and integrated water management beyond the catchment scale.
Global and continental scale risk assessment for natural hazards: methods, practice and open loss and risk assessment
The purpose of this session is to: (1) showcase the current state-of-the-art in global and continental scale natural hazard risk science, assessment, and application; (2) foster broader exchange of knowledge, datasets, methods, models, and good practice between scientists and practitioners working on different natural hazards and across disciplines globally; and (3) collaboratively identify future research avenues.
Reducing natural hazard risk is high on the global political agenda. For example, it is at the heart of the Sendai Framework for Disaster Risk Reduction and the Warsaw International Mechanism for Loss and Damage Associated with Climate Change Impacts. In response, the last 5 years has seen an explosion in the number of scientific datasets, methods, and models for assessing risk at the global and continental scale. More and more, these datasets, methods and models are being applied together with stakeholders in the decision decision-making process.
We invite contributions related to all aspects of natural hazard risk assessment at the continental to global scale, including contributions focusing on single hazards, multiple hazards, or a combination or cascade of hazards. We also encourage contributions examining the use of scientific methods in practice, and the appropriate use of continental to global risk assessment data in efforts to reduce risks. Furthermore, we encourage contributions focusing on globally applicable methods, such as novel methods for using globally available datasets and models to force more local models or inform more local risk assessment.
At various scales from global to local, many efforts on the collection and use of loss data related to natural hazards (e.g. cyclone, earthquake, flood, wildfire) as well as open datasets have been made in recent years. The integration of these socioeconomic loss databases and open datasets for loss and risk assessment allow for effective use for both science and policy, and to create a community linking academia, government and insurance.
Hydroinformatics: computational intelligence, systems analysis, optimisation, data science
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.
Spatio-temporal and/or (geo) statistical analysis of hydrological events, floods, extremes, and related hazards
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).
Advanced geostatistics for water, earth and environmental sciences
Geostatistical methods are commonly applied in the Water, Earth and Environmental sciences to quantify spatial variation, produce interpolated maps with quantified uncertainty and optimize spatial sampling designs. Space-time geostatistics explores the dynamic aspects of environmental processes and characterise the dynamic variation in terms of correlations. Geostatistics can also be combined with machine learning and mechanistic models to improve the modelling of real-world processes and patterns. Such methods are used to model soil properties, produce climate model outputs, simulate hydrological processes, and to better understand and predict uncertainties overall. Big data analysis and data fusion have become major topics of research due to technological advances and the abundance of new data sources from remote and proximal sensing as well as a multitude of environmental sensor networks. Methodological advances, such as hierarchical Bayesian modeling, machine learning, sparse Gaussian processes, local interaction models, as well as advances in geostatistical software modules in R and Python have enhanced the geostatistical toolbox.
This session aims to provide a forum where scientists from different disciplines can present and discuss innovative geostatistical methods targeting important problems in the Water, Earth and Environmental sciences. We also encourage contributions focusing on real-world applications of state-of-the-art geostatistical methods.
The topics of interest include:
1) Space-time geostatistics for hydrology, soil, climate system observations and modelling
2) Hybrid methods: Integration of geostatistics with optimization and machine learning approaches
3) Advanced parametric and non-parametric spatial estimation and prediction techniques
4) Big spatial data: analysis and visualization
5) Optimisation of spatial sampling frameworks and space-time monitoring designs
6) Algorithms and applications on Earth Observation Systems
7) Data Fusion, mining and information analysis
8) Application of covariance functions and copulas for the identification of spatio-temporal relationships
9) Geostatistical characterization of uncertainties and error propagation
10) Bayesian geostatistical analysis and hierarchical modelling
11) Functional data analysis approaches to geostatistics
12) Multiple point geostatistics
This session is co-sponsored by the International Association for Mathematical Geosciences (IAMG), https://www.iamg.org/
Machine learning (ML) is now widely used across Hydrology and the broader Earth Sciences and especially its subfield deep learning (DL) has recently enjoyed increased attention.. This session highlights the continued integration of ML, and its many variants, including deep learning (DL), 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 hydrological modeling. This might include, but is not limited to, the following:
(1) Development of novel DL models or modeling workflows.
(2) Integrating DL with process-based models and/or physical understanding.
(3) Improving understanding of the (internal) states/representations of ML/DL models.
(4) Understanding the reliability of ML/DL, including under nonstationarity.
(5) Deriving scaling relationships or process-related insights with ML/DL.
(6) Modeling human behavior and impacts on the hydrological cycle.
(7) Hazard analysis, detection, and mitigation.
(8) Natural Language Processing in support of models and/or modeling workflows
Clustering in hydrology: methods, applications and challenges
Clustering analysis is a well-known exploratory task for partitioning databases into smaller groups based on patterns or inherent similarity in data. Clustering methods have found many applications in many disciplines due to growing interest in unravelling the hidden and meaningful patterns that exist in large amounts of available data. Due to its unsupervised nature, clustering data is a complex task that requires attention to optimal choice alternatives regarding methods, model parameters and performance metrics. However, the suitability of clustering algorithms depends on their application. Different methods and approaches co-exist in a large pool. The challenge is to obtain application-specific insights while enabling a practical knowledge perspective for benchmarking. There are still research gaps in the wider clustering literature, and hydrology-specific knowledge is fragmented and difficult to find.
In hydrology, unsupervised classification of multivariate data is often used but typically in rather basic forms and as an intermediate step. Recently, the number of studies using clustering methods has rapidly increased. However, a clear and integrative vision on clustering algorithms is currently missing. Despite advances in other fields, the scope of hydrological studies is limited. Knowledge exchange on hydrology-specific ways of dealing with the issues related to clustering is needed.
The aim of this session is to explore theoretical and conceptual underpinnings of well-known clustering methods, offer fresh insights into applications of new clustering methods, gain thorough understanding of pearls and pitfalls in clustering algorithms, provide a critical overview of the main challenges associated with data clustering process, discuss major research trends and highlight open research issues. It is expected to improve scientific practice within the hydrology domain, and foster scientific debate on benchmarking in cluster analysis.
We invite contributions (case studies, comparative analyses, theoretical experiments) on a wide range of topics including (but not limited to): hard vs fuzzy clustering; comparison of clustering algorithms; benchmarking in cluster analysis; clustering as an exploratory tool vs clustering as a hypothesis testing tool; determination of number of clusters; selecting variables to cluster upon; evaluation of clustering performance; alternative clustering methods (sequential, evolutionary, deep, ensemble, etc.)
Innovative sensing techniques for water monitoring, modelling, and management: satellites, gauges and citizens
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.
Information theory provides a powerful conceptual framework for automated learning, model development, model evaluation, and experimental design in the Earth Sciences. Information theory extends probability theory, and provides potentially new or complementary perspectives on questions related to models, data, and analysis, including:
- How much information does a model or hypothesis contain about environmental systems?
- How much information does a data set contain about a particular phenomenon or behavior?
- How much information is shared among the different hypotheses or models?
- How much information is lost across the different steps of a prediction chain?
- How to combine information in the data with information in models?
- Can information theory complement or go beyond existing approaches (e.g. Bayesian) for uncertainty analysis?
- How does information about a system reflect the underlying dynamics?
This session invites contributions about information theory in the Earth Sciences that are related (but not limited to) to the following topics:
1-Fundamentals of probability theory and information theory
2-Practical aspects of working with distributions and information measures
3-Model evaluation and uncertainty analysis
4-Causal inference and process networks
5-Information-based hydrological learning and prediction
6-Learning interpreted as data compression
7-Information theory applied to spatial problems and/or remote sensing data analysis
8-Information-based design of observational networks
9-Physics of Information: mathematical foundations, physical theories and applications
Advances in diagnostics, sensitivity, uncertainty analysis, and hypothesis testing of Earth and environmental systems models
Proper characterization of uncertainty remains a major research and operational challenge in Environmental Sciences, and is inherent to many aspects of modelling impacting model structure development; parameter estimation; an adequate representation of the data (inputs data and data used to evaluate the models); initial and boundary conditions; and hypothesis testing. To address this challenge, methods for a) uncertainty analysis (UA) that seek to identify, quantify and reduce the different sources of uncertainty, as well as propagating them through a system/model, and b) the closely-related methods for sensitivity analysis (SA) that evaluate the role and significance of uncertain factors (in the functioning of systems/models), have proved to be very helpful.
This session invites contributions that discuss advances, both in theory and/or application, in methods for SA/UA applicable to all Earth and Environmental Systems Models (EESMs), which embraces all areas of hydrology, such as classical hydrology, subsurface hydrology and soil science.
Topics of interest include (but are not limited to):
1) Novel methods for effective characterization of sensitivity and uncertainty
2) Analyses of over-parameterised models enabled by AI/ML techniques
3) Single- versus multi-criteria SA/UA
4) Novel methods for spatial and temporal evaluation/analysis of models
5) The role of information and error on SA/UA (e.g., input/output data error, model structure error, parametric error, regionalization error in environments with no data etc.)
6) Novel approaches and benchmarking efforts for parameter estimation
7) Improving the computational efficiency of SA/UA (efficient sampling, surrogate modelling, parallel computing, model pre-emption, model ensembles, etc.)
Analysis of complex geoscientific time series: linear, nonlinear, and computer science perspectives
This interdisciplinary session welcomes contributions on novel conceptual and/or methodological approaches and methods for the analysis and statistical-dynamical modeling 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
- statistical inference for nonlinear time series, including empirical inference of causal linkages from multivariate data
- 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. We particularly aim at fostering a transfer of new methodological data analysis and modeling concepts among different fields of the geosciences.
Flash floods and rainfall induced hydro-geomorphic hazards: from observation to forecasting and warning
Heavy precipitation events in small and medium size catchments can trigger flash floods, which are characterized by very short response times and high specific peak discharges, and often occur in ungauged basins. Under appropriate geomorphological conditions, such rainstorms also cause debris flows or shallow landslides mobilizing large amounts of unconsolidated material. Although significant progress has been made in the management of these different hazards and related risks, they remain poorly understood and their predictability is affected by large uncertainties, due to the fast evolution of triggering rainfall events, the lack of appropriate observations, the high variabilities and non-linearities in the physical processes, and the high variability and complexity of societal vulnerability.
This session aims to illustrate current advances in monitoring, understanding, modelling, and forecasting flash floods and associated geomorphic processes, and documenting and anticipating the societal impacts and social responses.
Contributions on the following scientific themes are more specifically expected:
- Development of new measurement techniques adapted to flash floods and/or rainfall-induced geomorphic hazards monitoring (including in-situ sensors and remote sensing data, such as weather radar, and lightning ..), and quantification of the associated uncertainties,
- Identification of processes leading to flash flood events and/or rainfall-induced geomorphic hazards from data analysis and/or modelling, and of their characteristic space-time scales,
- Possible evolutions in hazard characteristics and frequency related to climate change,
- Development of short-range (0-6h) rainfall forecasting techniques adapted to heavy precipitation events, and representation of associated uncertainties,
- Development of hydro-meteorological forecasting chains for predicting flash floods and/or rainfall-induced geomorphic hazards in gauged and ungauged basins,
- Development of inundation mapping approaches specifically designed for an integration in flash floods monitoring or forecasting chains,
- Use of new criteria such as specific “hydrological signatures” (high water marks, impacts and damages, ..) or other proxy data for model and forecast evaluation,
- Observation, understanding and prediction of the societal vulnerability and social responses to flash floods and/or associated hydro-geomorphic hazards.
Drought and water scarcity: monitoring, modelling and forecasting to improve hydro-meteorological risk management
Drought and water scarcity are important issues in many regions of the Earth. While an increase in the severity and frequency of droughts can lead to water scarcity situations, particularly in regions that are already water-stressed, overexploitation of available water resources can exacerbate the consequences of droughts. In the worst case, this can lead to long-term environmental and socio-economic impacts. It is, therefore, necessary to improve both monitoring and sub-seasonal to seasonal forecasting for droughts and water availability and to develop innovative indicators and methodologies that translate the information provided into effective drought early warning and risk management. This session addresses statistical, remote sensing and physically-based techniques, aimed at monitoring, modelling and forecasting hydro-meteorological variables relevant to drought and/or water scarcity. These include, but are not limited to, precipitation, snow cover, soil moisture, streamflow, groundwater levels, and extreme temperatures. The development and implementation of drought indicators meaningful to decision-making processes, and ways of presenting and explaining them to water managers, policymakers and other stakeholders, are further issues that are addressed. The session aims to bring together scientists, practitioners and stakeholders in the fields of hydrology and meteorology, as well as in the field of water resources and/or risk management; interested in monitoring, modelling and forecasting drought and water scarcity, and in analyzing their interrelationships, hydrological impacts, and the feedbacks with society. Particularly welcome are applications and real-world case studies in regions subject to significant water stress, where the importance of drought warning, supported through state-of-the-art monitoring and forecasting of water resources availability is likely to become more important in the future. Contributors to the session are invited to submit papers to the Special Issue (SI) entitled "Recent advances in drought and water scarcity monitoring, modelling, and forecasting", to be published in the open-access journal Natural Hazard and Earth System Sciences (https://www.natural-hazards-and-earth-system-sciences.net/special_issues/schedule.html). Submission is open until 31 December 2020, for manuscripts that are not under consideration for publication elsewhere.
Ensemble and probabilistic hydro-meteorological forecasts: predictive uncertainty, verification and decision making
This session brings together scientists, forecasters, practitioners and stakeholders interested in exploring the use of ensemble hydro-meteorological forecast techniques in hydrological applications: e.g., flood control and warning, reservoir operation for hydropower and water supply, transportation, and agricultural management. It will address the understanding of sources of predictability and quantification and reduction of predictive uncertainty of hydrological extremes in deterministic and ensemble hydrological forecasting. Uncertainty estimation in operational forecasting systems is becoming a more common practice. However, a significant research challenge and central interest of this session is to understand the sources of predictability and development of approaches, methods and techniques to enhance predictability (e.g. accuracy, reliability etc.) and quantify and reduce predictive uncertainty in general. Ensemble data assimilation, NWP preprocessing, multi-model approaches or hydrological postprocessing can provide important ways of improving the quality (e.g. accuracy, reliability) and increasing the value (e.g. impact, usability) of deterministic and ensemble hydrological forecasts. The models involved with the methods for predictive uncertainty, data assimilation, post-processing and decision-making may include machine learning models, ANNs, catchment models, runoff routing models, groundwater models, coupled meteorological-hydrological models as well as combinations (multimodel) of these. Demonstrations of the sources of predictability and subsequent quantification and reduction in predictive uncertainty at different scales through improved representation of model process (physics, parameterization, numerical solution, data support and calibration) and error, forcing and initial state are of special interest to the session.
The session welcomes new experiments and practical applications showing successful experiences, as well as problems and failures encountered in the use of uncertain forecasts and ensemble hydro-meteorological forecasting systems. Case studies dealing with different users, temporal and spatial scales, forecast ranges, hydrological and climatic regimes are welcome.
The session is part of the HEPEX international initiative: www.hepex.org
Operational forecasting and warning systems for natural hazards: challenges and innovations
This interactive session aims to bridge the gap between science and practice in operational forecasting for different water-related natural hazards. Operational (early) warning systems are the result of progress and innovations in the science of forecasting. New opportunities have risen in physically based modelling, coupling meteorological and hydrological forecasts, ensemble forecasting, impact-based forecasting and real time control. Often, the sharing of knowledge and experience about developments are limited to the particular field (e.g. flood forecasting or landslide warnings) for which the operational system is used.
The focus of this session will be on bringing the expertise from different fields together as well as exploring differences, similarities, problems and solutions between forecasting systems for varying natural hazards. Real-world case studies of system implementations - configured at local, regional and national scales - will be presented, including trans-boundary issues. An operational warning system can include, for example, monitoring of data, analysing data, making forecasts, giving warning signals and suggesting response measures.
Contributions are welcome from both scientists and practitioners who are involved in developing operational forecasting and/or management systems for water-related natural or man-made hazards, such as flood, drought, tsunami, landslide, hurricane, hydropower, pollution etc.
Reducing the impacts of natural hazards through forecast-based action: from early warning to early action
The Sendai Framework for disaster risk reduction (SFDRR) and its seventh global target recognizes that increased efforts are required to develop risk-informed and impact-based multi-hazard early warning systems. Despite significant advances in disaster forecasting and warning technology, it remains challenging to produce useful forecasts and warnings that are understood and used to trigger early actions. Overcoming these challenges requires understanding of the reliability of forecast tools and implementation barriers in combination with the development of new risk-informed processes. It also requires a commitment to create and share risk and impact data and to co-produce impact-based forecasting models and services. To deal with the problem of coming into action in response to imperfect forecasts, novel science-based concepts have recently emerged. As an example, Forecast-based Financing and Impact-based Multi-Hazard Early Warning Systems are currently being implemented operationally by both governmental and non-governmental organisations in several countries as a result of increasing international effort by several organizations such as the WMO, World Bank, IFRC and UNDRR to reduce disaster losses and ensuring reaching the objectives of SFDRR. This session aims to showcase lessons learnt and best practices on impact-based multi-hazards early warning system from the perspective of both the knowledge producers and users. It presents novel methods to translate forecast of various climate-related and geohazards into an impact-based forecast. The session addresses the role of humanitarian agencies, scientists and communities at risk in creating standard operating procedures for economically feasible actions and reflects on the influence of forecast uncertainty across different time scales in decision-making. Moreover, it provides an overview of state-of-the-art methods, such as using Artificial Intelligence, big data and space applications, and presents innovative ways of addressing the difficulties in implementing forecast-based actions. We invite submissions on the development and use of operational impact-based forecast systems for early action; developing cost-efficient portfolios of early actions for climate/geo-related impact preparedness such as cash-transfer for droughts, weather-based insurance for floods; assessments on the types and costs of possible forecast-based disaster risk management actions; practical applications of impact forecasts.