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Soil moisture dynamics across scales
All current hydrological observatories distributed are providing soil moisture data from in-situ and proximal sensor network systems in different spatial and temporal resolutions. Moreover large-scale global coverage of soil moisture data is provided by various remote sensing platforms. The increase in the amount of soil moisture data across spatial and temporal scales is leading to the era of “Big Soil Moisture Data”. The exponential growth in computational power and advancements in machine learning algorithms are unlocking scientific insights at an unprecedented rate in soil-moisture-related processes leading to improved hydrological, ecological and agricultural modeling and forecasting. Yet the abundant soil moisture data collected by new-generation ground-based, airborne-based and space-borne platforms are still affected by uncertainties and have gaps in both space and time. In this session, we welcome contributions that analyze soil moisture dynamics that have been made available in hydrological observatories aiming at improving our understanding of hydrological processes. We also invite contributions that address the aforementioned challenges.
Our capacity to observe hydrological states and fluxes at the catchment scale has greatly increased over the last decades. Novel technologies have become available that allow an unprecedented spatial and temporal resolution of the components of the terrestrial hydrological balance. Data assimilation approaches have gained increasing interest as they allow for an optimal integration of data and models and they have been shown to be ideally suited for forecasting hydrological processes and managing water resources. Data assimilation also allows determining the value of measurement data and the accuracy gain/reduction if the density of the monitoring network is changed. It can therefore contribute to the design of monitoring networks that give an adequate balance between accuracy and costs. This session welcomes contributions that link observational data with hydrological models in order to improve our understanding of hydrological processes and for real-time management of water resources.
UAS- and satellited based remote sensing for hydrological observatories
Recent developments in unmanned aerial vehicles, imaging technology, and airborne and space borne remote sensing techniques allow high resolution imagining of the environment at unprecedented scale. Remote sensing bridges the scales from point-to-catchment scale observations by airborne UAV to continental scale observations from space. In this session, we welcome contributions addressing novel developments in those fields with strong relevance to characterizing physical states and fluxes as inputs to hydrologic modeling at high spatial and temporal resolution. The session welcomes remote sensing applications for catchment scale processes with strong implications to continental scale process dynamics driven by global change.
Despite powerful tracer tools for deciphering water sources, flow paths and transit times, hydrology remains a discipline that is measurement limited. With global change likely triggering increasingly intense hydro-meteorological events in the near future, there is a pressing need for new observational tools that collect data at unprecedented spatial and temporal scales. Recently, new momentum has come through the advent of novel geophysical field scale sensing technologies and field deployable high-resolution isotope and geochemical measurement systems. The emergence of such novel measurements has the potential for serving as a catalyst in critical zone process analyses – ultimately delivering entirely new datasets for model calibration and validation. In this session, we encourage submissions dealing with new types of sensing methods and data to investigate and model processes within and between the various compartments of the critical zone (e.g. wireless distributed sensors, novel use of hydro-geophysical methods, cosmic-ray neutron probes, high frequency hydro-chemical measurements, etc.). Submissions leveraging approaches from data science, including new methods to analyse (big) datasets and those that provide new insights based on established types of data are also welcome.
Keynote speakers: Dr. Rafael Rosolem (University of Bristol) and Dr. Matthias Sprenger (IDAEA-CSIC and North Carolina State University)
Quantifying impact of global and climate change on hydrology
Water is the key factor for sustaining natural and agricultural ecosystems. Food, feed and biomass production for energy consumption (e.g. bio-based economy) are controlled by its availability. The water cycle is strongly affected by climate and land use change but the extent and impact on ecosystems services and functioning are only roughly known. Hydrological extremes, such as floods and droughts, are expected to increase which may lead to severe economic, societal and ecological impacts. This session solicits contributions outlining the impacts of climate and global change, and exploring mitigation strategies for increasing hydrologic resilience in regions of high vulnerability .
In the past decade, hydrological and critical zone observatories have been established that produce massive amounts of data for a range of critical zone processes. As it remains challenging to analyze such data sets, we solicit submissions that present novel strategies to support critical zone studies in the light of big data.
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