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Dynamical river NETworks: climatic controls and biogeochemical function

Periodic Reporting for period 2 - DyNET (Dynamical river NETworks: climatic controls and biogeochemical function)

Reporting period: 2019-11-01 to 2021-04-30

Despite the ubiquity of expansion and retraction dynamics of flowing streams, river networks are often conceived as static elements of the landscape, and a coherent framework to quantify nature and extent of drainage network dynamics is lacking. The implications of this ubiquitous phenomenon extend far beyond hydrology and involve ecological and biogeochemical functions of riparian corridors, as well as policy actions for the protection of temporary streams. The research project will move beyond the traditional paradigm of static river networks to unravel physical causes and biogeochemical consequences of stream dynamics. In particular, the project plans to identify the climatic and geomorphic controls on the expansion/contraction of river networks, and quantify the length of temporary streams and their impact on catchment-scale biogeochemical processes and stream water quality.
These challenging issues aree addressed by developing a novel theoretical framework complemented by extensive field observations within four representative sites along a climatic gradient in the EU: i) the Rietholzbach catchment, in Switzerland; ii) the Valfredda catchment in Northern Italy; iii) the Montecalvello catchment, in Central Italy; iv) the Turbolo catchment in Southern ITaly. Field measurements performed include long-term high-frequency mapping of the active drainage network and daily hydro-chemical data across scales. The experimental dataset will be used to develop and inform a set of innovative modelling tools, including an analytical framework for the description of spatially explicit hydrologic dynamics driven by stochastic rainfall and a modular hydro-chemical model based on the concept of water age, able to account for the variable connectivity among soil, groundwater and channels as induced by stream network dynamics. The project will open new avenues to quantify freshwater carbon emissions – crucially dependent on the extent of ephemeral streams – and it will provide a robust basis to identify temporary rivers and maintain their biogeochemical function in times of global change.
In the first 30 months of the project, the following major actions have been implemented:

1) Most of the foreseen equipment for the monitoring of chemical and hydrological variables has been purchased and tested. This includes all the sensors for water quality and the physical properties of water, the autosamplers, the meteorologic stations, three drones with the related thermal cameras, water presence sensors, CO2 chambers, pressure transducters;
2) In the 4 study catchments permanent experimental installations have been set up. Likewise, extensive monitoring activities have been carried out in particular for the mapping of the river network and its time variability, as well as the quantification of biogeochemical processes taking place in streams (water quality and carbon dioxide fluxes measurements);
3) Hydrological and chemical data gathered in the staudy catchments have been validated and analyzed, and preliminary models able to simulate the dynamics of river networks have been developed;
4) A comparative assessment of the active length variations in different field sites has been undertaken and a novel theoretical framework for the stochastic descritpion of active length dynamics has been developed;
5) Carbon dioxide emissions as controlled by the microtopography of the river have been studied using theoretical analysis and field data.

The major results achieved during this 30 months period include:

1) testing and comparing different technologies for stream network mapping under different field and climatic conditions (visual inspection, water presence sensors, satellite images, thermal images from drones);
2) testing novel tools to monitor CO2 fluxes from stream to the atmosphere (a flexible floaitng chamber and the use of steady chambers located in fixed positions for measuring the equilibrium CO2 water concentration);
3) a unique dataset about stream network dynamics in different catchments characterized by heterogeneous climatic and geologic conditions has been created;
4) development of stochastic models for the description of active stream length changes and network dynamics: the model is based on Bayesian networks and Directed Acyclic Graphs. the model allows alinkage between the dynamics of the active length and the temporal variations of the hydrological status of the nodes;
5) a universal hierarchical rule for network expansion and contraction has been identified (the most persistent segments systematically activate first, regardless of the complexity of persistency patterns).
The monitoring of the stream netwrok performed in the DyNET sites reached a combination of size and temporal resolutions never reached before. For the first time joint data about water quality and stream netwrok dynamics will be collected, which open the avenue to analyze the impact of dynamical networks on biogeochemical function of rivers. We have developed a newly designed chamber for measuring CO2 fluxes from the streams to the atmosphere, which minimizes the turbulence induced on the water flow. We have developed a statistical model that relates precipitation, evapotranspiration and stream lenght across different timescales. We have identified a universal hierarchical rule in the activation and deactivation of temporary streams: the most persistent stretches are the first to be activated. The project plans to expand the available dataset and include a detailed characterization of stream network dynamics throughout the study sites, so as to unravel the impact of climatic drivers on river form and function.The project also plans to develop physically based models for stream netwrok expansion and contraction, and seeks to analyze the impact of these dynamics on carbon cycling in catchments, through the use of biogeochemical models and water quality data.
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