Periodic Reporting for period 1 - SEDAHEAD (Dynamic river catchments in a Global Change context: assessing the present, preparing for the future)
Okres sprawozdawczy: 2022-11-01 do 2025-04-30
In a Global Changing world sediment regime has emerged as a dominant actor in the modification of river catchments. The sediment regime refers to the sediment budget (amount, type and timing of sediment inputs, outputs and storage) of a river system as well as the way water and sediment interact to drive river conditions. Studies of sediment regime assessing the impact of Global Change (i.e. climatic forces and land cover/land use variability) are scarce and traditionally relies on deterministic approaches. However, at any given river catchment section, a complex imprint in the spatial-temporal distribution of sediment regime is observed. This imprint is caused by the temporal and spatial uneven production, storage activation and transport of sediments. Pure deterministic (and thus partial) solutions are not accounting for the natural variability of sediment regime and the inherent uncertainty due to Global Change. This means we are potentially missing half the story and that our attempts to forecast the current and future evolution of rivers areas, at both catchment- and reach-scale, may be more wrong than right.
Particular attention needs to be devoted to headwater rivers. These headwater streams are located in mountainous regions which are key locations in the sediment balance of river catchments, contributing to sediment production, fragmentation and export to lowland valleys. Rivers in mountainous regions tend to carry coarse sediment loads and are often gravel-bed rivers. They flow in many cases through narrow valleys that are intermittently supplied with sediment coming out of bed scour, bar migration, bank erosion mechanisms and hillslope processes. All of these processes are highly sensitive to Global Change and ultimately determine the sediment that reaches lowland alluvial valleys and the coast. For this reason, sediment transport in gravel-bed rivers located in mountainous regions plays a fundamental role in regulating the sediment cascade at the whole catchment scale
The tenet of this project is to describe and determine the Global Change impacts on sediment fluxes at all scales relevant for river catchment management by means of field work (using Unmanned Aerial Vehicle -UAVs, direct measurements and surrogate methods), theoretical and numerical modelling (with physically-based equations and data-driven models).
Particular attention needs to be devoted to headwater rivers. These headwater streams are located in mountainous regions which are key locations in the sediment balance of river catchments, contributing to sediment production, fragmentation and export to lowland valleys. Rivers in mountainous regions tend to carry coarse sediment loads and are often gravel-bed rivers. They flow in many cases through narrow valleys that are intermittently supplied with sediment coming out of bed scour, bar migration, bank erosion mechanisms and hillslope processes. All of these processes are highly sensitive to Global Change and ultimately determine the sediment that reaches lowland alluvial valleys and the coast. For this reason, sediment transport in gravel-bed rivers located in mountainous regions plays a fundamental role in regulating the sediment cascade at the whole catchment scale
The tenet of this project is to describe and determine the Global Change impacts on sediment fluxes at all scales relevant for river catchment management by means of field work (using Unmanned Aerial Vehicle -UAVs, direct measurements and surrogate methods), theoretical and numerical modelling (with physically-based equations and data-driven models).
Four headwater river sections were instrumented in the Upper Aragón river catchment (located in the Central Spanish Pyrenees). One river reach has snow-melt dominated floods and the other three river reaches have a combination of snow-melt and precipitation dominated floods. We recorded in each river section streamflow values, suspended sediment concentration values and water temperature values. In parallel, we ran aerial topographic surveys with an Unmanned Aerial Vehicle (UAV) and we collected sediment data such as superficial sediment grain size distribution (through ground surveys) and sediment size in suspension (through an ISCO sampler and lab analysis). This monitoring occurred before and after flood events capable of mobilizing sediment transport. Furthermore, the field measurements included the release of active Radio Frequency Identification (RFID) tags, which allowed to infer travelling distances, sediment velocities and sediment diffusion characteristics. We also deployed geophones on river banks to capture the ground vibrations caused by bedload particles interacting with the river bed. This allows to relate streamflow values and river seismic signals to infer bedload transport estimates. Finally, we implemented a robust, quasi-automatic, protocol to collect aerial photos taken by UAVs and to post process that information in order to infer topographic data and sedimentological data (e.g. superficial grain size distribution).
Conversely, we used data-driven methods to compare their performance with traditionally lumped process-based modeling approaches for streamflow prediction. In particular, we used Long Short-Term Memory (LSTM) machine-learning models for punctual streamflows prediction and Convolutional Neural Networks (CNN) for including spatial features within a catchment area. We used as a benchmark a rich database from the Ebro River Water Authorities (CHE), with daily information since before 1990. Data was curated and homogenized in order to be used with this type of data-driven models. Climatic forcing (precipitation, air temperature, radiation), atmospheric indices (NAO, WeMO) and drought indices (SPEI) were also taken into account. These data-driven methods require less spatially distributed information, less computational demand and can outperformed traditional process-based hydrological models. Furthermore, we tested a physical constraint imposed in the data-driven model to guarantee the mass conservation of the results. This physical constraint helps to better predict the timing and magnitude of flood events, with particular focus on extreme events. This in turn ease the computation of sediment transport estimates conveyed by each flood event.
We defined socio-economic scenarios (which include both climatic forcing and land use-land cover characteristics) at the spatial scale required to infer the impact of Global Change at headwater river systems. Climate data and land use information was downscaled to a regional (finer) resolution based on neighborhood rules and stochasticity in the placement of areas with significant changes. This allows to infer water and sediment fluxes future evolution at headwater river systems.
Conversely, we used data-driven methods to compare their performance with traditionally lumped process-based modeling approaches for streamflow prediction. In particular, we used Long Short-Term Memory (LSTM) machine-learning models for punctual streamflows prediction and Convolutional Neural Networks (CNN) for including spatial features within a catchment area. We used as a benchmark a rich database from the Ebro River Water Authorities (CHE), with daily information since before 1990. Data was curated and homogenized in order to be used with this type of data-driven models. Climatic forcing (precipitation, air temperature, radiation), atmospheric indices (NAO, WeMO) and drought indices (SPEI) were also taken into account. These data-driven methods require less spatially distributed information, less computational demand and can outperformed traditional process-based hydrological models. Furthermore, we tested a physical constraint imposed in the data-driven model to guarantee the mass conservation of the results. This physical constraint helps to better predict the timing and magnitude of flood events, with particular focus on extreme events. This in turn ease the computation of sediment transport estimates conveyed by each flood event.
We defined socio-economic scenarios (which include both climatic forcing and land use-land cover characteristics) at the spatial scale required to infer the impact of Global Change at headwater river systems. Climate data and land use information was downscaled to a regional (finer) resolution based on neighborhood rules and stochasticity in the placement of areas with significant changes. This allows to infer water and sediment fluxes future evolution at headwater river systems.
The main objective of SED@HEAD is to describe and determine the Global Change impact on sediment regime at the scales relevant for river catchment management, i.e. the headwaters streams. A model approach is searched to reproduce fluctuating (and realistic) sediment storage, supply limiting conditions, and thus determine sediment regime characteristics. To this end, the multi-level sediment information of the aerial photos taken by UAVs was thoroughly used. The theoretical contribution of this project has an immediate wide impact not only in the hydraulics and river catchment management community, but in other fields and application areas as well (risk assessment and insurance companies; hydropower operators; land managers). I describe now the related impact along the two main pillars built during the project:
(i) Establishment of a solid theoretical framework: The impact of Global Change was taken into account through several socio-economic scenarios. This allows to infer the variability of future water and sediment fluxes.
(ii) Development of robust algorithms and techniques to post-process sediment data: We developed a subset of techniques ready to be used by practitioners. The develop algorithms are robust and reliable and provide information at different sediment scales.
(i) Establishment of a solid theoretical framework: The impact of Global Change was taken into account through several socio-economic scenarios. This allows to infer the variability of future water and sediment fluxes.
(ii) Development of robust algorithms and techniques to post-process sediment data: We developed a subset of techniques ready to be used by practitioners. The develop algorithms are robust and reliable and provide information at different sediment scales.