Gianluca Botter and colleagues (among which a notable mention is needed to Nicola Durighetto) recent work in the ERC project Dynet is remarkable (as well as the older one) and a little of it is in this presentation they kindly prepared for the AboutHydrology blog.The first slides (you find them by clicking on the Figure below) are choreographic (slide 1 only a photo, slide 2 a photographic example of network dynamics).
Slide 2 shows that stream intermittency is a pervasive phenomenon in many riverscapes. All river networks, in fact, continuously expand and contract in response to time-varying climatic conditions. Consequently, the same reach can experience flowing water, ponding or no-water at all depending on the survey time, as shown by the examples reported in this slide.
Slides 3 movie (here) represents the simulated network dynamics in the Rio Valfredda, 5 km2 (BELLUNO, Dolomites). The movie has been built combining empirical observations and a hierarchical modeling framework. Network dynamics are very complex, with wet reaches that propagate upstream in response to rainfall events, but also active channels that extend in the downstream direction as the catchment wets up. Multiple disconnections are generated and removed as the network expands and contracts.
Slide 4 summarizes the conceptual model used to identify the timing and the duration of surface flow within a given location along the geomorphic network. According to the model, the presence of surface flow is produced by the imbalance between the local inflow Qin, which is made up by the sum of a surface and a subsurface component, and the maximum discharge capacity of the subsurface of that point, Q*. Consequently in this framework, the surface flow presence condition can be written as Q_in > Q*.
In slide 5 (movie) it is shown that surface flow presence is driven by the imbalance between the local inflow Q_in and the maximum subsurface discharge capacity in the hyporheic region, Q*. One important point of this formulation is that Q_in changes in time as a function of the catchment wetness, but also in space, with larger values of inflows that are associated to downstream sites with a larger contributing area. Instead, the maximum outflow Q* is typically constant in time, and does not necessarily exhibit significant scaling effects as it might depend on local features such as the slope or the subsurface transmissivity. As the catchment gets wet, the local Q_in increases along the network non-uniformly and activate larger and larger portions of the network, as shown in the upper movie of this slide. The same process can be also seen looking at the corresponding “specific” quantities, dividing both sides of the surface flow presence equation by the contributing area A. The advantage is that now the specific inflow (small q_in) can be assumed as nearly uniform along the network, while the max specific outflow, rho*, decreases downstream as the contributing area increases. From this new perspective, when the catchment wets up the local specific inflow increases almost uniformly everywhere in the network, and more and more sites experience surface flow starting from the most downstream nodes for which the max specific max outflow rho* is lower.
From a phenomenological view point (slide 6), this mechanistic formulation originates a hiearchical behaviour: during wetting, nodes are activated sequentially from from the most to the least persistent, thereby originating … a sequence of network configurations in which less and less persistent nodes activate as the network expands
Slides 7 shows that during drying, nodes dry out with an order that is the inverse of the one experienced during the wetting… Thus, more and more persistent nodes are progressively switched off as the network retracts. The hierarchical behaviour is observed also in case of dynamically fragmented networks, as in this example.
The hierachical structuring of channel network dynamics, shown in slide 8, has been the object of several past studies, in which the hierarchy was mathematically defined using graph theories, and the validity of the hierarchical scheme has been proved using empirical data from tens of catchments spread al lover the world. The hierarchical structuring proved to be a powerful tool to extrapolate in space incomplete empirical information on surface flow presence.
Slides 9 shows data about local persistency as derived from field surveys in different catchments belong to a broad range of geomorphoclimatic features, from humid settings (on the left) to a dry mediterranian catchment (shown on the right). As you can see there is a general tendency for the persistency to increase moving downstream along the network (indicated by blue-like colors), in line with the expected decrease of rho* for larger contributing areas. However, the observed patterns of local persistency are much more complex than expected in most cases, owing to spatial heterogeneity of hydromorphological features, such as slope and river bed permeability.
Activelength vs discharge plots in slide 10 are valuable to estimate the changes in the flowing length associated to changes in the catchment wetness. Different catchments show a vary different behaviour though…
Slide 11 (movie here) shows an example simulation derived using a stochastic model for simulating the spatio-temporal dynamics of temporary streams. The model takes advantage of few, widely available climatic and morphologic parameters to generate synthetic timeseries of rainfall, streamflow and active length. Furthermore, the approach allows the reconstruction of the time-varying configuration of the active network. Thanks to its simplicity and limited computational requirements, the model can be easily coupled with ecologic models to simulate specific in-stream processes taking place on temporary streams.
In slide 12 (movie here) the Authors combined synthetic dynamic networks with a metapopulation model. The model stochastically simulates the occupancy of a temporary stream by a target species, which is shown in dark blue in the lower plot. The available habitat for the focus species varies with time and is greatly reduced during droughts. Our results indicate that, when compared to a static network, temporary streams result in a lower average occupancy and a higher extinction probability.
To better understand the importance of network dynamics, slide 13 compares the simulated behavior of a fish species in two different conditions: a dynamic network, shown in the bottom panel, and a static network, shown at the top panel.
Even though the average length of the active network is the same in the two cases, the time variability of the available habitat inherent of the dynamic network results in a lower average occupancy and a higher time variability of the network length occupied by the metapopulation. Consequently, in the dynamic scenario there is also an increase in the probability of complete extinction of the target species within the network.
The results in slide 14 suggest that the presence of disconnections along the network lowers the mean occupancy and increases extinction probability. In fact, the target species always goes extinct in less than 1 year in the scenario characterized by the largest number of disconnections (upper panel), while it can survive in the other cases. Therefore, temporary disconnections produced by stream network dynamics are crucial to the ecological functioning of rivers.
This is an important result also in the frame of climate change, which is globally increasing stream intermittency.
References
- N. Durighetto, F. Vingiani, et al. (2020). Intraseasonal Drainage Network Dynamics in a Headwater Catchment of the Italian Alps. Water Resources Research. https://doi.org/10.1029/2019WR025563
- G. Botter, N. Durighetto (2020). The Stream Length Duration Curve: A Tool for Characterizing the Time Variability of the Flowing Stream Length. Water Resources Research. https://doi.org/10.1029/2020WR027282
- G. Botter, F. Vingiani, et al. (2021). Hierarchical climate-driven dynamics of the active channel length in temporary streams. Scientific Reports. https://doi.org/10.1038/s41598-021-00922-2
- N. Durighetto, G. Botter (2021). Time‐lapse visualization of spatial and temporal patterns of stream network dynamics. Hydrological Processes. https://doi.org/10.1002%2Fhyp.14053
- F. Zanetti, N. Durighetto, et al. (2022). Technical note: Analyzing river network dynamics and the active length–discharge relationship using water presence sensors. Hydrology and Earth System Sciences. https://doi.org/10.5194/hess-26-3497-2022
- N. Durighetto, V. Mariotto, et al. (2022). Probabilistic Description of Streamflow and Active Length Regimes in Rivers. Water Resources Research. https://doi.org/10.1029/2021WR031344
- N. Durighetto, G. Botter (2022). On the Relation Between Active Network Length and Catchment Discharge. Geophysical Research Letters. https://doi.org/10.1029/2022GL099500
- N. Durighetto, L. Bertassello, G. Botter (2022). Eco-hydrological modelling of channel network dynamics—part 1: stochastic simulation of active stream expansion and retraction. Royal Society Open Science. https://doi.org/10.1098/rsos.220944
- L. Bertassello, N. Durighetto, G. Botter (2022). Eco-hydrological modelling of channel network dynamics—part 2: application to metapopulation dynamics. Royal Society Open Science. https://doi.org/10.1098/rsos.220945
- N. Durighetto, S. Noto, et al. (2023). Integrating spatially-and temporally-heterogeneous data on river network dynamics using graph theory. I-Science. https://doi.org/10.1016/j.isci.2023.107417
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