Time‐lapse visualization of spatial and temporal patterns of stream network dynamics

Durighetto, Nicola; Botter, Gianluca

doi:10.1002/hyp.14053

Cited by 8 publications

(12 citation statements)

References 12 publications

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“…How-ever, these surveys were not homogeneous in space or systematic in time, thereby making a reliable estimation of the persistency of the network nodes impossible during the study period on a purely experimental basis. Therefore, we decided to estimate the persistency of the nodes using a model that links the spatial configuration of the network to rainfall data, as detailed in the study by Durighetto and Botter (2021). The main model assumptions and its performance in reproducing observed stream dynamics are detailed in Appendix C. The model was calibrated and validated based on 24 complete field surveys carried out for the whole Valfredda catchment from the summer of 2018 to the fall of 2020.…”

Section: Water Presence Data and Flow Persistencymentioning

confidence: 99%

“…During each survey, more than 500 nodes were classified as wet or dry, based on the observed hydrologic conditions of the network (Appendix A). In the light of the good performance of the model (Durighetto et al, 2020;Botter and Durighetto, 2020;Durighetto and Botter, 2021), this was used to estimate the persistency of the nodes (here defined as the fraction of time during which a node was simulated as active in the reference F. Zanetti et al: Technical note: Analyzing river network dynamics and active length period), exploiting information on the antecedent precipitation accumulated over 5 and 35 d.…”

Section: Water Presence Data and Flow Persistencymentioning

confidence: 99%

“…To correct the asynchronicity between the electrical signals recorded by the sensors and the persistencies of the corresponding nodes the model developed by Durighetto et al (2020), Botter and Durighetto (2020) and Durighetto and Botter (2021) was applied. It links the spatial configuration of the network to weather data, and here it was applied to estimate the persistency of the nodes between 4 September and 24 October 2019, knowing rainfall heights of that same period as well as the persistencies and precipitation data collected between July 2018 and January 2019 used to develop the model (Fig.…”

Section: Appendix B: Corrections Applied To the Datamentioning

confidence: 99%

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Technical note: Analyzing river network dynamics and the active length–discharge relationship using water presence sensors

Zanetti

Durighetto

Vingiani

et al. 2022

Hydrol. Earth Syst. Sci.

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Abstract. Despite the importance of temporary streams for the provision of key ecosystem services, their experimental monitoring remains challenging because of the practical difficulties in performing accurate high-frequency surveys of the flowing portion of river networks. In this study, about 30 electrical resistance (ER) sensors were deployed in a high relief 2.6 km2 catchment of the Italian Alps to monitor the spatio-temporal dynamics of the active river network during 2 months in the late fall of 2019. The setup of the ER sensors was customized to make them more flexible for the deployment in the field and more accurate under low flow conditions. Available ER data were compared to field-based estimates of the nodes' persistency (i.e., a proxy for the probability to observe water flowing over a given node) and then used to generate a sequence of maps representing the active reaches of the stream network with a sub-daily temporal resolution. This allowed a proper estimate of the joint variations of active river network length (L) and catchment discharge (Q) during the entire study period. Our analysis revealed a high cross-correlation between the statistics of individual ER signals and the flow persistencies of the cross-sections where the sensors were placed. The observed spatial and temporal dynamics of the actively flowing channels also highlighted the diversity of the hydrological behavior of distinct zones of the study catchment, which was attributed to the heterogeneity in catchment geology and stream-bed composition. Our work emphasizes the potential of ER sensors for analyzing spatio-temporal dynamics of active channels in temporary streams, discussing the major limitations of this type of technology emerging from the specific application presented herein.

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Section: Water Presence Data and Flow Persistencymentioning

confidence: 99%

Section: Water Presence Data and Flow Persistencymentioning

confidence: 99%

Section: Appendix B: Corrections Applied To the Datamentioning

confidence: 99%

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Technical note: Analyzing river network dynamics and the active length–discharge relationship using water presence sensors

Zanetti

Durighetto

Vingiani

et al. 2022

Hydrol. Earth Syst. Sci.

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“…In fact, the soil composition and the vegetation features change along the altimetric gradient of the catchment (Figure 3a ). The Valfredda catchment has been extensively studied in previous work (Botter & Durighetto, 2020 ; Durighetto & Botter, 2021 ; Durighetto et al., 2020 ; Zanetti et al., 2021 ). The Valfredda river has an alpine climate, characterized by cold snowy winters and wet summers.…”

Section: Case Studies and Model Applicationmentioning

confidence: 99%

“…Existing classifications tools for temporary rivers share the adoption of a local perspective, according to which a stream type is associated to every single river segment (or reach) in a network, depending on the hydrologic and ecologic regime observed in that particular location. While extremely useful, reach‐wise classifications are difficult to extrapolate in space (Gordon & Goñi, 2003 ; Levick et al., 2018 ) as the resulting stream types might be highly heterogeneous along the stream network for example, owing to the internal geological complexity of a catchment (Durighetto & Botter, 2021 ). Moreover, local approaches do not take into account the status of the river network upstream of the focus stretch, which instead might be highly influential for a proper definition of the ecological and bio‐geochemical function of streams.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Description of Streamflow and Active Length Regimes in Rivers

Durighetto

Mariotto

Zanetti

et al. 2022

Water Resources Research

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In spite of the prevalence of temporary rivers over a wide range of climatic conditions, they represent a relatively understudied fraction of the global river network. Here, we exploit a well‐established hydrological model and a derived distribution approach to develop a coupled probabilistic description for the dynamics of the catchment discharge and the corresponding active network length. Analytical expressions for the flow duration curve (FDC) and the stream length duration curve (SLDC) were derived and used to provide a consistent classification of streamflow and active length regimes in temporary rivers. Two distinct streamflow regimes (persistent and erratic) and three different types of active length regimes (ephemeral, perennial, and ephemeral de facto) were identified depending on the value of two dimensionless parameters. These key parameters, which are related to the underlying streamflow fluctuations and the sensitivity of active length to changes in the catchment discharge (here quantified by the scaling exponent b), originate seven different behavioral classes characterized by contrasting shapes of the underlying SLDCs and FDCs. The analytical model was tested using data gathered in three study catchments located in Italy and USA, with satisfactory model performances in most cases. Our analytical and empirical results show the existence of a structural relationship between streamflow and active length regimes, which is chiefly modulated by the scaling exponent b. The proposed framework represents a promising tool for the coupled analysis of discharge and river network length dynamics in temporary streams.

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How do different runoff generation mechanisms drive stream network dynamics? Insights from physics‐based modelling

Zanetti,

Camporese,

Botter

2024

Hydrological Processes

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Non‐perennial river catchments are characterized by an ever‐changing spatial configuration of their flowing streams. A combination of empirical data and simplified analytical frameworks has been frequently used in the literature to analyse the co‐evolution of the total active stream length () and the catchment discharge at the outlet (). However, despite the increasing availability of field data, understanding how runoff generation processes drive the spatio‐temporal dynamics of non‐perennial river reaches remains challenging. In this paper we use CATHY, an integrated surface–subsurface hydrological model (ISSHM), to investigate the impact of saturation‐excess (Dunnian) and infiltration‐excess (Hortonian) runoff generation on the stream network dynamics of two virtual catchments with spatially homogeneous subsurface properties but different morphology. The numerical simulations show that when surface runoff is triggered by saturation‐excess mechanisms, the subsurface domain is slowly saturated, and the stream network gradually expands upstream from the outlet towards the catchment divides. In these conditions, the specific inflow per unit contributing area is relatively uniform along the network, thereby implying that and display a monotonically increasing one‐to‐one relationship. On the other hand, infiltration‐excess mechanisms lead to more heterogeneous saturation patterns in the subsurface domain. In particular, during the wetting phase, Hortonian processes originate highly transient conditions and a non‐uniform spatial distribution of the specific inflow along the stream network. This is reflected by a hysteretic relation and a marked asymmetry between the wetting and drying phases of the event. The application of an ISSHM proved to be a useful tool to elucidate the processes that drive stream network expansion and retraction in non‐perennial rivers.

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Time‐lapse visualization of spatial and temporal patterns of stream network dynamics

Abstract: Temporary streams (i.e., streams that experience zero flow, at least in some branches of the network) represent a fundamental part of riverine systems. Conservative estimates indicate that they constitute

Cited by 8 publications

References 12 publications

Technical note: Analyzing river network dynamics and the active length–discharge relationship using water presence sensors

Technical note: Analyzing river network dynamics and the active length–discharge relationship using water presence sensors

Probabilistic Description of Streamflow and Active Length Regimes in Rivers

How do different runoff generation mechanisms drive stream network dynamics? Insights from physics‐based modelling

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