Integrating spatially-and temporally-heterogeneous data on river network dynamics using graph theory

Durighetto, Nicola; Noto, Simone; Tauro, Flavia; Grimaldi, Salvatore; Botter, Gianluca

doi:10.1016/j.isci.2023.107417

Cited by 4 publications

(6 citation statements)

References 71 publications

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“…Finally, this new product, with its high sensitivity to seasonal variations in inland waterbodies, not only wetlands but also rivers, might be a great tool to test theories related to river networks, their formations, and their sequential activation (Bertassello et al., 2022; Durighetto et al., 2023; Rinaldo et al., 2014). The tools being developed to better understand river networks are of crucial importance to understanding the hydrological response of river basins to extreme hydrological events, but data to appropriately test these theories have so far been very limited, both spatially and temporally.…”

Section: Discussionmentioning

confidence: 99%

Berkeley‐RWAWC: A New CYGNSS‐Based Watermask Unveils Unique Observations of Seasonal Dynamics in the Tropics

Pu,

Gerlein‐Safdi,

Xiong

et al. 2024

Water Resources Research

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The UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley‐RWAWC) is a new data product designed to address the challenges of monitoring inundation in regions hindered by dense vegetation and cloud cover as is the case in most of the Tropics. The Cyclone Global Navigation Satellite System (CYGNSS) constellation provides data with a higher temporal repeat frequency compared to single‐satellite systems, offering the potential for generating moderate spatial resolution inundation maps with improved temporal resolution while having the capability to penetrate clouds and vegetation. This paper details the development of a computer vision algorithm for inundation mapping over the entire CYGNSS domain (37.4°N–37.4°S). The sole reliance on CYGNSS data sets our method apart in the field, highlighting CYGNSS's indication of water existence. Berkeley‐RWAWC provides monthly, low‐latency inundation maps starting in August 2018 and across the CYGNSS latitude range, with a spatial resolution of 0.01° × 0.01°. Here we present our workflow and parameterization strategy, alongside a comparative analysis with established surface water data sets (SWAMPS, WAD2M) in four regions: the Amazon Basin, the Pantanal, the Sudd, and the Indo‐Gangetic Plain. The comparisons reveal Berkeley‐RWAWC's enhanced capability to detect seasonal variations, demonstrating its usefulness in studying tropical wetland hydrology. We also discuss potential sources of uncertainty and reasons for variations in inundation retrievals. Berkeley‐RWAWC represents a valuable addition to environmental science, offering new insights into tropical wetland dynamics.

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Section: Discussionmentioning

confidence: 99%

Berkeley‐RWAWC: A New CYGNSS‐Based Watermask Unveils Unique Observations of Seasonal Dynamics in the Tropics

Pu,

Gerlein‐Safdi,

Xiong

et al. 2024

Water Resources Research

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“…This perceptual model offers a simple framework to analyse the changes of total active length associated with the discharge variations observed at a given station, and explains the physical mechanisms that originate the so‐called hierarchical structuring of channel network dynamics. This means that the activation of non‐perennial reaches follows a fixed and repeatable sequence in which the persistence associated to each section of the stream (i.e., the probability of being observed as wet) determines the order of activation/deactivation of the section itself, that is, the least persistent sections activate only when the most persistent are already flowing (Botter et al, 2021; Durighetto et al, 2023; Noto et al, 2024).…”

Section: Methodsmentioning

confidence: 99%

“…Recently, our capability to gather empirical data on the spatial and temporal evolution of the flowing river network has been significantly enhanced by the use of sensor technology (e.g., Chapin et al, 2014; Constantz et al, 2002; Dralle et al, 2023; Jensen et al, 2019; Kaplan et al, 2019; Noto et al, 2022; Partington et al, 2021; Zanetti et al, 2022). Hi‐tech sensors, in fact, can efficiently integrate coarser representations of the active network extent gathered through field mapping (e.g., Day, 1980; Durighetto et al, 2020; Jaeger et al, 2007; Jensen et al, 2017; Senatore et al, 2021), particularly thanks to the use of algorithms designed to maximize the information contained in empirical datasets characterized by non‐uniform spatial and/or temporal resolutions (e.g., Botter et al, 2021; Durighetto et al, 2023; Noto et al, 2024). On‐site information on stream dynamics has been already used in the existing literature to develop empirical tools that link the total flowing stream length in a network (

L

) with the discharge observed at the outlet (

Q

) (Godsey & Kirchner, 2014; Jensen et al, 2017; Lapides et al, 2021; Senatore et al, 2021; Ward et al, 2018; Zanetti et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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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“…Notable examples include temperature sensors (Arismendi et al., 2017; Blasch et al., 2004; Constantz et al., 2001; Keery et al., 2007; Partington et al., 2021), stage camera systems (Herzog et al., 2022; Kaplan et al., 2019; Noto et al., 2022; Perks et al., 2016; Tauro et al., 2014; Tauro, Olivieri, et al., 2016; Tauro, Petroselli, et al., 2016), and electrical resistance sensors (Chapin et al., 2014; Floriancic et al., 2018; Goulsbra et al., 2014; Jensen et al., 2019; Kaplan et al., 2019; Paillex et al., 2020; Zanetti et al., 2022). All of them proved to be useful for collecting data at high temporal resolution and have the benefit of being cost‐effective and automatic, allowing scientists to observe the sequences of activation/deactivation of the different nodes of the network (Durighetto et al., 2023) and any discontinuity in the wet stream length at the event scale, rather than at the monthly or seasonal scale. On the other hand, these technologies have to be carefully supervised and monitored to ensure the reliability of the data collected, and their deployment in the field is typically highly time consuming.…”

Section: Introductionmentioning

confidence: 99%

Stream Network Dynamics of Non‐Perennial Rivers: Insights From Integrated Surface‐Subsurface Hydrological Modeling of Two Virtual Catchments

Zanetti,

Botter,

Camporese

2024

Water Resources Research

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Understanding the spatio‐temporal dynamics of runoff generation in headwater catchments is challenging, due to the intermittent and fragmented nature of surface flows. The active stream network in non‐perennial rivers contracts and expands, with a dynamic behavior that depends on the complex interplay among climate, topography, and geology. In this work, CATchment HYdrology, an integrated surface–subsurface hydrological model (ISSHM), is used to simulate the stream network dynamics of two virtual catchments with the same, spatially homogeneous, subsurface characteristics (hydraulic conductivity, porosity, water retention curves) but different morphology. We run two sets of simulations to reproduce a sequence of steady‐states at different catchment wetness levels and transient conditions and analyze the joint variations of the stream length (L) and discharge at the outlet (Q) with high spatio‐temporal resolutions. The shape of the L(Q) curves differs in the two catchments but does not depend on the climate forcing, as it is mainly controlled by the underlying topography. We then analyzed the suitability of the topographic wetness index and the contributing area to identify the spatial configuration of the maximum stream length in the two catchments. These two morphometric parameters provided a good estimate of the spatial distribution of the maximum flowing network in both the study catchments. Our numerical simulations indicate that ISSHMs have the potential to accurately describe the spatio‐temporal variations of the stream networks and the processes driving such dynamic behavior and that, overall, they can be useful tools to gain insights into the main physical drivers of non‐perennial streams.

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Integrating spatially-and temporally-heterogeneous data on river network dynamics using graph theory

Cited by 4 publications

References 71 publications

Berkeley‐RWAWC: A New CYGNSS‐Based Watermask Unveils Unique Observations of Seasonal Dynamics in the Tropics

Berkeley‐RWAWC: A New CYGNSS‐Based Watermask Unveils Unique Observations of Seasonal Dynamics in the Tropics

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

Stream Network Dynamics of Non‐Perennial Rivers: Insights From Integrated Surface‐Subsurface Hydrological Modeling of Two Virtual Catchments

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