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On the Relation Between Active Network Length and Catchment Discharge.

Nicola DurighettoGianluca Botter
Published in: Geophysical research letters (2022)
The ever-changing hydroclimatic conditions of the landscape induce ceaseless variations in the wet channel length ( L ) and the streamflow ( Q ) of a catchment. Here we use a perceptual model to analyze the links among (and the drivers of) four descriptors commonly used to characterize discharge and active length dynamics in streams, namely the L ( Q ) relationship and the cumulative distributions of local persistency, flowrate and active length. The model demonstrates that the shape of the L ( Q ) law is defined by the cumulative distribution of the specific subsurface discharge capacity along the network, a finding which provides a clue for the parametrization of L ( Q ) relations in dynamic streams. Furthermore, we show that L ( Q ) laws can be constructed combining the streamflow distribution with disjoint active length data. Our framework links previously unconnected formulations for characterizing stream network dynamics, and offers a novel perspective to describe the scaling between wet length and discharge in rivers.
Keyphrases
  • machine learning