Journal article
Insights On Streamflow Predictability Across Scales Using Horizontal Visibility Graph Based Networks
Frontiers in water, Vol.2
12/06/2019
DOI: 10.3389/frwa.2020.00017
Abstract
Streamflow is a dynamical process that integrates water movement in space and
time within basin boundaries. The authors characterize the dynamics associated
with streamflow time series data from about seventy-one U.S. Geological Survey
(USGS) stream-gauge stations in the state of Iowa. They employ a novel approach
called visibility graph (VG). It uses the concept of mapping time series into
complex networks to investigate the time evolutionary behavior of dynamical
system. The authors focus on a simple variant of VG algorithm called horizontal
visibility graph (HVG). The tracking of dynamics and hence, the predictability
of streamflow processes, are carried out by extracting two key pieces of
information called characteristic exponent, {\lambda} of degree distribution
and global clustering coefficient, GC pertaining to HVG derived network. The
authors use these two measures to identify whether streamflow process has its
origin in random or chaotic processes. They show that the characterization of
streamflow dynamics is sensitive to data attributes. Through a systematic and
comprehensive analysis, the authors illustrate that streamflow dynamics
characterization is sensitive to the normalization, and the time-scale of
streamflow time-series. At daily scale, streamflow at all stations used in the
analysis, reveals randomness with strong spatial scale (basin size) dependence.
This has implications for predictability of streamflow and floods. The authors
demonstrate that dynamics transition through potentially chaotic to randomly
correlated process as the averaging time-scale increases. Finally, the temporal
trends of {\lambda} and GC are statistically significant at about 40% of the
total number of stations analyzed. Attributing this trend to factors such as
changing climate or land use requires further research.
Details
- Title: Subtitle
- Insights On Streamflow Predictability Across Scales Using Horizontal Visibility Graph Based Networks
- Creators
- Ganesh R Ghimire - University of IowaNavid Jadidoleslam - University of IowaWitold F Krajewski - University of IowaAnastasios A Tsonis - University of Wisconsin–Milwaukee
- Resource Type
- Journal article
- Publication Details
- Frontiers in water, Vol.2
- DOI
- 10.3389/frwa.2020.00017
- ISSN
- 2624-9375
- eISSN
- 2624-9375
- Language
- English
- Date published
- 12/06/2019
- Academic Unit
- Civil and Environmental Engineering; IIHR--Hydroscience and Engineering
- Record Identifier
- 9984202250702771
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