2 Methodology and Materials
2.1 Data sources
The literature data adopted for this research were acquired from two
common and influential scientific databases, i.e. the Science Citation
Index Expanded (SCI-E) and the Social Science Citation Index (SSCI) of
WOS (Wu, 2015). These following terms were used to retrieve related
publications: TS = (“uncertain*” AND “water” AND (“modeling” OR
“simulat*”) AND (“basin” OR “watershed”) AND (“manage*” OR
“allocation”)) (“TS” represents an article subject and “*”
represents a fuzzy search). Under these conditions, a total of 2,020
documents with full bibliographic records were retrieved and downloaded
as related research from 1991 to 2018.
2.2 Statistical methods
CiteSpace is a popular and widespread tool in the bibliometric study,
visualizing frontier knowledge, and constructing a network in the domain
of science (Chen 2004). In the view maps generated by CiteSpace, nodes
represent items (each item has a keyword, author, journal, and country
information), and links represent a co-citation structure. Additionally,
every node illustrates three types of colours
and
different thicknesses to represent its centrality value (Chen, 2006; Xie
et al., 2015)).
For
example, the red ring on a node demonstrates a burst discovered while
the purple rim indicates high betweenness centrality (≥0.1), which
represents the significance of the whole network (Freeman, 1979; Liu et
al., 2015; Chen, 2010).
The following landscape views were acquired from publications in
1991-2018, and the 50 most cited journals were used to build a knowledge
network originally. Afterwards, each network was generated and enclosed
2,020 references. The time horizon from 1991 to 2018 was divided into
three periods (i.e., 1991-1999, 2000-2009, and 2010-2018). Five types
involving author, institution, country, keyword, and cited reference
were fixed according to the research need. A few parameters were set as
defaults. Then, the collaboration network and co-occurrence network were
analyzed based on the frequency, burst, and centrality to identify
research characteristics and trends in uncertainty watershed modeling
and management.