3 Results
3.1 Characteristics of
publications
According to a total of 2,020 publications
about
uncertainties in hydrologic simulation and water resources management,
they were divided into three document types for the period of
1991–2018. The dominant document type was articles, which comprise 95%
of the total publications. Proceedings papers and reviews were ranked
the second and the third, respectively. Fig. 1 presents the number of
publication outputs for each year during the 28 years. The first
publication on the uncertainty in the hydrological system was published
in 1992. Over time, the publication outputs increased rapidly from 1 in
1991 to 390 in 2018, and they especially dramatically in the last five
years of the study period. This result thoroughly explains why research
on uncertainty in hydrological modeling and management has attracted
increasing attention from scholars.
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Please place Fig. 1 here
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3.2 Journal co-citation
analysis
Table 1 presents the top ten frequently quoted journals in the related
domain. The top-tier item was Journal of Hydrology with 1,430
publications, while Water Resources Research was in second place with a
record of 1,368. These two journals contributed to the majority, making
them the major source of research on uncertainty in the hydrological
system. At the Impact Factor (IF) level, Water Resources Research and
Hydrology & Earth System Sciences had higher IF values (4.361 and
4.256), which indicated a more substantial influence in the related
areas.
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Please place Table 1 here
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3.3 Country/territory and institution cooperation
analysis
The research on the uncertainty in hydrologic simulation and water
resources management has attracted global attention. Table 2 shows the
top ten productive countries/territories and institutions in detail. The
results demonstrated that the USA had the maximum journals for the
field, subsequently were China, Canada, and Australia. China was the
only developing country of these selected countries/territories.
Additionally,
the 590 publications from the first 10 organizations contributed to
29.2% of the total. The Chinese Academy of Sciences was the leading
institution, with 113 publications, followed by Beijing Normal
University (China) and the University of Regina (Canada).
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Please place Table 2 here
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To
obtain more collaboration information about countries and institutions,
the first 20 co-country/territory and co-institution networks were
presented in detail in Fig. 2. There
existed
widespread cooperation among these 20 countries/territories and
institutions. The US and China played critical roles in global
collaboration, especially for Canada and Australia. The relationships
between China and Canada, as well as the Chinese Academy of Sciences and
Beijing Normal University were notable. Additionally, some countries and
institutions should improve international influence with more
publications or closer collaboration in the related domain, such as
India and Tsinghua University.
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Please place Fig. 2 here
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3.4 Author co-citation analysis
Fig. 3 is an item density visualization showing the most influential
authors in
the
field of uncertainty in hydrological systems. The results indicate that
the most prominent scholars were Arnold
(United
States Department of Agriculture) in the hydrological modeling field
(Arnold et al. 1998). Arnold’s researches were broadly quoted by others,
with a citation frequency of 354. Beven (Lancaster University, England)
was the second influential researcher, followed by Wilby (University of
Derby, England), Huang (University of Regina, Canada), and Bergstrom
(Karolinska Institution, Sweden). We find that these representative
scholars have made significant contributions to the related domain and
were closely linked.
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Please place Fig. 3 here
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3.5 Reference co-citation
analysis
Usually, a publication with a citation burst indicates it gained wide
attention in its related scientific field. Furthermore, a new research
field may be discovered by recognizing a cluster with bursts (Yu, 2015).
As the top ten references were shown in Table
3,
the whole length signified the time horizon from 1991 to 2018, while the
red line indicates the citation burst. The first milestone paper in this
field since 1991 was about model evaluation in simulations with a burst
strength of 24.61 (Moriasi et al. 2007). The second item ranked by
bursts was a paper on the calibration and validation of SWAT, with a
burst strength of 18.6 (Arnold et al. 2012). As can be seen in this
study, eight references were related to the SWAT model. This indicates
that SWAT might be the most popular model for hydrological modeling and
water resources management.
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Please place Table 3 here
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3.6 Subject categories co-occurrence
analysis
The dual-map overlays incorporate with more than 10,000 scientific
journals from WOS (Chen and Leydesdorff, 2014). Fig. 4 shows the
visualization of publications between 1991 and 2018 on the topic of
uncertainty in hydrological systems, in which the coloured curves
demonstrate the process from left to right. We can see that the citing
and cited maps were separate for several research fields at different
positions. Each color of the cluster is labelled by the corresponding
field. The publications on uncertainty in hydrologic modeling and water
resources management primarily appear in three domains: the blue was
labelled with Ecology/Earth/Marine; while the yellow with
Veterinary/Animal/Science, and the red with
Mathematics/Systems/Mathematical. Publications in the discipline with
blue citation links are built from at least five disciplines from the
upper right corner of the map. Thus, research on the uncertainty in
hydrological systems is based on broad areas, from environmental science
to ecology, geology, mathematics, and chemistry.
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Please place Fig. 4 here
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3.7 Keywords co-word
analysis
The main function of the co-word study was to recognize hotspots and the
structure of a related scientific domain ((Liu, 2015; Xiang et al.,
2017). For this study, the first 20 items were chosen to construct a
co-occurrence network (Fig. 5). Each cross represents one kind of
keyword; the larger the size illustrates the higher frequency. From Fig.
5, we can obtain the following information that the “Uncertainty” got
the highest frequency of occurrence with 932, and it was followed by
“basin” with 584, “model” with 492, “management” with 447, and
“climate change” with 437. Other commonly used words included system
(229), simulation (228), impact (209), SWAT (191), and optimization
(186). Also, the centrality represented the importance of that node in
the network. Among the top 20 keywords, “water quality”,
“management” and “simulation” have high centrality values, which
indicate their critical positions in
the
field of uncertainty in hydrological systems. This result also indicates
research on the uncertainties related to water quality has received more
attention.
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Please place Fig. 5 here
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3.8 Document co-citation
analysis
3.8.1 Research cluster
analysis
The
reference relationship reflects the objective law of scientific
development and intellectual structure (Yin et al. 2006). To further
determine the distribution rule of references, the top 20 references
between 1991 and 2018 were picked out. As shown in Fig. 6, the central
clusters for the domain of uncertainty in hydrologic modeling and water
resources management were demonstrated. The high modularity of 0.76
indicating a clear definition for the field (Chen, 2006; Chen, 2017; Yu,
2015). All of these references were separated into 96 clusters, 14 of
which had labels. In general, this study field on the uncertainty in
hydrologic modeling and water resources management includes many
aspects, such as management objects (water resource and water quality),
measures (stochastic programming and statistical learning), simulation
(SWAT), technique (GIS), and study areas (China and Canada). Thus,
uncertainty in hydrological modeling and integrated management has been
a relatively mature study area in recent years.
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Please place Fig. 6 here
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3.8.2 Timeline view of typical
clusters
A timeline visualization depicts the temporal progress for each
set. The top ten distinct clusters
along horizontal timelines are displayed in
Fig.
7. The three most cited references can be acquired under each timeline.
As shown in Fig. 7, individual cluster had long period of approximately
20 years, while a short time for several clusters. The SWAT model for
clusters #0 and #6 spans from 1994 to 2016 and a series of essential
achievements were obtained between 2005 and 2015. Cluster #4 on
uncertain management lasts 16 years and remains active. In contrast,
cluster #7 on the GIS-based model ends in 2007, which indicates new
specialties found in relevant research with other forms.
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Please place Fig. 7 here
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