1 Introduction
Hydrologic process is influenced by various aspects, including landuse
type, land surface conditions, and climate and meteorological
conditions.
Hydrological
processes are affected by several elements such as landuse type, surface
conditions, climatic and meteorological conditions, which vary spatially
and temporally (Akter and Babel, 2012; Cho, 2016; Pereira, 2016; Wang,
2016; Xue, 2018). As the hydrological model becomes more complicated and
the sources of required data are more diverse, the uncertainty in
hydrological simulation and prediction becomes increasingly prominent.
As a result, the prediction of water availability and integrated
watershed management becomes a necessary and challenging issue
restricted by the implementation of water shortages (Fonseca, 2014; Ma,
2017; Xia, 2011). Meanwhile, the enormous complexities associated with
human-environmental interactions make it even more challenging to
develop reliable models and schemes to support effective water resources
management.
Several scholars have previously applied stochastic analysis and fuzzy
mathematics to study the uncertainty in water resources systems.
Generally, there are three aspects of uncertainties in hydrological
modeling: systematic bias of model input, uncertainty parameters, and
structural uncertainty of hydrologic models (Montanari Brath, 2004;
Moriasi, 2007; Wu, 2015; Yin, 2006).
The most extensively studied aspect
is parameter uncertainty.
The
Generalized likelihood uncertainty estimation (GLUE) and Bayesian were
usually used to evaluate the uncertainty of model parameters (Beven et
al., 1992; Beven et al., 2001; Krzysztofowicz 1999). Both the GLUE and
Bayesian methods estimate parameter uncertainty based on likelihood
functions (Blasone, 2008; Bouda, 2012; Mantovan and Todini, 2006;
Vazquez, 2009).
As
there exist lots of uncertainties for water resources management, the
decision-makers are usually confronted with challenges to satisfy
numerous or contradictory requests (Du et al. 2013; Li, 2009). The
stochastic and fuzzy mathematical programming methods have been adopted
by various researchers to address such uncertainties (Huang, 2000; Guo,
2010; Li et al., 2011, 2014, 2018). Due to the uncertainties and
complexities of research on hydrologic simulation and water resources
management,
it is essential to keep up with the scientific structure and frontier in
a certain domain of science.
Although methodologies developed in previous studies can be effective in
addressing various uncertainties in hydrological modeling and water
resources management, very little analysis has been done from a
scientometric and bibliometric perspective. Furthermore, no previous
review has provided the development process and the structural
relationship of scientific knowledge through visual maps in this field.
Therefore, we will use CiteSpace, a graphical tool on account of
collaboration, co-citation, and co-occurrence networks,
to
provide an appreciated, appropriate, and elastic perspective to explore
the new emerging trends and recognize critical evidence Provide
valuable, timely, repeatable, and flexible methods to track the
development of emerging trends and identify critical evidence (Chen,
2004; Garcia-Lillo et al., 2016; Merigo, 2017; Wang, 2016). The process
of a systematic review on uncertainty analysis and quantification in
hydrological modeling and water resources management using the
visualization software.
The main objectives for this research are to supply cooperation,
co-citation, and co-occurrence networks with related references obtained
through the Web of Science (WOS) Core Collection.
First, the most creative scholars
were recognized from the viewpoint of countries and institutions.
Second, we will construct a distributed network from the viewpoint of
articles, authors, and journals. Third, we will disclose the chief types
and main subjects by co-occurrence analysis from the perspective of
keywords and classes. According to these analyses, the visual research
in this related field was conducted, the knowledge characteristics,
intellectual structure, and research fronts were inspected deeply from
different perspectives of time and space.