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.