Next Article in Journal
Environmental Flows in the Lower Ebro River and Delta: Current Status and Guidelines for a Holistic Approach
Previous Article in Journal
Groundwater Storage Changes in the Major North African Transboundary Aquifer Systems during the GRACE Era (2003–2016)
Open AccessArticle

Parallel Hydrological Model Parameter Uncertainty Analysis Based on Message-Passing Interface

by 1, 2,*, 2,3 and 2,3
State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300350, China
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Science and Technology Innovation Center for Smart Water, Northeastern University, Shenyang 110819, China
Author to whom correspondence should be addressed.
Water 2020, 12(10), 2667;
Received: 17 August 2020 / Revised: 14 September 2020 / Accepted: 15 September 2020 / Published: 23 September 2020
(This article belongs to the Section Hydrology and Hydrogeology)
Parameter uncertainty analysis is one of the hot issues in hydrology studies, and the Generalized Likelihood Uncertainty Estimation (GLUE) is one of the most widely used methods. However, the scale of the existing research is relatively small, which results from computational complexity and limited computing resources. In this study, a parallel GLUE method based on a Message-Passing Interface (MPI) was proposed and implemented on a supercomputer system. The research focused on the computational efficiency of the parallel algorithm and the parameter uncertainty of the Xinanjiang model affected by different threshold likelihood function values and sampling sizes. The results demonstrated that the parallel GLUE method showed high computational efficiency and scalability. Through the large-scale parameter uncertainty analysis, it was found that within an interval of less than 0.1%, the proportion of behavioral parameter sets and the threshold value had an exponential relationship. A large sampling scale is more likely than a small sampling scale to obtain behavioral parameter sets at high threshold values. High threshold values may derive more concentrated posterior distributions of the sensitivity parameters than low threshold values. View Full-Text
Keywords: hydrological model; parameter uncertainty; GLUE; parallel computing; MPI hydrological model; parameter uncertainty; GLUE; parallel computing; MPI
Show Figures

Figure 1

MDPI and ACS Style

Yin, Z.; Liao, W.; Lei, X.; Wang, H. Parallel Hydrological Model Parameter Uncertainty Analysis Based on Message-Passing Interface. Water 2020, 12, 2667.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop