Hydrological modeling has experienced rapid development and played a significant role in water resource management in recent decades. However, modeling uncertainties, which are propagated throughout model runs, may affect the credibility of simulation results and mislead management decisions. Therefore, analyzing and reducing uncertainty is of significant importance in providing greater confidence in hydrological simulations. To reduce and quantify parameter uncertainty, in this study, we attempted to introduce additional remotely sensed data (such as evapotranspiration (ET)) into a common parameter estimation procedure that uses observed streamflow only. We undertook a case study of an application of the Soil Water Assessment Tool in the Guijiang River Basin (GRB) in China. We also compared the effects of different combinations of parameter estimation algorithms (e.g., Sequential Uncertainty Fitting version 2, particle swarm optimization) on reduction in parameter uncertainty and improvement in modeling precision improvement. The results indicated that combining Sequential Uncertainty Fitting version 2 (SUFI-2) and particle swarm optimization (PSO) can substantially reduce the modeling uncertainty (reduction in the R-factor from 0.9 to 0.1) in terms of the convergence of parameter ranges and the aggregation of parameters, in addition to iterative optimization. Furthermore, the combined approaches ensured the rationality of the parameters’ physical meanings and reduced the complexity of the model calibration procedure. We also found the simulation accuracy of ET improved substantially after adding remotely sensed ET data. The parameter ranges and optimal parameter sets obtained by multi-objective calibration (using streamflow plus ET) were more reasonable and the Nash–Sutcliffe coefficient (NSE) improved more rapidly using multiple objectives, indicating a more efficient parameter optimization procedure. Overall, the selected combined approach with multiple objectives can help reduce modeling uncertainty and attain a reliable hydrological simulation. The presented procedure can be applied to any hydrological model.

Timeliness, budget consciousness, and quality are critical to the success of a project, and become increasingly challenging with increased project complexity. Five-dimensional building information modeling (BIM) integrates cost and schedule data with a 3D model, and enhances project management by addressing budgeting, timelines, and visualization simultaneously. However, a comprehensive assessment of 5D BIM’s impact on key performance indicators is currently lacking. This research aims to identify the critical factors influencing the adoption of 5D BIM and its impact on key project performance indicators. A thorough systematic literature review and qualitative analysis were conducted to achieve this goal. Relevant articles from the past decade (2014–2023) were examined from the Scopus and Web of Science databases, of which 222 were selected and screened using PRISMA procedures. This research found consistent and rapid updating of keywords, highlighting the dynamic evolution of 5D BIM and its expanding applications in the construction industry. Thirty critical factors affecting the adoption of 5D BIM were identified and categorized into the following six groups based on the technology–organization–environment (TOE) framework: technology, organization, environment, operator, project, and government policy. The 15 factors driving construction project performance in integrated 5D BIM were divided into cost, time, and quality performance based on key performance indicators. This review offers innovative insights into 5D BIM adoption, and can aid stakeholders in developing effective 5D BIM implementations.

Although 5D Building Information Modeling (5D BIM) offers potential for cost control, its implementation remains constrained by multiple barriers. This study examines the relative perceived importance of six factors associated with 5D BIM implementation and their direct associations with project cost performance among higher-tier general contractors in Guangdong Province, China. Survey data from 234 contractor organizations, each represented by a knowledgeable key informant, were analyzed using SPSS, PLS-SEM, and IPMA to rank the factors, estimate their direct associations, and identify managerial priorities. Environmental factors received the highest perceived-importance ranking. The structural model shows significant positive direct associations between project cost performance and four factors: technological, organizational, environmental, and government policy; government policy has the largest standardized path coefficient among the six predictors tested. Operational and project-related factors show no statistically significant direct associations. IPMA places organizational and environmental factors in the high-importance, high-performance area, whereas government policy has high importance but below-average performance. This first empirical test of the previously developed extended TOE framework distinguishes perceived implementation importance from direct cost–performance relevance and clarifies its theoretical boundary. These context-specific findings inform contractor management and policy implementation and support future validation across regions, contractor types, and stakeholder groups.