Investigating the Dynamic Influence of Hydrological Model Parameters on Runoff Simulation Using Sequential Uncertainty Fitting-2-Based Multilevel-Factorial-Analysis Method

Abstract

Hydrological model parameters are generally considered to be simplified representations that characterize hydrologic processes. Therefore, their influence on runoff simulations varies with climate and catchment conditions. To investigate the influence, a three-step framework is proposed, i.e., a Latin hypercube sampling (LHS-OAT) method multivariate regression model is used to conduct parametric sensitivity analysis; then, the multilevel-factorial-analysis method is used to quantitatively evaluate the individual and interactive effects of parameters on the hydrologic model output. Finally, analysis of the reasons for dynamic parameter changes is performed. Results suggest that the difference in parameter sensitivity for different periods is significant. The soil bulk density (SOL_BD) is significant at all times, and the parameter Soil Convention Service (SCS) runoff curve number (CN2) is the strongest during the flood period, and the other parameters are weaker in different periods. The interaction effects of CN2 and SOL_BD, as well as effective hydraulic channel conditions (CH_K2) and SOL_BD, are obvious, indicating that soil bulk density can impact the amount of loss generated by surface runoff and river recharge to groundwater. These findings help produce the best parameter inputs and improve the applicability of the model.

An Integrated Methodology to Analyze the Total Nitrogen Accumulation in a Drinking Water Reservoir Based on the SWAT Model Driven by CMADS: A Case Study of the Biliuhe Reservoir in Northeast China

“…In recent years, various atmospheric reanalysis datasets such as the JRA-55, the ERA-Interim, the CFSR, and the MERRA have been developed and used globally [19][20][21][22]. The China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) are the latest East Asia atmospheric reanalysis datasets developed by Dr. Xianyong Meng from the China Agricultural University (CAU), which have attracted widespread attention [23][24][25]. The CMADS series of datasets have been verified in different basins of China and Korea and have performed well in the Heihe basin, Manas River Basin, Qinghai-Tibet Plateau, Han River Basin and so on, however the application of CMADS mainly focuses on hydrological simulation and there are few studies about non-point source pollution simulation driven by the datasets, especially in the cold regions of Northeast China [26][27][28][29][30][31][32][33].…”

Establishment and Evaluation of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS)

“…For example, researchers from Korea used CMADS to drive SWAT in the Han River Basin in the Korean Peninsula with a satisfactory performance [30], and the results were acceptable. In China, scientists used CMADS to drive the hydro-meteorological model for the Qinghai-Tibet Plateau [31], the Yangtze River Basin [32], the Yellow River Basin [33][34][35], the Pearl River Basin [36], and the inland arid areas in Northwest China [37,38]. The above studies show that CMADS has been widely verified in many regions of East Asia.…”

Moisture Distribution in Sloping Black Soil Farmland during the Freeze–Thaw Period in Northeastern China

“…We The soil temperature data of the sunny slope is more correlated with CMADS-ST, which coincides with the selection of the sunny slope in the wild slope farmland as the test Site 2. The monitored hourly data can be found in the freeze-thaw cycle with the time of day, melting during the day, and freezing at night (refer to Figure 3, the sunny slope soil experienced 39 freeze-thaw cycles, and the shady slope soil 47 cycles); however, the data period is short and the monitoring points are limited; CMADS-ST daily data can only see a large freeze-thaw cycle in the winter of the yearly cycle (refer to Figure 12); however, CMADS has a lot of spatiotemporal data, applied to a wide range of areas with a long series [11,31,33,[38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54].Using fixed-point monitoring of refined soil temperature, soil moisture content, precipitation, temperature, nitrogen and phosphorus of nutrients, spatiotemporal CMADS data can be better promoted and applied.…”

“…In this paper, the distribution of soil moisture in sloping black soil farmland during freeze-thaw cycles in Northeastern China is discussed. Soil moisture has great impacts on food security, human CMADS has been used successfully in different basins, such as the Heihe River Basin, Juntanghu Basin, Manas River Basin, and Han River Basin, indicating good applicability of CMADS in East Asia [38][39][40][41][42][43][44][45][46][47][48][49][50]. However, the relative studies mainly focused on the surface hydrological process and meteorological data, whereas the application of the CMADS-ST to soil temperature and soil moisture distribution has been rarely studied, especially in the black soil zone [51][52][53][54].…”