Hierarchical sensitivity analysis for a large-scale process-based hydrological model applied to an Amazonian watershed

Abstract

Abstract. Sensitivity analysis methods have recently received much attention for identifying important uncertainty sources (or uncertain inputs) and improving model calibrations and predictions for hydrological models. However, it is still challenging to apply the quantitative and comprehensive global sensitivity analysis method to complex large-scale process-based hydrological models (PBHMs) because of its variant uncertainty sources and high computational cost. Therefore, a global sensitivity analysis method that is capable of simultaneously analyzing multiple uncertainty sources of PBHMs and providing quantitative sensitivity analysis results is still lacking. In an effort to develop a new tool for overcoming these weaknesses, we improved the hierarchical sensitivity analysis method by defining a new set of sensitivity indices for subdivided parameters. A new binning method and Latin hypercube sampling (LHS) were implemented for estimating these new sensitivity indices. For test and demonstration purposes, this improved global sensitivity analysis method was implemented to quantify three different uncertainty sources (parameters, models, and climate scenarios) of a three-dimensional large-scale process-based hydrologic model (Process-based Adaptive Watershed Simulator, PAWS) with an application case in an ∼ 9000 km2 Amazon catchment. The importance of different uncertainty sources was quantified by sensitivity indices for two hydrologic outputs of interest: evapotranspiration (ET) and groundwater contribution to streamflow (QG). The results show that the parameters, especially the vadose zone parameters, are the most important uncertainty contributors for both outputs. In addition, the influence of climate scenarios on ET predictions is also important. Furthermore, the thickness of the aquifers is important for QG predictions, especially in main stream areas. These sensitivity analysis results provide useful information for modelers, and our method is mathematically rigorous and can be applied to other large-scale hydrological models.

Evaluating the effects of topography and land use change on hydrological signatures: a comparative study of two adjacent watersheds

“…Given the heterogeneity of the basin, effective auto-calibration was challenging, leading to a preference for manual calibration as a common practice for ISSHMs (Shi et al, 2014;Thornton et al, 2022;Brandhorst and Neuweiler, 2023). We leveraged previous uncertainty analysis and parameter sensitivity studies (Baroni et al, 2010;Song et al, 2015;Liu et al, 2020) to select the most crucial parameters for this hands-on calibration process. Monitoring data from the entire period were utilized for calibration, focusing on enhancing model performance.…”

Sensitivity Analysis of the Physical Hydrological Model CASC2D-SED in Arid Area

“…The runoff transport capacity is reduced due to the transport of suspended sediment, and the remaining runoff transport capacity is calculated by the following formula: (10) The remaining runoff transport capacity is used to transport sand bed erosion. The formula for calculating the volume of erosion with particle size I from the source pixel to the receiving pixel is as follows: (11) BMvol i is the volume of sand bed erosion with particle size i in the source pixel, cubic meters. Once both suspended and sand-bed erosions have been migrated, if a certain migration capacity is still retained, this part of the erosional migration force will erode the soil parent material.…”

“…Liu Haifan improved the hierarchical sensitivity analysis method by defining a new set of sensitivity indicators for the subdivision parameters. A new classification method and Latin hypercube sampling method are proposed to estimate these new sensitivity indicators [11] .…”

Comparative Assessment of Two Global Sensitivity Approaches Considering Model and Parameter Uncertainty

“…Sensitivity analysis (SA) offers key tools to this purpose (Borgonovo & Plischke, 2016; Liu et al., 2020; Saltelli et al., 2008). It enables us to improve our ability to (a) appraise relationships between model components (including model inputs, structures, parameters) and outputs of interest; (b) quantify the relative contributions of uncertain model components to the uncertainty associated with model outputs; and (c) guide possible simplification of a given interpretive model and appropriately allocate resources.…”