气候变化领域集成服务门户(CCPortal): Hierarchical sensitivity analysis for a large-scale process-based hydrological model applied to an Amazonian watershed

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DOI	10.5194/hess-24-4971-2020
	Hierarchical sensitivity analysis for a large-scale process-based hydrological model applied to an Amazonian watershed
	Liu H.; Dai H.; Niu J.; Hu B.X.; Gui D.; Qiu H.; Ye M.; Chen X.; Wu C.; Zhang J.; Riley W.
发表日期	2020
ISSN	1027-5606
起始页码	4971
结束页码	4996
卷号	24 期号:10
英文摘要	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. © 2020 Author(s).
语种	英语
scopus关键词	Aquifers; Catchments; Climate models; Forecasting; Groundwater resources; Hydrogeology; Predictive analytics; Three dimensional computer graphics; Uncertainty analysis; Watersheds; Computational costs; Global sensitivity analysis; Hydrological modeling; Hydrological models; Large scale hydrological model; Latin hypercube sampling; Quantitative sensitivity; Uncertainty contributors; Sensitivity analysis; calibration; evapotranspiration; groundwater flow; hierarchical system; hydrological modeling; numerical method; parameterization; sensitivity analysis; streamflow; uncertainty analysis; watershed; Amazonia
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159280
作者单位	Liu, H., School of Water Resources and Environment, China University of Geosciences, Beijing, 100083, China; Dai, H., State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, 830011, China, Institute of Groundwater and Earth Sciences, Jinan University, Guangzhou, 510632, China; Niu, J., Institute of Groundwater and Earth Sciences, Jinan University, Guangzhou, 510632, China; Hu, B.X., Institute of Groundwater and Earth Sciences, Jinan University, Guangzhou, 510632, China; Gui, D., State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, 830011, China; Qiu, H., Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, United States; Ye, M., Department of Earth Ocean, and Atmospheric Science, Florida State University, Tallahassee, FL 32306, United States; Chen, X., Pacific Northwest National Laboratory, Richland, WA...
推荐引用方式 GB/T 7714	Liu H.,Dai H.,Niu J.,et al. Hierarchical sensitivity analysis for a large-scale process-based hydrological model applied to an Amazonian watershed[J],2020,24(10).
APA	Liu H..,Dai H..,Niu J..,Hu B.X..,Gui D..,...&Riley W..(2020).Hierarchical sensitivity analysis for a large-scale process-based hydrological model applied to an Amazonian watershed.Hydrology and Earth System Sciences,24(10).
MLA	Liu H.,et al."Hierarchical sensitivity analysis for a large-scale process-based hydrological model applied to an Amazonian watershed".Hydrology and Earth System Sciences 24.10(2020).