skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Classification of hydrological parameter sensitivity and evaluation of parameter transferability across 431 US MOPEX basins

Abstract

The Community Land Model (CLM) represents physical, chemical, and biological processes of the terrestrial ecosystems that interact with climate across a range of spatial and temporal scales. As CLM includes numerous sub-models and associated parameters, the high-dimensional parameter space presents a formidable challenge for quantifying uncertainty and improving Earth system predictions needed to assess environmental changes and risks. This study aims to evaluate the potential of transferring hydrologic model parameters in CLM through sensitivity analyses and classification across watersheds from the Model Parameter Estimation Experiment (MOPEX) in the United States. The sensitivity of CLM-simulated water and energy fluxes to hydrological parameters across 431 MOPEX basins are first examined using an efficient stochastic sampling-based sensitivity analysis approach. Linear, interaction, and high-order nonlinear impacts are all identified via statistical tests and stepwise backward removal parameter screening. The basins are then classified accordingly to their parameter sensitivity patterns (internal attributes), as well as their hydrologic indices/attributes (external hydrologic factors) separately, using a Principal component analyses (PCA) and expectation-maximization (EM) –based clustering approach. Similarities and differences among the parameter sensitivity-based classification system (S-Class), the hydrologic indices-based classification (H-Class), and the Koppen climate classification systems (K-Class) are discussed. Within each S-class with similar parameter sensitivitymore » characteristics, similar inversion modeling setups can be used for parameter calibration, and the parameters and their contribution or significance to water and energy cycling may also be more transferrable. This classification study provides guidance on identifiable parameters, and on parameterization and inverse model design for CLM but the methodology is applicable to other models. Inverting parameters at representative sites belonging to the same class can significantly reduce parameter calibration efforts.« less

Authors:
; ; ; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1331739
Report Number(s):
PNNL-SA-105598
Journal ID: ISSN 0022-1694; KP1703020
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Journal of Hydrology
Additional Journal Information:
Journal Volume: 536; Journal ID: ISSN 0022-1694
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
Community Land Model; MOPEX; parameter identifiability; parameter transferability; basin classification

Citation Formats

Ren, Huiying, Hou, Zhangshuan, Huang, Maoyi, Bao, Jie, Sun, Yu, Tesfa, Teklu K., and Leung, Lai-Yung R. Classification of hydrological parameter sensitivity and evaluation of parameter transferability across 431 US MOPEX basins. United States: N. p., 2016. Web. doi:10.1016/j.jhydrol.2016.02.042.
Ren, Huiying, Hou, Zhangshuan, Huang, Maoyi, Bao, Jie, Sun, Yu, Tesfa, Teklu K., & Leung, Lai-Yung R. Classification of hydrological parameter sensitivity and evaluation of parameter transferability across 431 US MOPEX basins. United States. doi:10.1016/j.jhydrol.2016.02.042.
Ren, Huiying, Hou, Zhangshuan, Huang, Maoyi, Bao, Jie, Sun, Yu, Tesfa, Teklu K., and Leung, Lai-Yung R. Sat . "Classification of hydrological parameter sensitivity and evaluation of parameter transferability across 431 US MOPEX basins". United States. doi:10.1016/j.jhydrol.2016.02.042.
@article{osti_1331739,
title = {Classification of hydrological parameter sensitivity and evaluation of parameter transferability across 431 US MOPEX basins},
author = {Ren, Huiying and Hou, Zhangshuan and Huang, Maoyi and Bao, Jie and Sun, Yu and Tesfa, Teklu K. and Leung, Lai-Yung R.},
abstractNote = {The Community Land Model (CLM) represents physical, chemical, and biological processes of the terrestrial ecosystems that interact with climate across a range of spatial and temporal scales. As CLM includes numerous sub-models and associated parameters, the high-dimensional parameter space presents a formidable challenge for quantifying uncertainty and improving Earth system predictions needed to assess environmental changes and risks. This study aims to evaluate the potential of transferring hydrologic model parameters in CLM through sensitivity analyses and classification across watersheds from the Model Parameter Estimation Experiment (MOPEX) in the United States. The sensitivity of CLM-simulated water and energy fluxes to hydrological parameters across 431 MOPEX basins are first examined using an efficient stochastic sampling-based sensitivity analysis approach. Linear, interaction, and high-order nonlinear impacts are all identified via statistical tests and stepwise backward removal parameter screening. The basins are then classified accordingly to their parameter sensitivity patterns (internal attributes), as well as their hydrologic indices/attributes (external hydrologic factors) separately, using a Principal component analyses (PCA) and expectation-maximization (EM) –based clustering approach. Similarities and differences among the parameter sensitivity-based classification system (S-Class), the hydrologic indices-based classification (H-Class), and the Koppen climate classification systems (K-Class) are discussed. Within each S-class with similar parameter sensitivity characteristics, similar inversion modeling setups can be used for parameter calibration, and the parameters and their contribution or significance to water and energy cycling may also be more transferrable. This classification study provides guidance on identifiable parameters, and on parameterization and inverse model design for CLM but the methodology is applicable to other models. Inverting parameters at representative sites belonging to the same class can significantly reduce parameter calibration efforts.},
doi = {10.1016/j.jhydrol.2016.02.042},
journal = {Journal of Hydrology},
issn = {0022-1694},
number = ,
volume = 536,
place = {United States},
year = {2016},
month = {2}
}