Attributing runoff changes to climate variability and human activities: Uncertainty analysis using four monthly water balance models

Li, Shuai; Xiong, Lihua; Li, Hongyi; Leung, Lai-Yung; Demissie, Yonas

doi:10.1007/s00477-015-1083-8

Attributing runoff changes to climate variability and human activities: Uncertainty analysis using four monthly water balance models

Journal Article · Mon Jan 04 04:00:00 EST 2016 · Stochastic Environmental Research and Risk Assessment

DOI:https://doi.org/10.1007/s00477-015-1083-8· OSTI ID:1700688

Li, Shuai ^[1]; Xiong, Lihua ^[2]; Li, Hongyi ^[1]; ^[1]; Demissie, Yonas ^[3]

BATTELLE (PACIFIC NW LAB)
Wuhan University
Washington State University

Hydrological simulations to delineate the impacts of climate variability and human activities are subjected to uncertainties related to both parameter and structure of the hydrological models. To analyze the impact of these uncertainties on the model performance and to yield more reliable simulation results, a global calibration and multimodel combination method that integrates the Shuffled Complex Evolution Metropolis (SCEM) and Bayesian Model Averaging (BMA) of four monthly water balance models was proposed. The method was applied to the Weihe River Basin (WRB), the largest tributary of the Yellow River, to determine the contribution of climate variability and human activities to runoff changes. The change point, which was used to determine the baseline period (1956-1990) and human-impacted period (1991-2009), was derived using both cumulative curve and Pettitt’s test. Results show that the combination method from SCEM provides more skillful deterministic predictions than the best calibrated individual model, resulting in the smallest uncertainty interval of runoff changes attributed to climate variability and human activities. This combination methodology provides a practical and flexible tool for attribution of runoff changes to climate variability and human activities by hydrological models.

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE

DOE Contract Number:: AC05-76RL01830

OSTI ID:: 1700688

Report Number(s):: PNNL-SA-104608

Journal Information:: Stochastic Environmental Research and Risk Assessment, Journal Name: Stochastic Environmental Research and Risk Assessment Journal Issue: 1 Vol. 30

Country of Publication:: United States

Language:: English

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