Evaluating Global Streamflow Simulations by a Physically Based Routing Model Coupled with the Community Land Model

Hong-Yi Li Pacific Northwest National Laboratory, Richland, Washington

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L. Ruby Leung Pacific Northwest National Laboratory, Richland, Washington

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Augusto Getirana NASA Goddard Space Flight Center, Greenbelt, and Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

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Maoyi Huang Pacific Northwest National Laboratory, Richland, Washington

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Huan Wu NASA Goddard Space Flight Center, Greenbelt, and Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

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Yubin Xu Pacific Northwest National Laboratory, Richland, Washington

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Jiali Guo Pacific Northwest National Laboratory, Richland, Washington

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Nathalie Voisin Pacific Northwest National Laboratory, Richland, Washington

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Abstract

Accurately simulating hydrological processes such as streamflow is important in land surface modeling because they can influence other land surface processes, such as carbon cycle dynamics, through various interaction pathways. This study aims to evaluate the global application of a recently developed Model for Scale Adaptive River Transport (MOSART) coupled with the Community Land Model, version 4 (CLM4). To support the global implementation of MOSART, a comprehensive global hydrography dataset has been derived at multiple resolutions from different sources. The simulated runoff fields are first evaluated against the composite runoff map from the Global Runoff Data Centre (GRDC). The simulated streamflow is then shown to reproduce reasonably well the observed daily and monthly streamflow at over 1600 of the world’s major river stations in terms of annual, seasonal, and daily flow statistics. The impacts of model structure complexity are evaluated, and results show that the spatial and temporal variability of river velocity simulated by MOSART is necessary for capturing streamflow seasonality and annual maximum flood. Other sources of the simulation bias include uncertainties in the atmospheric forcing, as revealed by simulations driven by four different climate datasets, and human influences, based on a classification framework that quantifies the impact levels of large dams on the streamflow worldwide.

Corresponding author address: Hong-Yi Li, Pacific Northwest National Laboratory, 902 Battelle Blvd., P.O. Box 999, MSIN K9-33, Richland, WA 99352. E-mail: hongyi.li@pnnl.gov

Abstract

Accurately simulating hydrological processes such as streamflow is important in land surface modeling because they can influence other land surface processes, such as carbon cycle dynamics, through various interaction pathways. This study aims to evaluate the global application of a recently developed Model for Scale Adaptive River Transport (MOSART) coupled with the Community Land Model, version 4 (CLM4). To support the global implementation of MOSART, a comprehensive global hydrography dataset has been derived at multiple resolutions from different sources. The simulated runoff fields are first evaluated against the composite runoff map from the Global Runoff Data Centre (GRDC). The simulated streamflow is then shown to reproduce reasonably well the observed daily and monthly streamflow at over 1600 of the world’s major river stations in terms of annual, seasonal, and daily flow statistics. The impacts of model structure complexity are evaluated, and results show that the spatial and temporal variability of river velocity simulated by MOSART is necessary for capturing streamflow seasonality and annual maximum flood. Other sources of the simulation bias include uncertainties in the atmospheric forcing, as revealed by simulations driven by four different climate datasets, and human influences, based on a classification framework that quantifies the impact levels of large dams on the streamflow worldwide.

Corresponding author address: Hong-Yi Li, Pacific Northwest National Laboratory, 902 Battelle Blvd., P.O. Box 999, MSIN K9-33, Richland, WA 99352. E-mail: hongyi.li@pnnl.gov
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