Quantifying the Interactions of Noah‐MP Land Surface Processes on the Simulated Runoff Over the Tibetan Plateau

Author:

Li Jianduo12ORCID,Gan Yanjun3ORCID,Zhang Guo12ORCID,Gou Jiaojiao4ORCID,Lu Xingjie5ORCID,Miao Chiyuan6ORCID

Affiliation:

1. CMA Earth System Modeling and Prediction Centre China Meteorological Administration Beijing China

2. State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing China

3. Department of Civil Engineering University of Texas at Arlington Arlington TX USA

4. Department of Geography, Faculty of Arts and Sciences Beijing Normal University Zhuhai China

5. School of Atmospheric Sciences Sun Yat‐sen University Guangzhou China

6. State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science Beijing Normal University Beijing China

Abstract

AbstractThe quantification of uncertainties in runoff over the Tibetan Plateau (TP), simulated by land surface models (LSMs), is of paramount importance for effective water resources management within this region. However, the interactions of land surface processes on simulated runoff, where the effectiveness of one process depends on the chosen scheme for another, have rarely been studied. To address this gap, we conducted ensemble simulations with the Noah‐MP (Noah with multiparameterization) LSM by varying the optional parameterization schemes of six land surface processes and quantified the sensitivities of the simulated runoff to these processes. Results showed that the simulated runoff over the TP was most sensitive to the RUN (runoff‐groundwater) process. The interplay of RUN and FRO (frozen soil permeability) accounted for up to 30% of the variation in the annual mean surface runoff in the TP's permafrost regions. The interactions of RUN and VEG (dynamic vegetation) on summer and autumn subsurface runoff exceeded 10% in the southeast TP. In regions where these interactions among land surface processes significantly affected simulated runoff, we observed elevated model errors and reduced model controllability. Therefore, this study underscores the imperative need to categorize land regions based on the interactions of land surface processes as a foundational step toward enhancing the performance of LSMs. Prioritizing improvements in model physics should be particularly directed toward regions marked by high interactions.

Publisher

American Geophysical Union (AGU)

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