Streamflows over a West African Basin from the ALMIP2 Model Ensemble

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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Aaron Boone CNRM-GAME/Météo-France, Toulouse, France

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Christophe Peugeot HydroSciences Montpellier, Montpellier, France

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ALMIP2 Working Group

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Abstract

Comparing streamflow simulations against observations has become a straightforward way to evaluate a land surface model’s (LSM) ability in simulating water budget within a catchment. Using a mesoscale river routing scheme (RRS), this study evaluates simulated streamflows over the upper Ouémé River basin resulting from 14 LSMs within the framework of phase 2 of the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project (ALMIP2). The ALMIP2 RRS (ARTS) has been used to route LSM outputs. ARTS is based on the nonlinear Muskingum–Cunge method and a simple deep water infiltration formulation representing water-table recharge as previously observed in that region. Simulations are performed for the 2005–08 period during which ground observations are largely available. Experiments are designed using different ground-based rainfall datasets derived from two interpolation methods: the Thiessen technique and a combined kriging–Lagrangian methodology. LSM-based total runoff (TR) averages vary from 0.07 to 1.97 mm day−1, while optimal TR was estimated as ~0.65 mm day−1. This highly affected the RRS parameterization and streamflow simulations. Optimal Nash–Sutcliffe coefficients for LSM-averaged streamflows varied from 0.66 to 0.92, depending on the gauge station. However, individual LSM performances show a wider range. A more detailed rainfall distribution provided by the kriging–Lagrangian methodology resulted in overall better streamflow simulations. The early runoff generation related to reduced infiltration rates during early rainfall events features as one of the main reasons for poor LSM performances.

Please see the appendix for the full list of working group members.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Augusto Getirana, augusto.getirana@nasa.gov

Abstract

Comparing streamflow simulations against observations has become a straightforward way to evaluate a land surface model’s (LSM) ability in simulating water budget within a catchment. Using a mesoscale river routing scheme (RRS), this study evaluates simulated streamflows over the upper Ouémé River basin resulting from 14 LSMs within the framework of phase 2 of the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project (ALMIP2). The ALMIP2 RRS (ARTS) has been used to route LSM outputs. ARTS is based on the nonlinear Muskingum–Cunge method and a simple deep water infiltration formulation representing water-table recharge as previously observed in that region. Simulations are performed for the 2005–08 period during which ground observations are largely available. Experiments are designed using different ground-based rainfall datasets derived from two interpolation methods: the Thiessen technique and a combined kriging–Lagrangian methodology. LSM-based total runoff (TR) averages vary from 0.07 to 1.97 mm day−1, while optimal TR was estimated as ~0.65 mm day−1. This highly affected the RRS parameterization and streamflow simulations. Optimal Nash–Sutcliffe coefficients for LSM-averaged streamflows varied from 0.66 to 0.92, depending on the gauge station. However, individual LSM performances show a wider range. A more detailed rainfall distribution provided by the kriging–Lagrangian methodology resulted in overall better streamflow simulations. The early runoff generation related to reduced infiltration rates during early rainfall events features as one of the main reasons for poor LSM performances.

Please see the appendix for the full list of working group members.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Augusto Getirana, augusto.getirana@nasa.gov
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