Evaluation and uncertainty attribution of the simulated streamflow from NoahMP-RAPID over a high-altitude mountainous basin

Abstract

Accurate hydrological simulations in high-altitude mountainous basins are important for optimizing reservoir operations in hydropower production and flood control. However, the simulations experience substantial uncertainties from the sparse precipitation measurements over complex mountainous topography and the imperfect hydrological process representations over the heterogeneous surface. At the Daduhe river basin, which is a high-altitude mountainous river basin located in the eastern slope of the Tibetan Plateau, daily streamflow over the 2-year period of 2014 and 2015 is simulated using the Noah land surface model with multi-parameterizations (NoahMP) and Routing Application for Parallel computation of Discharge (RAPID) coupled model driven by the Chinese Meteorological Forcing Dataset (CMFD). Two different runoff parameterization schemes of NoahMP are used for the simulations: SIMGM, which is derived from TOPMODEL and parameterizes surface runoff using water table depth, and NOAH, in which surface runoff is parameterized using soil moisture content based on the Philip infiltration model. The simulated streamflow is evaluated at two mainstream gauges on the Daduhe river, Tongjiezi and Longtoushi, in terms of Nash-Sutcliffe Efficiency (NSE), correlation coefficient, bias, and standard deviation. The uncertainty of the simulated streamflow is attributed to different runoff parameterization in NoahMP, the flow wave celerity parameter of RAPID, and the forcing CMFD precipitation data. Results show that the NOAH runoff parameterization scheme outperforms the SIMGM scheme. NOAH satisfactorily reproduced the observed streamflow anomaly and flood timing at the two mainstream gauges, and the correlation coefficients between the simulated and observed streamflow are above 0.85. The NSE for NOAH is approximately 0.3. The mathematical decomposition of NSE reveals that the relatively low NSE value is mainly attributed to the significant streamflow bias. Without the bias, NSE can be improved to approximately 0.7. The bias shows little dependency on the flow wave celerity parameter of RAPID and the runoff parameterization of NoahMP, whereas it closely corresponds to the forcing CMFD precipitation data bias. Comparing the estimated precipitation from Budykos curve and the observed streamflow, CMFD significantly underestimates the basin-averaged precipitation, which leads to the significant streamflow underestimation in the hydrological simulations. CMFD does not correctly represent the orographic effects on precipitation in the Daduhe river basin. Comparing the rain gauge measurements located in low-altitude river valleys, the CMFD precipitation is significant underestimated, suggesting a significantly underestimation in high-altitude precipitation. This study shows that streamflow bias can be reduced by densifying rain gauges at high altitudes. Despite the bias, the NoahMP RAPID coupled model can satisfactorily reproduce streamflow anomalies and flood timing in mountainous basins. As a dam model is already included, the coupled model can be further used to optimize reservoir operations for hydropower production and flood control.