CMADS Verification
Applicability Assessment and Uncertainty Analysis of Multi-Precipitation Datasets for the Simulation
Verification

Hydrologic models are essential tools for understanding hydrologic processes, such as precipitation, which is a fundamental component of the water cycle. For an improved understanding and the evaluation of different precipitation datasets, especially their applicability for hydrologic modelling, three kinds of precipitation products, CMADS, TMPA-3B42V7 and gauge-interpolated datasets, are compared. Two hydrologic models (IHACRES and Sacramento) are applied to study the accuracy of the three types of precipitation products on the daily streamflow of the Lijiang River, which is located in southern China. The models are calibrated separately with different precipitation products, with the results showing that the CMADS product performs best based on the Nash–Sutcliffe efficiency, including a much better accuracy and better skill in capturing the streamflow peaks than the other precipitation products. The TMPA-3B42V7 product shows a small improvement on the gauge-interpolated product. Compared to TMPA-3B42V7, CMADS shows better agreement with the ground-observation data through a pixel-to-point comparison. The comparison of the two hydrologic models shows that both the IHACRES and Sacramento models perform well. The IHACRES model however displays less uncertainty and a higher applicability than the Sacramento model in the Lijiang River basin.View Full-Text


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Figure 7. Two-dimensional projections of the pairwise correlation of behavioral parameters for the Sacramento model using CMADS (above diagonal) and TMPA-3B42V7 (below diagonal) precipitation datasets. The heavy dots represent the location of the best objective function value obtained from the GLUE sample.


Ref:Guo, B.; Zhang, J.; Xu, T.; Croke, B.; Jakeman, A.; Song, Y.; Yang, Q.; Lei, X.; Liao, W. Applicability Assessment and Uncertainty Analysis of Multi-Precipitation Datasets for the Simulation of Hydrologic Models. Water 2018, 10, 1611.