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Merging ground and satellite-based precipitation data sets for improved hydrological simulations in the Xijiang River basin of China

  • Tao Chen
  • Liliang RenEmail author
  • Fei Yuan
  • Tiantian Tang
  • Xiaoli Yang
  • Shanhu Jiang
  • Yi Liu
  • Chongxu Zhao
  • Limin Zhang
Original Paper
  • 104 Downloads

Abstract

Watershed management, disaster warning, and hydrological modeling require accurate spatiotemporal precipitation data sets. This paper presents a comprehensive assessment of a gauge-satellite-based precipitation product that merges the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) satellite precipitation product (SPP) and ground precipitation data at 134 rain gauges in the Xijiang River basin, South China. Two regression-based schemes, principal component regression (PCR) and multiple linear regression (MLR), were used to combine the gauge-based precipitation data and PERSIANN-CDR SPP and were compared at daily and annual scales. Furthermore, a hydrological model Variable Infiltration Capacity was used to calculate streamflow and to evaluate the impact of four different precipitation interpolation methods on the results of the hydrological model at the daily scale. The result shows that the PCR method performs better than MLR and can effectively eliminate the interpolation anomalies caused by terrain differences between observation points and surrounding areas. On the whole, the combined scheme consistently exhibits good performance and thus serves as a suitable tool for producing high-resolution gauge-and satellite-based precipitation datasets.

Keywords

PERSIANN-CDR Precipitation data merge Principal component regression Multiple linear regression Hydrological model 

Notes

Acknowledgements

This study was sponsored by the National Key Research and Development Program (under Grant No. 2016YFA0601500) approved by the Ministry of Science and Technology of China, the National Natural Science Foundation of China (Grant Nos. 51779070 and 4173075), the National Natura Science Foundation of China (Grant No. 51579066), the Fundamental Research Funds for the Central Universities (Grant No. 2019B10414).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Tao Chen
    • 1
    • 2
  • Liliang Ren
    • 1
    • 2
    Email author
  • Fei Yuan
    • 1
    • 2
  • Tiantian Tang
    • 2
  • Xiaoli Yang
    • 2
  • Shanhu Jiang
    • 2
  • Yi Liu
    • 2
  • Chongxu Zhao
    • 1
    • 2
  • Limin Zhang
    • 1
    • 2
  1. 1.State Key Laboratory of Hydrology-Water Resources and Hydraulic EngineeringHohai UniversityNanjingChina
  2. 2.College of Hydrology and Water ResourcesHohai UniversityNanjingChina

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