Water (Apr 2023)

Performance of Frequency-Corrected Precipitation in Ungauged High Mountain Hydrological Simulation

  • Hongyi Li,
  • Jiapei Ma,
  • Yaru Yang,
  • Liting Niu,
  • Xinyu Lu

DOI
https://doi.org/10.3390/w15081461
Journal volume & issue
Vol. 15, no. 8
p. 1461

Abstract

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Accurate precipitation data are essential for understanding hydrological processes in high mountainous regions with limited observations and highly variable precipitation events. While frequency-corrected precipitation data are expected to aid in understanding hydrological processes, its performance in ungauged high mountain hydrological simulation remains unclear. To clarify this issue, we conducted a numerical experiment that used reanalysis precipitation, frequency-corrected precipitation, and gridded precipitation to drive a distributed cold region hydrological model. We selected an ungauged basin in high mountain Asia (Manas River Basin in China) as the study area and employed a statistical parameter optimization method to avoid subjectivity in parameter selection. Our findings indicate that the frequency information from the few existing stations can aid in correcting the reanalysis precipitation data. The frequency correction approach can reduce the total volume of errors in the reanalysis precipitation data, especially when severe biases occur. Our findings show that frequency-corrected precipitation performs better in modeling discharge, runoff depth, and evaporation. Furthermore, the improvement in precipitation using frequency correction bears clear altitude differences, which implies that having more stations at different altitudes is necessary to measure precipitation accurately in similar areas. Our study provides a feasible flow for future precipitation preparation for similar ungauged high mountain areas. Frequency correction, instead of direct interpolation, may be a viable option for precipitation preparation. Our work has reference implications for future hydrological simulations in similar ungauged high mountains.

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