Hydrological Model Calibration in Data-Limited Catchments Using Non-Continuous Data Series with Different Lengths

7 pages•Published: September 20, 2018

Chuanzhe Li, Jia Liu, Fuliang Yu, Jiyang Tian, Yang Wang and Qingtai Qiu

Abstract

This paper evaluates the effects of calibration data series length on the performance of a hydrological model in data-limited catchments where data are non-continuous and fragmental. Non-continuous calibration periods were used for more independent streamflow data for SIMHYD model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.

Keyphrases: calibration data series length, data limited catchment, hydrological model, model performance, optimal parameter

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 1155-1161.

Links:	https://easychair.org/publications/paper/gLsG
	https://doi.org/10.29007/5hv1

BibTeX entry

@inproceedings{HIC2018:Hydrological_Model_Calibration_Data,
  author    = {Chuanzhe Li and Jia Liu and Fuliang Yu and Jiyang Tian and Yang Wang and Qingtai Qiu},
  title     = {Hydrological Model Calibration in Data-Limited Catchments Using Non-Continuous Data Series with Different Lengths},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {/publications/paper/gLsG},
  doi       = {10.29007/5hv1},
  pages     = {1155-1161},
  year      = {2018}}

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