Water Resources Management

, Volume 33, Issue 3, pp 1103–1121 | Cite as

A Recursive Approach to Long-Term Prediction of Monthly Precipitation Using Genetic Programming

  • Suning Liu
  • Haiyun ShiEmail author


Precipitation is regarded as the basic component of the global hydrological cycle. This study develops a recursive approach to long-term prediction of monthly precipitation using genetic programming (GP), taking the Three-River Headwaters Region (TRHR) in China as the study area. The daily precipitation data recorded at 29 meteorological stations during 1961–2014 are collected, among which the data during 1961–2000 are for calibration and the remaining data are for validation. To develop this approach, first, the preliminary estimations of annual precipitation are computed based on a statistical method. Second, the percentage of the monthly precipitation for each month of a year is calculated as the mean monthly precipitation divided by the mean annual precipitation during the study period, and then the preliminary estimation of monthly precipitation for each month of a year is obtained. Third, since GP can be used to improve the prediction results through establishing the relationship of the observations with the preliminary estimations at the past and current times, it is adopted to improve the preliminary estimations. The calibration and validation results reveal that the recursive approach involving GP can provide the more accurate predictions of monthly precipitation. Finally, this approach is used to predict the monthly precipitation over the TRHR till 2050. Overall, the proposed method and the obtained results will enhance our understanding and facilitate future studies regarding the long-term prediction of precipitation in such regions.


Monthly precipitation Recursive approach Long-term prediction Genetic programming Three-River Headwaters Region 



This study was supported by the Natural Science Foundation of Qinghai Province funded project (No. 2017-ZJ-911), Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control (No. 2017B030301012), and State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control. The authors are also grateful to Editor, Associate Editor, and the two anonymous reviewers who offered the insightful comments leading to improvement of this paper.

Compliance with Ethical Standards

Conflict of Interest Statement



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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and EngineeringSouthern University of Science and TechnologyShenzhenChina
  2. 2.Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control, School of Environmental Science and EngineeringSouthern University of Science and TechnologyShenzhenChina
  3. 3.Department of Civil EngineeringThe University of Hong KongHong KongChina
  4. 4.State Key Laboratory of Plateau Ecology and AgricultureQinghai UniversityXiningChina

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