Advertisement

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

  • Suning Liu
  • Haiyun ShiEmail author
Article
  • 49 Downloads

Abstract

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.

Keywords

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

Notes

Acknowledgments

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

None.

References

  1. Babovic V, Keijzer M (2000) Genetic programming as a model induction engine. J Hydroinf 2(1):35–60CrossRefGoogle Scholar
  2. Barnston AG, Smith TM (1996) Specification and prediction of global surface temperature and precipitation from global SST using CCA. J Clim 9:2660–2697CrossRefGoogle Scholar
  3. Barros R, Isidoro D, Aragüés R (2011) Long-term water balances in La Violada irrigation district (Spain): I. sequential assessment and minimization of closing errors. Agric Water Manag 102:35–45CrossRefGoogle Scholar
  4. Benestad RE (2013) Association between trends in daily rainfall percentiles and the global mean temperature. J Geophys Res-Atmos 118(19):10802–10810CrossRefGoogle Scholar
  5. Brassel KE, Reif D (1979) A procedure to generate Thiessen polygons. Geogr Anal 11:289–303CrossRefGoogle Scholar
  6. Cao LG, Pan SM (2014) Changes in precipitation extremes over the “Three-River Headwaters” region, hinterland of the Tibetan plateau, during 1960-2012. Quat Int 321:105–115CrossRefGoogle Scholar
  7. Chadalawada J, Havlicek V, Babovic V (2017) A genetic programming approach to system identification of rainfall-runoff models. Water Resour Manag 31(12):3975–3992CrossRefGoogle Scholar
  8. Chardon J, Hingray B, Favre A-C (2018) An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France. Hydrol Earth Syst Sci 22:265–286CrossRefGoogle Scholar
  9. Chen J, Shi HY, Sivakumar B, Peart MR (2016) Population, water, food, energy and dams. Renew Sust Energ Rev 56:18–28CrossRefGoogle Scholar
  10. China Meteorological Administration (2016) Daily meteorological observation data sets of China. http://data.cma.gov.cn/data/detail/dataCode/SURF_CLI_CHN_MUL_DAY_V3.0.html
  11. Chow VT, Maidment DR, Mays LW (1988) Applied hydrology. McGraw-Hill, New YorkGoogle Scholar
  12. Chu HJ (2012) Assessing the relationships between elevation and extreme precipitation with various durations in southern Taiwan using spatial regression models. Hydrol Process 26(21):3174–3181CrossRefGoogle Scholar
  13. Claußnitzer A, Névir P (2009) Analysis of quantitative precipitation forecasts using the dynamic state index. Atmos Res 94(4):694–703CrossRefGoogle Scholar
  14. Cramer NL (1985) A representation for the adaptive generation of simple sequential programs. In: John J (ed) Proc of an inter Conf on genetic algorithms and the applications Grefenstette. Carnegie Mellon University, PittsburghGoogle Scholar
  15. Duan AM, Hu J, Xiao ZX (2013) The Tibetan plateau summer monsoon in the CMIP5 simulations. J Clim 26(19):7747–7766CrossRefGoogle Scholar
  16. Fallah-Mehdipour E, Haddad OB, Mariño MA (2012) Real-time operation of reservoir system by genetic programming. Water Resour Manag 26(14):4091–4103CrossRefGoogle Scholar
  17. Garbrecht J, Van Liew M, Brown GO (2004) Trends in precipitation, streamflow, and evapotranspiration in the Great Plains of the United States. J Hydrol Eng 9(5):360–367CrossRefGoogle Scholar
  18. Gaur S, Deo MC (2008) Real-time wave forecasting using genetic programming. Ocean Eng 35:1166–1172CrossRefGoogle Scholar
  19. Gosling SN, Arnell NW (2011) Simulating current global river runoff with a global hydrological model: model revisions, validation, and sensitivity analysis. Hydrol Process 25:1129–1145CrossRefGoogle Scholar
  20. Groisman PY, Knight RW, Easterling DR, Karl TR, Hegerl GC, Razuvaev VN (2005) Trends in intense precipitation in the climate record. J Clim 18:1326–1350CrossRefGoogle Scholar
  21. Immerzeel WW, van Beek LPH, Bierkens MFP (2010) Climate change will affect the Asian water towers. Science 328:1382–1385CrossRefGoogle Scholar
  22. Intergovernmental Panel on Climate Change (IPCC) (2013) Summary for policymakers. In: Stocker TF et al (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  23. Khu ST, Liong SY, Babovic V, Madsen H, Muttil N (2001) Genetic programming and its application in real-time runoff forecasting. J Am Water Resour Assoc 37(2):439–451CrossRefGoogle Scholar
  24. Kim J, Oh H-S, Lim Y, Kang H-S (2017) Seasonal precipitation prediction via data-adaptive principal component regression. Int J Climatol 37:75–86CrossRefGoogle Scholar
  25. Kisi O, Cimen M (2011) A wavelet-support vector machine conjunction model for monthly streamflow forecasting. J Hydrol 399:132–140CrossRefGoogle Scholar
  26. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, CambridgeGoogle Scholar
  27. Koza JR (1994) Genetic programming II: automatic discovery of reusable programs. MIT Press, CambridgeGoogle Scholar
  28. Kulligowski RJ, Barros AP (1998) Localized precipitation from a numerical weather prediction model using artificial neural networks. Weather Forecast 13:1195–1205CrossRefGoogle Scholar
  29. Landman WA, Beraki A, DeWitt D, Lötter D (2014) SST prediction methodologies and verification considerations for dynamical mid-summer rainfall forecasts for South Africa. Water SA 40:615–622CrossRefGoogle Scholar
  30. Lee T, Ouarda TBMJ (2010) Long-term prediction of precipitation and hydrologic extremes with nonstationary oscillation processes. J Geophys Res Atmos 115:D13107.  https://doi.org/10.1029/2009JD012801 CrossRefGoogle Scholar
  31. Li JY, Li TJ, Liu SN, Shi HY (2018) An efficient method for mapping high-resolution global river discharge based on the algorithms of drainage network extraction. Water 10(4):533.  https://doi.org/10.3390/w10040533 CrossRefGoogle Scholar
  32. Liang LQ, Li LJ, Liu CM, Cuo L (2013) Climate change in the Tibetan plateau three Rivers source region: 1960-2009. Int J Climatol 33:2900–2916CrossRefGoogle Scholar
  33. Liong SY, Gautam TR, Khu ST et al (2002) Genetic programming: a new paradigm in rainfall runoff modeling. J Am Water Resour Assoc 38(3):705–718CrossRefGoogle Scholar
  34. Liu SN, Chui TFM (2018) Impacts of different rainfall patterns on hyporheic zone under transient conditions. J Hydrol 561:598–608CrossRefGoogle Scholar
  35. Liu XD, Yin ZY (2002) Sensitivity of east Asian monsoon climate to the uplift of the Tibetan plateau. Palaeogeogr Palaeoclimatol Palaeoecol 183(3–4):223–245CrossRefGoogle Scholar
  36. Muttil N, Lee JHW (2005) Genetic programming for analysis and real-time prediction of coastal algal blooms. Ecol Model 189(3–4):363–376CrossRefGoogle Scholar
  37. Naoum S, Tsanis IK (2004) Orographic precipitation modeling with multiple linear regression. J Hydrol Eng 9(2):79–102CrossRefGoogle Scholar
  38. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part 1 - a discussion of principles. J Hydrol 10(3):282–290CrossRefGoogle Scholar
  39. Nordin P, Banzhaf W (1997) Real time control of a Khepera robot using genetic programming. Control Cybern 26(3):533–561Google Scholar
  40. Ortiz-García EG, Salcedo-Sanz S, Casanova-Mateo C (2014) Accurate precipitation prediction with support vector classifiers: a study including novel predictive variables and observational data. Atmos Res 139:128–136CrossRefGoogle Scholar
  41. Park Y-Y, Buizza R, Leutbecher M (2008) TIGGE: preliminary results on comparing and combining ensembles. European Centre for Medium-Range Weather Forecasts, ReadingGoogle Scholar
  42. Partal T, Cigizoglu HK (2009) Prediction of daily precipitation using wavelet-neural networks. Hydrol Sci J 54:234–246CrossRefGoogle Scholar
  43. Richardson D (2005) The THORPEX Interactive Grand Global Ensemble (TIGGE). Geophysical Research Abstracts 7: Abstract EGU05-A-02815Google Scholar
  44. Shi HY, Fu XD, Chen J et al (2014) Spatial distribution of monthly potential evaporation over mountainous regions: case of the Lhasa River basin, China. Hydrol Sci J 59(10):1856–1871CrossRefGoogle Scholar
  45. Shi HY, Li TJ, Wei JH et al (2016) Spatial and temporal characteristics of precipitation over the Three-River Headwaters region during 1961-2014. J Hydrol Reg Stud 6:52–65CrossRefGoogle Scholar
  46. Shi HY, Li TJ, Wei JH (2017) Evaluation of the gridded CRU TS precipitation dataset with the point raingauge records over the Three-River Headwaters region. J Hydrol 548:322–332CrossRefGoogle Scholar
  47. Silverman D, Dracup JA (2000) Artificial neural networks and long-range precipitation prediction in California. J Appl Meteorol 39:57–66CrossRefGoogle Scholar
  48. Singh VP (1988) Hydrologic systems: watershed modeling. Prentice Hall, New JerseyGoogle Scholar
  49. Thiessen AJ, Alter JC (1911) Precipitation averages for large areas. Mon Weather Rev 39:1082–1984CrossRefGoogle Scholar
  50. Tong LG, Xu XL, Fu Y, Li S (2014) Wetland changes and their responses to climate change in the “Three-River Headwaters” Region of China since the 1990s. Energies 7:2515–2534CrossRefGoogle Scholar
  51. Wang GS, Xia J, Chen J (2009) Quantification of effects of climate variations and human activities on runoff by a monthly water balance model: a case study of the Chaobai River basin in northern China. Water Resour Res 45(7).  https://doi.org/10.1029/2007WR006768
  52. Wedge DC, Das A, Dost R et al (2009) Real-time vapour sensing using an OFET-based electronic nose and genetic programming. Sensors Actuators B Chem 143(1):365–372CrossRefGoogle Scholar
  53. Wei H, Li JL, Liang TG (2005) Study on the estimation of precipitation resources for rainwater harvesting agriculture in semi-arid land of China. Agric Water Manag 71:33–45CrossRefGoogle Scholar
  54. Westra S, Alexander LV, Zwiers FW (2013) Global increasing trends in annual maximum daily precipitation. J Clim 26(11):3904–3918CrossRefGoogle Scholar
  55. Xi Y, Miao CY, Wu JW et al (2018) Spatiotemporal changes in extreme temperature and precipitation events in the Three-Rivers Headwater Region, China. J Geophys Res-Atmos 123.  https://doi.org/10.1029/2017JD028226
  56. Xu CC, Liu P, Wang W, Zhang Y (2016) Real-time identification of traffic conditions prone to injury and non-injury crashes on freeways using genetic programming. J Adv Transp 50(5):701–716CrossRefGoogle Scholar
  57. Xue T, Xu J, Guan Z et al (2017) An assessment of the impact of ATMS and CrIS data assimilation on precipitation prediction over the Tibetan plateau. Atmos Meas Tech 10:2517–2531CrossRefGoogle Scholar
  58. Yi XS, Li GS, Yin YY (2013) Spatio-temporal variation of precipitation in the Three-River Headwater Region from 1961 to 2010. J Geogr Sci 23(3):447–464CrossRefGoogle Scholar

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

Personalised recommendations