Application of multivariate recursive nesting bias correction, multiscale wavelet entropy and AI-based models to improve future precipitation projection in upstream of the Heihe River, Northwest China
- 214 Downloads
Accurate projection of future precipitation is a major challenge due to the uncertainties arising from the atmospheric predictors and the inherent biases that exist in the global circulation models. In this study, we employed multivariate recursive nesting bias correction (MRNBC) and multiscale wavelet entropy (MWE) to reduce the bias and improve the projection of future (i.e., 2006–2100) precipitation with artificial intelligence (AI)-based data-driven models. Application of the developed method and the subsequent analyses are performed based on representative concentration pathway (RCP) scenarios: RCP4.5 and RCP8.5 of eight Coupled Model Intercomparison Project Phase-5 (CMIP5) Earth system models for the upstream of the Heihe River. The results confirmed the MRNBC and MWE were important statistical approaches prudent in simulation performance improvement and projection uncertainty reduction. The AI-based methods were superior to linear regression method in precipitation projection. The selected CMIP5 outputs showed agreement in the projection of future precipitation under two scenarios. The future precipitation under RCP8.5 exhibited a significantly increasing trend in relative to RCP4.5. In the future, the precipitation will experience an increase by 15–19% from 2020 to 2050 and by 21–33% from 2060 to 2090.
The authors thank the anonymous reviewers for their valuable comments and suggestions, which significantly improved this manuscript.
This study was supported by the National Key R&D Program of China (2017YFC0404302, 2016YFC0400908) and the Key Research Program of Frontier Sciences, CAS (QYZDJ-SSW-DQC031).
- Dabanlı İ, Şen Z (2017) Precipitation projections under GCMs perspective and Turkish Water Foundation (TWF) statistical downscaling model procedures. Theor Appl ClimatolGoogle Scholar
- Deo RC, Tiwari MK, Adamowski JF, Quilty MJ (2016) Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model. Stoch Env Res Risk A:1–30Google Scholar
- Draper N, Smith H (1981) Applied regression analysis, 709 pp. Wiley, New YorkGoogle Scholar
- Eghdamirad S, Johnson F, Sharma A (2017) Using second-order approximation to incorporate GCM uncertainty in climate change impact assessments. Clim Chang:1–16Google Scholar
- Hassan Z, Harun S (2012) Application of statistical downscaling model for long lead rainfall prediction in Kurau River catchment of Malaysia. Malays J Civil Eng (MJCE) 24:1–12Google Scholar
- Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feedforward neural networks, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541), pp. 985–990; vol. 982Google Scholar
- Jin X, Han J (2016) K-means clustering. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning and data mining. Springer US, Boston, pp 1–3Google Scholar
- Macqueen J (1967) Some methods for classification and analysis of multivariate observations, Proc. of Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297Google Scholar
- Montgomery DC, Peck EA, Vining GG (2012) Introduction to linear regression analysis. WileyGoogle Scholar
- Salvi K, Kannan S, Ghosh S (2013) High-resolution multisite daily rainfall projections in India with statistical downscaling for climate change impacts assessment. J Geophys Res: Atmos 118:3557–3578Google Scholar
- Santos JA, Belo-Pereira M, Fraga H, Pinto JG (2016) Understanding climate change projections for precipitation over Western Europe with a weather typing approach. J Geophys Res: Atmos 121:1170–1189Google Scholar
- Sillmann J, Kharin VV, Zhang X, Zwiers FW, Bronaugh D (2013) Climate extremes indices in the CMIP5 multimodel ensemble: part 1. Model evaluation in the present climate. J Geophys Res: Atmos 118:1716–1733Google Scholar
- Yang L, Feng Q, Yin Z, Deo RC, Wen X, Si J, Li C (2017a) Separation of the climatic and land cover impacts on the flow regime changes in two watersheds of Northeastern Tibetan Plateau. Adv Meteorol 2017:15Google Scholar