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Article

Reconstruction of Hydrometeorological Data Using Dendrochronology and Machine Learning Approaches to Bias-Correct Climate Models in Northern Tien Shan, Kyrgyzstan

1
Mountain Societies Research Institute, University of Central Asia, Bishkek 720001, Kyrgyzstan
2
Institute of Water Problems and Hydropower, National Academy of Science, Bishkek 720033, Kyrgyzstan
*
Author to whom correspondence should be addressed.
Academic Editor: Alexander Shiklomanov
Water 2022, 14(15), 2297; https://doi.org/10.3390/w14152297
Received: 17 June 2022 / Revised: 19 July 2022 / Accepted: 20 July 2022 / Published: 24 July 2022
(This article belongs to the Section Water and Climate Change)
Tree-ring-width chronologies for 33 samples of Picea abies (L.) Karst. were developed, and a relationship between tree growth and hydrometeorological features was established and analyzed. Precipitation, temperature, and discharge records were extrapolated to understand past climate trends to evaluate the accuracy of global climate models (GCMs). Using Machine Learning (ML) approaches, hydrometeorological records were reconstructed/extrapolated back to 1886. An increase in the mean annual temperature (Tmeana) increased the mean annual discharge (Dmeana) via glacier melting; however, no temporal trends in annual precipitation were detected. For these reconstructed climate data, root-mean-square error (RMSE), Taylor diagrams, and Kling–Gupta efficiency (KGE) were used to evaluate and assess the robustness of GCMs. The CORDEX REMO models indicated the best performance for simulating precipitation and temperature over northern Tien Shan; these models replicated historical Tmena and Pa quite well (KGE = 0.24 and KGE = 0.24, respectively). Moreover, the multi-model ensembles with selected GCMs and bias correction can significantly increase the performance of climate models, especially for mountains region where small-scale orographic effects abound. View Full-Text
Keywords: annual discharge trends; CMIP6; CORDEX; data reconstruction; Kyrgyzstan; tree rings; Tien Shan annual discharge trends; CMIP6; CORDEX; data reconstruction; Kyrgyzstan; tree rings; Tien Shan
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MDPI and ACS Style

Isaev, E.; Ermanova, M.; Sidle, R.C.; Zaginaev, V.; Kulikov, M.; Chontoev, D. Reconstruction of Hydrometeorological Data Using Dendrochronology and Machine Learning Approaches to Bias-Correct Climate Models in Northern Tien Shan, Kyrgyzstan. Water 2022, 14, 2297. https://doi.org/10.3390/w14152297

AMA Style

Isaev E, Ermanova M, Sidle RC, Zaginaev V, Kulikov M, Chontoev D. Reconstruction of Hydrometeorological Data Using Dendrochronology and Machine Learning Approaches to Bias-Correct Climate Models in Northern Tien Shan, Kyrgyzstan. Water. 2022; 14(15):2297. https://doi.org/10.3390/w14152297

Chicago/Turabian Style

Isaev, Erkin, Mariiash Ermanova, Roy C. Sidle, Vitalii Zaginaev, Maksim Kulikov, and Dogdurbek Chontoev. 2022. "Reconstruction of Hydrometeorological Data Using Dendrochronology and Machine Learning Approaches to Bias-Correct Climate Models in Northern Tien Shan, Kyrgyzstan" Water 14, no. 15: 2297. https://doi.org/10.3390/w14152297

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