Study of the impact of climate change on the runoff of the Varzob River Basin in Tajikistan
Article Sidebar
Main Article Content
Abstract
The Varzob River is one of the main tributaries of the Kofarnihon River and the main water supply of the city of Dushanbe. Changes in water balance, exacerbated by climate change, threaten water security of the whole region. This study aims to study the impact of climate change on the runoff of Varzob River basin using SRM modelling, to simulate the runoff and make predictions. Increasing temperature trends were identified across multiple meteorological stations within the basin in the past decades. Additionally, historical water discharge and precipitation levels indicated weak downward trends. The simulated monthly discharge closely matched the observed values throughout both the calibration and validation periods. On average, the coefficient of determination (R2) value of the simulated annual runoff across all years was 0.925. The average Nash-Sutcliffe model efficiency coefficient (NSE) and the average volume difference (Dv) were 0.8912 and 1.022% respectively during the validation period. According to a simulated climate change scenario, precipitation increase by 10mm leads to rise in runoff by up to 9.57 %. Similarly, another scenario revealed that temperature increase by 2.5 0C leads to an increase in runoff by as high as 5.47 %. The peaks in discharge do not shift under examined climate change scenario. The highest change in discharge occurred during the summer months under both climate change scenarios. The SRM model highlights that climate change significantly impacts precipitation and runoff.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
By submitting a manuscript to Evidence in Earth Science, the author(s) agree to the following terms:
-
Copyright Ownership:
Authors retain copyright to their submitted work. Upon acceptance for publication, the authors grant Evidence in Earth Science a license to publish the work under the Creative Commons Attribution 4.0 International License (CC-BY 4.0). -
Open Access License (CC-BY 4.0):
The articles published in Evidence in Earth Science are open access and distributed under the Creative Commons Attribution 4.0 International License (CC-BY 4.0). This means that:- Users are free to share, copy, modify, distribute, and display the work, including for commercial purposes, as long as they provide appropriate attribution to the original author(s).
- The work can be adapted or used in derivative works, but proper credit must be given.
For more detailed information on the licensing terms, please refer to the Creative Commons Attribution 4.0 International License (CC-BY 4.0).
References
Bolch, T., 2007, Climate change and glacier retreat in northern Tien Shan (Kazakhstan/Kyrgyzstan) using remote sensing data: Global and Planetary Change, 56(1-2), 1-12, https://doi.org/10.1016/j.gloplacha.2006.07.009. DOI: https://doi.org/10.1016/j.gloplacha.2006.07.009
Butt, M. J. & Bilal, M., 2011, Application of snowmelt runoff model for water resource management: Hydrological Processes. 25(24), 3735-3747, https://doi.org/10.1002/hyp.8099. DOI: https://doi.org/10.1002/hyp.8099
Chen, F., Huang, W. & Jin, L., 2012, Characteristics and spatial differences of precipitation in arid region of Central Asia under the background of global warming: Chin. Sci. Earth Sci. 41, 1647-1657.
Christiane, B., Alex, K., Carolyne, D., Matthias, B., Emmanuelle, B., Viktor, N. & Otto, S., 2009, Climate Change in Central Asia: A Visual Synthesis, based on official country information from the communications to the UNFCCC, scientific papers and news reports: Zoi Environment Network, Imprimerie Nouvelle Gonnet, F-01303 Belley, France, https://archive.zoinet.org/web/sites/default/files/publications/CCCA_dec2009_0.pdf.
Choudhury, A., & Kosorok, M. R., 2020, Missing data imputation for classification problems: arXiv preprint arXiv: 2002.10709.
Feng, R., Yu, R., Zheng, H. & Gan, M., 2018, Spatial and temporal variations in extreme temperature in Central Asia: International Journal of Climatology, 38, e388-e400, https://doi.org/10.1002/joc.5379. DOI: https://doi.org/10.1002/joc.5379
Feng, Y., et al., 2021, Imputation of missing PM2.5 observations in a network of air quality monitoring stations by a new kNN method. Atmosphere, 13(11), 1934. DOI: https://doi.org/10.3390/atmos13111934
García-Laencina P. J. et al., 2003, K nearest neighbours with mutual information for simultaneous classification and missing data imputation: Neurocomputing, 72. 1483-1493. DOI: https://doi.org/10.1016/j.neucom.2008.11.026
Gulahmadov, N., Chen, Y., Gulakhmadov, M., Satti, Z., Naveed, M., Davlyatov, R., Ali, S. & Gulakhmadov, A., 2023, Assessment of temperature, precipitation, and snow cover at different altitudes of the Varzob River Basin in Tajikistan: Applied Sciences, 13, 5583. DOI: https://doi.org/10.3390/app13095583
Gulayozov, M., Amirzoda, O. & Kobuli, Z., 2023, Water resources and the integral assessment of the water Quality of the Varzob river: Water Resources, 556.3, 1-12.
Gulayozov, M. & Fazylov, A. R., 2022a, Geographic-hydrological and environmental conditions of the Varzob river basin: Water Resources, 556.5, 1-14.
Gulayozov, M. & Fazylov, A. R., 2022b, Rational use and protection of water resources in the Varzob river basin: Water Resources, 556.5, 45-53.
Gulayozov, M. S., 2022, Geographical, hydrological and ecological assessment of the state of the Varzob river basin: National Academy of Sciences of Tajikistan, 1-60.
Hu, Z., Zhang, C., Hu, Q. & Tian, H., 2014, Temperature changes in Central Asia from 1979 to 2011 based on multiple datasets: Journal of Climate, 27, 1143-1167, https://doi.org/10.1175/JCLI-D-13-00064.1. DOI: https://doi.org/10.1175/JCLI-D-13-00064.1
Hock, Regine, et al., 2019, "High mountain areas." IPCC special report on the ocean and cryosphere in a changing climate. H.-O. Pörtner, DC Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, NM Weyer, 131-202.
Ich, I., Sok, T., Kaing, V., Try, S., Chan, R. & Oeurng, C., 2022, Climate change impact on water balance and hydrological extremes in the Lower Mekong Basin: a case study of Prek Thnot River Basin, Cambodia: Journal of Water and Climate Change, 13, 2911-2939. DOI: https://doi.org/10.2166/wcc.2022.051
IPCC., 2021, Climate Change. The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, https://www.ipcc.ch/report/ar6/wg1/.
Klein Tank, A. M., Peterson, T., Quadir, D., Dorji, S., Zou, X., Tang, H., Santhosh, K., Joshi, U., Jaswal, A. & Kolli, R., 2006, Changes in daily temperature and precipitation extremes in central and south Asia: Journal of Geophysical Research: Atmospheres, 111, https://doi.org/10.1029/2005JD006316. DOI: https://doi.org/10.1029/2005JD006316
Kushwaha, N. L., Rajput, J., Elbeltagi, A., Elnaggar, A. Y., Sena, D. R., Vishwakarma, D. K., Mani, I. & Hussein, E. E., 2021, Data intelligence model and meta-heuristic algorithms-based pan evaporation modelling in two different agro-climatic zones: a case study from Northern India: Atmosphere, 12, 1654, https://doi.org/10.3390/atmos12121654. DOI: https://doi.org/10.3390/atmos12121654
Labat, D., 2010, Cross wavelet analyses of annual continental freshwater discharge and selected climate indices: Journal of Hydrology, 385, 269-278, https://doi.org/10.1016/j.jhydrol.2010.02.029. DOI: https://doi.org/10.1016/j.jhydrol.2010.02.029
Li, J., & Zhang, X., 2013, Application of K-Nearest Neighbor imputation for missing data in climate studies: Environmental Modelling & Software, 48, 110-118.
Li, T., Peng, J., Au, T. F. & Li, J., 2024, April–September minimum temperature reconstruction based on Sabina tibetica ring-width chronology in the central eastern Tibetan Plateau, China, Journal of Forestry Research, 35, 37. DOI: https://doi.org/10.1007/s11676-023-01682-7
Liu, J., Xue, B., Sun, W. & Guo, Q., 2020, Water balance changes in response to climate change in the upper Hailar River Basin, China, Hydrology Research, 51, 1023-1035, https://doi.org/10.2166/nh.2020.032 DOI: https://doi.org/10.2166/nh.2020.032
Maillard, É., McConkey, B. G., Luce, M. S., Angers, D. A. & Fan, J., 2018, Crop rotation, tillage system, and precipitation regime effects on soil carbon stocks over 1 to 30 years in Saskatchewan, Canada: Soil and Tillage Research, 177, 97-104, https://doi.org/10.1016/j.still.2017.12.001 DOI: https://doi.org/10.1016/j.still.2017.12.001
Mannig, B., Müller, M., Starke, E., Merkenschlager, C., Mao, W., Zhi, X., Podzun, R., Jacob, D. & Paeth, H., 2013, Dynamical downscaling of climate change in Central Asia, Global and planetary change. 110, 26-39, https://doi.org/10.1016/j.gloplacha.2013.05.008 DOI: https://doi.org/10.1016/j.gloplacha.2013.05.008
Martinec, J., Rango, A. & Roberts, R., 2008a, Snowmelt Runoff Model (SRM) User’s Manual: New Mexico State University Press, New Mexico, USA, 19-39
Martinec, J., Rango, A. & Roberts, R., 2008b, Snowmelt Runoff Model (SRM) user’s manual, updated edition for Windows: Spec. Rep, 100
Müller, M., & Wessels, H., 2018, Evaluation of imputation methods for meteorological data: Atmospheric Science Letters, 19(5), 250-257. https://doi.org/10.1002/asl.830 DOI: https://doi.org/10.1002/asl.830
Organization, W. M., 1992, Simulated real-time intercomparison of hydrological models: Secretariat of the World meteorological organization.
Pujianto, U., Wibawa, A. P., & Akbar, M. I., 2019, K-nearest neighbor (k-NN) based missing data imputation: In 2019 5th International Conference on Science in Information Technology (ICSITech), 83-88. IEEE. DOI: https://doi.org/10.1109/ICSITech46713.2019.8987530
Pan, Liqiang, and Jianzhong Li., 2010, "K-nearest neighbor based missing data estimation algorithm in wireless sensor networks: Wireless Sensor Network, 2. 115-122, doi: 10.4236/wsn.2010.22016. DOI: https://doi.org/10.4236/wsn.2010.22016
Pan, S., Tian, H., Dangal, S. R., Zhang, C., Yang, J., Tao, B., Ouyang, Z., Wang, X., Lu, C. & Ren, W., 2014, Complex spatiotemporal responses of global terrestrial primary production to climate change and increasing atmospheric CO2 in the 21st century: PloS one, 9, e112810, https://doi.org/10.1371/journal.pone.0112810. DOI: https://doi.org/10.1371/journal.pone.0112810
Pan, X., Wang, W., Liu, T., Huang, Y., Maeyer, P. D., Guo, C., Ling, Y. & Akmalov, S., 2020, Quantitative detection and attribution of groundwater level variations in the Amu Darya Delta: Water. 12, 2869, https://doi.org/10.3390/w12102869. DOI: https://doi.org/10.3390/w12102869
Patle, G., Singh, D., Sarangi, A., Rai, A., Khanna, M. & Sahoo, R., 2015, Time series analysis of groundwater levels and projection of future trend: Journal of the Geological Society of India, 85, 232-242. DOI: https://doi.org/10.1007/s12594-015-0209-4
Prasad, V. H. & Roy, P. S., 2010, Estimation of snowmelt runoff in Beas Basin, India[J]. Geocarto International. (2005) 20, 41-47. DOI: https://doi.org/10.1080/10106040508542344
Profile, U. c. Property Rights and Resource Governance: Tajikistan. U.S. Agency for International Development, U.S WATER PARTNERSHIP.
Sorg, Annina, Tobias Bolch, Markus Stoffel, Olga Solomina, and Martin Beniston., 2012, Climate change impacts on glaciers and runoff in Tien Shan (Central Asia), Nature Climate Change 2, no. 10. 725-731. DOI: https://doi.org/10.1038/nclimate1592
Stillinger, T., Alan, H., Bales, R., & Painter, T., 2019, Landsat-based snow cover monitoring in complex terrain: Limitations from cloud cover and temporal resolution. Remote Sensing of Environment, 224, 139–154. https://doi.org/10.1016/j.rse.2019.01.026 DOI: https://doi.org/10.1016/j.rse.2019.01.026
Singh P, Kumar N., 1997, Effect of orography on precipitation in the western Himalayan region: Journal of Hydrology, 1;199(1-2):183-206, https://doi.org/10.1016/S0022-1694(96)03222-2. DOI: https://doi.org/10.1016/S0022-1694(96)03222-2
Shukla, R., Kumar, P., Vishwakarma, D. K., Ali, R., Kumar, R. & Kuriqi, A., 2021, Modeling of stage-discharge using back propagation ANN-, ANFIS-, and WANN-based computing techniques: Theoretical and applied climatology, 1-23. DOI: https://doi.org/10.21203/rs.3.rs-696059/v1
Tahir, A. A., Chevallier, P., Arnaud, Y., Neppel, L. & Ahmad, B., 2011, Modeling snowmelt-runoff under climate scenarios in the Hunza River basin, Karakoram Range, Northern Pakistan, Journal of Hydrology, 409, 104-117, https://doi.org/10.1016/j.jhydrol.2011.08.035. DOI: https://doi.org/10.1016/j.jhydrol.2011.08.035
Glaciers of Tajikistan., 2003, The Main Directorate for Hydrometeorology and Observations of the Environment. Ministry of Nature Protection of the Republic of Tajikistan , 1-35, http://www.cawater-info.net/library/rus/glaciers_tj.pdf.
Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., & Altman, R. B., 2001, Missing value estimation methods for DNA microarrays: Bioinformatics, 17(6), 520-525, https://doi.org/10.1093/bioinformatics/17.6.520. DOI: https://doi.org/10.1093/bioinformatics/17.6.520
Vishwakarma, D. K., Pandey, K., Kaur, A., Kushwaha, N., Kumar, R., Ali, R., Elbeltagi, A. & Kuriqi, A., 2022, Methods to estimate evapotranspiration in humid and subtropical climate conditions: Agricultural Water Management, 261, 107378, https://doi.org/10.1016/j.agwat.2021.107378. DOI: https://doi.org/10.1016/j.agwat.2021.107378
WMO (World Meteorological Organization)., 1986, Intercomparison of models of snowmelt runoff.
Xie, S., Du, J., Zhou, X., Zhang, X., Feng, X., Zheng, W., Li, Z. & Xu, C.-Y., 2018, A progressive segmented optimization algorithm for calibrating time-variant parameters of the snowmelt runoff model (SRM): Journal of Hydrology, 566, 470-483, https://doi.org/10.1016/j.jhydrol.2018.09.030. DOI: https://doi.org/10.1016/j.jhydrol.2018.09.030
Yao, J., & Chen, Y., 2015, Trend analysis of temperature and precipitation in the Syr Darya Basin in Central Asia: Theoretical and applied climatology, 120, 521-531. DOI: https://doi.org/10.1007/s00704-014-1187-y
Yao, M., Tang, H., & Huang, G., 2024, Inter‐model uncertainty in projecting precipitation changes over central Asia under global warming: Geophysical Research Letters, 51(24), e2024GL111989, https://doi.org/10.1029/2024GL111989. DOI: https://doi.org/10.1029/2024GL111989
Zhang, C., & Ren, W., 2017, Complex climatic and CO2 controls on net primary productivity of temperate dryland ecosystems over central Asia during 1980–2014. Journal of Geophysical Research: Biogeosciences, 122(9), 2356-2374, https://doi.org/10.1002/2017JG003781. DOI: https://doi.org/10.1002/2017JG003781
Zhang, Y., et al., 2020, Machine-learning-based imputation method for filling missing values in ground meteorological observation data: Sensors, 16(9), 422. DOI: https://doi.org/10.3390/a16090422
Zhu, X., & Liu, H., 2015, Combining KNN and regression for missing data imputation: International Journal of Computer Science and Network Security, 15(8), 9-16.