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Tytuł artykułu

Integrating advanced approaches for climate change impact assessment on water resources in arid regions

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Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This research addresses the growing complexity and urgency of climate change’s impact on water resources in arid regions. It combines advanced climate modelling, machine learning, and hydrological modelling to gain profound insights into temperature variations and precipitation patterns and their impacts on the runoff. Notably, it predicts a continuous rise in both maximum and minimum air temperatures until 2050, with minimum temperatures increasing more rapidly. It highlights a concerning trend of decreasing basin precipitation. Sophisticated hydrological models factor in land use, vegetation, and groundwater, offering nuanced insights into water availability, which signifies a detailed and comprehensive understanding of factors impacting water availability. This includes considerations of spatial variability, temporal dynamics, land use effects, vegetation dynamics, groundwater interactions, and the influence of climate change. The research integrates data from advanced climate models, machine learning, and real-time observations, and refers to continuously updated data from various sources, including weather stations, satellites, ground-based sensors, climate monitoring networks, and stream gauges, for accurate basin discharge simulations (Nash-Sutcliffe efficiency - NSERCP2.6 = 0.99, root mean square error - RMSERCP2.6 = 1.1, and coefficient of determination R2RCP2.6 = 0.95 of representative concentration pathways 2.6 (RCP)). By uniting these approaches, the study offers valuable insights for policymakers, water resource managers, and local communities to adapt to and manage water resources in arid regions.
Wydawca
Rocznik
Tom
Strony
149--156
Opis fizyczny
Bibliogr. 27 poz., rys., tab., wykr.
Twórcy
  • Tashkent State Pedagogical University, Vice-Rector for Scientific Affairs, 27 Bunyodkor Ave, 100070, Tashkent, Uzbekistan
Bibliografia
  • Apurv, T. et al. (2015) “Impact of climate change on floods in the Brahmaputra basin using CMIP5 decadal predictions,” Journal of Hydrology, 527, pp. 281–291. Available at: https://doi.org/10.1016/j.jhydrol.2015.04.056.
  • Aryal, A., Shrestha, S. and Babel, M.S. (2019) “Quantifying the sources of uncertainty in an ensemble of hydrological climate-impact projections,” Theoretical and Applied Climatology, 135(1–2), pp. 193–209. Available at: https://doi.org/10.1007/s00704-017-2359-3.
  • Bajracharya, A.R. et al. (2018) “Climate change impact assessment on the hydrological regime of the Kaligandaki Basin, Nepal,” Science of The Total Environment, 625, pp. 837–848. Available at: https://doi.org/10.1016/j.scitotenv.2017.12.332.
  • Bhatta, B. et al. (2019) “Evaluation and application of a SWAT model to assess the climate change impact on the hydrology of the Himalayan River Basin,” CATENA, 181, 104082. Available at: https://doi.org/10.1016/j.catena.2019.104082.
  • Buytaert, W. et al. (2010) “Uncertainties in climate change projections and regional downscaling in the tropical Andes: Implications for water resources management,” Hydrology and Earth System Sciences, 14(7), pp. 1247–1258. Available at: https://doi.org/10.5194/hess-14-1247-2010.
  • Deom, J.-M. and Sala, R. (2022) “The arid regions of Daryalyk Takyr and Telikol: Ethno-geoarchaeological study of a strategic trans-humance rangeland on the right bank of the Syr Darya delta, Kazakhstan,” Studia Quaternaria, 39(2), pp. 95–111. Available at: https://doi.org/10.24425/sq.2022.140886.
  • Duan, Z. et al. (2019) “Hydrological evaluation of open-access precipitation and air temperature datasets using SWAT in a poorly gauged basin in Ethiopia,” Journal of Hydrology, 569, pp. 612–626. Available at: https://doi.org/10.1016/j.jhydrol.2018.12.026.
  • Ercan, M.B. et al. (2020) “Estimating potential climate change effects on the Upper Neuse Watershed water balance using the SWAT model,” Journal of the American Water Resources Association, 56 (1), pp. 53–67. Available at: https://doi.org/10.1111/1752-1688.12813.
  • Fereidoon, M. and Koch, M. (2018) “SWAT-MODSIM-PSO optimization of multi-crop planning in the Karkheh River Basin, Iran, under the impacts of climate change,” Science of The Total Environment, 630, pp. 502–516. Available at: https://doi.org/10.1016/j.scitotenv.2018.02.234.
  • Golmohammadi, G. et al. (2017) “Predicting the temporal variation of flow contributing areas using SWAT,” Journal of Hydrology, 547, pp. 375–386. Available at: https://doi.org/10.1016/j.jhydrol.2017.02.008.
  • González, P. et al. (2010) “Global patterns in the vulnerability of ecosystems to vegetation shifts due to climate change,” Global Ecology and Biogeography, 19(6), pp. 755–768. Available at: https://doi.org/10.1111/j.1466-8238.2010.00558.x.
  • Guo, T. et al. (2018) “Impact of number of realizations on the suitability of simulated weather data for hydrologic and environmental applications,” Stochastic Environmental Research and Risk Assessment, 32(8), pp. 2405–2421. Available at: https://doi.org/10.1007/s00477-017-1498-5.
  • Hendy, Z.M. et al. (2023) “The modelling of tomato crop response to the climate change with different irrigation schemes,” Journal of Water and Land Development, 58, pp. 42–52. Available at: https://doi.org/10.24425/jwld.2023.145360.
  • Ikegwuoha, D.C. and Dinka, M.O. (2020) “Drought prediction in the Lepelle River basin, South Africa under general circulation model simulations,” Journal of Water and Land Development, 45, pp. 42–53. Available at: https://doi.org/10.24425/jwld.2020.133044.
  • Kim, J. et al. (2022) “Development of a simulation method for paddy fields based on surface FTABLE of hydrological simulation program – FORTRAN,” Agricultural Water Management, 271, 107694. Available at: https://doi.org/10.1016/j.agwat.2022.107694.
  • Mohammed, I.N., Bomblies, A. and Wemple, B.C. (2015) “The use of CMIP5 data to simulate climate change impacts on flow regime within the Lake Champlain Basin,” Journal of Hydrology: Regional Studies, 3, pp. 160–186. Available at: https://doi.org/10.1016/j.ejrh.2015.01.002.
  • Ndhlovu, G.Z. and Woyessa, Y.E. (2020) “Modelling impact of climate change on catchment water balance, Kabompo River in Zambezi River Basin,” Journal of Hydrology: Regional Studies, 27, 100650. Available at: https://doi.org/10.1016/j.ejrh.2019.100650.
  • Nilawar, A.P. and Waikar, M.L. (2019) “Impacts of climate change on streamflow and sediment concentration under RCP 4.5 and 8.5: A case study in Purna River basin, India,” Science of The Total Environment, 650, pp. 2685–2696. Available at: https://doi.org/10.1016/j.scitotenv.2018.09.334.
  • Organiściak, K. and Borkowski, J. (2020) “Single-ended quality measurement of a music content via convolutional recurrent neural networks,” Metrology and Measurement Systems, 27(4), pp. 721–733. Available at: https://doi.org/10.24425/mms.2020.134849.
  • Oseke, F.I. et al. (2021) “Predicting the impact of climate change and the hydrological response within the Gurara reservoir catchment, Nigeria,” Journal of Water and Land Development, 51, pp. 129–143. Available at: https://doi.org/10.24425/jwld.2021.139023.
  • Tan, M.L. et al. (2017) “Climate change impacts under CMIP5 RCP scenarios on water resources of the Kelantan River Basin, Malaysia,” Atmospheric Research, 189, pp. 1–10. Available at: https://doi.org/10.1016/j.atmosres.2017.01.008.
  • Tuo, Y. et al. (2016) “Evaluation of precipitation input for SWAT modeling in Alpine catchment: A case study in the Adige river basin (Italy),” Science of the Total Environment, 573, pp. 66–82. Available at: https://doi.org/10.1016/j.scitotenv.2016.08.034.
  • Venkataraman, K. et al. (2016) “21st century drought outlook for major climate divisions of Texas based on CMIP5 multimodel ensemble: Implications for water resource management,” Journal of Hydrology, 534, pp. 300–316. Available at: https://doi.org/10.1016/j.jhydrol.2016.01.001.
  • Wang, R. and Kalin, L. (2018) “Combined and synergistic effects of climate change and urbanization on water quality in the Wolf Bay watershed, southern Alabama,” Journal of Environmental Sciences, 64, pp. 107–121. Available at: https://doi.org/10.1016/j.jes.2016.11.021.
  • Wang, Y. and Luo, X. (2021) “Analyzing rear-end crash severity for a mountainous expressway in China via a classification and regression tree with random forest approach,” Archives of Civil Engineering, 67(4), pp. 591–604. Available at: https://doi.org/10.24425/ace.2021.138520.
  • Zhang, Y. et al. (2016) “Impacts of climate change on streamflows under RCP scenarios: A case study in Xin River Basin, China,” Atmospheric Research, 178–179, pp. 521–534. Available at: https://doi.org/10.1016/j.atmosres.2016.04.018.
  • Zhou, J. et al. (2015) “Integrated SWAT model and statistical downscaling for estimating streamflow response to climate change in the Lake Dianchi watershed, China,” Stochastic Environmental Research and Risk Assessment, 29(4), pp. 1193–1210. Available at: https://doi.org/10.1007/s00477-015-1037-1.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e0a92df5-98c6-423f-8ec2-ba30820eaef9
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