Integrating advanced approaches for climate change impact assessment on water resources in arid regions
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 im...
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2024-03-01
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| Series: | Journal of Water and Land Development |
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| Online Access: | https://journals.pan.pl/Content/130728/2024-01-JWLD-16.pdf |
| _version_ | 1797238938640842752 |
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| author | Barno S. Abdullaeva |
| author_facet | Barno S. Abdullaeva |
| author_sort | Barno S. Abdullaeva |
| collection | DOAJ |
| description | 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 – NSE RCP2.6 = 0.99, root mean square error – RMSE RCP2.6 = 1.1, and coefficient of determination R 2 RCP2:6= 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. |
| first_indexed | 2024-04-24T17:43:36Z |
| format | Article |
| id | doaj.art-18ff8cd2b91246d4aeea4708bbc7586a |
| institution | Directory Open Access Journal |
| issn | 2083-4535 |
| language | English |
| last_indexed | 2024-04-24T17:43:36Z |
| publishDate | 2024-03-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Journal of Water and Land Development |
| spelling | doaj.art-18ff8cd2b91246d4aeea4708bbc7586a2024-03-27T15:12:35ZengPolish Academy of SciencesJournal of Water and Land Development2083-45352024-03-01No 60149156https://doi.org/10.24425/jwld.2024.149116Integrating advanced approaches for climate change impact assessment on water resources in arid regionsBarno S. Abdullaeva0https://orcid.org/0009-0004-7240-014XTashkent State Pedagogical University, Vice-Rector for Scientific Affairs, 27 Bunyodkor Ave, 100070, Tashkent, UzbekistanThis 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 – NSE RCP2.6 = 0.99, root mean square error – RMSE RCP2.6 = 1.1, and coefficient of determination R 2 RCP2:6= 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.https://journals.pan.pl/Content/130728/2024-01-JWLD-16.pdfarid regionsclimate changehydrological modellingmachine learningwater resources |
| spellingShingle | Barno S. Abdullaeva Integrating advanced approaches for climate change impact assessment on water resources in arid regions Journal of Water and Land Development arid regions climate change hydrological modelling machine learning water resources |
| title | Integrating advanced approaches for climate change impact assessment on water resources in arid regions |
| title_full | Integrating advanced approaches for climate change impact assessment on water resources in arid regions |
| title_fullStr | Integrating advanced approaches for climate change impact assessment on water resources in arid regions |
| title_full_unstemmed | Integrating advanced approaches for climate change impact assessment on water resources in arid regions |
| title_short | Integrating advanced approaches for climate change impact assessment on water resources in arid regions |
| title_sort | integrating advanced approaches for climate change impact assessment on water resources in arid regions |
| topic | arid regions climate change hydrological modelling machine learning water resources |
| url | https://journals.pan.pl/Content/130728/2024-01-JWLD-16.pdf |
| work_keys_str_mv | AT barnosabdullaeva integratingadvancedapproachesforclimatechangeimpactassessmentonwaterresourcesinaridregions |