Spatiotemporal analysis of transition probabilities of wet and dry days under SSPs scenarios in the semi-arid Susurluk Basin, Türkiye

Şan, Murat; Nacar, Sinan; Kankal, Murat; Bayram, Adem

doi:10.1016/j.scitotenv.2023.168641

Cited by 6 publications

(1 citation statement)

References 94 publications

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“…Similarly, when it comes to Turkey, several studies based on CMIP were conducted to assess climate change (Aziz & Yucel, 2023; Bağçaci et al, 2021; Bayar et al, 2023; Çetin et al, 2024; Gumus et al, 2023; Oruc et al, 2023; Nacar et al, 2022; Oruc, 2022; Şan et al, 2023, 2024; Seker & Gumus, 2022; Senocak et al, 2023; Yavaşlı & Erlat, 2023).…”

Section: Introductionmentioning

confidence: 99%

Machine learning‐based streamflow forecasting using CMIP6 scenarios: Assessing performance and improving hydrological projections and climate change

Kartal

2024

Hydrological Processes

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Water is essential for humans as well as for all living organisms to sustain their lives. Therefore, any climate‐driven change in available resources has significant impacts on the environment and life. Global climate models (GCMs) are one of the most practical methods to evaluate climate change. Based on this, this research evaluated the capability of GCMs from the Coupled Model Intercomparison Project 6 (CMIP6) to reproduce the historical flow of climate prediction centre data for the Konya Closed basin and to project the climate of the basin using the selected GCMs. Global climate models based on the CMIP6 under the scenario of common socioeconomic pathways (SSP245 and SSP 585) were used to analyse the climate change effect on streamflow of the study area by Bias Correction of GCM Models using Long Short‐Term Memory (LSTM), Bidirectional LSTM (BiLSTM), AdaBoost, Gradient Boosting, Regression Tree, and Random Forest methods. The coefficient of determination (R2), mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE) were used to assess the performance of the methods. Findings show that the Random Forest Model consistently outperformed other models in both the testing and training phases. A significant downward in the volume of water flowing through the region's rivers and streams in the next decades. It is critical to enhance climate‐resilient water infrastructure financing, establish an early warning system for drought, introduce best management practices, implement integrated water resource management, public awareness, and support water research to alleviate the negative consequences of drought and increase resilience against the effects of climate change on Turkey's water resources.

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Section: Introductionmentioning

confidence: 99%

Machine learning‐based streamflow forecasting using CMIP6 scenarios: Assessing performance and improving hydrological projections and climate change

Kartal

2024

Hydrological Processes

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A CMIP6-ensemble-based evaluation of precipitation and temperature projections

Yılmaz,

Aras,

Nacar

2024

Theor Appl Climatol

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Understanding climate change’s effects on dam basins is very important for water resource management because of their important role in providing essential functions such as water storage, irrigation, and energy production. This study aims to investigate the impact of climate change on temperature and precipitation variables in the Altınkaya Dam Basin, which holds significant potential for hydroelectric power generation in Türkiye. These potential impacts were investigated by using ERA5 reanalysis data, six GCMs from the current CMIP6 archive, and two Shared Socioeconomic Pathways (SSP2 − 4.5 and SSP5 − 8.5) scenario data. Four Multi-Model Ensemble (MME) models were developed by using an Artificial Neural Network (ANN) approach (ENS1), simple averaging (ENS2), weighted correlation coefficients (ENS3), and the MARS algorithm (ENS4), and the results were compared to each other. Moreover, quantile delta mapping (QDM) bias correction was used. The 35-year period (1980–2014) was chosen as the reference period, and further evaluations were conducted by dividing it into three future periods (near (2025–2054), mid-far (2055–2084), and far (2085–2100)). Considering the results achieved from the MMEs, variations are expected in the monthly, seasonal, and annual assessments. Projections until the year 2100 indicate that under optimistic and pessimistic scenarios, temperature increases could reach up to 3.11 °C and 5.64 °C, respectively, while precipitation could decrease by as much as 19% and 43%, respectively. These results suggest that the potential changes in temperature and precipitation within the dam basin could significantly impact critical elements such as future water flow and energy production.

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Remote sensing and monitoring of water resources: A comparative study of different indices and thresholding methods

Günen,

Atasever

2024

Science of The Total Environment

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Spatiotemporal analysis of transition probabilities of wet and dry days under SSPs scenarios in the semi-arid Susurluk Basin, Türkiye

Cited by 6 publications

References 94 publications

Machine learning‐based streamflow forecasting using CMIP6 scenarios: Assessing performance and improving hydrological projections and climate change

Machine learning‐based streamflow forecasting using CMIP6 scenarios: Assessing performance and improving hydrological projections and climate change

A CMIP6-ensemble-based evaluation of precipitation and temperature projections

Remote sensing and monitoring of water resources: A comparative study of different indices and thresholding methods

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