A new wavelet combined innovative polygon trend analysis (W-IPTA) approach for investigating the trends in the streamflow regime in the Konya Closed Basin, Turkey

Sezen, Cenk

doi:10.1007/s00704-022-04328-6

Cited by 10 publications

(1 citation statement)

References 72 publications

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“…Different climatic conditions play a role in the climate in the basin. The continental climate with cold winters in the central and northern parts of the basin, the desert climate with hot and dry summers in and around Karapınar, and the Mediterranean climate with hot and dry summers and mild and rainy winters in the south of the basin are dominant (Koycegiz & Buyukyildiz, 2024). The precipitation is generally observed in winter and spring and the average annual precipitation is approximately 382 mm.…”

Section: Study Area and Data Methodsmentioning

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: Study Area and Data Methodsmentioning

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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Standardized Innovative Polygon Trend Analysis for Climate Change Assessment (S-IPTA)

Alashan,

Abu Arra,

Şişman

2024

Pure Appl. Geophys.

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Research and applications on trend analysis have recently been on the agenda and are top priorities in many disciplines due to the effects of climate change. After a thorough evaluation of the literature, it is noted that different hydro-meteorological variables, such as precipitation, temperature, etc., are studied and analyzed individually. This research proposes a new innovative polygon trend analysis application (S-IPTA) using the standardization concept to fill this gap in classical trend applications and comprehensively compare the trends of different variables to temporal and spatial patterns. Firstly, using statistical standardization, S-IPTA adjusts the original data sets and makes them dimensionless. Then, the innovative trend analyses are conducted and interpreted on one single graph (S-IPTA). The S-IPTA methodology is applied to monthly precipitation and temperature time series of Konya Basin in Türkiye at ten meteorological stations between 1959 and 2022. For precipitation, the S-IPTA did not exhibit a consistent polygon across all stations within the study area, while the temperature polygon was more regular, indicating that the temperature mean was generally stable with a positive trend. Also, S-IPTA shows the difference between the average value for each month and the newly proposed long-term average value (0). S-IPTA also provides a basis for a better interpretation of climate change and its effects by providing a common denominator for various trend characteristics, such as trend magnitudes and trend transitions in different hydro-meteorological time series.

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Trend and variability analysis in rainfall and temperature records over Van Province, Türkiye

Esit,

Yuce,

Deger

et al. 2023

Theor Appl Climatol

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A new wavelet combined innovative polygon trend analysis (W-IPTA) approach for investigating the trends in the streamflow regime in the Konya Closed Basin, Turkey

Cited by 10 publications

References 72 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

Standardized Innovative Polygon Trend Analysis for Climate Change Assessment (S-IPTA)

Trend and variability analysis in rainfall and temperature records over Van Province, Türkiye

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