Hydrological Response to Climate Change: McGAN for Multi-Site Scenario Weather Series Generation and LSTM for Streamflow Modeling DOI Creative Commons
Jian Sha,

Yaxin Chang,

Yaxiu Liu

и другие.

Atmosphere, Год журнала: 2024, Номер 15(11), С. 1348 - 1348

Опубликована: Ноя. 9, 2024

This study focuses on the impacts of climate change hydrological processes in watersheds and proposes an integrated approach combining a weather generator with multi-site conditional generative adversarial network (McGAN) model. The incorporates ensemble GCM predictions to generate regional average synthetic series, while McGAN transforms these averages into spatially consistent data. By addressing spatial consistency problem generating this tackles key challenge site-scale impact assessment. Applied Jinghe River Basin west-central China, generated daily temperature precipitation data for four stations under different shared socioeconomic pathways (SSP1-26, SSP2-45, SSP3-70, SSP5-85) up 2100. These were then used long short-term memory (LSTM) network, trained historical data, simulate river flow from 2021 results show that (1) effectively addresses correlation generation; (2) future is likely increase flow, particularly high-emission scenarios; (3) frequency extreme events may increase, proactive policies can mitigate flood drought risks. offers new tool hydrologic–climatic assessment studies.

Язык: Английский

Koreksi Bias Data Hujan Proyeksi Coupled Model Intercomparison Project Phase 6 (Cmip6) Di Kota Bima DOI Creative Commons

I Putu Hartawan,

Muhamad Zaky Ibnu Malik,

Laifhan Setyo Qhairaan

и другие.

Cerdika Jurnal Ilmiah Indonesia, Год журнала: 2025, Номер 5(1), С. 57 - 66

Опубликована: Янв. 15, 2025

Perubahan iklim global telah memengaruhi pola curah hujan, khususnya di wilayah tropis, termasuk Kota Bima. Model proyeksi seperti Coupled Intercomparison Project Phase 6 (CMIP6) menyediakan data penting untuk memprediksi perubahan iklim, namun sering kali mengandung bias yang signifikan. Penelitian ini bertujuan melakukan koreksi pada hujan CMIP6 menggunakan lima model dalam skenario SSP5-8.5, yaitu CMCC-CM2-SR5, CESM2-WACCM, ACCESS-CM2, CESM2, dan AWI-CM-1-1-MR, dengan mengintegrasikan historis dari Global Precipitation Climatology Centre (GPCC) lokal BMKG. Hasil penelitian menunjukkan bahwa GPCC memiliki korelasi sangat kuat BMKG, nilai koefisien sebesar 0,97 RMSE 34,41 mm. empat (CESM2-WACCM, AWI-CM-1-1-MR) tren serupa berdasarkan analisis Weibull plotting. Sementara itu, CMCC-CM2-SR5 penyimpangan Implikasi adalah meningkatkan akurasi mendukung perencanaan mitigasi risiko bencana pengelolaan sumber daya air juga membuka peluang pengembangan metode lebih efisien teknologi pembelajaran mesin rinci.

Процитировано

0
Investigating the Future Precipitation Changes Over the Kingdom of Bahrain Using CMIP6 Projections DOI Creative Commons

J. Drisya,

Waleed Al-Zubari

Earth Systems and Environment, Год журнала: 2025, Номер unknown

Опубликована: Фев. 5, 2025

Язык: Английский

Процитировано

0
Future Projections of Clouds and Precipitation Patterns in South Asia: Insights from CMIP6 Multi-Model Ensemble Under SSP5 Scenarios DOI Open Access

Praneta Khardekar,

Rohini Bhawar,

Vinay Kumar

и другие.

Climate, Год журнала: 2025, Номер 13(2), С. 36 - 36

Опубликована: Фев. 8, 2025

Projecting future changes in monsoon rainfall is crucial for effective water resource management, food security, and livestock sustainability South Asia. This study assesses precipitation, total cloud cover (categorized by top pressure), outgoing longwave radiation (OLR) across the region using Coupled Model Intercomparison Project Phase 6 (CMIP6) data. A multi-model ensemble (MME) approach employed to analyze projections under Shared Socio-Economic Pathway (SSP5-8.5) scenario, which assumes radiative forcing will reach 8.5 W/m2 2100. The MME projects a ~1.5 mm/day increase during 2081–2100. Convective stratiform precipitation are expected expand spatially, with convective increasing from 3 historical simulations 3.302 far future. Stratiform also shows an 0.822 0.962 over same period. notable decrease OLR (~60 along Western Ghats) high suggest intensified rainfall. pattern correlation coefficient (PCC) reveals reduced scenarios (PCC ~0.77 vs. ~0.81 historically), likely due feedback mechanisms. These results highlight enhanced monsoonal activity warming scenarios, implications regional climate adaptation.

Язык: Английский

Процитировано

0
Can CMIP6 Models Accurately Reproduce Terrestrial Evapotranspiration Across China? DOI Open Access
Hui Shen, Jianduo Li, Guocan Wu

и другие.

International Journal of Climatology, Год журнала: 2025, Номер unknown

Опубликована: Фев. 24, 2025

ABSTRACT Terrestrial evapotranspiration (ET) plays a fundamental role in the climate system. The Coupled Model Intercomparison Project Phase 6 (CMIP6) provides valuable framework for assessing global model performance, but gaps remain evaluating its ET estimates, particularly China. To fill this gap, we employed Global Land Evaporation Amsterdam (GLEAM) and water balance method to validate CMIP6 outputs from 1980 2014 at both national river basin scales. Key findings include: (1) GLEAM performs comparably method, making it reliable validating outputs. From 2014, annual mean China ranges 355 411 mm/year. In contrast, most models overestimate ET, with multi‐model ensemble (MME) ranging 524 542 mm/year, showing considerable variation among models. Spatially, MME overestimates across over 90% of Bayesian averaging (BMA) results align closely reference data, overestimation concentrated southwest (2) At scale, trends range −0.36 0.58 mm/year 2 , which contrasts sharply trend 1.27 . compared GLEAM, discrepancies evident major basins. smallest difference simulation occurs Northwest River basin, where distributions are more concentrated, while largest appear Pearl performance is scattered. Furthermore, signal‐to‐noise ratio (SNR) analysis reveals high consistency regions such as Haihe, Yellow, Yangtze, Songliao basins, indicating these areas. This study contributes enhancing reliability accuracy projections, essential informed decision‐making policy formulation atmospheric science.

Язык: Английский

Процитировано

0
Projection of precipitation and temperature in major cities of Pakistan using multi-model ensembles DOI Creative Commons
Shah Fahad, Ayyoob Sharifi

Urban Climate, Год журнала: 2025, Номер 61, С. 102430 - 102430

Опубликована: Апрель 17, 2025

Язык: Английский

Процитировано

0
Detection, attribution, and modeling of climate change: Key open issues DOI Creative Commons
Nicola Scafetta

Gondwana Research, Год журнала: 2025, Номер unknown

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
Historical and projected extreme climate changes in the upper Yellow River Basin, China DOI Creative Commons
Shihao Chen, Baohui Men,

Jinfeng Pang

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 30, 2025

Considering plateau climate and complex terrain of the upper Yellow River Basin, understanding changes in extremes has become increasingly urgent. This study highlighted historical from 1960 to 2022 based on 20 extreme indices, future until 2100 under two Shared Socioeconomic Pathways (SSP126 SSP585) Coupled Model Intercomparison Project phase 6 (CMIP6) models. We found that spatial temporal evolutions precipitation (PEs) temperature (TEs) primarily exhibit increasing trends. The frequency intensity PEs show an trend, while duration shows a decreasing trend. Both cold extremes, as well intensity, frequency, warm Future TEs are expected continue intensify even most ideal scenario (i.e., SSP126), these anticipated further with radiative forcing levels greenhouse gas concentrations. Results could provide scientific references for better coping regions scarce observation station.

Язык: Английский

Процитировано

0
Hydrological Response to Climate Change: McGAN for Multi-Site Scenario Weather Series Generation and LSTM for Streamflow Modeling DOI Creative Commons
Jian Sha,

Yaxin Chang,

Yaxiu Liu

и другие.

Atmosphere, Год журнала: 2024, Номер 15(11), С. 1348 - 1348

Опубликована: Ноя. 9, 2024

This study focuses on the impacts of climate change hydrological processes in watersheds and proposes an integrated approach combining a weather generator with multi-site conditional generative adversarial network (McGAN) model. The incorporates ensemble GCM predictions to generate regional average synthetic series, while McGAN transforms these averages into spatially consistent data. By addressing spatial consistency problem generating this tackles key challenge site-scale impact assessment. Applied Jinghe River Basin west-central China, generated daily temperature precipitation data for four stations under different shared socioeconomic pathways (SSP1-26, SSP2-45, SSP3-70, SSP5-85) up 2100. These were then used long short-term memory (LSTM) network, trained historical data, simulate river flow from 2021 results show that (1) effectively addresses correlation generation; (2) future is likely increase flow, particularly high-emission scenarios; (3) frequency extreme events may increase, proactive policies can mitigate flood drought risks. offers new tool hydrologic–climatic assessment studies.

Язык: Английский

Процитировано

0