Evaluation and comparison of separated precipitation types from multi-sources data in the Chinese Tianshan mountainous region

Journal of Mountain Science, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 30, 2025

Language: Английский

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Season-dependent climate sensitivity of the surface runoff of major rivers in Changbai Mountain

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 643, P. 131936 - 131936

Published: Sept. 1, 2024

Language: Английский

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A comprehensive comparison of bias correction methods in climate model simulations: application on ERA5-Land across different temporal resolutions

Heliyon, Journal Year: 2024, Volume and Issue: 10(23), P. e40352 - e40352

Published: Nov. 14, 2024

Climate data plays a crucial role in water resources management, which is becoming an increasingly relevant asset all types of hydrological analysis not only for climate change studies but various horizon forecasting. Though the ever-improving accuracy models' spatial and temporal resolution has surged validity their outputs, products global regional models need to be corrected reliably used local purposes. Here, we propose comprehensive statistical univariate multivariate, as well machine learning methods bias correction, are compared on different scales, ranging from hourly time steps monthly aggregations, environment complex Alpine orthography, using ERA5-Land reanalysis data. The results reveal trends performance correction precipitation temperature across resolutions.

Language: Английский

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Performances of reanalysis products in representing the temperature climatology of Ethiopia

Theoretical and Applied Climatology, Journal Year: 2025, Volume and Issue: 156(1)

Published: Jan. 1, 2025

Language: Английский

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Daily reference evapotranspiration prediction in Iran: A machine learning approach with ERA5-land data

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 59, P. 102343 - 102343

Published: March 30, 2025

Language: Английский

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Review of bias correction methods for climate model outputs in hydrology

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133213 - 133213

Published: April 1, 2025

Language: Английский

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Weakening trends of glacier and snowmelt-induced floods in the Upper Yarkant River Basin, Karakoram during 1961‒2022

Advances in Climate Change Research, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

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Improved evapotranspiration estimation using the Penman-Monteith equation with a deep learning (DNN) model over the dry southwestern US: Comparison with ECOSTRESS, MODIS, and OpenET

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133460 - 133460

Published: May 1, 2025

Language: Английский

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Convection‐permitting dynamical downscaling of ERA5 for Europe and the Mediterranean basin

Quarterly Journal of the Royal Meteorological Society, Journal Year: 2025, Volume and Issue: unknown

Published: May 26, 2025

Abstract As the European continent and Mediterranean Sea experience rapid warming trends diverse manifestations of extreme weather, there is an urgent need to understand mitigate impacts climate change in these regions. This study introduces Computational Hydrometeorology with Advanced Performance Enhanced Realism (CHAPTER) high‐resolution dynamical downscaling Centre for Medium‐Range Weather Forecasts Reanalysis v5 (ERA5) global reanalysis made Research Forecasting numerical model. CHAPTER covers Europe basin at a convection‐resolving grid resolution 3 km by km. CHAPTER's performances representing precipitation temperature are evaluated compared state‐of‐the‐art datasets like ERA5‐Land. The focus put on seasonal spatial distributions bias root mean square error, fuzzy verification techniques used validate outputs. results reveal that performance aligns closely well‐recognized downscalings ERA5 but has, addition, advantage providing rich portfolio variables hourly temporal different terrain, following model levels. Therefore, valuable resource studying weather events, offering insights crucial adaptation mitigation efforts region.

Language: Английский

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Large-Scale Hydrological Models and Transboundary River Basins

Water, Journal Year: 2024, Volume and Issue: 16(6), P. 878 - 878

Published: March 19, 2024

Large-scale hydrological modeling is an emerging approach in river hydrology, especially regions with limited available data. This research focuses on evaluating the performance of two well-known large-scale models, namely E-HYPE and LISFLOOD, for five transboundary rivers Greece. For this purpose, discharge time series at rivers’ outlets from both models are compared observed datasets wherever possible. The comparison conducted using well-established statistical measures, namely, coefficient determination, Percent Bias, Nash–Sutcliffe Efficiency, Root-Mean-Square Error, Kling–Gupta Efficiency. Subsequently, models’ bias corrected through scaling factor, linear regression, delta change, quantile mapping methods, respectively. outputs then re-evaluated against observations same measures. results demonstrate that neither consistently outperformed other, as one model performed better some basins while other excelled remaining cases. bias-correction process identifies regression most suitable methods case study basins. Additionally, assesses influence upstream waters water budget. highlights significance presents a methodological their applicability any basin global scale, underscores usefulness cooperative management international waters.

Language: Английский

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