Projected changes in precipitation and air temperature over the Volga River Basin from bias-corrected CMIP6 outputs DOI Creative Commons
S. Mahya Hoseini, Mohsen Soltanpour,

Mohammad R. Zolfaghari

и другие.

Numerical Methods in Civil Engineering, Год журнала: 2023, Номер 8(2), С. 36 - 47

Опубликована: Дек. 1, 2023

This paper investigates future changes in annual mean precipitation and air temperature across the Volga River basin, which serve as significant drivers of climate-induced River's discharge, primary input to Caspian Sea. The thirteen Global Climate Models (GCMs) outputs under four Shared Socioeconomic Pathways (SSPs) scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, SSP5–8.5) from sixth phase Coupled Model Intercomparison Project (CMIP6) are used for this study. In historical period (1950-2014), using comprehensive rating metrics Taylor diagram, GCMs ranked according their ability capture temporal spatial variability temperature. Multi-Model Ensemble (MME) is generated, bias-correction techniques utilized reduce uncertainties correct biases CMIP6 outputs. Bias-correction assessed average proper methods projections (2015-2100). 21st century, show that basin could mainly experience a increase 0.4°C 7.5°C, alongside rise 0.7% 37%, depending on considered. Comparison with an observational dataset 2015 2017 indicates SSP2–4.5 more likely scenario represent climate basin.

Язык: Английский

Climate change impacts on temperature and precipitation over the Caspian Sea DOI
S. Mahya Hoseini, Mohsen Soltanpour,

Mohammad R. Zolfaghari

и другие.

International Journal of Water Resources Development, Год журнала: 2024, Номер unknown, С. 1 - 26

Опубликована: Март 7, 2024

The projected changes in precipitation and air temperature over the Caspian Sea (CS) are studied using 13 Global Climate Models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6) under four Shared Socioeconomic Pathways (SSPs) scenarios. Multi-Model Ensemble (MME) downscaling/bias-correction techniques applied to reduce uncertainties correct biases CMIP6 outputs. Future projections indicate a warmer climate (0.4–3°C) CS 21st century, with up 2.3% decrease or 20% increase based on These pose significant environmental challenges that require mitigation adoption strategies for sustainable development.

Язык: Английский

Процитировано

4
CA-discharge: Geo-Located Discharge Time Series for Mountainous Rivers in Central Asia DOI Creative Commons
Beatrice Marti, Andrey Yakovlev, Dirk Nikolaus Karger

и другие.

Scientific Data, Год журнала: 2023, Номер 10(1)

Опубликована: Сен. 4, 2023

Abstract We present a collection of 295 gauge locations in mountainous Central Asia with norm discharge as well time series river from 135 these collected hydrological yearbooks Asia. Time have monthly, 10-day and daily temporal resolution are available for different duration. A third-party data allows basin characterization all gauges. The is validated using standard quality checks. Norm against literature values by water balance approach. novelty the consists combination rivers which not anywhere else. geo-located can be used modelling training forecast models runoff

Язык: Английский

Процитировано

9
Bias-corrected high-resolution precipitation datasets assessment over a tropical mountainous region in Colombia: A case of study in Upper Cauca River Basin DOI Creative Commons

Clara Marcela Romero-Hernández,

Alvaro Ávila-Díaz, Benjamín Quesada

и другие.

Journal of South American Earth Sciences, Год журнала: 2024, Номер 140, С. 104898 - 104898

Опубликована: Апрель 24, 2024

Surface gauge measurements have been commonly employed to analyze the precipitation's high spatial and temporal variability. However, incomplete area coverage deficiencies over most tropical complex topography mean significant limitations of this measurement type. Satellite-derived datasets, combined with integration in-situ observations satellite data, are an alternative address these by offering a more spatially homogeneous temporally comprehensive for scarce data areas globe. Nevertheless, applying bias correction technique on precipitation datasets is still necessary before they used research due their considerable bias. Here, we performance CHIRPS, WorldClim, TerraClimate compared from 30 rain stations South-West Colombia, specifically in Upper Cauca River Basin-UCRB between 1981 2018. Additionally, applied Quantile Mapping all gridded products, subsequently, corrected rainfall observed monthly, seasonal, annual scale. Our results show that CHIRPS dataset better captures seasonal monthly presents best during less rainy seasons at low elevation zones (900-2,000 meters above sea level-m.a.s.l.), followed TerraClimate. Utilizing methodology, generated new, corrected, reliable time series each location products. found presented across spatiotemporal scales UCRB .Therefore, study provides accurate database topographic region limited availability.

Язык: Английский

Процитировано

2
Spatio-Temporal Network for Sea Fog Forecasting DOI Open Access
Jinhyeok Park, Young Jae Lee, Yongwon Jo

и другие.

Sustainability, Год журнала: 2022, Номер 14(23), С. 16163 - 16163

Опубликована: Дек. 3, 2022

Sea fog can seriously affect schedules and safety by reducing visibility during marine transportation. Therefore, the forecasting of sea is an important issue in preventing accidents. Recently, order to forecast fog, several deep learning methods have been applied time series data consisting meteorological oceanographic observations or image predict fog. However, these only use a single without considering temporal characteristics. In this study, we propose multi-modal method improve accuracy using convolutional neural network (CNN) gated recurrent unit (GRU) models. CNN GRU extract useful features from closed-circuit television (CCTV) images multivariate data, respectively. CCTV collected at Daesan Port South Korea 1 March 2018 14 February 2021 Hydrographic Oceanographic Agency (KHOA) were used evaluate proposed method. We compare with that consider information spatial information. The results indicate both same shows superior accuracy.

Язык: Английский

Процитировано

8
Short‐Term Sea Fog Area Forecast: A New Data Set and Deep Learning Approach DOI Creative Commons
Keran Chen, Yuan Zhou, 田中 義人

и другие.

Journal of Geophysical Research Machine Learning and Computation, Год журнала: 2024, Номер 1(3)

Опубликована: Июль 2, 2024

Abstract Prompt and precise forecast of sea fog regions ensures maritime navigational safety. This paper establishes a prompt that ensure multivariable (MV‐SFF) data set proposes deep learning‐based method named rich‐element aggregated (REA) for short‐term forecasts. The MV‐SFF contains 122 events from 2010 to 2020. Each event in the includes meteorological variables reanalysis geostationary ocean color imager satellite images captured hourly on day occurrence. aims comprehensively utilize elements remote sensing observations predict spatial changes areas next 7 hours. proposed REA model can extract integrate features historical different types, times, locations. In addition, utilizes position‐aware edge detection mechanism locate exact position fog. We perform seven‐hour ahead starting 09:16 local time show promising forecasting results, which demonstrate area ability our model. Compared with existing advanced learning networks, is superior performance stability.

Язык: Английский

Процитировано

1
Monitoring Sea Fog over the Yellow Sea and Bohai Bay Based on Deep Convolutional Neural Network DOI
Bin Huang, Shibo Gao,

Run Yu

и другие.

Journal of Tropical Meteorology, Год журнала: 2024, Номер 30(3), С. 223 - 230

Опубликована: Авг. 14, 2024

Язык: Английский

Процитировано

1
Projected changes in Caspian sea level under CMIP6 climate change scenarios: probabilistic and deterministic approaches DOI
S. Mahya Hoseini, Mohsen Soltanpour,

Mohammad R. Zolfaghari

и другие.

Climate Dynamics, Год журнала: 2024, Номер 63(1)

Опубликована: Дек. 18, 2024

Язык: Английский

Процитировано

0
Projected changes in precipitation and air temperature over the Volga River Basin from bias-corrected CMIP6 outputs DOI Creative Commons
S. Mahya Hoseini, Mohsen Soltanpour,

Mohammad R. Zolfaghari

и другие.

Numerical Methods in Civil Engineering, Год журнала: 2023, Номер 8(2), С. 36 - 47

Опубликована: Дек. 1, 2023

This paper investigates future changes in annual mean precipitation and air temperature across the Volga River basin, which serve as significant drivers of climate-induced River's discharge, primary input to Caspian Sea. The thirteen Global Climate Models (GCMs) outputs under four Shared Socioeconomic Pathways (SSPs) scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, SSP5–8.5) from sixth phase Coupled Model Intercomparison Project (CMIP6) are used for this study. In historical period (1950-2014), using comprehensive rating metrics Taylor diagram, GCMs ranked according their ability capture temporal spatial variability temperature. Multi-Model Ensemble (MME) is generated, bias-correction techniques utilized reduce uncertainties correct biases CMIP6 outputs. Bias-correction assessed average proper methods projections (2015-2100). 21st century, show that basin could mainly experience a increase 0.4°C 7.5°C, alongside rise 0.7% 37%, depending on considered. Comparison with an observational dataset 2015 2017 indicates SSP2–4.5 more likely scenario represent climate basin.

Язык: Английский

Процитировано

0