Quantifying the climate change impacts on the magnitude and timing of hydrological extremes in the Baro River Basin, Ethiopia

ENVIRONMENTAL SYSTEMS RESEARCH, Journal Year: 2024, Volume and Issue: 13(1)

Published: Jan. 9, 2024

Abstract Extreme hydrological events, like floods and droughts, exert considerable effects on both human natural systems. The frequency, intensity, duration of these events are expected to change due climate change, posing challenges for water resource management adaptation. In this study, the Soil Water Assessment Tool plus (SWAT +) model was calibrated validated simulate flow under future shared socioeconomic pathway (SSP2-4.5 SSP5-8.5) scenarios in Baro River Basin with R2 values 0.88 0.83, NSE 0.83 0.74, PBIAS 0.39 8.87 during calibration validation. Six bias-corrected CMIP6 Global Climate Models (GCM) were selected utilized investigate magnitude timing extremes. All simulation results suggest a general increase streamflow emission SSP5-8.5). multi-model ensemble projections show yearly increases 4.8% 12.4% mid-term (MT) (2041–2070) long-term (LT) (2071–2100) periods SSP2-4.5, 15.7% 35.6% SSP5-8.5, respectively. Additionally, analysis revealed significant shifts projected annual 1 day, 3 7 30 day maximum flows, whereas minimum fluctuations do not present distinct trend scenario compared baseline (1985–2014). study also evaluated extremes, focusing low peak utilizing analysis. An earlier occurrence noted SSP2-4.5 scenario, while later observed SSP5-8.5 baseline. conclusion, showed effect river hydrology extreme highlighting their importance informed sustainable planning.

Language: Английский

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Hydrological Drought and Flood Projection in the Upper Heihe River Basin Based on a Multi-GCM Ensemble and the Optimal GCM

Atmosphere, Journal Year: 2024, Volume and Issue: 15(4), P. 439 - 439

Published: April 1, 2024

To ensure water use and resource security along “the Belt Road”, the runoff hydrological droughts floods under future climate change conditions in upper Heihe River Basin were projected this study, based on observed meteorological data from 1987 to 2014, 10 GCMs 2014 2026 2100, using SWAT model, Standardized Runoff Index, run length theory, entropy-weighted TOPSIS method. Both multi-GCM ensemble (MME) optimal model used assess drought flood responses change. The results showed that (1) multi-year average MME was be close historical period SSP245 scenario increase by 2.3% SSP585 scenario, those CMCC-ESM2 decrease both scenarios; (2) duration intensity decrease, while intensity, peak, duration, peak scenarios their levels; (3) most after 2080, according MME. reached a consensus sign of extreme characteristic change, but differences magnitude trends.

Language: Английский

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Impact of land-use and climate change on future extreme flows: a study for three dam watersheds in Alborz and Tehran provinces of Iran

Applied Water Science, Journal Year: 2025, Volume and Issue: 15(3)

Published: Feb. 18, 2025

Language: Английский

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Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models

Water, Journal Year: 2023, Volume and Issue: 15(18), P. 3222 - 3222

Published: Sept. 10, 2023

Runoff from the high-cold mountains area (HCMA) is most important water resource in arid zone, and its accurate forecasting key to scientific management of resources downstream basin. Constrained by scarcity meteorological hydrological stations HCMA inconsistency observed time series, simulation reconstruction mountain runoff have always been a focus cold region research. Based on observations Yurungkash Kalakash Rivers, upstream tributaries Hotan River northern slope Kunlun Mountains at different periods, atmospheric circulation indices, we used feature analysis machine learning methods select input elements, train, simulate, preferences models runoffs two watersheds, reconstruct missing series River. The results show following. (1) Air temperature driver variability mountainous areas River, had strongest performance terms Pearson correlation coefficient (ρXY) random forest importance (FI) (ρXY = 0.63, FI 0.723), followed soil 0.043), precipitation, hours sunshine, wind speed, relative humidity, were weakly correlated. A total 12 elements selected as data. (2) Comparing simulated eight methods, found that gradient boosting performed best, AdaBoost Bagging with Nash–Sutcliffe efficiency coefficients (NSE) 0.84, 0.82, 0.78, while support vector regression (NSE 0.68), ridge 0.53), K-nearest neighbor 0.56), linear 0.51) poorly. (3) application four boosting, forest, AdaBoost, bagging, simulate for 1978–1998 was generally outstanding, NSE exceeding 0.75, reconstructing data period (1999–2019) could well reflect characteristics intra-annual inter-annual changes runoff.

Language: Английский

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Near future variations in temperature extremes in northeastern Iran under CMIP6 projections

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(10)

Published: Sept. 23, 2024

Language: Английский

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