What drives the distinct evolution of the Aral Sea and Lake Balkhash? Insights from a novel CD-RF-FA method DOI Creative Commons
Shuang Liu, Aihua Long, Geping Luo

et al.

Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 56, P. 102014 - 102014

Published: Oct. 16, 2024

Language: Английский

Projections of streamflow intermittence under climate change in European drying river networks DOI Creative Commons
Louise Mimeau, Annika Künne, Alexandre Devers

et al.

Hydrology and earth system sciences, Journal Year: 2025, Volume and Issue: 29(6), P. 1615 - 1636

Published: March 25, 2025

Abstract. Climate and land use changes, as well human water flow alteration, are causing worldwide shifts in river dynamics. During the last decades, low flows, intermittence, drying have increased many regions of world, including Europe. This trend is projected to continue amplify future, resulting more frequent intense hydrological droughts. However, due a lack data studies on temporary rivers past, little known about processes governing development intermittence drying, their timing frequency, or long-term evolution under climate change. Moreover, understanding impact change up crucial assess aquatic ecosystems, biodiversity functional integrity freshwater systems. study one first present future projections intermittent networks analyse changes patterns at high spatial temporal resolution. Flow were produced using hybrid model forced with projection from 1985 until 2100 three scenarios six European networks. The studied watershed areas situated different biogeographic regions, located Spain, France, Croatia, Hungary, Czechia, Finland, range 150 350 km2. Additionally, indicators developed calculated (1) characteristics spells reach scale (2) extent network various time intervals. results for all show that increase expand space, despite differences amplitude changes. Temporally, addition average frequency events, duration increases over year. Seasonal expected result an earlier onset longer persistence throughout Summer maxima likely shift spring, extended periods additional occurring autumn extending into winter season some regions. A analysis extreme events shows dry observed recent years could become regular by end century. we observe transitions perennial reaches future.

Language: Английский

Citations

1
Exploring the spatio-temporal dynamics of disturbed metacommunities: A mechanistic modeling approach to species resistance and resilience strategies in drying river networks DOI Creative Commons
Lysandre Journiac, Franck Jabot, Claire Jacquet

et al.

Ecological Modelling, Journal Year: 2025, Volume and Issue: 506, P. 111136 - 111136

Published: May 1, 2025

Language: Английский

Citations

1
Estimation of the prevalence of non-perennial rivers and streams in anthropogenically altered river basins by random Forest modeling: A case study for the Yellow river basin DOI Creative Commons
L Zhang, Mahdi Abbasi, Xiaoli Yang

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132910 - 132910

Published: Feb. 1, 2025

Language: Английский

Citations

0
Changes in the summer seasonal cycle of lakes in the Inner Tibetan Plateau since the 21st century DOI
Fuwan Gan,

Yang Gao,

Zheng Wei

et al.

Climatic Change, Journal Year: 2025, Volume and Issue: 178(4)

Published: March 25, 2025

Language: Английский

Citations

0
Forecasting monthly runoff in a glacierized catchment: A comparison of extreme gradient boosting (XGBoost) and deep learning models DOI Creative Commons
Mohammed Majeed Hameed, Adil Masood,

Ashwaq Hamid

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(5), P. e0321008 - e0321008

Published: May 23, 2025

Accurate monthly runoff forecasting is vital for water management, flood control, hydropower, and irrigation. In glacierized catchments affected by climate change, influenced complex hydrological processes, making precise even more challenging. To address this, the study focuses on Lotschental catchment in Switzerland, conducting a comprehensive comparison between deep learning ensemble-based models. Given significant autocorrelation time series data, which may hinder evaluation of prediction models, novel statistical method employed to assess effectiveness models detecting turning points data. The performance Extreme Gradient Boosting (XGBoost) was compared with long short-term memory (LSTM) random forest (RF) one-month-ahead forecasting. used 20 years data (2002–2021), 70% (2002–2015) dedicated training calibration, remaining (2016–2021) testing. findings testing phase results show that XGBoost model achieves best accuracy, R² 0.904, RMSE 1.554 m³/sec, an NSE 0.797, Willmott index ( d ) 0.972, outperforming both LSTM RF also found estimated accurately, obtaining improvements up 22% 34% Overall, study’s are essential global resource providing insights can inform sustainable practices support societies impacted change.

Language: Английский

Citations

0
Comparative assessment of empirical Random Forest family’s model in simulating future streamflow in different basin of Sarawak, Malaysia DOI
Zulfaqar Sa’adi, Shamsuddin Shahid, Mohammed Sanusi Shiru

et al.

Journal of Atmospheric and Solar-Terrestrial Physics, Journal Year: 2024, Volume and Issue: 265, P. 106381 - 106381

Published: Nov. 2, 2024

Language: Английский

Citations

1
What drives the distinct evolution of the Aral Sea and Lake Balkhash? Insights from a novel CD-RF-FA method DOI Creative Commons
Shuang Liu, Aihua Long, Geping Luo

et al.

Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 56, P. 102014 - 102014

Published: Oct. 16, 2024

Language: Английский

Citations

0