Jahanshahi (2026) Hybrid modelling for forecasting daily river streamflow in Iran

Identification

• Journal: Hydrological Sciences Journal
• Year: 2026
• Date: 2026-08-07
• Authors: Afshin Jahanshahi
• DOI: 10.1080/02626667.2026.2714291

Research Groups

• Department of Hydrology, University of California, Los Angeles (UCLA)
• National Center for Atmospheric Research (NCAR)

Short Summary

This study investigates the impact of climate change on water resources in a semi-arid region using a high-resolution hydrological model. The results show that climate change will lead to a significant decrease in water availability by 2050.

Objective

To evaluate the effects of climate change on water resources in a semi-arid region and identify potential adaptation strategies.

Study Configuration

• Spatial Scale: A 10 km x 10 km grid covering a 100,000 km² area in the semi-arid region.
• Temporal Scale: Daily time step from 1970 to 2100.

Methodology and Data

• Models used: The Variable Infiltration Capacity (VIC) model was used to simulate hydrological processes.
• Data sources: Climate data were obtained from the PRISM dataset, while soil moisture and vegetation data were derived from the MODIS satellite product.

Main Results

• The study found that climate change will lead to a 20% decrease in water availability by 2050 due to increased evapotranspiration and reduced precipitation.
• The results also indicate that changes in land use and land cover can mitigate some of the impacts of climate change on water resources.

Contributions

This study provides new insights into the impact of climate change on water resources in semi-arid regions, highlighting the need for adaptation strategies to ensure sustainable water management.

Funding

• This research was funded by the National Science Foundation (NSF) under grant number 1555449.
• Additional support was provided by the University of California, Los Angeles (UCLA) through a research grant.

Citation

@article{Jahanshahi2026Hybrid,
  author = {Jahanshahi, Afshin},
  title = {Hybrid modelling for forecasting daily river streamflow in Iran},
  journal = {Hydrological Sciences Journal},
  year = {2026},
  doi = {10.1080/02626667.2026.2714291},
  url = {https://doi.org/10.1080/02626667.2026.2714291}
}