Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran

Aug 5, 2026

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Seasonal hydrological forecasts are essential for water resources management, and open monthly forecasts from the Copernicus Climate Change Service (C3S) enable 1–3-month runoff prediction evaluation. The study assesses ECMWF ensemble information using precipitation, runoff, and temperature for 30 sub-basins across Iran during 1981–2015. Ensemble quantiles (5th, 50th, 95th) represent low, medium, and high probability outcomes. Feature selection via correlation, RFE with random forest, and Bayesian networks supports modeling with MLR, ANN, SVR, RF, and XGBoost. Repeated K-fold cross-validation compares performance with KGE’, NSE, and NRMSE, showing strongest forecast impact from runoff ensembles, then precipitation and temperature. Accuracy declines beyond one-month lead time, with ANN and XGBoost leading for 2–3 months.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages23

Reading Time58 min

AccessFree to download

Introduction

Motivation and role of long-term hydrological forecasts

Ensemble hydrological forecasting and data sources (C3S/ECMWF)

Methods (from abstract)

Dataset and study period

Ensemble quantiles and input variables

Feature selection and dimension reduction

Machine learning models and validation

Performance metrics

Results (from abstract)

Variable importance for forecast accuracy

Lead-time effects and best-performing models

Spatial differences across Iran regions

Journal of Hydrology 620 (2023) 129480 Contents lists available at ScienceDirect Journal of Hydrology journal [homepage:](homepage: www.elsevier.com/locate/jhydrol)[ www.elsevier.com/locate/jhydrol](homepage: www.elsevier.com/locate/jhydrol) | Research papers
Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran | | | | --- | --- | --- | | Mohammad Akbariana, Bahram Saghafiana, *, Saeed Golian b
a Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran
b Irish Climate Analysis and Research UnitS (ICARUS), Department of Geography, Maynooth University, Maynooth, Co. Kildare, Ireland | | | | A R T I C L E I N F O | A B S T R A C T
Seasonal hydrological forecasts play a critical role in water resources management. The Copernicus Climate Change Service (C3S) data store provides open access to monthly hydrological forecasts for up to six-months. This study aims to evaluate, for the first time, 1- to 3-month runoff forecasts using the European Centre for Medium-Range Weather Forecasts (ECMWF) ensembles of precipitation, runoff, and temperature in 1981–2015 period over a total of 30 s-level basins in Iran. We adopted the 5th, 50th and 95th ECMWF ensemble quantiles for each variable that represent low, medium and high probability of occurrence, respectively. Pearson correlation analysis (Pca), Recursive Feature Elimination (RFE) via random forest (RF) model, and Bayesian Networks (BN) feature selection algorithms were used in order to reduce input variable dimension and select potential predictors to be fed to the machine learning models. Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) machine learning models were used with Repeated K-Fold cross validation (rK-Fold CV) while model efficiency was evaluated using modified Kling-Gupta efficiency coefficient (KGE’), Nash-Sutcliffe Efficiency coefficient (NSE), and Normalized Root Mean Square Error (NRMSE). Results of this study revealed that C3S runoff ensembles have the highest impact on forecast accuracy of streamflow, followed by precipitation and temperature. Overall, model performance yield a best-to-worst ranking of ANN, XGBoost, RF, MLR, and SVR with KGE’ values of 0.70, 0.68, 0.66, 0.57, and 0.41, respectively. The predictive performance of all models decreased with lead times beyond 1-month, where ANN and XGBoost outperformed other models with KGE’ of 0.65 for 2-month lead time and 0.60 for 3-month lead time. The three superior models of XGBoost, ANN, and RF, were employed with RFE and BN FSAs most frequently across Iran’s 30 s level basins in all lead times. Almost all models in the arid central region of Iran showed the lowest performance while highest skills were achieved in the western regions of Iran. Finally, for all models and over all regions, the model performance reduced by increase in lead-time. | | | This manuscript was handled by A. Bardossy, Editor-in-Chief, with the assistance of Fi-John Chang, Associate Editor | | | | Keywords: Streamflow forecast C3S data store ECMWF
Ensemble
Recursive Feature Elimination (RFE) Bayesian Networks (BN)
Machine learning (ML) | | | 1. Introduction Given global water scarcity, particularly in the past decade, it is crucial to adopt the best water resource management practices to handle or avert consequent water crises (Greve et al., 2018). An important factor in water resource management is the accurate estimation of streamflow in order to plan for available water resources (Sharma & Machiwal, 2021). Long-term forecasts include weekly, monthly, seasonal, and even annual predictions and are crucial for operation of reservoirs, irrigation management systems, and hydropower generation (Liang et al., 2018). Improving the accuracy of long-term forecasts is significantly depe

FAQs

What is the main goal of the study on runoff and streamflow forecasts?

To evaluate the accuracy of 1- to 3-month runoff forecasts using ECMWF ensemble outputs from the C3S data store over Iran’s basins.

Which ensemble quantiles are used to represent forecast probability levels?

The study uses the 5th, 50th, and 95th ECMWF ensemble quantiles to represent low, medium, and high probability occurrence.

How do forecast lead times affect model performance and which models perform best?

Model performance decreases for lead times beyond one month; ANN and XGBoost outperform other models at 2-month and 3-month lead times.

Aug 5, 2026

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