Interpretable monthly streamflow estimation for seasonal water-resource management in highland watershed
- 1University of Science – Viet Nam National University
- 2Ho Chi Minh City University of Technology and Engineering
- 3Ho Chi Minh City University of Natural Resources and Environment
Abstract
Monthly streamflow information supports seasonal water-resource planning in tropical highland basins, where dry-season scarcity and wet-season runoff require different management responses. This study evaluated an integrated hydrological machine-learning and socio-economic water-pressure screening framework (Hydro-ML–SEWP) for the Da Tam watershed, Lam Dong Province, Vietnam. The hydrological models estimate monthly discharge conditionally on target-month rainfall and temperature together with antecedent streamflow, lagged rainfall, rolling hydroclimatic indicators, and seasonal cyclic terms; they are therefore within-month estimators rather than strict lead-time forecasts. A seasonal-naive benchmark, Ridge regression, support vector regression with a radial basis function kernel (SVR-RBF), Random Forest, and Gradient Boosting were assessed using a chronological 2019–2024 hold-out period and rolling-origin evaluation for 2011–2024. Ridge regression gave the highest R 2 and lowest RMSE in both evaluations (hold-out R 2 = 0.863, RMSE = 0.904 m3 s−1; rolling-origin R 2 = 0.831, RMSE = 1.047 m3 s−1), whereas Random Forest gave the lowest MAE and the highest normalized composite score. Target-month rainfall, seasonal cyclicity, 1-month lagged discharge, and 1-month lagged rainfall represented 77.1% of the Random Forest impurity-based importance, although correlated predictors and the use of concurrent rainfall limit causal interpretation. Screening of the 1995–2024 drainage-area-transferred streamflow series identified February–March and September–October as the principal low-flow and high-flow periods. Because the source dataset does not identify the reference gauge and the available socio-economic records are not temporally aligned across all four proposed SEWP components, absolute discharge magnitudes and annual SEWP values are not treated as fully verified basin-specific quantities. The framework is therefore presented as a transparent conditional-estimation and scenario-screening approach whose operational use requires documented reference-basin metadata, target-month meteorological forecasts or lag-only predictors, and watershed-scale socio-economic data.
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BibTeX
@article{pham2026interpretable,
title = {{Interpretable monthly streamflow estimation for seasonal water-resource management in highland watershed}},
author = {Anh The Pham and Canh Thanh Truong and An Xuan Bui and Bao Quoc Tran},
journal = {Water Science},
year = {2026},
month = oct,
doi = {10.1007/s44533-026-00087-9},
url = {https://doi.org/10.1007/s44533-026-00087-9},
note = {CC BY},
}
RIS
TY - JOUR
TI - Interpretable monthly streamflow estimation for seasonal water-resource management in highland watershed
AU - Anh The Pham
AU - Canh Thanh Truong
AU - An Xuan Bui
AU - Bao Quoc Tran
PY - 2026
DA - 2026/10/01
JO - Water Science
DO - 10.1007/s44533-026-00087-9
UR - https://doi.org/10.1007/s44533-026-00087-9
AB - Monthly streamflow information supports seasonal water-resource planning in tropical highland basins, where dry-season scarcity and wet-season runoff require different management responses. This study evaluated an integrated hydrological machine-learning and socio-economic water-pressure screening framework (Hydro-ML–SEWP) for the Da Tam watershed, Lam Dong Province, Vietnam. The hydrological models estimate monthly discharge conditionally on target-month rainfall and temperature together with antecedent streamflow, lagged rainfall, rolling hydroclimatic indicators, and seasonal cyclic terms; they are therefore within-month estimators rather than strict lead-time forecasts. A seasonal-naive benchmark, Ridge regression, support vector regression with a radial basis function kernel (SVR-RBF), Random Forest, and Gradient Boosting were assessed using a chronological 2019–2024 hold-out period and rolling-origin evaluation for 2011–2024. Ridge regression gave the highest R 2 and lowest RMSE in both evaluations (hold-out R 2 = 0.863, RMSE = 0.904 m3 s−1; rolling-origin R 2 = 0.831, RMSE = 1.047 m3 s−1), whereas Random Forest gave the lowest MAE and the highest normalized composite score. Target-month rainfall, seasonal cyclicity, 1-month lagged discharge, and 1-month lagged rainfall represented 77.1% of the Random Forest impurity-based importance, although correlated predictors and the use of concurrent rainfall limit causal interpretation. Screening of the 1995–2024 drainage-area-transferred streamflow series identified February–March and September–October as the principal low-flow and high-flow periods. Because the source dataset does not identify the reference gauge and the available socio-economic records are not temporally aligned across all four proposed SEWP components, absolute discharge magnitudes and annual SEWP values are not treated as fully verified basin-specific quantities. The framework is therefore presented as a transparent conditional-estimation and scenario-screening approach whose operational use requires documented reference-basin metadata, target-month meteorological forecasts or lag-only predictors, and watershed-scale socio-economic data.
SN - 2357-0008
SN - 1110-4929
ER -