Openly in HSJ 
Open Access papers from recent issues
Issue 7
Research article
Extra constraint on actual evaporation in a semi-distributed conceptual model to improve model physical realism
Shu-Chen Hsu et al.
Hydrological models are typically only calibrated on discharge data and can lead to poor estimates of other fluxes in the model. In this work, a semi-distributed conceptual model was further constrained by actual evaporation datasets. The results show that constraining actual evaporation during calibration indirectly constrains the estimation of other fluxes, allowing the water balance to be closed more realistically.
Review
Panta Rhei: a decade of progress in research on change in hydrology and society
Heidi Kreibich et al.
This fundamental community paper provides a synthesis of the Panta Rhei scientific decade and marks its conclusion, highlighting the need for integrated approaches to understand human–water system co-evolution and progress in harnessing new data sources. It also emphasizes advances in detecting hydrological changes and attributing their drivers, suggesting future directions for broader understanding, discipline development and innovative solution-finding for global water issues that avoid the unintended consequences of human interventions.
Issue 8
Special issue: History of hydrology
Emergence of, and developments in, hydrology (suimongaku) in Japan from the late 19th century to 1970
Shinichiro Nakamura et al.
This paper provides an interesting and enlightening history of hydrology in Japan, from its initial efforts to translate and introduce Western hydrological knowledge, to becoming a leader in contributing original research advances and methodologies in the field. The paper also explores the effects of the Second World War in shaping hydrological studies in Japan.
The rise of the Nash-Sutcliffe efficiency in hydrology
Lieke A. Melsen et al.
Have you ever wondered why Nash-Sutcliffe Efficiency (NSE) has become so widely used in scientific fields? This study investigates this very question by uncovering the social and technological factors that led to the NSE becoming the dominant model evaluation metric in hydrology. The historical journey of the NSE reveals how community acceptance and influential endorsements, rather than inherent technical superiority, solidified its widespread use in hydrological modelling. This is a must-read before using NSE in your next study.
Special issue: Twenty-first century hydrological challenges and opportunities
in Africa
Bias reduction of CFSR data with random forest for applications in water resources modelling in Ogun River
Oluwatobi Aiyelokun et al.
This study uses random forest to correct biases in CFSR climate data, aligning it more closely with observed weather patterns. The improved RF-CFSR dataset significantly reduces errors and enhances key performance metrics, offering a reliable alternative to traditional bias correction methods.
Issue 9
Value of water level class observations for parameter set selection in hydrological modelling
Franziska Clerc-Schwarzenbach et al.
Citizen-reported water level observations, combined with minimal discharge data, can effectively support parameter selection for the HBV hydrological model. Even approximate discharge estimates improve model performance, making hydrological modelling more accessible in data-scarce regions.
Study of multi-source river discharge data assimilation in collaboration with EKF and MIKE11HD
Zhang Yinqin et al.
Data assimilation is being used increasingly in conjunction with hydrological modelling output to improve estimates of discharge. This paper presents a novel data assimilation approach that incorporates several different data types (remote sensing imagery, observed discharge data and model simulations) and demonstrates the improved accuracy of
these discharge estimates over traditional data assimilation methods.
Co-editors’ choice
One article in every issue is singled out by the Co-editors to be featured for its excellence or innovation, and is awarded six months’ free access; in addition, the editors periodically choose an article of particular topical interest. Recent selections include the following:
Vol. 70 Issue 7
Featured article - free to view for six months
Multifractal comparison of rainfall measurement with the help of a disdrometer and a mini vertically pointing Doppler radar
Mateus Seppe Silva et al.
Selecting optimal rainfall measurement tools is crucial yet complex for hydrological applications. This study addresses that challenge by conducting a multifractal comparison of rainfall data from disdrometers and mini Doppler radars. By revealing insights into their scaling behaviours and the influence of environmental factors across various scales and rain conditions, this research helps determine the most reliable device for diverse rainfall scenarios.
Vol. 70 Issue 8
Featured article - free to view for six months
Assessing atmospheric influences for improving time-varying data-driven decadal predictions of Mediterranean spring discharge
Nazzareno Diodato et al.
The study introduces a novel auto-regressive time series model to predict spring discharge at decadal time scales. The data-driven model capitalizes on 123 years of available spring discharge data in the central Mediterranean. Results emphasize the importance of extreme rainfall events in determining spring discharge and highlight the complex relationship between climate change and groundwater systems.
Hot topic
Integrating machine learning and data envelopment analysis for reliable reservoir water quality index assessment considering uncertainty
Mohammad Sadegh Zare et al.
The authors propose a machine learning-based framework to determine the water quality index for a reservoir. A tree-based ensemble technique for variable selection reduces reliance on subjective expert judgement. The water quality index thus obtained is analysed across varying depths, seasons and sampling locations. The proposed framework aims to assist stakeholders in their decision-making processes regarding water quality management.
Vol. 70 Issue 9
Featured article - free to view for six months
Rainfall nowcasting models: state of the art and possible future perspectives
Davide Luciano De Luca et al.
The authors review widely used rainfall nowcasting models, including deep learning approaches, and discuss the benefits of blending these models to improve early warning systems. They further explore the critical role of predictive uncertainty evaluation in enhancing informed decision making, based on the forecasts of single- and multi-model early warning systems. | 
