https://hess.copernicus.org/articles/ Combined list of the recent articles of the journal Hydrology and Earth System Sciences and the recent discussion forum Hydrology and Earth System Sciences Discussions en https://doi.org/10.5194/hess-30-3575-2026 Decoding multicomponent hydrochemical anomalies: a synergistic detection model for earthquake forecasting Weiye Shao, Ying Li, Xiaocheng Zhou, Zhi Chen, Huajiao Liu, Zhaofei Liu, Chang Lu, Yuwen Wang, Zhaojun Zeng, Yun Wang, Hongyi He, and Shaohui Fan Hydrol. Earth Syst. Sci., 30, 3575–3596, https://doi.org/10.5194/hess-30-3575-2026, 2026 A five-year study of hot springs at a fault intersection on the southeastern Tibetan Plateau developed an anomaly detection model that links synchronous changes in water chemistry to earthquakes with magnitude ≥4. The model combines multiple components to improve accuracy of earthquake timing forecasting and identify reliable predictors. Stronger or closer earthquakes show more components with synchronous anomalies, providing a valuable reference for real-time forecasting in high-risk areas. Fri, 12 Jun 2026 10:47:16 +0200 https://doi.org/10.5194/hess-30-3549-2026 Multi-site learning for hydrological uncertainty prediction: the case of quantile random forests Taha-Abderrahman El Ouahabi, François Bourgin, Charles Perrin, and Vazken Andréassian Hydrol. Earth Syst. Sci., 30, 3549–3574, https://doi.org/10.5194/hess-30-3549-2026, 2026 To improve hydrological uncertainty estimation, recent studies have explored machine learning (ML)-based post-processing approaches. Among these, quantile random forests (QRF) are increasingly used for their balance between interpretability and performance. We develop a hydrologically informed QRF trained in a multi-site setting. Our results show that the regional QRF approach is beneficial, particularly in catchments where local information is insufficient. Fri, 12 Jun 2026 10:47:16 +0200 https://doi.org/10.5194/hess-30-3497-2026 Testing discharge assimilation strategies to enhance short-range AI-based operational rainfall–runoff forecasts Bob E. Saint-Fleur, Eric Gaume, Florian Surmont, Nicolas Akil, and Dominique Theriez Hydrol. Earth Syst. Sci., 30, 3497–3527, https://doi.org/10.5194/hess-30-3497-2026, 2026 This paper highlights the importance of discharge assimilation (DA) for artificial intelligence (AI)-based operational discharge forecasting. Using two public datasets from France and the USA, simulated discharge from two rainfall-runoff models, and a multilayer perceptron for implementation, we evaluate three DA strategies under both deterministic and probabilistic forecasting approaches. Results show that DA is crucial and that model performance may decrease between the two forecasting cases. Thu, 11 Jun 2026 10:47:16 +0200 https://doi.org/10.5194/hess-30-3529-2026 Projections of future hydrological drought in a reservoir-regulated region: the roles of climate change and reservoir operation Shaokun He, Sirui Sun, Yanghe Liu, Kebing Chen, Lingling Zhu, and Yu Gong Hydrol. Earth Syst. Sci., 30, 3529–3547, https://doi.org/10.5194/hess-30-3529-2026, 2026 Climate change and human activities jointly shape river droughts, yet their combined impacts remain uncertain. We pair a data-driven river model with scenario-based climate projections to assess future water shortages in China’s Upper Hanjiang River. We also evaluate improved reservoir operating rules. Results show rising risk of prolonged drought, while refined reservoir operations ease short events but cannot offset long-term deficits, informing resilient water-energy planning. Thu, 11 Jun 2026 10:47:16 +0200 https://doi.org/10.5194/hess-30-3455-2026 Cause-effect discovery in hydrometeorological systems: evaluation of causal discovery methods Vivek Kumar Yadav, Murray C. Peel, Keirnan Fowler, Dongryeol Ryu, and Bramha Dutt Vishwakarma Hydrol. Earth Syst. Sci., 30, 3455–3496, https://doi.org/10.5194/hess-30-3455-2026, 2026 Identifying drivers is crucial for process understanding and predictions. In Hydrometeorological systems, many variables are closely related, and common methods often rely on correlation. We describe theoretically distinct methods of discovering cause-effect relations from data. We evaluate them in a large simulated environment. Results show that finding cause-effect relations provides a parsimonious picture and to obtain robust predictions, especially under changing environmental conditions. Fri, 05 Jun 2026 10:47:16 +0200 https://doi.org/10.5194/hess-30-3439-2026 Technical note: Benchmarking large-domain model performance under sampling uncertainty Gaby J. Gründemann, Wouter J. M. Knoben, Yalan Song, Katie van Werkhoven, and Martyn P. Clark Hydrol. Earth Syst. Sci., 30, 3439–3453, https://doi.org/10.5194/hess-30-3439-2026, 2026 The quality of large-domain hydrologic model simulations is often quantified with so-called accuracy metrics. Here we use simple benchmarks to provide relevant context for these accuracy metrics. Results show that areas where the model cannot beat the benchmarks do not always align with areas where the accuracy metrics are low. This suggests that model improvements are possible in regions that under more typical model evaluation approaches (i.e., without benchmarks) might not be obvious. Fri, 05 Jun 2026 10:47:16 +0200 https://doi.org/10.5194/hess-30-3399-2026 Comprehensive Global Assessment of 24 Gridded Precipitation Datasets Across 18 428 Catchments Using Hydrological Modeling Ather Abbas, Yuan Yang, Ming Pan, Yves Tramblay, Chaopeng Shen, Haoyu Ji, Solomon H. Gebrechorkos, Florian Pappenberger, JongCheol Pyo, Dapeng Feng, George Huffman, Phu Nguyen, Christian Massari, Luca Brocca, Jackson Tan, and Hylke E. Beck Hydrol. Earth Syst. Sci., 30, 3399–3423, https://doi.org/10.5194/hess-30-3399-2026, 2026 Our study evaluated 24 precipitation datasets using a hydrological model at global scale to assess their suitability and accuracy. We found that MSWEP (Multi-Source Weighted-Ensemble Precipitation) V2.8 excels due to its ability to integrate data from multiple sources, while others, such as IMERG (Integrated Multi-satellitE Retrievals for Global Precipitation Mission) and GDAS (Global Data Assimilation System), demonstrated strong regional performances. This research assists in selecting the appropriate dataset for applications in water resource management, hazard assessment, agriculture, and environmental monitoring. Wed, 03 Jun 2026 10:47:16 +0200 https://doi.org/10.5194/hess-30-3425-2026 Symbolic regression-based regionalization of baseflow separation parameter using catchment-scale characteristics Yongen Lin, Dagang Wang, Yiwen Mei, Jinxin Zhu, Huan Wu, Shuo Wang, Zhonghou Xu, Asaad Y. Shamseldin, and Emmanouil N. Anagnostou Hydrol. Earth Syst. Sci., 30, 3425–3438, https://doi.org/10.5194/hess-30-3425-2026, 2026 Understanding how baseflow contributes to river flow is essential for managing water resources. We studied a widely used method for separating baseflow and found that a key parameter was often estimated too simply. Using symbolic regression and data from 855 catchments, we uncovered new formulas that greatly improve accuracy and reveal how soil, snow, and catchment size jointly influence baseflow estimation. Wed, 03 Jun 2026 10:47:16 +0200 https://doi.org/10.5194/hess-30-3367-2026 Long-term hydro-sedimentary dynamics of the Ucayali River (Amazon Basin) revealed through combined observations, remote sensing, and SWAT-Amazon modelling William Santini, Alexandre Delort-Ylla, Waldo Lavado-Casimiro, Benoît Camenen, Joana Roussillon, Jhonatan Jr. Pérez Arévalo, Jorge Molina-Carpio, and Jean Michel Martinez Hydrol. Earth Syst. Sci., 30, 3367–3397, https://doi.org/10.5194/hess-30-3367-2026, 2026 The Ucayali River is the major Andean conveyor of sediment to the Amazon. By combining field measurements, satellite data and modelling over decades, we show that floodplains trap 36% of this sediment while recycling 22% back into the river during flood recession. During high waters, flooding reduces the river's capacity to carry sand, capturing 14% at peak discharge. These findings show that floodplains act as dynamic regulators of sediment transport in large tropical rivers. Tue, 02 Jun 2026 10:47:16 +0200 https://doi.org/10.5194/hess-30-3351-2026 Regulatory role of permanent gullies in dissolved nitrogen and phosphorus transport under different rainfall types Zhuoxin Chen, Mingming Guo, Lixin Wang, Xin Liu, Jinshi Jian, Qiang Chen, and Xingyi Zhang Hydrol. Earth Syst. Sci., 30, 3351–3366, https://doi.org/10.5194/hess-30-3351-2026, 2026 We examined how permanent gully in farmland regulate the transport of runoff and dissolved nitrogen and phosphorus during natural rainfall. Measurements at both the gully head and the outlet showed that the gully facilitates runoff production, yet diluted nutrient concentrations. High-erosivity storms triggered disproportionately large nutrient losses and markedly altered the gully’s contribution. These findings provide insights for improving nutrient management in gully-dominated landscapes. Mon, 01 Jun 2026 10:47:16 +0200 https://doi.org/10.5194/hess-30-3331-2026 Can streamflow observations constrain snow mass reconstructions? Lessons from two synthetic numerical experiments Pau Wiersma, Jan Magnusson, Nadav Peleg, Bettina Schaefli, and Gregoire Mariethoz Hydrol. Earth Syst. Sci., 30, 3331–3350, https://doi.org/10.5194/hess-30-3331-2026, 2026 Streamflow observations contain information about snow, but their potential to constrain seasonal snow mass reconstructions remains underexplored. Using inverse hydrological modeling, we show that streamflow is particularly effective at constraining catchment-aggregated melt rates, but that non-uniqueness in the snow–streamflow relationship and uncertainties in the inverse modeling chain can easily limit inversion performance. Thu, 28 May 2026 10:47:16 +0200 https://doi.org/10.5194/hess-30-3313-2026 Isotopic insights into the dynamics of soil water pools along an elevation gradient Jiri Kocum, Kristyna Falatkova, Vaclav Sipek, Karel Patek, Jan Haidl, Ondrej Gebousky, Jan Hnilica, Michal Jenicek, Martin Sanda, Lukas Trakal, and Lukas Vlcek Hydrol. Earth Syst. Sci., 30, 3313–3330, https://doi.org/10.5194/hess-30-3313-2026, 2026 This study investigates soil water dynamics along an elevation gradient and distinguishes individual soil water pools (mobile vs. tightly bound water). Varying persistence of winter-derived soil water was documented, with longer residence times in lowland areas despite the absence of snow cover. A new method for direct extraction of tightly bound soil water, together with a correction procedure, also revealed distinct seasonal behavior of soil water pools, particularly during spring and autumn. Thu, 28 May 2026 10:47:16 +0200 https://doi.org/10.5194/hess-30-3263-2026 A simplified model to investigate the hydrological regimes of temporary wetlands: the case study of Doñana marshland (Spain) Claudia Panciera, Alessandro Pagano, Vito Iacobellis, Manuel Bea Martínez, and Ivan Portoghese Hydrol. Earth Syst. Sci., 30, 3263–3281, https://doi.org/10.5194/hess-30-3263-2026, 2026 Wetlands are crucial environments, increasingly threatened by anthropogenic activities. The present work proposes a simplified model of wetland dynamics 'WetMAT'), useful to understand the state and potential evolution of the system in a multiplicity of conditions (e.g., climate change). Referring to the Doñana wetland (Spain) we aim at using WetMAT to support estimating water needs in such a complex and fragile ecosystem, providing useful insights for water resources planning and management. Wed, 27 May 2026 10:47:16 +0200 https://doi.org/10.5194/hess-30-3283-2026 A novel classifier-guided ensemble framework for global terrestrial evapotranspiration estimates Le Ni, Weiguang Wang, Jianyu Fu, and Mingzhu Cao Hydrol. Earth Syst. Sci., 30, 3283–3312, https://doi.org/10.5194/hess-30-3283-2026, 2026 Existing global evapotranspiration algorithms rely on sparse local measurements and each comes with its own strengths and weaknesses. Here, we proposed an ensemble framework that employed a machine learning system to dynamically select the most appropriate algorithm to be used across spatial and temporal scales, thus fully utilizing the distinct strengths of each method. In multi-scale validations, our framework exhibited enhanced extrapolation performance, stability, and interpretability. Wed, 27 May 2026 10:47:16 +0200 https://doi.org/10.5194/hess-30-3245-2026 Technical note: An innovative monitoring approach to measure spatio-temporal throughfall patterns in forests Lea Dedden and Markus Weiler Hydrol. Earth Syst. Sci., 30, 3245–3261, https://doi.org/10.5194/hess-30-3245-2026, 2026 Throughfall in forests varies in space and time creating distinct patterns. We developed a novel throughfall monitoring approach for continuous, automated measurement that features 60 self-built and cost effective throughfall samplers. Collected data show the potential of the approach to capture throughfall variability at small distances, among and within rainfall events and between different trees species. Tue, 26 May 2026 10:47:16 +0200 https://doi.org/10.5194/hess-30-3221-2026 Reconstruction of climate-driven global terrestrial water storage variations (2002–2021) using a four-parameter linear recursive model Pu Xie and Shuang Yi Hydrol. Earth Syst. Sci., 30, 3221–3244, https://doi.org/10.5194/hess-30-3221-2026, 2026 We present a global 0.5° × 0.5° daily reconstruction of terrestrial water storage anomalies from 2002–2021, using a novel four-parameter linear recursive model driven only by precipitation and temperature. The model exhibits strong physical interpretability, efficiently quantifies the precipitation-to-storage conversion fraction, and achieves faster parameter convergence. It outperforms existing models in 89 % of basins, with Nash–Sutcliffe efficiency values exceeding 0.7 in 84 basins.  Tue, 26 May 2026 10:47:16 +0200 https://doi.org/10.5194/hess-30-3203-2026 Derivation and validation of estimation model of rainfall kinetic energy under the canopy Zixi Li and Fuqiang Tian Hydrol. Earth Syst. Sci., 30, 3203–3219, https://doi.org/10.5194/hess-30-3203-2026, 2026 Forests can change the kinetic energy of rain below them. We built a new model that breaks down the canopy into layers, and tracks two types of raindrop: direct splashes and water dripping from leaves. The model was validated through nine rainfall events. The canopy doesn't always reduce the rain's force, and sometimes it increases it, depending on the specific structure of the leaves and branches. Fri, 22 May 2026 10:47:16 +0200 https://doi.org/10.5194/hess-30-3165-2026 A hybrid Kolmogorov–Arnold Networks-based model with attention for predicting Arctic river streamflow Renjie Zhou and Shiqi Liu Hydrol. Earth Syst. Sci., 30, 3165–3183, https://doi.org/10.5194/hess-30-3165-2026, 2026 Arctic rivers move enormous amounts of water and carbon into the ocean, influencing global climate, but their flow is hard to predict because the region is remote and the frozen ground behaves in unusual ways. This research combines artificial intelligence with the physics of snow and permafrost to forecast river flow more accurately. Demonstrated on the Kolyma River, the new model outperforms existing approaches and provides a robust framework for understanding Arctic hydrological systems. Fri, 22 May 2026 10:47:16 +0200 https://doi.org/10.5194/hess-30-3185-2026 Shifting water scarcities: irrigation alleviates agricultural green water deficits while increasing blue water scarcity Heindriken Dahlmann, Lauren S. Andersen, Sibyll Schaphoff, Fabian Stenzel, Johanna Braun, Christoph Müller, and Dieter Gerten Hydrol. Earth Syst. Sci., 30, 3185–3201, https://doi.org/10.5194/hess-30-3185-2026, 2026 Green water stress can negatively affect agricultural production and is often mitigated through irrigation. In this global modelling study, we investigate where and to what extent the implementation of irrigation helps to compensate for green water stress but at the same time leads to an increase in blue water scarcity. Our findings highlight the need to consider both water stresses together, along with their dynamic interactions, for sustainable water management. Fri, 22 May 2026 10:47:16 +0200 https://doi.org/10.5194/hess-30-3095-2026 Simulating carbon fluxes in boreal catchments: WSFS-Vemala model development and key insights Marie Korppoo, Inese Huttunen, Markus Huttunen, Maiju Narikka, Jari Silander, Tom Jilbert, Martin Forsius, Pirkko Kortelainen, Niina Kotamäki, Cintia Uvo, and Anna-Kaisa Ronkanen Hydrol. Earth Syst. Sci., 30, 3095–3119, https://doi.org/10.5194/hess-30-3095-2026, 2026 The development of carbon processes in the water quality model WSFS (Watershed Simulation and Forecasting System)-Vemala presents a significant advancement in simulating both total organic and inorganic carbon dynamics, burial and emissions through a river/lake network. The addition of organic acids to the total alkalinity definition improved pH simulations and thus the simulation of CO2 emissions in the acidic and organic rich waters of Finland. The new Vemala model provides a robust foundation to support water management in the future. Thu, 21 May 2026 10:47:16 +0200