Hydrological Research Articles

Exploring the Ecological Effectiveness of Taiwan’s Ecological Check and Identification Mechanism in Coastal Engineering

Extreme weather events from climate change challenge infrastructure stability. While water-related engineering enhances disaster resilience, it also impacts ecosystems. Taiwan has implemented Ecological Check and Identification (ECI) since 2003, yet challenges remain in standards, resource allocation, and effectiveness. This study analyzes 35 coastal engineering cases and participated in two engineering projects from five key perspectives. The results show that there are regional differences in the types of projects implemented for ECI. Landscape engineering was the main type in northern Taiwan (31%), water resource engineering was the main type in southern Taiwan (43%), and no cases were found in eastern Taiwan. Most inspections occur in the proposal (24%), planning (22%), and design (22%) stages, with limited post-construction monitoring (14%). Furthermore, ecological assessments were lacking in 49% of cases, and aquatic ecosystems were underrepresented. Inconsistent inspection formats and low species documentation (57% of cases) reduce data comparability and conservation effectiveness. To address these gaps, some recommendations were made, including standardizing inspections, integrating Sustainable Development Goals (SDGs), promoting low-carbon approaches, strengthening public participation, and establishing long-term monitoring. The findings provide policy insights to enhance ECI, supporting sustainable coastal engineering while balancing infrastructure benefits and environmental conservation.

A review of soil moisture data in China: definition, sources, and applications in hydrometeorology research

Abstract Soil moisture (SM) is a key land surface hydrological variable that regulates energy, water, and carbon exchanges between land and atmosphere, as well as connecting hydrological, meteorological and environmental sciences. For decades, SM has been measured, simulated, and then utilized for various research in China. In this paper, we summarize recent progresses of SM data and their hydrometeorological applications in China and then propose some future research prospects. First, we introduce the terminology and description of SM in various formats, followed by the primary SM data sources along with their strengths and weaknesses. Then, recent advances in SM applications for drought detection, land-atmosphere interactions, and climate prediction are thoroughly discussed. Based on the above findings, it is recommended that: (1) future developments of SM data should undertake more intensive in-situ measurements, carry out high-quality modeling experiments along with upgraded numerical models, and build novel data fusion technology; (2) ongoing efforts should be emphasized on enhancing representation of SM-related physical processes and taking into account the anthropogenic influences on SM in the land surface models; (3) SM drought should reinforce its onset and demise mechanisms, as well as its precursor signals in both terrestrial and atmospheric systems; and (4) more innovative methods are needed to distinguish the impacts of SM on climate variability from other drivers. With these efforts, research on SM would help us to understand the mechanisms underlying its interconnected changes in the earth system.

An Immersive Hydroinformatics Framework with Extended Reality for Enhanced Visualization and Simulation of Hydrologic Data

This study introduces a novel framework with the use of extended reality (XR) systems in hydrology, particularly focusing on immersive visualization of hydrologic data for enhanced environmental planning and decision making. The study details the shift from traditional 2D data visualization methods in hydrology to more advanced XR technologies, including virtual and augmented reality. Unlike static 2D maps or charts that require cross-referencing disparate data sources, this system consolidates real-time, multivariate datasets, such as streamflow, precipitation, and terrain, into a single interactive, spatially contextualized 3D environment. Immersive information systems facilitate dynamic interaction with real-time hydrological and meteorological datasets for various stakeholders and use cases, and pave the way for metaverse and digital twin systems. This system, accessible via web browsers and XR devices, allows users to navigate a 3D representation of the continental United States. The paper addresses the current limitations in hydrological visualization, methodology, and system architecture while discussing the challenges, limitations, and future directions to extend its applicability to a wider range of environmental management and disaster response scenarios. Future application potential includes climate resilience planning, immersive disaster preparedness training, and public education, where stakeholders can explore scenario-based outcomes within a virtual space to support real-time or anticipatory decision making.

An intelligent algorithm to fast and accurately detect chaotic correlation dimension

AbstractDetecting the complexity of natural systems, such as hydrological systems, can help improve our understanding of complex interactions and feedback between variables in these systems. The correlation dimension method, as one of the most useful methods, has been applied in many studies to investigate the chaos and detect the intrinsic dimensions of underlying dynamic systems. However, this method often relies on manual inspection due to uncertainties from identifying the scaling region, making the correlation dimension value calculation troublesome and subjective. Therefore, it is necessary to propose a fast and intelligent algorithm to solve the above problem. This study implies the distinct windows tracking technique and fuzzy C‐means clustering algorithm to accurately identify the scaling range and estimate the correlation dimension values. The proposed method is verified using the classic Lorenz chaotic system and 10 streamflow series in the Daling River basin of Liaoning Province, China. The results reveal that the proposed method is an intelligent and robust method for rapidly and accurately calculating the correlation dimension values, and the average operation efficiency of the proposed algorithm is 30 times faster than that of the original Grassberger‐Procaccia algorithm.

Thermal Regime Characteristics of Alpine Springs in the Marginal Periglacial Environment of the Southern Carpathians

Mountain watersheds play a crucial role in sustaining freshwater resources, yet they are highly vulnerable to climate change. In this study, we investigated the summer water temperature of 35 alpine springs in the highest part of the Retezat Mountains, Southern Carpathians, between 2020 and 2023. During the four-year monitoring period, water temperatures across all springs ranged from 1.2 °C to 10.5 °C. Springs emerging from rock glaciers had the lowest average temperature (2.37 °C), while those on cirque and valley floors were the warmest (6.20 °C), followed closely by springs from meadow-covered slopes (6.20 °C) and those from scree and talus slopes (4.70 °C). However, only four springs recorded summer temperatures below 2 °C, suggesting a direct interaction with ground ice. The majority of springs exhibited temperatures between 2 and 4 °C, exceeding conventional thresholds for permafrost presence. This challenges the applicability of traditional thermal indicators in marginal periglacial environments, where reduced ground ice content within rock glaciers and talus slopes can lead to spring water temperatures ranging from 2 °C to 4 °C during summer. Additionally, cold springs emerging from rock glaciers displayed minimal daily and seasonal temperature fluctuations, highlighting their thermal stability and decoupling from atmospheric conditions. These findings underscore the critical role of rock glaciers in maintaining alpine spring temperatures and acting as refugia for cold-adapted organisms. As climate change accelerates permafrost degradation, these ecosystems face increasing threats, with potential consequences for biodiversity and hydrological stability. This study emphasizes the need for long-term monitoring and expanded investigations into water chemistry and discharge dynamics to improve our understanding of high-altitude hydrological systems. Furthermore, it provides valuable insights for the sustainable management of water resources in Retezat National Park, advocating for conservation strategies to mitigate the impacts of climate change on mountain hydrology and biodiversity.

A First Look at River Discharge Estimation From SWOT Satellite Observations

AbstractThe Surface Water and Ocean Topography (SWOT) satellite has the potential to transform global hydrologic science by offering simultaneous and synoptic estimates of river discharge and other hydraulic variables. Discharge is estimated from SWOT observations of water surface elevation, width, and slope. A first assessment using just the highest quality SWOT measurements, over the first 15 months (March 2023–July 2024) of the mission evaluated at 65 gauged reaches shows results consistent with pre‐launch expectations. SWOT estimates track discharge dynamics without relying on any gauge information: median correlation is 0.73, with a correlation interquartile range of 0.51–0.89. SWOT estimates capture discharge magnitude correctly in some cases but are biased (median bias is 50%) in others. There are already a total of 11,274 ungauged global locations with highest quality SWOT measurements where SWOT discharge is expected to accurately track discharge variations: this value will increase as SWOT data record length grows, algorithms are refined and SWOT measurements are reprocessed. This first look indicates that SWOT discharge is performing as expected for SWOT data that achieve performance requirements, providing observed information on discharge variations in ungauged basins globally.

Comparative analysis of evapotranspiration and water yield in basins of the Northern Gulf of America between the past and next 40 years.

Comparative analysis of evapotranspiration and water yield in basins of the Northern Gulf of America between the past and next 40 years.

River Thermal Dynamics and Heatwaves of Polish Rivers Under Climate Change

AbstractProgression of global warming poses a significant risk to river ecosystems. However, how river heatwaves' characteristics across complex hydrological systems alter under climate change is still poorly understood. In this study, long‐term reconstructed daily river water temperatures (RWTs) from 125 hydrological stations in 70 rivers across Poland, were used. Bayesian estimator of abrupt change, seasonal change, and trend (BEAST) method was used to track the abrupt changes of RWTs. Moreover, the characteristics of river heatwaves, including number, duration, intensity, and category, were evaluated. BEAST analysis revealed pronounced spatiotemporal variability in RWT trends in Poland, influenced by natural and anthropogenic factors. Notably, the maximum abrupt changes of RWT were observed during the 1980s and 1990s. Southern Poland, particularly mountainous regions, exhibited more pronounced river temperature changes and severe heatwaves compared to the milder northern regions. Our results also showed a statistically significant increase in frequency and intensity of river heatwaves at 121 out of the 125 studied stations (p‐value < 0.05), which were consistent with the warming of air temperatures. For all the designated stations, the majority of river heatwaves belonged to the category “moderate,” followed by “strong,” “severe,” and “extreme.” Number, duration, and intensity of the river heatwaves were highly correlated with air temperatures, with the correlation coefficients being 0.624, 0.631, and 0.604, respectively. Our findings further suggest that mitigation measures shall be taken to reduce the effects of climate warming on Polish river ecosystems, especially under low flow conditions which are more vulnerable to the intensified river heatwaves.

TVDLF Method to Simulate Two-Dimensional Flow through Large Hydrologic Systems with Wetlands and Hillslopes

TVDLF Method to Simulate Two-Dimensional Flow through Large Hydrologic Systems with Wetlands and Hillslopes

Climate Warming-Induced Hydrological Regime Shifts in Cold Northeast Asia: Insights from the Heilongjiang-Amur River Basin

Rapid climate warming and intensified human activities are causing profound alterations in terrestrial hydrological systems. Understanding shifts in hydrological regimes and the underlying mechanisms driving these changes is crucial for effective water resource management, watershed planning, and flood disaster mitigation. This study examines the hydrological regimes of the Heilongjiang-Amur River Basin, a transboundary river basin characterized by extensive permafrost distribution in northeastern Asia, by analyzing long-term daily meteorological (temperature, precipitation, evaporation) and hydrological data from the Komsomolsk, Khabarovsk, and Bogorodskoye stations. Missing daily runoff data were reconstructed using three machine learning methods: Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), and Convolutional Long Short-Term Memory Networks (CNN-LSTM). Trend analysis, abrupt change detection, and regression techniques revealed significant warming and increased actual evapotranspiration in the basin from 1950 to 2022, whereas precipitation and snow water equivalent showed no significant trends. Climate warming is significantly altering hydrological regimes by changing precipitation patterns and accelerating permafrost thaw. At the Komsomolsk station, an increase of 1 mm in annual precipitation resulted in a 0.48 mm rise in annual runoff depth, while a 1 °C rise in temperature led to an increase of 1.65 mm in annual runoff depth. Although annual runoff exhibited no significant long-term trend, low-flow runoff demonstrated substantial increases, primarily driven by temperature and precipitation. These findings provide critical insights into the hydrological responses of permafrost-dominated river basins to climate change, offering a scientific basis for sustainable water resource management and strategies to mitigate climate-induced hydrological risks.

Integrating Prediction of Precipitation and Hydrology for Early Actions: The InPRHA project within the World Weather Research Programme

Abstract Despite advancements in science and technology, floods prediction and preparedness remain challenging due to uncertainties in forecasting atmospheric and hydrologic processes, limited real-time data, and communication barriers. The Integrating Prediction of Precipitation and Hydrology for Early Actions (InPRHA) project, a five-year initiative under the WMO’s World Weather Research Programme, is the first to bring together meteorology, hydrology, and social sciences within a steering committee to address these challenges. Building on knowledge from the HiWeather project, InPRHA focuses on multi-hazard flood forecasting across the entire warning value chain from minutes to days, in a rapidly changing world. A key emphasis is understanding flood predictability and how uncertainties cascade through forecasting systems and are perceived, communicated, and acted upon by diverse stakeholders. This includes bridging research and operations, examining socio-economic, cultural, and environmental challenges that influence risk perception and response. We propose key scientific questions across seven themes that address critical gaps in integrating predictions along the flood warning value chain. Addressing these gaps requires collaboration across disciplines and agencies. The project is structured into four work packages: DEFINE (identifying challenges), CONSTRUCT (gathering case studies), EXPERIMENT (scientific evaluations), and ENGAGE (community collaboration). Research will span rural, urban, and underdeveloped regions as well as countries with established warning systems, ensuring broad applicability. We invite scientists and practitioners from meteorology, hydrology, hydraulics, impacts, communication, human behavior and economics to collaborate. By integrating disciplines and fostering transdisciplinary research, InPRHA aims to advance the science and practice of flood forecasting and early warnings to better protect vulnerable communities at risk.

A Model for Regional‐Scale Water Erosion and Sediment Transport and Its Application to the Yellow River Basin

AbstractOn catchment scales, sediment discharge depends on both sediment transport capacity and sediment availability. The quantification of sediment discharge at the regional scales is important but is rarely adequately represented in regional hydrological models. Here, we introduce a regional water erosion and sediment transport model, Atmospheric and Hydrological‐Sediment Modeling System (AHMS‐SED). This model integrates the Atmospheric and Hydrological Modeling System (AHMS) with the improved CASCade 2‐Dimensional SEDiment (CASC2D‐SED) model and incorporates gully erosion as a significant factor affecting sediment supply. A gully area index is introduced to quantify the fraction of the gully area and the enhancement of water erosion induced by concentrated flow in gullies. We use the AHMS‐SED to simulate the sediment processes in the Yellow River Basin from 1979 to 1987 at a 20 km resolution. We find quantitative agreement between the observations and model predictions for monthly sediment fluxes at five major hydrological stations along the Yellow River, with excellent performance metrics (modified Kling‐Gupta efficiency = 0.90, Nash–Sutcliffe model efficiency coefficient = 0.81) at the basin outlet. The results demonstrate the strong performance of the AHMS‐SED and the robustness of the sediment supply estimates. We also use AHMS‐SED to investigate how changes in climate and human activities affect sediment discharge in the Yellow River. The model shows that halving precipitation intensity substantially reduces sediment discharge, halving precipitation amount reduces it by 60%, and doubling irrigation reduces it by 10%.

Proposal of Siphon Operation Guidelines for Reducing Downstream Flood Damage in Small Agricultural Reservoirs

Climate change has altered rainfall patterns, thereby increasing the frequency of extreme floods and threatening small agricultural reservoirs with insufficient discharge facilities. Most reservoirs lack flood control gates, making them vulnerable to excessive inflow. Thus, portable siphons provide a practical alternative. This study used the U.S. Army Corps of Engineers River Analysis System developed by the Hydrologic Engineering Center. (HEC-RAS) to analyze the effects of siphon specifications and pre-release duration on reservoir discharge and downstream inundation, thereby presenting optimized siphon operation guidelines. The results showed that increasing the number of siphons and extending the pre-release periods effectively reduces peak discharge and inundation. Based on these findings, an allowable flood threshold was established to minimize downstream damage and prevent reservoir failure. Site-specific siphon operation standards can enhance emergency discharge systems for aging reservoirs.

EDITORIAL: INTEGRATION OF HYDROLOGICAL MODELS AND MACHINE LEARNING TECHNIQUES FOR WATER RESOURCES MANAGEMENT

Hydrology and water resources management ensure the sustainable use, conservation, and allocation of water in natural and engineered systems. Climate change, urbanization, and rising water demand necessitate advanced modeling approaches to enhance water security and resilience to extreme hydrological events. This editorial scope explores the integration of conventional hydrological models with machine learning to improve predictive accuracy, decision-making, and resource optimization. Physics-based models such as SWAT, VIC, and HEC-HMS simulate watershed processes, while hydraulic models like HEC-RAS and MIKE SHE assess flood risks. Groundwater models (e.g., MODFLOW) analyze aquifer dynamics, and optimization models support efficient reservoir and watershed management. Despite their reliability, these models require extensive calibration, high-resolution data, and struggle with capturing nonlinear hydrological complexities. Advancements in computational power and data availability enable machine learning to complement traditional models. Algorithms such as ANNs, SVMs, and RF enhance hydrological forecasting, while deep learning methods (LSTMs, CNNs) improve spatio-temporal predictions. Hybrid models integrating physical-based simulations with machine learning-driven corrections reduce uncertainties, enhance computational efficiency, and enable adaptive water management. Machine learning applications extend to flood forecasting, drought risk assessment, and climate change impact analysis, strengthening disaster mitigation efforts. Integrating AI with hydrological models offers promising advancements in real-time monitoring, infrastructure resilience, and water governance. However, challenges related to data availability, model interpretability, and computational complexity remain. Future research should focus on explainable AI, refined hybrid modeling, and machine learning-based decision-support systems. As AI, remote sensing, and big data evolve, their convergence with hydrological sciences will drive more intelligent and sustainable water management solutions.

Assessment of Baseflow Characteristics and Environmental Flow Allocation in the Welo Sub-Watershed, Central Java

This study assesses baseflow characteristics and environmental flow (EF) requirements in the Welo Sub-Watershed, Central Java, Indonesia—a region increasingly affected by land use change and climate variability. Using 30 years of daily streamflow data (1994–2023), baseflow was separated using the Fixed Interval Method with analysis conducted in Excel and the Hydrologic Engineering Center’s Hydrograph Analysis (HEC-HMS). EF was estimated using both the Tennant Method and Flow Duration Curve (FDC) analysis. Results indicate that average dry-season baseflow is 0.79 m³/s, while the reliable flow (Q80) averages 1.02 m³/s. EF estimates are 0.43 m³/s (Tennant) and 0.54 m³/s (FDC). Under normal hydrological conditions, baseflow exceeds EF thresholds. However, peak irrigation demand reaching 1.30 m³/s surpasses both baseflow and Q80 during dry periods. This suggests periods of ecological stress and potential conflict among water users. These findings underscore the need for integrating EF targets into local water resource planning to safeguard ecosystem function and ensure sustainable water allocation.

Red Maple Tree Root Water Uptake Depths are Influenced by Neighboring Tree Species Composition.

Understanding how mixed-species forests uptake subsurface water sources is critical to projecting future forest water use and stress. Variation in Root Water Uptake (RWU) depths and volumes is common among trees but it is unclear how it is affected by species identity, local water availability, or neighboring tree species compositions. We evaluated the hypothesis that RWU depths and the age of water (i.e., time since water entered soils as precipitation) taken up by red maples (Acer rubrum) varied significantly between two forested plots, both containing red maples, similar soils, topography, and hydrologic conditions, but having different neighboring tree species. We measured soil moisture contents as well as stable isotopes (δ2H, δ18O) in plant xylem water and soil moisture across two years. These data were used to calibrate process-based stand-level ecohydrological models for each plot to estimate species-level RWU depths. Model calibration suggested significant differences in red maple tree RWU depths, transpiration rates, and the ages of water taken up by maples across the two stands. Maple trees growing with ash and white spruce relied on significantly deeper and older water from the soil profile than maple trees growing with birch and oak. The drought risk profile experienced by maple trees differed between the plots as demonstrated by strong correlations between precipitation and model simulated transpiration on a weekly time scale for maples taking up shallow soil moisture and a monthly time scale for maples reliant on deeper soil moisture. These findings carry significant implications for our understanding of water competition in mixed-species forests and for the representation of forest rooting strategies in hydrologic and earth systems models.

Socio-hydrological frameworks for adaptive governance: addressing climate uncertainty in South Asia

In an era of growing climate change impacts, there is an increasing need to grasp the complex connection between human society and hydrological systems. Socio-hydrology, an interdisciplinary area between hydrology, sociology, and economics, provides essential insights to uncover how people's conduct impacts water and climate systems and resources. In this context, this paper looks at modern socio-hydrology advances and what they suggest for creating resistance or insensitivity to climate evolution. By synthesizing numerous theoretical backgrounds, empirical works, and case analyses within the concept of socio-hydrology, this paper tries to show that the socio-hydrological approach can provide insights for decision-making and policy intervention for building resilience at different levels. In the complex landscape of South Asia, where water resources are intricately linked across borders, socio-hydrology emerges as a crucial framework for fostering collaboration and resilience. By recognizing the socio-economic and political dynamics that influence water management, transboundary water issues can be approached holistically. Socio-hydrological principles explain how human behavior, cultural norms, and governance structures intersect with hydrological processes. This understanding enables the development of inclusive policies, equitable agreements, and cooperative strategies for sustainable water use and conflict resolution. In particular, the analysis supports the prospect of integrating socio-hydrological factors by recognizing the social components of water management, including human perception, cognition, behavior, and institutions. This paper examines modern socio-hydrology advances and what they suggest for creating resistance or insensitivity to climate evolution. It also explores potential theoretical frameworks and models like integrated assessment models (IAMs), system dynamic models, agent-based models (ABMs), and scenario planning models in socio-hydrology for planning and risk assessment to help facilitate adaptive governance. We find that socio-hydrology could provide an essential framework for enhancing climate resilience and sustainable water governance in South Asia. Adaptive governance approaches, collaboration amongst key stakeholders, and inclusive strategies are necessary to navigate tricky transboundary water disputes, socio-economic disparities, and the vulnerability of marginalized communities, all problems emblematic of the region. Further research in this field is necessary to harness socio-hydrology's potential in addressing the interconnected challenges climate change poses.

Optimal Water Level Prediction and Control of Great Lakes Based on Multi-Objective Planning and Fuzzy Control Algorithm

The optimal water level prediction and control of the Great Lakes is critical for balancing ecological, economic, and societal demands. This study proposes a multi-objective planning model integrated with a fuzzy control algorithm to address the conflicting interests of stakeholders and dynamic hydrological complexities. First, a network flow model is established to capture the interconnected flow dynamics among the five Great Lakes, incorporating lake volume equations derived from paraboloid-shaped bed assumptions. Multi-objective optimization aims to maximize hydropower flow while minimizing water level fluctuations, solved via a hybrid Ford–Fulkerson and simulated annealing approach. A fuzzy controller is designed to regulate dam gate openings based on water level deviations and seasonal variations, ensuring stability within ±0.6096 m of target levels. Simulations demonstrate rapid convergence (T = 5 time units) and robustness under environmental disturbances, with sensitivity analysis confirming effectiveness in stable conditions (parameter ≥ 0.2). The results highlight the framework’s capability to harmonize stakeholder needs and ecological sustainability, offering a scalable solution for large-scale hydrological systems.

Inferring causal associations in hydrological systems: a comparison of methods

Inferring causal associations in hydrological systems: a comparison of methods

Lithium from magma to mine in an early Yellowstone hotspot caldera

Renewable energy technologies rely on the extraction of metals not historically in high demand, such as lithium (Li), for which ore deposit models are incompletely understood. One of the world’s largest Li deposits is hosted in lake sediments of the 16.4 Ma McDermitt caldera, which formed during the early stages of Yellowstone hotspot volcanism in the western United States. Eruptive and posteruptive mobility of Li are major challenges in elucidating deposit formation. Melt inclusions preserved in quartz crystals provide a means to assess pre-eruptive magmatic Li contents. Concentrations of Li determined by ion microprobe for melt inclusions in a McDermitt rhyolite lava are 400−1350 ppm, compared to 20−70 ppm Li in matrix rhyolite glasses. Synthesis with melt inclusion data for eight additional calderas demonstrates a recurrence of Li-rich rhyolitic magmas (200−2000 ppm Li) in the western part of the Yellowstone hotspot track. However, unlike the multicyclic caldera complexes with overlapping fault networks that may have compromised Li retention, the McDermitt caldera remained a closed hydrologic system throughout its evolution. Modeling indicates 100 km3 of resurgent magma could yield 25−150 Mt Li in a magmatic fluid and supports accumulation of Li-rich magmatic fluid in a closed intracaldera lake, followed by evaporative concentration and sequestration of Li within clay minerals to generate the McDermitt deposit.