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Blockchain technology has been used for the digitalization of physical asset management and data management processes in many areas in the industry and academia, including the water domain. Its potential as an immutable data storage system and smart contract integration has provided a plethora of use cases and utility in the domain of hydrology and water resources. This systematic review critically examines the application of blockchain technology in the field of water resources and hydrology. By analyzing 104 academic publications and 37 non-academic studies from 2017 to 15 October 2022, this paper identifies the current state of blockchain applications in water management, delineates their potential use cases, and assesses their practical utility and scalability. Despite the theoretical promise of blockchain for enhancing water governance, data security, and stakeholder trust, the review reveals a noticeable gap between theoretical potential and the existence of workable, real-time applications specifically in water resources management. The findings indicate that while blockchain technology has been effectively implemented in various sectors, its adoption in hydrological domain is still emerging, with limited empirical evidence to support full-scale deployment. The paper concludes with a call for more empirical research to validate theoretical benefits, address scalability and interoperability challenges, and integrate blockchain technology with real-time data networks for sustainable water management practices. [ABSTRACT FROM AUTHOR]

In this paper, a combined air/water source heat pump water heater is presented. At ordinary time, the heater can be used for hot water heating in water-source mode to improve the energy efficiency. While in cold season, it gives priority to air-source mode for water heating to improve operational safety. Experiments were conducted to investigate the thermodynamic performance of the heater in each operation mode, and the experiment results were analyzed. The research indicated that its operation was reliable during the long-time running. This paper offers a promising energy-saving method in future. [ABSTRACT FROM AUTHOR]

The Journal of Hydrologic Engineering is a major journal in the field of hydrologic science and engineering. It has been published for 27 years and covers a wide range of topics related to hydrology and water resources. The journal recently revised its aims and scope to reflect changes in the field, including advancements in technology and emerging areas of study. The journal publishes various types of papers, including technical papers, case studies, and state-of-the-art reviews. The review process is efficient and fair, with a typical turnaround time of 2 months. The journal encourages researchers to submit their work and also invites them to serve as reviewers. Additionally, the journal publishes special collections and offers opportunities for young professionals to join the Early Career Editorial Board. [Extracted from the article]

Assessment of mining impact on groundwater is one of critical considerations for longwall extension and sustainability, however usually constrained by limited data availability, hydrogeological variation, and the complex coupled hydro-mechanical behaviour. This paper aims to determine the factors and mechanism of groundwater depressurisation and identify knowledge gaps and methodological limitations for improving groundwater impact assessment. Analysis of dewatering cases in Australian, Chinese, and US coalfields demonstrates that piezometric drawdown can further lead to surface hydrology degradation, while the hydraulic responses vary with longwall parameters and geological conditions. Statistical interpretation of 422 height of fracturing datasets indicates that the groundwater impact positively correlates to panel geometry and depth of cover, and more pronounced in panel interaction and top coal caving cases. In situ stress, rock competency, clay mineral infillings, fault, valley topography, and surface–subsurface water interaction are geological and hydrogeological factors influencing groundwater hydraulics and long-term recovery. The dewatering mechanism involves permeability enhancement and extensive flow through fracture networks, where interconnected fractures provide steep hydraulic gradients and smooth flow pathways draining the overlying water to goaf of lower heads. Future research should improve fracture network identification and interconnectivity quantification, accompanied by description of fluid flow dynamics in the high fracture frequency and large fracture aperture context. The paper recommends a research framework to address the knowledge gaps with continuous data collection and field-scale numerical modelling as key technical support. The paper consolidates the understanding of longwall mining impacting mine hydrology and provides viewpoints that facilitate an improved assessment of groundwater depressurisation. [ABSTRACT FROM AUTHOR]

[Objective] SWAT is a model widely used to simulate various processes including hydrological processes and crop growth at various scales. This paper reviews its application and development from 1994 to 2022. [Method] The analysis is based on published papers that have been indexed by the Web of Science (WOS). Three bibliometric tools, VOSviewer, CiteSpace and Biblioshiny were used in econometric analysis of the literature. The levels of attention, knowledge and research frontiers in SWAT application and development were analyzed based on the number of published papers, publishing journals, affiliated institutions, paper citation, keywords and research topics. [Result] ① A total of 3 496 papers related to SWAT were published from 1994 to 2022. After 2006, the number of published papers involving SWAT increased rapidly. Since 2008, the number of SWAT papers published by Chinese researchers has grown rapidly, surpassing the United States in 2019 and becoming the lead country in publishing SWAT papers. ② The journals that publish SWAT papers most are "Journal of Hydrology", "Hydrological Processes" and "Water ". Non-Chinese authors who published most SWAT papers are Srinivasan R and Arnold J, both from the United States. Chinese authors who published most SWAT papers are Shen Zhenyao, Chen Lei and Ouyang Wei. ③ Beijing Normal University, the University of Texas, the University of Chinese Academy of Sciences, Hohai University, Wuhan University are the institutions that contributed most SWAT papers. Cooperation between countries and research institutions has promoted globalization and development of SWAT. ④ The application of SWAT is mainly in water resources, environmental science, and ecology. 5 The hotspots in SWAT are runoff simulation, assessment of the impact of land use and climate change on hydrological processes, soil erosion, pollution load. [Conclusion] SWAT has been widely used in different areas but its practical application still has some challenges. These include parameter calibrations and the associated uncertainties, model construction for different watersheds as well as the influence of different factors, impact of climate change and human activities. [ABSTRACT FROM AUTHOR]

Urban territorial expansion and forest disclosure are two examples of large-scale modifications necessary because of population growth and the rising demand for land. The paper analyzes the various facets of urban development and the essential role that hydrology plays in planning construction projects and water infrastructure. Hydrology study becomes essential for engineers designing and developing structures involving water resources, especially when flood conditions arise. The paper explores two commonly used flood hydrograph approaches, the Nakayasu Synthetic and the Soil Conservation Service (SCS) Unit Synthetic, concentrating on the design of flood discharge and its significance in flood management. The study provides insight into the feasibility of existing hydrograph techniques for determining flood discharge and is based on bibliometric data from earlier studies. [ABSTRACT FROM AUTHOR]

The retrieval of continuous snow water equivalent (SWE) directly from passive microwave observations is hampered by ambiguity, which can potentially be mitigated by incorporating knowledge on snow hydrological processes. In this paper, we present a data assimilation (DA)-based SWE retrieval framework coupling the QCA-Mie scattering (DMRT-QMS) model (a dense medium radiative transfer (RT) microwave scattering model) and a one-dimensional column-based multiple-layer snow hydrology model. The snow hydrology model provides realistic estimates of the snowpack physical parameters required to drive the DMRT-QMS model. This paper devises a strategy to specify those internal parameters in the snow hydrology and RT models that lack observational records. The modeled snow depth is updated by assimilating brightness temperatures (Tbs) from the X, Ku, and Ka bands using an ensemble Kalman filter (EnKF). The updated snow depth is then used to predict the SWE. The proposed framework was tested using the European Space Agency's Nordic Snow Radar Experiment (ESA NoSREx) dataset for a snow field experiment from 2009 to 2012 in Sodankylä, Finland. The achieved SWE retrieval root mean square error of 34.31 mm meets the requirements of NASA and ESA snow missions and is about 70% less than the open-loop SWE. In summary, this paper introduces a novel SWE retrieval framework that leverages the combined strengths of a snow hydrology model and a radiative transfer model. This approach ensures physically realistic retrievals of snow depth and SWE. We investigated the impact of various factors on the framework's performance, including observation time intervals and combinations of microwave observation channels. Our results demonstrate that a one-week observation interval achieves acceptable retrieval accuracy. Furthermore, the use of multi-channel and multi-polarization Tbs is preferred for optimal SWE retrieval performance. [ABSTRACT FROM AUTHOR]

Although enhancing the knowledge of nitrogen (N) dynamics in aquatic systems is crucial for basin N management, there is still a lack of theories on the patterns of basin N sources and transport because of the intricate influence of human activities, climatic conditions, landscape patterns, and topography on the trajectory of basin N. To shed new light on the patterns of basin N sources and transport in the Chinese subtropical monsoon region, this study provides a comprehensive approach combining multiple isotopes and hydrological model based on monthly records of hydro-chemistry and isotopes ( 18 O-NO 3 - / 15 N-NO-3 and 18 O-H 2 O / 2 H-H 2 O) for river water, groundwater and rainfall in three basins over multiple years. Our observations of hydro-chemistry showed that fluvial N levels in highly urbanized basins (3.05 ± 1.42 mg·L -1 ) were the highest and were characterized by higher levels in the dry season. In the agricultural basin, fluvial N levels in February and March were approximately 1.9 times higher than those in the other months. The fluvial N load was higher in agricultural basins (0.624-0.728 T N km -2 y -1 ) than in urban basins (0.558 T N km -2 y -1 ), primarily because of variations in sewage treatment rates and fertilizer application. In highly urbanized basin, manure and sewage (46.9 %) were the dominant sources of fluvial N, which were discharged into rivers after treatment. In the plain agricultural basin, a substantial portion of diffused residential sewage leaches into aquifers and is stored. In the hilly agro-forest mixed basin, the high baseflow coefficient (75.8 %) and the key role of groundwater N, mainly from soil N (27.3 %), chemical fertilizers (20.2 %), manure and sewage (46.6 %), to fluvial N (26.5 %) indicated that a high proportion of the N sources leached into the aquifer and were then transported to rivers. For the first time, this study integrated multiple methods to substantiate the proposed typical patterns of N sources and transport within the basins. These findings have significant implications for tailored basin-specific N management strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Water management has shifted from solely technical and engineering approaches towards nature-based solutions (NBS), like natural water retention measures (NWRM), offering benefits beyond hydrology, such as improved well-being and biodiversity conservation. Determining the best type and location of these measures is challenging due to diverse options with varying benefits and effects depending on measure type and location characteristics. While most studies regarding the optimal allocation and implementation of NBS focus on the urban environment, this study presents a methodology for decision-makers focusing on inter-urban regions with limited data on NWRM implementation. Through hydrological modeling and cost-benefit analysis (CBA), we identify Pareto optimal NWRM sites and types, considering water quantity and quality alongside economic, environmental, and social objectives. We defined optimal locations that seek the most significant reduction of runoff, sediment, and pollutants, whilst optimal NWRM types are defined to seek the most cost-effective measures based on hydrological, ecological, and social criteria. Using the Open Non-point Source Pollution and Erosion Comparison Tool (OpenNSPECT), we simulated increased infiltration in different inter-urban areas and identified the optimal placement. The criteria for selecting suitable NWRM types for the identified areas are derived from the EU Directorate General for the Environment (DG-ENV) NWRM database. The results show different effective areas for reducing runoff, sediment, and pollutants. While one NWRM (natural bank stabilization) was identified as most beneficial for reducing sediment, several measures were selected for runoff reduction. Interestingly, measures with high potential for pollutant reduction seem to offer limited social and biodiversity benefits, suggesting conflicting objectives and highlighting the importance of accounting for multiple criteria. By employing simplified models and qualitative benefit assessments, this paper presents a practical decision-making approach to facilitate NWRM implementation in data-scarce areas., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Global warming is altering the frequency of extreme rainfall events and introducing uncertainties for non-point source pollution (NPSP). This research centers on orchard-influenced planting areas (OIPA) in the Wulong River Watershed of Shandong Province, China, which are known for their heightened nitrogen (N) and phosphorus (P) pollution. Leveraging meteorological data from both historical (1989-2018) and projected future periods (2041-2100), this research identified five extreme rainfall indices (ERI): R10 (moderate rain), R20 (heavy rain), R50 (rainstorm), R95p (Daily rainfall between the 95th and 99th percentile of the rainfall), and R99p (>99th percentile). Utilizing an advanced watershed hydrological model, SWAT-CO 2 , this study carried out a comparison between ERI and average conditions and evaluated the effects of ERI on the hydrology and nutrient losses in this coastal watershed. The findings revealed that the growth multiples of precipitation in the OIPA for five ERI varied between 16 and 59 times for the historical period and 14 to 65 times for future climate scenarios compared to the average conditions. The most pronounced increases in surface runoff and total phosphorus (TP) loss were observed with R50, R95p, and R99p, showing growth multiples as high as 352 and 330 times, and total nitrogen (TN) growth multiples varied between 4.6 and 30.3 times. The contribution rates of R50 and R99p for surface runoff and TP loss in the OIPA during all periods exceeded 55%, however, TN exhibited the opposite trend, primarily due to the dominated NO 3 -N leaching in the sandy soil. This research revealed how the OIPA reacts to different ERI and pinpointed essential elements influencing water and nutrient losses., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Hydrological connectivity is crucial for the healthy operation of wetland ecosystems. However, the current design of ecological corridors in wetland biodiversity networks is mostly based on species migration resistance, neglecting the important role of hydrological connectivity. How to incorporate hydrological connectivity into the wetland ecological corridor system (ECS) is still unclear. To answer the question, we proposed a framework for constructing a wetland ECS with the goal of improving conservation value of previously identified wetland biodiversity hotspots based on hydrological connectivity. In the proposed framework, we clarified the function-level-dimension of each corridor based on the dynamics of conservation value of biodiversity hotspots, the hierarchical classification of rivers and the dimension of hydrological connectivity. Then we determined the spatial distribution and functional zoning of the corridors by least cost model (LCM) using indicators that reflect wetland hydrological connectivity resistance, including water coverage, water use efficiency of vegetation, and land use suitability. The results are as follows: (1) to improve the overall hydrological connectivity and conservation value of biodiversity hotspots, 25 corridors should be constructed for vertical hydrological connectivity (with 3 for maintaining the status quo, 6 for improving and 16 for restoring connectivity) and 3 corridors should be constructed for lateral hydrological connectivity; (2) total area of all corridors are 11 km 2 , accounting for 6.79% of the study area (2.47% of core zone and 4.32% of buffer zone); (3) low suitability areas of hydrological vegetation gradient (HVG) are the most extensive, followed by low suitability areas of land use/cover change (LUCC) and the average fraction coverage of water surface (AFCW), accounting for 65.08%, 47.87% and 6.76% of the corridor coverage, respectively. The proposed framework of constructing wetland ECS in this study has the potential to provide the post-2020 global biodiversity framework and sustainable development goals with specific technical support and more targeted-control strategies for building a hydrological connected wetland biodiversity network., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Reticular river networks, essential for ecosystems and hydrology, pose challenges in assessing longitudinal connectivity due to complex multi-path structures and variable flows, exacerbated by human-made infrastructures like sluices. Existing tools inadequately track water flow's spatiotemporal changes, highlighting the need for targeted methods to gauge connectivity within complex river network systems. The Hydraulic Capacity Connectivity Index (HCCI) was developed adopting complex network theory. This involves river networks mapping, nodes and edges construstion, weight factor definition, maximum flow and resistance distance calculation. The connectivity between nodes is represented by the product of the maximum flow and the inverse of the resistance distance. The mean connectivity of each node with all other nodes, denoted as the node connectivity capacity C i , and the HCCI of the whole river network is defined as the mean of the C i for all nodes. The HCCI was firstly applied to a symmetrical virtual river network to investigate the factors influencing the HCCI. The results revealed that C i showed a radial decreasing pattern from the obstructed river reach outwards, and the boundary rivers play the most significant role in regulating the flow dynamics. Subsequently, the HCCI was applied to a real river network in the Yandu district, followed by spatiotemporal statistical analysis comparing with 1D hydraulic model's simulated river discharge. Results showed a high correlation (Pearson coefficient of 0.89) between the HCCI and monthly average river discharge at the global scale. At the local scale, the geographically weighted regression model demonstrated the strong explanatory power of C i in predicting the distribution of river reach discharge. This suggests that the HCCI addresses multi-path connectivity assessment challenge in reticular river networks, precisely characterizing spatiotemporal flow dynamics. Furthermore, since HCCI is based on a complex network model that can calculate the connectivity between all river node pairs, it is theoretically applicable to other types of river networks, such as dendritic river networks. By identifying low-connectivity areas, HCCI can guide managers in developing scientifically sound and effective strategies for restoring river network hydrodynamics. This can help prevent water stagnation and degradation of water quality, which is beneficial for environmental protection and water resource management., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Hydrological models are vital tools in environmental management. Weaknesses in model robustness for hydrological parameters transfer uncertainties to the model outputs. For streamflow, the optimized parameters are the primary source of uncertainty. A reliable calibration approach that reduces prediction uncertainty in model simulations is crucial for enhancing model robustness and reliability. The optimization of parameter ranges is a key aspect of parameter calibration, yet there is a lack of literature addressing the optimization of parameter ranges in hydrological models. In this paper, we introduce a parameter calibration strategy that applies a clustering technique, specifically the Self-Organizing Map (SM), to intelligently navigate the parameter space during the calibration of the Soil and Water Assessment Tool (SWAT) model for monthly streamflow simulation in the Baishan Basin, Jilin Province, China. We selected the representative algorithm, the Sequential Uncertainty Fitting version 2 (SUFI-2), from the commonly used SWAT Calibration and Uncertainty Programs for comparison. We developed three schemes: SUFI-2, SUFI-2-Narrowing Down (SUFI-2-ND), and SM. Multiple diagnostic error metrics were used to compare simulation accuracy and prediction uncertainty. Among all schemes, SM outperformed the others in describing watershed streamflow, particularly excelling in the simulation of spring snowmelt runoff (baseflow period). Additionally, the prediction uncertainty was effectively controlled, demonstrating the SM's adaptability and reliability in the interval optimization process. This provides managers with more credible prediction results, highlighting its potential as a valuable calibration tool in hydrological modeling., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The quantity and quality of dissolved organic matter (DOM) exported from source areas are closely related to hydrological linkage between source areas and streams, that is hydrological connectivity. However, understanding of how hydrological connectivity regulates the export of catchment DOM components remains inadequate. In this study, high-frequency monitoring of groundwater and runoff from subtropical humid catchment was conducted for 20 months, and hydrological connectivity was quantitatively characterized by considering both surface and subsurface hydrological processes. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was utilized to investigate the DOM molecular composition. Results showed that over half of the areas in the catchment could not persistently establish hydrological connectivity with the stream during the rainfall. The average proportion of lignin was the highest in DOM components, followed by tannin and proteins. Additionally, both modified aromaticity index and double bond equivalence reached maximums at peak discharge, reflecting terrestrial materials could increase DOM aromaticity and unsaturated degree. Partial least square-structural equation modeling revealed significantly direct effects of rainfall, antecedent conditions, and hydrological connectivity on dissolved organic carbon (DOC) export. Furthermore, nonlinear relationships were observed between hydrological connectivity and DOC, tannin, and condensed aromatics. Specifically, the instantaneous DOC flux increased dramatically when the hydrological connectivity strength exceeded 0.14; tannin and condensed aromatics exhibited a rapid increase with rising connectivity strength, but remained stable at connectivity strength above 0.25. However, hydrological connectivity showed no significant correlation with unstable components (such as lipids, protein, amino sugars, and carbohydrates). These results provide new insights into hydrological controls on the quantity and quality of DOM export and contribute to developing appropriate catchment management strategies for carbon storage., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Climate change and intensified human activities are exacerbating the frequency and severity of extreme precipitation events, necessitating more precise and timely flood risk assessments. Traditional models often fail to dynamically and accurately assess flood risks due to their static nature and limited handling of spatiotemporal variations. This study confronts these challenges head-on by developing a novel coupled hydrological-hydrodynamic model integrated with a Block-wise use of the TOPMODEL (BTOP) and the Rainfall-Runoff-Inundation (RRI) model. This integrated approach enables the rapid acquisition of high-precision flood inundation simulation results across large-scale basins, addressing a significant gap in dynamic flood risk assessment and zoning. A critical original achievement of this research lies in developing and implementing a comprehensive vertical-horizontal combined weighting method that incorporates spatiotemporal information for dynamic evaluation indicators, significantly enhancing the accuracy and rationality of flood risk assessments. This innovative method successfully addresses the challenges posed by objective and subjective weighting methods, presenting a balanced and robust framework for flood risk evaluation. The findings from the Min River Basin in China, as a case study, demonstrate the effectiveness of the BTOP-RRI model in capturing the complex variations in runoff and the detailed simulations of flood processes. The model accurately identifies the timing of these peaks, offering insights into the dynamic evolution of flood risks and providing a more precise and timely assessment tool for policymakers and disaster management authorities. The flood risk assessment results demonstrate good consistency with the actual regional conditions. In particular, high-risk areas exhibit distinct characteristics along the river channel, with the distribution area significantly increasing with a sudden surge in runoff. Intense precipitation events expand areas classified as moderate and high risk, gradually shrinking as precipitation levels decrease. This study significantly advances flood risk assessment methodologies by integrating cutting-edge modeling techniques with comprehensive weighting strategies. This is essential for improving the scientific foundation and decision-making processes in regional flood control efforts., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

To clarify the impacts of tidal hydrological process shifts caused by sea level rise on the blue carbon cycle, a typical coastal wetland in Jiaozhou Bay was selected for this study. The soils of Suaeda salsa (SS) and Phragmites australis (PA) wetlands were collected to simulate the effects of three types of tidal hydrological processes (Neap tide group, NT; Middle tide group, MT; Spring tide group, ST) on the soil-water dissolved inorganic carbon (DIC) dynamic. The results showed that the concentration of water dissolved inorganic carbon (WDIC) increased rapidly (115% higher) at early stage (days 0-4) under the influence of the tidal hydrological processes. Significant differences were found in WDIC concentration during different tidal hydrological processes (P < 0.05), which were expressed as MT (52.7 ± 13.3 mg L -1 ) > ST (52.5 ± 12.9 mg L -1 ) > NT (48.4 ± 10.1 mg L -1 ). After experiencing the tidal hydrological processes, the soil DIC content showed a net accumulation (55.1 ± 1.29 mg L -1 vs. 46.7 ± 1.76 mg L -1 , P < 0.001), whereas the soil inorganic carbon (SIC) decreased (2.73 ± 1.64 mg L -1 vs. 4.61 ± 1.71 mg L -1 ), which may be attributed to the dissolution of SIC caused by the uptake of CO 2 to form DIC. The accumulation of soil DIC was directly related to the SIC (λ = 1.03, P < 0.01), and indirectly related to soil nutrients (SOC substrate, λ = -0.003) and microbes (microbial biomass, λ = -0.10), and was mainly dominated by abiotic processes (abiotic: 58.1 ± 1.8% to 82.7 ± 2.46% vs. biotic: 17.4 ± 2.46% to 41.9 ± 1.76%). The increase of tidal frequency generally inhibited the accumulation of soil DIC content and promoted the output of WDIC. However, the response of soil DIC in different wetland types to tidal frequency was divergent, which was mainly regulated by the trade-off between soil nutrients and SIC content. Taken together, tidal hydrological processes and their frequency changes reshaped DIC dynamics, promoted the dissolution of SIC and the potential uptake of CO 2 . These findings enhance the comprehension of the inorganic carbon cycle within coastal wetlands, particularly amidst the backdrop of climate change and the rising sea levels., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The potential effect of hydrological conditions on distribution and loadings of Hg species was investigated in the microtidal Hyeongsan River Estuary (HRE). Dissolved Hg (DHg) and dissolved methylmercury (DMeHg) from the creek receiving industrial wastes were effectively settled to sediment during the post-typhoon period, while persistent input from the Hg-contaminated creek without settling was observed during the dry periods. The event-based mean approach was applied to explore the hydrological effects on the annual flux of Hg. The largest inputs of DHg and particulate Hg (PHg) were found in the Hg-contaminated creek, and DHg input was higher in the dry than wet periods whereas PHg input was higher in the wet than dry periods. In sediment, Hg and MeHg concentrations decreased after the typhoon, attributed to erosion of surface sediments. Overall, the HRE serves as an effective sink of Hg that reduces the degree of Hg contamination in coastal water., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Eutrophication is a serious threat to water quality and human health, and chlorophyll-a (Chla) is a key indicator to represent eutrophication in rivers or lakes. Understanding the spatial-temporal distribution of Chla and its accurate prediction are significant for water system management. In this study, spatial-temporal analysis and correlation analysis were applied to reveal Chla concentration pattern in the Fuchun River, China. Then four exogenous variables (wind speed, water temperature, dissolved oxygen and turbidity) were used for predicting Chla concentrations by six models (3 traditional machine learning models and 3 deep learning models) and compare the performance in a river with different hydrology characteristics. Statistical analysis shown that the Chla concentration in the reservoir river segment was higher than in the natural river segment during August and September, while the dominant algae gradually changed from Cyanophyta to Cryptophyta. Moreover, air temperature, water temperature and dissolved oxygen had high correlations with Chla concentrations among environment factors. The results of the prediction models demonstrate that extreme gradient boosting (XGBoost) and long short-term memory neural network (LSTM) were the best performance model in the reservoir river segment (NSE = 0.93; RMSE = 4.67) and natural river segment (NSE = 0.94; RMSE = 1.84), respectively. This study provides a reference for further understanding eutrophication and early warning of algal blooms in different type of rivers., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Systematic literature reviews can provide an objective global overview of background research on hydrological questions. This study presents a methodology to ensure rigor, traceability, and replicability in evaluating state-of-the-art hydrological topics. The proposed methodology involved systematic, objective, and explicit steps. Strategies with defined selection criteria were used to search the relevant literature comprehensively and accurately on hydrological uncertainty as a case study. The most pertinent documents were filtered to build a critical state-of-the-art synthesis for evaluating their quality and relevance. This methodology allows systematic literature analysis to provide an objective summary of the evidence, with a structured procedure for state-of-the-art reviews, which promotes transparency in the search, unlike typical review papers that lack reproducible methodologies that may lead to obtaining a potentially subjective reference selection. Consequently, the proposed methodology improves reference reliability and study reproducibility while generalizing the methodology. This procedure has proven effective and practical for building state-of-the-art research in the study case (State-of-the-art Uncertainty in Hydrological forecasting from 2017 to 2023) and can be considered a relevant tool in hydrology and other STEM disciplines, providing a rigorous and transparent approach that enhances evidence quality and reliability. [ABSTRACT FROM AUTHOR]

This document is an editorial that discusses the importance of water in agriculture, particularly in arid and semiarid regions, and the impact of irrigation on hydrological processes. It covers various research fields within agricultural hydrology, such as water-heat-salt-nutrients transport and evapotranspiration modeling, as well as remote sensing applications. The document provides an overview of published papers on this topic, highlighting research gaps and challenges in integrated agro-hydrological modeling, water resource management, non-point source pollution control, soil salinization, data-driven algorithms, and the use of remote sensing technology. It also lists research topics related to hydrology, including remote sensing, sustainability, ecohydrology, crop water requirement, drought assessment, and groundwater, with studies conducted in different regions such as China, South Africa, Pakistan, Sri Lanka, Brazil, and the USA. The document includes a list of research articles covering various subjects, such as the impacts of climate change on streamflow, water footprint of the animal breeding industry, reference evapotranspiration estimation methods, and the effects of controlled tile drainage on crop growth. The articles are authored by researchers from different countries, including China, Spain, the USA, and Sri Lanka. The document also provides information on the authors' contributions, data availability, and conflicts of interest. [Extracted from the article]

This paper provides an overview of the functionality of Arc Hydro, one of the components within ArcGIS. The purpose of using GIS, specifically the Arc Hydro component, is to prepare geospatial data (e.g., preprocessing digital terrain models, identifying river networks and watershed areas, etc.) during the pre-modeling phase within HEC-HMS. Arc Hydro provides a set of standardized attributes and a toolset for implementing the data model. The Arc Hydro tools generate several datasets that collectively describe the drainage pattern of a catchment. These tools are installed as an Arc Hydro toolbar, which includes the Terrain Preprocessing menu. This menu provides functions dealing with DEM processing and is mostly used once to prepare spatial information for later use. Arc Hydro is not a hydrologic modelling software but an excellent free tool to facilitate hydrologic modelling of watersheds. It is a unique GIS program that maintains the connectivity of hydrologic features. Using the example of the Bioštica River Basin, this paper will demonstrate the pre-modeling steps utilizing Arc Hydro. Additionally, the results obtained during the pre-modeling phase serve as the foundation for the watershed delineation method, using the Point Delineation option (it allows interactive delineation of a watershed upstream of a user-specified point). In this way, the study defines the watershed area for the Hydrological Station HS Bioštica, Bioštica River. Compared to the manual subwatershed and stream network delineation approach, the Arc Hydro toolbar offers significant time savings. [ABSTRACT FROM AUTHOR]

The Budyko framework, widely used to quantify the watershed hydrological response to the watershed characteristics and climate variabilities, is continuously refined to overcome the disadvantages of steady state assumption. However, dynamic variations in vegetations and climate variables are not fully integrated including coverages and precipitation regimes of intensity, frequency, and duration. To address this, we developed an innovative approach for determining the parameter ω in the Budyko framework to quantify the hydrological effects of vegetation restoration in a mesoscale watershed located in northern China. We found that fractional vegetation coverage (FVC), heavy precipitation amount (95pTOT), and the number of precipitation days (R01mm) are significant variables for estimating ω to improve the predictive capability of the watershed response. This extended time-varying Budyko framework can rigorously capture the temporal variations and underlying mechanisms of interactions between vegetation dynamic and precipitation regime partitioning precipitation (P) to R. Under the Budyko-Fu framework, compared to constant ω (ω‾) or ω that only considers FVC (ω P ) or precipitation regimes (ω FVC ) for simulating R, using ω that integrated FVC and precipitation regimes (ω P-FVC ) can improve Nash-Sutcliffe efficiency coefficient (NSE) by 24.81%, while reduced the root mean squared error (RMSE) and relative error (RE) by 64.08% and 65.77%, respectively. Although the increase in climatic dryness (PET/P) resulted in decreased R, the increase in FVC has also a significant contribution to this decrease due to vegetation restoration. We highlight that decrease precipitation intensity (95pTOT) and frequency (R01mm) amplified the hydrological effects of vegetation restoration, causing a 79.09∼100.31% increase in R compared to the independent impact of changes in FVC. We conclude that the extended time-varying Budyko framework by precipitation regime is more rigorous for quantifying the hydrological effects of ecological restoration under climate change and providing more reliable approach for adaptive watershed management., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Humans have played a fundamental role in altering lake wetland ecosystems, necessitating the use of diverse data types to accurately quantify long-term changes, identify potential drivers, and establish a baseline status. We complied high-resolution historical topographic maps and Landsat imagery to assess the dynamics of the lake wetlands in the Yangtze Plain over the past century, with special attention to land use and hydrological connectivity changes. Results showed an overall loss of 45.6 % (∼11,859.5 km 2 ) of the lake wetlands over the past century. The number of lakes larger than 10 km 2 decreased from 149 to 100 due to lake dispersion, vanishing, and shrinkage. The extent of lake wetland loss was 3.8 times larger during the 1930s-1970s than that in the 1970s-1990s. Thereafter, the lake wetland area remained relatively stable, and a net increase was observed during the 2010s-2020s in the Yangtze Plain. The significant loss of lake wetland was predominately driven by agricultural activities and urban land expansion, accounting for 81.1 % and 4.9 % of the total losses, respectively. In addition, the changes in longitudinal and lateral hydrological connectivity further exacerbated the lake wetland changes across the Yangtze Plain through isolation between lakes and the Yangtze River and within the lakes. A total of 130 lakes have been isolated from the Yangtze River due to the construction of sluices and dykes throughout the Yangtze Plain, resulting in the decrease in the proportion of floodplain marsh from 28.3 % in the 1930s to 8.0 % in the 2020s. Furthermore, over 260 sub-lakes larger than 1 km 2 (with a total area of 1276.4 km 2 ) are experiencing a loss of connectivity with their parent lakes currently. This study could provide an improved historical baseline of lake wetland changes to guide the conservation planning to wetland protection and prioritization area in the Yangtze Plain., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Future changes in land use/land cover (LULC) and climate (CC) affect watershed hydrology. Despite past research on estimating such changes, studies on the impacts of both these nonstationary stressors on urban watersheds have been limited. Urban watersheds have several important details such as hydraulic infrastructure that call for fine-scale models to predict the impacts of LULC and CC on watershed hydrology. In this paper, a fine-scale hydrologic model-Personal Computer Storm Water Management Model (PCSWMM)-was applied to predict the individual and joint impacts of LULC changes and CC on surface runoff attributes (peak and volume) in 3800 urban subwatersheds in Midwest Florida. The subwatersheds a range of characteristics in terms of drainage area, surface imperviousness, ground slope and LULC distribution. The PCSWMM also represented several hydraulic structures (e.g., ponds and pipes) across the subwatersheds. We analyzed changes in the runoff attributes to determine which stressor is most responsible for the changes and what subwatersheds are mostly sensitive to such changes. Six 24-h design rainfall events (5- to 200-year recurrence intervals) were studied under historical (2010) and future (year 2070) climate and LULC. We evaluated the response of the subwatersheds in terms of runoff peak and volume to the design rainfall events using the PCSWMM. The results indicated that, overall, CC has a greater impact on the runoff attributes than LULC change. We also found that LULC and climate induced changes in runoff are generally more pronounced in greater recurrence intervals and subwatersheds with smaller drainage areas and milder slopes. However, no relationship was found between the changes in runoff and original subwatershed imperviousness; this can be due to the small increase in urban land cover projected for the study area. This research helps urban planners and floodplain managers identify the required strategies to protect urban watersheds against future LULC change and CC., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Nasrin Alamdari reports financial support was provided by Florida State University. Nasrin Alamdari reports a relationship with Florida State University that includes: employment and funding grants., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Understanding the relationship between hydrological connectivity (HC) and water level (WL) is crucial for effective water resource management and wetland restoration. However, current knowledge regarding this relationship is limited. This study proposed an integrated nonstationary and uncertain analysis framework (INUAF) to investigate the HC-WL relationship with reference to the Baiyangdian wetland, which is a fragmented wetland in North China. With the INUAF, the interannual and intra-annual variations of both HC and WL were examined, together with the wavelet coherence and lag effects between the two variables at multiple scales. The results highlighted marked nonstationarity in HC, WL, and the relationship between them. Scale-dependent lag effects revealed that HC lags WL by 37 days (131 days) at the 1 a scale (4 a scale), and leads WL by 190 days at the 8 a scale, indicating a complex coupled relationship between HC and WL. Additionally, the INUAF was applied to evaluating the uncertainty in the response of lagged HC to varied WL. Results indicated that every 0.2-m increase in WL led to a 2.2%-2.4% higher probability of maintaining high HC for WL between 6.0 and 8.0 m, but a 10%-11% higher probability for WL between 8.0 and 9.0 m. We suggest that a WL of > 8.4 m would produce a probability of > 50% for achieving high HC. These findings provide valuable insights into the HC-WL relationship and could contribute to wetland restoration efforts., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Hydrological forecasting is of great importance for water resources management and planning, especially given the increasing occurrence of extreme events such as floods and droughts. The physics-informed machine learning (PIML) models effectively integrate conceptual hydrologic models with machine learning (ML) models. In this process, the intermediate variables of PIML models serve as bridges between inputs and outputs, while the impact of intermediate variables on the performance of PIML models remains unclear. To fill this knowledge gap, this study aims to encompass the construction of PIML models based on various hydrologic models, conduct comparative analyses of different intermediate variables based on a case study of 205 CAMELS basins, and further explore the relationship between the performance of PIML models and catchment characteristics. The optimal ML model for constructing PIML is first selected among four ML models within the 205 basins. The PIML models are then developed based on five monthly water balance models, namely TM, XM, MEP, SLM, and TVGM. To quantify the potential impact of difference in intermediate variables, two sets of experiments are further designed and performed, namely S1 with actual evapotranspiration as the intermediate variable and S2 with soil moisture as the intermediate variable. Results show that five PIML models generally outperformed the optimal standalone ML models, i.e., the Lasso model. Specifically, regardless of the choice of intermediate variables, the PIML-XM model consistently outperformed the other models within the same basins. Almost all constructed PIML models are affected by the intermediate variables in monthly runoff simulations. Typically, S1 exhibited better performance compared to S2. A greater impact of aridity index, forest fraction, and catchment area on model performance is observed in S2. These findings improve our understanding of constructing PIML models in hydrology by emphasizing their excellent performance in runoff simulations and highlighting the importance of intermediate variables., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In this paper, we address the critical task of 24-h streamflow forecasting using advanced deep-learning models, with a primary focus on the transformer architecture which has seen limited application in this specific task. We compare the performance of five different models, including persistence, long short-term memory (LSTM), Seq2Seq, GRU, and transformer, across four distinct regions. The evaluation is based on three performance metrics: Nash-Sutcliffe Efficiency (NSE), Pearson's r , and normalized root mean square error (NRMSE). Additionally, we investigate the impact of two data extension methods: zero-padding and persistence, on the model's predictive capabilities. Our findings highlight the transformer's superiority in capturing complex temporal dependencies and patterns in the streamflow data, outperforming all other models in terms of both accuracy and reliability. Specifically, the transformer model demonstrated a substantial improvement in NSE scores by up to 20% compared to other models. The study's insights emphasize the significance of leveraging advanced deep learning techniques, such as the transformer, in hydrological modeling and streamflow forecasting for effective water resource management and flood prediction., Competing Interests: The authors declare there is no conflict., (© 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).)

Dams and reservoirs have significantly altered river flow dynamics worldwide. Accurately representing reservoir operations in hydrological models is crucial yet challenging. Detailed reservoir operation data is often inaccessible, leading to relying on simplified reservoir operation modules in most hydrological models. To improve the capability of hydrological models to capture flow variability influenced by reservoirs, this study proposes a hybrid hydrological modeling framework, which combines a process-based hydrological model with a machine-learning-based reservoir operation module designed to simulate runoff under reservoir operations. The reservoir operation module employs an ensemble of three machine learning models: random forest, support vector machine, and AutoGluon. These models predict reservoir outflows using precipitation and temperature data as inputs. The Soil and Water Assessment Tool (SWAT) then integrates these outflow predictions to simulate runoff. To evaluate the performance of this hybrid approach, the Xijiang Basin within the Pearl River Basin, China, is used as a case study. The results highlight the superiority of the SWAT model coupled with machine learning-based reservoir operation models compared to alternative modeling approaches. This hybrid model effectively captures peak flows and dry period runoff. The Nash-Sutcliffe Efficiency (NSE) in daily runoff simulations shows substantial improvement, ranging from 0.141 to 0.780, with corresponding enhancements in the coefficient of determination (R 2 ) by 0.098-0.397 when compared to the original reservoir operation modules in SWAT. In comparison to parameterization techniques lacking a dedicated reservoir module, NSE enhancements range from 0.068 to 0.537, and R 2 improvements range from 0.027 to 0.139. The proposed hybrid modeling approach effectively characterizes the impact of reservoir operations on river flow dynamics, leading to enhanced accuracy in runoff simulation. These findings offer valuable insights for hydrological forecasting and water resources management in regions influenced by reservoir operations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Rainfall is a key hydro meteorological variable. Climate change is disrupting the hydrological cycle and altering the usual cycle of rainfall, which frequently results in long-lasting storms with significant rainfall. A first step in hydrologic design of project is to determine the design storm or rainfall events to be used. For deriving design storm, researchers concluded that instead of using generalized readily available curves or maps, it is better to estimate design storm based on site specific historical rainfall data. The objective of the study is to analyze the rainfall data in the koyna watershed area in order to evaluate the design storm, which will be further used as an input data for HEC-HMS event based hydrological modelling of flood peak attenuation of design storm flow at koyna dam during extreme rainfall event. In this study, 40 years (1982-2021) of rainfall data from 8 rain gauge stations in Koyna Dam Catchment area is used initially for performing trend analysis through statistical and graphical techniques and then for Isopluvial analysis. The Sen's slope test and the Mann-Kendall test are the statistical techniques employed, and Innovative Trend Analysis is the graphical technique used. IDF approach is used for deriving design storm, and using Gumbel's frequency distribution method Isohyetal maps, IDF tables and curves are prepared for 2,10,25,50,75 and 100 year return periods and 6,12,24,48 and 96 h durations. Results obtained from statistical and graphical trend analysis of annual rainfall series are consistent. No statistically significant trend in annual rainfall series is observed, however there is rising and falling trend was observed in annual as well as monthly rainfall series. From the results of design storm study, the design storm hyetograph of 10 years return period and 96 h duration is selected, which gives the rainfall intensity of 10.88 mm/h for the koyna catchment. There are various dams nearby koyna catchment, The Isohyet maps, IDF curves and table output available from this study can be more reliably used during planning and design of hydraulic structure for other areas near by koyna catchment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The Earth and life sciences are replete with portmanteau (blended) words and neologisms. Researchers at the interfaces between the traditional disciplines within the Earth and life sciences have coined dual titles for 'new' disciplines, such as geobiology/biogeology and ecohydrology/hydroecology. An upsurge in such coinage over the last few decades reflects a healthy willingness of many researchers to cross traditional disciplinary boundaries and to include the human sphere within their domains. However, some of the titles cause confusion because their meanings are not self-evident; rather, they sometimes demand extra research to reveal their meanings, thus rendering science less 'open' at a time when Open Science is being vigorously encouraged. After discussing the question of disciplinary neologisms, the paper probes the interchangeability of 15 dual 'new' discipline titles and concludes that six are more-or-less synonymous, three are nonsynonymous, and six are indeterminate for varying reasons. We question the usefulness of some, but by no means all, disciplinary neologisms, given that their meanings are not immediately apparent. Looking beyond portmanteau titles and other neologisms, the paper discusses the rise of interdisciplinary and transdisciplinary research, as seen in the concepts of the Critical Zone, multifunctional landscapes, and Earth System Science, the last of which supports a conceptual model of the planet as an interdependent set of spheres from global to local scales that has the advantages of combining the biotic, abiotic, and human spheres. [ABSTRACT FROM AUTHOR]

Machine learning (ML) applications in hydrology are revolutionizing our understanding and prediction of hydrological processes, driven by advancements in artificial intelligence and the availability of large, high-quality datasets. This review explores the current state of ML applications in hydrology, emphasizing the utilization of extensive datasets such as CAMELS, Caravan, GRDC, CHIRPS, NLDAS, GLDAS, PERSIANN, and GRACE. These datasets provide critical data for modeling various hydrological parameters, including streamflow, precipitation, groundwater levels, and flood frequency, particularly in data-scarce regions. We discuss the type of ML methods used in hydrology and significant successes achieved through those ML models, highlighting their enhanced predictive accuracy and the integration of diverse data sources. The review also addresses the challenges inherent in hydrological ML applications, such as data heterogeneity, spatial and temporal inconsistencies, issues regarding downscaling the LSH, and the need for incorporating human activities. In addition to discussing the limitations, this article highlights the benefits of utilizing high-resolution datasets compared to traditional ones. Additionally, we examine the emerging trends and future directions, including the integration of real-time data and the quantification of uncertainties to improve model reliability. We also place a strong emphasis on incorporating citizen science and the IoT for data collection in hydrology. By synthesizing the latest research, this paper aims to guide future efforts in leveraging large datasets and ML techniques to advance hydrological science and enhance water resource management practices. [ABSTRACT FROM AUTHOR]

This study coupled the green water and blue water accounting with the existing standard Budyko framework and Fu's 1-parameter Budyko framework to diagnose the basin hydrological behavior. Both Budyko frameworks were employed to determine green water consumption (ET Green ) and blue water consumption (ET Blue ) which, in turn, were used to map the blue water index (BWI) hotspots and green water index (GWI) bright spots. The relative contributions of green water and blue water were quantified for sustaining water and food security. A new methodology is proposed using BWI and GWI for partitioning the Gross Primary Production (GPP) and Water Use Efficiency (WUE) into GPP Blue , GPP Green and WUE Blue and WUE Green . The methodology was applied to five sub-basins of the Central Godavari River Basin (CGRB): Purna, Dhalegaon, GR Bridge, Yeli and Delta. Results showed that all five basins exhibited larger deviations from the theoretical Budyko curve of Fu's 1-parameter Budyko framework than did the standard Budyko framework and the Dhalegaon basin showed the largest deviations. The partitioning of GPP and WUE by the proposed methodology showed that the proportion of GPP Green to the total GPP was much higher than that of the GPP Blue . Similarly, the proportion of WUE Green to WUE was more than that of WUE Blue . The mapping of GPP Blue and GPP Green , and WUE Blue and WUE Green showed that the Delta and Yeli basins had the highest values of both GPP Green & GPP Blue and WUE Blue and WUE Green (bright spot basins) and the Dhalegaon and parts of GR Bridge basin had the lowest values (hot spot basins). The proposed partitioning of GPP and WUE will help identify the relative contributions of green water and blue water (for managing agricultural waters) and formulate agronomical and engineering practices for stakeholders and policy makers for increasing the overall WUE and GPP to sustain water and food security., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Rainfall-runoff (RR) modelling is a challenging task in hydrology, especially at the regional scale. This work presents an approach to simultaneously predict daily streamflow in 86 catchments across the US using a sequential CNN-LSTM deep learning architecture. The model effectively incorporates both spatial and temporal information, leveraging the CNN to encode spatial patterns and the LSTM to learn their temporal relations. For training, a year-long spatially distributed input with precipitation, maximum temperature, and minimum temperature for each day was used to predict one-day streamflow. The trained CNN-LSTM model was further fine-tuned for three local sub-clusters of the 86 stations, assessing the significance of fine-tuning in model performance. The CNN-LSTM model, post fine-tuning, exhibited strong predictive capabilities with a median Nash-Sutcliffe efficiency (NSE) of 0.62 over the test period. Remarkably, 65% of the 86 stations achieved NSE values greater than 0.6. The performance of the model was also compared to different deep learning models trained using a similar setup (CNN, LSTM, ANN). An LSTM model was also developed and trained individually to predict for each of the stations using local data. The CNN-LSTM model outperformed all the models which was trained regionally, and achieved a comparable performance to the local LSTM model. Fine-tuning improved the performance of all models during the test period. The results highlight the potential of the CNN-LSTM approach for regional RR modelling by effectively capturing complex spatiotemporal patterns inherent in the RR process., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The objective of this study was to conduct a detailed analysis of the available flow series in the Chiriquí Viejo River basin in Panama. This paper examines the patterns of variation within these series and calculates various hydrological indexes indicative of the region's hydrology. Utilizing advanced hydrological indexes within the Chiriquí Viejo River basin in Panama, which spans an area of 1376 km2 and supports an estimated population of 100,000 inhabitants, analytical methods were employed to compute indexes such as the Daily Flow Variation Index (QVAR), the Slope of the Flow Duration Curve (R2FDC), the Hydrological Regulation Index (IRH), and the average duration of low (DLQ75) and high (DHQ25) flow pulses. The results indicate moderate flow variability (QVAR of 0.72) and a Hydrological Regulation Index (IRH) of 2.32, signifying a moderate capacity for flow regulation. Notably, low flow events (DLQ75) lasted approximately 3.73 days, while high flow events (DHQ25) lasted around 4.08 days. The study highlights a significant capacity to respond to extreme events, with maximum annual flows reaching 80.25 m3/s and minimum flows dropping to 3.01 m3/s. Despite the significant contribution of the basin to hydroelectric power generation and other economic activities, there is an observed need for sustainable management that accommodates hydrological fluctuations and promotes resource conservation. The conclusions indicate that these findings are critical for future planning and conservation strategies in the region, emphasizing the importance of integrating multidisciplinary approaches for Hydro-Social Sustainability. This novel and holistic approach underscores the interdependence between hydrological dynamics, socio-economic activities, and environmental sustainability, aiming to ensure the long-term resilience of the Chiriquí Viejo basin and its communities. [ABSTRACT FROM AUTHOR]

Distribution forecasts P over future quantities or events are routinely made in hydrology but usually traded for a (likelihood‐weighted) mean or median prediction to accommodate error measures or scoring functions such as the mean absolute error or mean squared error. Case in point is the so‐called KG efficiency (KGE) of Gupta et al. (2009, https://doi.org/10.1016/j.jhydrol.2009.08.003) and improvements thereof (Lamontagne et al., 2020, https://doi.org/10.1029/2020wr027101), which have rapidly gained popularity among hydrologists as alternative scoring functions to the commonly used Nash and Sutcliffe (1970, https://doi.org/10.1016/0022‐1694(70)90255‐6) efficiency, but are equally exclusive in how they quantify model performance using only single‐valued output of the quantities of interest. This point‐valued mapping necessarily implies a loss of information about model performance. This paper advocates the use of probabilistic watershed model training, evaluation and diagnostics. Distribution evaluation opens a mature literature on scoring rules whose strong statistical underpinning provides, as we will demonstrate, the theory, context and guidelines necessary for the development of robust information‐theoretically principled metrics for watershed signatures. These so‐called hydrograph functionals are scalar‐valued mappings of major behavioral watershed functions embodied in a strictly proper scoring rule. We discuss past developments that led to the current state‐of‐the‐art of distribution evaluation in hydrology and review scoring rules for dichotomous and categorical events, quantiles (intervals) and density forecasts. We are particularly concerned with elicitable functionals and scoring rule propriety, discuss the decomposition of scoring rules into a sharpness, reliability and entropy term and present diagnostically appealing strictly proper divergence scores of hydrograph functionals for flood frequency analysis, flow duration and recession curves. The usefulness and power of distribution‐based model evaluation and diagnostics by means of scoring rules is demonstrated on simple illustrative problems and discharge distributions simulated with watershed models using random sampling and Bayesian model averaging. The presented theory (a) enables a more complete evaluation of distribution forecasts, (b) offers a statistically principled means for watershed model training, evaluation, diagnostics and selection using hydrograph functionals and/or extreme events and (c) provides a universal framework for metric development of watershed signatures, promoting metric standardization and reproducibility. Plain Language Summary: The past decades have witnessed an unbridled growth in goodness‐of‐fit metrics of hydrologic models. These metrics may satisfy the needs of hydrologists but lack conforming theory and principles. This state of affairs (a) elicits improper model training and evaluation, (b) provokes and supports misguided inferences, (c) impedes statistically‐principled uncertainty quantification, metric standardization and development of universal model benchmarks and (d) obfuscates determination of whether the model has finished learning. What is more, most hydrologic model evaluation metrics in use today are rather exclusive in how they quantify model performance using only single‐valued simulated output of the quantities of interest. Predictive distributions derived from (quasi)‐Bayesian methods or ensembles are usually traded for a (likelihood‐weighted) mean or median prediction to accommodate error measures (scoring functions) such as the mean absolute error. This implies a large loss of information. This paper develops a distribution‐based approach to hydrologic model evaluation and diagnostics. Distribution evaluation opens the necessary theory and guidelines for development of robust information‐theoretically principled metrics of watershed signatures. These so‐called hydrograph functionals are scalar‐valued mappings of major behavioral watershed functions embodied in a strictly proper scoring rule. The hydrograph functionals offer a statistically principled means for hydrologic model evaluation, diagnostics and selection. Key Points: Scoring rules of hydrograph functionals provide an information‐theoretically principled means for watershed model training, evaluation, and diagnosticsWe present strictly proper (divergence) scores for flood frequency analysis, flow duration, and recession curvesPropriety and elicitability offer useful working paradigms for metric development of hydrograph functionals [ABSTRACT FROM AUTHOR]

Background: Testate amoebae are a polyphyletic group of unicellular eukaryotic organisms that are characterised by a rigid shell and inhabit mostly freshwater and terrestrial ecosystems. They are particularly abundant in peatlands, especially in Sphagnum-dominated biotopes. Peatland hydrology is the most important influence on testate amoebae communities. The good preservation of the shells in peat deposits and their response to hydrological regime changes are the principles for palaeohydrological reconstructions. Any changes in the water balance of mires should be expected to have far-reaching effects on biogeochemical cycles, productivity, carbon dioxide and methane exchange. New information: This paper presents a dataset (Darwin Core Archive -- DwC-A) on the distribution of Sphagnum-dwelling testate amoebae in nine mires located in the forest-steppe subzone of the East European Plane. The dataset includes information about 86 taxa belonging to 29 genera and contains 3,123 occurrences of 49,874 individuals. The following environmental variables are provided: microtopography, oxidising and reducing potential, total mineralisation, substrate temperature, acidity, substrate wetness and water table depth. These data might be used for biogeographical and palaeoecological studies, including quantitative reconstructions. [ABSTRACT FROM AUTHOR]

The BELSAR dataset consists of high-resolution multitemporal airborne mono- and bistatic fully-polarimetric synthetic aperture radar (SAR) data in L-band, alongside concurrent measurements of vegetation and soil biogeophysical variables measured in maize and winter wheat fields during the summer of 2018 in Belgium. Its collection was funded by the European Space Agency (ESA) to address the lack of publicly-accessible experimental datasets combining multistatic SAR and in situ measurements. As such, it offers an opportunity to advance the development of SAR remote sensing science and applications for agricultural monitoring and hydrology. This paper aims to facilitate its adoption and exploration by offering comprehensive documentation and integrating its multiple data sources into a unified, analysis-ready dataset. [ABSTRACT FROM AUTHOR]

Floodplain wetland ecosystems experience significant seasonal water fluctuation over the year, resulting in a dynamic hydroperiod, with a range of vegetation community responses. This paper assesses trends and changes in land cover and hydroclimatological variables, including air temperature, river discharge, and water level in the Upper Columbia River Wetlands (UCRW), British Columbia, Canada. A land cover classification time series from 1984 to 2022 was generated from the Landsat image archive using a random forest algorithm. Peak river flow timing, duration, and anomalies were examined to evaluate temporal coincidence with observed land cover trends. The land cover classifier used to segment changes in wetland area and open water performed well (kappa of 0.82). Over the last 4 decades, observed river discharge and air temperature have increased, precipitation has decreased, the timing of peak flow is earlier, and the flow duration has been reduced. The frequency of both high-discharge events and dry years have increased, indicating a shift towards more extreme floodplain flow behavior. These hydrometeorological changes are associated with a shift in the timing of snowmelt, from April to mid-May, and with seasonal changes in the vegetative communities over the 39-year period. Thus, woody shrubs (+ 6 % to + 12 %) have expanded as they gradually replaced marsh and wet-meadow land covers with a reduction in open-water area. This suggests that increasing temperatures have already impacted the regional hydrology, wetland hydroperiod, and floodplain land cover in the Upper Columbia River valley. Overall, there is substantial variation in seasonal and annual land cover, reflecting the dynamic nature of floodplain wetlands, but the results show that the wetlands are drying out with increasing areas of woody/shrub habitat and loss of aquatic habitat. The results suggest that floodplain wetlands, particularly marsh and open-water habitats, are vulnerable to climatic and hydrological changes that could further reduce their areal extent in the future. [ABSTRACT FROM AUTHOR]

Accurately estimating snow hydrology parameters, including snow coverage mapping and snow depth, plays a significant role in comprehending water resource dynamics, flood forecasting, and environmental management in regions influenced by snow cover. These parameters are critical for hydrological models that simulate snowmelt and runoff, which are essential for predicting water availability and managing water resources in snow-covered areas. Traditional methods for estimating these parameters often rely on manual measurements or simplistic models, which can be inadequate for capturing the complexity of snow-related processes. In recent years, there has been a growing interest in leveraging deep learning techniques for snow hydrology parameter estimation, offering the potential to overcome these limitations. This review paper comprehensively analyzes the current state, challenges, and future directions of image-based approaches in snow hydrology parameter estimation. By harnessing the power of automated methods, particularly deep learning, these approaches demonstrate the ability to capture intricate spatial and temporal relationships present in image data. A comparative analysis between traditional and image-based methods highlights the strengths of automated approaches, including scalability and accuracy. Integration of image sensors, such as satellite imagery and crowd-sourced data, is explored as a crucial component of snow hydrology parameter estimation. Various satellite image sources, including Sentinel 1-2, Landsat, and MODIS, are discussed in terms of their suitability for snow hydrological applications. Despite the promise of image-based approaches, challenges remain, including data availability, model interpretability, and transferability. The paper identifies future research directions, emphasizing the exploration of novel deep learning architectures and uncertainty quantification techniques to address these challenges. In conclusion, this review underscores the importance of image-based approaches for advancing snow hydrology parameter estimation. By addressing challenges and maximizing potential impact, these approaches have the potential to revolutionize snow hydrological modeling and environmental management. [ABSTRACT FROM AUTHOR]

Based on urban flood hydrology processes and hydrodynamic principles, the stormwater management model (SWMM) was improved upon. The coupling and implementation methods of the SWMM and two-dimensional hydrodynamic model are proposed. The improved SWMM was coupled with the hydrodynamic model both from the vertical and horizontal directions. The hydrology and hydrodynamic coupling model (HHDCM) was constructed and verified by using extreme rainstorm data. Taking July.20 extreme rainstorms (from July 17 to July 20, 2021, i.e., July.20 extreme rainstorm) in Zhengzhou city, Henan Province, China, as an example and using the HHDCM model, the flood disaster caused by July.20 extreme rainstorm was simulated. Based on the simulation results, an inundation distribution map was drawn for the urban area. A comparison between the simulated and measured results reveals that the maximum relative error in the simulated results is 12.5%. Therefore, the HHDCM model proposed in this paper has desirable accuracy and reliability for simulating extreme urban rainstorms and flood disasters. [ABSTRACT FROM AUTHOR]

The soil–water characteristic curve (SWCC) is widely used as a tool in geotechnical, geo-environmental, hydrology, and soil science fields for predicting and interpreting hydro-mechanical behaviors of unsaturated soils. Several previous studies focused on investigating the influence of initial water content, stress history, temperature, and salt content on the SWCC behavior. However, there is still limited understanding to be gained from the literature on how we can systematically incorporate the influence of complex thermo-hydro-mechanical-chemo (THMC) effects into interpreting and predicting the behavior of unsaturated soils. To address that knowledge gap, in this study, the coupled influence of temperature, initial stress state, initial density, soil structure, and chemical solution effects was modeled using established SWCC equations from the literature. The methodology for incorporating the coupled effects of these influential factors is presented herein. Furthermore, we evaluated the SWCC models proposed in this study, enabling us to provide a comprehensive discussion of their strengths and limitations, using the published SWCC data from the literature. The developments outlined in this paper contribute toward facilitating a rigorous approach for analyzing the THMC behaviors of unsaturated soils. [ABSTRACT FROM AUTHOR]

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal. [ABSTRACT FROM AUTHOR]

While deep learning (DL) models are effective in rainfall-runoff modelling, their dependence on data and lack of physical mechanisms can limit their use in hydrology. As there is yet no consensus on the consideration of the fundamental water balance for DL models, this paper presents an in-depth investigation of the effects of water balance constraint on the long-short term memory (LSTM) network. Specifically, based on the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset, the LSTM and its architecturally mass-conserving variant (MC-LSTM) are trained basin-wise to provide rainfall-runoff prediction and then the robustness of the LSTM and MC-LSTM against data sparsity, random parameters initialization and contrasting climate conditions are assessed across the contiguous United States. Through large-sample tests, the results show that the water balance constraint evidently improves the robustness of the basin-wise trained LSTM. On the one hand, as the amount of training data increases from 1 year to 15 years, the incorporation of the water balance constraint into the LSTM network decreases the sensitivity from 95.0 % to 32.7 %. On the other hand, the water balance constraint contributes to the stability of the LSTM for 450 (85 %) basins when there are 3 years' training data. In the meantime, the water balance constraint improves the transferability of the LSTM from the driest years to the wettest years for 318 (67 %) basins. Overall, the in-depth investigations of this paper facilitate insights into the use of DL models for rainfall-runoff modelling. [ABSTRACT FROM AUTHOR]

In the world, research devoted to adjusting the results of heuristic methods based on forecasting, recognition, classification, and determining the absolute extremum of a multidimensional function is relevant and widely used in such fields as medicine, geology, hydrology, management, and computer technology. In this regard, it is important to construct optimal correctors of heuristic algorithms based on control materials. Therefore, checking the completeness of classes of k-valued logical functions and developing methods and algorithms for minimizing functions in the class of canonical normal forms, estimating the number of monotonic functions of k-valued logic, constructing minimal bases of special classes of correcting functions for correcting incorrect algorithms remains one of the important problems of computational and discrete science. mathematics. Currently, a lot of scientific research is being carried out around the world aimed at expanding the integration of science and industry, in particular the development of the theory of k-valued logical functions for correcting the results of heuristic algorithms. In this case, an important role is played by the construction of formulas in the class of canonical normal forms, the coding of elementary conjunctions and the application of the rules of gluing, absorption and idempotency for them, and checking the completeness of systems of correcting functions. Consequently, the development of effective numerical computational methods and algorithms for constructing correction functions based on k-valued logic to improve the accuracy of the results of heuristic methods is considered a targeted scientific research. The paper considers the representation of k-valued logical functions in the class of disjunctive normal forms. Various classes of monotone functions of k-valued logic are studied. Theorems are proved on the coincidence of abbreviated and shortest disjunctive normal forms of k-valued functions. For a certain class of k-valued monotone functions, we prove an estimate for the number of functions from this class. criteria for the absorption of elementary conjunctions by a first-order neighborhood of disjunctive normal forms of k-valued functions are proved. [ABSTRACT FROM AUTHOR]

This article discusses the issue of the origin of a rod fastening defect. At the beginning of operation, the rod is rigidly fixed at the edges. During operation, over time, certain defects may appear at the ends of the rod. We need to find out what defects may occur? Then it is necessary to trace the further behavior of the emerging defects at the ends of the rod. This paper discusses the diagnostics of types of fastening of a structure made of interconnected rods. In this work, the state of fastening types in individual parts of the structure is determined and a number of results are obtained using mathematical analysis. Most of them assume how failures begin at the end connections of the rods, and then the scenario for their further development. Mathematical models are presented to determine the state of the rod attachments relative to the proposed scenario, and then the state in which they are in is carefully examined. Defects in fastening objects made from a system of rods are investigated using identification problems. The difference between this article and other works is that instead of the shape of the area, the size of the object, or the state of its location, defects that occur in fasteners are studied. This work is devoted to the search for types of fastening that provide the required range of vibration frequencies. [ABSTRACT FROM AUTHOR]

Copulas are quite a popular, flexible, and useful tool for multivariate modeling in many fields of applications, including finance and insurance, actuarial sciences, biomedical studies, geostatistics, and hydrology. In this paper, we use the concept of diagonal function to build new bivariate copulas based on the well known and important Ali–Mikhail–Haq (AMH), Farlie–Gumbel–Morgenstern (FGM) and Plackett families. We also study some dependence properties of the newly constructed semilinear copulas, namely, the Spearman's ρ coefficient. In particular, we find that the range of this rank correlation coefficient varies between weak and moderate values for the transformed AMH and FGM families, although it varies between moderate and strong values for the transformed Plackett families. Moreover, in all cases, the Spearman's ρ coefficient reaches negative and positive values. Finally, we present a synthetic data generation algorithm, as well as a classical method of estimation of the copula association parameter for each of the six transformed bivariate families. [ABSTRACT FROM AUTHOR]

Sediment connectivity at the catchment scale includes the processes linking sediment sources, sinks and the river outlet. Soil erosion models usually estimate yields without considering riverine processes or human infrastructure that may affect sediment connectivity. Quantifying these processes at a large scale is a determinant of understanding sediment transfers from continental lands to marine ecosystems. This study tries to fill this gap by coupling the soil erosion model WaTEM/SEDEM (WS) with the riverine connectivity tool CASCADE to quantify sediment fluxes in the Rhône watershed. The coupling returned a good fit, with deviations of −51.7%. WS alone predicts the exported fluxes better with a deviation of −34.9%. Nevertheless, this paper shows the importance of considering connectivity and transport capacity to develop a more realistic representation of fine sediment dynamics at a large scale. However, connectivity tools depend on the quality of the models (soil erosion and hydrology) and the geomorphological data on which they depend, which is a limiting factor in large‐scale studies. [ABSTRACT FROM AUTHOR]

Hydrology has a long history, but is still considered a young science due to its lack of a solid scientific foundation as a natural science. Field experimentation is crucial when investigating hydrological processes and mechanisms, and is essential if hydrology is to have a solid, science‐based foundation. Professor Wei‐Zu Gu (1932–2022) was an internationally renowned scientist in the field of hydrology and is recognized as the greatest pioneer of experimental hydrology and isotope hydrology in China. He created the Hydrohill experimental catchment, which serves as both a great public facility for experimental hydrology and a valuable legacy for researchers that will enable them to conduct advanced hydrological experiments in the future. This legacy consists of innovative infrastructure that bridges the gap between natural watershed experiments and artificial physical models. The Hydrohill is an intensively instrumented experimental catchment that allows different elements of the hydrological cycle and their tracing indicators to be comprehensively measured. To provide an in‐depth understanding of the Hydrohill, this paper presents a short history of the site, its experimental objectives, a site description (including location, construction and instrumentation), site conditions (such as soil, hydrological and meteorological properties), and its contributions to hydrological science. We acknowledge Professor Gu for creating the Hydrohill experimental hydrology facility and enhancing our understanding of hydrological processes and mechanisms. Finally, we hope that Chuzhou Scientific Hydrology Laboratory, along with support from Professor Gu's friends, will ensure the continued growth of the Hydrohill so that it can address unsolved problems in hydrology. [ABSTRACT FROM AUTHOR]