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This paper shares an early-career perspective on potential themes for the upcoming International Association of Hydrological Sciences (IAHS) Scientific Decade (SD). This opinion paper synthesizes six discussion sessions in western Europe identifying three themes that all offer a different perspective on the hydrological threats the world faces and could serve to direct the broader hydrological community: "Tipping points and thresholds in hydrology," "Intensification of the water cycle," and "Water services under pressure." Additionally, four trends were distinguished concerning the way in which hydrological research is conducted: big data, bridging science and practice, open science, and inter- and multidisciplinarity. These themes and trends will provide valuable input for future discussions on the theme for the next IAHS SD. We encourage other early-career scientists to voice their opinion by organizing their own discussion sessions and commenting on this paper to make this initiative grow from a regional initiative to a global movement. [ABSTRACT FROM AUTHOR]

The article discusses opinion paper titled "Hydrology and change" by professor of hydrology Demetris Koutsoyiannis. It mentions Hurst-Kolmogorov (HK) statistical effect which is the focus of the paper and suggests invention of tools with HK effect for future day predictions. It also presents discussion on the physical changes along with time such as change in climatic conditions due to human activities including innovation in agriculture, industrial advancement and electronic revolution.

On the eve of my retirement as co-editor of the International Association of Hydrological Sciences (IAHS) and Hydrological Sciences Journal (HSJ), I would like to take the opportunity to address the HSJ audience with some reflections about the journal and the publishing system. [ABSTRACT FROM AUTHOR]

This paper considers what we know about the potential for disinformation in hydrological data when used for the evaluation of hydrological models. This will generally arise from epistemic uncertainties associated with hydrological observations, particularly from nonstationary or extrapolated rating curves for discharges, and poor rainfall and snowmelt information when interpolated over basin areas. Approaches based on information theory are not well suited to consideration of such epistemic uncertainties in model evaluation and an alternative approach based on setting limits of acceptability independent of any model runs is suggested. This allows for both the rejection of all models tried, and for acceptability of models across different model structures and parameter sets. The paper concludes with some suggestions for future research on defining disinformative data for both point and spatial observables, studying model failures, and defining new observations with a view to having the greatest impact on reducing model uncertainties. [ABSTRACT FROM AUTHOR]

Rangecroft et al. provide an important and interesting paper on the challenges of interdisciplinary research and fieldwork with participants in water resource management. The paper shows the challenges of interaction between their research areas and demonstrates the importance of how a researcher interacts with their selected study sites. My key points reflect the use of different methodologies within social and natural sciences and across them as well as the main challenge of who has the power to influence the research directions. Research is not value-free and is highly influenced by one's own training and knowledge, which needs to be addressed in the research activities. Finally, an option might be to move beyond interdisciplinary constraints and to work within a stronger transdisciplinary framework. Water research very much needs to interact with non-academic people to understand the challenges and possible solutions. [ABSTRACT FROM AUTHOR]

Socio-hydrology has expanded and been effective in exposing the hydrological community to ideas and approaches from other scientific disciplines, and social sciences in particular. Yet it still has much to explore regarding how to capture human agency and how to combine different methods and disciplinary views from both the hydrological and the social sciences to develop knowledge. A useful starting ground is noting that the complexity of human–water relations is due to interactions not only across spatial and temporal scales but also across different organizational levels of social systems. This calls for consideration of another analytical scale, the human organizational scale, and interdisciplinarity in study methods. Based on the papers published in this journal's Special Issue Advancing Socio-hydrology over 2019–2022, this paper illuminates how the understanding of coupled human–water systems can be strengthened by capturing the multi-level nature of human decision making and by applying an interdisciplinary multi-method approach. [ABSTRACT FROM AUTHOR]

The article reports on bringing innovation and improving impact assessment in journal publications in hydrology. Editors of several journals in the field of hydrology met during the Assembly of the International Association of Hydrological Sciences (IAHS) in Prague, Czech Republic in June 2015 to review the current status of the journals and the publication process.

Copyright of Hydrological Sciences Journal/Journal des Sciences Hydrologiques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a numerical test problem that was used as the basis for ranking the river basin management models that participated in a tender for a World Bank-funded project in India. The problem is deterministic, and it involves a set of constraints and management objectives that were of interest to the Narmada Control Authority. The test problem had five reservoirs, seven off-stream and four on-stream water demands, steady-state inflows based on a 10-daily time step over a period of nine years, and relatively simple reservoir operating rules. The paper describes the problem and discusses the solutions that were obtained from various vendors. The principal finding is that simulation models should not be used to solve optimization problems. The paper also highlights the importance of establishing benchmarks as an important tool for model evaluation. Input data and the current benchmark solution are available for download. [ABSTRACT FROM AUTHOR]

Addressing post-fire impacts largely depends on burn "severity." A singular severity classification that encompasses the holistic effects of fire on all ecosystem processes does not currently exist. Lumping vegetation burn severity and soil burn severity into one metric, or using them interchangeably, can induce large inaccuracies and uncertainties in the intended ecosystem response to forcing. Often, burn "severity" reflects fire impacts on vegetation, which can be measured through remote sensing. Vegetation burn severity is likely more apropos for ecological research, whereas soil burn severity is more relevant for hydrological analyses. This paper reviews different remotely sensed vegetation severity products currently (mis)used for hydrological modeling, provides examples of when vegetation burn severity may (not) match soil burn severity, and summarizes the potential synergistic future of remote sensing with in situ severity metrics. While the focus in this paper is on the western United States, the lessons and principles apply universally. [ABSTRACT FROM AUTHOR]

This paper presents the WEB.BM model for multipurpose multireservoir river basin management that uses Mixed Integer Linear Programming (MILP) solver to optimize basin operation over single or multiple time steps, with flexible time step length and optional use of both reservoir and hydrological channel routing. The principal novelty is the capability of the model to solve multiple time step optimization while simultaneously properly accounting for hydrological channel routing as constraints to optimization, which is demonstrated on two numerical tests presented in this paper, which can be used as benchmark by other researchers. There are no other known optimization-based river basin management models with this capability. The model can include multiple reservoirs, hydropower plants, environmental flow targets, return flows from irrigation, diversion volume licences, apportionment agreements between neighbouring states and net evaporation. Operating priorities among users are represented by a selection of user-defined weight factors. The model can be accessed at . [ABSTRACT FROM AUTHOR]

We are very grateful to the authors Aksoy and Cavus for their constructive contributions to the discussion of our earlier paper "Drought assessment in a south Mediterranean transboundary catchment." Our paper focused on the variability of rainfall and drought patterns in the Medjerda catchment in Tunisia and analysed the underlying causes resulting in these changes. Scope exists to improve the integration of the space–time variability in the drought assessment methodology. Different drought indices and statistical methods are employed in the study. The discussion of the article concentrates almost exclusively on the use of the Standardized Precipitation Index (SPI) and some mistyped equations. In this reply, we clarify the problematic points of misunderstanding and discuss the classification of droughts based on the SPI values. [ABSTRACT FROM AUTHOR]

This paper presents the development of hybrid machine learning models to forecast the natural flows of water bodies. Five models were considered under the analysis: extreme gradient boosting (XGB), extreme learning machine (ELM), support vector regression (SVR), elastic net linear model (EN), and multivariate adaptive regression splines (MARS). The grey wolf optimization algorithm (GWO) optimized all of the models' internal parameters. A feature selection approach was embedded in the hybrid model to reduce the number of input variables. The hybrid model performed the forecasts considering one, three, five, and seven days ahead on data collected from Cahora Bassa dam, Mozambique. In the experiments conducted in this paper, XGB outperformed EN, ELM, MARS, and SVR, presenting lower prediction error and uncertainty. The proposed XGB model arises as an alternative to help with flow prediction, which is crucial for hydroelectric power plant activity. [ABSTRACT FROM AUTHOR]

The research paper by Akçay et al. applied innovative polygon trend analysis (IPTA) to derive trend length/volume and trend slope between two consecutive months for rainfall/streamflow in the eastern Black Sea basin, Turkey. Although the trend length/volume equation is correct, the trend slope equation is fundamentally incorrect. A brief discussion is presented to apprise the research community of the correct trend slope equation. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to forecast the spatiotemporal water change trends in Urmia Lake through 2030. Three space–time cube-based models were applied. The forest-based forecast model, with a mean of 0.14 forecast root mean square error (RMSE) and 0.39 validation RMSE, had a better performance. According to the model’s results, the peripheral parts of the lake will mainly stay completely arid with a 42.5% extension in the entire period, 21.7% in spring, and a 27% reduction in winter. In the middle parts, the aridity will increase by 121% in summer, 93% in fall, and 38% in spring. Eventually, the centric areas will not be completely arid; however, the water patterns’ extent will decrease by 53.46% in fall, 34.6% in spring, and 28.5% in summer. Additionally, southern, eastern, western, and northern areas will experience drier conditions, respectively. The findings can be used for water resource management and water restoration plans for the lake. [ABSTRACT FROM AUTHOR]

Rainfall properties and drop spectra characterization is essential when designing and using rainfall simulators to properly replicate natural rainfall characteristics. This study considered a small-scale rainfall simulator operating under varying nozzle, pressure and rotating disc scenarios. A dynamic rainfall gauge system developed at the Federal University of Lavras was used to quantify the impact velocity of rainfall droplets and kinetic energy, and to conduct radial uniformity analyses. The results showed that the rainfall intensity, impact velocity of droplets, kinetic energy, and uniformity of distribution of rainfall are influenced by effects of the spray nozzle, operating pressure and the rotating disc opening of the rainfall simulator. Lower values of simulated rainfall kinetic energy were observed compared to the empirical natural rainfall kinetic energy at equivalent rainfall intensities. This paper highlights the influence of rainfall simulator design specifications on resultant rainfall spectra and validates simulated rainfall against empirically based natural rainfall characteristics. [ABSTRACT FROM AUTHOR]

In this study, a bibliometric analysis technique is used for performance analysis and science mapping of artificial intelligence (AI) applications in streamflow research. This paper examines the current trends in the literature using the Scopus database over the last 37 years. RStudio Bibliometrix software was used to analyse the titles, keywords, abstracts, and full texts of 3000 publications to identify trends in AI models, publication types, journals, citations, authors, countries, and regions. The highest frequency AI-related keyword is "artificial neural networks," which was used in a total of 25587 times. The most common publication type, at 82.1%, is journal articles, and the highest rate of country production is 25% for China. In recent years, streamflow research studies have significantly increased their use of AI applications. [ABSTRACT FROM AUTHOR]

Slichter’s model is a theoretical model that can be used for quantifying the permeability of non-cohesive materials. An inaccurate form of the equation has prevailed, undermining this model, which has been considered unreliable. This paper revises Slichter’s work and proposes a corrected formulation of his equation by redefining key parameters such as the referential grain size, the porosity function, and the area of the pores. This approach was tested for poorly graded sands and well-graded sandy gravels in northern Croatia and a literature control group of heterogeneous sediments. The results were compared with permeabilities determined from: (1) pumping and slug tests, and (2) the well-established Hazen’s experimental and Kozeny-Carman theoretical formulae. High correlativity between the different methods and with the field results confirmed the validity of the corrected Slichter’s equation for predicting the permeability of non-cohesive materials, regardless of their gradation and mean geometric grain size. [ABSTRACT FROM AUTHOR]

In this work, we formulate a regionalization framework for rainfall intensity–time scale–return period relationships which is applied over the Greek territory. The methodology for single-site estimation is based on a stochastic framework for multi-scale modelling of rainfall intensity which is outlined in the companion paper. Five parameters are first fitted independently for each site and the resulting parameter variability is assessed. Following a systematic investigation of uncertainty and variability patterns, two parameters, i.e. the tail-index and a time scale parameter, are identified as constant in space and estimated using data pooling techniques. The other three parameters are regionalized over Greece by means of spatial interpolation and smoothing techniques that are assessed through cross-validation in a multi-model framework. The regionalization scheme is implemented in a sequential order that allows exploiting rainfall information both from rainfall stations with sub-daily resolution and from the more reliable network of daily raingauges. [ABSTRACT FROM AUTHOR]

This work presents a stochastic framework for the construction of rainfall intensity–time scale–return period relationships, which was applied in the recent regionalization of design rainfall curves over the Greek territory, described in a companion paper. The methodology outlined herein builds upon a widely-used mathematical framework, which has been recently revisited and upgraded, and incorporates two different versions: (a) a theoretically consistent stochastic model applicable for rainfall intensity over any scale of interest; and (b) a simplified version valid over small scales, which makes parameter estimation easier. Special attention is given to the presentation of the simplified version, which suffices for most engineering tasks. Parameter estimation approaches are presented in detail, including the K-moments framework that allows for reliable high-order moment estimation and handling of bias due to spatiotemporal dependence. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article "Discussion on the Opinion Paper Hydrology and change" by Arie Ben-Zvi published in the 2014 issue of the journal.

Potential changes in hydro-meteorological events have been causing mass damage to the economy and lives. Among several other factors, the progression of climate change over a long time is expected to cause non-stationarity in annual maximum rainfall. Understanding the characteristics of annual maximum rainfall series is crucial for coastal cities as they are highly vulnerable due to the greatly varying weather patterns. In this paper, we propose stationary and non-stationary methods to model the effect of non-stationarity on the differing duration of annual maximum rainfall and demonstrate the impacts on nine coastal cities spread across the Arabian Sea and Bay of Bengal stretch of India. The Bayesian inference parameter estimation technique was used. It was found that while stationary models often fit well for longer-duration rainfall, non-stationary models often best fit the short duration. [ABSTRACT FROM AUTHOR]

This paper aims to fill in the missing time series of hourly surface water levels of some stations installed along the River Seine, using the long short-term memory (LSTM) algorithm. In our study, only the water level data from the same station, containing many missing parts, were used as input and output variables, in contrast to other works where several features are available to take advantage of e.g. other station data/physical variables. A sensitive analysis is presented on both the network properties and how the input and output data are reentered to better determine the appropriate strategy. Numerous scenarios are presented, each an updated version of the previous one. Ultimately, the final version of the model can impute missing values of up to one year of hourly data with great flexibility (one-year Root-Mean-Square Error (RMSE) = 0.14 m) regardless of the location of the missing gaps in the series or their size. [ABSTRACT FROM AUTHOR]

Rangecroft et al. (2021) offer a set of principles for conducting interdisciplinary research and fieldwork with participants from a hydrologist perspective. In this invited paper, I present some thoughts from a social scientist's perspective, not to disagree with their points but to add to them. Specifically, I use my sociology background and interdisciplinary experiences to reflect on qualitative evaluative criteria, power dynamics in the scientific community, barriers to interdisciplinary research, and approaches to overcome obstacles. Individual researchers can educate themselves about other disciplines, and there are also opportunities for institutional change on the part of universities, funders, and journals to support interdisciplinary work. I am enthusiastic about the emerging hydrology–social science collaborations I am witnessing. Indeed, I hope that more of my social science colleagues will see the unlimited potential of studying water systems with hydrologists and engineers, as I have. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including hydrology, sustainable development and scientific knowledge.

This paper presents an evaluation of the parameter sensitivity of a process-based model at the global scale using large-sample data. The analysis was carried out using the HYdrological Prediction of the Environment (HYPE) model, for which soil and snow parameters were evaluated using 187 river flow gauges spread worldwide. As a result, 6 out of 12 soil parameters and 7 out of 10 snow parameters were found to be sensitive. Taking advantage of the global dataset, an additional analysis was used to investigate links between catchment characteristics and parameter sensitivity. Different patterns of sensitivity were observed for different Köppen climate classes, which indicates that parameter regionalization would benefit from calibration based on climate zones. This numerical sensitivity method was compared with the judgement of a set of expert HYPE modellers to understand how numerical results compare with modellers' experience. [ABSTRACT FROM AUTHOR]

The flood of the Senegal River is a resource for the populations of the Senegal valley, who practise agriculture, fishing and livestock farming in the annually flooded areas. The objective of this paper is to investigate the hydrological evolution of the Senegal River flood since the drought of the 1970s and the construction and management of the Manantali Dam. To this purpose, the paper analyses the evolution of several parameters of the flood hydrograph and defines the factors that determine the intensity of the flood. Also, a follow-up of the flooded areas in the valley is carried out using remote sensing. The results show that the flood hydrograph has been similar since the 1970s drought, due to the reduction in contributions from the Bafing after the construction of the Manantali Dam. As a result, the high floods depend now on the rainfall pattern in the Sudano-Sahelian zone of the watershed. [ABSTRACT FROM AUTHOR]

Ethiopia, marked by diverse geography and rapid land use/land cover (LULC) changes, grapples with formidable water resource management challenges. Understanding the hydrological response to these changes is paramount for sustainability. This review paper provides an overview of studies that investigated the hydrological impacts of LULC changes in Ethiopian basins. The review discusses methodologies, key findings, implications for sustainable land and water resource management, research gaps, and future directions. Emphasizing results from diverse studies that utilized hydrological and geospatial modelling, LULC significantly affects surface runoff, streamflow patterns, and groundwater recharge. The observed consequences are increased surface runoff, modified streamflow timing, and reduced groundwater recharge. The review underscores the urgency for integrated land and water management strategies to alleviate adverse effects. Future research priorities include basin-specific studies, long-term monitoring, integrated modelling, consideration of socio-economic factors, and incorporating climate change scenarios to enhance understanding and guide sustainable water resource management in Ethiopian basins. [ABSTRACT FROM AUTHOR]

Calibration and regionalization are two key tasks in hydrological modelling, and the availability of increased data and computing resources has enhanced our ability to complete these tasks. This paper addresses both tasks, i.e. (i) it proposes novel model structure-based calibration strategies with the goal to improve estimation of high streamflows, and (ii) it explores the association of soil hydraulic properties with optimal parameters to estimate streamflows in ungauged catchments. Both tasks are demonstrated by employing a conceptual hydrological model, Sacramento Soil Moisture Accounting (SAC-SMA), on multiple catchments in the Narmada River Basin. The initial calibrated model exhibited good model performance, with high flows being underestimated. Seven calibration strategies via revising parameter space are explored, and improvement in high flow performance is observed at nine out of 12 catchments. Estimated streamflows based on soil hydraulic parameters demonstrate regionalization potential. The results indicate successful application of the proposed methods which are transferable to other basins. [ABSTRACT FROM AUTHOR]

This paper aimed to estimate the reference evapotranspiration (ET0) due to some limitations of the Food and Agriculture Organization-56 Penman-Monteith (FAO 56-PM) approach by using five alternative machine learning models. The study makes an important contribution to the ET0 estimation success for of the ET0 of 12 stations with variable climate characteristics in the Central Anatolian Region (CAR). The performances of the models were compared with the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) metrics that are frequently cited in the literature, and also with the performance index (PI). Long short-term memory (LSTM), artificial neural networks (ANN), and multivariate adaptive regression splines (MARS) models provided the best performance in eight, three, and one stations, respectively. The R2, MAE, RMSE, and PI values of the selected models from each station vary in the range of 0.987-0.999, 1.948-4.567, 2.671-6.659, and 1.544-4.018, respectively. [ABSTRACT FROM AUTHOR]

This review paper focuses on the question of what will happen to the future water resource system of Grand Renaissance Dam of Ethiopia based on the projected hydrology of the Upper Blue Nile River basin. For this, the hydrological extremes, the baseline hydrological trends, and projected hydrological extremes of the Upper Blue Nile River basin were reviewed from various published and unpublished sources. Changes in the future hydrology of the Upper Blue Nile River basin due to climate change or any other natural or manmade modification of the river basin deserve concerted attention in the future water resources system of the Grand Renaissance Dam of Ethiopia, because the water resources of the dam mainly depend on the water potential nature of the Upper Blue Nile River basin. [ABSTRACT FROM AUTHOR]

A paper published by Anagnostopoulos et al. in volume 55 of the Hydrological Sciences Journal (HSJ) concludes that climate models are poor based on temporal correlation between observations and individual simulations. This interpretation hinges on a common misconception, that climate models predict natural climate variability. This discussion underlines fundamental differences between hydrological and climatological models, and hopes to clear misunderstandings regarding the proper use of climate simulations. Citation Huard, D. (2011) A black eye for the Hydrological Sciences Journal. Discussion of ‘A comparison of local and aggregated climate model outputs with observed data’ by G.G. Anagnostopoulos et al. (2010, Hydrol. Sci. J. 55 (7), 1094–1110). Hydrol. Sci. J. 56(7), 1330–1333. [ABSTRACT FROM PUBLISHER]

Copyright of Hydrological Sciences Journal/Journal des Sciences Hydrologiques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With climate change, decreases in winter snow storage and increases in precipitation duration and intensities will alter the occurrence of floods in high-latitude countries. The state-of-the-art hydrological climate-impact model chain consists of one or more global climate models, downscaling and bias-correction techniques, and one or more hydrological models. Machine learning offers a complementary approach to hydrological climate-impact modelling by facilitating direct downscaling from large-scale atmospheric variables to streamflow. This paper presents the development of multilayer perceptron (MLP) and long short-term memory (LSTM) neural networks benchmarked against regression tree models for reconstruction of daily streamflow and floods from atmospheric reanalysis data with comparable resolution to global climate model outputs. Catchment-specific, physically-based input features representing the dominant flood drivers were identified for 27 catchments in Norway. Overall, the LSTM obtained the highest accuracy. This article provides a springboard for future research on hydrological climate-impact modelling with neural networks in high-latitude countries. [ABSTRACT FROM AUTHOR]

Rapid climate change is impacting water resources in Afghanistan. The consequences are poorly known. Suitable mitigation and adaptation strategies have not been developed. Thus, this paper summarizes current status of knowledge in relation to Afghan water resources. More than 130 scientific articles, reports and data sources are synthesized to review the potential impacts of climate change on the cryosphere, streamflow, groundwater and hydrological extremes. The available information suggests that Afghanistan is currently witnessing significant increases in temperature, less so precipitation. There is evidence of shifts in the intra-annual distribution of streamflow, with reduced summer flows in non-glaciated basins and increased winter and spring streamflow. However, in the short-term there will be an increase in summer ice melt in glaciated basins, a "glacial subsidy", which sustains summer streamflow, despite reduced snow accumulation. The future prognosis for water resources is likely to be more serious when this glacier subsidy ends. [ABSTRACT FROM AUTHOR]