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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]

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]

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]

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]

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]

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]

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]

Natural streamflow data is required in many hydrological applications. However, many basins are located in data-scarce regions or are impacted by human construction and activities. In this paper, we explore three machine learning algorithms, namely artificial neural networks, random forest and light gradient boosting machine, to simultaneously estimate all the parameters of the coupled modèle du Génie Rural à 4 paramètres Journaliers (GR4J) and snow accounting routine called CemaNeige model. A database of 675 basins in the USA and Quebec is used to train and test ensembles. After using the estimated parameters in GR4J, the resulting naturalized streamflow series are compared with those obtained by the established drainage area ratio and spatial proximity transfer methods in 11 test basins. The results indicate that the machine learning algorithms outperform the drainage area ratio and spatial proximity transfer methods. Among machine learning algorithms, random forests obtain lower (better) continuous ranked probability scores than the other methods for 10 out of 11 test basins. [ABSTRACT FROM AUTHOR]

The Iberian Peninsula is a climate change hotspot, where the temperature is increasing faster than the global annual mean surface temperature, with the largest reduction of precipitation. Consequently, freshwater availability is expected to decrease substantially. In this context, freshwater systems are especially vulnerable in terms of meeting the water demands and ecosystem requirements we know today. In this paper, we present an extension of the eco-engineering decision scaling (EEDS) method to explore trade-offs in agricultural and ecologic metrics at the catchment scale across a range of unknown future hydrological and climate states. The extended EEDS method evaluates current water resource management rules focusing on agricultural and ecologic objectives, identifies climate hazards that make the system fail and assesses climate risk in three time horizons for the design of adaptation measures. The case study is the Serpis River basin, Spain, where 72% of available water is used for agricultural purposes. [ABSTRACT FROM AUTHOR]

The urban stormwater flood and its ecological effects are becoming increasingly prominent underlying the rapid urbanization of China. Nanning is a typical southern city with frequent flood disasters and an urgent need to conduct quantitative research and explore effective countermeasures. Therefore, the quantitative contributions of rainfall, evapotranspiration (ET), and landscape patterns to urban stormwater runoff are analysed using the Budyko framework in this paper. The results show that the rainfall, landscape pattern, evapotranspiration and runoff in Nanning have significant synergistic interaction. The change of rainfall has a positive effect on runoff increment, accounting for 5.9~12.3% of the annual rainfall. The change of the underlying surface parameter (n) has a negative effect on runoff decrement, accounting for −14.8%. Evidently, the blue (water space), green and grey infrastructure of the underlying surface and its spatial pattern largely determine the quantity and spatial distribution of storm runoff in Nanning City. [ABSTRACT FROM AUTHOR]

Rainstorm and waterlogging disasters of underground rail transit have recently become a vital safety problem. In this paper, a model of vulnerability assessment is established, and 14 indicators are selected to establish an indicator system for 109 stations in the central area of Tianjin. Analytic Hierarchy Process(AHP) and Entropy Weight Method (EWM) were used to determine the subjective and objective weights of indicators. A distance function was introduced to calculate the combined weights and improve the weights' rationality. The results show that there are 34 high and medium-high vulnerability stations, and 41 low and medium-low vulnerability stations. Except for Line 2, the vulnerability level of subway stations on the rail lines decreased from the edge to the centre. Nankai District had the most high-vulnerability stations (seven of the 20 high-vulnerability stations). The main influence factors were the waterproofing capacity of the subway, the digital elevation model, the station type, the height of the exit, and the entrance steps. [ABSTRACT FROM AUTHOR]

Missing data is ubiquitous in hydrology. This phenomenon poses difficulty in the development of datadriven models. Events of missing data in groundwater pollution monitoring networks may occur due to failure of recording devices, malfunctioning of sensors, etc. Handling such missing data implies filling the missing portions of the data structure. Though several studies are available for dealing with missing data in the field of hydrology, literature dealing with such scenarios in groundwater pollution prediction is scarce. This paper assesses four imputation techniques-viz. linear, cubic spline, piece-wise cubic Hermite and modified Akima with cubic Hermite interpolation methods-for developing groundwater pollution prediction models using artificial neural network (ANN). The study employs the development of cascade-forward back-propagation ANN models using missing data ranging from 5% to 75% and evaluating their performance. Results show that imputation techniques can be effective in such circumstances. [ABSTRACT FROM AUTHOR]

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]

This paper provides an assessment of hydrological research activity conducted in the Oceania Region. Literature from three databases (Web of Science (WOS), Scopus, and Google Scholar) was collected and analysed. Results show large gaps in the variety of themes and poor collaborative efforts between regional water specialists. Results also suggest that local specialists potentially lack the ability to conduct quality hydrological research or that, due to the region's developing status, more focus has been placed on project-based objectives rather than building a body of research. A solution could be the development of a self-reliant base for conducting hydrological research by empowering and improving capabilities of local citizens. Firstly, an overhaul of organization policies regarding the introduction of research and performance monitoring is needed. Secondly, education in hydrology must be improved to suit local capabilities while addressing the complex socio-cultural environment with transdisciplinary measures. This assessment could benefit the future management of hydrological services. [ABSTRACT FROM AUTHOR]

Accurate predictions of water surface elevation (WSE) in rivers at high spatial and temporal resolution are important for flood/drought risk assessment and flood/drought forecasting and management. WSE in a river is controlled by three main factors: discharge, riverbed geometry, and hydraulic roughness. In remote and poorly instrumented rivers, discharge and riverbed geometry are highly uncertain and WSE is therefore hard to predict. ICESat-2 laser altimetry provides accurate elevation transects across the river at very high spatial resolution (70 cm along track). This paper demonstrates how ICESat-2 elevation transects can be used to parameterize a basin-scale hydraulic model of a continental-scale river. The workflow is demonstrated for the transboundary Amur River in North-East Asia. Simulated WSE is subsequently validated against a large dataset of in situ and satellite altimetry observations, and we demonstrate that the model can reproduce available WSE observations throughout the basin with an accuracy of 1–2 m. [ABSTRACT FROM AUTHOR]

Rainfall is considered a major input in designing stormwater management measures, especially for any low-impact development (LID) control design. With the impact of climate change, rainfall frequency and its patterns are changing continuously. Quantification of these changes and their impact on the performance of LID design becomes crucial. This paper presents a methodology to quantify the change in rainfall patterns using the Coupled Model Intercomparison Project 5 (CMIP5) climate model and to select the most feasible LID for a catchment with haphazard development. Interconnected decentralization-based LID controls are evaluated with the objective of emulating a pre-urbanized scenario. The overall analyses indicated that green roof (GR) followed by infiltration trenches (IT), rooftop disconnection (RTD), and permeable pavement (PP) showed better performance. Furthermore, a combination of IT, PP, and RTD accomplishes better efficiency for extreme rainfall events. Implementation of the most feasible combination will provide the additional benefit of water recycle and reuse. [ABSTRACT FROM AUTHOR]

This paper investigates how hydrological drought affected suspended sediment yield (SSY) in the Mediterranean Ofanto River basin, southern Italy, and in its five sub-basins from 1951 to 1989. The Standardized Precipitation Evaporation Index over a scale of 12 months (SPEI12) has been used to compute the hydrological drought in the investigated span of time. SPEI12, mean and maximum monthly discharge (Qmean and Qmax), monthly rainfall erosivity, monthly simple daily intensity index and monthly SSY were used to assess the relationships between dry/wet cycles, streamflow and SSY through the Pearson correlation matrix. Qmean and Qmax are significantly correlated with SSY, while SPEI12 and rainfall intensity do not show good correlation with SSY. Furthermore, from the overall analyses it emerges that sediment yield estimations were higher mainly during the wet period following a period of drought or during a drought period. [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]

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]

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]

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]

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 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]

A correction notice has been issued for an article titled "Multisite calibration of a semi-distributed hydrologic and thermal model in a large Canadian watershed." The correction addresses an error in the calculation of vapour pressure in the original paper. The correction did not significantly impact the results of the research, with only minor changes occurring in certain parameters and metrics. The correction notice also includes the original published version of Table 6, which provides thermal parameter values for different calibration scenarios. The corrected version of Table 7 shows the root mean square error (RMSE) values for each station in Method 1 and the overall RMSE for different scenarios. The overall conclusions of the original paper remain unchanged. [Extracted from the article]

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]

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]

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]

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]

In the present paper, we propose a new approach for monthly streamflow prediction based on the extreme learning machine (ELM) and the metaheuristic bat algorithm (Bat-ELM). The performance of the Bat-ELM was compared to that of ELM, support vector regression (SVR), Gaussian process regression (GPR), multilayer perceptron neural network (MLPNN), and generalized regression neural network (GRNN). The proposed models were applied using data from three hydrometric stations located in the Cheliff Basin, Algeria. The results showed that the Bat-ELM was more satisfactory than the standalone models. The Bat-ELM achieved the highest numerical performance with correlation coefficient and Nash-Sutcliffe efficiency ranging from 0.927 to 0.973 and from 0.846 to 0.944, respectively, much higher than the respective values obtained using the MLPNN, GRNN, SVR, GPR and ELM approaches. The obtained results demonstrate that the Bat-ELM is an interesting alternative algorithm for predicting high and extreme streamflow. [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]

A paucity of precipitation data often precludes the application of traditional unit hydrograph and synthetic unit hydrograph methods for generating the design flood hydrograph (DFH) at a catchment outlet. However, flow data, either recorded or derived by deterministic approaches, are readily obtained for many catchments. In this paper, analytical methods for generating a DFH having its peak matching the peak flow of a specified return period, or a flood hydrograph beneath a known peak flow at a site, are presented. Only flow data in the case of a gauged site, and flow data together with data of physical descriptors of catchments at gauged sites in the vicinity in the case of an ungauged site, are used to develop a parametric semi-dimensionless characteristic flood hydrograph (CFH); finally, the CFH, which also enables volume estimation, is scaled up for fleshing out a DFH beneath a specified peak flow at that site. [ABSTRACT FROM AUTHOR]

In November 2021, Member States unanimously adopted the United Nations Educational Scientific and Cultural Organization (UNESCO) Recommendation on Open Science. As attention now turns to the implementation of the Recommendation, time is ripe for the science community to take an active role in governance of the evolving science system if a new era of open science is to serve the global public good. This paper discusses open science in terms of the paradigm shift needed, the potential and directions for the evolution of global science, and governance arrangements in the broader perspective of Anthropocene-related challenges, with the hydrological entry point at its core. [ABSTRACT FROM AUTHOR]

Data assimilation (DA) offers immense potential for uncertainty identification, improving the initial estimates for hydrological and atmospheric modelling. This paper reviews the studies in hydrological DA using Kalman filters. Recent applications of Kalman filters in hydrological and atmospheric DA are summarized. Existing challenges for DA studies are briefly described. In addition, three case study examples are presented highlighting the effects of: (a) soil moisture DA in the Noah land surface model; (b) variational assimilation for improving precipitation forecasts in the WRF (Weather Research Forecast) model; and (c) simulating AMSR-2 (Advanced Microwave Scanning Radiometer-2) radiances towards DA. Although there are many unresolved issues in DA that warrant further research, it has immense potential for predicting variables at a better lead time for hydrometeorology. [ABSTRACT FROM AUTHOR]

Two thirds of hydrological observation networks in developing countries are reported to be in poor or declining condition. At the same time innovation in sensor technologies and data processing are presenting opportunities for enhancing observation networks that are not being realized. The World Meteorological Organization's Global Hydrometry Support Facility, or WMO HydroHub, was launched in 2016 to transform assistance to operational water monitoring agencies around the world. If successful, the initiative will increase the amount of hydrometric data available to researchers, catchment managers and water policy makers. To those unfamiliar with UN organizations, however, the nature of such initiatives, the reasoning behind the decisions taken to establish them and the mechanisms by which they try to deliver benefits for society, can be opaque. This paper adopts a novel dialogue-style format to explore the set-up of the WMO HydroHub and build awareness amongst those who ultimately may benefit from its approaches. [ABSTRACT FROM AUTHOR]

The French Critical Zone research infrastructure, OZCAR-RI, gathers 20 observatories sampling various compartments of the critical zone, each having developed their own data management and distribution systems. A common information system (Theia/OZCAR IS) was built to make their in situ observation FAIR (findable, accessible, interoperable, reusable). The IS architecture was designed after consultation of the users, data producers and IT teams involved in data management. A common data model based on various metadata standards was defined to create information fluxes between observatories' ISs and the Theia/OZCAR IS. Controlled vocabularies were defined to develop a data discovery web portal offering a faceted search with various criteria, including variables names and categories that were harmonized in a thesaurus published on the web. This paper describes the IS architecture, the pivot data model and open-source solutions used to implement data discovery, and future steps to implement data downloading and interoperability services. [ABSTRACT FROM AUTHOR]

Hydro-meteorological monitoring systems are essential sources of information for designing sustainable water management and developing equitable policies. In this paper we discuss the role of a regional monitoring network in Switzerland, Canton Ticino, and identify a list of challenges and actions to be considered to address future expected issues. The identification is based on a retrospective review of the monitoring network and analysis of the state of the art. Based on our experience, the most critical aspects relate to data density and quality, improvement of archiving and dissemination, modernization of the network for increased quality of service and better interoperability to foster the transition towards an innovative and interdisciplinary water resources management. [ABSTRACT FROM AUTHOR]

In this paper, a comprehensive semi-distributed water balance model is presented for basins under anthropogenic impacts. It benefits from data in different time resolutions for estimation of surface and groundwater exchanges and water withdrawals for various purposes. Ground-based data such as vegetation cover and topography have been used to determine the model parameters. The performance of the proposed model was tested for 38 sub-basins in Iran representing various climate conditions, vegetation covers, and agricultural practices. The results demonstrated a good performance in terms of estimating streamflows at the basin outlet, with more than 90% of sub-basins having a correlation coefficient above 0.9. However, the model showed a relatively weaker performance in the estimation of annual aquifer storage volume change. Correlation coefficients above 0.6 were calculated for more than half of the aquifers in the study area. [ABSTRACT FROM AUTHOR]

Bridge failures due to scour occur more frequently than expected, although bridge design considers conservative scenarios, evidencing a stochastic scour behaviour and the need for improvements in current design procedures. In this paper, a new method for determination of design local scour depth at bridge piers is proposed. The results show that peaks-over-threshold series are better suited than annual-maximum series of maximum scour depths for frequency analysis. Extreme scour depths follow the heavy-tailed Cauchy distribution. Scour depths with a 100-year return period resulted in 22 to 202% of the equilibrium scour depth currently used in design, evidencing the need for a different criterion to select the design scour depth. The proposed method computes the design local scour depth based on its probability of occurrence, introducing a novel probabilistic approach in bridge design for enhanced infrastructure risk management compatible with multi-hazard analysis. [ABSTRACT FROM AUTHOR]

Assessment of the spatial and temporal distribution of surface water balance (SWB) components for a river basin having complex topography with limited observed data is challenging. This paper focused on estimating the spatial-temporal variability of long-term average monthly, seasonal, and annual SWB components using the modified Water Energy Transfer between Soil, Plants, and Atmosphere in Steady State (WetSpass-M) physically based hydrological model in Ethiopia's data-scarce Omo River basin. Further, the model output was used for estimating the spatial variation of the average monthly crop water deficit. A sensitivity analysis was performed on the global and local parameters of the model for analysing the relative variation in the SWB components. It was found that actual evapotranspiration (AET) and interception are insensitive to average rainfall intensity while surface runoff is highly sensitive. The variation of SWB under different combinations of land-use/land cover (LU/LC) and soil type indicates that SWB types such as surface runoff, AET, and interception are influenced more by LU/LC than by soil type. [ABSTRACT FROM AUTHOR]

This paper outlines the importance of multiple time step optimization (MTO) in river basin allocation. The principal novelty of the work presented here is to provide a methodology for how to use MTO solutions in river basin planning and real-time operation. Two approaches for using the MTO results were tested on Damodar River basin in India and are presented in the paper. Using the proposed approach, the model managed flood flows without exceeding the downstream full bank channel capacity in 35 years of available historical data, while at the same time increasing generated hydropower on average by 63% annually, and supplying an additional 350 million m3 to irrigation and industry compared to the historical levels. The results presented in this study were obtained using the new Web-based Basin Management (WEB.BM) water management model, the only water allocation model with full Linear Programming (LP) optimization capabilities available online free of charge. [ABSTRACT FROM AUTHOR]

In small catchments, the time interval most commonly used for simulation – daily or monthly – may not be sufficient to accurately capture the time distribution of hydrological processes. In this paper, the Soil and Water Assessment Tool (SWAT) was used to perform an hourly long-term streamflow and sediment load simulation in the small (4.8 km2) and forested Aixola catchment (northern Spain). From this simulation, 10 runoff events were tested; the most satisfactory results for streamflow were obtained under wet antecedent conditions. However, simulated sediment load was underestimated during the peaks and remained high towards the end of the event. Furthermore, the influence of the precipitation time step (1–4 h, daily) was not relevant in the streamflow simulation but does influence the sediment simulation. The best results were achieved with the daily step simulations obtained at an hourly time step. This paper shows that sub-daily modelling improves water and especially sediment yield results; however, improvements are still needed in timing-related routines. [ABSTRACT FROM AUTHOR]

This paper proposes runoff models based on machine learning to estimate daily streamflows in Oued Sebaou watershed, a Mediterranean coastal basin located in northern Algeria. Therefore, we applied random forest (RF), artificial neural networks (ANN – under different training algorithms), and locally weighted linear regression (LWLR) using as input combinations of current and past rainfall amounts and previous values of streamflow. We selected streamflow and rainfall records to calibrate and validate the stated approaches. We used root mean square error (RMSE) and correlation coefficient (R) to evaluate the accuracy of the models. Analyses of the results show that RF provided the best outcomes for both training (RMSE = 4.7458 and R = 0.9834) and validation (RMSE = 2.3617 and R = 0.9719). The ANN calibrated with the Levenberg-Marquardt algorithm presented the second-best result, outperforming its counterparts and LWLR. [ABSTRACT FROM AUTHOR]

This paper explores how crowdsourced social media data complements urban flood modelling to improve model performance and achieve a better classification of impacts. In addition to georeferencing flood impacts, Twitter allows monitoring the events in terms of hazards and impacts, and YouTube facilitates a retrospective analysis from audiovisual data. The analysis of 2800 tweets collected during four storm events and of almost 900 videos of the recent history of the basin, together with the implementation of a high-resolution model, contributed to the expansion of the capacity to represent the temporal and spatial scales of the problem. The complementation of crowdsourced social media data and urban modelling enhances the understanding of the flood dynamics, thus offering a framework of greater certainty for the generation of flood risk management products. [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]

Robust detection of patterns at low signal-to-noise ratios (SNRs) is a fundamental challenge of analysing high-frequency data, particularly in water quality monitoring. When conducting water quality data analysis, an indispensable step is to clean the data noise. In this paper, a new method named ADAPTIVE-EWT-MFE, based on empirical wavelet transform (EWT) and multiscale fuzzy entropy (MFE), is proposed to implement time series data cleaning. EWT-MFE can decompose the spectrum into different intrinsic mode functions (IMFs). According to different characteristics of the IMFs, an adaptive and adjustable parameter based on MFE, which reflects the intrinsic characteristics, is introduced into the threshold function to improve the performance of noise cleaning. Finally, this hybrid data cleaning method is designed to filter the high-frequency noise on the IMFs. Results show that our proposed method not only has great potential to improve the noise-cleaning performance but also does not distort noise-free data. [ABSTRACT FROM AUTHOR]

Models that can integrate aspects of society such as institutions, perceptions and behaviours with aspects of the natural system such as rainfall, runoff and water quality may help us understand and manage complex human–water systems. In this paper, a socio-hydrological model is developed for the Black Volta (Mouhoun) watershed in Burkina Faso. The model captures the relationships between the awareness of water quality issues and capacity of local organizations, land use in the riparian zone, agricultural practices and suspended sediment concentration as an indicator of water quality. Scenarios are generated for the current situation and for plausible pathways to achieve improved water quality through different riparian land management strategies. Scenario comparison show how water quality improvements are generated if institutional support, resources and capacity of local level organizations are substantially increased compared to current levels. [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.