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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]

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.)

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.

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

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.)

The calculation of runoff and infiltration parameters is a fundamental task for water budget estimation and water resources management. The "Kenessey method" allows these values to be calculated and its use is widespread among water scientists and practitioners. Due to language barriers and inefficient translation in past decades, the method has undergone some misinterpretations and is now far from its original usefulness and purpose. The use of the modified method can give unreal results for specific climatic zones. The purpose of this paper is to point out these methodological variations and to return to the original approach, improving it with modern technology. [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]

The article focuses on modelling reference crop evapotranspiration using vector machines. It relates the evapotranspiration modelling work of O. Kisi and M. Cimen wherein support vector machines and artificial neural networks were applied to a non-complex hydrological modelling problem. It mentions that four meteorological input measurements were utilized in the modeling work including solar radiation (Rs), air temperature (T), and relative humidity (RH). It suggests that measurement units for all input and output variables must be provided in each scientific reporting of data in data-driven modelling paper. It adds that each paper must consist of a numerical description of every variable used in the model.

The opinion paper by Kreibich et al. provides interesting insights into the interplay between floods and droughts, which are often ignored. The structure of the paper is original and the findings of high interest. It may however be difficult to obtain the required data to pursue the research agenda in actual catchments. This discussion paper suggests various ways in which the linkages between floods and droughts can be explored even if recorded data of sufficient detail is not available. [ABSTRACT FROM AUTHOR]

Climate change impact search methodologies, for a single hydro-meteorological record, are based mostly on probabilistic, statistical and stochastic processes. The application of these methodologies requires some restrictive assumptions for their validation, which may not be embedded in the structure of the record. Either transformations or approximations are taken into consideration, with idealization and simplification assumptions. In this paper, a series of conceptual trend polygon methodologies are presented without mathematical expression or restrictive assumptions. These are serial, cross- and double polygonal trend methods, in addition to trend star graphs. They help in identifying, interpreting and calculating monthly shifts, producing useful scientific information that cannot be obtained by the classical monotonic trend identification approaches. The methodology is applied to hydro-meteorological series from different regions of the world including precipitation and discharge records. [ABSTRACT FROM AUTHOR]

To explore and address complex water-related issues, true collaborative, interdisciplinary research at the interface of hydrology and social science is necessary. Accordingly, hydrologists are increasingly working with social sciences and becoming involved in fieldwork with participants. With the overarching aim of facilitating collaboration and interdisciplinary water research, here we discuss important considerations and guiding principles for hydrologists, both those new to and those already familiar with interdisciplinary research, who are: (i) involved in fieldwork with participants; and (ii) working more collaboratively with social scientists. Drawing on first-hand experiences, this paper combines theory and experience from hydrologists and social scientists from their various interdisciplinary research projects to better understand key ethical, theoretical and practical considerations when working with participants. Complementary to this, we discuss the barriers and opportunities in collaborative interdisciplinary research. Facilitating these practices and understandings for hydrologists is essential to strengthen collaboration and to develop more holistic, successful research. [ABSTRACT FROM AUTHOR]

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.)

Precipitation prediction is central in hydrology and water resources planning and management. This paper introduces a semi-empirical predictive model to predict monthly precipitation and compares its predictive skill with those of machine learning (ML) methods. The stochastic method presented herein estimates monthly precipitation with one-step-ahead prediction properties. The ML predictive skill of the algorithms is evaluated by predicting monthly precipitation relying on the statistical association between precipitation and environmental and topographic factors. The semi-empirical predictive model features non-negative matrix factorization (NMF) for investigating the influence of multiple predictor variables on precipitation. The semi-empirical predictive model's parameters are optimized with the hybrid genetic algorithm (GA) and Levenberg-Marquardt algorithm (LM), or GALMA, yielding a validated model with high predictive skill. The methodologies are illustrated with data from Hubei Province, China, which comprise 27 meteorological station datasets from 1988–2017. The empirical results provide valuable insights for developing semi-empirical rainfall prediction models. [ABSTRACT FROM AUTHOR]

Flooding events can produce significant disturbances in underground transport systems within urban areas and lead to economic and socioenvironmental well-known consequences, which can be worsened by variations in the occurrence of weather and climate extremes. A better comprehension of these impacts and their conditions is consequently needed. Hence, this paper presents a state-of-the-art literature review on flood impact assessment in "metro" systems, analysing their purposes and their shortcomings. This document shows the adaptation measures dealing with specific classes of pluvial flood damages, besides identifying prospective paths towards the application of suitable actions facing actual and projected hazards in metro systems worldwide. [ABSTRACT FROM AUTHOR]

This paper reviews socio-hydrology and hydrosocial research, finding a sophisticated relationship with emergent syntheses. We examined 419 papers by topic, region of study, theories implemented, journal, and year published to ascertain trends in both subfields. We found important overlap and considerable difference between subfields. Whereas hydrosocial research took years to develop, socio-hydrology commenced with an inaugural paper in 2012. While the former focuses on power and scale in studying water demand, the latter concentrates on practical responses to climate extremes. Hydrosocial research usually relies on qualitative methods, and socio-hydrology research the quantitative. In the geographic regions where the former does not focus, the latter does. The former often relies on post-structuralist theory, whereas the latter uses positivist approaches. Our review concludes that socio-hydrology and hydrosocial research exist in a complex epistemological relationship, offering fertile grounds for lively discussions from which both will continue to benefit. [ABSTRACT FROM AUTHOR]

Advances in open data science serve large-scale model developments and, subsequently, hydroclimate services. Local river flow observations are key in hydrology but data sharing remains limited due to unclear quality, or to political, economic or infrastructure reasons. This paper provides methods for quality checking openly accessible river-flow time series. Availability, outliers, homogeneity and trends were assessed in 21 586 time series from 13 data providers worldwide. We found a decrease in data availability since the 1980s, scarce open information in southern Asia, the Middle East and North and Central Africa, and significant river-flow trends in Africa, Australia, southwest Europe and Southeast Asia. We distinguish numerical outliers from high-flow peaks, and integrate all investigated quality characteristics in a composite indicator. We stress the need to maintain existing gauging networks, and highlight opportunities in extending existing global databases, understanding drivers for trends and inhomogeneity, and in innovative acquisition methods in data-scarce regions. [ABSTRACT FROM AUTHOR]