# Panta Rhei: a decade of progress in research on change in hydrology and society

Heidi Kreibich, Murugesu Sivapalan, Amir Aghakouchak, Nans Addor, Hafzullah Aksoy, Berit Arheimer, Karsten Arnbjerg-Nielsen, Cynthia Vail-Castro, Christophe Cudennec, Mariana Madruga de Brito, et al.

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## To cite this version:

Heidi Kreibich, Murugesu Sivapalan, Amir Aghakouchak, Nans Addor, Hafzullah Aksoy, et al. Panta Rhei: a decade of progress in research on change in hydrology and society. Hydrological Sciences Journal, 2025, pp.1-27. 10.1080/02626667.2025.2469762. hal-05025482

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Hydrological Sciences Journal

# Panta Rhei: a decade of progress in research on change in hydrology and society

Heidi Kreibich, Murugesu Sivapalan, Amir AghaKouchak, Nans Addor, Hafzullah Aksoy, Berit Arheimer, Karsten Arnbjerg-Nielsen, Cynthia Vail-Castro, Christophe Cudennec, Mariana Madruga de Brito, Giuliano Di Baldassarre, David C. Finger, Keirnan Fowler, Wouter Knoben, Tobias Krueger, Junguo Liu, Elena Macdonald, Hilary McMillan, E. Mario Mendiondo, Alberto Montanari, Marc F. Muller, Saket Pande, Fuqiang Tian, Alberto Viglione, Yongping Wei, Attilio Castellarin, Daniel Peter Loucks, Taikan Oki, María J. Polo, Huub Savenije, Anne F. Van Loon, Ankit Agarwal, Camila Alvarez-Garreton, Ana Andreu, Marlies H. Barendrecht, Manuela Brunner, Louise Cavalcante, Yonca Cavus, Serena Ceola, Pedro Chaffe, Xi Chen, Gemma Coxon, Zhao Dandan, Kamran Davary, Moctar Dembélé, Benjamin Dewals, Tatiana Frolova (Bibikova), Animesh K. Gain, Alexander Gelfan, Mohammad Ghoreishi, Thomas Grabs, Xiaoxiang Guan, David M. Hannah, Joerg Helmschrot, Britta Höllermann, Jean Hounkpè, Elizabeth Koebele, Megan Konar, Frederik Kratzert, Sara Lindersson, Maria Carmen Llasat, Alessia Matanó, Maurizio Mazzoleni, Alfonso Mejia, Pablo Mendoza, Bruno Merz, Jenia Mukherjee, Farzin Nasiri Saleh, Bertil Nlend, Rodric Merime Nonki, Christina Orieschnig, Katerina Papagiannaki, Gopal Penny, Olga Petrucci, Rafael Pimentel, Sandra Pool, Elena Ridolfi, Maria Rusca, Nivedita Sairam, Adarsh Sankaran Namboothiri, Ana Carolina Sarmento Buarque, Elisa Savelli, Lukas Schoppa, Kai Schröter, Anna Scolobig, Mojtaba Shafiei, Anna E. Sikorska-Senoner, Magdalena Smigaj, Claudia Teutschbein, Thomas Thaler, Andrijana Todorovic, Faranak Tootoonchi, Roshanak Tootoonchi, Elena Toth, Ronald van Nooijen, Franciele Maria Vanelli, Nicolás Vásquez, David W. Walker, Marthe Wens, David J. Yu, Heidar Zarei, Changrang Zhou & Günter Blöschl

**To cite this article:** Heidi Kreibich, Murugesu Sivapalan, Amir AghaKouchak, Nans Addor, Hafzullah Aksoy, Berit Arheimer, Karsten Arnbjerg-Nielsen, Cynthia Vail-Castro, Christophe Cudennec, Mariana Madruga de Brito, Giuliano Di Baldassarre, David C. Finger, Keirnan Fowler, Wouter Knoben, Tobias Krueger, Junguo Liu, Elena Macdonald, Hilary McMillan, E. Mario Mendiondo, Alberto Montanari, Marc F. Muller, Saket Pande, Fuqiang Tian, Alberto Viglione, Yongping Wei, Attilio Castellarin, Daniel Peter Loucks, Taikan Oki, María J. Polo, Huub Savenije, Anne F. Van Loon, Ankit Agarwal, Camila Alvarez-Garreton, Ana Andreu, Marlies H. Barendrecht, Manuela Brunner, Louise Cavalcante, Yonca Cavus, Serena Ceola, Pedro Chaffe, Xi Chen, Gemma Coxon, Zhao Dandan, Kamran Davary, Moctar Dembélé, Benjamin Dewals, Tatiana Frolova (Bibikova), Animesh K. Gain, Alexander Gelfan, Mohammad Ghoreishi, Thomas Grabs, Xiaoxiang Guan, David M. Hannah, Joerg Helmschrot, Britta Höllermann, Jean Hounkpè, Elizabeth Koebele, Megan Konar, Frederik Kratzert, Sara Lindersson, Maria Carmen Llasat, Alessia Matanó, Maurizio Mazzoleni, Alfonso Mejia, Pablo Mendoza, Bruno Merz, Jenia Mukherjee, Farzin Nasiri Saleh, Bertil Nlend, Rodric Merime Nonki, Christina Orieschnig, Katerina Papagiannaki, Gopal Penny, Olga Petrucci, Rafael Pimentel, Sandra Pool, Elena Ridolfi,
Maria Rusca, Nivedita Sairam, Adarsh Sankaran Namboothiri, Ana Carolina Sarmento Buarque, Elisa Savelli, Lukas Schoppa, Kai Schröter, Anna Scolobig, Mojtaba Shafiei, Anna E. Sikorska-Senoner, Magdalena Smigaj, Claudia Teutschbein, Thomas Thaler, Andrijana Todorovic, Faranak Tootoonchi, Roshanak Tootoonchi, Elena Toth, Ronald van Nooijen, Franciele Maria Vanelli, Nicolás Vásquez, David W. Walker, Marthe Wens, David J. Yu, Heidar Zarei, Changrang Zhou & Günter Blöschl (03 Apr 2025): Panta Rhei: a decade of progress in research on change in hydrology and society, Hydrological Sciences Journal, DOI: 10.1080/02626667.2025.2469762

To link to this article: https://doi.org/10.1080/02626667.2025.2469762

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© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

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Panta Rhei: a decade of progress in research on change in hydrology and society

Heidi Kreibich IDa, Murugesu Sivapalan Idb, Amir AghaKouchak Idc, Nans Addor Idd, Hafzullah Aksoy Ide, Berit Arheimer Idf, Karsten Arnbjerg-Nielsen Idg, Cynthia Vail-Castro Idh, Christophe Cudennec Iid, Mariana Madruga de Brito Idj, Giuliano Di Baldassarre Idk, David C. Fingerl, Keirnan Fowlerm, Wouter Knoben Idn, Tobias Krueger Ido, Junguo Liu Idp, Elena Macdonalda, Hilary McMillan Idq, E. Mario Mendiondo Ir, Alberto Montanari Ids, Marc F. Muller Idt, Saket Pandeu, Fuqiang Tian Idv, Alberto Viglione Idw, Yongping Wei x, Attilio Castellarin Ids, Daniel Peter Loucks Idy, Taikan Oki Idz, María J. Polo Ida, Huub Savenijeu, Anne F. Van Loon Idb, Ankit Agarwal Idd, Camila Alvarez-Garretondd, Ana Andreu Ida, Marlies H. Barendrecht ee, Manuela Brunner Idf, Louise Cavalcantegg, Yonca Cavus Idh, Serena Ceola Ids, Pedro Chaffe Idi, Xi Chen Idj, Gemma Coxonkk, Zhao Dandan Idl, Kamran Davarymm, Moctar Dembélé Idnn, Benjamin Dewals oo, Tatiana Frolova (Bibikova)pp, Animesh K. Gain Idqq, Alexander Gelfanrr, Mohammad Ghoreishi Idss, Thomas Grabsk, Xiaoxiang Guan a, David M. Hannah Dt, Joerg Helmschrotuu, Britta Höllermann Idv, Jean Hounkpèww, Elizabeth Koebele Idx, Megan Konarb, Frederik Kratzert Idy, Sara Lindersson Idk, Maria Carmen Llasatzz, Alessia Matanóbb, Maurizio Mazzoleni Idbb, Alfonso Mejia Ida, Pablo Mendoza Idbbb, b, b, b, Bruno Merz Ida, c, ccc, Jenia Mukherjee ccc, Farzin Nasiri Saleh ddd, Bertil Nlend Idee, Rodric Merime Nonki If, Christina Orieschnig IJg, Katerina Papagiannaki IJhh, Gopal Penny IId, Olga Petrucci Idjj, Rafael Pimentel Ida, Sandra Pool Id, Elena Ridolfi Idkk, Maria Rusca III, Nivedita Sairam Ida, Adarsh Sankaran Namboothiri Idmmm, Ana Carolina Sarmento Buarque IIdnn, Elisa Savelli IIdoo, Lukas Schoppa Ida, Kai Schröter Idpp, Anna Scolobigqq, Mojtaba Shafiei Idrr, Anna E. Sikorska-Senoner Idss, Magdalena Smigaj IJg, Claudia Teutschbein Idk, Thomas Thaler Idtt, Andrijana Todorovic IJuu, Faranak Tootoonchi IJvv, Roshanak Tootoonchiwww, Elena Toth Ids, Ronald van Nooijen Iu, Franciele Maria Vanelli IJxxx, Nicolás Vásquez IJbb, David W. Walker IJg, Marthe Wens IJb, David J. Yu IJyy, Heidar Zareizz, Changrang Zhou IJu and Günter Blöschl Is, aaaa

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<footnote>aSection Hydrology, GFZ Helmholtz Centre for Geosciences, Potsdam, Germany; bThe Grainger College of Engineering, Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA; cDepartment of Civil and Environmental Engineering, University of California, Irvine, CA, USA; dFathom, Bristol, UK; and Geography, University of Exeter, Exeter, UK; eDepartment of Civil Engineering, Istanbul Technical University, Istanbul, Turkey; fHydrological Research, Swedish Meteorological and Hydrological Institute (SMHI), Norrkoping, Sweden; gDanish Meteorological Institute, Copenhagen, Denmark; hResearch Applications Laboratory, NSF National Center for Atmospheric Research, Boulder, CO, USA; IInstitut Agro, INRAE, SAS, Rennes, France; and Imperial College – CNRS International Research Center on Transformational Sciences and Technologies, London, UK; jDepartment of Urban and Environmental Sociology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany; kDepartment of Earth Sciences, Uppsala University, Uppsala, Sweden; lDepartment of Engineering, Reykjavik University, Reykjavik, Iceland; mUniversity of Melbourne, Melbourne, Australia; nUniversity of Calgary, Calgary, AB, Canada; oGeography Department and IRI THEsys, Humboldt-Universität zu Berlin, Berlin, Germany; pHenan Provincial Key Laboratory of Hydrosphere and Watershed Water Security, North China University of Water Resources and Electric Power, Zhengzhou, China; qDepartment of Geography, San Diego State University, San Diego, CA, USA; rSao Carlos School of Engineering, University of Sao Paulo, Sao Carlos-SP, Brazil; sDepartment of Civil, Chemical, Environmental, and Materials Engineering, Alma Mater Studiourium, Università di Bologna, Bologna, Italy; tEawag – Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland; uDelft University of Technology, Delft, Netherlands; vDepartment of Hydraulic Engineering and State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, China; wDepartment of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Torino, Italy; xThe University of Queensland, Brisbane, Australia; yCivil and Environmental Engineering, Cornell University, Ithaca, NY, USA; zGraduate School of Engineering, The University of Tokyo, Tokyo, Japan; aaDepartment of Agronomy, Unit of Excellence María de Maeztu (DAUCO), Fluvial Dynamics and Hydrology Research Group, Andalusian Institute for Earth System Research (IISTA), University of Córdoba, Córdoba, Spain; bbInstitute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam, Netherlands; ccDepartment of Hydrology, Indian Institute of Technology Roorkee, Roorkee, India; ddCenter for Climate and Resilience Research (CR)2, University of Chile, Santiago, Chile; eeDepartment of Geography, King’s College London, London, UK; ffInstitute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland; and WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland; and Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC, Davos Dorf, Switzerland; ggWageningen University, Wageningen, Netherlands; hhDepartment of Civil Engineering, Istanbul Beykent University, Istanbul, Turkey; iiDepartment of Sanitary and Environmental Engineering, Universidade Federal de Santa Catarina, Florianopolis, Brazil; iiiDepartment of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, OH, USA; kkUniversity of Bristol, Bristol, UK; llInstitute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin, China; mmmDepartment of Water Engineering, Ferdowsi University of Mashhad, Mashhad, Iran; nnInternational Water Management Institute (IWMI), Accra, Ghana; ooUrban and Environmental Engineering (UEE), University of Liège, Liège, Belgium; ppInstitute of Geography RAS, Moscow, Russia; qqSchool of Environmental and Conservation Sciences, Murdoch University, Murdoch, Australia; rrFaculty of Geography, Lomonosov Moscow State University, Moscow, Russia; ssGlobal Institute for Water Security (GIWS), University of Saskatchewan, Saskatoon, Saskatchewan, Canada; ttBirmingham Institute for Sustainability and Climate Action, and School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK; uuStellenbosch University Water Institute, Stellenbosch, South Africa; wwInstitute of</footnote>
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Geography, Osnabrueck University, Osnabrück, Germany; ⁂wNational Water Institute, University of Abomey Calavi, Abomey-Calavi, Benin; ⁂xDepartment of Political Science, University of Nevada, Reno, NV, USA; ⁂yGoogle Research, Vienna, Austria; ⁂zUniversity of Barcelona, Barcelona, Spain; ⁂aaDepartment of Civil and Environmental Engineering, The Pennsylvania State University, State College, PA, USA; ⁂bbDepartment of Civil Engineering, Universidad de Chile, Santiago, Chile; ⁂ccDepartment of Humanities and Social Sciences, Indian Institute of Technology Kharagpur, Kharagpur, India; ⁂ddDepartment of Water Engineering, Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran; ⁂eeUniversity of Douala, Douala, Cameroon; ⁂ffLaboratory for Environmental Modeling and Atmospheric Physics (LEMAP), University of Yaoundé 1, Yaoundé, Cameroon; ⁂ggUMR G-EAU, IRD, Montpellier, France; ⁂hhNational Observatory of Athens, Institute for Environmental Research and Sustainable Development, Athens, Greece; ⁂iiEnvironmental Defense Fund, San Francisco, CA, USA; ⁂iiiCNR-IRPI-Research Institute for Geo-Hydrological Protection, CNR (Italian National Research Council), Cosenza, Italy; ⁂kkDepartment of Civil, Constructional and Environmental Engineering, Sapienza Università di Roma, Rome, Italy; ⁂llUniversity of Manchester, Manchester UK; ⁂mmDepartment of Civil Engineering, TKM College of Engineering, Kollam, India; ⁂nnUniversidade de Sao Paulo, Sao Paulo, Brazil; ⁂ooMiddle East North Africa and Europe Regional Hub, Mercy Corps, Portland, OR, USA; ⁂ppLeichtweiß-Institute for Hydraulic Engineering and Water Resources, Division Hydrology and River Basin Management, Technische Universität Braunschweig, Braunschweig, Germany; ⁂qqInstitute for Environmental Sciences, University of Geneva, Geneva, Switzerland; ⁂rrDivision of Environment and Natural Resources, Norwegian Institute of Bioeconomy Research (NIBIO), Aas, Norway; ⁂ssFederal Office of Meteorology and Climatology MeteoSwiss, Zurich-Airport, Switzerland; and Center for Climate Systems Modeling C2SM, ETH Zurich, Zurich, Switzerland; ⁂ttInstitute of Landscape Planning, BOKU University, Vienna, Austria; ⁂uuFaculty of Civil Engineering, Institute for Hydraulic and Environmental Engineering, University of Belgrade, Belgrade, Serbia; ⁂vvDepartment of Crop Production Ecology, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden; ⁂wwwDepartment of Civil, Environmental and Mechanical Engineering, University of Trento, Trento, Italy; ⁂xxxIndependent researcher, Joinville, Brazil; ⁂yyLyles School of Civil and Construction Engineering; Department of Political Science, Purdue University, West Lafayette, IN, USA; ⁂zzDepartment of Hydrology and Water Resources, Shahid Chamran University of Ahvaz, Ahvaz, Iran; ⁂aaaaInstitute of Hydraulic Engineering and Water Resources Management, Vienna University of Technology, Vienna, Austria; ⁂bbbbAdvanced Mining Technology Center (AMTC), Universidad de Chile, Santiago, Chile; ⁂ccccInstitute for Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany

## ABSTRACT

To better understand the increasing human impact on the water cycle and the feedbacks between hydrology and society, the International Association of Hydrological Sciences (IAHS) organized the scientific decade "Panta Rhei – Everything Flows: Change in hydrology and society" (2013–2022). A key finding is the need to use integrated approaches to assess the co-evolution of human-water systems in order to avoid unintended consequences of human interventions over long periods of time. Additionally, substantial progress has been made in leveraging new data sources on human behaviour, e.g. through text mining of social media posts. Much has been learned about detecting hydrological changes and attributing them to their drivers, e.g. quantifying climate effects on floods. To achieve further progress, we recommend broadening the understanding, the discipline and training activities, while at the same time pursuing synthesis by focusing on key themes, developing innovative approaches and finding sustainable solutions to the world's water problems.

## ARTICLE HISTORY

Received 10 July 2024
Accepted 31 January 2025

## EDITOR

A. Fiori

## ASSOCIATE EDITOR

(not assigned)

## KEYWORDS

socio-hydrology; predictions
under change; integrated
water resources
management; human-water
feedbacks; change

# 1 Introduction

The feedbacks between hydrology and society have accelerated in recent decades, highlighting the need for the hydrological community to better understand the interactions between these systems (Montanari et al. 2013, Brondizio et al. 2016). Climate change, land use and socio-economic changes significantly alter the water cycle, leading to changes in water availability, quality and distribution, and related hazards. For instance, flood and drought impacts have already significantly increased in many regions and are expected to increase further (IPCC 2012, 2022). Freshwater scarcity is becoming a major limiting factor for societal development and security (United Nations 2018, GCEW 2023). Thus, it is important to understand, assess, predict and manage these accelerating changes in order to mitigate their adverse impacts and to ensure sustainability (Montanari et al. 2013, Ceola et al. 2016, McMillan et al. 2016, Di Baldassarre et al. 2019). This review aims to present key scientific advances on change in hydrology and society, with a focus on the feedbacks between humans and water, particularly over decadal to centennial time scales.

## 1.1 The IAHS scientific decade: Panta Rhei – Everything Flows: Change in hydrology and society

The overall aim of the International Association of Hydrological Sciences (IAHS) science decades is to coordinate efforts in order to accelerate research progress on a particular hydrological problem. The success of the scientific decades PUB – Predictions in Ungauged Basins 2003–2012 and Panta Rhei – Everything Flows: Change in hydrology and society 2013–2022 led to the current scientific decade, “Science for solutions: Hydrology Engaging Local People IN one Global world (HELPING),” 2023–2032 (Arheimer et al. 2024). At the close of the PUB scientific decade (Blöschl et al. 2013, Hrachowitz et al. 2013), the IAHS community started a global discussion to identify the most relevant societal challenges to shape the next IAHS scientific decade. The discussions on a blog, which attracted thousands of visits and many comments, converged on the understanding that “change” was the keyword for hydrological sciences in the 21st century and that a broad perspective on global change is necessary. The new decade should highlight the key role of hydrology in predicting future trends of environmental dynamics shaped by human-water feedbacks (Montanari et al. 2013).
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<figure><img src="image_23.png" /><figcaption>**Figure 1.** Links and cooperation between the Panta Rhei working groups and the IAHS commissions. International Commission on Snow and Ice Hydrology (ICSIH), International Commission on Continental Erosion (ICCE), International Commission on Groundwater (ICGW), International Commission on Tracers (ICT), International Commission on Coupled Land-Atmosphere Systems (ICCLAS), International Commission on Statistical Hydrology (ICSH), International Commission on Water Quality (ICWQ), International Commission on Remote Sensing (ICRS), International Commission on Surface Water (ICSW), International Commission on Water Resources Systems (ICWRS).</figcaption></figure>

To emphasize the focus on change, this decade was called “Panta Rhei – Everything Flows: Change in hydrology and society” after the aphorism attributed to the Greek philosopher Heraclitus of Ephesus, which conveys the idea that nature and societies are continuously changing. Supporting a community-based bottom-up organization, an open call for Working Groups (WGs) was issued, which resulted in over 30 groups that initiated joint studies, scientific papers, conference sessions and workshops within the frame of the IAHS scientific decade. An overview of the Panta Rhei working groups and their cooperation with IAHS commissions (Fig. 1) emphasizes the variety of scientific challenges being addressed and the diversity of approaches to solving them.

During the decade, the substantial increase in the network of hydrologists and scientists in a range of disciplines, including social sciences, stimulated large-scale cooperation based on the exchange of knowledge and data, which was supported

by the emergence of the open science paradigm (UNESCO (United Nations Educational Scientific and Cultural Organization) 2021; Cudennec et al. 2022b, Hall et al. 2022). Examples are the Panta Rhei opinion paper series in the *Hydrological Sciences Journal* (Kreibich et al. 2017) and the international collaborative effort to collect and analyse the Panta Rhei benchmark dataset of paired events of floods and droughts, to which more than 90 scientists contributed (Kreibich et al. 2022b, 2023). Remarkable progress in understanding interconnected change in hydrology and society has also been made due to relevant research projects and programmes supported by governmental agencies and funding organizations. Furthermore, the long-term partnership of IAHS with several agencies of the United Nations (UN) and the UN Water coordination mechanism allowed strong synergies with, and scientific inputs to, multilateral efforts, including the implementation of Sustainable Development
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<figure><img src="image_25.png" /><figcaption>**Figure 2.** Panta Rhei research encompasses three domains – socio-hydrology (SH), predictions under change (PUC), and integrated water resources management (IWRM) – to achieve its three targets (figure adapted from Montanari *et al.* 2013, Thompson *et al.* 2013).</figcaption></figure>

Goal (SDG) 6: “Clean Water and Sanitation” and interlinkages within Agenda 2030 (e.g. Young *et al.* 2015, Cudennec *et al.* 2020, 2022a, Mahé *et al.* 2021, Dixon *et al.* 2022, ISC 2023).

## 1.2 The three domains of Panta Rhei research

The Science Plan of Panta Rhei organized the scientific work around three targets and six science questions (Montanari *et al.* 2013). The three targets are closely related to the three domains: (1) socio-hydrology (Target 1), (2) predictions under change (Target 2), and (3) integrated water resources management (IWRM, Target 3), as Panta Rhei aimed to bridge past developments with new opportunities (Fig. 2).

The domain of socio-hydrology attempts to understand the complex interactions and feedbacks between human and water systems (Sivapalan et al. 2012). It contributes to deciphering hydrological change and its interaction with societies (Target 1 in Montanari et al. 2013). The innovation of socio-hydrological research is to model the co-evolution of human-water systems with an integrated approach to better understand the above-mentioned feedbacks and unintended consequences of human interventions over long periods of time. Along with empirical research across scales and places, stylized models based on differential equations are promising tools that can help explore socio-hydrological dynamics and contribute to theory development (Di Baldassarre et al. 2015). In addition, socio-hydrology draws on tools developed in research on socio-ecological and complex systems to expand socio-hydrological knowledge (Troy et al. 2015). With these tools, however, predictability is debatable in view of the contingent nature of some environmental and societal processes, as well as the importance of retroactive loops and the possible presence of tipping points (Sivapalan and Blöschl 2015, Bai et al. 2016). The goal is, rather, the projection of alternative, plausible and co-evolving trajectories of the socio-hydrological system, which may help stakeholders identify safe or desirable

operating spaces (Srinivasan et al. 2017a). As such, socio-hydrology aims to be a use-inspired science to inform the complex water sustainability challenges faced in the Anthropocene (Sivapalan et al. 2014, Sivapalan and Blöschl 2015, Di Baldassarre et al. 2019) and be applied to policy-making (Troy et al. 2015).

The domain of predictions under change aims to understand and model changes in hydrological systems in response to various environmental and human-induced drivers. It improves the estimation and prediction of hydrological processes under change, including design variables for flood and drought risk mitigation (Target 2 in Montanari et al. 2013). The drivers of change include climate change, river regulation, land use change, water abstraction or storage, and others (e.g. Milly et al. 2008). Detection and attribution of past changes help to understand trends (IPCC 2022). While detection demonstrates that a change has been observed and is statistically significantly different from what can be explained by natural variability, attribution associates detected changes with the corresponding drivers and rules out alternative explanations that are not causally associated with observed outcomes (Merz et al. 2012). On this basis, models and methods are developed to predict future changes in hydrological systems under changing conditions, supporting decision making in the management and planning of water resources.

The domain of integrated water resources management (IWRM) is a holistic approach to managing water resources that considers the multiple uses and users of water within a given area (Biswas 2004, Uysal et al. 2024). It has high societal relevance and, therefore, aims for iterative exchanges among science, technology, and societies. It brings science into practice, including policymaking and implementation (Target 3 in Montanari et al. 2013). IWRM aims to ensure that water resources are managed in an equitable, sustainable and efficient manner that considers both social and environmental aspects. Key principles of IWRM include a focus on basin-
<figure><img src="image_26.png" /><figcaption>**Table 1.** Organization of this review along the Panta Rhei science questions (Montanari *et al.* 2013).</figcaption></figure>

<table><thead><tr><th>Panta Rhei Science Questions (Montanari et al. 2013)</th><th>Sections of review</th></tr></thead><tbody><tr><td>How can we advance our monitoring and data analysis capabilities to predict and manage hydrological change?</td><td>2. Monitoring and data analysis</td></tr><tr><td>What are the external drivers and internal system properties of change? How can boundary conditions be defined for the future?</td><td>3. Drivers of change</td></tr><tr><td>How do changes in hydrological systems interact with, and feedback to, natural and social systems driven by hydrological processes? What are the boundaries of coupled hydrological and societal systems?</td><td>4. Understanding socio-hydrological systems</td></tr><tr><td>How can we use improved knowledge of coupled hydrological-social systems to improve model predictions, including estimation of predictive uncertainty and assessment of predictability?</td><td>5. Modelling and prediction</td></tr><tr><td>How can we support societies to adapt to changing conditions by considering the uncertainties and feedbacks between natural and human-induced hydrological changes?</td><td>6. Water management and adaptation to change</td></tr><tr><td>What are the key gaps in our understanding of hydrological change?</td><td>7. Summary of achievements<br>8. Recommendations</td></tr></tbody></table>

level planning, stakeholder participation, the integration of water management across different sectors, and the consideration of social, economic, and environmental factors. The approach also emphasizes the need for adaptive management, which involves continuously monitoring and assessing water resources, and adapting management strategies as needed to meet changing conditions (Medema *et al.* 2008, Kreibich *et al.* 2014).

This review is organized along the Panta Rhei science questions (Montanari *et al.* 2013), as shown in Table 1. The aim is to present scientific progress and to illustrate it using specific research findings from the scientific decade.

As a basis for this review, a collection of 351 key scientific papers that contribute to answering these science questions was compiled (see Supplementary material). The spreadsheet for the collection of key papers (see Supplementary material) has been made publicly available and the authors of the present article have each contributed up to five key papers. The collection also contains a brief summary of the most important results and scientific advances for each paper, as well as information on which of the scientific questions of Panta Rhei the paper contributes to answering (see Supplementary material). With 58 to 89 papers per question, i.e. with shares between 17% and 25%, the distribution of papers among the questions to be answered is fairly even. This collection demonstrates the recent progress by many experts in the field of change in hydrology and society worldwide.

## 2 Scientific progress on monitoring and data analysis

Improving our understanding of the long-term co-evolution of hydrological systems has required associating geophysical and anthropogenic processes that have historically been observed at disparate temporal, spatial, and social scales. Improving data interoperability and accessibility to enable interdisciplinary

research was therefore an essential component of the Panta Rhei scientific decade. Many initiatives and approaches have improved data accessibility, discovered new, unconventional data, developed innovative approaches to data integration and analyses, and used citizen science, thus contributing to answering the science question “How can we advance our monitoring and data analysis capabilities to predict and manage hydrological change?” The Panta Rhei collection of key scientific papers contains 58 papers (17%) that contribute to answering this question (see Supplementary material).

### 2.1 Improved data accessibility

Over the past decade there have been major innovations in data collection, in the combination of disparate data into easy-to-use large-sample datasets, and in data sharing and open-access initiatives that improved the accessibility of hydrological and socio-economic data. For instance, flow monitoring at thousands of stations over decades has been the basis for detecting changes in high flows and seasonality that were attributed to climate change across Europe (Hall *et al.* 2014, Blöschl *et al.* 2019b) and globally (Wang *et al.* 2024). New data have enabled advances in detecting human influence on river flow, for instance by showing that water abstractions aggravate droughts (Van Loon *et al.* 2022) and must be taken into account to successfully predict the baseflow index (Bloomfield *et al.* 2021). Analysis of paired events identified improved governance and high investment in integrated risk management as success factors in managing unprecedented flood and drought events (Kreibich *et al.* 2019, 2022b). Newly released global datasets, such as freshwater withdrawal and consumption rates, enabled Huggins *et al.* (2022) to map socio-ecological vulnerability to freshwater stress and storage loss and identify hotspots for prioritizing interventions such as IWRM practices.

Considerable effort has been spent on making data more accessible and useful via collation across locations and domains (Gupta *et al.* 2014). For example, the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS), Caravan and EStreams datasets combine daily hydro-meteorological time series with landscape attributes (e. g. reservoir type and capacity, water abstraction and return, consumptive water use, and surface and groundwater rights) for more than 20 000 catchments in over 35 countries (Newman *et al.* 2015, Addor *et al.* 2017, Alvarez-Garreton *et al.* 2018, Chagas *et al.* 2020, Coxon *et al.* 2020, Fowler *et al.* 2021, Höhe *et al.* 2023, Kratzert *et al.* 2023, Do Nascimento *et al.* 2024). These datasets have been instrumental in demonstrating that wastewater discharges dominate urban hydrology signals across England and Wales (Coxon *et al.* 2024), that water uses exacerbated hydrological drought conditions during the megadrought in central Chile after 2010 (Álamos *et al.* 2024) and that stream water losses are higher in areas of extensive groundwater pumping (Uchôa *et al.* 2024). Other studies target specific environments that are sensitive to change, such as high-mountain snow cover in semi-arid regions (Polo *et al.* 2019); or focus on anthropogenic processes, e.g. storage and release policies for approximately 2000 reservoirs in the US (Turner *et al.* 2021).
<figure><img src="image_27.png" /></figure>

Important progress has been made in the last decade through the structured documentation of extreme events and the recording of their impacts in databases (De Groeve et al. 2014, Rudari et al. 2017). Examples include flood fatality data across 12 territories in Europe and its surroundings (Papagiannaki et al. 2022), drought impact data extracted from nearly 5000 reports (Stahl et al. 2016), and object-specific flood damage data from fluvial, pluvial and groundwater flooding stored in the Flood Damage Database HOWAS 21 (Kellermann et al. 2020).

## 2.2 New, unconventional data

The increasing availability and volume of digital data have also opened up new opportunities for the prediction and management of hydrological change by including unstructured and qualitative data types in the research design. For example, analysing the minutes of water board committee meetings, Carvalho et al. (2024) found that water allocation decisions were increasingly based on seasonal forecasts and data on oceanic indices in Northeast Brazil from 1997 to 2021. An analysis of the number of news articles published about drought revealed that single-family customers reduced their water consumption most quickly following heavy drought-related news coverage (Quesnel and Ajami 2017, Roby et al. 2018). Web-scraping and text mining have made social media popular for analysing public opinion on extreme events (Cervone et al. 2016, Kryvasheyeu et al. 2016, Smith et al. 2017), improving flood mapping (Fohringer et al. 2015, Scotti et al. 2020), and monitoring the occurrence of disasters (Kryvasheyeu et al. 2016). Data collected through car navigation apps such as Waze or Mapbox have been shown to be powerful in estimating the extent of traffic impacts due to flooding (Praharaj et al. 2021, Safaei Moghadam et al. 2023), as well as anomalies in human activity (Farahmand et al. 2022). Similarly, Google Trends has emerged as a way to measure public awareness regarding drought (Kam et al. 2019, Kim et al. 2019, Alencar et al. 2024), track flood disasters (Thompson et al. 2021), and understand the dynamic social response to past droughts (Gonzales and Ajami 2017).

Earth observation products have become common for assessing key environmental variables at large scale, such as Landsat data employed for surface water dynamics (Pekel et al. 2016), Gravity Recovery and Climate Experiment (GRACE) data used for terrestrial water storage evolution (Chen and Rodell 2021, Kvas et al. 2024), and the Surface Water and Ocean Topography (SWOT) mission aimed at monitoring river hydraulic properties (Frasson et al. 2019). Local-scale monitoring has recently been fostered by low-cost innovative wireless sensor networks (WSN), employed for example in the meteorological, hydrological, agricultural, water management and services sectors (Ojha et al. 2015, Marais et al. 2016, Pimentel et al. 2017, Tauro et al. 2018, Bárdossy et al. 2021).

## 2.3 Data integration and machine learning

The combination of datasets with both process-based modeling and machine learning (ML) approaches can be integrated in tools that decision makers can use to investigate the long-term effects of their management decisions (Xia et al. 2021).

Furthermore, alongside large-scale or large-sample efforts, there are bespoke small-scale efforts to harness local hydrological understanding for improved social outcomes. For example, Hund et al. (2018) developed a data-based drought early warning system for communities dependent on an aquifer in Costa Rica, with predictions based on the local understanding of what climatic conditions typically lead to drought-induced hardship.

Interdisciplinary perspectives that integrate qualitative and quantitative data are needed to understand complex human-water systems (Di Baldassarre et al. 2021, Rangecroft et al. 2021, Vanelli et al. 2022). While quantitative data allow researchers to identify generalizable patterns and dynamics, qualitative data provide insights into the socio-political drivers of water management through detailed analyses of local contexts (Riedlinger and Berkes 2001, Ruska and Di Baldassarre 2019, Alexander et al. 2020). Several innovative approaches have been developed that combine qualitative and quantitative data in a meaningful way, in particular for nexus studies (Liu et al. 2017a, Cudennec et al. 2018, Heal et al. 2022). Another example is provided by Ferdous et al. (2018), who triangulated quantitative data from household surveys and qualitative data from focus group discussions in a socio-hydrological study. Sarmento Buarque et al. (2020) present a sequential mixed design, where a modelling-based quantitative analysis was supported by qualitative data obtained from newspapers and photographs. Van Loon et al. (2015) analysed quantitative and qualitative data in an iterative manner to investigate the frequency of occurrence of different drought types in cold climates.

With the increasing accessibility of big data from diverse data sources, artificial intelligence (AI) and ML approaches are increasingly used to overcome the challenges posed by the high complexity, non-linearity, and non-stationarity of change in hydrology and society (Kratzert et al. 2019, Ke et al. 2020, Mao et al. 2021, Yu et al. 2023). For instance, ML is used to automatically label built-up areas based on night-time lights or buildings and map roads using aerial or satellite imagery (Alshehhi and Marpu 2017, Jia et al. 2022). Other examples include real-time identification or mapping of floods based on social media posts (Annis and Nardi 2019), and analyses of flood damage processes using decision tree or Bayesian approaches (Carisi et al., 2018, Schoppa et al. 2020, Paprotny et al. 2021). Human perceptions and decisions were assessed based on insurance uptake using interpretable ML (Knighton et al. 2021, Veigel et al. 2023).

## 2.4 Citizen science

Citizen science and related data acquisition techniques such as volunteered geographic information (VGI), participatory tools and crowdsourcing have emerged to complement observations, raise awareness, promote innovative thinking, and encourage scientist-citizen cooperation in addressing water management issues (Woolley et al. 2010, Buytaert et al. 2014). Citizen science and related methods have a significant role in improving community sensitivity and engagement with water-related issues. Through citizen science initiatives, people can actively participate in data collection, analysis, and interpretation, promoting universal and equitable access to scientific data and information (de
<figure><img src="image_28.png" /></figure>

Sherbinin *et al.* 2021). Additionally, citizen science projects can have educational and outreach aspects, promoting awareness and understanding of water issues among the broader public, and even increasing citizen engagement in local governance processes (Nardi *et al.* 2022).

Citizen science has gained increasing prominence in hydrology, addressing the need for more dispersed and diverse observations of multiple water-related variables (Nardi et al. 2022) and is used to collect large amounts of data over wide areas (Buytaert et al. 2014, Walker et al. 2021). It additionally enables the observation of social, economic, educational, and behavioural dynamics that are difficult to capture (Jollymore et al. 2017).

Applications of citizen science in hydrology can range from local-scale studies involving a single volunteer to global-scale studies involving tens of thousands of volunteers (Walker et al. 2021). Examples of data commonly acquired include water levels (Lowry and Fienen 2013, Jan et al. 2019), water quality (Rangecroft et al. 2023, 2024), building footprints obtained from OpenStreetMap (Cerri et al. 2021), and meteorological observations (“Met Office WOW – Home Page” n.d.). Comprehensive overviews of citizen science projects in the field of hydrology are provided by Buytaert et al. (2014), Anna et al. (2019), Njue et al. (2019), See (2019), Kelly-Quinn et al. (2022), and Nath and Kirschke (2023).

Summary on monitoring and data analysis: Our monitoring and data analysis capabilities to predict and manage hydrological change have advanced significantly: (1) Accessibility and usefulness of (time series) data has increased by sharing and combining data across locations and domains, including quantified human impacts. Examples are the CAMELS datasets (e.g. Alvarez-Garreton et al. 2018, Fowler et al. 2021, Höhe et al. 2023), Panta Rhei benchmark datasets (e.g. Kreibich et al. 2023) and impact datasets (e.g. Stahl et al. 2016, Papagiannaki et al. 2022). (2) Repurposing and combining of data and increased exploration of new, unconventional data sources such as social media, novel sensors (e.g. Fohringer et al. 2015, Kryvasheyeu et al. 2016, Scotti et al. 2020) and new methods of analysis such as machine learning and text mining (e.g. Knighton et al. 2021, Paprotny et al. 2021, Veigel et al. 2023) have increased the availability and potential of qualitative and quantitative data. (3) Advancements in citizen science have demonstrated its value in monitoring various processes, promoting community engagement and supporting education in hydrology (e.g. Jollymore et al. 2017, Nardi et al. 2022).

## 3 Scientific progress on drivers of change

The pace and scope of change of hydrological systems has accelerated, and with them the risks to society and the environment. This has also increased the importance of assessing the drivers of change. Effects of climate, land use and socio-economic changes on freshwater quantity and quality trends were frequently assessed, and new approaches for attribution were developed to answer the following scientific questions: “What are the external drivers and internal system properties of change? How can boundary conditions be defined for the future?” The Panta Rhei collection of key scientific papers contains 67 papers (19%) that contribute to answering these questions (see Supplemental material).

### 3.1 Climate change

Climate change is expected to significantly influence the water cycle, through changes in the global atmospheric circulation and the larger water-holding capacity of a warmer atmosphere. Using 7250 observations around the world covering the years 1971–2010, Gudmundsson et al. (2021) found evidence for the role of anthropogenic climate change as a causal driver of recent trends in river flow. Wang et al. (2024) detected a clear trend of weakening seasonality in river flow in high-latitude regions of the Northern Hemisphere, which is closely linked to anthropogenic climate change. Yang et al. (2021) showed that, at a global scale, long-term annual streamflow has remained stationary in 79% of catchments with minimal human disturbance, while the percentage is only 38% for those catchments where substantial human interventions have occurred.

Climate change and human behaviour also jointly drive changes in hydrological extremes and exacerbate their effects (Arheimer et al. 2017, Caretta et al. 2022, Chagas et al. 2022). Based on a meta-analysis, Merz et al. (2021) found that in more than half of catchments worldwide, floods have increased in recent decades. River floods in Europe have increased in magnitude in the northwest and decreased in the south and east in the last 60 years (Blöschl et al. 2019b, Bertola et al. 2020). Changing seasonality of floods has been detected, more clearly than for their magnitudes (Blöschl et al. (2017) for Europe, Collins (2019) for the US, Chagas et al. (2022) for Brazil). These studies usually consider river flooding, but flash flooding is also expected to increase due to increased atmospheric convection in a warmer climate (Llasat et al. 2016, Huang et al. 2022).

Changes in drought frequency and severity have been detected with various confidence levels depending on the drought type (Van Loon 2015). While meteorological droughts have increased in a few regions of Africa and South America, socio-hydrological droughts have increased in megacities (Souza et al. 2022) and agricultural (soil-moisture) droughts have increased in several regions on all continents (IPCC 2022). Brunner et al. (2023) find that high-elevation catchments in the Alps have experienced a stronger change in drought type (from rainfall-driven to temperature-driven) and drought severity (shorter and higher deficit) than low-elevation catchments. Brunner and Tallaksen (2019) found that four regions in Europe, i.e. southeast England, southeast France, central Norway, and the Pre-Alpine area, may become more affected by multi-year droughts in the future as streamflow becomes less snow influenced. The increasing trend in drought severity in the Po River basin (Italy) was found to be mainly driven by the type and seasonality of precipitation, rather than its total amount, and the expansion of irrigated areas (Montanari et al. 2023).

### 3.2 Land use and socio-economic change

Land use changes such as deforestation and urbanization have often caused increased surface runoff and a decreased baseflow (Levy et al. 2018, Müller et al. 2021). This effect, along with the regulation of river flows, e.g. for hydropower production,
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industrial use or flood protection, has substantially affected discharge regimes in many parts of the world (Vorogushyn and Merz 2013, Wang et al. 2017, Arheimer and Lindström 2019, Shrestha et al. 2022).

Considering the combined effects of anthropogenic alterations to natural water streams and changing climate has resulted in a new framework of droughts, that defines anthropogenic drought as a compound multidimensional and multi-scale phenomenon (AghaKouchak et al. 2015, Van Loon et al. 2016). Anthropogenic droughts are governed by the combination of natural water variability, climate change, human decisions and activities, and altered micro-climatic conditions due to changes in land and water management (AghaKouchak et al. 2021). Human activities have a major impact on hydrological droughts as well, in some cases exacerbating the effects of climate change, despite management efforts (Van Loon et al. 2022). Alborzi et al. (2018) report on the combined effects of meteorological drought and unsustainable water resource management, which contributed to the rapid shrinkage of Lake Urmia in Iran, after it had reached a tipping point. Van Oel et al. (2018) document the exacerbating effect of reservoir operations on downstream hydrological drought in a river basin in Brazil, while a continental-scale study in the US shows that reservoirs can also alleviate drought severity in many instances (Brunner 2021). Increasing water demand and decreasing surface water availability are frequent causes of groundwater overexploitation (Nlend et al. 2018). Declining groundwater resources are exacerbated by misaligned incentives associated with the common-pool nature of the resource (Mullen et al. 2022).

Flood impacts are also strongly influenced by changes in land use and socio-economic processes, next to atmospheric drivers (Formetta and Feyen 2019, Merz et al. 2021). Shifts in socio-economic systems foster human encroachment into floodplains and increase flood exposure. Thus, increasing exposure was the main driver of the increase in flood losses during recent decades, in Europe (Stevens et al. 2016, Paprotny et al. 2018) and elsewhere (Tanoue et al. 2016, McAneney et al. 2019). It is expected that future flood impacts will continue to increase (Rojas et al. 2013, Dottori et al. 2018), due to a combination of changes in hazard, exposure and vulnerability (Rojas et al. 2013, Vousdoukas et al. 2018, Steinhausen et al. 2022, Schoppa et al. 2024). Sauer et al. (2021) quantified hazard, exposure and vulnerability changes for flood events globally, finding that for Europe the increase in flood losses was driven almost entirely by exposure, with some small decline in hazard and vulnerability.

## 3.3 Changes in water quality

Climate change in terms of rising temperatures, changes in precipitation patterns, and extreme weather events have affected the water cycle, also leading to changes in water quality (e.g. Meier et al. 2014, Bartosova et al. 2019). In coastal areas, sea level rise, storm surges, drought, land subsidence and erosion were reported to affect salinity and water quality in soils, estuaries and aquifers (Dasgupta et al. 2015, Jasechko et al. 2020, Philips et al. 2020). Water scarcity also impacts water

quality, as pollution is more concentrated, so that recent scientific advances have been in the direction of quality-related and ecological water scarcity (Liu et al. 2016, 2022). Integrated assessments of water quality, quantity, and environmental flows have been widely applied at global, national, and local levels (Liu et al. 2017b, van Vliet et al. 2017, Ma et al. 2020).

Urbanization and changes in land use have resulted in increased impervious surfaces, such as roads, which can lead to higher levels of pollutants, e.g. nutrients and chemicals being washed into water bodies (Dailey et al. 2014). Diffuse pollution that remains in the environment for a very long time makes it challenging to achieve water quality goals (Van Meter et al. 2018). In particular, new science questions on the use, fate and impacts of persistent anthropogenic chemicals, such as PFAS (Ackerman Grunfeld et al. 2024) and microplastics (Eerkes-Medrano et al. 2015), were raised during the Panta Rhei scientific decade.

At the same time, traditional water-quality problems due to agricultural activities have not yet been solved, e.g. the use of fertilizers and animal waste that result in nutrient runoff and contamination of water bodies, leading to eutrophication (Finger et al. 2013) and intensive irrigation that increases salinity in downstream water bodies (Thorslund et al. 2021). Direct implications for human health are expected from industrial discharges, including the release of pollutants and chemicals that contaminate water sources (Ma et al. 2020), and mobilization of geogenic contaminants (e.g. arsenic) due to groundwater overuse (Erban et al. 2013).

Addressing these complex and interlinked water quality challenges requires a holistic approach that includes sustainable water management, land use planning, pollution control and public awareness (Hipsey and Arheimer 2013, Rahman et al. 2019). Modelling was found to be instrumental in planning remedial measures at the catchment scale (Arheimer et al. 2015) and regionally (Bartosova et al. 2021). Nature-based solutions have proven to be efficient in addressing some of these challenges (Huang et al. 2020, Oral et al. 2021, Carvalho et al. 2022) although their effect at large scale has been questioned, e.g. regarding wetland constructions for nutrient reduction (Arheimer and Pers 2017). Technological advances have contributed to significantly improve both detection and treatment of water contaminants. Stricter environmental policies, regulations and standards are needed to reduce pollution, by improving wastewater treatment, reducing the impact of agricultural practices, and managing landscapes (Hanrahan et al. 2018, Cheng et al. 2022, Penny et al. 2022).

## 3.4 Methodological advancements in the attribution of change

Hydrological systems are spatially heterogeneous and tightly coupled with human and ecological systems at a variety of spatial and temporal scales (Kingston et al. 2020, Bertassello et al. 2021). Studying changes in these human-water systems requires addressing the twin challenges of detection and attribution. Detecting hydrological change implies distinguishing persistent changes in hydrological outcomes from the effects of stationary but long-memory climate variability
<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /><figcaption>**Figure 3.** Approximative typology of attribution approaches: DL (diagnostic learning), XAI (explainable artificial intelligence), TS (time series analysis), GC (Granger causality analysis), CCM (convergent cross-mapping), BI (Bayesian inference), PC (paired catchments), PA (panel analysis), IV (instrumental variable), RD (regression discontinuity), DiD (difference-in-difference), NB (narrative-based analysis), MH (multiple hypotheses), FO (field observation), SH (socio-hydrological modelling), PB (process-based or physical modelling), FP (fingerprinting).</figcaption></figure>

and random observation errors (Hall et al. 2014, Koutsoyiannis and Montanari 2015, Milly et al. 2015, Serialaldi and Kilsby 2015, Yang et al. 2019, Villarini and Wasko 2021). Much methodological development during the Panta Rhei decade has focused on addressing the second challenge of attribution, which investigates the causal relationship between changes and their hypothesized drivers (Merz et al. 2012). Elucidating such causal relationships is necessary to improve predictions (Srinivasan et al. 2017a, Müller and Levy 2019) and to develop and evaluate policies to avert or mitigate these changes (Thompson et al. 2013). This subsection discusses current attribution approaches with regard to their deductive (model-based) vs. inductive (data-based) nature and their focus on internal “Newtonian” (small sample size) vs. external “Darwinian” (large sample size) variability (Fig. 3).

Deductive process-based models are developed, calibrated and validated to test causal hypotheses about the key physical processes assumed to govern hydrological dynamics (Ferraro et al. 2019), such as hydroclimatic change (Chiang et al. 2021), changes in streamflow (Hundecha and Merz 2012, Duethmann et al. 2015, Badjana et al. 2017, Mao and Liu 2019, Collar et al. 2022) and flood risk (Metin et al. 2018). In a related approach, hydrological change is analysed by identifying a fingerprint: specific signatures of changes in the hypothesized drivers (Viglione et al. 2016, Arheimer and Lindström 2019, Bertola et al. 2019, 2021, Kemter et al. 2020). For example, Viglione et al. (2016) leverage the fact that different processes govern floods in catchments of different sizes to identify the most likely drivers of changing flood characteristics. Challenges to this approach are related to data scarcity and the complexity of systems, where feedbacks with social and ecological processes can be both drivers and outcomes of hydrological change (Srinivasan et al. 2017b, Duethmann et al. 2020).

Data-based inductive approaches use statistical models that rely on the detection and interpretation of statistical relationships, in time (Arheimer and Lindström 2019, Lan et al. 2020) or with observable covariates (Khazaei et al. 2019, Shao et al. 2022), or both (Chagas and Chaffe 2018, Franceschinis et al. 2021, Müller et al. 2021). In terms of attribution, three alternative strategies are deployed. First, the structure of the data themselves can be used to infer causal relationships, for instance through time series analysis such as Granger causality analysis (Singh and Borrok 2019) or convergent cross-mapping (Bonotto et al. 2022). Second, the characteristics of the data-generating process can be leveraged by identifying so-called natural experiments (Müller and Levy 2019), for instance through panel regression analysis (Blum et al. 2020, Davenport et al. 2020, Mondino et al. 2021) or covariate matching (Wagenaar et al. 2018, Brunner 2021). Third, ML can be leveraged to explicitly control for all plausible sources of variations, for instance using explainable AI (Althoff et al. 2021, Veigel et al. 2023) or autoencoders (Bassi et al. 2024).

Complementary to the previously described Darwinian (large sample) approaches are the Newtonian (small sample) ones that tackle attribution by seeking to reconstruct a plausible narrative to explain the observed phenomena for a limited number of cases (internal validity) (Harman and Troch 2014). Approaches seeking to elucidate the internal mechanics of a small number of units, through either statistical analysis or process-based modelling, fall under the latter category, along with other approaches, including comparative case studies (Kreibich et al. 2017, Garcia et al. 2019), socio-hydrological or agent-based models (Kandasamy et al. 2014, Mustafa et al. 2018, Penny et al. 2021, Schoppa et al. 2022) and narrative-based approaches (Treuer et al. 2017, Leong 2018).
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Figure 4. Causal loop diagram showing how hydrological, economic, political, technological, and social processes are all interlinked and gradually coevolve (continuous thin arrows), while being abruptly altered (continuous thick arrows) by the sudden occurrence of an extreme event. Depending on the choice of specific state variables and feedback mechanisms it can help simulate phenomena, e.g. unintended consequences such as the levee effect (figure adapted from Di Baldassarre et al. 2013, Sivapalan and Blöschl 2015).

Summary on drivers of change: Significant advancements were achieved in detecting and attributing hydrological changes: (1) Climate change leads to both increasing and decreasing trends of hydrological extremes in different regions of the world (e.g. Blöschl et al. 2019b, Merz et al. 2021, Brunner and Tallaksen 2019) and for different types of events (e.g. Van Loon 2015, Huang et al. 2022). (2) Land use and socio-economic change, such as the construction of hydraulic structures, were also identified as drivers of change, particularly in terms of flood and drought impacts (e.g. Vorogushyn and Merz 2013, Nlend et al. 2018, Paprotny et al. 2018). (3) Climate and global change, e.g. urbanization, leads to higher levels of pollutants and changes in water quality (e.g. Dailey et al. 2014, Meier et al. 2014, Bartosova et al. 2019). (4) The development of various attribution approaches, e.g. deductive (model-based) vs. inductive (data-based) ones, led to a better quantification of the interactions between drivers and a better separation of the individual contributions of drivers to change (e.g. Viglione et al. 2016, Arheimer and Lindström 2019, Ferraro et al. 2019).

# 4 Scientific progress on socio-hydrological systems

The impact of humans on water systems has increased and with it the need to understand the interactions and feedbacks between social and hydrological systems. To this end, new socio-hydrological concepts and approaches were developed to answer the following questions: "How do changes in hydrological systems interact with, and feedback to, natural and social systems driven by hydrological processes? What are the boundaries of coupled hydrological and societal systems?" The Panta Rhei collection of key scientific papers contains 89 papers (25%) that contribute to answering these questions (see Supplementary material).

## 4.1 Concepts for socio-hydrological systems

It is well known that human societies increasingly influence the hydrological regime, deliberately or otherwise, by: (a) building dams and reservoirs to store water for different purposes; (b) diverting water flows for urban, industrial or agricultural use; (c) changing the characteristics of watersheds via land use change, including deforestation, urbanization, or

drainage of wetlands; and (d) altering the regional or global climate via greenhouse gas emissions (Savenije et al. 2014).

Concurrently, changes in the hydrological regime, including the occurrence of extreme events, influence human societies. Water crises, droughts and floods impact societies in multiple ways, and can cause serious human and economic losses. Moreover, individuals, communities, and societies adapt and respond to extreme events by changing policies or social contracts (Adger et al. 2013) as well as collective behaviour, or patterns of human settlements (Mård et al. 2018).

An important scientific advancement in relation to the change in hydrology and society is the concept of socio-hydrological systems, which is based on a two-way coupling between human actions and water quantity and quality (Sivapalan et al. 2012, Sivapalan and Blöschl 2015). To illustrate this, Fig. 4 shows a causal loop diagram, consisting of system states and feedbacks. It illustrates how hydrological, economic, political, technological and social processes are all interlinked and either gradually co-evolve or are abruptly altered by the sudden occurrence of an extreme event, e.g. a flood (Di Baldassarre et al. 2013, Sivapalan and Blöschl 2015). In general, while humans influence hydrological flows, water storage, and the distribution of floods and droughts, they also respond to hydrological risk by changing (deliberately or not) demography, behaviour, water governance and infrastructure. Thus, human influences on and adaptive responses to hydrological processes are changing in space and time, indicating that simulations without sufficient inclusion of human interaction tend to underestimate temporal dynamics of human awareness and actions that alter hydrology (Di Baldassarre et al. 2015, Van Loon et al. 2016, AghaKouchak et al. 2021).

These complex interactions and feedbacks between human and water systems (e.g. Fig. 4) can generate socio-hydrological phenomena, i.e. patterns across places or even across contexts (Sivapalan and Blöschl 2015, Di Baldassarre et al. 2019). These phenomena consist of actual outcomes, paradoxical dynamics, or unintended consequences that arise from water management to achieve a desired societal objective (Table 2). The large range of socio-hydrological phenomena was organized into a
<figure><img src="image_34.png" /><figcaption>Table 2. Examples of archetypes and socio-hydrological phenomena (adapted from Di Baldassarre et al. 2019).</figcaption></figure>

<table><thead><tr><th>Archetype</th><th>Archetype definition</th><th>General phenomenon</th><th>Characteristics of phenomenon</th><th>Sub-phenomena</th></tr></thead><tbody><tr><td rowspan="2">Fixes that fail</td><td rowspan="2">Shortcut solutions might seem to work in the short term, but often fail in the long run. In this way, they will aggravate the original problem or create even more challenging problems.</td><td><strong>Safe-development paradox</strong><br>(Kates et al. 2006, Fusinato et al. 2024)</td><td>Protection measures generate a false sense of security that reduces coping capacities thereby increasing social vulnerability.</td><td><em>Levee effect</em><br>(White 1945)</td></tr><tr><td><strong>Rebound effect</strong><br>(Alcott 2005)</td><td>Increasing efficiency leads to higher consumption.</td><td><em>Reservoir effect</em><br>(Di Baldassarre et al. 2018)</td></tr><tr><td rowspan="3">Limits to growth</td><td rowspan="3">Continuous and accelerating growth of demand makes the system go beyond the limits unintentionally, thus experiencing a subsequent decline.</td><td><strong>Supply-demand cycle</strong><br>(Kallis 2010)</td><td>Increasing supply enables growth that in turn generates higher demands.</td><td><em>Irrigation efficiency paradoxes</em><br>(Dumont et al. 2013)</td></tr><tr><td><strong>Adaptation effect</strong><br>(Di Baldassarre et al. 2015)</td><td>Frequent extreme events increase coping capacities, thereby reducing social vulnerability. Adaptation to drought can worsen flood losses, and vice versa.</td><td><em>Fixes that backfire</em><br>(Gohari et al. 2013)</td></tr><tr><td><strong>Pendulum swing</strong><br>(Kandasamy et al. 2014)</td><td>Changing priorities from pursuing economic prosperity or environmental protection.</td><td><em>Flood risk adaptation</em><br>Kreibich et al. (2017)</td></tr><tr><td rowspan="3">Success to the successful</td><td rowspan="3">Good performance secures more resources relative to others, enabling the generation of further success which in turn secures still more resources.</td><td><strong>Aggregation effect</strong></td><td>Desirable outcomes at the system scale from aggregated optimal decisions at the individual scale. Desirable outcomes at the system scale from aggregated inequalities at the individual scale.</td><td><em>Sequence effect</em><br>(Di Baldassarre et al. 2017)</td></tr><tr><td><strong>Institutional complexity</strong></td><td>Trade-off between resilience and efficiency or between resilience to different disturbance regimes.</td><td><em>Peak water paradoxes</em><br>Gleick and Palaniappan (2010)</td></tr><tr><td></td><td></td><td><em>Environmental Kuznets curve</em><br>(Dinda 2004)</td></tr><tr><td></td><td></td><td><strong>Aggregation effect</strong></td><td>Undesirable outcomes at the system scale from aggregated optimal decisions at the individual scale. Desirable outcomes at the system scale from aggregated inequalities at the individual scale.</td><td><em>Collective action</em><br>(Olson 1965, Ostrom 1990)</td></tr><tr><td></td><td></td><td><strong>Institutional complexity</strong></td><td>Trade-off between resilience and efficiency or between resilience to different disturbance regimes.</td><td><em>Water injustice</em><br>(Zwarteveen et al. 2017)</td></tr><tr><td></td><td></td><td><strong>Aggregation effect</strong></td><td>Undesirable outcomes at the system scale from aggregated optimal decisions at the individual scale. Desirable outcomes at the system scale from aggregated inequalities at the individual scale.</td><td><em>Robustness-fragility trade-off</em><br>(Csete and Doyle 2002)</td></tr></tbody></table>

small number of system archetypes (Table 2). For instance, the most common example of the “fixes that fail” archetype is the levee effect (Di Baldassarre et al. 2018, 2019).

## 4.2 Approaches for assessing human-water systems

The Panta Rhei initiative has successfully contributed to a societal impact assessment that goes beyond project evaluation to include, for example, feedback mechanisms and the legacy of past and projected future changes based on implemented or proposed actions on a multi-decadal or centennial scale. Many conceptualizations of mechanisms and potential boundaries have been suggested (e.g. Elshafei et al. 2014, Müller et al. 2024). System dynamics models based on causal loop diagrams seem to be a promising way to study and validate long-term dynamics (Di Baldassarre et al. 2015, Barendrecht et al. 2017, Schoppa et al. 2022).

Models for large-scale studies primarily focus on the water-energy-food nexus or other aspects within the framework of the SDGs and have been adopted by institutional investors such as the World Bank (Liu et al. 2017a, Payet-Burin et al. 2019). Recently we have seen the development of models with very fine resolutions based on agent-based modelling (Wens et al. 2020, Ghoreishi et al. 2021) or various applications of statistical or ML methods to study interactions on the micro-scale. The purpose of modelling has shifted, to some degree, from finding universal modelling paradigms to finding suitable boundaries that ensure a simplicity that enables decision making while having the complexity that allows for robust assessment of the main impacts (Arnbjerg-Nielsen et al. 2022). Approaches have been developed to integrate

quantitative and qualitative information in order to better understand the hydrological, socio-political, economic, and cultural contexts in different locations (Rangecroft et al. 2018, Vanelli et al. 2022), supported by socio-hydrology (Sivapalan and Blöschl 2015).

In detail, conceptual models have been proposed to demonstrate that demographic and socio-economic characteristics such as income levels or social status further differentiate population vulnerabilities to water and livelihood insecurities (Haeffner et al. 2017, Teweldebrihan et al. 2020, Savelli et al. 2021, Savelli and Mazzoleni 2023). Understanding and modelling the co-evolution of water institutions has shown that vulnerabilities interact with livelihood insecurity in cities and floodplains (Yu et al. 2017, Muneepeerakul et al. 2020).

The Panta Rhei community has progressed our understanding of drought through the lens of human influences and coupled system co-evolution (Park et al. 2018, Cavus and Aksoy 2020, Wens et al. 2020). Such studies have revealed a strong linkage between human behaviour and drought effects across increasing time scales, which help to form a foundation for understanding and communicating such complexities within operational drought management (Cavus et al. 2022). Similarly, the conceptual basis for connecting social processes (adaptation, management) with flood events has been strengthened by incorporating, for instance, bounded rationality and prospect theories (Di Baldassarre et al. 2015, Kreibich et al. 2017, Michaelis et al. 2020). Progress has continuously been made in predicting basin-scale socio-hydrological dynamics of water use for agricultural and environmental
<figure><img src="image_35.png" /></figure>

purposes and its effects on societal conditions such as migration into agricultural basins and flood plains (Di Baldassarre et al. 2017, Roobavannan et al. 2018). There has also been progress in simulating the interplay between multiple hazards, water management, and societies. For example, Mazzoleni et al. (2021) showed that changes in flood and drought awareness can help contribute to the emergence of multiple human-water phenomena (e.g. sequence effect, reservoir effect, supply-demand cycle, and levee effect).

Comparative studies across socio-economic and cultural gradients of human water relations as well as hydroclimatic gradients provided a better understanding of the interplay between water hazards and societal responses, e.g. with respect to flood protection and poor water quality (Gupta et al. 2014, Kreibich et al. 2017, 2022a, Daniel et al. 2022). An example of this is disentangling the effect of social norms on the way water is abstracted for intensive agriculture from the effect the latter has on the formation of norms that encourage such water use (Troy et al. 2015, Alam et al. 2022). Another example is provided by Zhao et al. (2019), who introduced comparative advantage theory to track the driving forces of virtual water trade based on the spatial-temporal distribution of resource productivity and opportunity costs of land, labour and water use in agricultural and non-agricultural sectors across Chinese provinces.

Summary on understanding socio-hydrological systems: Significant advancements were achieved in conceptualizing and assessing socio-hydrological systems: (1) A better understanding of the feedbacks between hydrology and society has been achieved, based on the concept of a two-way coupling between human actions and water quantity and quality (e.g. Sivapalan et al. 2012, Sivapalan and Blöschl 2015). These complex feedbacks can generate phenomena such as the levee effect (e. g. Di Baldassarre et al. 2013, 2018). The generic and transferable descriptions of socio-hydrological phenomena and their organization into system archetypes should be considered in decision making (e.g. Di Baldassarre et al. 2019). (2) Integrated approaches were developed to assess the co-evolution of human-water systems in order to avoid unintended consequences of human interventions over long periods of time, described as phenomena. The development of socio-hydrological models made it possible to simulate long-term developments, including future projections (e.g. Barendrecht et al. 2017, Schoppa et al. 2022). Synthesis studies stressed the importance of space-time aspects as well as of understanding causalities to even better address important societal challenges (e.g. Van Loon et al. 2016, Zhao et al. 2019).

# 5 Scientific progress on modelling and prediction

The evolution of hydrological systems motivates the need to improve modelling and prediction to support better risk assessment, planning, and infrastructure design. Various approaches and models were developed in response to the following question: "How can we use improved knowledge of coupled hydrological-social systems to improve model predictions, including estimation of predictive uncertainty and assessment of predictability?" The Panta Rhei collection of key scientific papers contains 61 papers (17%) that contribute to answering this question (see Supplementary material).

## 5.1 Recognition of the change in hydrology and society led to advances in Modelling

Although we know that “stationarity is dead” (Milly et al. 2008) due to the changes observed over time in hydrological response (Montanari et al. 2013, Ceola et al. 2016, McMillan et al. 2016), it can still be useful to model hydrological processes under known conditions to make reliable predictions, such as for the design of civil structures (Koutsoyiannis 2011, Lins and Cohn 2011, Matalas 2012, Koutsoyiannis and Montanari 2015). Nevertheless, gradual and sudden changes in the form of a trend, a jump or a shift (Fowler et al. 2022, Volpi et al. 2024) due to the natural variation of a hydrological process or anthropogenic interventions should not be ignored, as, for instance, they have the potential to increase the frequency and intensity of extreme hydrological events. Similarly, in the more complex context of human-water systems, inertia in culture and institutions, poor governance and the hierarchical and cross-sectoral size of organizations influence human decision making. Roobavannan et al. (2018) and Amirkhani et al. (2022) incorporated changing beliefs about how important the environment is with respect to agricultural production as a function of community sensitivity to environmental degradation. Statistical techniques such as breakpoint analysis have been used, for example, to evaluate the impact on flow from human-induced changes in catchment characteristics (Arheimer and Lindström 2019) or to identify changes in reservoir operating rules and to develop amended rules using inverse modelling (Giuliani and Castelletti 2016).

## 5.2 Quantitative and qualitative human-water systems modelling

Traditional hydrological models are best suited for simulation and prediction in natural catchments, assuming that conditions have not been influenced by societal interaction. Human influences were often only included as management scenarios during the simulation, frequently at a specific point in time (Montanari et al. 2013). The predictive capabilities of traditional hydrological models are based on empirical observations, with which the models are calibrated and validated (Aguilar et al. 2017). However, complex human-water system models must reflect human and social dynamics such as changing water institutions. The data needed to calibrate such models often include observations of choices made by humans or the evolution of institutions (Sarmento-Buarque et al. 2020). Further, modelling concepts have gone beyond the physics-based principles to include the governing principles behind human actions such as rules based on behavioural theories and evolutions of water institutions and governance that are a result of long-term slow-moving processes of values, norms and culture (Sivapalan and Blöschl 2015, Wesselink et al. 2017, Bartosova et al. 2021, Schrieks et al. 2021). For instance, a system-of-systems regional flood model was used to quantify the effect of changes in various risk components, including changes in land use, assets, and vulnerability, on flood risk (Metin et al. 2018). Recent models of human-water decision making have benefited from the novel
<figure><img src="image_36.png" /></figure>

application of concepts that exist in the social sciences domain, such as game theoretic concepts, agent-based models, and behavioural models (Bartosova et al. 2021, Schrieks et al. 2021). For example, heterogeneous decision making of farmers has been extensively modelled using agent-based models (Tamburino et al. 2020, Wens et al. 2020, 2022). Yu et al. (2017) used game theoretic concepts to incorporate collective action in a stylized human-water system model of flood resilience. The model rules which describe how humans interact with their water environment were also inspired by behavioural theories such as the theory of planned behaviour, so that the models provided realistic predictions of societal inequities and unintended consequences of agricultural water interventions (Pouladi et al. 2020, Alam et al. 2022). Integrating empirical data, e.g. from recorded events, into socio-hydrological models supports the simulation of real, long-term processes in human-flood systems, including future projections (Schoppa et al. 2024). Using Bayesian inference allows models to be calibrated with qualitative and quantitative data and even to include expert knowledge as a prior (Barendrecht et al. 2019).

The application of hydrological models as well as human-water system models is not objective and models' subjectivity should be better recognized (Lane 2014, Merz et al. 2015, Beck and Krueger 2016, Melsen et al. 2018, Addor and Melsen 2019, Yu et al. 2022). It is now acknowledged that the predictability of human-water systems is affected by factors such as biased selection in choosing stakeholders for model co-development, social effects that stem from model results, mutual reinforcement of model development and model shaping by the involved parties (modellers, scientists, stakeholders), a lack of neutrality in political implications, and difficulties with transdisciplinary collaboration between academic and non-academic actors (Melsen et al. 2018). Yu et al. (2022) have highlighted that the complexities of human-water systems, such as decision making at various spatial, temporal and organizational scales, affect system predictability.

In line with the modelling traditions of social sciences, where mixed methods are often used, models have been calibrated on narratives or narratives are built on model predictions (Leong 2018, Mostert and Mostert 2018, Rangecroft et al. 2018, Yu et al. 2022). Such an interplay of qualitative and quantitative methods to improve predictions and their significance for societies is important in the coupled modelling of human-water systems.

It is increasingly acknowledged that human-water models developed to capture extremely long-term phenomena should be explicit about their uncertainty when applied to short-term decision making (Srinivasan et al. 2017a). Merz et al. (2015) argue that surprise is particularly important in attempting to overcome potential cognitive biases within coupled human-water management. Techniques such as behavioural experiments and surveys have been proposed to test hypotheses about human behaviour and biases in decision making (Tian et al. 2019, Yu et al. 2022). As such, the concept of scale, and how human-water processes may shift according to the lens through which they are studied and by whom, are of importance in bridging the gap between

understanding human-water co-evolution and utilizing such insights for prediction. In this light, a means for defining, capturing, and communicating human-water model uncertainty, especially in narratives or qualitative causal loop diagrams developed for diverse decision makers, is essential (e.g. Höllermann and Evers 2019). Formal Bayesian and other methods have been proposed to analyse uncertainty in such models. Barendrecht et al. (2019) incorporated survey data in a human-flood systems model and provided quantitative uncertainty information based on Bayesian statistics.

## 5.3 Approaches to predict future trajectories

A spectrum of data and modelling methods were developed, to unravel complex human-societal phenomena in order to predict future trajectories of human-water systems in diverse contexts. For instance, novel concepts describing community sensitivity to drought and flood events were used to understand vulnerability dynamics in the past and predict possible future trajectories (Di Baldassarre et al. 2017, Roobavannan et al. 2018, Wens et al. 2021, Rusca et al. 2023).

Several socio-hydrological studies, mostly in human-agricultural and human-flood systems, have used diverse data sources to simultaneously calibrate social parameters, such as perception of risk to flooding, alongside hydrological parameters of the models using novel calibration strategies (Roobavannan et al. 2018, Barendrecht et al. 2019, Schoppa et al. 2024). Such calibrated models were then used to identify conditions under which the coupled system would sustainably evolve. For example, using a lumped socio-hydrological model at basin scale, Roobavannan et al. (2018) found that a higher level of diversification in the basin economy increases sustainability and makes it less reliant on water availability. Schoppa et al. (2024) calibrated a socio-hydrological model for flood risk assessment with survey data and simulated a wide range of potential futures. Results showed that integrated adaptation strategies (i.e. combined structural and non-structural measures) can reduce the average flood risk by up to 60%.

Summary on modelling and prediction: Progress in modelling and predicting future trajectories was achieved: (1) Various powerful socio-hydrological model approaches were developed which describe feedbacks; examples are stylized models, system-of-systems models and agent-based models (e.g. Yu et al. 2017, Metin et al. 2018, Wens et al. 2020). These approaches allow, for example, the incorporation of changes in risk perceptions, beliefs and community sensitivities into (long-term) modelling (e.g. Giuliani and Castelletti 2016, Amirkhani et al. 2022). (2) Using Bayesian inference, qualitative and quantitative data as well as expert knowledge can be used for model parameterization (e.g. Rangecroft et al. 2018, Yu et al. 2022). The combination of socio-hydrological modelling and empirical data provides additional insights into human-water systems to realistically explore possible system evolutions comprehensively, including unlikely futures (e.g. Barendrecht et al. 2019, Schoppa et al. 2024). (3) Calibrated socio-hydrological models are used to predict future trajectories of human-water systems in diverse contexts and to identify conditions under which the systems would sustainably evolve (e.g. Roobavannan et al. 2018, Wens et al. 2021, Schoppa et al. 2024).
<figure><img src="image_37.png" /></figure>

# 6 Scientific progress on water management and adaptation to change

Since it is not possible to plan under stable hydrological conditions, adaptive management approaches need to be developed that are more flexible to changing conditions. The development of realistic long-term scenarios, adaptive management and participatory governance are suggested approaches to answer the following question: “How can we support societies to adapt to changing conditions by considering the uncertainties and feedbacks between natural and human-induced hydrological changes?” The Panta Rhei collection of key scientific papers contains 76 papers (22%) that contribute to answering this question (see Supplementary material).

## 6.1 Scenarios and possibility spaces

Prediction is central to water resources management and planning. Socio-hydrological models aim to show under what circumstances sustainable development or a “lock-in” situation can arise (Ceola et al. 2016, Schoppa et al. 2024). Various socio-hydrological models have been developed to describe possible consequences of both “hard” infrastructure and “soft-path” solutions (Garcia et al. 2022, Genova and Wei 2023).

The predictions obtained from the socio-hydrological models are not mere scenarios that represent snapshots of the world at some specific future points in time, as is usual in conventional water resources planning. Predictions produced from the socio-hydrological models are alternative, plausible and co-evolving trajectories of coupled human-water systems. Collectively, these trajectories map out the future possibility space of socio-hydrological systems (Sivapalan and Blöschl 2015, Srinivasan et al. 2016). The possibility space creates a range of options by exploring the future more independently of initial views regarding probability and desirability. It covers future pathways involving disruptive changes, i.e. changes that do not necessarily follow the pattern of past transitions and are impossible to obtain through scenario analyses, and it greatly expands the possibility range by simulating various combinations of multiple variables within the system boundaries of the models. This possibility space makes it easier to be imaginative, systematic and explicit about hypothetical “What if?” questions. It can assist in identifying safe or desirable solutions for water availability and use while warning against maladaptive actions for socio-hydrological systems with alternate stable states of multiple variables (Rockström et al. 2009). The possibility space provides the basis for developing adaptive and participatory water governance.

## 6.2 Adaptive water management

Adaptive water management is a planning process that is decidedly adaptive, aims to keep multiple pathways to the future open, and incorporates the knowledge and perspectives of stakeholders (Versteeg et al. 2021). In this way, it aims to avoid the following three problems that often lead to the failure of planning processes in water management: (1) traditional planning

processes often emphasize the technical aspects of water management while ignoring the practices and knowledge of water users and other stakeholders; (2) they are based on an overly rational and linear ideal of the controllability of hydrology and infrastructure, which is untenable in a time of environmental change, non-stationarity and uncertainty; and (3) the planning processes are often not suitable for balancing the competing interests of stakeholders while keeping an eye on the feasibility and economic viability of the measures now and in the future (Butsch et al. 2022b, Conallin et al. 2022, Pham et al. 2022, Ward et al. 2020).

The Panta Rhei initiative has supported adaptive water management through inter- and transdisciplinary research and collaboration between hydrologists, social scientists, and a range of stakeholders, considering non-stationarity, uncertainty and change in hydrology and society. Furthermore, new ideas and advancements are created by meeting changing social needs (Sivapalan and Blöschl 2017). In the community paper that launched the IAHS Prague statement on the adaptation of water resource systems, Ceola et al. (2016) promote resilient, adaptive water resources systems management and advocate for a bottom-up approach that starts with analysing the vulnerabilities of a particular system in context and with stakeholders, rather than adopting a one-size-fits-all (“top-down”) perspective. van Nooijen and Kolechkina (2021) applied control theory for a water resources control system with time-varying delays in the feedback loop in a changing and unpredictable environment. Garcia et al. (2020) modelled reservoir dynamics before proposing a multi-level approach to flood and drought management which includes consideration of cognitive biases and systematic errors in decision making (Garcia et al. 2022). Kreibich et al. (2014) suggested integrating the cost assessment cycle into the risk management cycle so that continuous monitoring of the costs associated with natural hazards and their management enables early identification of inefficient risk mitigation strategies and supports adaptation. Such solutions provide tools to support the planning, monitoring, implementation and evaluation of adaptive water management under changing climatic and socio-economic conditions over long periods of time.

## 6.3 Participatory water governance

Participatory water governance approaches are particularly suited to managing complex, integrated, dynamic human-water systems. These approaches are adaptive and nested, and span scales of problems and jurisdictions; they actively involve communities and stakeholders, and incorporate all kinds of knowledge to inform decision making (Lemos 2015, Carnohan et al. 2020). The growing importance of participation in water management can generally be attributed to its potential to initiate social learning processes and build capacity (Evers et al. 2016). Understanding the conflicting demands and views of stakeholders can strengthen trust between them and enables the inclusion of local knowledge and different values, interests and perspectives in planning and management processes, which promotes acceptance of the proposed measures (Gooch and Huitema 2008, Evers et al. 2016).
<figure><img src="image_38.png" /></figure>

<figure><img src="image_39.png" /><figcaption>**Figure 5.** Summary of progress in research on change in hydrology and society in terms of hydrological science and practical water management.</figcaption></figure>

As the following examples demonstrate, the Panta Rhei initiative's contributions to supporting participatory water governance range from novel approaches to theoretical frameworks, inclusion and quantification of social variables and the participatory implementation of water management. Rangecroft et al. (2021) developed a working approach for bridging the gap between hydrologists and social scientists by embracing the concepts of research ethics, power dynamics and communication barriers. Di Baldassarre et al. (2019) discuss the role of socio-hydrology as a disciplinary framework to accommodate social heterogeneity, power relations, cultural beliefs and cognitive biases. Godinez-Madrigal et al. (2020) have shown how scientists were involved in the long-standing controversies surrounding the Zapotillo dam and water transfer project in Mexico, and how a participatory approach to hydrological modelling can give voice to previously marginalized concerns and proposals.

An implementation example is the transdisciplinary restoration of the damaged aquatic ecosystem in the Heihe River catchment area in China. Experts in hydrology, social development and ecosystem health together with authorities and other stakeholders implemented an interdisciplinary network approach leading to satisfactory restoration results (Liu et al. 2019).

In another case, hydrologists worked with the Scottish government to develop a web-based tool to help prioritize the location of riparian tree planting to provide shade for preventing water temperature extremes and protect fisheries as a climate change adaptation strategy (Jackson et al. 2018, 2021). Many other examples demonstrate how co-design with potential end-users from the public and private sector as well as civil society organizations lead to improved preparedness, early warning and resilience to floods and droughts (Löschner et al. 2016, Rangecroft et al. 2018, Lienert et al. 2022).

However, caution needs to be taken, as social learning can be characterized by power differences and strategic behaviour (Bou Nassar et al. 2021, Niccollier et al. 2022), and foregrounding integration, consensus and neutrality in transdisciplinary research may reinforce differences in value, knowledge and power (Ruska and Di Baldassarre 2019, Brelsford et al. 2020, Hayashi et al. 2021).

Summary on water management and adaptation to change: (1) Water management can consider future scenarios that consist of plausible, co-evolving trajectories of human-water systems and form possibility spaces that enable an assessment of the circumstances under which sustainable development may arise (e.g. Sivapalan and Blöschl 2015, Srinivasan et al. 2016). (2) Adaptive management concepts, which anticipate changes over time, keep multiple pathways to the future open, and incorporate the perspectives of stakeholders, have been developed (e.g. Garcia et al. 2020, Versteeg et al. 2021). Water management is seen as a continuous process with regular monitoring and revisiting management decisions, e.g. via the integrated cost assessment cycle (Kreibich et al. 2014). (3) Participatory and inclusive governance is needed as it initiates social learning processes, builds capacity, enables the inclusion of local knowledge and promotes acceptance of the proposed measures (e.g. Evers et al. 2016, Godinez-Madrigal et al. 2020). Advice from the scientific community should also play an essential role in participatory governance, as promoted in the Prague statement of the International Association of Hydrological Sciences in 2015 (Ceola et al. 2016).

# 7 Summary of scientific achievements

Inter- and transdisciplinary collaboration has generated concepts, methods, results and applications that have filled many important gaps in our understanding of change in hydrology and society and led to progress in science and practical water management, as presented in the different sections of this review, which we visualize in Fig. 5 and summarize as follows.

## 7.1 Cross-cutting

In addition to the creation of knowledge, an important outcome of the Panta Rhei initiative is non-tangible, namely the large and diverse community that formed during the decade, in line with the IAHS mandate. Cooperation of hydrologists, social scientists, and practitioners at local, regional, and international levels led to mutual benefits and new outcomes. Transdisciplinary project teams transformed our understanding of human-water systems, improving predictions and decision making. Close communication between scientists and stakeholders was essential, as new ideas and advancements
<figure><img src="image_40.png" /></figure>

are often generated by addressing changing societal needs with new approaches and technologies. The co-alignment of research with the UNESCO Intergovernmental Hydrological Programme (IHP) priorities for a water secure world in a changing environment, and with the efforts of the World Meteorological Organization (WMO) to support operational hydrology, enabled more stakeholders to participate in the creation of a new and sustainable water culture through co-creative knowledge and transformative education actions at several scales of governance.

## 7.2 Monitoring and data analysis

The accessibility and usefulness of data have increased significantly, particularly due to increasing community data-sharing initiatives, which match hydrological data with socio-economic and behavioural data, e.g. CAMELS initiatives or Panta Rhei benchmark data compilations. Open and equitable data sharing is supported by international principles such as the FAIR data principles of findability, accessibility, interoperability, and reusability (FAIR) (Wilkinson et al. 2016), the CARE Principles for Indigenous Data Governance which are Collective benefit, Authority to control, Responsibility, and Ethics (CARE) (Carroll et al. 2020) and open science principles (Ramachandran et al. 2021).

New methods of analysis (e.g. ML, text mining), repurposing of data and increased exploration of new, unconventional data sources (e.g. social media, novel sensors) have increased the availability of data in general, but especially of data on socio-economic aspects and human behaviour. The value of citizen science for monitoring, but also in terms of community sensitization, educational aspects and knowledge generation through the involvement of multiple points of view, was further confirmed and consolidated.

## 7.3 Drivers of change

Significant advancements have been achieved in detecting and attributing hydrological changes, particularly on the basis of monitoring and data analyses. Especially, the effects of climate change and land use change were quantified for past and potential future developments. Additionally, other socio-economic processes, such as urbanization, the construction of hydraulic structures or groundwater exploitation, have also been identified as drivers of change.

In particular, assessments that considered many, in some cases all, relevant drivers of change led to a better quantification of the interactions between drivers and a better separation of their individual contributions to change. These comprehensive, mainly model-based (deductive), but occasionally also data-based (inductive) analyses improved our understanding of the long-term developments of complex human-water systems, and stressed the importance of human actions, e.g. to mitigate flood and drought risks.

## 7.4 Understanding socio-hydrological systems

Socio-hydrological research, based on both the analysis of long time series and the in-depth assessment of case studies, has led

to a better understanding of the processes in human-water systems. It is crucial to understand and consider the causalities and feedbacks that can lead to phenomena such as the levee effect. The development of socio-hydrological models made it possible to simulate long-term developments, including future projections. Combinations of model- and data-based approaches increase the relevance for practical water management.

Comparative studies enabled the identification of commonalities and differences between places and the recognition of patterns. As such, generic and transferable descriptions of long-term changes that involve a two-way coupling between human actions and water quantity or quality were developed, which also led to organizing the range of socio-hydrological phenomena into a small number of system archetypes (e.g. fixes that fail). Archetypes are expressed in terms of generic causal loop diagrams. Syntheses and meta-analyses across socio-hydrological studies stressed the importance of space and space-time aspects as well as of understanding causalities to even better address important societal challenges.

## 7.5 Modelling and prediction

Various powerful socio-hydrological model approaches have been developed which describe feedbacks, e.g. causal loops, and include new conceptualizations of human behaviour such as risk awareness and community sensitivity. Examples are stylized models (i.e. system characteristics simplified into a set of differential equations), system-of-systems models (spatially explicit coupled models that capture different hydrological and socio-economic processes of the system) and agent-based models (theory-based models that describe the decisions and interactions between agents).

Significant progress in parameter estimation has been achieved thanks to improved accessibility as well as new, unconventional data that also describe new parameters like community sensitivity. The use of Bayesian inference allows modellers to introduce their degree of belief in certain processes as priors. Further, it opens up the possibility to integrate empirical qualitative and quantitative data. Both these advancements in modelling improved the simulation of past and future complex pathways, e.g. including tipping points and non-linear system dynamics.

## 7.6 Water management and adaptation to change

Future scenarios (and partly possibility spaces) are now commonly considered in water management, e.g. as required by the EU Water Framework Directive and the Floods Directive. Adaptive management concepts, which do not rely on design values but anticipate changes over time, have been developed. Water management is seen as a continuous process with regular monitoring and revisiting management decisions. Preferences for a particular measure are not only determined by cost-benefit analyses, but the flexibility and adaptability of the measures are also considered.

Participatory and inclusive governance is needed, involving all relevant stakeholders (users, planners and policymakers at all levels, in particular at the river basin scale, thus from
<figure><img src="image_41.png" /></figure>

<figure><img src="image_42.png" /><figcaption>Figure 6. Recommendations to make progress by broadening understanding, discipline and training while synthesizing and focusing on key themes, the development of innovative approaches and sustainable solutions.</figcaption></figure>

different countries if relevant). Advice from the scientific community should also play an essential role in participatory governance as promoted in the IAHS' 2015 Prague statement.

## 8 Recommendations

The IAHS Panta Rhei scientific decade has ended, but change is still ongoing – everything is still flowing, literally. We understand flow and change better now than in 2013. However, we also realize that the more our knowledge of nature and humans increases, the larger is the number of relevant interactions and feedbacks that will come to our attention, and as such the greater the complexity and uncertainty in our understanding and predictions. We continue our endeavour to answer the question “What are the key gaps in our understanding of hydrological change” and to fill these gaps. Thus, we need both continued excellent science on change in hydrology and society and a pragmatic and holistic approach to translating scientific innovation into policy and practice.

The Panta Rhei scientific decade inspired worldwide research efforts on change in hydrology and society that have created a vibrant and productive community of natural, social and interdisciplinary scientists and practitioners (Pande et al. 2022), which is an important and lasting outcome of this initiative. Intensive transdisciplinary collaboration on changes in hydrology and society has resulted in many new concepts, approaches, results and applications that have already improved practical water management for the benefit of societies, as illustrated in this review. We recommend continued effort and support for transdisciplinary collaboration in this field, by providing mid- to long-term funding for transdisciplinary research, supporting improved interdisciplinary education, improving the mechanisms to assess the value of scholarly work, and bringing together scientists and practitioners from various disciplines within the framework of IAHS and beyond (Kreibich et al. 2022a). These are all recommendations geared towards a broadening of our activities.

As we expand knowledge, we should also equally consolidate and synthesize, to avoid fragmentation of the field.

We need a clear science agenda for future research on water and societies, which the new IAHS International Commission on Human-Water Feedbacks (ICHWF) is designed to spearhead. We must synthesize knowledge to identify patterns in the apparent disorder and high complexity, using both scientific discourse and targeted efforts such as periodic meta-analyses. Finally, our improved knowledge and predictive capabilities regarding human-water systems should be leveraged to solve water problems in a way that accounts for the long-term feedbacks between humans and water.

We therefore recommend that the community takes a broader view of the hydrological sciences in three dimensions, while at the same time pursuing synthesis, also in three dimensions (Fig. 6).

### Broadening:

* **Broadening the understanding** of hydrological sciences by promoting comparative studies across spatial gradients of socio-economic and hydro-climatic systems, which can be supported by making data freely available.

* **Broadening the discipline** by mainstreaming the concept of coupled human-water systems in hydrology, because people are affected by, and affecting, all aspects of water systems.

* **Broadening the training** and education in hydrology towards more interdisciplinary understanding of integrated systems.

### Synthesis:

* **Focusing on key themes**, e.g. as proposed by the Unsolved Problems in Hydrology initiative (UPH; Blöschl et al. 2019a), in order to strengthen the coherence within the discipline and its impact on other disciplines and societies.

* **Developing innovative approaches** by drawing upon new ideas and technologies (e.g. inter- and
<figure><img src="image_43.png" /></figure>

transdisciplinary approaches; analysing new data with ML and AI) in order to advance the hydrological sciences even further in a coherent way.

* Finding sustainable solutions as proposed by the new IAHS scientific decade (2023–2032) on “Science for solutions: Hydrology Engaging Local People IN one Global world (HELPING)” (Arheimer *et al.* 2024).

## Disclosure statement

No potential conflict of interest was reported by the author(s).

## ORCID

Heidi Kreibich http://orcid.org/0000-0001-6274-3625

Murugesu Sivapalan http://orcid.org/0000-0003-3004-3530

Amir AghaKouchak http://orcid.org/0000-0003-4689-8357

Nans Addor http://orcid.org/0000-0002-6057-3930

Hafzullah Aksoy http://orcid.org/0000-0001-5807-5660

Berit Arheimer http://orcid.org/0000-0001-8314-0735

Karsten Arnbjerg-Nielsen http://orcid.org/0000-0002-6221-9505

Cynthia Vail-Castro http://orcid.org/0000-0002-8825-9900

Christophe Cudennec http://orcid.org/0000-0002-1707-8926

Mariana Madruga de Brito http://orcid.org/0000-0003-4191-1647

Giuliano Di Baldassarre http://orcid.org/0000-0002-8180-4996

Wouter Knoben http://orcid.org/0000-0001-8301-3787

Tobias Krueger http://orcid.org/0000-0002-4559-6667

Junguo Liu http://orcid.org/0000-0002-5745-6311

Hilary McMillan http://orcid.org/0000-0002-9330-9730

E. Mario Mendiondo http://orcid.org/0000-0003-2319-2773

Alberto Montanari http://orcid.org/0000-0001-7428-0410

Marc F. Muller http://orcid.org/0000-0001-6406-8184

Fuqiang Tian http://orcid.org/0000-0001-9414-7019

Alberto Viglione http://orcid.org/0000-0002-7587-4832

Attilio Castellarin http://orcid.org/0000-0002-6111-0612

Daniel Peter Loucks http://orcid.org/0000-0001-8095-3474

Taikan Oki http://orcid.org/0000-0003-4067-4678

María J. Polo http://orcid.org/0000-0002-6296-2198

Anne F. Van Loon http://orcid.org/0000-0003-2308-0392

Ankit Agarwal http://orcid.org/0000-0001-8572-7046

Ana Andreu http://orcid.org/0000-0001-8346-5417

Manuela Brunner http://orcid.org/0000-0001-8824-877X

Yonca Cavus http://orcid.org/0000-0002-0528-284X

Serena Ceola http://orcid.org/0000-0003-1757-509X

Pedro Chaffe http://orcid.org/0000-0002-9918-7586

Xi Chen http://orcid.org/0000-0002-0293-7417

Zhao Dandan http://orcid.org/0000-0002-9797-2046

Moctar Dembélé http://orcid.org/0000-0002-0689-2033

Animesh K. Gain http://orcid.org/0000-0003-3814-693X

Mohammad Ghoreishi http://orcid.org/0000-0002-5051-2763

David M. Hannah http://orcid.org/0000-0003-1714-1240

Britta Höllermann http://orcid.org/0000-0001-5545-3515

Elizabeth Koebele http://orcid.org/0000-0001-9133-2710

Frederik Kratzert http://orcid.org/0000-0002-8897-7689

Sara Lindersson http://orcid.org/0000-0001-9359-6218

Maurizio Mazzoleni http://orcid.org/0000-0002-0913-9370

Alfonso Mejia http://orcid.org/0000-0003-3891-1822

Pablo Mendoza http://orcid.org/0000-0002-0263-9698

Bruno Merz http://orcid.org/0000-0002-5992-1440

Bertil Nlend http://orcid.org/0000-0001-7961-1901

Rodric Merime Nonki http://orcid.org/0000-0003-2466-6272

Christina Orieschnig http://orcid.org/0000-0001-5182-7110

Katerina Papagiannaki http://orcid.org/0000-0003-4433-5841

Gopal Penny http://orcid.org/0000-0001-6628-1026

Olga Petrucci http://orcid.org/0000-0001-6918-1135

Rafael Pimentel http://orcid.org/0000-0001-6990-4874

Sandra Pool http://orcid.org/0000-0001-9399-9199

Elena Ridolfi http://orcid.org/0000-0002-4714-2511

Nivedita Sairam http://orcid.org/0000-0003-4611-9894

Adarsh Sankaran Namboothiri http://orcid.org/0000-0001-8223-043X

Ana Carolina Sarmento Buarque http://orcid.org/0000-0002-9489-4214

Elisa Savelli http://orcid.org/0000-0002-8948-0316

Lukas Schoppa http://orcid.org/0000-0002-3443-4603

Kai Schröter http://orcid.org/0000-0002-3173-7019

Mojtaba Shafiei http://orcid.org/0000-0001-8894-1208

Anna E. Sikorska-Senoner http://orcid.org/0000-0002-5273-1038

Magdalena Smigaj http://orcid.org/0000-0002-2763-4666

Claudia Teutschbein http://orcid.org/0000-0002-3344-2468

Thomas Thaler http://orcid.org/0000-0003-3869-3722

Andrijana Todorovic http://orcid.org/0000-0002-1001-9739

Faranak Tootoonchi http://orcid.org/0000-0001-5262-1712

Elena Toth http://orcid.org/0000-0002-9652-7901

Ronald van Nooijen http://orcid.org/0000-0001-7783-487X

Francieke Maria Vanelli http://orcid.org/0000-0001-8763-5786

Nicolás Vásquez http://orcid.org/0000-0002-4651-8935

David W. Walker http://orcid.org/0000-0002-2486-4677

Marthe Wens http://orcid.org/0000-0002-0133-5924

David J. Yu http://orcid.org/0000-0001-9929-1933

Changrang Zhou http://orcid.org/0000-0001-8466-192X

Günter Blöschl http://orcid.org/0000-0003-2227-8225

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