Displays

A recent paper (Bloeschl, et al. 2019) reported on the outcome of a multi-year effort involving over 200 scientists identifying the 23 most unsolved scientific issues facing the hydrologic community today.  The purpose of this exercise was to motivate the hydrologic research community to focus their work on these issues to better understand the major causes of how water behaves in our catchments, watersheds and river basins, often in different ways at various space and time scales, and under the influence of various degrees of human interactions. Aside from the scientific value that this increased understanding might bring, this presentation focuses on two questions: Why and how might this increased understanding be beneficial and who would benefit? In other words, who should care and why? This interactive presentation attempts to provide some answers to these two questions for each of the 23 identified unsolved scientific problems. But in general it is clear much of the impact that humans are having on our environment is driven by how the hydrologic cycle fits in with the needs of humans and our supporting ecosystems. Water in our environment affects the spread of contaminants and pathogens, the energy and food and industrial goods we produce, the ecosystem services we enjoy, and the duration and extent of floods and droughts some endure. Understanding these links and their economic, health, and social consequences will allow us to manage our water resources and their use more effectively, and perhaps even reduce the risks of reaching tipping points that could forever change how we all will live and survive in the future.    

How to cite: Loucks, D.: Solving the 23 Major Mysteries in Hydrology: Who Cares and Why? , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-364, https://doi.org/10.5194/egusphere-egu2020-364, 2020.

This contribution summarizes the steps of, and experiences with, a wide consultation process, led by the International Association of Hydrological Sciences (IAHS) that resulted in a list of 23 major unsolved scientific problems (UPH) in hydrology.

Step 1) Launch of a YouTube video, outlining the purpose of the initiative and its vision.

Step 2) Discussion via a LinkedIn group leading to a total of about 200 contributions and responses.

Steps 3-4) Two ‘in-person’ meetings organised in April 2019 in Vienna: one (Step 3) at the EGU General Assembly (attended by about 60 scientists), in order to solicit additional questions, at the end of which about 260 candidate problems had been compiled; the second one (Step 4) at the Vienna Catchment Science Symposium (VCSS) at the Vienna University of Technology (attended by about 110 scientists), to sort, merge, split, reword and prioritise the proposed questions. Through an iteration of parallel sessions (repeated twice, mixing the participants) and a final plenary voting session, a list of 16 ‘gold‘ and 29 ‘silver‘ questions was identified.

Step 5) Synthesis carried out by a small working group, involving representatives and members of IAHS, IAH, EGU and AGU, to consolidate, interpret and synthesise the questions, as well as to address potential biases in their selection that might have arisen from the composition of the participants at the meetings. The working group also pooled the questions into seven themes for clarity and communication. As a result of the synthesis process, the working group finally listed a set of 23 questions, presented in a community paper with over 200 authors (Blöschl et al., 2019, https://doi.org/10.1080/02626667.2019.1620507).

The UPH initiative is a proof of concept that this kind of broad consultation process is actually feasible, and is well received by the hydrological scientific community. Thus, equally important as the final list, is the community-level learning process of such a consultation, involving a large number of hydrologists and the four main learned societies in the field.

Consultations such as this could and should be repeated in the future for the benefit of our discipline, since providing common research subjects will increase the coherence of the scientific process in hydrology and promote the co-building of scientific strategies and synergy towards accelerated progress in hydrological sciences and applications.

This PICO presentation gives a short overview of the consultation process and of each of the 23 questions, shares the experiences from the process and proposes some possible future steps.

How to cite: Cudennec, C., Arheimer, B., Blöschl, G., Ramos, M. H., and Toth, E.: The community consultation process leading to the compilation of the 23 Unsolved Problems in Hydrology (UPH), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11302, https://doi.org/10.5194/egusphere-egu2020-11302, 2020.

This work could contribute to solve UPH #1: is the hydrological cycle regionally accelerating/decelerating under climate and environmental change, and are there tipping points (irreversible changes)?

This fundamental question hinges upon the Nature of the hydrologic cycle itself, and for which a geological perspective is needed.  To begin to solve this problem, we thus must have a clear picture of how the water cycle has changed throughout Earth’s History.  However, current narratives of the history of Earth's water cycle lack a coherent description of how life altered water cycling on land. Here I review a body of evidence of plant evolution events in Earth's history and propose how rainfall runoff mechanisms evolved through five key evolutionary phases.  This review reveals that for most of Earth's history, water cycling on land was likely very different from today, with fewer mechanisms available to store water between rainfall events in the critical surface zone, with implications for water availability and surface climate.  A key tipping point occurred during the Silurian-Devonian periods with the greening of the planet. This deep-time perspective illustrates the step-by-step process through which plants optimized the water cycle in which it increased the distribution in space and time, culminating in the development of forests in the late Devonian. Lastly, I review how the past may serve as a key to the future, discussing how the historical perspective illustrates key areas needed to improve our current conceptualization of water availability so that we may better understand and predict changes of water availability during the Anthropocene.

How to cite: Sterling, S.: A new deep-time historical perspective of the terrestrial water cycle that is needed to solve UPH #1: , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10606, https://doi.org/10.5194/egusphere-egu2020-10606, 2020.

The identification of tipping points in the water cycle has been recently ranked Nr. 1 in the list of the top 23 unresolved problems in Hydrology by the International Association of Hydrological Sciences (IAHS) and as a priority in the field of hydrology and water resources by several studies. Such daunting task is mainly attributed to the concerns that greenhouse gas emission climate change may tip the water cycle into an unfavorable new state. Up to date, tipping points occurring in complex dynamical systems have been identified across a large set of disciplines. In most proven tipping points, hydrologic variables are always taken as the control variables, as changes in water fluxes and stocks are known to act as stressors of socioecological systems, and the affected aquatic and terrestrial ecosystems as the response variables. The main objective of this study is to explore the existence of tipping points in catchment-scale freshwater availability, that is, the tipping points were the response variable is catchment water storage. We first review the existence of reported tipping points in the field of hydrology and water resources, to establish a coherent framework for the identification of hydrological tipping points. We explore their mathematical existence at the catchment scale by Linear Stability Analysis, illustrating cases with potential functions and bifurcation diagrams. We then explore any possible contribution to the existence of hydrological tipping points by adding complexity to the hydrological dynamic system through the inclusion of sociological feedbacks. We find that even with the inclusion of the moisture feedback of evapotranspiration to precipitation, constant socioecological conditions will most likely not present tipping points of water storage in the catchment. However, the inclusion of socioecological feedbacks does generate tipping points under certain assumptions, even without assuming a moisture feedback between evapotranspiration and precipitation. We hope that this study sheds some light on the existence, conditions, assumptions and characteristics of large-scale hydrological tipping points with long-term implications.

How to cite: Jaramillo, F., Manzoni, S., Crepin, A.-S., Rocha, J., Wang-Erlandson, L., Zipper, S., Gleeson, T., and D’Odorico, P.: Exploring the existence of hydrological tipping points at the catchment-scale , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19397, https://doi.org/10.5194/egusphere-egu2020-19397, 2020.

Long period annual rainfall data series from nine raingauge stations throughout eastern India were analysed. Those data series were for the years 1901 to 1965 for Aijal (Mizoram); 1901 to 1984 for Imphal (Manipur); 1901 to 1986 for Guwahati (Assam), Shillong, Cherrapunji (Meghalaya); 1901 to 1987 for Cuttack (Odisha), Patna (Bihar), Agartala (Tripura), Krishnanagar (West Bengal). Incomplete annual rainfall data were found out by taking average of data of preceding and following years. Each annual rainfall series was divided into modelled period (1901 to 1980 for eight stations except Aijal with 1901 to 1960) and predicted period (data for years left in the series after modelled period for evaluation of the model for prediction of future rainfalls). Each annual rainfall series in the modelled period was first converted into percentage values of the mean annual rainfall and then plotted against year, which showed the oscillations of the historigram about the mean line (Tomlinson, 1987 for New Zealand rainfalls). Such type of characteristic historigrams for all stations showed periodic nature of annual rainfalls throughout eastern India. So, autoregressive integrated moving average (ARIMA) model (Clarke, 1973) was used to evolve a useful model for prediction of future rainfalls. As the ARIMA model was biased for periodicity due to inclusion of both the ‘sin’ and ‘cos’ functions and period length as 12, modelled data series were analysed for polynomial regression. The accepted degrees of polynomials were decided on the basis of analysis of variance (ANOVA). Acceptance of either ARIMA model or polynomial regression was done on the basis of -test. In most of the cases in the observed historigrams the lengths of periods were less than eight years and in some cases those were eight to 12 years and from polynomial regressions in most cases the period lengths varied in between 8 to 12 years, 13 to 22 years and 23 to 37 years; and in rare cases those lengths were 38 years and more. Considering all the limitations in the observed data and 95% confidence interval for ARIMA model, a particular amount of annual rainfall occurred at about 12 years (i.e. almost resembling a Solar Cycle) and that might be concluded after minute analysis of more observed data. Recurrence of flood and drought years can be predicted from such analysis and also by following probability analysis of excess and deficit runs of annual rainfalls (Panda et al., 1996).

References:

Clarke, R.T.1973. Mathematical models in hydrology. FAO Irrigation and Drainage Paper No. 19. FAO of the United Nations, Rome. pp.101-108.

Panda, S.; Datta, D.K. and Das, M.N. (1996). Prediction of drought and flood years in Eastern India using length of runs of annual rainfall. J. Soil Wat. Conserv. India. 40(3&4):184-191.

          https://www.academia.edu/15034719/Prediction_of_drought_and_flood_years_in_eastern_%20%09India%20using_length_of_runs_of_annual_rainfall

Tomlinson, A.I. (1987). Wet and dry years – seven years on. Soil & Water. Winter 1987: 8-9. ISSN 0038-0695    

How to cite: Panda, D. S.: Periodic occurrences of annual rainfalls in Eastern India [UPH No. 9 (theme: Variability of extremes) and UPH No.19 (theme: Modelling methods)], EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4004, https://doi.org/10.5194/egusphere-egu2020-4004, 2020.

Climatic variations can have a significant impact on a number of water-related sectors. Managing such variations through accurate predictions is thus crucial. Continental hydro-climate services have recently received attention to address various user needs. However, predictions for months ahead can be limited at catchment scale, highlighting the need for data tailoring. Here, we address how seasonal forecasts from continental services can be used to address user needs at the catchment scale. We compare a continentally-calibrated process-based model (E-HYPE) and a catchment-specific parsimonious model (GR6J) to forecast streamflow in a set of French catchments.

This work provides insights into UPH 20 (How can we disentangle and reduce model structural/parameter/input uncertainty in hydrological prediction?) by proposing a skill assessment framework that isolates gains from hydrological model forcings and forecast initialisation. Our results show that a good estimation of the hydrologic states, such as soil moisture or lake levels, prior to the prediction is the most important factor in obtaining accurate streamflow predictions in both setups. We also show that the spread in internal model states varies largely between the two systems, reflecting the differences in their setups and calibration strategies, and highlighting that caution is needed before extracting hydrologic variables other than streamflow.

This work also provides insights into UPH 21 (How can the (un)certainty in hydrological predictions be communicated to decision makers and the general public?). Despite the expected high performance from the catchment setup against observed streamflow, the continental setup can, in some catchments, match the catchment-specific setup for 3-month aggregations and when looking at statistics relative to model climatology, such as anomalies. Nevertheless, differences in the setups can result in different uncertainties for variables such as soil water content.

How to cite: Crochemore, L., Ramos, M.-H., and Pechlivanidis, I.: Can Continental Models Convey Useful Seasonal Hydrologic Information at the Catchment Scale?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6126, https://doi.org/10.5194/egusphere-egu2020-6126, 2020.

This talk addresses aspects of three of the seven UPH themes: (i) time variability and change, (ii) space variability and scaling, and (iii) modeling methods. 

During the community contribution phase of the 23 Unsolved Problems effort, one of the suggested questions was “Does Machine Learning have a real role in hydrological modeling?” The final UPH paper claimed that “Most hydrologists would probably agree that [extrapolating to changing conditions] will require a more process-based rather than calibration-based approach as calibrated conceptual models do not usually extrapolate well.” In this talk we will present a collection of recent experiments that demonstrate how catchment models based on deep learning can account for both temporal nonstationarity and spatial information transfer (e.g., from gauged to ungauged catchments), often achieving significantly superior predictive performance compared to other state-of-the-art (process-based) modeling strategies, while also providing interpretable results. This is due to the fact that deep learning can learn, exploit, and explain catchment and hydrologic similarity in ways and with accuracies that the community has not been able to achieve using traditional methods. 

We argue that the results we have obtained motivate a path forward for hydrological modeling that centers around ‘physics-informed machine learning.’ Future model development might focus on building hybrid (AI + process-informed) models with three objectives: (i) integrating known catchment behaviors into models that are also able to learn directly from data, (ii)  building explainable deep learning models that allow us to extract scientific insights, and (iii) building hybrid models that are also able to simulate unobserved or sparsely observed variables. We argue further that while the sentiments expressed in the UPH paper about process-based modeling are common, the community currently lacks an evidence-based understanding of where and when process-based understanding is important for future predictions, and that addressing this question in a meaningful way will require true hybrids between different modeling approaches.

We will conclude by providing two fundamentally novel examples of physics-informed machine learning applied to catchment-scale and point-scale modeling: (i) conservation-constrained neural network architectures applied to rainfall-runoff processes, and (ii) integrating machine learning into existing process-based models to learn unmodeled hydrologic behaviors. We will show results from applying these strategies to the CAMELS dataset in a rainfall-runoff context, and also to FluxNet soil moisture data sets.

How to cite: Nearing, G., Kratzert, F., Pelissier, C., Klotz, D., Frame, J., and Gupta, H.: Machine Learning is Central to the Future of Hydrological Modeling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6111, https://doi.org/10.5194/egusphere-egu2020-6111, 2020.

We tackle the unsolved problem in hydrology “How can we extract information from available data on human and water systems in order to inform the building process of socio-hydrological models and conceptualisations?”

In the framework of the Panta Rhei initiative we compile and analyse a benchmark dataset, which shall be used to calibrate and apply socio-hydrological models. The compilation and analyses of the benchmark dataset will be undertaken as follows: 1) selection of suitable socio-hydrological models; 2) identification of the variables necessary to calibrate and apply the selected models; 3) collection of time series data of the selected variables for as many catchments as possible; 4) calibration and application of the socio-hydrological models; 5) comparative analyses across different models and catchments.

A minimum of two, preferably more socio-hydrological models for floods and droughts shall be selected. Data collection will be undertaken with the support of the Panta Rhei community, particularly the members of the Panta Rhei working groups “Changes in flood risk” and “Droughts in the Anthropocene”. For the socio-hydrological model calibration we plan to follow the example of Barendrecht et al. (2019). This PICO presentation shall be used to discuss and finalise the concept for data compilation and analyses, to promote this initiative and to motivate as many colleague as possible to contribute to the data collection and comparative analyses.

Reference: Barendrecht, M. H., Viglione, A., Kreibich, H., Merz, B., Vorogushyn, S., Blöschl, G. (2019): The value of empirical data for estimating the parameters of a socio-hydrological flood risk model. WRR, 55, 2, 1312-1336. DOI: http://doi.org/10.1029/2018WR024128

How to cite: Kreibich, H., di Baldassarre, G., van Loon, A., Schröter, K., Ward, P., Tian, F., Viglione, A., Sivapalan, M., and Blöschl, G.: Panta Rhei Benchmark Dataset, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10001, https://doi.org/10.5194/egusphere-egu2020-10001, 2020.

Understanding how different societal groups respond to drought or flood events is one of the unsolved problems in hydrology (UPH), concerning the interfaces with society. More specifically, there is a need to decipher the relationship between potential impacts of unprecedented events, distribution of sociohydrological risk as well as future adaptation and recovery trajectories. In this presentation, we introduce a new analytical approach to answer the question of how contemporary societies might adapt to and recover from unprecedented drought and flood events in an inclusive and sustainable fashion. In doing so, this presentation deepens our understandings of the interface between hydrological extremes and society. Addressing this question requires creating new forms of knowledge that integrate analyses of the past, i.e. historical and political processes of risk and adaptation and the underlying power relations, with hydroclimatic projections of unprecedented events. We thus combine three aspects which have been studied individually, but never integrated: a. scenarios based on social science theories on disaster management; b. case studies of past hydroclimatic events which were unprecedented at the time of their occurrence; c. conceptual transfer across case studies - that is, learning something about potential future unprecedented events at one location by leveraging events which occurred elsewhere. Some of the scenarios developed may already be emerging in current times, whilst others are plausible hypotheses in humanity’s future space. This approach, at the nexus between social and hydrological sciences, has the concrete advantage of providing an impacts-focussed vision of future risk, beyond what is achievable within conventional disciplinary boundaries. 

How to cite: Rusca, M., Di Baldassarre, G., and Messori, G.: Unsolved problems in hydrology: societal responses to unprecedented events, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7431, https://doi.org/10.5194/egusphere-egu2020-7431, 2020.

Water is at the core of many current and future global challenges, which involve hydrological, technical and social processes. Therefore, successful interdisciplinary research on how water-related issues interact with human activities, actions and responses is increasingly important. Qualitative data and diverse perspectives provide much-needed information to improve our understanding and management of water-related issues. To collect this information, hydrologists are increasingly conducting fieldwork with human participants (e.g. individuals, policy-makers, community leaders, government representatives, etc.) themselves, and collaboratively with others. Although collaboration between hydrologists and social scientists in interdisciplinary projects is becoming more common, several barriers, including lack of understanding and experience, can result in hydrologists and social scientists remaining somewhat separate during research, leading to suboptimal research outcomes. Hydrologists who are planning and undertaking fieldwork involving human participants may be underprepared because they are unfamiliar with key social science approaches and concepts. Therefore, here, we help guide hydrologists to better understand some important issues to consider when working with human participants, to facilitate more collaborative research.

As a group of social, natural, and interdisciplinary scientists, we discuss a number of important elements of fieldwork involving human participants that hydrologists might be unfamiliar with, or might have different approaches to than social scientists. These elements include good ethical practice, research question frameworks, power dynamics, communication of science (e.g. participatory mapping, photovoice, videography, and interactive graphs), and post-fieldwork reflections. There are also issues to consider when working collaboratively with social scientists, such as vocabulary differences and different methodologies and data collection approaches (e.g. interviews, focus groups, questionnaires, workshops, ethnography).

We believe that by introducing hydrologists (and natural scientists in general) to some of the key considerations when working with human participants in the field, more holistic, ethical, and successful research outcomes can be achieved. We also want to stress that collaboration with social scientists stays important and research ethics, design, participant involvement, and results, may be compromised without the input and experience of social scientists themselves. Facilitating these collaborations between the natural and social sciences will improve interdisciplinary water research, resulting in a better understanding of the interactions between water and society.

How to cite: Rangecroft, S., Banks, E., Day, R., Di Baldassarre, G., Frommen, T., Hayashi, Y., Höllermann, B., Lebek, K., Mondino, E., Rohse, M., Rusca, M., Wens, M., and Van Loon, A.: Social science for hydrologists: considerations when doing fieldwork with human participants, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5221, https://doi.org/10.5194/egusphere-egu2020-5221, 2020.

Hydro-metrological events cause substantial economic damage and social disruption in our society to date. These climate-related risks will become even more severe in the future, driven by changes in the frequency and magnitude of natural hazard events, an increasing exposure of buildings or infrastructure, as well as vulnerability and resilience developments of residents and businesses. Although these long-term developments are of major social and economic relevance, decisions in disaster risk management and their (potential) impacts are typically assessed as singular events and potential alternative solutions, which have not been considered, are out of scope. Recent research therefore suggests to employ the concept of iterative climate risk management (CRM), in order to align disaster risk management and climate change adaptation policy and practice. This is supposed to increase the awareness of how complex and dynamic the challenge of comprehensively tackling climate-related risks is.

Pathways aims to fill this gap by analysing the long-term development of past and future decisions. The arenas in which these decisions are made are characterised by (1) competing interests from various policy areas, (2) ad-hoc decisions often taking precedence over strategic planning for long-term CRM, and (3) previous decisions providing carry-over, follow-up or creating even lock-in effects for later decisions. Focusing on two climate-adaptation regions in Austria (so-called KLAR!-regions), Pathways paints a comprehensive picture of how local adaptation pathways were developed in the past, how these pathways led to specific decisions at specific points in time, and which impacts these choices had on community development with respect to the choices and pathways not taken. Pathways learns from the past to inform the future with the aim to provide capacity building at the local level. By understanding how earlier decisions enabled or constrained the later decisions, pathways offers policy guidance for making robust decisions in local CRM.

Pathways applies a mixed-method approach to integrate quantitative and qualitative social science research methods and to triangulate the research objectives from different perspectives. Semi-structured interviews with key CRM actors at various levels of government, geo-spatial analysis, secondary analysis of census data and archival research jointly inform the reconstruction of past decision points and related pathways. This approach allows to test, compare, confirm, and control the collected data and the interpreted results from different perspectives, while avoiding narrow, oversimplifying explanations. Building on the lessons learnt from the past, future pathways are co-designed with local stakeholders in Formative Scenario workshops. Pathways restricts its scope to climate-related risks from extreme hydro-meteorological events and geological mass movements, such as riverine floods and pluvial torrents, mud and debris flow, landslides or avalanches. This risk domain requires governance structures for immediate response to the disaster as well as for prevention and relief/reconstruction. Pathways aims to improve the knowledge base for respective governance efforts.

How to cite: Thaler, T., Babcicky, P., Clar, C., Schinko, T., and Seebauer, S.: Learning from the past for strategic decision-making in climate risk management: Connecting historic and future adaptation pathways , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1485, https://doi.org/10.5194/egusphere-egu2020-1485, 2020.

The U.S. Geological Survey, through the National Water Census, has produced a near real-time, operational concept map of water availability for the conterminous United States. Currently, this map aggregates “natural” landscape-dimension storage volumes (e.g. soil moisture, snowpack, and surface depression storage) and relates these values to historic averages for a given spatial unit for the given time of year. The purpose of this operational concept map is to improve communication of current water availability to the general public using the best available knowledge and technology. Current operational model deployment is an application of nationally-consistent methods; however, the degree to which regionalization and local knowledge might be applied and interwoven into the national product are current topics of exploration. In addition, future development for this model and visualization process will include adding water quality and water use as variables that contribute to the overall availability of water. Adding these transdisciplinary components to the existing physical model is not straightforward; the differences in model structure and data types needed for specific disciplines will need to be overcome to present a truly integrated water availability estimate that can provide useful information for the public as well as the technical research community. In this presentation, we explore the successes and challenges of the existing operational model used for the National Water Census, including transdisciplinary model integration, calibration, and uncertainty, with the goal of improving communication of water availability.

How to cite: Driscoll, J. M. and Farmer, W. H.: Integrated, operational water availability estimates for the conterminous United States: transdisciplinary data and modeling successes and challenges, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10811, https://doi.org/10.5194/egusphere-egu2020-10811, 2020.

Climate adaptation of brook catchments is much needed in the studied regions of England, Belgium and the Netherlands. With the continuous rise of global temperatures and global change, these regions suffer from the impacts of extreme weather events such as drought and flooding. Extreme weather and climate change impacts are spatially non-uniform, uncertain and can have different strengths at local and regional level. Therefore, cities and regions need to adapt to climate change in an ambiguous way. Accordingly, there is no uniformity in the adaptive capacity of individuals, groups within society, organisations and governments or how they can respond to current and future climate change impacts.

To better understand the interlinkages in nature-based climate adaptation between the socio-economic and climate change drivers, we studied these drivers in the hydrological modelling in 3 pilot studies in the UK, the Netherlands and Belgium. Focus is on how co-creation, defined as active participation is incorporated in the hydrological modelling process, (1) within each brook catchment and (2) between the professionals, as cross border knowledge transfer. Data on the co-creation process was collected with workshops on each of the semi-annual partner meetings of each catchment. Data on the modelling process was collected by semi-structured interviews of the professionals and by using assessment of professional learning in the network (field trips). Findings on co-creation processes of nature based solutions in hydrological modelling will be compared in the UK, the Netherlands and Belgium. In the end, existing co-creation processes will be joined to a framework for co-creation which can be improved and adapted based on the gathered data. This would include: identification of stakeholder groups and their needs, the level of intended participation, the identified climate problem by the stakeholders and by the policy-makers, the planned modelling approach, the NbS etc.

Keywords: climate change, hydrology, nature-based solutions, stakeholders, climate adaptation, framework.

How to cite: Bogatinoska, B., Lansu, A., Floor, J., Huitema, D., and Dekker, S.: Co-creation processes of nature based solutions in hydrological modelling – case studies in the UK, Belgium and the Netherlands, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11432, https://doi.org/10.5194/egusphere-egu2020-11432, 2020.

New and unconventional sources of data that enhance our understanding of internal interactions between socio-economic and hydrological processes is central to sociohydrological modelling. Participatory modelling (PM) departs from conventional modelling tools by informing and conceptualizing sociohydrological models through stakeholder engagement. However, the implementation of most PM processes remains biased, particularly in regions where marginalized communities are present. Most PM processes are not cognizant of differentiation and diversity within a society and tend to treat communities as homogeneous units with similar capabilities, needs, and interests. This undifferentiation leads to the exclusion of key actors, many of whom are associated with marginalized communities. In this study, a participatory model-building framework (PMBF), aiming to ensure the inclusiveness of marginalized stakeholders - who (1) have low literacy, (2) are comparatively powerless, and/or (3) are associated with a minoritized language - in participatory sociohydrological modelling is proposed. The adopted approach employs interdisciplinary storylines to inform and conceptualize system dynamics-based sociohydrological models.The suggested method is underpinned by the Multi-level Perspective (MLP) framework, which was developed by Geels et al. (2002) to conceptualize socio-technical transitions and modified in this study to accommodate the development of interdisciplinary storylines. A case study was conducted in Atitlán Basin, Guatemala, to understand the relationships that govern the lake’s cultural eutrophication problem. This research integrated key stakeholders from the indigenous Mayan community, associated with diverse literacy ranges, and emerging from three different minoritized linguistic backgrounds (Kaqchikel, Tz’utujil, and K’iche’), in the PM activity. The generated model serves as a decision support system for managing nutrient discharge into Lake Atitlán, allowing stakeholders to investigate trends of different policy and management scenarios. The participatory model-building activity helped eliminate the impact of power imbalances in water resources management and empower community-based decision-making.

How to cite: Bou Nassar, J., Malard, J., Adamowski, J., Ramírez Ramírez, M., and Tuy, H.: The use of interdisciplinary storylines to ensure the inclusiveness of marginalized stakeholders in participatory sociohydrological modelling: A case study in Tz’olöj Ya’, Mayan Guatemala, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11178, https://doi.org/10.5194/egusphere-egu2020-11178, 2020.

Hydrological modeling tools are usually used to obtain broad scale understandings of ecological and hydrological interconnections in a basin. They have also been presented as useful to support collaborative decision processes by visually displaying hydrological systems connections, uncertainties and gaps, as well conflicting preferences over water management strategies. However, many challenges remain at capturing and communicating the complexity of couple human-hydrological systems. The Aculeo basin in Chile is an internationally publicized case due to the disappearance of a 12 km² lake that leaded to increasing conflicts over water scarcity and the cause of the catastrophe. A traditional hydrological model study and a separate collaborative agreement process were implemented in parallel to find answers and discuss solutions to the water scarcity crisis. The model initially designed to answer a single water balance question, was finally turned in a question-driven socio-hydrological modeling process used to explore a diversity of uncertainties emanating from the collaborative agreement process. Model development and some results of this integration are presented, displaying how science-policy process forces adjusting model structure, challenging official information and searching for alternatives sources and approaches to find answers. This research presents how a hydrological model can be used as a dynamic framework to address poor knowledge on the system behavior, disagreements on the water crisis causes and contradictions on the management options proposed. However, it also shows that participation can be an instance used by stakeholders to question and challenge the rigidity, scope and accuracy of the model information being presented. Therefore, flexible approaches and research agendas should support the exploration of this type of synergies towards more collaboration and production of useful and legitimate socio-hydrological models. 

How to cite: Ocampo-Melgar, A., Barria, P., and Chadwick, C.: Cooperation under conflict: a framework for participatory modeling under severe social and climate change pressures, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20035, https://doi.org/10.5194/egusphere-egu2020-20035, 2020.