|The BINGO project has received funding from the European Union's Horizon 2020 Research and Innovation programme, under the Grant Agreement number 641739.|
The remarkable number of key innovative scientific & practical achievements was not so unexpected due to the high quality of the team. What was surprising more was the inspiring, valuable and transformative process of engaging stakeholders in the Communities of Practice (CoP) and the joint co-production of knowledge. For all the team members it was an incredible rich experience. The power of true collaboration, trust and commitment made the difference in BINGO.
BINGO brought its key outcomes to different sectors in society and using several ways of communication, fit to the different audiences. They are: oral and written technical and scientific communications; workshops with stakeholders (e.g.: water and water related sector, civil protection, decision makers); videos with testimonials and animations explaining key guidelines; games and scripts for role playing (farmer and politicians dealing with climate change) and a musical performance, to be used in schools, in senior universities, in associations, in families, etc. There materials can be easily find and accessible at the BINGO website!
BINGO was one of the 3 H2020 projects co-organising ECCA 2019, together with PLACARD and RESCCUE. The event took place in Lisbon, Portugal, 28-31 May 2019, gathering 1200 participants - it was the most participated ECCA so far. Moreover, the final conference of BINGO was embedded in ECCA 2019: the project main results and outcomes were communicated through 27 presentations, among oral presentations, sessions and posters, covering all the six themes of the conference.
At first stages of the project it was not expected that such a great connection among the different case studies would develop. Although events such as annual meetings and workpackage meetings were scheduled, the team imagined a scenario in which every local team would developed their project mainly on its own, just sharing some results and conclusions with the others in these events.
Not only partners not directly involved to any specific case study such as FUB, SPI and InterSus have been crucial making this link strong, but also the structure of Work packages with its leaders from different teams which was designed. Moreover, the annual meetings have allowed every single member of the BINGO team getting to know the reality of the other case studies, getting everybody involved and generating many profitable discussions for the benefit of the whole project, regardeless of the case study.
To sum up, this duality of case studies and workpages structures was a complete success, an effective way to turn the local teams into the global BINGO team.
A Community of Practice (CoP) is a group of significant and diverse stakeholders who share a concern or a passion for something they do, and learn how to do it better as they interact regularly. A CoP is not necessarily consensus-based, but rather fed by diversity, enabling exchanges, mutual awareness, joined perceptions, implication and actions. Member’s interactions over time produce resources that affect their practice, whether they engage in actual practice together or separately. They enable collaborative co-production of knowledge, the development of holistic and actionable strategies to address challenges at hand and help the innovative potential of research results to cross the barrier to practice.
One of BINGO´s key objectives right from the beginning was to establish CoPs to apply a collaborative integrated approach to climate challenges. The BINGO CoPs involved researchers and key stakeholders and practitioners who are relevant to address climate change challenges for the local water cycles or systems. In order to embrace true knowledge transfer and bring practical knowledge and experience into the project, BINGO settled and facilitated a CoP in each research site, according to their own context specificity, guided by a common interactions (workshops) roadmap. Additionally, BINGO created dedicated virtual interaction “rooms” in its Basecamp Platform to support each site´s CoP exchanges in each native CoP languages and a “CoP room” to support interactions and exchanges between the different site CoPs in a whole global BINGO CoP.
Reproducing the effects of climate change by modelling climate and up-following impacts are producing a huge amount of data. The ability to transfer that into "easy" understandable information for acceptance and decision-making processes is from our experience still weak. Dealing with uncertainties and probabilities for finding more dynamic adaptive measurements is an ongoing process we still have to learn.
The process of communication and building a network between so many great experts all over Europe with their different expertise and problems helped a lot to understand which changes and regarding challenges we are facing. Because we can only solve the unsolvable together, it is important to see it from all the different perspectives and participate in. The most valuable outcomes from BINGO for the challenges we are facing already, therefor were for me the motivation I get from working with the all the ambitious people and coming and working together as an European community on our future.
Decadal predictions are – like weather forecasts – initial value problems. This means that an accurate decadal prediction or weather forecast is dependent on an accurate measurement of the state of the earth system (i.e. atmosphere, ocean, etc.). Using fundamental equations, an accurate measurement of the earth system’s properties (e.g. temperature, pressure, etc.) can be used to calculate the future evolution of the earth system from this measured initial state.
Measurements of the earth system, however, possess an inherent level of uncertainty. It is simply not possible to perfectly measure the state of the earth system. When the fundamental equations are applied to compute decadal predictions (or weather forecasts), small inaccuracies in the measurement of the earth system’s initial state will propagate over time to eventually produce an unreliable prediction.
To account for the inherent measurement uncertainty and how this can influence the final predictions, the so-called “ensemble prediction” technique is adopted. Here, instead of using the best estimate of the earth system’s state to begin a single prediction, a large number of initial states are used to produce a large number of predictions. This set of predictions is known as the “ensemble” and the final prediction is simply the average of all ensemble members. In weather forecasting, the different initial states are typically produced by applying random perturbations to the best estimate initial state. In the MiKlip project, the different initial states for the decadal predictions are produced by simply staggering the start time of the predictions by 24 hours.
By taking the final prediction as the average of all ensemble members’ predictions, the uncertainty due to inaccuracies in the measured state of the earth system can be greatly reduced, giving more reliable predictions. The divergence between the different ensemble members can also be used to estimate the confidence in the ensemble mean prediction: little divergence representing higher and large divergence lower confidence. Individual ensemble members can furthermore be isolated to study best- and worst-case outcomes.
Why is it important to downscale decadal climate predictions to medium-high resolution?”
Water managers and decision-makers have to make decisions for the short term (2-5 years) without compromising the long term. On the other side, most adaptation measures are mostly local dependent. Regional climate models are not sufficiently detailed in space and time to support water management decisions. Decadal predictions allow for detailed prediction of the relevant climate variables decision makers need at the appropriate scale and time they need. These means that one can test and validate the existing “adaptation plans” and improve them in a timeline that fits the decision process horizon.
The BINGO methodology was designed to be applied in the very distinct research sites involved in the project, all of them different in terms of climate, geography, socio-economic landscape, local governance and social connections. As a single hard methodology could not be used in such a diverse set of case studies, the BINGO methodology was developed to be flexible, allowing the work from BINGO had to be adjusted according to the specific characteristics of each site, resulting in several nuances in the application of the BINGO methodology.
The BINGO project, now that it has ended, aims to serve as inspiration for other municipalities across Europe, having in mind that successfully applying the methodologies shared in this e-book requires adaptation to the local characteristics. This is why we share guidelines for specific common points in each step of the BINGO overall methodology that can be used as the basis to be replicated and adapted.
We developed an online portfolio of adaptation measures that have been collected and analysed in the BINGO project. This portfolio is available here: http://beta.tools.watershare.eu/bingo/$/
The information is focused on strategists, decision makers and policy makers in different sectors, such as water resource management, urban drainage, public water supply and agriculture. The information in the database is primarily focus on governance aspects of the measures, using the three-layer-framework that has also been used in BINGO.
We want to motivate all other regions in Europe to find a fitting adaptation measure for their issue – but with the awareness that each measure should serve as inspiration and be adjusted to the local specificities of the municipality which will apply them.
BINGO gave us the opportunity to significantly improve our experience in the usage and interpretation of climate data, based on reanalysis products, decadal predictions, and climate projections (Work Package 2). For instance, in order to capture accurately extreme convective rainfall events, fine spatial and temporal resolution are of crucial importance. Additionally, the determination of indices like SPI (Standardised Precipitation Index) and SPEI (Standardised Evapotranspiration-Precipitation Index) demonstrated to be a robust method for comparison between several data sets, where differences between bias and not bias corrected data sets were negligible. This approach can be applied to other research sites worldwide, serving as a tool which supports decision making processes for reservoir management.
All simulated climate scenarios (decadal predictions as well as climate projections) show a negative trend in the current decade in terms of reservoir storage, where none of the data sets reach the maximum storage volume by the end of 2024. Water stress at the Große Dhünn reservoir is therefore not an unlikely scenario. On the other hand, anthropogenic influences like predicted land and water use were also considered under BINGO. This demonstrated that climate change alone will not influence future water availability at the Große Dhünn reservoir: while estimated land use changes for the next decade will not play a significant role in runoff generation, reservoir volume proved to be highly sensitive to different water use scenarios, based on future water demand and low flow augmentation (used for ecological flow). Thus, implementation of risk reduction measures should be implemented.
Results from the cost-benefit analysis preformed in Badalona have estimated that, if no adaptation measures are taken to adapt de city to the impacts derived from climate change, the expected annual damage (EAD) derived from urban flooding can increase in a 30% (from 1.5€ to 1.9€) considering direct and indirect damages and the expected annual damage derived from combined sewer overflows can represent 1.4 M€/year.
The Early Warning System (EWS) is the most beneficial measure among the 3 analyzed ones. Indeed, the EWS can significantly reduce flood vulnerability (not hazard), expected annual damage (EAD) and risk for limited costs. Sustainable Urban Drainage Systems (SUDS) S are the second most beneficial measure. Despite the fact that the analyzed SUDS can only slightly reduce flood hazard (not vulnerability), EAD and risk, they have lots of other socio-economic- environmental benefits (i.e., CO2 depletion, heat island reduction, ecosystem services, aesthetic value, etc.). The structural measures proposed (addition of new inlets, sewers and retention tanks) are the least convenient from a CBA point of view because the flood EAD reduction is not high enough to compensate the high investment and annual costs of structural measures.
SUDS are the most beneficial measure in terms of net benefit. They involve high socio-economic benefits mainly derived from the ecosystem services they provide (habitat creation, leisure/social spaces, etc.) but also from heat island reduction or air purification. On the other hand, structural measures do not provide net benefits given that the investment and operational costs are not compensated by the socio-economic benefits they provide.
As less precipitation falls and more water evaporates, the groundwater level at the Veluwe decreases. One dry year has little effect on the groundwater level at the Veluwe and the discharge of streams. The high parts of the Veluwe react slowly to changes in precipitation and evaporation. In several dry years in a row, the groundwater level decreases further than usual in dry periods. Also the discharge of the brooks decrease. They can even decrease to zero discharge.
Demand for drinking water is increasing as it will become warmer. Approximately 30% of the drinking water in the province of Gelderland is produced at the Veluwe. In the event of extreme drought, other parts of the Netherlands may also want to make use of the groundwater reserves at the Veluwe.
The farmers will extract more groundwater to prevent damage to the crops. The growing season will also become longer, increasing evaporation and further increasing water demand.
As the temperature rises, the Veluwe will become even more attractive as a recreational area. As a result, the demand for water increases during the summer season. The warmer climate increases the growth of algae and bacteria, affecting quality of surface water and the water systems dependent on that (streams, springs and recreational water). Also the risk of forest fires will increase.
In the BINGO project, three measures have been investigated to increase the groundwater supply at the Veluwe: a ban on irrigation for farmers on the edges of the Veluwe, large-scale infiltration of surface water and the felling of coniferous forests. Infiltration of 30 million m3 of surface water per year or felling of large areas of coniferous forest appear to be the most effective measures to raise the groundwater level. Prohibiting irrigation or felling of smaller areas of coniferous forest has hardly any effect. Because the Veluwe is a large, slow-response groundwater system, large-scale measures are required.
On behalf of Aigües de Barcelona, the company which deals with the sewerage network and the Wastewater Treatment Plants (WWTPs) of the Barcelona Metropolitan Area, I must admit that Climate Change issues were not seriously considered in the past while designing infraestructures such as sewers or WWTPs.
Due to the long lifespan of these infraestructures, both current and future conditions must be taken into account while designing. The Bingo project has shown that future climate predictions are not easy to make. Choosing both the proper numerical model and the suitable scale requires great experience and sometimes these tools are expensive and time demanding. Whatever the circumstances, the BINGO project has shown very good approaches to be considered either in Combined Sewer Overflows, CSOs (short term predictions, 2015-2024) or flooding (long term predictions, 2100), although every location would require a specific study.
Yes (partly). Within BINGO, a classification algorithm was developed for identifying days with an elevated risk of extreme precipitation at a given catchment. These days were then targeted for selective dynamical downscaling to O(1 km) resolution, reducing computational expense by over 90% and still realistically representing the extremes (Meredith et al., 2018).
Meredith, E. P., Rust, H. W., and Ulbrich, U. (2018) A classification algorithm for selective dynamical downscaling of precipitation extremes, Hydrol. Earth Syst. Sci., 22, 4183-4200, https://doi.org/10.5194/hess-22-4183-2018.
The relatively new field of decadal climate prediction aims to simulate both the climate response to future anthropogenic forcing and the future evolution (from the present) of the climate due to internal climate variability. This differs from the approach taken in climate projections, e.g. the CMIP5 project, where the focus is on the response of the climate to anthropogenic forcing and the impacts of internal climate variability are (supposed to be) nullified via multi-decadal climate model integrations. Unlike in climate projections, the earth system models (ESMs) used in decadal prediction systems are initialized with an observed state of the climate system, i.e. ocean, atmosphere, soil, ice, etc. Skill in predicting internal climate variability on a decadal scale is derived from the long-term memory (i.e. sensitivity to the initial state) of certain components of the climate system, predominantly the ocean. As such, decadal predictions (unlike climate projections) are reliant on a high-quality initialization of the ESM for those components which exhibit long-term memory.
Due to inherent uncertainty in the initial conditions, however, the best-estimate initialization can never be completely correct. Errors in the initial conditions will propagate over time, making the longer-term predictions less reliable (this is like the well-known problem in weather forecasting). To account for this inherent uncertainty, several sets of initial conditions are created by applying random perturbations to the best-estimate initial conditions. These perturbed initial conditions then form the basis for further predictions over the same time period. Together, all separate decadal predictions (often referred to as "realizations") form a decadal-prediction ensemble. The most accurate prediction will be found in the ensemble mean. For other applications, however, it may be of interest to consider the most extreme members, i.e. the members furthest above/below the ensemble mean. Which approach to take depends on the aims of the analysis and needs to be decided by each user based on their needs.
One of the goals of the BINGO project was to select and analyse adaptation measures as part of an adaptation strategy. We have applied a stepwise approach to prioritize between measures. The approach relies on active participation of stakeholders which was organized, in BINGO, through Communities of Practice but also consultation of experts beyond the CoPs. The BINGO guidelines “Prioritisation between adaptation measures” can be used to help municipalities qualify and select adaptation measures suitable to their specific context. The six steps of the approach include:
A Multi-Criteria-Analysis is a method to evaluate options (such as measures) using a broad range of indicators, related to socio-economic or other (e.g. environmental) factors. In this way, the wider socio-economic effects and side effects of adaptation measures can be assessed to a broader extent than looking at costs and the direct effect on risk reduction. A Multi-Criteria-Analysis can also be used to analyse effects that are difficult to quantify (e.g. acceptability or environmental side effects). To perform a Multi-Criteria-Analysis, a set of indicators/criteria must be selected to score the measures against. Which indicators/criteria are most suitable is very dependent on local circumstances, therefore this step can best be performed at the level of the research site, involving local stakeholders.
Social justice is considered an increasingly important topic in climate change adaptation. Therefore, to support decision making on adaptation it is important to take this issue into account. In the social justice analysis, the focus lies on the distribution of costs/negative impacts and benefits of the adaptation measures to different actors or groups in society. Social justice can be analysed by answering a set of questions for each adaptation measure.
The governance analysis is useful to prepare a successful implementation of measures. "Governance needs" are the governance requirements that need to be met to be able to implement the measure, such as knowledge requirements, administrative requirements and legal-operational requirements. The governance analysis is based on a three layer framework which has been developed by the Water Governance Council to assess the policy and governance situation in light of climate change adaptation.
A CBA helps the researcher, consultant or engineer to focus and point out the main expected benefits as well as necessary resources input to implement an adaptation measure. Furthermore, it serves as a good tool to compare alternative solutions. One of the main advantages is that results of the analysis are presented in one, easy to read key figure for the decision maker(s). It also delivers information necessary to organize the financing of final measures and thus is a valuable tool to speed up the implementation after decisions are taken for specific climate change adaptation in water systems.
A CEA is a fitting framework, once many different risk reduction solutions are technically feasible with a comparable output. This is especially helpful in situations, where it is hard to express the benefit in monetary terms, but where a non-monetary indicator, eg. a technical indicator can be used to express the benefit of a measure (i.e. the risk reduction effectiveness), this can be e.g. reduced combined-sewer-overflow in m³ or additional available raw water in m³. The CEA can help to identify the low-cost solution among a set of fitting measures. Overall the CEA is a fitting tool integrating multiple disciplines in one framework: Engineering and economic perspectives to characterize measures, hydrologic and climatic perspectives for the risk and effectiveness of adaptation measures. This is quite useful for a thorough climate change adaptation of water systems.