The BINGO project has received funding from the European Union's Horizon 2020 Research and Innovation programme, under the Grant Agreement number 641739.

Deliverables

You are here

Deliverables

Get access to the BINGO project outputs.

 

D2.1

 

Dynamical downscaling of European reanaysis to 12km and daily values for the period 1979/2014

The European Reanalysis project (ERA-Interim) data has been dynamically downscaled to a 0.11 degree resolution (about 12km) using the regional climate model COSMO-CLM. Those surface variables relevant for hydrological modelling (e.g. precipitation, temperature, wind, pressure, etc) have been bias corrected at the daily time scale and made available for use with hydrological models.

A web-based and on-demand extraction and conversion tool was set up, allowing the hydrological modelling groups to search and extract the data and then convert it to their needs.

This project deliverable (in particular, values for periods 2009-2015 and 1979-1989) can be made available upon request. If you’re interested in having access to the data, get in contact with us.

 

 

D3.1

 

Characterization of the catchments and the water systems

This deliverable evaluates the state of water resources (surface and groundwater, quantity and quality) at the six BINGO research sites. A consistent characterization of the catchments and surface, groundwater, and estuarine water bodies, including land use, pollution sources and anthropogenic water abstractions is included. It includes measured data, results from previous research, literature, knowledge. This document serves as an overview of all six BINGO research sites.

 

 
 

 
 

 

D4.1

 

Context for risk assessment at the six research sites, including criteria to be used in risk assessment

This deliverable aims at setting up a common and clear understanding of all important variables at each research site, taking into consideration the end user’s views for the choice of the criteria and methodologies to be used. This report will be the basis for the future development of the three steps of risk assessment that will be performed in WP4: risk identification (Task 4.2) and risk analysis and risk evaluation (Task 4.3), and provide important input and basic information about the research sites into the work done in WP5.

 

D3.2

 

Future Land and Water Use Scenarios

This document reports the future land and water use scenarios at the six BINGO research sites. The scenarios consist of qualitative developments, for each of two future scenarios Economy First and Sustainability Eventually, developed within SCENES at the research sites. These qualitative developments are then translated into quantitative land use and water use tables. Prior to the results, the scope and methodology are explained and justified in the respective chapters. The results from this deliverable will be used to analyze the effect of land and water use on the hydrology of the research sites, compared to the effects of extreme weather events.

 

D3.3

 

Calibrated water resources models for past conditions

This Deliverable presents the model applications at all six research sites. Special focus in this Deliverable is being paid to the modelling objectives, the model types, the data used and produced, the model results, as well as the model evaluation and discussion. D3.3 shows the wide variability of models developed and applied in order to bring innovation into water management practices. This is due to the fact that European water problems are diverse and BINGO aims at providing as many solutions as possible to mitigate those climate change related problems.

 

 

 

 

 

 

 

D5.4

 

Report on the assessment of the current governance situation and recommendations for improvement at the research sites using the three layer framework (part 1)

Deliverable 5.4 assesses the current policy and governance contexts for adaptation to climate change at the six BINGO research sites. For each research site, governance strengths and weaknesses are identified, based on which recommendation for improvement are offered. The report finds a common governance strength in a strong capacity to deal with existing water-related risks. For example, the Netherlands deals well with water safety risks, Cyprus has a clear policy framework on water scarcity and in the Norwegian city of Bergen, waste water risks are well managed. The downside of this focus on present-day risks is that new risks posed by climate change are insufficiently taken into account. Thus, health risks in Cyprus and the Netherlands and the risk of storm floods in Bergen remain largely untreated. The high degree of governmental fragmentation is identified as another common governance challenge. Because of this, information about the local water system is scattered and coordinative efforts, key to developing adaptation strategies, are hampered.

 

 

D2.3

 

Definition of extremal circulation patterns, present climate

This deliverable addresses the issue of identifying large scale atmospheric patterns with high probability of producing extreme precipitation events for the RS. The Wupper catchment, located in West Germany, has been used in this text to exemplify the approach followed in WP2. The methods used here combine the definition of weather types based on a sophisticated clustering algorithm (Simulated ANnealing and Diversified RAndomisation, SANDRA) and a regression approach based on a logistic model (special case of generalized linear models, GLM). This combination exploits the benefits of including highly non-linear drivers of extreme precipitation via a discrete set of weather types and the flexibility of a generalized linear model to include continuous variables such as convectively available potential energy (CAPE), relative humidity and wind speed.


This revised version of the deliverable report has been amended to (i) take account of the latest status of the availability of the high-resolution test-simulations for the Tagus research site (page 6), which are now fully available, and (ii) to clarify that the Cyprus research site employs the same methodology for the identification of extremal weather patterns as the other research sites (page 10), albeit with a slightly different modelling strategy (page 6).

 

 

 

 

D2.5

 

Ensembles for present climate extremal episodes downscaled to 7km/6h (3-1km/1h); maps of return levels for Cyprus research site

This deliverable consists of data for present climate extremal pattern episodes (15 days duration) downscaled to 12km and 4km/6h (20 ensemble members) and to 1km/1h (5 ensemble members) for the Cyprus research site. Five extreme rainfall events over Cyprus were identified from observations and were dynamically downscaled from the ERA-Interim (EI) dataset with the Weather Research and Forecasting model (WRF), for 15-day periods centered around the peak of each event. For validation we used a 1-km gridded observational dataset over Cyprus. Additionally, we explored the potential added value gained from the EI downscaling with WRF. This was done in both terms of timing and rainfall amounts for each horizontal resolution nest of the downscaling process. Simulations with WRF captured rainfall over the eastern Mediterranean reasonably well for three of the five selected extreme events. For these three cases, the higher spatial resolution WRF simulations were found to improve the ERA-Interim precipitation amounts, which strongly underestimated these rainfall extremes over Cyprus. The best model performance was obtained for the January 1989 event, which was simulated with an average negative bias of 4% and a modified Nash-Sutcliff efficiency of 0.72 for a 5-member ensemble of the 1-km simulations. Our 1-km simulations overall indicate a higher added value, especially over regions of highelevation. Interestingly, for some cases the intermediate 4-km nest was found to outperform the 1-km simulations for low-elevation coastal parts of Cyprus.

 

 

D2.6

 

Ensembles for decadal prediction extremal episodes downscaled to 3-1km/ 1h); Spatial stochastic precipitation generator for catchments

This deliverable consists of two components. First, a spatial stochastic precipitation generator is developed for the catchments. Second, extremal episodes are identified from the decadal predictions and downscaled with the COSMO-CLM to a resolution of 2.2-km and 1 hour for all sites except Veluwe (see changes with respect to the DoW) and Cyprus. The corresponding high-resolution downscaling for Cyprus is performed with WRF and presented in D2.7.

 

 

 

 

 

 

 

D2.7

 

Ensembles for decadal prediction extremal episodes downscaled to 1km/1h for Cyprus research site

This deliverable is about the generation of high temporal and spatial resolution data for future extremal precipitation episodes (15 days duration). This was achieved by applying the dynamical downscaling method. Three extreme rainfall events over Cyprus were identified from the global MiKlip decadal prediction system. Then they were dynamically downscaled with the Weather Research and Forecasting model(WRF), for 15-day periods centered over the peak of each event. An ensemble set of five model configurations was used for the downscaling. According to WRF all events are producing significant rainfall amounts, particularly over the Troodos mountains. Case 2023 can be characterised as extra-ordinary as, according to the model simulations, its 5-day total precipitation amounts (420 mm) breaks the observation records over the semi-mountainous regions of the BINGO research site of Cyprus (408mm). The present report describes the downscaling tools and methodology while it also includes the results in the form of precipitation maps and hyetographs.

 

BINGO Partners

  

The BINGO project has received funding from the European Union's Horizon 2020 Research and Innovation programme, under the Grant Agreement number 641739.

 

The project is coordinated at European level by Laboratório Nacional de Engenharia Civil (LNEC, Portugal).

 

© 2017 Bingo. All rights reserved.

 

 

This website reflects only the author’s view and the Commission is not responsible for any use that may be made of the information it contains.

We're on Facebook!

 

Twitter Feed