Use of participatory scenario modelling as platforms in stakeholder dialogues

A participatory methodology, based on dialogues between stakeholders and experts has been developed and tested in the drainage area to Kaggebo Bay in the Baltic Sea. This study is focused on the EU Water Framework Directive, with emphasis on reduction of eutrophication. The drainage area is included in the WFD administrative area of the Motala Ström River basin. A similar approach is now applied in a recently initiated project in the Thukela River basin, with focus on impacts of climate change on water resources. The methodology is based on the idea that a catchment model serves as a platform for the establishment of a common view of present conditions and the causes behind these conditions. In the following steps, this is followed by model-assisted agreement on environmental goals (i.e. what do we want the future to look like?) and local agreement on a remedy or mitigation plans in order to reduce environmental impact (e.g. eutrophication); alternatively to adapt to conditions that cannot be determined by local actions (e.g. climate change). By involving stakeholder groups in this model-supported stepwise process, it is ensured that all stakeholder groups involved have a high degree of confidence in the presented model results, and thereby enable various actors involved to share a common view, regarding both present conditions, goals and the way to reach these goals. Although this is a process that is time- (and cost-) consuming, it is hypothesised that the use of this methodology is two-pronged: it increases the willingness to carry out remedies or necessary adaptations to a changing environment, and it increases the level of understanding between the various groups and therefore ameliorates the potential for future conflicts. Compared to traditional use of model results in environmental decision-making, the experts’ role is transformed from a one-way communication of final results to assistance in the various steps of the participatory process.Keywords: participatory, catchment, coastal zone, modelling, nutrient

The Water SWITCH-ON, Spatial Information Platform (SIP)

The amount of open data available for hydrology research is continually growing and provides opportunities for new science and products. Although the existing digital infrastructures (GEOSS Portal, INSPIRE community geoportal and other initiatives) provide access to open data, many hydrologists still encounter difficulties in finding and using open data. Since the time spent on collecting and preparing data usually amounts to more than the time spent on an experiment, any improvement on finding, understanding, accessing and using open data is greatly beneficial. The Spatial Information Platform (SIP) has been developed to tackle these issues within the SWITCH-ON European funded FP7 project. The SIP has been designed as a toolbox of interconnected software components based on open standards that provide to the user all the necessary functionalities as described in the Publish-Find-Bind (PFB) pattern. In other words, this means that the SIP enables users to locate relevant and suitable data for the task they are carrying out and to access and transform it (filtering, extraction, selection, conversion, aggregation). Moreover, the SIP can be used to provide descriptive information about the data and to publish it so others can find it and use it. The SIP is based on existing open data protocols such as OGC-CSW, OGC-WMS, OpenDAP and open-source technologies such as PostgreSQL/PostGIS, GeoServer and pyCSW among others. The SIP is divided in three main user interfaces: the BYOD (Browse your open dataset) web interface, the Expert GUI tool and the Upload Data and Metadata web interface. The BYOD (Browse Your Own Data) HTML5 client is the main entry point for end users that want to search and browse open data in the SIP. The BYOD has a map interface based on Leaflet JavaScript libraries so that the users can search more efficiently. The Expert GUI is an integrated desktop application which can be run by verified experts and members of the SWITCH-ON project only and provides full metadata editing capabilities. The web-based Open Data Registration Tool is designed to provide a user-friendly upload and metadata description interface the end users community. In conclusion, the Spatial Information Platform (SIP) provides to the hydrological science community a set of tools for better understanding and ease of use of hydrological open-data. Moreover, the SIP has been based on well-known OGC standards that will allow the connection and data harvesting from popular EU open data portals such as the GEOSS system of systems

A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers

The geophysical and hydrological processes governing river flow formation exhibit persistence at several timescales, which may manifest itself with the presence of positive seasonal correlation of streamflow at several different time lags. We investigate here how persistence propagates along subsequent seasons and affects low and high flows. We define the high-flow season (HFS) and the low-flow season (LFS) as the 3-month and the 1-month periods which usually exhibit the higher and lower river flows, respectively. A dataset of 224 rivers from six European countries spanning more than 50 years of daily flow data is exploited. We compute the lagged seasonal correlation between selected river flow signatures, in HFS and LFS, and the average river flow in the antecedent months. Signatures are peak and average river flow for HFS and LFS, respectively. We investigate the links between seasonal streamflow correlation and various physiographic catchment characteristics and hydro-climatic properties. We find persistence to be more intense for LFS signatures than HFS. To exploit the seasonal correlation in the frequency estimation of high and low flows, we fit a bi-variate meta-Gaussian probability distribution to the selected flow signatures and average flow in the antecedent months in order to condition the distribution of high and low flows in the HFS and LFS, respectively, upon river flow observations in the previous months. The benefit of the suggested methodology is demonstrated by updating the frequency distribution of high and low flows one season in advance in a real-world case. Our findings suggest that there is a traceable physical basis for river memory which, in turn, can be statistically assimilated into high- and low-flow frequency estimation to reduce uncertainty and improve predictions for technical purposes

Future socioeconomic conditions may have a larger impact than climate change on nutrient loads to the Baltic Sea

The Baltic Sea is suffering from eutrophication caused by nutrient discharges from land to sea, and these loads might change in a changing climate. We show that the impact from climate change by mid-century is probably less than the direct impact of changing socioeconomic factors such as land use, agricultural practices, atmospheric deposition, and wastewater emissions. We compare results from dynamic modelling of nutrient loads to the Baltic Sea under projections of climate change and scenarios for shared socioeconomic pathways. Average nutrient loads are projected to increase by 8% and 14% for nitrogen and phosphorus, respectively, in response to climate change scenarios. In contrast, changes in the socioeconomic drivers can lead to a decrease of 13% and 6% or an increase of 11% and 9% in nitrogen and phosphorus loads, respectively, depending on the pathway. This indicates that policy decisions still play a major role in climate adaptation and in managing eutrophication in the Baltic Sea region.Peer reviewe

The impact of climatic extreme events on the feasibility of fully renewable power systems: a case study for Sweden

Long term time series of variable renewable energy (VRE) generation and electricity demand (load) provide important insights into the feasibility of fully renewable power systems. The coverage of energy statistics is usually too short or the temporal resolution too low to study effects related to interannual variability or the impact of climatic extreme events. We use time series simulated from climate data to assess the frequency, duration, and magnitude of extreme residual load events of two fully renewable power scenarios with a share of VRE generation (wind and solar PV) of about 50% for the case of Sweden. We define residual load as load – wind – PV – nuclear generation. Extreme residual load events are events that exceed the balancing or ramping capacities of the current power system. For our analysis, we use 29 years of simulated river runoff and wind and PV generation. Hourly load is derived from MERRA reanalysis temperature data by applying statistical models. Those time series are used along with historic capacity and ramping restrictions of hydro and thermal power plants in an optimization model to minimize extreme residual load events. Our analysis shows that even highly flexible power systems, as the Swedish one, are affected by climatic extreme events if they increase their VRE shares. Replacing current nuclear power capacities by wind power results on average in three extreme residual load events per year that exceed the current power system’s flexibility. Additional PV generation capacities instead of wind increase the number of extreme residual load events by about 4 %, as most events occur during the winter month when solar generation is close to zero and thus not able to counterbalance low wind events. Contrarily, overproduction and the need to curtail VRE generation become more pressing with higher shares of PV. In the discussion we highlight measures that could provide additional balancing capabilities to cope with the more frequent and severe residual load events in a fully renewable power system with high shares of VRE generation

Intercomparison of regional-scale hydrological models and climate change impacts projected for 12 large river basins worldwide—a synthesis

An intercomparison of climate change impacts projected by nine regional-scale hydrological models for 12 large river basins on all continents was performed, and sources of uncertainty were quantified in the framework of the ISIMIP project. The models ECOMAG, HBV, HYMOD, HYPE, mHM, SWAT, SWIM, VIC and WaterGAP3 were applied in the following basins: Rhine and Tagus in Europe, Niger and Blue Nile in Africa, Ganges, Lena, Upper Yellow and Upper Yangtze in Asia, Upper Mississippi, MacKenzie and Upper Amazon in America, and Darling in Australia. The model calibration and validation was done using WATCH climate data for the period 1971–2000. The results, evaluated with 14 criteria, are mostly satisfactory, except for the low flow. Climate change impacts were analyzed using projections from five global climate models under four representative concentration pathways. Trends in the period 2070–2099 in relation to the reference period 1975–2004 were evaluated for three variables: the long-term mean annual flow and high and low flow percentiles Q10 and Q90, as well as for flows in three months high- and low-flow periods denoted as HF and LF. For three river basins: the Lena, MacKenzie and Tagus strong trends in all five variables were found (except for Q10 in the MacKenzie); trends with moderate certainty for three to five variables were confirmed for the Rhine, Ganges and Upper Mississippi; and increases in HF and LF were found for the Upper Amazon, Upper Yangtze and Upper Yellow. The analysis of projected streamflow seasonality demonstrated increasing streamflow volumes during the high-flow period in four basins influenced by monsoonal precipitation (Ganges, Upper Amazon, Upper Yangtze and Upper Yellow), an amplification of the snowmelt flood peaks in the Lena and MacKenzie, and a substantial decrease of discharge in the Tagus (all months). The overall average fractions of uncertainty for the annual mean flow projections in the multi-model ensemble applied for all basins were 57% for GCMs, 27% for RCPs, and 16% for hydrological models.Intercomparison of regional-scale hydrological models and climate change impacts projected for 12 large river basins worldwide—a synthesispublishedVersio