PONDERFUL: Pond Ecosystems for Resilient FUture Landscapes in a changing climate

Because of their small size, the significance of ponds has long been underestimated. They are, for example, largely excluded from the Water Framework Directive in Europe, even though the Directive is actually intended to protect ‘all waters’. In North America, their inclusion in the protections provided by the Clean Water Act are contested, and in other areas they lie largely outside regulatory systems. However, research over the last 10-15 years has shown that, because of their abundance, heterogeneity, exceptional biodiversity, inherent naturalness and biogeochemical potency, ponds play a role in catchments, landscapes, and potentially at continental scale which is completely out of proportion to their small size.

The main aims of the research in PONDERFUL will be to increase understanding of the ways in which ponds, as a Nature-Based Solution (NBS), can help society to mitigate and adapt to climate change, protect biodiversity and deliver ecosystem services. The project starts in December 2020, and runs for 4 years.

The project has five main components:

  1. Developing a strategic approach to engagement with stakeholders, to ensure that they are able to effectively implement the benefits of ponds as Nature-Based Solutions
  2. Through the generation of extensive new biodiversity and ecosystem services datasets, to better establish the relationship between pond biodiversity and the delivery of ecosystem services
  3. Establish models that enable us to test and optimise practical scenarios for the use of ponds and Nature-Based Solutions
  4. Create a set of demonstration sites across Europe which show to practitioners and policy makers how ponds can help to mitigate and adapt to the effects of climate change
  5. Ensure that the project’s outputs are widely known to policy makes, practitioners and other stakeholder.

The full project team comprises: the University of Vic – Central University of Catalonia; IGB Leibniz-Institute of Freshwater Ecology and Inland Fisheries; Katholieke Universiteit Leuven; Haute Ecole Specialisée de Suisse Occidentale; Universitat de Girona; Ecologic Institute, Berlin; University College London; Middle East Technical University; CIIMAR – Centro Interdisciplinar de Investigação Marinha e Ambiental; Aarhus Universitet; Uppsala Universitet; Bangor University; Technische Universitaet Muenchen; Institut Superieur d’agriculture Rhone Alpes I.S.A.R.A; Freshwater Habitats Trust; Universidad de la Republica, Uruguay; Randbee Consultants and Amphi International.

Impacts of recent El-Niño Southern Oscillation (ENSO) on the Water-Food-Energy Nexus in South Asia

India’s agriculture, economy, water resources and societal well-being heavily rely on the Indian summer monsoon rainfall (ISMR). But ISMR is strongly influenced by the El-Nino Southern Oscillation (ENSO) phenomenon. In fact, most of the historic droughts over India were associated with El-Nino years (i.e., the warm phase of ENSO). The most recent El-Nino event during 2015-2016 was one of the strongest and longest lasting events on record, during which many regions suffered from rainfall deficits, which affected food, energy and water security as well as livelihoods of local communities and farmers in particular. Given the importance of reservoirs and dams for a continuous year-round water supply, irrigation and hydropower generation in India and neighboring low-income countries, there is an urgent need to assess the impact of El-Nino on the complex water-food-energy nexus. For a region like South Asia, which is under rapid economic development, this need is even more central and calls for an urgent assessment of the role of operating policies in balancing the competing demands for irrigation supply, ecosystem services and hydropower generation.
Through collaborations between Uppsala University (Sweden), the Indian Institute of Technology (Mandi, India) and Amrita Vishwa Vidyapeetham (Coimbatore, India) in combination with an active engagement of local stakeholders, this project will foster a more sustainable development of the South Asian water sector through enhanced management and operational policies, which in turn will ensure that stakeholders in the region are equipped with appropriate tools to cope with and adapt to future El-Niño events and their impacts on the complex water-food-energy nexus.

Through the collection of hydro-climatic data, the application of big-data analyses methods and hydrological/agricultural modelling techniques, we will fill existing knowledge gaps in terms of how El-Niño events influence

  • key hydrological processes and water use
  • performance of major water resources infrastructures during El-Niño events in meeting the water and energy demands of the region under existing operational practices
  • agricultural activities (such as cropping patterns or planting periods)

Reducing uncertainties in hydrological climate change impact research

Vision: Five important knowledge gaps currently limit much of the present effort to understand and project climate change impacts on water resources. The goal of the proposed project is to fill these existing knowledge gaps and strengthen the basis for robust projections of future hydrological climate change impacts by carefully assessing existing approaches and by developing new dynamic and multi-dimensional methods to reduce uncertainties.

Hydrological climate change impact studies rely on complex serial modeling chains (Figure 1) which typically involve several methodological decisions including choices of greenhouse gas emission scenarios as a basis for a global climate model (GCM). GCM simulations are then used as boundary conditions for regional climate models (RCMs). Climate ensemble simulations of such RCMs with resolutions of 10-50 km for Europe can be downloaded from public web portals such as CORDEX1 or ENSEMBLES2. For hydrological impact studies at the catchment scale, RCM-simulated variables such as temperature (T) and precipitation (P) are most commonly used to drive a hydrological model. These RCM variables are, however, often affected by considerable systematic model errors3,4 (also called biases), and consequently require suitable bias correction approaches5. Bias correction is the process of re-scaling climate model outputs to reduce the effects of systematic errors in the climate models and to make the output more suitable as driving force for the hydrological model, which in turn provides simulations of a hydrological component (e.g., streamflow) under varying climate conditions. Bias correction methods typically do not consider the physical reasons of the biases and can introduce inconsistencies between simulated variables (knowledge gap 1). Furthermore, a crucial assumption of common bias correction methods is that the RCM simulation bias is assumed to be invariant over time (i.e. stationary), which is often not the case in reality (knowledge gap 2). Another limitation of the above mentioned type of modeling chain is that the decisions made along the way have a considerable impact on the resulting hydrological projections, because different models/methods have different skill levels6 and therefore cause highly variable results (knowledge gap 3). This does not only apply to the climate models, but also to the hydrological models at the end of the modeling chain. Especially the way of calculating certain hydrological components within the model (e.g. potential evaporation or snow pack) and their dependence on the driving RCM data is a source of large uncertainties (knowledge gap 4). And finally, stationary model parameterization (i.e., fixed model parameter values) can be problematic as it does not allow the hydrological model to respond to physical changes in the catchments caused by climate change or anthropogenic modifications (knowledge gap 5).

  1. Bridge knowledge gap 1: Develop a higher-dimensional bias correction (i.e., multivariate distribution scaling, MVDS) that is able to maintain physical links among multiple variables to effectively reduce RCM biases and evaluate its suitability for hydrological impacts simulations in Sweden
  2. Bridge knowledge gap 2: Add a dynamic component to MVDS, so that it will become sensitive to time-variant RCM biases for hydrological impacts simulations in Sweden
  3. Bridge knowledge gap 3: Investigate the skill levels of different hydrological models under climate conditions unlike those used for model training, while considering different ensemble averaging techniques when examining inter-period transferability
  4. Bridge knowledge gap 4: Benchmark approaches for deriving potential evaporation conditions (for Swedish climate) based on RCM simulations in a changing climate context
  5. Bridge knowledge gap 5: Evaluate whether time-varying hydrological model parameters better reflect changes in hydrological processes in a changing climate