Earth and environmental models are relied upon to investigate system responses that cannot otherwise be examined. In simulating physical processes, models have adjustable parameters which may, or may not, have a physical meaning. Determining the values to assign to these model parameters is an enduring challenge for earth and environmental modellers. Selecting different error metrics by which the models results are compared to observations will lead to different sets of calibrated model parameters, and thus different model results. Furthermore, models may exhibit ‘equifinal’ behaviour, where multiple combinations of model parameters lead to equally acceptable model performance against observations. These decisions in model calibration introduce uncertainty that must be considered when model results are used to inform environmental decision-making. This presentation focusses on the uncertainties that derive from the calibration of a four parameter lumped catchment hydrological model (GR4J). The GR models contain an inbuilt automatic calibration algorithm that can satisfactorily calibrate against four error metrics in only a few seconds. However, a single, deterministic model result does not provide information on parameter uncertainty. Furthermore, a modeller interested in extreme events, such as droughts, may wish to calibrate against more low flows specific error metrics. In a comprehensive assessment, the GR4J model has been run with 500,000 Latin Hypercube Sampled parameter sets across 303 catchments in the United Kingdom. These parameter sets have been assessed against six error metrics, including two drought specific metrics. This presentation compares the two approaches, and demonstrates that the inbuilt automatic calibration can outperform the Latin Hypercube experiment approach in single metric assessed performance. However, it is also shown that there are many merits of the more comprehensive assessment, which allows for probabilistic model results, multi-objective optimisation, and better tailoring to calibrate the model for specific applications such as drought event characterisation. Modellers and decision-makers may be constrained in their choice of calibration method, so it is important that they recognise the strengths and limitations of their chosen approach.

The European Centre for Medium-Range Weather Forecasts (ECMWF) mission is to deliver high-quality global medium-range numerical weather predictions and monitoring of the Earth system including hydrology and water resources to its member states for decision making processes. Challenges in this area include the integration of innovative observations into the Earth system; realistic representations of water, energy and carbon cycles; coupling and initialisation of all Earth system components; adequate representation of uncertainties; and supporting the development of user-specific products to enable optimal decision-making under uncertainty. ECMWF is also the operational centre of the European Union’s Copernicus Emergency Management Service (CEMS) providing Global Flood and Fire forecasting systems issuing seasonal and sub-seasonal forecasts. These forecasts, along with reanalysis and reforecast data, are now openly available through the Copernicus Climate Data Store allowing them to be input to other applications and used by decision makers. These data and service enhancements open numerous possibilities to improve integration with water decision-makings systems and processes. However, ensuring these forecasts can be used for such purposes is challenging due to the scale disconnect between a continental or global forecast system and the local scale at which decisions are made. Overcoming this challenge can be achieved by co-designing and optimising the forecasting systems together with the applications sector. This will allow to fully integrate Earth System and impacts modelling in the forecasting systems, thereby enhancing simulation realism. It will also to better tailor specific end products to user requirements and facilitate an improved decision making. An example is the TAMIR project which aims to connect flood forecasts to end user decision making through an impact matrix. This matrix combines flood hazard forecasts, derived by blending hourly radar based nowcasts and medium-range numerical weather predictions, with locally relevant exposure information regarding population and critical infrastructure. Continuous end user engagement ensures that the design of the forecast system remains relevant for their decision-making purposes. Another example is the recently commenced I-CISK project, which will tailor environmental forecast data to meet the requirements set out by end users. This project aims to build upon the wealth of existing data and services by incorporating local knowledge across multiple sectors, timescales and hazards. Working with decision-makers at every step of the project to co-design, co-create, co-implement and co-evaluate a range of tailored climate services that are specific to user needs, will help to provide information that is useful, useable, and used at the local scale. This will include collaborating with users to design effective forecast visualisations and undertaking user-driven evaluation to answer specific questions users have about forecast performance.