A model chain for studying the link between climate and the dispersion of waterborne pathogens involving downscaling and hydrological models

Downscaled key statistics for 24-hr daily rainfall, the wet-day frequency and wet-day mean precipitation, can provide a basis for input to hydrological models with the help of a simple weather generator. A demonstration is provided by the SPRINGS project (https://www.springsproject.eu/) that models the link between climate and public health. It focuses on some specific pilot regions, which include Ghana, Romania, Tanzania and Italy, and the lessons learned may also benefit other parts of the world. For example, water quality and quantity is modelled for the study of the dispersion of waterborne pathogens that lead to diarrhoea in the Volta catchment of Ghana and provide challenges for the water supply in Timisoara, Romania. A model chain employed by the SPRINGS project is presented together with preliminary results, starting from global climate models and ending with specific regional policy advice. The work includes empirical-statistical downscaling (ESD) of large multi-model CMIP5/6 ensembles of global climate models and the estimation of the number of days with heavy 24-hr rainfall. It is important to produce robust and reliable information that can provide a basis for decision-making. While robust and reliable downscaled results imply large multi-model ensembles, various downscaling strategies, and downscaling statistical parameters, hydrological models typically need time series of hourly or daily weather. Hence, an optimal chain of models, involving climate, downscaling, hydrological, epidemiological and economical models, needs to make some compromises. Here, a strategy for cross-disciplinary coproduction of knowledge is presented, with a particular emphasis on climate and hydrology while keeping in mind the needs for practical policy-making.