Real-time predictions of pesticide runoff risk: linking multiscale runoff models and weather data APIs to improve water quality

This paper describes a novel integration of soil water interaction models with open weather data. Such models require a large amount of inputs about for example describing field conditions, soil wetness and so on. Here proxies for these were developed using spatially distributed antecedent and predicted rainfall data from live APIs. A spatiotemporal data cube past and projected weather data allows the soil water interaction model to be parametrised by inferring soil water status from long runs of historical data. This research suggests that there a number of opportunities for revisiting soil-water interaction models, which are driven by soil wetness and plant-soil interactions, in conjunction with live feeds to very large datasets such as climate data, to infer soil water balance and to estimate antecedent soil conditions and thus runoff in real-time. The proposed framework is generic and can be used to model any kind of agricultural runoff with minimum model specification. It demonstrates how modified soil water interaction models can be used with real time, spatially distributed by highly localised environmental data.