A coupled human–natural system to assess the operational value of weather and climate services for agriculture

Abstract

Abstract. Recent advances in weather and climate (W&C) services are showing increasing forecast skills over seasonal and longer timescales, potentially providing valuable support in informing decisions in a variety of economic sectors. Quantifying this value, however, might not be straightforward as better forecast quality does not necessarily imply better decisions by the end users, especially when forecasts do not reach their final users, when providers are not trusted, or when forecasts are not appropriately understood. In this study, we contribute an assessment framework to evaluate the operational value of W&C services for informing agricultural practices by complementing traditional forecast quality assessments with a coupled human-natural system behavioural model which reproduces farmers' decisions. This allows a more critical assessment of the forecast value mediated by the end users' perspective, including farmers' risk attitudes and behavioural factors. The application to an agricultural area in northern Italy shows that the quality of state-ofthe-art W&C services is still limited in predicting the weather and the crop yield of the incoming agricultural season, with ECMWF annual products simulated by the IFS/HOPE model resulting in the most skillful product in the study area. However, we also show that the accuracy of estimating crop yield and the probability of making optimal decisions are not necessarily linearly correlated, with the overall assessment procedure being strongly impacted by the behavioural attitudes of farmers, which can produce rank reversals in the quantification of the W&C services operational value depending on the different perceptions of risk and uncertainty.

Skilful seasonal forecasts of streamflow over Europe?

“…the February 2017 streamflow for a forecast made on 1 January 2017), etc. Monthly averages were selected for the analysis presented in this paper as it is a valuable aggregation time step for decision-makers for many water-related applications (as shown in the literature for applications such as, for example, navigation (Meißner et al, 2017), reservoir management (Viel et al, 2016;Turner et al, 2017), drought-risk management , irrigation (Chiew et al, 2003;Li et al, 2017) and hydropower (Hamlet et al, 2002)). …”

“…What is a useful scientific finding does not automatically translate into usable information which will fit into any user's decision-making chain (Soares and Dessai, 2016). While several authors have already investigated the usability of seasonal streamflow forecasts for applications such as navigation (Meißner et al, 2017), reservoir management (Viel et al, 2016;Turner et al, 2017), drought-risk management Yuan et al, 2013;Crochemore et al, 2017), irrigation (Chiew et al, 2003;Li et al, 2017), water resource management and hydropower (Hamlet et al, 2002), its application to flood preparedness is still left mostly unexplored. One exception being Neumann et al (in review), who look at the use of the CM-SSF to predict the 2013/14 Thames basin floods.…”

“…Translating the quality of a forecast into an added value for decision-making and incorporating new forecasting products into established decision-making chains are not easy tasks. This has been explored for many water-related applications, such as navigation (Meißner et al, 2017), reservoir management (Viel et al, 2016;Turner et al, 2017), drought-risk management Crochemore et al, 2017), irrigation (Chiew et al, 2003;Li et al, 2017), water resource management and hydropower (Hamlet et al, 2002), but seasonal streamflow forecasts have yet to be adopted by the flood preparedness community.…”

Non-renewable groundwater use and groundwater depletion: a review

“…Sociohydrology states that the concept of economic efficiency is not sufficient to understand the mechanisms behind non-sustainable water use, nor the effects of economic policy thereon. Inspired by related work on coupled human-natural systems modelling (Liu et al 2007), socio-ecological principles (Ostrom 1990) and socio-environmental modelling (Filatova et al 2016), socio-hydrology combines insights from the social sciences, including behavioural economics (Kahneman andTversky 1979, Camerer et al 2004), with models from complexity theory, such as stylized conceptual models, game theory, machine learning approaches, and agent-based models to describe human-water interactions (Girard et al 2016, Giuliani et al 2016, Li et al 2017. Many of these methods have yet to be applied to the study of sustainable groundwater use, but the first papers start to appear (O'Keeffe et al 2018).…”

Mapping the landscape of climate services

“…Several European and international initiatives have stimulated a vibrant community: third World Climate Conference (in 2009), the Climate Services Partnership (in 2011), the International Conference on Climate Services (in 2011), the Global Framework of Climate Services (in 2012), the European Roadmap for Climate Services (in 2015), and the Climate Services for Resilient Development Partnership (in 2017). Climate services can improve efficiency and speed innovative methods and processes in agriculture (Amissah-Arthur 2003, Stigter 2008, Lechthaler and Vinogradova 2017, Li et al 2017, food security (Vogel and O'Brien 2006), disaster risk reduction (van den Hurk et al 2016), urban planning (Jones et al 2017, Lindberg et al 2018, health (Goddard et al 2010, Bruno Soares et al 2017, tourism Lemieux 2010, Scott et al 2011), and other climatesensitive sectors.…”