From skill to value: isolating the influence of end-userbehaviour on seasonal forecast assessment

Abstract

Abstract. Recent improvements in initialization procedures and representation of large scale hydro-meteorological processes contributed in advancing the accuracy of hydroclimatic forecasts, which are progressively more skillful over the seasonal and longer timescales. These forecasts are potentially valuable for informing strategic multisector decisions, including irrigated agriculture, where they can improve crop choices and irrigation scheduling. In this operational context, the accuracy associated with the forecast system setup does not necessarily yield proportional marginal benefit, as this is also affected by how forecasts are employed by end-users. This paper contributes a novel framework to quantify the value of hydroclimatic forecasts by extending traditional accuracy assessments with estimates of potential economic benefit to the end-users. We also explore the sensitivity of this benefit to both forecast system setup and end-user behavioral factors. The approach is demonstrated on the Lake Como system (Italy), a regulated lake operated for flood protection and irrigation supply. Our framework relies on an integrated modeling chain composed of three building blocks: bias-adjusted seasonal meteorological forecasts are used as input to the continentally-calibrated E-HYPE hydrological model; predicted lake inflows are used for conditioning the daily lake operations; the resulting lake releases feed an agricultural model to estimate the net profit of the farmers in a downstream irrigation district. Results suggest that despite the gain on average conditions is negligible, during intense drought episodes informing the operations of Lake Como based on seasonal hydrological forecasts allows gaining about 15 % of the farmers' profit with respect to a baseline solution not informed by any forecast. Moreover, our analysis suggests that behavioral factors capturing different perceptions of risk and uncertainty significantly impact on the quantification of the benefit to the end-users, where the estimated forecast value is potentially undermined by different levels of end-user risk aversion. Lastly, our results show an exponential skill-to-value relation where large gains in forecast skills are necessary to generate moderate gains in end-user profit, with the ratio that becomes less demanding during extreme drought events.

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