Abstract
Abstract. Recent improvements in initialization procedures and representation of large-scale hydrometeorological processes have contributed to advancing the accuracy of hydroclimatic forecasts, which are progressively more skillful over seasonal and longer timescales. These forecasts are potentially valuable for informing strategic multisector decisions, including irrigated agriculture, for which 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 aims at quantifying the value of hydroclimatic forecasts in terms of potential economic benefit to the end users, which allows for the inference of a relation between gains in forecast skill and gains in end user profit. We also explore the sensitivity of this benefit to both forecast system setup and end user behavioral factors. These analyses are supported by an evaluation framework 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; and 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 in average conditions being negligible, informing the operations of Lake Como based on seasonal hydrological forecasts during intense drought episodes allows about 15 % of the farmers' profit to be gained 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 the quantification of the benefit to the end users, whereby the estimated forecast value is potentially undermined by different levels of end user risk aversion. Lastly, our results show an intricate skill-to-value relation modulated by the underlying hydrologic conditions, which is well aligned over an exponential function in dry years, while the gains in profit are almost insensitive to the improvements in forecast skill in wet years.
Highlights
Recent advances in initialization procedures (e.g., Ceglar et al, 2018) and representation of large-scale hydrometeorological processes (e.g., Krysanova et al, 2017) have contributed to greatly advancing the accuracy of hydroclimatic services
We analyze the isolated sources of forecast value in terms of both forecast system setup and end user behavioral factors, and we infer a relation between gains in forecast skill and gains in end user value
Numerical results demonstrate the potential of the EHYPE hydrological forecast to inform the operations of Lake Como, generating an average EUR 290 000 yr−1 gain in the net profit of the farmers served by the lake releases
Summary
Recent advances in initialization procedures (e.g., Ceglar et al, 2018) and representation of large-scale hydrometeorological processes (e.g., Krysanova et al, 2017) have contributed to greatly advancing the accuracy of hydroclimatic services. State-of-the-art meteorological and hydrological forecast products are increasingly skillful over seasonal and longer timescales and are becoming valuable assets for informing strategic decisions contributing to flood protection (e.g., Coughlan de Perez et al, 2017; Neumann et al, 2018), drought management (e.g., Crochemore et al, 2017; Turco et al, 2017), or hydropower production (e.g., Block, 2011; Boucher and Ramos, 2018). M. Giuliani et al.: From forecast skill to end user value ing decisions (e.g., Li et al, 2017; Guimarães Nobre et al, 2019), which strongly depend on the expected hydrometeorological conditions. Giuliani et al.: From forecast skill to end user value ing decisions (e.g., Li et al, 2017; Guimarães Nobre et al, 2019), which strongly depend on the expected hydrometeorological conditions In such operational contexts, it is key to communicate forecast accuracy along with hydroclimatic services (Contreras et al, 2020). Probabilistic forecasts are often used to convey these uncertainties, potentially adding value for decision-making (see Georgakakos and Graham, 2008; Cloke and Pappenberger, 2009, and references therein)
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