Value [def.]: The importance or worth of something for someone

Seasonal hydrometeorological forecasts, often incorporated in a climate service, may provide (probabilistic) predictions or outlooks of water resources availability and early warning of drought to agriculture, hydropower, humanitarian, tourism, forestry, and other sectors. Availability of seasonal forecast data and of climate services have mushroomed in recent decades, but research shows that the actual uptake and use of these has not quite kept pace. Several reasons for this lag are identified, and it is acknowledged that three key dimensions that foster uptake are the credibility, salience and legitimacy of data and services provided; from the perspective of intended users.

As showcased in many contributions in this session, significant effort is dedicated to improving the consistency and quality of (probabilistic) seasonal forecasts through the understanding of uncertainty, data-assimilation, and bias-reduction. This is important, as an accurate forecast contributes to this being considered good, and the opinion users hold on its credibility. However, accuracy alone is not sufficient. Users must also consider the forecast salient, or relevant, to the decisions it is intended to support. Even the most precise and technically robust forecasts may fail to be considered if they do not align with the specific needs, priorities and contexts of users. This eludes to the title of this contribution, which is the dictionary definition of the word value. If users consider the predictions or outlooks actionable in informing their decision-making processes, then these hold value to them. Conversely, researchers and developers of seasonal hydrometeorological forecasts often find other aspects important. They may value forecasts that provide accurate predictions of hydrometeorological variables, and use forecast verification through a range of metrics to evaluate forecast goodness. While these metrics are essential from a technical perspective, they may be less meaningful to users.

In this contribution we explore how seasonal forecasts can be co-evaluated, together with users, and through user-oriented (verification) metrics. An essential step is the identification of needs through a common understanding of the decisions that users take and how they use data and knowledge in making those decisions. We show how this has been developed with users in Living Labs established in the I-CISK project, an EU research initiative. We find that this is a highly iterative process, with tools such as interviews, surveys, focus group discussions and co-developed decision timelines giving rich insight to what decisions are made, thresholds that are used, and when decisions are made when these vary seasonally. We also evaluate through interviews with users a selection of commonly used verification metrics. Results from these interviews show that decision-oriented metrics such as contingency tables are considered more informative than other metrics, as are visual inspection of forecast performance for past events. They also show that discussing these in the co-evaluation process contributed to the opinion users had on the credibility of the forecasts and how these are of value to them. Significantly, the co-evaluation of forecasts was also found to help build trust, contributing to legitimacy; the third important dimension of uptake.