TY - JOUR
T1 - Bridging forecast verification and humanitarian decisions
T2 - A valuation approach for setting up action-oriented early warnings
AU - Lopez, Ana
AU - Coughlan de Perez, Erin
AU - Bazo, Juan
AU - Suarez, Pablo
AU - van den Hurk, Bart
AU - van Aalst, Marteen
PY - 2020/3
Y1 - 2020/3
N2 - Empirical evidence shows that acting on early warnings can help humanitarian organizations reduce losses, damages and suffering while reducing costs. Available forecasts of extreme events can provide the information required to automatically trigger preparedness measures, while ‘value of information’ approaches can, in principle, guide the selection of forecast thresholds that make early action preferable to inaction. We acknowledge here that, for real-world humanitarian situations, the value of information approach accurately estimates the value of forecasts only if key factors relevant for the humanitarian sector are taken into account. First, the negative consequences of acting in vain are significant and must be factored in. Secondly, the “most valuable” forecast thresholds depend on criteria beyond expenses reduction, and this choice must be explicitly considered in funding mechanisms for early warning products and services. Two options to guide this selection are examined: a maximizing criterion for cost effectiveness, and a satisficing criterion for loss avoidance. Third, decision-makers must be able to confidently assess whether the forecast threshold they are selecting is robust to all possible cost/loss structures for the action in question. Based on these considerations, we explore the application of the valuation approach to select which forecasts (magnitude, probability and lead time) should trigger humanitarian actions. Using a basic example of ensemble precipitation forecast to prepare for potential floods, we discuss how the valuation approach can be used to select probability thresholds that trigger early action, and some of the generalisations required to make this applicable to a wider range of humanitarian situations.
AB - Empirical evidence shows that acting on early warnings can help humanitarian organizations reduce losses, damages and suffering while reducing costs. Available forecasts of extreme events can provide the information required to automatically trigger preparedness measures, while ‘value of information’ approaches can, in principle, guide the selection of forecast thresholds that make early action preferable to inaction. We acknowledge here that, for real-world humanitarian situations, the value of information approach accurately estimates the value of forecasts only if key factors relevant for the humanitarian sector are taken into account. First, the negative consequences of acting in vain are significant and must be factored in. Secondly, the “most valuable” forecast thresholds depend on criteria beyond expenses reduction, and this choice must be explicitly considered in funding mechanisms for early warning products and services. Two options to guide this selection are examined: a maximizing criterion for cost effectiveness, and a satisficing criterion for loss avoidance. Third, decision-makers must be able to confidently assess whether the forecast threshold they are selecting is robust to all possible cost/loss structures for the action in question. Based on these considerations, we explore the application of the valuation approach to select which forecasts (magnitude, probability and lead time) should trigger humanitarian actions. Using a basic example of ensemble precipitation forecast to prepare for potential floods, we discuss how the valuation approach can be used to select probability thresholds that trigger early action, and some of the generalisations required to make this applicable to a wider range of humanitarian situations.
KW - UT-Hybrid-D
UR - http://www.scopus.com/inward/record.url?scp=85044612927&partnerID=8YFLogxK
U2 - 10.1016/j.wace.2018.03.006
DO - 10.1016/j.wace.2018.03.006
M3 - Article
AN - SCOPUS:85044612927
VL - 27
JO - Weather and climate extremes
JF - Weather and climate extremes
SN - 2212-0947
M1 - 100167
ER -