Statistical Models for Characterizing and Reducing Uncertainty in Seasonal Rainfall Pattern Forecasts to Inform Decision Making
Creators: name: Bandar Saud AlMutairi
Publisher: name: Carnegie Mellon University
Year: 2017
Subjects: subject: 90799 Environmental Engineering not elsewhere classified
subject: FOS: Environmental engineering
subject: FOS: Environmental engineering
subject: 90599 Civil Engineering not elsewhere classified
subject: FOS: Civil engineering
subject: FOS: Civil engineering

description: Uncertainty in rainfall forecasts affects the level of quality and assurance for decisions made to manage water resource-based systems. However, eliminating uncertainty in a complete manner could be difficult, decision-makers thus are challenged to make decisions in the light of uncertainty. This study provides statistical models as an approach to cope with uncertainty, including: a) a statistical method relying on a Gaussian mixture (GM) model to assist in better characterize uncertainty in climate model projections and evaluate their performance in matching observations; b) a stochastic model that incorporates the El Niño–Southern Oscillation (ENSO) cycle to narrow uncertainty in seasonal rainfall forecasts; and c) a statistical approach to determine to what extent drought events forecasted using ENSO information could be utilized in the water resources decision-making process. This study also investigates the relationship between calibration and lead time on the ability to narrow the interannual uncertainty of forecasts and the associated usefulness for decision making. These objectives are demonstrated for the northwest region of Costa Rica as a case study of a developing country in Central America. This region of Costa Rica is under an increasing risk of future water shortages due to climate change, increased demand, and high variability in the bimodal cycle of seasonal rainfall. First, the GM model is shown to be a suitable approach to compare and characterize long-term projections of climate models. The GM representation of seasonal cycles is then employed to construct detailed comparison tests for climate models with respect to observed rainfall data. Three verification metrics demonstrate that an acceptable degree of predictability can be obtained by incorporating ENSO information in reducing error and interannual variability in the forecast of seasonal rainfall. The predictability of multicategory rainfall forecasts in the late portion of the wet season surpasses that in the early portion of the wet season. Later, the value of drought forecast information for coping with uncertainty in making decisions on water management is determined by quantifying the reduction in expected losses relative to a perfect forecast. Both the discrimination ability and the relative economic value of drought-event forecasts are improved by the proposed forecast method, especially after calibration. Positive relative economic value is found only for a range of scenarios of the cost-loss ratio, which indicates that the proposed forecast could be used for specific cases. Otherwise, taking actions (no-actions) is preferred as the cost-loss ratio approaches zero (one). Overall, the approach of incorporating ENSO information into seasonal rainfall forecasts would provide useful value to the decision-making process - in particular at lead times of one year ahead.