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Accueil du site → Doctorat → États-Unis → 2019 → Development of Sub-seasonal to Seasonal Watershed-scale Hydroclimate Forecast Techniques to Support Water Management

University of Colorado at Boulder (2019)

Development of Sub-seasonal to Seasonal Watershed-scale Hydroclimate Forecast Techniques to Support Water Management

Baker, Sarah A.

Titre : Development of Sub-seasonal to Seasonal Watershed-scale Hydroclimate Forecast Techniques to Support Water Management

Auteur : Baker, Sarah A.

Université de soutenance : University of Colorado at Boulder

Grade : Doctor of Philosophy (PhD) 2019

Résumé
Operational sub-seasonal to seasonal (S2S) climate predictions have advanced in skill in recent years but are not yet broadly utilized by stakeholders in the water management sector. While some of the challenges that relate to fundamental predictability are difficult or impossible to surmount, other hurdles related to forecast product formulation, translation, and accessibility can be directly addressed. An example of S2S climate forecast use in water management is through streamflow forecasting. Streamflow forecasts inform many water management decisions such as reservoir operations, water allocation, flood control, and instream supported releases. More skillful streamflow forecasts would benefit water managers through improved projections of future basin conditions for planning and decision making purposes. This dissertation is motivated by the need to reduce hurdles in water manager adoption of S2S climate forecasts. To this end, this dissertation makes four contributions. (1) Two S2S climate forecast products, Climate Forecast System version 2 (CFSv2) and North American Multi-model Ensemble (NMME), are processed to develop real-time watershed-based climate forecast products. A prototype S2S climate data products website was built to disseminate real-time forecasts of CFSv2-based bi-weekly climate forecasts (weeks 1-2, 2-3, and 3-4) and NMME-based monthly and seasonal prediction products on a watershed scale. (2) Biweekly S2S climate forecast of temperature and precipitation were post-processed to enhance the skill and reliability of raw CFSv2 climate forecasts using partial least squares regression (PLSR). (3) An experimental streamflow forecasting method was developed with a simple stochastic trace weighting technique that ingests watershedbased climate forecasts in the Colorado River Basin. The experimental forecasting technique was compared to the traditional streamflow forecasting method, Ensemble Streamflow Prediction (ESP). (4) The experimental and operational streamflow forecasts were compared and analyzed through a testbed framework that was developed to assess how streamflow forecast performance affects operational projections in the Colorado River Basin at a lead time of two years using the Bureau of Reclamation’s Mid-term Probabilistic Operations Model (MTOM

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