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Shukla S.,University of California at Santa Barbara | Wood A.W.,Research Applications LaboratoryNational Center for Atmospheric ResearchBoulder | Cheng L.,University of Colorado BoulderBoulder | Svoboda M.,National United University
Water Resources Research | Year: 2016

Improving water management in water stressed-regions requires reliable seasonal precipitation predication, which remains a grand challenge. Numerous statistical and dynamical model simulations have been developed for predicting precipitation. However, both types of models offer limited seasonal predictability. This study outlines a hybrid statistical-dynamical modeling framework for predicting seasonal precipitation. The dynamical component relies on the physically based North American Multi-Model Ensemble (NMME) model simulations (99 ensemble members). The statistical component relies on a multivariate Bayesian-based model that relates precipitation to atmosphere-ocean teleconnections (also known as an analog-year statistical model). Here the Pacific Decadal Oscillation (PDO), Multivariate ENSO Index (MEI), and Atlantic Multidecadal Oscillation (AMO) are used in the statistical component. The dynamical and statistical predictions are linked using the so-called Expert Advice algorithm, which offers an ensemble response (as an alternative to the ensemble mean). The latter part leads to the best precipitation prediction based on contributing statistical and dynamical ensembles. It combines the strength of physically based dynamical simulations and the capability of an analog-year model. An application of the framework in the southwestern United States, which has suffered from major droughts over the past decade, improves seasonal precipitation predictions (3-5 month lead time) by 5-60% relative to the NMME simulations. Overall, the hybrid framework performs better in predicting negative precipitation anomalies (10-60% improvement over NMME) than positive precipitation anomalies (5-25% improvement over NMME). The results indicate that the framework would likely improve our ability to predict droughts such as the 2012-2014 event in the western United States that resulted in significant socioeconomic impacts. © 2016. American Geophysical Union. All Rights Reserved. Source

Clark M.P.,Research Applications LaboratoryNational Center for Atmospheric ResearchBoulder | Kavetski D.,University of South Australia | Rupp D.E.,Oregon State University | Gutmann E.D.,Research Applications LaboratoryNational Center for Atmospheric ResearchBoulder | And 6 more authors.
Water Resources Research | Year: 2015

This work advances a unified approach to process-based hydrologic modeling, which we term the "Structure for Unifying Multiple Modeling Alternatives (SUMMA)." The modeling framework, introduced in the companion paper, uses a general set of conservation equations with flexibility in the choice of process parameterizations (closure relationships) and spatial architecture. This second paper specifies the model equations and their spatial approximations, describes the hydrologic and biophysical process parameterizations currently supported within the framework, and illustrates how the framework can be used in conjunction with multivariate observations to identify model improvements and future research and data needs. The case studies illustrate the use of SUMMA to select among competing modeling approaches based on both observed data and theoretical considerations. Specific examples of preferable modeling approaches include the use of physiological methods to estimate stomatal resistance, careful specification of the shape of the within-canopy and below-canopy wind profile, explicitly accounting for dust concentrations within the snowpack, and explicitly representing distributed lateral flow processes. Results also demonstrate that changes in parameter values can make as much or more difference to the model predictions than changes in the process representation. This emphasizes that improvements in model fidelity require a sagacious choice of both process parameterizations and model parameters. In conclusion, we envisage that SUMMA can facilitate ongoing model development efforts, the diagnosis and correction of model structural errors, and improved characterization of model uncertainty. © 2015. American Geophysical Union. All Rights Reserved.. Source

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