This project proposes a methodology that improves capabilities to forecast 1- to 6-month precipitation and water supply anomalies. Predicting and understanding droughts' effects on water resource systems is essential to securing sustainable water resources. This project will make unique contributions through a comprehensive and challenging plan to advance research in drought monitoring and prediction. UCI has recently developed and implemented the Global Integrated Drought Monitoring and Prediction System (GIDMaPS) that provides near real-time drought information based on multiple indicators and NASA data sets. The research plan consists of three complementary parts to be integrated into the current version of GIDMaPS: (a) Integrate NASA’s Atmospheric Infrared Sounder (AIRS) relative humidity and water vapor to improved drought early onset detection and prediction; (b) develop a multivariate modeling framework for composite drought assessment; and (c) develop a framework for quantitative and probabilistic assessment of drought through integrating satellite data into an analog-based drought prediction model.
Geographic Focus
California
Application Readiness Level
TRL-4: Initial Integration & Verification (Prototype Developed)
Milestone 1: Components of eventual application system have been brought together.
Principal Investigator
Amir AghaKouchak, University of California, Irvine
Project Team
Soroosh Sorooshian, University of California, Irvine
Kaolin Hsu, University of California, Irvine
Collaborators and Stakeholders
California Department of Water Resources
Technical Overview
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 project 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). Our latest 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.
The proposed work contributes to the objectives of the NASA Water Resources program through developing an innovative and practical applied research that will be integrated into water resources management and decision-making. The project is coordinated with the California Department of Water Resources.
Related Research Areas