Towards an Enhanced Probabilistic National Drought Forecasting

Improved long-range hydrologic drought forecasts can help mitigate drought impacts-they provide guidance to agencies responsible for water allocation, water conservation, and the mitigation of other adverse impacts such as wild land fires. Both observations and models have indicated an increasing trend in drought events that could be linked to recent global climate change.

With respect to drought monitoring and prediction, the U.S. Drought Monitor (DM) and the NOAA Climate Prediction Center’s (CPC) US Drought Outlook (DO), are the primary operational tools available to water managers dealing with drought contingencies. However, both the DM and DO depend on a range of tools from simple statistical indices to water balance models driven by statistical forecasts. MAPP supports developing an integrated drought prediction capability that incorporates research advances into operational seasonal to intra-seasonal climate and hydrologic prediction by means of multiple data sources, hydrologic modeling and data assimilation. The program puts special emphasis on integration of dynamical prediction systems with statistical methods where improved initial condition and probabilistic prediction are considered the key elements in advancing the drought prediction system. The probabilistic framework is expected to improve the characterization of uncertainties, the accuracy, reliability and confidence in drought prediction as the main undertakings under this program.

Uncertainties are an unfortunate yet inevitable part of any forecasting system. Within the context of seasonal hydrologic predictions, these uncertainties can be attributed to three causes: our imperfect characterization of initial conditions, an incomplete knowledge of future climate and errors within computational models. In order to effectively manage these uncertainties, each of these factors must be quantified, providing a framework to reduce uncertainty and accurately convey persistent predictive uncertainty. Remote sensing-based real-time snow extent fields can, in some regions, help characterize one of the key components used to define drought, which is critically important for identifying conditions that could flicker seasonal droughts.

Here, we propose a three-year collaborative research project that will quantify and reduce the major uncertainties involved in drought forecasting by implementing state of the art of data assimilation methods and multivariate statistical drought forecasting by means of copula functions. This is a combined dynamical-statistical approach that is designed to characterize the uncertainties. Multivariate framework allows developing joint distribution function of drought predictors including initial condition estimated in the form of snow and soil moisture storages using data assimilation, and also streamflow observation and simulation. The framework is compared with a purely dynamical framework where the ensemble streamflow prediction (ESP) is generated by deriving the hydrologic model with the bias-corrected climate forecasts. We will focus in particular on those observed variables that have the greatest potential for improving hydrological forecasts in different regions of the U.S. During this process we examine the effectiveness of newly developed postprocessing methods applicable for climate forecast in generating the ESP and accordingly drought forecast. Assessment of prediction skills using deterministic and probabilistic approaches that evaluate the reliability and confidence is conducted.

This proposal responds to the second area solicited on the MAPP Information Sheet “B. Advancing the Development of a National Drought Monitoring and Prediction System, in particular the consideration of the opportunity to integrate state-of-the-art dynamical prediction systems, statistical methodologies, and improved initial conditions through data assimilation to develop more accurate and skillful regional-scale probabilistic drought predictions on intra-seasonal to seasonal time scales”. Assessing the prediction skills will be according to the metrics identified by the MAPP drought task force, working group 1 (http://mappdroughttaskforce.wikispaces.com/) where the PI has been actively engaged in identifying and selecting those metrics. The proposed research will expand on past NOAA-MAPP, -CSTAR and -CPPA funded research by the PI and others in the development and application of hydrologic forecasting with a special attention to drought. The proposed project aligns well with the NOAA’s long-term climate goal as described in NOAA’s Next Generation Strategic Plan in particular on: 1) improved scientific understanding of the changing climate system and its impacts, and 2) mitigation and adaptation choices supported by sustained, reliable, and timely climate services.