Imtiaz Rangwala co-author on new paper, “Seasonal to multi-year soil moisture drought forecasting”

Imtiaz Rangwala co-author on new paper, “Seasonal to multi-year soil moisture drought forecasting”


NC CASC Climate Science Lead, Imtiaz Rangwala, is co-author of a new paper published in Nature, “Seasonal to multi-year soil moisture drought forecasting”.

Advances in long lead-time soil moisture prediction will greatly augment the ongoing drought early warning efforts for agriculture and natural ecosystems. This study demonstrates the potential for successfully predicting soil moisture conditions across North America on seasonal to multi-year time periods based on the latest advances in Earth System Modeling and our improved understanding of land surface processes that influence soil moisture behavior on longer timescales (i.e., months to years). 

The study examines processes at seasonal to decadal (S2D) timescales from the Community Earth System Modeling (CESM) decadal prediction experiments and provides new insights into the interplay between atmospheric (precipitation) and land-surface processes on those timescales that can boost the predictability (i.e., ability to successfully forecast) of soil moisture on both seasonal and multi-year timescales. 

One important finding from the study is that the predictability for soil moisture on seasonal time periods is three times greater than that for precipitation. Regionally, the study finds that this potential soil moisture predictability is higher for the central and western United States particularly on longer lead times that could span multiple years. 

This study further demonstrates that the soil moisture prediction on S2D timescales can be significantly improved by including the information on observationally-constrained land surface initial conditions in the decadal prediction experiments of the Earth System Models.

The long-term goal of this research, which is in part funded by a USDA-NIFA project (Grant Number: 2020-67021-32476), is to develop the Next Generation Interactive Soil Moisture Forecasting System (NG-ISMFS) that combines the latest advances in Earth System Modeling with Big-Data Analytics to provide an interactive soil moisture forecasting application for agriculture producers and other users with an ability to incorporate data on local soil moisture conditions.

Citation: Esit, M., S. Kumar, A. Pandey, D. Lawrence, I. Rangwala, S. Yeager (2021). Seasonal to Multi-Year Soil Moisture Drought Forecasting. npj Climate and Atmospheric Science.