Representing climate change and drought intensity with the standardized aridity index (SAI) accurate assessment of drought in a changing climate requires (1) a drought index that remains…

The National Oceanic and Atmospheric Administration awarded a cooperative agreement totaling $424,515 to Texas A & M University on July 1, 2026, under the Climate and Atmospheric Research program (CFDA 11.431) to develop and test a standardized aridity index that accurately represents drought intensity in a changing climate.

The research will develop a drought index that represents precipitation relative to potential evapotranspiration, designed to remain well-calibrated to drought impacts as climate changes and to better capture sensitivity to temperature-driven dryness, particularly in arid regions. Texas A & M will test the index's performance through numerical experiments using hydrologic and crop models, examine how different drought definitions affect severity representations under climate change scenarios, and validate the index through engagement with stakeholder groups preparing Texas recommendations for the U.S. Drought Monitor. This real-time comparison of actual conditions against objective indices will help local stakeholders understand conceptual and practical differences between drought definitions and identify approaches most consistent with observed drought patterns.

Work is performed at Texas A & M University in College Station, Texas, with a period of performance from July 1, 2026, through June 30, 2028. The award obligates $424,515 in federal funds.

Representing climate change and drought intensity with the standardized aridity index (SAI) accurate assessment of drought in a changing climate requires (1) a drought index that remains well-calibrated to drought impacts even as the climate changes and (2) a clear understanding of what drought intensity is intended to represent. In addition to the obvious possibility of changes in precipitation affecting long-term drought frequency, temperature and other factors affect dryness through changes in potential evapotranspiration (ETO). We propose a standardized drought index that represents the amount of precipitation occurring for a given amount of ETO. Such an index, an improvement on the closely-related reconnaissance drought index and aridity index, shares the flexibility of time scale with the standardized precipitation evapotranspiration index but is likely to better represent sensitivity of drought to ETO, particularly in arid areas. We will test the performance of this drought index in a changing climate through a series of numerical experiments using hydrologic and crop models. We will then examine the impact of DI\erent definitions of drought in a changing climate on representations of drought severity, using indices that embody a range of sensitivities to warming temperatures. Besides a systematic analysis, we use real-time experiences with new and old drought indices within stakeholder groups engaged in the preparation of texas recommendations for the US drought monitor. By comparing actual conditions to objective indices, the local stakeholders will come to understand the conceptual and practical differences between DI\erent drought definitions and help articulate which drought definition and nonstationary approach is most consistent with their understanding and observations of drought. This research, if successful, will lead simultaneously to a better common understanding of drought in a changing climate as well as a more accurate index framework for real-time monitoring of drought intensity in a changing climate. This research aligns with NOAA's long-term research goal to help the nation address climate-related challenges and develop solutions. IT addresses three of the five exemplars of research falling under this competition: assessment of the effectiveness of existing and new drought indices in the context of nonstationarity, evaluation of evolving differences in drought severity in different parts of the united states, and improving drought assessment and predictability in a changing climate