Comparison of SPEI drought index values for the cumulative 3-month observed period from 1993 to 2022 D values: Panel (to) SPEI based on observations from 1981-2010, Panel (b) SPEI based on LOCA2 projected conditions for 2031-2060 and Panel (do) SPEI based on conditions projected by the Working Group for 2031–2060. A SPEI ≤−1.5 represents severe drought conditions. Based on the observed climate description for the period 1981–2010 in the Panel (to), 17 severe drought conditions occurred between 1993 and 2022, with a calculated minimum SPEI of -2.1. Using LOCA2 2031–2060 conditions, four severe droughts occurred between 1993 and 2022, with a minimum SPEI of -1.9. In the 1993–2022 observations analyzed using WG 2031–2060 conditions, no severe droughts occurred and the minimum SPEI was -1.4. This comparison demonstrates that the WG 2031–2060 climate description is significantly warmer and drier on average than the LOCA2 2031–2060 description.
Climate change is transforming our world in ways we are only just beginning to understand. One of the most pressing challenges is predicting how these changes will affect water resources, crucial to agriculture, industry, and daily life. The concept of weather attribution, which examines the likelihood of specific weather events occurring under different climatic conditions, has emerged as a vital tool in this effort. By understanding the role of human-induced climate change in altering weather patterns, scientists can better predict and prepare for the future. This approach is particularly relevant to water resources management, as it helps forecast conditions such as droughts that can have severe economic and environmental impacts.
Weather attribution has become increasingly vital as the effects of climate change intensify. Recent research led by Nick Martin, formerly of the Southwest Research Institute in San Antonio, Texas, explores how incorporating weather attribution into water balance projections can improve our understanding of future drought conditions. This work, published in the journal Hydrology, compares expectations of future severe droughts between historical observations, CMIP6 downscaled to LOCA2 future climate simulation results and statistically projected future climate guided by meteorological attribution. Stochastic weather generators (WG) are the statistical simulation tool used to predict future climate constrained by weather attribution.
Meteorological attribution estimates the probability of observed meteorological events occurring under different climate scenarios and, therefore, the change in the probability of occurrence of severe droughts under human-induced climate change. climate attribution study The method used to guide the statistical projection of future climate in this work suggests that a severe three-month drought is five times more likely to occur given human-induced climate change. Conceptually, five times more likely means that a 1-in-25-year drought in 2000 is now a 1-in-5-year drought in the 2020s. The synthetic future climate produced by the WG is constrained, or “calibrated,” to produce a severe drought five times more likely during 2031–2060. This method simulates future climate patterns, including droughts, in a way that reflects historical data, recently observed climate, and projected future climate changes.
“Meteorological attribution provides the observed change in the probability of extreme events, including drought, that is necessary to assess, plan and prepare for mitigation of future risk to water resources from human-induced climate change. Once the change in probability is attributed, synthetic statistical projections of future climate, incorporating the new probability of extreme events, provide a framework for water resources planning and risk assessment,” Martin said, highlighting the potential of this approach to provide water budget forecasts that describe the inherent uncertainty and risk associated with future conditions.
The implementation site was the Frio River Basin in south-central Texas, an area crucial for water resources management due to its direct communication between surface waters and the Edwards Aquifer. A working group was calibrated to synthetically produce a stochastic climate for the period 2031–2060 that provides a climate description in which a severe three-month drought is five times more likely to occur relative to historical observations. This increased drought probability is based on expectations of significantly higher temperatures and lower soil moisture in the future compared to historical norms. Expectations of increasing temperatures and decreasing soil moisture are supported by CMIP6 future climate simulation results and meteorological attribution studies based on recently observed climate.
This study describes the magnitude and probability of a three-month drought using the Standardized precipitation evapotranspiration index (SPEI)The SPEI is based on precipitation and temperature data and provides a climate drought index sensitive to global warming. The three-month observed water deficit (D), calculated as the depth of precipitation minus the depth of potential evapotranspiration, is the drought measure that is transformed, standardized, and normalized to generate the SPEI. The “standardization” portion provides the probability of three-month drought magnitudes based on the precipitation and temperature data set used to calculate the SPEI. Drought categories by range of SPEI values and cumulative probabilities for selected SPEI values are shown in the table below.
The significant increase in the probability of observed drought conditions for recent extreme events is the critical factor guiding water resources planning. The difference in the probability of the observed three-month drought in January 2000, shown in the figure above, identifies divergent expectations between historical conditions, the results of the CMIP6 downscaled LOCA2 future climate simulation, and the future climate projected by the Meteorological Attribution-Guided Working Group. The observed three-month water deficit (D) for January 2000 is -217 mm. When calculated from observations from 1981 to 2010, the SPEI for -217 mm is -1.9 with a cumulative probability of 0.03 corresponding to severe drought. When determined using WG-constrained meteorological attribution projections for 2031–2060, the SPEI for D of -217 mm is -0.9 with a cumulative probability of 0.17 corresponding to mild drought. This identifies a three-month D of -217 mm for November, December, and January as being 5.7 (0.17/0.03 = 5.67) times more likely to occur in the WG-projected climate for 2031–2060 than in the observed climate during 1981–2010. When calculated using downscaled CMIP6 LOCA2 climate simulation results for 2031–2060, the SPEI for a D of -217 mm is -1.6 with a cumulative probability of 0.05 denoting severe drought. Historically observed severe drought (D of -217 mm in November, December, and January) is 3.4 (0.17/0.05 = 3.4) times more likely to occur in the WG projected climate than in the downscaled CMIP6 LOCA2 climate simulation results for 2031–2060.
| SPEI value | Drought Category | Cumulative probability |
|---|---|---|
| 0.0 | 0.50 | |
| Mild drought | ||
| -1.0 | 0.16 | |
| Moderate drought | ||
| -1.5 | 0.07 | |
| Severe drought | ||
| -2.0 | 0.02 | |
| Extreme drought |
The importance of these findings lies in their potential applications for water resources management and planning. By providing an improved description of the likelihood of future extreme events, the study’s methodology can guide water conservation and allocation strategies, helping to mitigate the effects of severe droughts. This approach can be extended to other regions and water systems, offering a valuable tool for addressing the challenges and risks posed by climate change.
In summary, this study demonstrates the critical role of meteorological attribution in improving the characterization of uncertainty in future water balance projections. The findings underscore the need for innovative approaches to water resource management, particularly as climate change continues to alter the frequency and intensity of extreme weather events. As Martin concluded, the ability to predict and prepare for severe droughts is essential for sustainable water management and the resilience of communities that depend on these vital resources.
Journal reference
Martin, Nick. “Incorporating meteorological attribution into future water budget projections.” Hydrology, 2023, 10, 219. DOI: https://doi.org/10.3390/hidrología10120219
About the author
Nick Martin Nick is a water scientist working at Vodanube LLC and RESPEC, based in Fort Collins, Colorado. He has experience as a surface and groundwater hydrologist and software developer. He focuses on risk assessment, risk mitigation, reliability, resilience, and sustainability analyses related to climate change and legacy infrastructure in natural and manmade systems. Nick specializes in probabilistic analysis and modeling to quantify uncertainty and define environmental and economic risk. His technical interests include uncertainty analysis for decision support and data assimilation such as water movement, transportation modeling, machine learning, and deep learning studies.
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