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   https://doi.org/10.1007/s00442-020-04793-8  

   Carroll, Charles J.; Slette, Ingrid J.; Griffin-Nolan, Robert J.; Baur, Lauren E.; Hoffman, Ava M.; Denton, Elsie M.; Gray, Jesse E.; Post, Alison K.; Johnston, Melissa K.; Yu, Qiang; et al (January 2021, Oecologia)

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2. Probabilistic estimates of drought impacts on agricultural production: Drought Impacts on Agriculture

   https://doi.org/10.1002/2017GL073606  

   Madadgar, Shahrbanou; AghaKouchak, Amir; Farahmand, Alireza; Davis, Steven J. (August 2017, Geophysical Research Letters)

   Increases in the severity and frequency of drought in a warming climate may negatively impact agricultural production and food security. Unlike previous studies that have estimated agricultural impacts of climate condition using single-crop yield distributions, we develop a multivariate probabilistic model that uses projected climatic conditions (e.g., precipitation amount or soil moisture) throughout a growing season to estimate the probability distribution of crop yields. We demonstrate the model by an analysis of the historical period 1980–2012, including the Millennium Drought in Australia (2001–2009). We find that precipitation and soil moisture deficit in dry growing seasons reduced the average annual yield of the five largest crops in Australia (wheat, broad beans, canola, lupine, and barley) by 25–45% relative to the wet growing seasons. Our model can thus produce region- and crop-specific agricultural sensitivities to climate conditions and variability. Probabilistic estimates of yield may help decision-makers in government and business to quantitatively assess the vulnerability of agriculture to climate variations. We develop a multivariate probabilistic model that uses precipitation to estimate the probability distribution of crop yields. The proposed model shows how the probability distribution of crop yield changes in response to droughts. During Australia's Millennium Drought precipitation and soil moisture deficit reduced the average annual yield of the five largest crops. 

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3. Climate Change and Drought: a Perspective on Drought Indices

   https://doi.org/10.1007/s40641-018-0098-x  

   Mukherjee, Sourav; Mishra, Ashok; Trenberth, Kevin E. (October 2018, Current Climate Change Reports)
4. The bedrock of forest drought

   https://doi.org/10.1038/s41561-022-01015-z  

   Tague, Christina (September 2022, Nature Geoscience)
5. A quantitative evaluation of the issue of drought definition: a source of disagreement in future drought assessments

   https://doi.org/10.1088/1748-9326/ac2348  

   Satoh, Yusuke; Shiogama, Hideo; Hanasaki, Naota; Pokhrel, Yadu; Boulange, Julien Eric; Burek, Peter; Gosling, Simon Newland; Grillakis, Manolis; Koutroulis, Aristeidis; Müller Schmied, Hannes; et al (September 2021, Environmental Research Letters)

   Abstract Droughts are anticipated to intensify in many parts of the world due to climate change. However, the issue of drought definition, namely the diversity of drought indices, makes it difficult to compare drought assessments. This issue is widely known, but its relative importance has never been quantitatively evaluated in comparison to other sources of uncertainty. Here, encompassing three drought categories (meteorological, agricultural, and hydrological droughts) with four temporal scales of interest, we evaluated changes in the drought frequency using multi-model and multi-scenario simulations to identify areas where the definition issue could result in pronounced uncertainties and to what extent. We investigated the disagreement in the signs of changes between drought definitions and decomposed the variance into four main factors: drought definitions, greenhouse gas concentration scenarios, global climate models, and global water models, as well as their interactions. The results show that models were the primary sources of variance over 82% of the global land area. On the other hand, the drought definition was the dominant source of variance in the remaining 17%, especially in parts of northern high-latitudes. Our results highlight specific regions where differences in drought definitions result in a large spread among projections, including areas showing opposite signs of significant changes. At a global scale, 7% of the variance resulted independently from the definition issue, and that value increased to 44% when 1st and 2nd order interactions were considered. The quantitative results suggest that by clarifying hydrological processes or sectors of interest, one could avoid these uncertainties in drought assessments to obtain a clearer picture of future drought change. 

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