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1. Drought and small-bodied herbivores modify nutrient cycling in the semi-arid shortgrass steppe

   https://doi.org/10.1007/s11258-019-00908-1  

   Lemoine, Nathan P. ; Smith, Melinda D. ( February 2019 , Plant Ecology)
2. Adapting to extreme events: small drinking water system manager perspectives on the 2012–2016 California Drought

   https://doi.org/10.1007/s10584-021-03305-8  

   Klasic, Meghan ; Fencl, Amanda ; Ekstrom, Julia A. ; Ford, Amanda ( February 2022 , Climatic Change)

   Under a changing climate, droughts are projected to increase in frequency and duration in the Southwestern USA. Between 2012 and 2016, California experienced one of the region’s worst droughts, with record high temperatures and low snowpack, runoff, and precipitation. This study documents small drinking water system managers’ experiences during the 2012–2016 Drought. We contribute to research on water system drought resilience by elevating small drinking water system manager perspectives and expertise. We are especially focused on small systems that are not reliant on imports from state or federal water projects. A mixed-method approach ensures each data collection period informs the next to gather statewide perspectives and experiences of managers.

   Based on an analysis of drinking water manager reflections, the types of impacts, responses, and barriers differed based on both system size and water source portfolio. Common disadvantages that hinder small drinking water systems’ drought resilience and, similarly, climate adaptation include staff and administrative capacity; the financial burden of promoting water conservation over revenue compounded by onerous reporting and funding support programs; consumer awareness challenges; and challenges to consolidation from both local political differences and physical limitations. Systems that built technical, managerial, or financial capacity prior to the Drought were at an advantage over systems that lacked this capacity. In the long term, we found a dearth of adaptation planning among small water systems. Documentation of experiences from the 2012–2016 Drought can inform future planning for droughts and more broadly highlight needs for climate adaptation.

    

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3. Is a drought a drought in grasslands? Productivity responses to different types of drought

   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)

   null (Ed.)
4. Drought Impact on Desert Ecosystems, Drought Network precipitation manipulation experiment in desert grasslands

   https://doi.org/10.6073/pasta/5c2eb1c145a3babf399a0587d0189c2e  

   Ohlert, Timothy J ; Collins, Scott L ( January 2021 , Environmental Data Initiative)

   Climate change amplifies the global water cycle, making droughts more frequent and more severe. The hot deserts of the U.S. rely on the stability and frequency of water availability in order to sustain biological communities, making these ecosystems incredibly vulnerable to anticipated alterations in the water cycle. This project seeks to understand which biotic and abiotic variables are principle in determining desert ecosystem sensitivity to drought? To answer these questions, we have installed a drought manipulation that will simulate an extreme drought event by reducing annual precipitation by 66% in seven desert sites. Plant abundance data are collected annually to track changes in the plant community. Data collection began in Spring 2018. Treatments at three Sevilleta sites began in Fall 2018 after data collection in October 2018. Treatments started at four sites in Arizona and California in March and April of 2019 and spring pretreatment data collection. The treatments will last for four years. 

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5. Automated Analysis of the US Drought Monitor Maps With Machine Learning and Multiple Drought Indicators

   https://doi.org/10.3389/fdata.2021.750536  

   Hatami Bahman Beiglou, Pouyan ; Luo, Lifeng ; Tan, Pang-Ning ; Pei, Lisi ( October 2021 , Frontiers in Big Data)

   The US Drought Monitor (USDM) is a hallmark in real time drought monitoring and assessment as it was developed by multiple agencies to provide an accurate and timely assessment of drought conditions in the US on a weekly basis. The map is built based on multiple physical indicators as well as reported observations from local contributors before human analysts combine the information and produce the drought map using their best judgement. Since human subjectivity is included in the production of the USDM maps, it is not an entirely clear quantitative procedure for other entities to reproduce the maps. In this study, we developed a framework to automatically generate the maps through a machine learning approach by predicting the drought categories across the domain of study. A persistence model served as the baseline model for comparison in the framework. Three machine learning algorithms, logistic regression, random forests, and support vector machines, with four different groups of input data, which formed an overall of 12 different configurations, were used for the prediction of drought categories. Finally, all the configurations were evaluated against the baseline model to select the best performing option. The results showed that our proposed framework could reproduce the drought maps to a near-perfect level with the support vector machines algorithm and the group 4 data. The rest of the findings of this study can be highlighted as: 1) employing the past week drought data as a predictor in the models played an important role in achieving high prediction scores, 2) the nonlinear models, random forest, and support vector machines had a better overall performance compared to the logistic regression models, and 3) with borrowing the neighboring grid cells information, we could compensate the lack of training data in the grid cells with insufficient historical USDM data particularly for extreme and exceptional drought conditions. 

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