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The U.S. Drought Monitor (USDM), a weekly map depicting severity and spatial extent of drought, is used to communicate about drought in state and federal decision-making, and as a trigger in response policies, including the distribution of hundreds of millions of dollars for agricultural financial relief in the United States annually. An accompanying classification table helps interpret the map and includes a column of possible impacts associated with each level of drought severity. However, the column describing potential drought impacts is generalized for the entire United States. To provide more geographically specific interpretation of drought, state and regionally specific drought impact classification tables were developed by linking impacts chronicled in the Drought Impact Reporter (DIR) to USDM severity levels across the United States and Puerto Rico and identifying recurrent themes at each level. After creating state-level tables of impacts observed for each level of drought, a nationwide survey was administered to drought experts and decision-makers (n = 89), including the USDM authors, to understand whether the tables provided accurate descriptions of drought impacts in their state. Seventy-six percent of respondents indicated the state table was an acceptable or good characterization of drought impacts for their respective state. This classification scheme was created with a reproducible qualitative methodology that used past observations to identify themes in drought impacts across multiple sectors to concisely describe expected impacts at different levels of drought in each state. [ABSTRACT FROM AUTHOR]

What: More than 80 participants, representing local government, Alaska Native communities, state and federal agencies, nongovernmental organizations, academia, businesses, utilities, and the media, met in person and remotely to gather information about drought impacts in southeast Alaska and improve understanding of what constitutes a drought in a temperate rain forest. Given that the current drought in SEAK is the most significant drought in the wet season in decades, many current residents have not experienced drought conditions of the duration and severity seen by previous generations. Drought monitoring involves the continuous assessment of natural indicators of drought severity, spatial extent, and impacts. Recent research developed a methodology to link multisector impacts to drought severity levels to develop state-specific impact tables for the USDM ([7]) based on impacts captured in the Drought Impact Reporter. [Extracted from the article]

Drought is an abstract and complex phenomenon that can be difficult for many people to comprehend. Proactive planning to improve response during drought events is necessary but complicated because it involves stakeholders and decision-makers with competing interests. A category of games, called serious games, have proven to be helpful when learning about abstract concepts, and for improving communication and conflict resolution with respect to water-related issues. In this study, we present a new in-person role-playing game that serves as a drought educational tool in both classroom and professional settings. The message of the game emphasizes the importance of communication and cooperation between various communities and sectors that can be affected by drought. Furthermore, it also encourages discussions and collaborations between stakeholders involved in drought planning and can serve as an icebreaker activity. The game has been played in a variety of settings including university classes, university extension workshops, and drought workshops. This study describes the game itself, its development, and the results of surveys from game participants that were used to evaluate the usability of the game as an educational and icebreaker activity. [ABSTRACT FROM AUTHOR]

Land management practices and disturbances (e.g. overgrazing, fire) have substantial effects on grassland forage production. When using satellite remote sensing to monitor climate impacts, such as drought stress on annual forage production, minimizing land management practices and disturbance effects sends a clear climate signal to the productivity data. This study investigates the effect of this climate signal by: (1) providing spatial estimates of expected biomass under specific climate conditions, (2) determining which drought indices explain the majority of interannual variability in this biomass, and (3) developing a predictive model that estimates the annual biomass early in the growing season. To address objective 1, this study uses an established methodology to determine Expected Ecosystem Performance (EEP) in the Nebraska Sandhills, US, representing annual forage levels after accounting for non-climatic influences. Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) data were used to approximate actual ecosystem performance. Seventeen years (2000–2016) of annual EEP was calculated using piecewise regression tree models of site potential and climate data. Expected biomass (EB), EEP converted to biomass in kg*ha−1*yr−1, was then used to examine the predictive capacity of several drought indices and the onset date of the growing season. Subsets of these indices were used to monitor and predict annual expected grassland biomass. Independent field-based biomass production data available from two Sandhills locations were used for validation of the EEP model. The EB was related to field-based biomass production (R2 = 0.66 and 0.57) and regional rangeland productivity statistics of the Soil Survey Geographic Database (SSURGO) dataset. The Evaporative Stress Index (ESI), the 3- and 6-month Standardized Precipitation Index (SPI), and the U.S. Drought Monitor (USDM), which represented moisture conditions during May, June and July, explained the majority of the interannual biomass variability in this grassland system (three-month ESI explained roughly 72% of the interannual biomass variability). A new model was developed to use drought indices from early in the growing season to predict the total EB for the whole growing season. This unique approach considers only climate-related drought signal on productivity. The capability to estimate annual EB by the end of May will potentially enable land managers to make informed decisions about stocking rates, hay purchase needs, and other management issues early in the season, minimizing their potential drought losses. [ABSTRACT FROM AUTHOR]