Intense periods of drought across the western U.S. present severe threats to a wide range of shareholders tasked with managing natural resources. In an era of intensifying human-driven climate change, the severity and frequency of these droughts will likely increase ( Cravens et al., 2021 ). While managing water is a critical approach to mitigating and responding to drought, water management does not encompass the wide array of impacts, management strategies, and shareholders related to drought management. For example, aridification from lack of precipitation has agricultural and natural resource implications. Effective drought response involves interdisciplinary collaboration and the expertise and experience of diverse actors including private landowners, business owners, scientists, non-governmental organizations (NGOs), and managers and policymakers within Tribal, local, state, and federal government agencies. However, it is not clear how their differing professional, cultural, educational, and jurisdictional expertise can complicate collaboration. A team of researchers funded by the Climate Adaptation Science Centers (CASCs) sought to understand the strengths, weaknesses, and outcomes of these interdisciplinary collaborations to understand how and why the diverse actors involved in making decisions about drought take certain actions. Their analysis resulted in a typology to support actors navigating complex drought management projects that accounts for multiple scales and dynamics of drought choices. Typologies help capture important aspects of a concept, process, activity, or network ( Cravens et al., 2021 ) to provide a high-level understanding of that concept, process, activity, or network. Without a drought decision-making typology, issues that arise from complexity and subject-matter discrepancies may remain unchecked. Using a typology, actors can streamline the process of understanding the complexity of their drought issue and correct potential stakeholder exclusion, unexpected impacts, or partner misunderstandings early in project/management plan development phases. 

This guide is intended to provide managers, decision makers, and other practitioners with advice on conducting a rapid assessment of the social dimensions of drought. Findings from a rapid assessment can provide key social context that may aid in decision making, such as when preparing a drought plan, allocating local drought resilience funding, or gathering the support of local agencies and organizations for collective action related to drought mitigation. Part I—In the introduction to Part I, we describe the unique problems associated with drought—particularly its slow onset and long duration, which make it difficult to define drought—and highlight five major types of drought (see Box 1). We introduce a few social dimensions of drought (such as economic and institutional perspectives), demonstrate how these dimensions can be interrelated, and describe a few of the modern challenges (such as transformational change and cascading risks) that practitioners face. We also provide background on the rapid assessment method, first describing it as a “snapshot” of the social landscape, then providing some key advantages of the method (it can be quicker and cheaper than more in-depth methods), and lastly describing how secondary data and other methods can help overcome some of the disadvantages of rapid assessments. Then, after summarizing the process of developing this guide, we outline the process of using the guide. Importantly, we compare the guide to a travel guide, which provides many different types of information and is best approached with specific interests in mind. Ultimately, we hope for this guide to be malleable enough that it can be helpful to researchers and practitioners in many different contexts, using many different research methods. Related to how to use the guide, we characterize the type of person who might be motivated to use this guide. We also specify key qualifications for a researcher conducting a rapid assessment, drawing particular attention to training on ethical considerations. We sketch out key considerations when choosing social dimensions of drought to focus on, and the type of data used for analysis. First, it is important to note that in this guide we provide nine important social dimensions of drought, but this is by no means a comprehensive list, and a researcher may find that other dimensions better fit their local context. Second, we provide some pros and cons to a narrow (focusing on just a few dimensions or at a smaller scale) versus broad research focus. Lastly, we describe the pros and cons of using primary versus secondary data (one strategy is to use both, sequentially) and qualitative versus quantitative data. Ultimately, Part I of this guide functions as an exploration of the various decisions a researcher will make when designing a rapid assessment. These decisions will inform the type of findings and other outcomes that result from the rapid assessment. Part II—Part II of this guide introduces nine key social dimensions of drought: defining the problem of drought, individual perceptions, social relationships, technology, economics and livelihoods, water governance, decision making, information, and social vulnerability. Each section provides background and key considerations related to a particular dimension, as well as ideas for how to explore the dimension via a rapid assessment. Part III—Part III of this guide provides two hypothetical examples of how one might use this guide to aid the practitioner in implementing the lessons learned here. In the first example, a watershed group uses two dimensions, defining the problem of drought and social relationships, to inform a community meeting about protecting fisheries from drought. In the second example, a resource manager uses the economics and livelihoods and social vulnerability dimensions to inform the development of a livestock grazing drought management plan.

In this study, we combined two actual evapotranspiration datasets (ET), one obtained from a root zone water balance model and another from an energy balance model, to partition annual ET into green (rainfall-based) and blue (surface/groundwater) water sources. Time series maps of green water ET (GWET) and blue water ET (BWET) are produced for the conterminous United States (CONUS) over 2001–2015.

Paleohydrologic records provide a valuable perspective on the variability of streamflow and hydroclimate that is critical for water resource planning and placing present day and future conditions into a long-term context. Until now, key insights gained from streamflow reconstructions in the other river basins across the Western U.S. have been lacking in the Milk and St. Mary River Basin. Here we utilize a new database of naturalized streamflow records for the Milk and St. Mary Rivers and an expanded network of tree-ring records from the region to reconstruct streamflow at eight gaging locations located in the mountains, foothills, and plains reaches of the basins. The network of streamflow reconstructions presented here were generated for use by the Bureau of Reclamation and Montana Department of Natural Resources and Conservation in the Basin Study update for the Milk and St. Mary Rivers, and provides important data resources to water managers balancing increasing water demands for hydropower, irrigation, and ecological resources with increasing drought and flood risk in the basin.

Wildfires and housing development have increased since the 1990s, presenting unique challenges for fire management. However, it is unclear how the relative influences of housing growth and changing wildfire occurrence have contributed to risk to homes. We fit a random forest using weather, land cover, topography, and past fire history to predict burn probabilities and uncertainty intervals. Then, we estimated risk at 1-km resolution and monthly intervals from 1990 through 2019 by combining predicted burn probabilities with housing density across the Southern Rocky Mountains. We used 3 scenarios to evaluate how housing growth and changes in burn probability influenced risk individually and combined (observed, 1990 housing, and 1990 weather). This data release includes python scripts used for all processing steps and a readme file describing where to acquire original datasets used by the random forest model, instructions for running the python scripts, and descriptions of outputs. Preprocessed model inputs were too large to share. However, raster layers are included for modeled burn probability and risk for the 3 scenarios.