ALBERT

Description: The evolution of a flash drought event, characterized by a period of rapid drought intensification, is assessed using standard drought monitoring datasets and on-the-ground reports obtained via a written survey of agricultural stakeholders after the flash drought occurred. The flash drought impacted agricultural production across a five-state region centered on the Black Hills of South Dakota during the summer of 2016. The survey asked producers to estimate when certain drought impacts, ranging from decreased soil moisture to plant stress and diminished water resources, first occurred on their land. The geographic distribution and timing of the survey responses were compared to the U.S. Drought Monitor and to datasets depicting anomalies in evapotranspiration, precipitation, and soil moisture. Overall, the survey responses showed that this event was a multifaceted drought that caused a variety of impacts across the region. Comparisons of the survey reports to the drought monitoring datasets revealed that the topsoil moisture dataset provided the earliest warning of drought development, but at the expense of a high false alarm rate. Anomalies in evapotranspiration were closely aligned to the survey reports of plant stress and also provided a more focused depiction of where the worst drought conditions were located. This study provides evidence that qualitative reports of drought impacts obtained via written surveys provide valuable information that can be used to assess the accuracy of high-resolution drought monitoring datasets.

Description: Rural towns are especially susceptible to the effects of drought because their economies are dependent on natural resources. However, they are also resilient in many ways to natural hazards because they are rich in civic engagement and social capital. Because of the diverse nature of drought’s impacts, understanding its complex dynamics and its effects requires a multidisciplinary approach. To study these dynamics, this research combines appreciative inquiry, the Community Capitals Framework, and a range of climatological monitoring data to assess the 2012–14 Great Plains drought’s effect on McCook, Nebraska. Community coping measures, such as water-use reduction and public health programs, were designed to address the immediate effects of heat and scant rainfall during the initial summer and the subsequent years. Residents generally reported the community was better prepared than in previous droughts, including the persistent multiyear early-2000s drought. However, the results highlight wide variation in community perspectives about the drought’s severity and impacts, as well as divergent experiences and coping responses. Despite these factors, we find evidence of the transformative potential of moving from drought coping to drought mitigation. We attribute the city’s resilience to the ability to draw upon prior experience with droughts, having a formal municipal plan, and strong human and social capital to coordinate individual knowledge and expertise across agencies. We suggest that droughts have served a catalytic function, prompting the community to transform land-use practices, water conservation planning, and built infrastructure in lasting ways.

Description: LDAS-Monde is a global land data assimilation system (LDAS) developed by Centre National de Recherches Météorologiques (CNRM) to monitor land surface variables (LSV) at various scales, from regional to global. With LDAS-Monde, it is possible to jointly assimilate satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the interactions between soil biosphere and atmosphere (ISBA) land surface model (LSM) in order to analyze the soil moisture profile together with vegetation biomass. In this study, we investigate LDAS-Monde’s ability to predict LSV states up to two weeks in the future using atmospheric forecasts. In particular, the impact of the initialization, and the evolution of the forecasted variables in the LSM are addressed. LDAS-Monde is an offline system normally driven by atmospheric reanalysis, but in this study is forced by atmospheric forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) for the 2017–2018 period over the contiguous United States (CONUS) at a 0.2° × 0.2° spatial resolution. These LSV forecasts are initialized either by the model alone (LDAS-Monde open-loop, without assimilation) or by the analysis (assimilation of SSM and LAI). These two forecasts are then evaluated using satellite-derived observations of SSM and LAI, evapotranspiration (ET) estimates, as well as in situ measurements of soil moisture from the U.S. Climate Reference Network (USCRN). Results indicate that for the three evaluation variables (SSM, LAI, and ET), LDAS-Monde provides reasonably accurate and consistent predictions two weeks in advance. Additionally, the initial conditions after assimilation are shown to make a positive impact with respect to LAI and ET. This impact persists in time for these two vegetation-related variables. Many model variables, such as SSM, root zone soil moisture (RZSM), LAI, ET, and drainage, remain relatively consistent as the forecast lead time increases, while runoff is highly variable.

Description: The connection between drought early warning information and the timing of rangeland managers’ response actions is not well understood. This study investigates U.S. Northern Plains range and livestock managers’ decision-making in response to the 2016 flash drought, by means of a postdrought survey of agricultural landowners and using the Protective Action Decision Model theoretical framework. The study found that managers acted in response to environmental cues, but that their responses were significantly delayed compared to when drought conditions emerged. External warnings did not influence the timing of their decisions, though on-farm monitoring and assessment of conditions did. Though this case focused only on a one-year flash drought characterized by rapid drought intensification, waiting to destock pastures was associated with greater losses to range productivity and health and diversity. This study finds evidence of unrealized potential for drought early warning information to support proactive response and improved outcomes for rangeland management.

Description: The evolution of a flash drought event, characterized by a period of rapid drought intensification, is assessed using standard drought monitoring datasets and on-the-ground reports obtained via a written survey of agricultural stakeholders after the flash drought occurred. The flash drought impacted agricultural production across a five-state region centered on the Black Hills of South Dakota during the summer of 2016. The survey asked producers to estimate when certain drought impacts, ranging from decreased soil moisture to plant stress and diminished water resources, first occurred on their land. The geographic distribution and timing of the survey responses were compared to the U.S. Drought Monitor and to datasets depicting anomalies in evapotranspiration, precipitation, and soil moisture. Overall, the survey responses showed that this event was a multifaceted drought that caused a variety of impacts across the region. Comparisons of the survey reports to the drought monitoring datasets revealed that the topsoil moisture dataset provided the earliest warning of drought development, but at the expense of a high false alarm rate. Anomalies in evapotranspiration were closely aligned to the survey reports of plant stress and also provided a more focused depiction of where the worst drought conditions were located. This study provides evidence that qualitative reports of drought impacts obtained via written surveys provide valuable information that can be used to assess the accuracy of high-resolution drought monitoring datasets.