skip to main content


Title: Lessons Learned from the 2017 Flash Drought across the U.S. Northern Great Plains and Canadian Prairies
Abstract The 2017 flash drought arrived without early warning and devastated the U.S. northern Great Plains region comprising Montana, North Dakota, and South Dakota and the adjacent Canadian Prairies. The drought led to agricultural production losses exceeding $2.6 billion in the United States, widespread wildfires, poor air quality, damaged ecosystems, and degraded mental health. These effects motivated a multiagency collaboration among academic, tribal, state, and federal partners to evaluate drought early warning systems, coordination efforts, communication, and management practices with the goal of improving resilience and response to future droughts. This essay provides an overview on the causes, predictability, and historical context of the drought, the impacts of the drought, opportunities for drought early warning, and an inventory of lessons learned. Key lessons learned include the following: 1) building partnerships during nondrought periods helps ensure that proper relationships are in place for a coordinated and effective drought response; 2) drought information providers must improve their understanding of the annual decision cycles of all relevant sectors, including, and beyond, direct impacts in agricultural sectors; and 3) ongoing monitoring of environmental conditions is vital to drought early warning, given that seasonal forecasts lack skill over the northern Great Plains.  more » « less
Award ID(s):
1633831
NSF-PAR ID:
10279141
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ; ; ; ; ;
Date Published:
Journal Name:
Bulletin of the American Meteorological Society
Volume:
101
Issue:
12
ISSN:
0003-0007
Page Range / eLocation ID:
E2171 to E2185
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract. Flash droughts tend to be disproportionately destructive because theyintensify rapidly and are difficult to prepare for. We demonstrate that the2017 US Northern Great Plains (NGP) flash drought was preceded by abreakdown of land–atmosphere coupling. Severe drought conditions in the NGPwere first identified by drought monitors in late May 2017 and rapidlyprogressed to exceptional drought in July. The likelihood of convectiveprecipitation in May 2017 in northeastern Montana, however, resembled that ofa typical August when rain is unlikely. Based on the lower tropospherichumidity index (HIlow), convective rain was suppressed by theatmosphere on nearly 50% of days during March in NE Montana and centralNorth Dakota, compared to 30% during a normal year. Micrometeorologicalvariables, including potential evapotranspiration (ETp), were neither anomalouslyhigh nor low before the onset of drought. Incorporating convective likelihoodto drought forecasts would have noted that convective precipitation in theNGP was anomalously unlikely during the early growing season of 2017. It maytherefore be useful to do so in regions that rely on convectiveprecipitation.

     
    more » « less
  2. Flash drought often leads to devastating effects in multiple sectors and presents a unique challenge for drought early warning due to its sudden onset and rapid intensification. Existing drought monitoring and early warning systems are based on various hydrometeorological variables reaching thresholds of unusually low water content. Here, we propose a flash drought early warning approach based on spaceborne measurements of solar-induced chlorophyll fluorescence (SIF), a proxy of photosynthesis that captures plant response to multiple environmental stressors. Instead of negative SIF anomalies, we focus on the subseasonal trajectory of SIF and consider slower-than-usual increase or faster-than-usual decrease of SIF as an early warning for flash drought onset. To quantify the deviation of SIF trajectory from the climatological norm, we adopt existing formulas for a rapid change index (RCI) and apply the RCI analysis to spatially downscaled 8-d SIF data from GOME-2 during 2007–2018. Using two well-known flash drought events identified by the operational US Drought Monitor (in 2012 and 2017), we show that SIF RCI can produce strong predictive signals of flash drought onset with a lead time of 2 wk to 2 mo and can also predict drought recovery with several weeks of lead time. While SIF RCI shows great early warning potential, its magnitude diminishes after drought onset and therefore cannot reflect the current drought intensity. With its long lead time and direct relevance for agriculture, SIF RCI can support a global early warning system for flash drought and is especially useful over regions with sparse hydrometeorological data. 
    more » « less
  3. null (Ed.)
    The ornate box turtle (Terrapene ornata Agassiz) is a species of greatest conservation need in South Dakota. Habitat loss through agricultural development and fragmentation is the main threat to the species throughout its range, which extends from Wisconsin and northern Indiana through the central Great Plains, and from southern South Dakota to Arizona, northern Mexico, and the Gulf Coast of Texas. The objectives of this study were to determine the ornate box turtle’s preferred vegetation characteristics (microhabitat) compared to the available habitat (macrohabitat) on the Pine Ridge Reservation, South Dakota Sandhills region, during 2010–2011. In both years, using a modified Robel pole method, we determined that turtles selected microhabitat with greater visual obstruction readings (VORs) than those within the random available macrohabitat (P < 0.01), with means of 22 cm and 15 cm, respectively. Higher VOR values indicate greater vegetation height and/or density. Canopy cover results showed that ornate box turtles exhibited high selection (P < 0.01) for sand sagebrush (Artemisia filifolia Torr.) coverage (38%) but selected lower cover than available within the macrohabitat for total grasses (37%), total forbs (19%), and bare ground (14%). Shrubs, such as sand sagebrush, are an important component of box turtle microhabitat, as they facilitate thermoregulation by providing cool areas during the summer and favorable hibernation sites during the winter. Shrub coverage is highly recommended for consideration when developing habitat management plans that aim to increase or sustain ornate box turtle populations in the Sandhills ecological type. 
    more » « less
  4. The Ogallala Aquifer is one of the most productive agricultural regions and is referred to as the “breadbasket of the world”. It covers approximately 225,000 square miles beneath the Great Plains region spanning the states of Texas, New Mexico, Oklahoma, Kansas, Nebraska, South Dakota, Wyoming, and Colorado. The aquifer is a major water source for the region, with its use exceeding recharge. Previous studies have documented climate changes and their impacts in the region. However, this is the first study to document temperature and precipitation changes over the entire Ogallala region from 35 General Circulation Models participating in Phase 5 of the Climate Model Intercomparison Project (CMIP5). The main study objectives were (1) to provide estimates of present and future climate change scenarios for the High Plains Aquifer, (2) to translate the temperature and precipitation changes to agro-ecosystem indicator changes for Kansas using scenario funnels, and (3) to make recommendations for water resource and ecosystem managers to enable effective planning for the future availability of ecosystem services. The temperature change ranged from −4 °C to 8 °C, while the precipitation changes were between −50% to +50% over the region. This study improves the understanding of climate change on water resources and agro-ecosystems. This knowledge can be used to evaluate similar resources where the replenishment rate is slow. 
    more » « less
  5. Jianguo (Ed.)
    Yellow sweetclover (Melilotus officinalis; YSC) is an invasive biennial legume that bloomed across the Northern Great Plains in 2018–2019 in response to above-average precipitation. YSC can increase nitrogen (N) levels and potentially cause substantial changes in the composition of native plant species communities. There is little knowledge of the spatiotemporal variability and conditions causing substantial widespread blooms of YSC across western South Dakota (SD). We aimed to develop a generalized prediction model to predict the relative abundance of YSC in suitable habitats across rangelands of western South Dakota for 2019. Our research questions are: (1) What is the spatial extent of YSC across western South Dakota? (2) Which model can accurately predict the habitat and percent cover of YSC? and (3) What significant biophysical drivers affect its presence across western South Dakota? We trained machine learning models with in situ data (2016–2021), Sentinel 2A-derived surface reflectance and indices (10 m, 20 m) and site-specific variables of climate, topography, and edaphic factors to optimize model performance. We identified moisture proxies (Shortwave Infrared reflectance and variability in Tasseled Cap Wetness) as the important predictors to explain the YSC presence. Land Surface Water Index and variability in summer temperature were the top predictors in explaining the YSC abundance. We demonstrated how machine learning algorithms could help generate valuable information on the spatial distribution of this invasive plant. We delineated major YSC hotspots in Butte, Pennington, and Corson Counties of South Dakota. The floodplains of major rivers, including White and Bad Rivers, and areas around Badlands National Park also showed a higher occurrence probability and cover percentage. These prediction maps could aid land managers in devising management strategies for the regions that are prone to YSC outbreaks. The management workflow can also serve as a prototype for mapping other invasive plant species in similar regions. 
    more » « less