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.
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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.
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- Award ID(s):
- 1633831
- NSF-PAR ID:
- 10279141
- 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
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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.
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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
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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
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Abstract Land management can moderate or intensify the impacts of a warming atmosphere. Since the early 1980s, nearly 116,000 km2of cropland that was once held in fallow during the summer is now planted in the northern North American Great Plains. To simulate the impacts of this substantial land cover change on regional climate processes, convection‐permitting model experiments using the Weather Research and Forecasting model were performed to simulate modern and historical amounts of summer fallow. The control simulation was extensively validated using multiple observational data products as well as eddy covariance tower observations. Results of these simulations show that the transition from summer fallow to modern land cover led to ∼1.5°C cooler temperatures and decreased vapor pressure deficit by ∼0.15 kPa during the growing season across the study region, which is consistent with observed cooling trends. The cooler and wetter land surface with vegetation led to a shallower planetary boundary layer and lower lifted condensation level, creating conditions more conducive to convective cloud formation and precipitation. Our model simulations however show little widespread evidence of land surface changes effects on precipitation. The observed precipitation increase in this region is more likely related to increased moisture transport by way of the Great Plains Low Level Jet as revealed by the ERA5 reanalysis. Our results demonstrate that land cover change is consistent with observed regional cooling in the northern North American Great Plains but changes in precipitation cannot be explained by land management alone.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1175/BAMS-D-19-0272.1
