Comparison of Agricultural Stakeholder Survey Results and Drought Monitoring Datasets during the 2016 U.S. Northern Plains Flash Drought

Abstract

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.

Drought Early Warning and the Timing of Range Managers’ Drought Response

“…Each dataset was aggregated to the zip code level for the study region. As reported in Otkin et al [29], respondents' recollection of the timing of top soil moisture depletion generally aligned with drought development as indicted by NLDAS TS.…”

“…To do so, U.S. Northern Great Plains livestock producers were surveyed about their coping decisions (related to feed, forage, and grazing pressure) in response to a 2016 flash drought. As reported in Otkin et al [29], the 2016 drought event developed by the end of March, in response to a prolonged period of warmer than normal temperatures and near-to below-normal precipitation during the preceding fall and winter. April brought wetter conditions to much of the region, but by the end of May, very dry conditions returned to most of the region, and temperatures were also much cooler than normal.…”

“…e North American Land Data Assimilation System topsoil and total column moisture (NLDAS TS and TC) provide model-based objective measures of topsoil and subsoil moisture that correlate with respondent-perceived emergence of depleted topsoil and subsoil moisture. ese measures are based on models that simulate changes in soil moisture and temperature throughout layers of the soil profile [29]. For this analysis, gridded soil moisture analyses were obtained from NLDAS at 0.1258 degree resolution [34], with soil moisture anomalies computed over 4-week time periods using data from 1979-2017.…”

“…e Evaporative Stress Index (ESI) was examined to depict moisture-related stress in vegetation, both related to vegetation health and to soil moisture availability [35]. e ESI depicts standardized anomalies in the ratio of actual to reference evapotranspiration, and deteriorating conditions as measured by ESI have been shown to correspond with the timing of respondent reports of plant stress [29]. For this analysis, the ESI was computed at 4 km horizontal grid spacing, with 4 week ESI anomalies at weekly intervals using data from 2001 to 2017 [29,35].…”

“…e ESI depicts standardized anomalies in the ratio of actual to reference evapotranspiration, and deteriorating conditions as measured by ESI have been shown to correspond with the timing of respondent reports of plant stress [29]. For this analysis, the ESI was computed at 4 km horizontal grid spacing, with 4 week ESI anomalies at weekly intervals using data from 2001 to 2017 [29,35]. Each dataset was aggregated to the zip code level for the study region.…”