A Methodology for Flash Drought Identification: Application of Flash Drought Frequency across the United States

With the increasing use of the term ‘‘flash drought’’ within the scientific community, Otkin et al. provide a general definition that identifies flash droughts based on their unusually rapid rate of intensification. This study presents an objective percentile-based methodology that builds upon that work by identifying flash droughts using standardized evaporative stress ratio (SESR) values and changes in SESR over some period of time. Four criteria are specified to identify flash droughts: two that emphasize the vegetative impacts of flash drought and two that focus on the rapid rate of intensification. The methodology was applied to the North American Regional Reanalysis (NARR) to develop a 38-yr flash drought climatology (1979–2016) across the United States. It was found that SESR derived from NARR data compared well with the satellite-based evaporative stress index for four previously identified flash drought events. Furthermore, four additional flash drought cases were compared with the U.S. Drought Monitor (USDM), and SESR rapidly declined 1–2 weeks before a response was evident with the USDM. From the climatological analysis, a hot spot of flash drought occurrence was revealed over the Great Plains, the Corn Belt, and the western Great Lakes region. Relatively few flash drought events occurred over mountainous and arid regions. Flash droughts were categorized based on their rate of intensification, and it was found that the most intense flash droughts occurred over the central Great Plains, Corn Belt, and western Great Lakes region

Land Surface Temperature Estimation from the Next Generation of Geostationary Operational Environmental Satellites: GOES M–Q

The next generation of Geostationary Operational Environmental Satellites (GOES M–Q) will have only one thermal window channel instead of the current two split-window thermal channels. There is a need to evaluate the usefulness of this new configuration to retrieve parameters that presently are derived by utilizing the split-window characteristics. Two algorithms for deriving land surface temperatures (LSTs) from the GOES M–Q series have been developed and will be presented here. Both algorithms are based on radiative transfer theory; one uses ancillary total precipitable water (TPW) data, and the other is a two-channel (3.9 and 11.0 mm) algorithm that aims to improve atmospheric correction by utilizing the middle infrared (MIR) channel. The proposed algorithms are compared with a well-known generalized split-window algorithm. It is found that by adding TPW to the 11.0-mm channel, similar results to those from the generalized split-window algorithm are attained, and the combination of 3.9 and 11.0 mm yields further improvement. GOES M–Q retrievals (simulated with GOES-8 observations), when evaluated against skin temperature observations from the Oklahoma Mesonet, show that with the proposed two-channel algorithm, LST can be determined at an rms accuracy of about 2 K. The proposed algorithms are also applicable for the derivation of sea surface temperatures (SSTs) for which less restrictive assumptions on surface emissivity apply. 1

Vegetation drought monitoring from MODIS imagery and soil moisture data in Oklahoma Mesonet sites

Drought is a normal and recurrent climatic phenomenon, and is considered one of the most costly natural disasters in the United States. Grassland vegetation is sensitive to weather and climate, and persistent drought impacts goods and ecological services that grasslands provide (e.g., wildlife habitats, feedstock for the livestock industry, and recreational services). Droughts have extremely large spatial and temporal variations in areal coverage and intensity making drought monitoring a challenging task. Using soil and atmospheric data from the Oklahoma Mesonet and surface reflectance data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, this study examined the hypothesis that the satellite-derived Land Surface Water Index (LSWI) is sensitive to drought conditions and can potentially be used as an indicator or tool for drought monitoring. The sensitivity of LSWI to summer drought was first analyzed at 10 Mesonet sites that are homogeneous and representative of different types of grassland vegetation, soils and climate across Oklahoma. A summer drought event is defined, based on threshold values of LSWI and the Fractional Water Index (FWI) derived from soil moisture data at each site.Secondly, the LSWI-based drought algorithm was evaluated at103 Oklahoma Mesonet sites. Finally, the LSWI-based droughtalgorithm was used to map spatial patterns and temporal dynamicsof drought-affected land surface during 2001-2010 acrossOklahoma. The results from this study demonstrated the potentialof LSWI-based drought algorithm for tracking and mappingdrought-affected grassland vegetation in Oklahoma with 3%commission error in the Oklahoma Mesonet sites during 2001-2010.La sequía es un fenómeno climático normal y recurrente, y es considerado uno de los desastres naturales más costosos en Estados Unidos. La vegetación de pastizales es sensible al estado del tiempo y el clima, y la persistencia de la sequía afecta a los bienes y servicios ecológicos que proporcionan los pastizales (por ejemplo, son hábitats de vida silvestre, proveen materia prima para la industria ganadera, así como servicios de esparcimiento). La cobertura de área e intensidad de las sequías presentan grandes variaciones espaciales y temporales, haciendo que el monitorea de sequías sea una tarea difícil. Usando datos atmosféricos y de suelos de la Oklahoma Mesonet, y datos de reflectancia de la superficie terrestre del espectrorradiómetro de imágenes de resolución moderada (MODIS, por sus siglas en inglés) a bordo de los satélites Terra y Aqua, este estudio examinó la hipótesis de que el índice de agua de la superficie del terreno (LSWI, por sus siglas en inglés) es sensible a condiciones de sequía y potencialmente puede utilizarse como un indicador o herramienta para la monitoreo de sequías. La sensibilidad del LSWI a la sequía estival se analizó inicialmente en 10 sitios Mesonet que son homogéneos y representativos de los diferentes tipos de vegetación de pastizales, los suelos y el clima a través de Oklahoma. Un evento de sequía estival se define, en base a los valores de umbral de LSWI y el Índice de Agua fraccional (FWI) derivado de los datos de humedad del suelo en cada sitio. Posteriormente, el algoritmo de sequía basado en LSWI se evaluó en 103 sitios Oklahoma Mesonet. Por último, se utilizó el algoritmo de sequía basado en LSWI para mapear los patrones espaciales y la dinámica temporal de la superficie de la tierra afectada por la sequía durante 2001-2010 a través de Oklahoma. Los resultados de este estudio demostraron el potencial del algoritmo de sequía basado en LSWI para el seguimiento y la cartografía de vegetación de pradera afectada por la sequía en Oklahoma con un 3% de error de comisión en los sitios Oklahoma Mesonet durante 2001-201

A Methodology for Flash Drought Identification: Application of Flash Drought Frequency across the United States

With the increasing use of the term ‘‘flash drought’’ within the scientific community, Otkin et al. provide a general definition that identifies flash droughts based on their unusually rapid rate of intensification. This study presents an objective percentile-based methodology that builds upon that work by identifying flash droughts using standardized evaporative stress ratio (SESR) values and changes in SESR over some period of time. Four criteria are specified to identify flash droughts: two that emphasize the vegetative impacts of flash drought and two that focus on the rapid rate of intensification. The methodology was applied to the North American Regional Reanalysis (NARR) to develop a 38-yr flash drought climatology (1979–2016) across the United States. It was found that SESR derived from NARR data compared well with the satellite-based evaporative stress index for four previously identified flash drought events. Furthermore, four additional flash drought cases were compared with the U.S. Drought Monitor (USDM), and SESR rapidly declined 1–2 weeks before a response was evident with the USDM. From the climatological analysis, a hot spot of flash drought occurrence was revealed over the Great Plains, the Corn Belt, and the western Great Lakes region. Relatively few flash drought events occurred over mountainous and arid regions. Flash droughts were categorized based on their rate of intensification, and it was found that the most intense flash droughts occurred over the central Great Plains, Corn Belt, and western Great Lakes region

Evaluation of the North American Land Data Assimilation System over the southern Great Plains during the warm season

North American Land Data Assimilation System (NLDAS) land surface models have been run for a retrospective period forced by atmospheric observations from the Eta analysis and actual precipitation and downward solar radiation to calculate land hydrology. We evaluated these simulations using in situ observations over the southern Great Plains for the periods of May-September of 1998 and 1999 by comparing the model outputs with surface latent, sensible, and ground heat fluxes at 24 Atmospheric Radiation Measurement/Cloud and Radiation Testbed stations and with soil temperature and soil moisture observations at 72 Oklahoma Mesonet stations. The standard NLDAS models do a fairly good job but with differences in the surface energy partition and in soil moisture between models and observations and among models during the summer, while they agree quite well on the soil temperature simulations. To investigate why, we performed a series of experiments accounting for differences between model-specified soil types and vegetation and those observed at the stations, and differences in model treatment of different soil types, vegetation properties, canopy resistance, soil column depth, rooting depth, root density, snow-free albedo, infiltration, aerodynamic resistance, and soil thermal diffusivity. The diagnosis and model enhancements demonstrate how the models can be improved so that they can be used in actual data assimilation mode.</p