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Vegetation growth is one of the important indicators of drought events. Greenness-related vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) are often used for the assessment of agricultural drought. There is a need to evaluate the sensitivity of water-related vegetation indices such as Land Surface Water Index (LSWI) to assess drought and associated impacts. Moderate-Resolution Imaging Spectroradiometer (MODIS) derived time series NDVI, EVI and LSWI data during 2000–2013 were compared for their sensitivity to drought at two tallgrass prairie sites in the Oklahoma Mesonet (Marena and El Reno). Each site has continuous soil moisture measurements at three different depths (5, 25 and 60 cm) and precipitation data for the study period (2000–2013) at 5-min intervals. As expected, averaged values of vegetation indices consistently lower under drought conditions than normal conditions. LSWI decreased the most in drought years (2006, 2011 and 2012) when compared to its magnitudes in pluvial years (2007, 2013), followed by EVI and NDVI, respectively. Because green vegetation has positive LSWI values (>0) and dry vegetation has negative LSWI values (<0), much longer durations of LSWI < 0 were found in the summer periods of drought years rather than in pluvial years. A LSWI-based drought severity scheme (LSWI > 0.1; 0 < LSWI ⩽ 0.1; −0.1 < LSWI ⩽ 0; LSWI ⩽ −0.1) corresponded well with the drought severity categories (0; D0; D1: D2; D3 and D4) defined by the United States Drought Monitor (USDM) at these two study sites. Our results indicate that the number of days with LSWI < 0 during the summer and LSWI-based drought severity scheme can be simple, effective and complementary indicator for assessing drought in tallgrass prairie grasslands at a 500-m spatial resolution. [ABSTRACT FROM AUTHOR]

This paper describes important characteristics of an uncoupled high-resolution land data assimilation system (HRLDAS) and presents a systematic evaluation of 18-month-long HRLDAS numerical experiments, conducted in two nested domains (with 12- and 4-km grid spacing) for the period from 1 January 2001 to 30 June 2002, in the context of the International H2O Project (IHOP_2002). HRLDAS was developed at the National Center for Atmospheric Research (NCAR) to initialize land-state variables of the coupled Weather Research and Forecasting (WRF)–land surface model (LSM) for high-resolution applications. Both uncoupled HRDLAS and coupled WRF are executed on the same grid, sharing the same LSM, land use, soil texture, terrain height, time-varying vegetation fields, and LSM parameters to ensure the same soil moisture climatological description between the two modeling systems so that HRLDAS soil state variables can be used to initialize WRF–LSM without conversion and interpolation. If HRLDAS is initialized with soil conditions previously spun up from other models, it requires roughly 8–10 months for HRLDAS to reach quasi equilibrium and is highly dependent on soil texture. However, the HRLDAS surface heat fluxes can reach quasi-equilibrium state within 3 months for most soil texture categories. Atmospheric forcing conditions used to drive HRLDAS were evaluated against Oklahoma Mesonet data, and the response of HRLDAS to typical errors in each atmospheric forcing variable was examined. HRLDAS-simulated finescale (4 km) soil moisture, temperature, and surface heat fluxes agreed well with the Oklahoma Mesonet and IHOP_2002 field data. One case study shows high correlation between HRLDAS evaporation and the low-level water vapor field derived from radar analysis. [ABSTRACT FROM AUTHOR]

Flash droughts are characterized by a period of rapid intensification over sub-seasonal time scales that culminates in the rapid emergence of new or worsening drought impacts. This study presents a new flash drought intensity index (FDII) that accounts for both the unusually rapid rate of drought intensification and its resultant severity. The FDII framework advances our ability to characterize flash drought because it provides a more complete measure of flash drought intensity than existing classification methods that only consider the rate of intensification. The FDII is computed using two terms measuring the maximum rate of intensification (FD_INT) and average drought severity (DRO_SEV). A climatological analysis using soil moisture data from the Noah land surface model from 1979–2017 revealed large regional and interannual variability in the spatial extent and intensity of soil moisture flash drought across the US. Overall, DRO_SEV is slightly larger over the western and central US where droughts tend to last longer and FD_INT is ~75% larger across the eastern US where soil moisture variability is greater. Comparison of the FD_INT and DRO_SEV terms showed that they are strongly correlated (r = 0.82 to 0.90) at regional scales, which indicates that the subsequent drought severity is closely related to the magnitude of the rapid intensification preceding it. Analysis of the 2012 US flash drought showed that the FDII depiction of severe drought conditions aligned more closely with regions containing poor crop conditions and large yield losses than that captured by the intensification rate component (FD_INT) alone. [ABSTRACT FROM AUTHOR]