The Drought Monitor was started in spring 1999 in response to a need for improved information about the status of drought across the United States. It serves as an example of interagency cooperation in a time of limited resources. The Drought Monitor process also illustrates the creative use of Internet technologies to disseminate authoritative information about drought and to receive regional and local input that is in turn incorporated into the product. This paper describes the Drought Monitor and the interactive process through which it is created. The Drought Monitor was started in spring 1999 in response to a need for improved information about the status of drought across the United States. It serves as an example of interagency cooperation in a time of limited resources. The Drought Monitor process also illustrates the creative use of Internet technologies to disseminate authoritative information about drought and to receive regional and local input that is in turn incorporated into the product. This paper describes the Drought Monitor and the interactive process through which it is created.

Flash droughts are a recently recognized type of extreme event distinguished by sudden onset and rapid intensification of drought conditions with severe impacts. They unfold on subseasonal-to-seasonal timescales (weeks to months), presenting a new challenge for the surge of interest in improving subseasonal-to-seasonal prediction. Here we discuss existing prediction capability for flash droughts and what is needed to establish their predictability. We place them in the context of synoptic to centennial phenomena, consider how they could be incorporated into early warning systems and risk management, and propose two definitions. The growing awareness that flash droughts involve particular processes and severe impacts, and probably a climate change dimension, makes them a compelling frontier for research, monitoring and prediction.

Precipitation‐based drought indices are most commonly used in drought monitoring and early warning systems whereas impacts of drought are often related to other domains of the hydrological cycle such as streamflow. Precipitation droughts do not always coincide with streamflow droughts, as the propagation from precipitation to streamflow is affected by climate, catchment properties, and human influences. For monitoring in ungauged catchments it is the question to what extent drought indices solely based on precipitation or other (more recently developed) meteorological drought indices that include evaporation or snowmelt, have a stronger correlation with streamflow, and whether this correlation is weaker in catchments where streamflow is altered by human influences. Results of a correlation exercise between various meteorological drought indices and streamflow showed that the strongest correlation was often found for meteorological drought indices that include evaporation (especially in drier climates) or snow processes (especially in colder climates). Most catchments with an indicated presence of human influences showed a maximum correlation between meteorological drought indices and streamflow that was comparable in strength to the same correlation for catchments with near‐natural flow. However, up to 15% of catchments with an indicated presence of human influences show weaker correlations. Drought indices derived from these influenced records with a weaker correlation do not necessarily correspond to reported drought impacts. In conclusion, knowing which meteorological drought index has the strongest correlation with streamflow in different climate zones has the potential of improving large‐scale drought monitoring and early warning systems in ungauged areas or regions that lack real‐time streamflow availability.

The increasing drought severities and consequent devastating impacts on society over the Indian semi-arid regions demand better drought monitoring and early warning systems. Operational agricultural drought assessment methods in India mainly depend on a single input parameter such as precipitation and are based on a sparsely located in-situ measurements, which limits monitoring precision. The overarching objective of this study is to address this need through the development of an integrated agro-climatological drought monitoring approach, i.e., combined drought indicator for Marathwada (CDI_M), situated in the central part of Maharashtra, India. In this study, satellite and model-based input parameters (i.e., standardized precipitation index (SPI-3), land surface temperature (LST), soil moisture (SM), and normalized difference vegetation index (NDVI)) were analyzed at a monthly scale from 2001 to 2018. Two quantitative methods were tested to combine the input parameters for developing the CDI_M. These methods included an expert judgment-based weight of each parameter (Method-I) and principle component analysis (PCA)-based weighting approach (Method-II). Secondary data for major types of crop yields in Marathwada were utilized to assess the CDI_M results for the study period. CDI_M maps depict moderate to extreme drought cases in the historic drought years of 2002, 2009, and 2015–2016. This study found a significant increase in drought intensities (p ≤ 0.05) and drought frequency over the years 2001–2018, especially in the Latur, Jalna, and Parbhani districts. In comparison to Method-I (r ≥ 0.4), PCA-based (Method-II) CDI_M showed a higher correlation (r ≥ 0.60) with crop yields in both harvesting seasons (Kharif and Rabi). In particular, crop yields during the drier years showed a greater association (r > 6.5) with CDI_M over Marathwada. Hence, the present study illustrated the effectiveness of CDI_M to monitor agricultural drought in India and provide improved information to support agricultural drought management practices.