Search

Recent years have seen growing appreciation that rapidly intensifying flash droughts are significant climate hazards with major economic and ecological impacts. This has motivated efforts to inventory, monitor, and forecast flash drought events. Here we consider the question of whether the term "flash drought" comprises multiple distinct classes of event, which would imply that understanding and forecasting flash droughts might require more than one framework. To do this, we first extend and evaluate a soil moisture volatility–based flash drought definition that we introduced in previous work and use it to inventory the onset dates and severity of flash droughts across the contiguous United States (CONUS) for the period 1979–2018. Using this inventory, we examine meteorological and land surface conditions associated with flash drought onset and recovery. These same meteorological and land surface conditions are then used to classify the flash droughts based on precursor conditions that may represent predictable drivers of the event. We find that distinct classes of flash drought can be diagnosed in the event inventory. Specifically, we describe three classes of flash drought: "dry and demanding" events for which antecedent evaporative demand is high and soil moisture is low, "evaporative" events with more modest antecedent evaporative demand and soil moisture anomalies, but positive antecedent evaporative anomalies, and "stealth" flash droughts, which are different from the other two classes in that precursor meteorological anomalies are modest relative to the other classes. The three classes exhibit somewhat different geographic and seasonal distributions. We conclude that soil moisture flash droughts are indeed a composite of distinct types of rapidly intensifying droughts, and that flash drought analyses and forecasts would benefit from approaches that recognize the existence of multiple phenomenological pathways. [ABSTRACT FROM AUTHOR]

Abstract: In coastal and estuarine environments, near-surface salinity varies significantly in space and time. As absolute salinity and salinity gradients are central to many physical and ecological processes in these environments, reliable and consistent salinity estimates are a priority for marine research and application communities. Satellite remote sensing has a great potential to meet this need, yet sensors and algorithms designed to monitor open ocean salinity are typically ill-suited for high resolution applications to coastlines and estuaries. Here we present results of multiple statistical models that predict daily, gridded surface salinity at 1km resolution across Chesapeake Bay as a function of level 2 surface reflectance estimates from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS), onboard the Aqua platform. Eight statistical methods were tested and it was found that sea surface salinity can be accurately predicted via remote sensed products with an accuracy that is more than sufficient for many physical and ecological applications. For the best-performing statistical model, mean absolute error was 1.82 relative to mean Chesapeake Bay salinity of 16.5. [Copyright &y& Elsevier]