Droughts can be characterized by their severity, frequency and duration, and areal extent. Depth–area–duration analysis, widely used to characterize precipitation extremes, provides a basis for the evaluation of drought severity when storm depth is replaced by an appropriate measure of drought severity. Gridded precipitation and temperature data were used to force a physically based macroscale hydrologic model at 1/2° spatial resolution over the continental United States, and construct a drought history from 1920 to 2003 based on the model-simulated soil moisture and runoff. A clustering algorithm was used to identify individual drought events and their spatial extent from monthly summaries of the simulated data. A series of severity–area–duration (SAD) curves were constructed to relate the area of each drought to its severity. An envelope of the most severe drought events in terms of their SAD characteristics was then constructed. The results show that (a) the droughts of the 1930s and 1950s were the most severe of the twentieth century for large areas; (b) the early 2000s drought in the western United States is among the most severe in the period of record, especially for small areas and short durations; (c) the most severe agricultural droughts were also among the most severe hydrologic droughts, however, the early 2000s western U.S. drought occupies a larger portion of the hydrologic drought envelope curve than does its agricultural companion; and (d) runoff tends to recover in response to precipitation more quickly than soil moisture, so the severity of hydrologic drought during the 1930s and 1950s was dampened by short wet spells, while the severity of the early 2000s drought remained high because of the relative absence of these short-term phenomena.

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Using observation-driven simulations of global terrestrial hydrology and a cluster algorithm that searches for spatially connected regions of soil moisture, the authors identified 296 large-scale drought events (greater than 500 000 km2 and longer than 3 months) globally for 1950–2000. The drought events were subjected to a severity–area–duration (SAD) analysis to identify and characterize the most severe events for each continent and globally at various durations and spatial extents. An analysis of the variation of large-scale drought with SSTs revealed connections at interannual and possibly decadal time scales. Three metrics of large-scale drought (global average soil moisture, contiguous area in drought, and number of drought events shorter than 2 years) are shown to covary with ENSO SST anomalies. At longer time scales, the number of 12-month and longer duration droughts follows the smoothed variation in northern Pacific and Atlantic SSTs. Globally, the mid-1950s showed the highest drought activity and the mid-1970s to mid-1980s the lowest activity. This physically based and probabilistic approach confirms well-known droughts, such as the 1980s in the Sahel region of Africa, but also reveals many severe droughts (e.g., at high latitudes and early in the time period) that have received relatively little attention in the scientific and popular literature.

[1] The effects of forest canopies on snow accumulation and ablation processes can be very important for the hydrology of midlatitude and high-latitude areas. A mass and energy balance model for snow accumulation and ablation processes in forested environments was developed utilizing extensive measurements of snow interception and release in a maritime mountainous site in Oregon. The model was evaluated using 2 years of weighing lysimeter data and was able to reproduce the snow water equivalent (SWE) evolution throughout winters both beneath the canopy and in the nearby clearing, with correlations to observations ranging from 0.81 to 0.99. Additionally, the model was evaluated using measurements from a Boreal Ecosystem-Atmosphere Study (BOREAS) field site in Canada to test the robustness of the canopy snow interception algorithm in a much different climate. Simulated SWE was relatively close to the observations for the forested sites, with discrepancies evident in some cases. Although the model formulation appeared robust for both types of climates, sensitivity to parameters such as snow roughness length and maximum interception capacity suggested the magnitude of improvements of SWE simulations that might be achieved by calibration.

Surface water elevation profiles for a reach of the Ohio River were produced by the Jet Propulsion Laboratory Instrument Simulator to represent satellite measurements representative of those that would be observed by a wide swath altimeter being considered jointly by U.S. and European space agencies. The Ensemble Kalman filter with a river hydrodynamics model as its dynamical core was used to assimilate the water elevation synthetic observations, and to estimate river discharge. The filter was able to recover water depth and discharge, reducing the discharge RMSE from 23.2% to 10.0% over an 84‐day simulation period, relative to a simulation without assimilation. An autoregressive error model was instrumental in correcting boundary inflows, and increasing the persistence of error reductions between times of observations. The nominal 8‐day satellite overpass produced discharge relative errors of 10.0%, while 16‐day and 32‐day overpass frequencies resulted in errors of 12.1% and 16.9% respectively.