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Dongyue Li, Dennis P. Lettenmaier, Steven A. Margulis, and Konstantinos Andreadis

Abstract

Previous studies have shown limited success in improving streamflow forecasting for snow-dominated watersheds using physically based models, primarily due to the lack of reliable snow water equivalent (SWE) information. Here we use a hindcasting approach to evaluate the potential benefit that a high-resolution, spatiotemporally continuous, and accurate SWE reanalysis product would have on the seasonal streamflow forecast in the snow-dominated Sierra Nevada mountains of California if such an SWE product were available in real time. We tested the efficacy of a physically based ensemble streamflow prediction (ESP) framework when initialized with the reanalysis SWE. We reinitialized the SWE over the Sierra Nevada at the time when the Sierra Nevada had domain-wide annual maximum SWE for each year in 1985–2015, and on 1 February of the driest years within the same period. The early season forecasts on 1 February provide valuable lead time for mitigating the impact of drought. In both experiments, initializing the ESP with the reanalysis SWE reduced the seasonal streamflow forecast errors; compared with existing operational statistical forecasts, the peak-annual SWE insertion and the 1 February SWE insertion reduced the overall root-mean-square error of the seasonal streamflow forecasts by 13% and 23%, respectively, over the 13 major rivers draining the Sierra Nevada. The benefits of the reanalysis SWE insertion are more pronounced in areas with greater snow accumulation, while the complex snow and runoff-generation processes in low-elevation areas impede the forecasting skill improvement through SWE reinitialization alone.

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Lu Su, Qian Cao, Mu Xiao, David M. Mocko, Michael Barlage, Dongyue Li, Christa D. Peters-Lidard, and Dennis P. Lettenmaier

Abstract

We examine the drought variability over the conterminous United States (CONUS) for 1915–2018 using the Noah-MP land surface model. We examine different model options on drought reconstruction, including optional representation of groundwater and dynamic vegetation phenology. Over our 104-yr reconstruction period, we identify 12 great droughts that each covered at least 36% of CONUS and lasted for at least 5 months. The great droughts tend to have smaller areas when groundwater and/or dynamic vegetation are included in the model configuration. We detect a small decreasing trend in dry area coverage over CONUS in all configurations. We identify 45 major droughts in the baseline (with a dry area coverage greater than 23.6% of CONUS) that are, on average, somewhat less severe than great droughts. We find that representation of groundwater tends to increase drought duration for both great and major droughts, primarily by leading to earlier drought onset (some due to short-lived recovery from a previous drought) or later demise (groundwater anomalies lag precipitation anomalies). In contrast, representation of dynamic vegetation tends to shorten major droughts duration, primarily due to earlier drought demise (closed stoma or dead vegetation reduces ET loss during droughts). On a regional basis, the U.S. Southwest (Southeast) has the longest (shortest) major drought durations. Consistent with earlier work, dry area coverage in all subregions except the Southwest has decreased. The effects of groundwater and dynamic vegetation vary regionally due to differences in groundwater depths (hence connectivity with the surface) and vegetation types.

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