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Creators/Authors contains: "Abolafia���Rosenzweig, Ronnie"

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  1. Abstract

    Drought monitoring and forecasting systems are used in the United States (U.S.) to inform drought management decisions. Drought forecasting efforts have often been conducted and evaluated at coarse spatial resolutions (i.e., >10‐km), which miss key local drought information at higher resolutions. Addressing the importance of forecasting drought at high resolutions, this study develops statistical models to evaluate 1‐ to 3‐month lead time predictability of meteorological and agricultural summer drought across the western U.S. at a 4‐km resolution. Our high‐resolution drought predictions have statistically significant skill (p ≤ 0.05) across 70%–100% of the western U.S., varying by evaluation metric and lead time. 1‐ to 3‐month lead time drought forecasts accurately represent monitored summer drought spatial patterns during major drought events, the interannual variability of drought area from 1982 to 2020 (r = 0.84–0.93), and drought trends (r = 0.94–0.97). 71% of western U.S summer drought area interannual variability can be explained by cold‐season (November–February) climate conditions alone allowing skillful 3‐month lead time predictions. Pre‐summer drought conditions (represented by drought indices) are the most important predictors for summer drought. Thus, the statistical models developed in this study heavily rely on the autocorrelation of chosen agricultural and meteorological drought indices which estimate land surface moisture memory. Indeed, prediction skill strongly correlates with persistence of drought conditions (r ≥ 0.73). This study is intended to support future development of operational drought early warning systems that inform drought management.

     
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  2. Abstract

    The Noah‐MP land surface model (LSM) relies on the Monin‐Obukhov (M‐O) Similarity Theory (MOST) to calculate land‐atmosphere exchanges of water, energy, and momentum fluxes. However, MOST flux‐profile relationships neglect canopy‐induced turbulence in the roughness sublayer (RSL) and parameterize within‐canopy turbulence in an ad hoc manner. We implement a new physics scheme (M‐O‐RSL) into Noah‐MP that explicitly parameterizes turbulence in RSL. We compare Noah‐MP simulations employing the M‐O‐RSL scheme (M‐O‐RSL simulations) and the default M‐O scheme (M‐O simulations) against observations obtained from 647 Snow Telemetry (SNOTEL) stations and two AmeriFlux stations in the western United States. M‐O‐RSL simulations of snow water equivalent (SWE) outperform M‐O simulations over 64% and 69% of SNOTEL sites in terms of root‐mean‐square‐error (RMSE) and correlation, respectively. The largest improvements in skill for M‐O‐RSL occur over closed shrubland sites, and the largest degradations in skill occur over deciduous broadleaf forest sites. Differences between M‐O and M‐O‐RSL simulated snowpack are primarily attributable to differences in aerodynamic conductance for heat underneath the canopy top, which modulates sensible heat flux. Differences between M‐O and M‐O‐RSL within‐canopy and below‐canopy sensible heat fluxes affect the amount of heat transported into snowpack and hence change snowmelt when temperatures are close to or above the melting point. The surface energy budget analysis over two AmeriFlux stations shows that differences between M‐O and M‐O‐RSL simulations can be smaller than other model biases (e.g., surface albedo). We intend for the M‐O‐RSL physics scheme to improve performance and uncertainty estimates in weather and hydrological applications that rely on Noah‐MP.

     
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