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drought and risk assessments by factoring in both science and societal impacts ( Kumar et al. 2014a ). Simulated data similar to SMAP products were used in agricultural models to show the usefulness of soil moisture for crop yield estimation at sites where the full time sequence of precipitation and other critical weather variables were not available or subject to measurements errors (El Sharif et al. 2014, manuscript submitted to J. Hydrometeor. ) and the increase in streamflow forecast skill
drought and risk assessments by factoring in both science and societal impacts ( Kumar et al. 2014a ). Simulated data similar to SMAP products were used in agricultural models to show the usefulness of soil moisture for crop yield estimation at sites where the full time sequence of precipitation and other critical weather variables were not available or subject to measurements errors (El Sharif et al. 2014, manuscript submitted to J. Hydrometeor. ) and the increase in streamflow forecast skill
is often made based on broader societal applications, it is imperative that the OSSEs also capture the potential benefits of observations on actual end-use applications. Here, the end-use applications considered are droughts and floods. The first contribution of this article is the development of an L-band OSSE to measure improvement in the estimation of the risk of drought and floods. Drought and floods are arguably the two most societally important hydrologic applications, impacting famine
is often made based on broader societal applications, it is imperative that the OSSEs also capture the potential benefits of observations on actual end-use applications. Here, the end-use applications considered are droughts and floods. The first contribution of this article is the development of an L-band OSSE to measure improvement in the estimation of the risk of drought and floods. Drought and floods are arguably the two most societally important hydrologic applications, impacting famine
atmospheric forcing prior to its application in the offline (land-only) system versus those achieved with bias-corrected streamflow products from the original seasonal forecast system. As these examples demonstrate, a given offline-modeling system can serve as a powerful test bed for addressing the science underlying streamflow prediction. Given the potential societal benefits of accurate streamflow predictions, and given the fact that many aspects of the science underlying the predictions still require
atmospheric forcing prior to its application in the offline (land-only) system versus those achieved with bias-corrected streamflow products from the original seasonal forecast system. As these examples demonstrate, a given offline-modeling system can serve as a powerful test bed for addressing the science underlying streamflow prediction. Given the potential societal benefits of accurate streamflow predictions, and given the fact that many aspects of the science underlying the predictions still require
