Multivariate data assimilation (DA), a novel way to couple big data with land surface models, was extensively employed in forecasting-reanalyzing systems (FRSs), for example, ECMWF and GLDAS. Meanwhile, most (distributed) hydrological models, like soil and water assessment tool (SWAT), have not been equipped with straightforward ways to link to DA algorithms. Therefore, it is one of the main barriers to utilizing such hydrological models in FRSs. This paper deals with multivariate DA into SWAT (DA-SWAT), which is complicated since the original model does not provide full access to the models' initial conditions (ICs) at the hydrologic response unit (HRU) scale. The preceding DA-SWAT works commonly used an integrated approach in which the DA and SWAT codes were implemented in the same programming environment. We discuss how this approach complicates and prevents the application of DA-SWAT in multivariate, multimodel, and multisensor systems. Accordingly, we proposed a new approach for DA-SWAT by which SWAT can be perfectly linked with any DA algorithm of interest coded in any desired programming environment. Our framework utilizes input/output text files to access ICs and to link DA with SWAT. Moreover, we designed some univariate and multivariate scenarios for assimilating in situ streamflow measurement and MODIS's snow cover fraction (SCF) data, which has not yet been focused on in the SWAT calibration context. Results show that compared to the univariate assimilation of streamflow (SCF), the multivariate assimilation mitigates the equifinality problem and more accurately estimates SCF (streamflow) by improving NS and PBIAS measures with the differences of 0.4 (0.86), 12% (64%), respectively.

Black carbon (BC) is an important aerosol constituent in the atmosphere and climate forcer. A good understanding of the radiative forcing of BC and associated climate feedback and response is critical to minimize the uncertainty in predicting current and future climate influenced by anthropogenic aerosols. One reason for this uncertainty is that current emission inventories of BC are mostly obtained from the so-called bottom-up approach, an approach that derives emissions based on categorized emitting sources and emission factors used to convert burning mass to emissions. In this work, we provide a first global-scale top-down estimation of global BC emissions, as well as an estimated error range, by using a Kalman Filter. This method uses data of both column aerosol absorption optical depth and surface concentrations from global and regional networks to constrain our fully coupled climate-aerosol-urban model and thus to derive an optimized estimate of BC emissions as 17.85.6 Tg/yr, a factor of more than 2 higher than commonly used global BC emissions data sets. We further perform 22 additional optimization simulations that incorporate the known uncertain ranges of various important physical, model, and measurement parameters and still yield an optimized value within the above given range, from a low of 14.6 Tg/yr to a high of 22.2 Tg/yr. Furthermore, we show that the emissions difference between our optimized and a priori estimation is not uniform, with East Asia, Southeast Asia, and Eastern Europe underestimated, while North America is overestimated in the a priori inventory.