Treffer: SWAT_DA: Sequential Multivariate Data Assimilation‐Oriented Modification of SWAT.

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. Key Points: We propose a new approach to link SWAT with data assimilation (DA) algorithms based on source code modificationSnow cover fraction (SCF) data of MODIS are assimilated to estimate HRU‐scaled snow‐related parameters of SWATBivariate streamflow and MODIS SCF data assimilation mitigates the equifinality problem [ABSTRACT FROM AUTHOR]

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