This collection of Matlab scripts implements the data driven approach introduced by Vishwakarma et al. 2016 and Vishwakarma et al. 2017. The method published in Vishwakarma et al. 2017 is superior to the method in Vishwakarma et al. 2016, because it can produce reliable results for small catchments also. The minimum catchment size corresponding to an RMS error of 2 cm is approx. 65 000 sq. km. These findings can be found in an open access article at : https://www.mdpi.com/2072-4292/10/6/852 .
This collection of Matlab scripts implements the data driven approach introduced by Vishwakarma et al. 2016. The method works for hydrology, and it has been demonstrated in aforementioned publication. It uses a few Matlab functions from Filter bundle and GSHS bundle, which are using the uberall bundle. Therefore we suggest that please dowload all the bundles. Please use the following citation if you use this work:
Vishwakarma, B. D., Horwath, M., Devaraju, B., Groh, A., & Sneeuw, N. (2017). A data-driven approach for repairing the hydrological catchment signal damage due to ﬁltering of GRACE products. Water Resources Research, 53, 9824–9844.doi: 10.1002/2017WR021150
Vishwakarma, B. D., B. Devaraju, and N. Sneeuw (2016), Minimizing the effects of filtering on catchment scale GRACE solutions, Water Resour. Res., 52, doi:10.1002/2016WR018960.
The mask file—Zbn.mat—has many catchments. Each catchments is denoted by a number, which is the key. One can find the corresponding catchment in the Basin_ID_Catchments.xls file and vice-versa.
Please note that this is the first release and it can only implement Gaussian filter along with a destriping filter. User can choose the filter radius and switch the destriping filter on and off.
We hope that you have a good experience. Please contact us via E-Mail for any questions.
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