Matching process based modelling and remote sensing

This blog post summarizes a recent discussion I have had with Luca Brocca concerning the use of remote sensing data in hydrological applications. As you know, I have expertise on the description of the hydrological processes and on the development/implementation of physically-based hydrological models (i.e., GEOtop [1,2]). Luca has experience on the use and assimilation of remote sensing products of soil moisture and rainfall into hydrological model for improving hydrological predictions (e.g. flood [3] and landslide [4] forecasting).

Our starting viewpoints on remote sensing products are quite dissimilar. Luca has a lot of confidence on satellite data and he found in his research activity that remote sensing could be highly important for improving the modelling and prediction of hydrological processes (see, e.g., his recent interview on Research Gate and [5]). On my side, I believe that remote sensing products are derived from sensors data without strong reference to reasonable hydrological modelling. In fact, most of the times, remote sensing products are not the results of assimilation (or fusion) of data with hydrological models, but the outcome of procedures that may involve strong hydrological assumptions that remain implicit. This in my view constitutes a bad practice and a source of large mismatching between the results of the two communities.

In a recent post, it is highlighted how even physically-based and fully 3D hydrological models may fail in reproducing the spatial variability of soil moisture (e.g., [6]), and similar results were found in the comparison of satellite and modelled soil moisture data (e.g. [7]). Studies that attempted to used satellite rainfall data as input (see in [8]) or assimilated satellite soil moisture data [9] into rainfall-runoff models usually found several issues that still need to be addressed.

These issues can be addressed in two ways. I told Luca that a model of the sensor should be available from the side of the hydrological models, in which the process-based models can give all the information necessary to reproduce the expected results as seen from the sensors. In this way, a more direct assimilation could be made without undeclared passages that introduce bias in the products. Luca replied on the need to improve the structure of conceptual hydrological models (usually employed in most of the studies) to better fit what is measured from satellite sensors (see the discussion paragraph in [3]). In both cases, we are suggesting that the two communities, hydrologists and remote sensing scientists, should start a stronger and closer collaboration. It should not happen that hydrologists use satellite data simply as end-user without giving feedback to remote sensing scientists, and viceversa remote sensing scientists should take care of the suggestions and criticism made by hydrologists. From the close collaboration both communities can highly benefit providing improved models and satellite products each other!

REFERENCES

[1] Rigon, R., Bertoldi, G., and T.M. Over,  GEOtop: A distributed hydrological model with coupled water and energy budgets, Jour. of Hydrommet. , 7(3), 371-388, 2006

[2] Endrizzi, S., Gruber, S., Dall'Amico, M., and Rigon, R. (2014). GEOtop 2.0: simulating the combined energy and water balance at and below the land surface accounting for soil freezing, snow cover and terrain effect. Geosci. Model Dev., 7, 2831-2857, doi:10.5194/gmd-7-2831-2014.

[3] Brocca, L., Moramarco, T., Melone, F., Wagner, W., Hasenauer, S., Hahn, S. (2012). Assimilation of surface and root-zone ASCAT soil moisture products into rainfall-runoff modelling. IEEE Transactions on Geoscience and Remote Sensing, 50(7), 2542-2555, doi:10.1109/TGRS.2011.2177468.

[4] Brocca, L., Ponziani, F., Moramarco, T., Melone, F., Berni, N., Wagner, W. (2012). Improving landslide forecasting using ASCAT-derived soil moisture data: A case study of the Torgiovannetto landslide in central Italy. Remote Sensing, 4(5), 1232-1244, doi:10.3390/rs4051232.

[5] Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., Levizzani, V. (2014). Soil as a natural rain gauge: estimating global rainfall from satellite soil moisture data. Journal of Geophysical Research, 119(9), 5128-5141, doi:10.1002/2014JD021489.

[6] Cornelissen, T., Diekkrüger, B. Bogena, H.R. (2014). Significance of scale and lower boundary condition in the 3D simulation of hydrological processes and soil moisture variability in a forested headwater catchment. Journal of Hydrology, 516, 140-153, doi: 10.1016/j.jhydrol.2014.01.060.

[7] Li, B. and Rodell, M. (2013). Spatial variability and its scale dependency of observed and modeled soil moisture over different climate regions. Hydrol. Earth Syst. Sci., 17, 1177-1188, doi:10.5194/hess-17-1177-2013.

[8] Alvarez-Garreton, C., Ryu, D., Western, A. W., Su, C.-H., Crow, W. T., Robertson, D. E., and Leahy, C. (2014). Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes. Hydrol. Earth Syst. Sci. Discuss., 11, 10635-10681, doi:10.5194/hessd-11-10635-2014.