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.

Using geostatistics to integrate satellite information and modelling on soil moisture

This paper has a long history and explore the idea that geostatistics can be used to integrate satellite information when this is missing. At the same time the whole information is used for assimilated for better driving the Community Land Model. Thank you to Han Xujun for pursuing the publication, when I abandoned any hope, notwithstanding that the paper is a good one.

The paper is entitled: Soil Moisture Estimation by Assimilating L-Band Microwave Brightness Temperature with Geostatistics and Observation Localization, and my co-authors are (in order):
Han Xujun, Xin Li, Rui Jin, and Stefano Endrizzi.  The paper has been accepted by PLOSONE, and you can find the pre-print  here.

Other papers by Xujun are available from his Research Gate Profile.

A little of further bibliography:

Han, X. J., et al. (2014). "Soil moisture and soil properties estimation in the Community Land Model with synthetic brightness temperature observations." Water Resources Research 50(7): 6081-6105.

Han, X. J., et al. (2013). "Joint Assimilation of Surface Temperature and L-Band Microwave Brightness Temperature in Land Data Assimilation." Vadose Zone Journal 12(3).

Han, X., et al. (2012). "Spatial horizontal correlation characteristics in the land data assimilation of soil moisture." Hydrology and Earth System Sciences 16(5): 1349-1363.

Luca Brocca interview on Research Gate

Luca Brocca recently was very much interviewed for one of his achievements about the use of remote sensing in hydrology. He had this smart idea of obtaining rainfall from soil-moisture data. His SM2RAIN is a simple algorithm for estimating rainfall from soil moisture data that you can find in his web page together with  other interesting stuff:

• MISDc Rainfall-Runoff Model (and MILc)
• Soil Moisture Model
• Neyman-Scott Rectangular Pulse Model
• FARIMA
• Satellite vs in situ soil moisture observations
• Long-term (1989-2012) soil moisture data set

The paper that generate a big wave was:

Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data,  available here. He also had the honour of a Nature Research Highlight mention. All of this deserve mention by itself. However, he was so kind to mention me in this recent Research Gate Interview. Thank you Luca !

Luca's references on soil moisture spatial variability and remote sensing

In trying to extract the publishable results from Ageel Bushara Ph.D. thesis,  a good work indeed, but weakened by my ignorance in remote sensing, I started a conversation with Luca Brocca, one of the most prominent young italian hydrologists.  As befits in good conversations, Luca suggested some initial readings.

Here they are:

REFERENCES

1) Teuling et al. 2005 GRL they obtained good results comparing the spatial variability of the data but they do not have lateral flow of water

2) Brocca et al. 2013 JoH. As for Teuling, good results in the estimation of the spatial variability (however, the model is calibrated in any single point). Here we had a different scope, which was to obtain a lon soil moisture time series.

3) Walker et al. 2002 HYP using soil moisture estimates from SAR and comparison with ground data. IMHO not very good results (in Australia).

4) Li and Rodell 2013 HESS: they obtain that the spatial variability of in situ data (SCAN) is very different from the one modelled (Noah land surface model) and also different from the one obtain by another satellite (AMSRE, microwave passive sensor, 25 km). The study covers all the USA (CONUS).

SMAPEx and Rocco Panciera work in his seminar at Trento

Today Rocco gave his presentation here at his Alma Mater in Trento. Was a nice coming back and a beautiful presention of his work:

 Towards Global Remote Sensing of Soil Moisture: Australian Experiments

Abstract: Rocco panciera graduated from the University of trento in June 2013 with a thesis on distributed hydrological modelling under the supervision of Dr. Riccardo Rigon, and soon thereafter departed for the southern emisphere. He completed a Ph.D. at the University of Melbourne in 2010, with a thesis on remote sensing of near-surface soil moisture from airborne and spaceborne sensors, and since then has been working as a Research Fellow for the Australian Research Council, focusing on the estimation of soil moisture from airborne and spaceborne Synthetic Aperture Radar (SAR). Global monitoring of soil moisture using spaceborne passive microwave and SAR sensors is rapidly evolving from research into application, with the launch of soil moisture dedicated missions such as the Soil Moisture and ocean Salinity (SMOS, 2009) and the future Soil Moisture Active Passive (SMAP, 2014) missions. Moreover, there is an increasing trend in the availability of global coverage from Synthetic Aperture Radar (SAR) active microwave sensors such as SAOCOM, Sentinel-1, ALOS 2, Cosmo-SkyMed,  providing the opportunity for long-term temporally-dense series of microwave observations which are suitable for soil moisture monitoring at fine resolution (<1km).  Rocco was heavily involved in series of large-scale airborne experiments conducted in Australia in the 2005-2011 time frame that provided airborne data in support of algorithm development for soil moisture estimation from such missions. This presentation reports on such experiments and a number of research activities emerging from those related to the estimation of near-surface soil moisture and vegetation type and biomass from passive microwave and SAR observations.

Click on the figure to see the presentation slides.

Luca Brocca seminar at Trento, September 25th, 2014

On september 25 Luca Brocca, one of the most promising young Italian hydrologists, will give a seminar on soil moisture modelling in  Hydrology. His presentation is already on-line on slideshare (click on the image below).

and therefore, you can have a preview of what he will be saying. Hope many will come. Living experience is, obviously, a must.
The abstract of the conference:

soil moisture governs the partitioning of mass and energy fluxes between the land surface and the atmosphere, thus playing a fundamental role for many scientific and operational applications, including flood forecasting, climate modelling, landslide prediction, numerical weather prediction, irrigation scheduling, to cite a few. Nowadays, soil moisture estimates from satellite sensors are becoming more readily available with a spatial and temporal resolution that is suitable for hydrological applications. Moreover, the accuracy of the satellite-derived soil moisture retrievals is found to be satisfactorily in many countries worldwide and mainly in the Mediterranean region.

This study aims at showing the reliability of the satellite soil moisture product derived by the Advanced Scatterometer (ASCAT) over Europe and, mainly, to understand how these observations impact the modelling of extremes in the Mediterranean region by considering three different applications.
The first application addresses flood forecasting (Brocca et al., 2010; 2012a), by assimilating the ASCAT soil moisture into a multi-layer continuous and distributed rainfall-runoff model, named MISDc. The Ensemble Kalman filter is adopted to optimally incorporate the soil moisture data into MISDc. Several catchments located in different climatic regions over Europe are used as case studies. Results reveal that the ASCAT soil moisture product can be conveniently used to improve runoff prediction, mainly if the soil wetness conditions before a storm event are highly uncertain or unknown. However, reliability differs according to the climatic region, the soil/land use conditions and the size of the catchments under investigation. Therefore, the open issues that should be addressed in future studies are also given.

The second application investigates the use of the ASCAT soil moisture product for predicting the movement of a rock slope located in central Italy, the Torgiovannetto landslide (Brocca et al., 2012b). By using a statistical approach, the opening of the tension cracks, recorded by an extensometers network operating in the area, as a function of rainfall and soil moisture conditions prior the occurrence of rainfall, are predicted in the period 2007-2009. Results indicate that the regression performance (in terms of correlation coefficient) significantly increases if the ASCAT soil moisture product is included.

Finally, the third application aims at estimating rainfall starting from soil moisture observations (Brocca et al., 2012c). Specifically, by inverting the soil water balance equation, a simple analytical relationship for estimating rainfall accumulations from the knowledge of soil moisture time series is obtained. Satellite and soil moisture observations from three sites in Europe are used to test the developed approach that showed reasonable results thus opening new opportunities for rainfall estimation at catchment/global scale. 

Stochastic Soil Water Dynamics, and Ecohydrology, According to Amilcare Porporato

Last March 19 and 20, 2012, was there was the occasion to celebrate Ignacio Rodriguez-Iturbe' 70. In Princeton a lot of friends met and some interesting seminars on the topic covered and/or initiated by Ignacio took place. All of this fantastic material should be available soon on Princeton's web sites. Meanwhile they format the videos, I am summarizing here the first of the talks, the one given by Amilcare Porporato (on the right  in the picture - with purple jacket- with Eric VanMarcke on the left).

The idea behind Amilcares (Ignacio's own, and others like Luca Ridolfi, Paolo d'Odorico and Andrea Rinaldo, naming just the seniors of the group) is assuming a stochastic forcing of the soil water balance as given by rainfall (Ignacio worked a lot on  random model of precipitation, e.g., see the classic Random Function and Hydrology with Raphael Bras) incorporated into a probabilistic description of soil water balance. Soil moisture, in fact, is thougth as the key driver of ecohydrological, biogeochemical and hydrometeorological processes and what comes as a consequence. According to Amilcare, the progenitor of this research has to be found in the paper by P. Eagleson, 1978, and followed by some other relevant contribution by Bras and Cordova, 1981, Rodriguez-Iturbe et al., 1991, Milly, 1993 and Rodriguez-Iturbe et al., 1999.

To these pioneering works followed several works of "generalizations": D'Odorico et al., 2000, Porporato et al., 2006, containing the interannual variability and the "superstatistics"; Salvucci et al., 2000, with experimental verifications; Laio et al., 2001 with a more realistic loss function; Porporato et al., 2004 with classification of soil water balance; Daly et al., 2004, introducing arguments on the plant photosynthesis; Ridolfi et al., 2005,2006 including the water table and the capillary rise.

In the whole set of these papers the strategy followed was:

• Extract low-dimensional components of the dynamics;

• Surrogate external forcing and internal heterogeneities (high-dimensional components) with suitable noise (probabilistic description)

• Use minimalist models:
– analytical solutions;
– general relationships among fundamental groups;
– Coupling with other processes (plant,nutrients, etc.)

• Interpreting more complete simulations and schemes

Other papers cited in the presentation were: Daly et al. AWR, 2008; Gollan et al., 1985 (about the stomata response); Federer, 1979 (about a simple plant-atmosphere model), Porporato et al.,  2004, Daly and Porporato, 2010 (studying analitical steady state solutions of the master equation derived from the application of the assumptions above).

All the previous papers deal with local, point dynamics. However, some papers also have some spatial dynamics and/or descrition. Just looking at the vertical profile: Celia et al., 2001; Laio, 2006, D'Odorico and Ridolfi, 2000. Looking at the spatial soil moisture variability and about scaling of soil moisture: Rodriguez-Iturbe et al., 1995; Isham et al., 2006; about spatial Poisson processes: Manfreda et al., 2006 ; Using a Reynolds averaging approach: Katul et al., 2002; Albertson and Montalto, 2003; looking at the role of topography: Caylor et al., 2007; Instanbulluoglu and Bras et al., 2006.

Some papers, finally, tried to link soil moisture with the rainfall processes and precipitation recycling (D'Odorico and Porporato, 2004; Porporato and D'Odorico, 2004)

The above about the recent past: enough, I think for a novel reader.

However, the more recent work try to embrace a complexity of interactions of which an example is the work he made with Vico: Vico and Porporato, WRR 2010; AWR 2011a,b; Manzoni and Porporato, EOS (2006), Ecology (2012) an other papers of which you can find the reference and sometimes the pdf below.


References

Albertson, J. D., and N. Montaldo, Temporal dynamics of soil moisture variability: 1. Theoretical basis, Water Resour. Res., 39(10), 1274, doi:10.1029/2002WR001616, 2003.

Bras, R. L. and J. R. Cordova (1981), Intraseasonal water allocation in deficit irrigation, Water Resour. Res., 17(4), 866–874, doi:10.1029/WR017i004p00866 ?

Caylor K.K, Manfreda S. and Rodriguez-Iturbe, I., On the coupled geomorphological and ecohydrological organization of river basins, Advances in Water Resour., 28, 69-86, 2005

Daly, Edoardo, Amilcare Porporato, Ignacio Rodriguez-Iturbe, 2004: Coupled Dynamics of Photosynthesis, Transpiration, and Soil Water Balance. Part II: Stochastic Analysis and Ecohydrological Significance. J. Hydrometeor, 5, 559–566

Daly E. Oishi A.C., Porporato A., Katul G., A stochastic model for daily subsurface CO2 concentration and related soil respiration, Advances in Water Resour., 31,987-994, 2008

Daly and Porporato, Effect of different jump distributions on the dynamics of jump processes, Phys. Rev. E., 2010

D'Odorico, P., L. Ridolfi, A. Porporato, and I. Rodriguez-Iturbe (2000), Preferential states of seasonal soil moisture: The impact of climate fluctuations, Water Resour. Res., 36(8), 2209–2219, doi:10.1029/2000WR900103.

D'Odorico, P. and Porporato, A., Preferential states in soil moisture and climate dynamics, PNAS, 2004

Eagleson, P., Climate, Soil, and Vegetation 3. A simplified model of soil moisture movement in the liquid phase, Water Resour. Res., 14(5), 1978

Federer, C. A. (1979), A soil-plant-atmosphere model for transpiration and availability of soil water, Water Resour. Res., 15(3), 555–562, doi:10.1029/WR015i003p00555.

T. Gollan, N. C. Turner and E. -D. Schulze, The responses of stomata and leaf gas exchange to vapour pressure deficits and soil water content, Oecologia, 65 (3), 365-362, 1985

Guswa, A. J., M. A. Celia, and I. Rodriguez-Iturbe, Models of soil moisture dynamics in ecohydrology: A comparative study,Water Resour. Res., 38(9), 1166, doi:10.1029/2001WR000826, 2002

Isham, V., Cox, D.R., Rodríguez-Iturbe,  I., Porporato, A., Manfreda, S. (2005).
Mathematical characterization of the space-time variability of soil moisture, Proceedings
of the Royal Society A: Mathematical, Physical and Engineering Sciences, 461(2064),
4035–4055.

Istanbulluoglu, E. and R. L. Bras (2006), On the dynamics of soil moisture, vegetation, and erosion: Implications of climate variability and change, Water Resour. Res., 42, W06418, doi:10.1029/2005WR004113

Katul, G., P. Wiberg, J. Albertson, and G. Hornberger (2002), A mixing layer theory for flow resistance in shallow streams, Water Resour. Res., 38(11), 1250, doi:10.1029/2001WR000817.

Katul, G., Porporato, A., and Orem R., Stochastic Dynamics of Plant-Water Interactions., Annu. Rev. Ecol. Evol. Syst. 2007. 38:767–91

Laio, F., A. Porporato, C. P. Fernandez-Illescas, and I. RodriguezIturbe, Plants in water-controlled ecosystems: Active role in hydrologic processes and response to water stress, IV, Discussion of real cases, Adv. Water Resour., 24(7), 745–762, 2001.

Laio, F. (2006), A vertically extended stochastic model of soil moisture in the root zone, Water Resour. Res., 42, W02406, doi:10.1029/2005WR004502.

Manfreda, S. and I. Rodríguez-Iturbe (2006), On the spatial and temporal sampling of soil moisture fields, Water Resour. Res., 42, W05409, doi:10.1029/2005WR004548.

Manzoni, A., and Porporato, A., Soil Biology and Biochemistry, A theoretical analysis of nonlinearities and feedbacks in soil carbon and nitrogen cycles, Volume 39, Issue 7, July 2007, Pages 1542–1556 (http://c-h2oecology.env.duke.edu/Duke-FACE/PDF/sbb39-07.pdf)

Manzoni, S.,  Schimel, J.P, and Porporato, A., Responses of soil microbial communities to water stress: results from a meta-analysis, Ecology, 2012, (doi: 10.1890/11-0026.1)

Milly, P. C. D. (1993), An analytic solution of the stochastic storage problem applicable to soil water, Water Resour. Res.,29(11), 3755–3758, doi:10.1029/93WR01934
Porporato, A. & D'Odorico, P. (2004), State transitions driven by state-dependent Poisson Noise, Phys. Rev. Lett. 92

Porporato, A., Daly, E., Rodriguez-Iturbe, I., Soil Water Balance and Ecosystem Response to Climate Change, The American Naturalist, 164(5), 2004

Porporato, A. , Vico, G. , Fay, Philip A., Superstatistics of hydro-climatic fluctuations and interannual ecosystem productivity, NAL, 2006

Rigby, J.R and  Porporato, A., Simplified stochastic soil-moisture models: a look at infiltration, Hydrol. Earth Syst. Sci., 10, 861-871, 2006 www.hydrol-earth-syst-sci.net/10/861/2006/doi:10.5194/hess-10-861-2006

Rodriguez-Iturbe, I., P. D'Odorico, F. Laio, L. Ridolfi, and S. Tamea (2007), Challenges in humid land ecohydrology: Interactions of water table and unsaturated zone with climate, soil, and vegetation, Water Resour. Res., 43, W09301, doi:10.1029/2007WR006073. ???

Rodriguez-Iturbe, I., D. Entekhabi, and R. L. Bras (1991), Nonlinear Dynamics of Soil Moisture at Climate Scales: 1. Stochastic Analysis, Water Resour. Res., 27(8), 1899–1906, doi:10.1029/91WR01035
Rodriguez-Iturbe, I, A. Porporato, L. Ridolfi, V. Isham, and D.R. Cox, Probabilistic modelling of water balance at a point: the role of climate, soil and vegetation, PRSA, 1999

Ridolfi, L., P. D'Odorico, F. Laio, S. Tamea, and I. Rodriguez-Iturbe (2008), Coupled stochastic dynamics of water table and soil moisture in bare soil conditions, Water Resour. Res., 44, W01435, doi:10.1029/2007WR006707.

Rodriguez-Iturbe, I, Gregor K. Vogel, R. Rigon, D. Entekhabi, F. Castelli and A. Rinaldo, On the spatial organization of soil moisture fields, Journal of Geophysical Research, 22(20), 2757-2760, 1995

Salvucci, G. D. (2001), Estimating the moisture dependence of root zone water loss using conditionally averaged precipitation, Water Resour. Res., 37(5), 1357–1365, doi:10.1029/2000WR900336

Tamea, S., F. Laio, and L. Ridolfi (2005), Probabilistic nonlinear prediction of river flows, Water Resour. Res., 41, W09421, doi:10.1029/2005WR004136.

Vico, G. and A. Porporato (2010), Traditional and microirrigation with stochastic soil moisture, Water Resour. Res., 46, W03509, doi:10.1029/2009WR008130.

Vico, Giulia and Porporato, Amilcare, From rainfed agriculture to stress-avoidance irrigation: I. A generalized irrigation scheme with stochastic soil moisture, Advances in Water Resources, vol 34 no. 2 (2011), pp. 263--271.

Vico, G. and Porporato, A., From rainfed agriculture to stress-avoidance irrigation: II. Sustainability, crop yield, and profitability, Advances in Water Resources, vol 34 no. 2 (2011), pp. 272--281.

A New International Network for in Situ Soil Moisture Data

is available from TU Wien. Announcement can be found here on the EOS AGU Journal. The site can be found at https://ismn.geo.tuwien.ac.at/

This is an Initiative of the International Soil Moisture Network about which is available an open access paper on HESS by Dorigo et al.