Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Hydrology and Hydromechanics

The Journal of Institute of Hydrology SAS Bratislava and Institute of Hydrodynamics CAS Prague

4 Issues per year


IMPACT FACTOR 2016: 1.654

CiteScore 2016: 1.72

SCImago Journal Rank (SJR) 2016: 0.440
Source Normalized Impact per Paper (SNIP) 2016: 0.969

Open Access
Online
ISSN
0042-790X
See all formats and pricing
More options …
Volume 63, Issue 3 (Sep 2015)

Issues

Rainfall estimation from in situ soil moisture observations at several sites in Europe: an evaluation of the SM2RAIN algorithm

Luca Brocca
  • Corresponding author
  • Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Via Madonna Alta 126, 06128 Perugia, Italy
  • Email:
/ Christian Massari
  • Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Via Madonna Alta 126, 06128 Perugia, Italy
/ Luca Ciabatta
  • Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Via Madonna Alta 126, 06128 Perugia, Italy
/ Tommaso Moramarco
  • Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Via Madonna Alta 126, 06128 Perugia, Italy
/ Daniele Penna
  • Faculty of Science and Technology, Free University of Bozen-Bolzano, Piazza dell'Università 5, Bolzano, Italy
/ Giulia Zuecco
  • Department of Land and Agroforest Environments, University of Padova, Via dell'Università 16, Legnaro, Italy
/ Luisa Pianezzola
  • Department of Land and Agroforest Environments, University of Padova, Via dell'Università 16, Legnaro, Italy
/ Marco Borga
  • Department of Land and Agroforest Environments, University of Padova, Via dell'Università 16, Legnaro, Italy
/ Patrick Matgen
  • Luxembourg Institute of Science and Technology (LIST), ERIN, Avenue Des Hauts-Fourneaux 5, Esch-Sur-Alzette, Luxembourg
/ José Martínez-Fernández
  • Centro Hispano Luso de Investigaciones Agrarias, USAL, Calle del Duero 12, Villamayor, Spain
Published Online: 2015-06-25 | DOI: https://doi.org/10.1515/johh-2015-0016

Abstract

Rain gauges, weather radars, satellite sensors and modelled data from weather centres are used operationally for estimating the spatial-temporal variability of rainfall. However, the associated uncertainties can be very high, especially in poorly equipped regions of the world. Very recently, an innovative method, named SM2RAIN, that uses soil moisture observations to infer rainfall, has been proposed by Brocca et al. (2013) with very promising results when applied with in situ and satellite-derived data. However, a thorough analysis of the physical consistency of the SM2RAIN algorithm has not been carried out yet. In this study, synthetic soil moisture data generated from a physically-based soil water balance model are employed to check the reliability of the assumptions made in the SM2RAIN algorithm. Next, high quality and multiyear in situ soil moisture observations, at different depths (5-30 cm), and rainfall for ten sites across Europe are used for testing the performance of the algorithm, its limitations and applicability range.

SM2RAIN shows very high accuracy in the synthetic experiments with a correlation coefficient, R, between synthetically generated and simulated data, at daily time step, higher than 0.940 and an average Bias lower than 4%. When real datasets are used, the agreement between observed and simulated daily rainfall is slightly lower with average R-values equal to 0.87 and 0.85 in the calibration and validation periods, respectively. Overall, the performance is found to be better in humid temperate climates and for sensors installed vertically. Interestingly, algorithms of different complexity in the reproduction of the underlying hydrological processes provide similar results. The average contribution of surface runoff and evapotranspiration components amounts to less than 4% of the total rainfall, while the soil moisture variations (63%) and subsurface drainage (30%) terms provide a much higher contribution. Overall, the SM2RAIN algorithm is found to perform well both in the synthetic and real data experiments, thus offering a new and independent source of data for improving rainfall estimation, and consequently enhancing hydrological, meteorological and climatic studies.

Keywords : Rainfall; Soil moisture; In situ observations; Experimental sites; SM2RAIN.

References

  • Borga, M., 2002. Accuracy of radar rainfall estimates for streamflow simulation. J. Hydrol., 267, 1, 26-39.Google Scholar

  • Brocca, L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner, W., Dorigo, W., Matgen, P., Martínez- Fernández, J., Llorens, P., Latron, J., Martin, C., Bittelli, M., 2011. Soil moisture estimation through ASCAT and AMSRE sensors: an intercomparison and validation study across Europe. Remote Sens. Environ., 115, 3390-3408.Google Scholar

  • 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.Google Scholar

  • Brocca, L., Melone, F., Moramarco, T., Wagner, W., 2013. A new method for rainfall estimation through soil moisture observations. Geophys. Res. Lett., 40, 5, 853-858.CrossrefWeb of ScienceGoogle Scholar

  • Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., Levizzani, V., 2014a. Soil as a natural rain gauge: estimating global rainfall from satellite soil moisture data. J. Geophys. Res. Atmos., 119, 9, 5128-5141.Web of ScienceGoogle Scholar

  • Brocca, L., Camici, S., Melone, F., Moramarco, T., Martinez- Fernandez, J., Didon-Lescot, J.-F., Morbidelli, R., 2014b. Improving the representation of soil moisture by using a semi-analytical infiltration model. Hydrol. Process., 28, 4, 2103-2115.CrossrefWeb of ScienceGoogle Scholar

  • Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., Levizzani, V., 2014c. Estimating rainfall from global satellite soil moisture data: recent improvements and applications. Poster presented at “Satellite soil moisture validation & application workshop”, Amsterdam, the Netherlands, 10-11 July 2014.Google Scholar

  • Ciabatta, L., Brocca, L., Moramarco, T., Wagner, W., 2015. Comparison of different satellite rainfall products over the Italian territory. Engineering Geology for Society and Territory, 3, 623-626.Google Scholar

  • Chen, F., Crow, W.T., Holmes, T.H., 2012. Improving longterm, retrospective precipitation datasets using satellite-based surface soil moisture retrievals and the soil moisture analysis rainfall tool. J. Appl. Remote Sens., 6, 1, 063604.Web of ScienceGoogle Scholar

  • Chen, F., Crow, W.T., Ryu, D., 2014. Dual Forcing and State Correction via Soil Moisture Assimilation for Improved Rainfall-Runoff Modeling. J. Hydrometeor., 15, 1832-1848.Google Scholar

  • Corradini, C., Melone, F., Smith, R.E., 1997. A unified model for infiltration and redistribution during complex rainfall patterns. J. Hydrol., 192, 104-124.Google Scholar

  • Crow, W.T., Huffman, G.F., Bindlish, R., Jackson, T.J., 2009. Improving satellite rainfall accumulation estimates using spaceborne soil moisture retrievals. J. Hydrometeorol., 10, 199-212.CrossrefWeb of ScienceGoogle Scholar

  • Crow, W.T., van Den Berg, M.J., Huffman, G.F., Pellarin, T., 2011. Correcting rainfall using satellite-based surface soil moisture retrievals: The Soil Moisture Analysis Rainfall Tool (SMART). Water Resour. Res., 47, W08521.CrossrefWeb of ScienceGoogle Scholar

  • Doorenbos, J, Pruitt, W.O., 1977. Background and Development of Methods to Predict Reference Crop Evapotranspiration (ETo). Appendix II in FAO-ID-24, 108-119.Google Scholar

  • Dorigo, W.A., Wagner, W., Hohensinn, R., Hahn, S., Paulik, C., Drusch, M., Mecklenburg, S., Van Oevelen, P., Robock, A., and Jackson, T., 2011. The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements, Hydrol. Earth Syst. Sci., 15, 1675-1698.Web of ScienceCrossrefGoogle Scholar

  • Draper, C., Reichle, R., de Jeu, R., Naeimi, V., Parinussa, R., Wagner, W., 2013. Estimating root mean square errors in remotely sensed soil moisture over continental scale domains. Remote Sens. Environ., 137, 288-298.Web of ScienceGoogle Scholar

  • Ebert, E.E., Janowiak, J., Kidd, C., 2007. Comparison of near real time precipitation estimates from satellite observations and numerical models. Bull. Am. Meteorol. Soc., 88, 47-64.CrossrefWeb of ScienceGoogle Scholar

  • Famiglietti, J.S., Wood, E.F., 1994. Multiscale modeling of spatially variable water and energy balance processes. Water Resour. Res., 11, 3061-3078.CrossrefGoogle Scholar

  • Georgakakos KP, Baumer OW., 1996. Measurement and utilization of on-site soil moisture data. J. Hydrol., 184, 131-152.Google Scholar

  • Herrnegger, M., Nachtnebel, H.P., Schulz, K., 2014. From runoff to rainfall: inverse rainfall-runoff modelling in a high temporal resolution. Hydrol. Earth Syst. Sci. Discuss., 11, 13259-13309.CrossrefGoogle Scholar

  • Hong, Y., Adler, R.F., Huffman, G.J., 2007. An experimental global prediction system for rainfall triggered landslides using satellite remote sensing and geospatial datasets. IEEE Trans. Geosci. Remote Sens., 45, 1671-1680.Web of ScienceGoogle Scholar

  • Hou, A.Y., Kakar, R.K., Neeck, S., Azarbarzin, A.A., Kummerow, C.D., Kojima, M., Oki, R., Nakamura, K., Iguchi, T., 2014. The Global Precipitation Measurement (GPM) mission. Bull. Amer. Meteor. Soc., 95, 701-722.Google Scholar

  • Kidd, C., Huffman, G., Kirschbaum, D., Skofronick-Jackson, G., Joe, P., Muller, C., 2014. So, how much of the Earth’s surface is covered by rain gauges? Geophysical Research Abstracts, 16, EGU2014-10300.Google Scholar

  • Kirchner, J.W., 2009. Catchments as simple dynamical systems: catchment characterization, rainfall-runoff modeling, and doing hydrology backward. Water Resour. Res., 45, W02429.Web of ScienceCrossrefGoogle Scholar

  • Krier, R., Matgen, P., Görgen, K., Pfister, L., Hoffmann, L., et al., 2012. Inferring catchment precipitation by doing hydrology backwards: a test in 24 small and mesoscale catchments in Luxembourg. Water Resour. Res., 48, W10525.Web of ScienceCrossrefGoogle Scholar

  • Kucera, P.A., Ebert, E.E, Turk, F.J., Levizzani, V., Kirschbaum, D., Tapiador, F.J., Loew, A., Borsche, M., 2013. Precipitation from space: Advancing earth system science. Bull. Amer. Meteor. Soc., 94, 365-375.Google Scholar

  • Martinez-Fernandez, J., Ceballos, A., 2005. Mean soil moisture estimation using temporal stability analysis. J. Hydrol., 312, 1-4, 28-38.Google Scholar

  • Massari, C., Brocca, L., Moramarco, T., Tramblay, Y., Didon Lescot, J.-F., 2014. Potential of soil moisture observations in flood modelling: estimating initial conditions and correcting rainfall. Advances in Water Resources, 74, 44-53.Google Scholar

  • Matgen, P., Heitz, S., Hasenauer, S., Hissler, C., Brocca, L., Hoffmann, L., Wagner, W., Savenije, H.H.G., 2012. On the potential of METOP ASCAT-derived soil wetness indices as a new aperture for hydrological monitoring and prediction: a field evaluation over Luxembourg. Hydrol. Process., 26, 2346-2359.Web of ScienceGoogle Scholar

  • Melone, F., Corradini, C., Morbidelli, R., Saltalippi, C., Flammini, A., 2008. Comparison of theoretical and experimental soil moisture profiles under complex rainfall patterns. Journal of Hydrologic Engineering, 13, 1170-1176.Web of ScienceGoogle Scholar

  • Morbidelli, R., Corradini, C., Saltalippi, C., Flammini, A., Rossi, E., 2011. Infiltration-soil moisture redistribution under natural conditions: experimental evidence as a guideline for realizing simulation models. Hydrol. Earth Syst. Sci., 15, 2937-2945.Web of ScienceCrossrefGoogle Scholar

  • Pellarin, T., Louvet, S., Gruhier, C., Quantin, G., Legout, C., 2013. A simple and effective method for correcting soil moisture and precipitation estimates using AMSR-E measurements. Remote Sens. Environ., 136, 28-36.Web of ScienceGoogle Scholar

  • Penna, D., van Meerveld, I., Oliviero, O., Zuecco, G., Assendelft, R.S., Dalla Fontana, G., Borga, M., 2014. Seasonal changes in run-off generation in a small forested mountain catchment. Hydrol. Process., doi: 10.1002/hyp.10347. (In press.) CrossrefWeb of ScienceGoogle Scholar

  • Ramarohetra, J., Sultan, B., Baron, C., Gaiser, T., Gosset, M., 2013. How satellite rainfall estimate errors may impact rainfed cereal yield simulation in West Africa. Agricultural and Forest Meteorology, 180, 118-131.Web of ScienceGoogle Scholar

  • Tian, Y., Liu, Y., Arsenault, K.R., Behrangi, A., 2014. A new approach to satellite-based estimation of precipitation over snow cover. International Journal of Remote Sensing, 35, 13, 4940-4951.Google Scholar

  • Tramblay, Y., Bouvier, C., Martin, C., Didon-Lescot, J.F., Todorovik, D., Domergue, J.M., 2010. Assessment of initial soil moisture conditions for event-based rainfallrunoff modelling. J. Hydrol., 387, 3-4, 176-187.Web of ScienceGoogle Scholar

  • Tuttle, S.E., Salvucci, G.D., 2014. A new approach for validating satellite estimates of soil moisture using largescale precipitation: Comparing AMSR-E products. Remote Sens. Environ., 142, 207-222.Web of ScienceGoogle Scholar

  • Villarini, G., Krajewski, W.F., 2010. Sensitivity studies of the models of radar-rainfall uncertainties. J. Appl. Meteor. Climatol., 49, 288-309.CrossrefGoogle Scholar

  • Wake, B., 2013. Flooding costs. Nature Climate Change, 3, 778.Google Scholar

  • Zreda, M., Shuttleworth, W.J., Zeng, X., Zweck, C., Desilets, D., Franz, T., Rosolem, R., 2012. COSMOS: The COsmicray Soil Moisture Observing System. Hydrol. Earth Syst. Sci., 16, 4079-4099. Web of ScienceCrossrefGoogle Scholar

About the article

Received: 2014-11-08

Accepted: 2015-01-13

Published Online: 2015-06-25

Published in Print: 2015-09-01


Citation Information: Journal of Hydrology and Hydromechanics, ISSN (Online) 0042-790X, DOI: https://doi.org/10.1515/johh-2015-0016.

Export Citation

© 2015. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

Comments (0)

Please log in or register to comment.
Log in