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Advancing the Use of Satellite Rainfall Datasets for Flood Prediction in Ungauged Basins: The Role of Scale, Hydrologic Process Controls and the Global Precipitation Measurement Mission

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Quantitative Information Fusion for Hydrological Sciences

Part of the book series: Studies in Computational Intelligence ((SCI,volume 79))

Floods account for about 15% of the total death toll related to natural disasters, wherein typically more than 10 million lives are either displaced or lost each year internationally (Hossain, 2006). Rainfall is the primary determinant of floods and its intimate interaction with the landform (i.e., topography, vegetation and channel network) magnified by highly wet antecedent conditions leads to catastrophic flooding in medium (i.e., 1000 ~ 5000 km2) and large (i.e., >5000 km2) river basins. Furthermore, floods are more destructive overtropical river basins that lack adequate surface stations necessary for real-time rainfall monitoring — i.e., the ungauged river basins (Hossain and Katiyar, 2006) (see Figure 1, left panel).

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References

  • Anagnostou, E.N. (2004). Overview of Overland Satellite Rainfall Estimation for Hydro-Meteorological Applications. Surveys in Geophysics, 25(5–6), pp. 511–537.

    Article  Google Scholar 

  • Astin, I. (1997). A survey of studies into errors in large scale space-time averages of rainfall, cloud cover, sea surface processes and the earth’s radiation budget as derived from low orbit satellite instruments because of their incomplete temporal and spatial coverage. Surveys in Geophysics, 18, pp. 385–403.

    Article  Google Scholar 

  • Bell, T.L. (1987). A space-time stochastic model of rainfall for satellite remote-sensing studies. Journal of Geophysical Research, 92D, pp. 9631–9643.

    Article  Google Scholar 

  • Bell, T.L., Abdullah, A., Martin, R.L., and North, G.R. (1990). Sampling errors for satellite-derived tropical rainfall—Monte Carlo study using a space-time stochastic model. Journal of Geophysical Research, 95D, pp. 2195–2205.

    Article  Google Scholar 

  • Bellerby, T., and Sun, J. (2005). Probabilistic and ensemble representations of the uncertainty in IR/Microwave precipitation product. Journal of Hydrometeorology, 6, pp. 1032–1044.

    Article  Google Scholar 

  • Beven, K.J., and Freer, J. (2000). Equifinality, data assimilation, and uncertainty estimation in mechanistic modeling of complex environmental systems using the GLUE methodology. Journal of Hydrology 249, pp. 11–29.

    Article  Google Scholar 

  • Beven, K.J., Binley, AM. (1992). The future of distributed models: model calibration and uncertainty prediction. Hydrological Processes, 6, pp. 279–298.

    Article  Google Scholar 

  • Gebremichael, M., and Krajewski, W.F. (2004). Characterization of the temporal sampling error in space-time-averaged rainfall estimates from satellites.Journal of Geophysical Research 109(D11), (doi: D11110 0.1029/2004JD004509).

    Google Scholar 

  • Georgakakos, K.P., Seo, D-J., Gupta, H., Schaake, J.C., and Butts, M. (2004). Towards the characterization of stream-flow simulation uncertainty through multimodel ensembles. Journal of Hydrology, 298, pp. 223–241.

    Article  Google Scholar 

  • Griffith, C.G., Woodley, W.L., and Grube, P.G. (1978). Rain estimation from geosynchronous satellite imagery-visible and infrared studies. Monthly Weather Review 106, pp. 1153–1171.

    Article  Google Scholar 

  • Hossain, F. (2006). Towards formulation of a fully space-borne system for early warning of floods: Can cost-effectiveness outweigh flood prediction uncertainty? Natural Hazards, 37(3), pp. 263–276 (doi :10.1007/s11069-005-4645-0).

    Article  MathSciNet  Google Scholar 

  • Hossain, F., and Anagnostou, E.N. (2006a). A two-dimensional satellite rainfall error model. IEEE Transactions Geosciences and Remote Sensing, 44(6), pp. 1511–1522 (doi: 10.1109/TGRS.2005.863866).

    Article  Google Scholar 

  • Hossain, F., and Anagnostou, E.N. (2006b). Assessment of a multi-dimensional satellite rainfall error model for ensemble generation of satellite rainfall data. IEEE Geosciences and Remote Sensing Letters, 3(3), pp. 419–423.

    Article  Google Scholar 

  • Hossain, F., and Lettenmaier, D.P. (2006). Flood Prediction in the Future: Recognizing Hydrologic Issues in Anticipation of the Global Precipitation Measurement Mission. Water Resources Research, 44, (doi:10.1029/2006WR005202) .

    Google Scholar 

  • Hossain, F., and Katiyar, N. (2006). Improving flood forecasting in international river basins. EOS Transactions (AGU) 87(5), pp. 49–50.

    Google Scholar 

  • Hossain, F., and Anagnostou, E.N. (2005a). Numerical investigation of the impact of uncertainties in satellite rainfall estimation and land surface parameters for simulation of soil moisture. Advances in Water Resources, 28(12), pp. 1336–1350 (doi:10.1016/j.advwatres.2005.03.013).

    Article  Google Scholar 

  • Hossain, F. and Anagnostou, E.N. (2005b). Using a multi-dimensional satellite rainfall error model to characterize uncertainty in soil moisture fields simulated by an offline land surface model Geophysical Research Letters, 32(L15402) (doi:10.1029/2005GL023122).

    Article  Google Scholar 

  • Hossain, F., Anagnostou, E.N., and Dinku, T. (2004a). Sensitivity analyses of satellite rainfall retrieval and sampling error on flood prediction uncertainty. IEEE Transactions on Geosciences and Remote Sensing, 42(1).

    Google Scholar 

  • Hossain, F., Anagnostou, E.N., Dinku, T., and Borga, M. (2004b). Hydrological model sensitivity to parameter and radar rainfall estimation uncertainty. Hydrological Processes 18 (doi:10.1002/hyp.5659).

    Google Scholar 

  • Hossain, F., Anagnostou, E.N., and Lee K-H. (2004c). A non-linear and stochastic response surface method for Bayesian estimation of uncertainty in soil moisture simulation from a land surface model. INVITED PAPER—Special Issue in Non-Linear Processes in Geophysics, 11, pp. 1–15 (SREF-ID: 1607-7946/npg/2004-11-1).

    Google Scholar 

  • Huffman, G.J., Adler, R.F. Morrissey, M.M. and others. (2001). Global precipitation at one-degree daily resolution from multisatellite observations. Journal of Hydrometeorology, 2, pp 36–50.

    Article  Google Scholar 

  • Huffman, G.J., Adler, R.F., Bolvin, D.T., Gu, G., Nelkin, E.J., Bowman, K.P., Hong, Y., Stocker, E.F., and Wolff, D.B. (2007). The TRMM multi-satellite precipitation analysis: Quasi-global, multi-year, combined sensor precipitation estimates at fine scales, Journal of Hydrometeorology, 8, pp. 28–55.

    Article  Google Scholar 

  • Joyce, R.J. and Janowiak, J.E., Arkin, P.E., and Xie, P. (2004). CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of Hydrometeorology, 5(3), pp. 487–503.

    Article  Google Scholar 

  • Katiyar, N., and Hossain, F. (2007). An open-book watershed model for prototyping space-borne flood monitoring systems in international river basins. Environmental Modeling and Software, 22(12), pp. 1720–1731 (doi:10.1016/j.envsoft.2006.12.005).

    Article  Google Scholar 

  • Kavetski, D., Kuczera, G., and Franks, S.W. (2006a). Bayesian analysis of input uncertainty in hydrological modeling: 2 Application. Water Resources Research, 42(W03408) (doi:10.1029/2005WR004376).

    Google Scholar 

  • Kavetski, D., Kuczera, G., and Franks, S.W. (2006b). Bayesian analysis of input uncertainty in hydrological modeling: 1 Theory. Water Resources Research, 42 (W03407) (doi:10.1029/2005WR004368).

    Google Scholar 

  • Kremer, J.N. (1983). Ecological implications of parameter uncertainty in stochastic simulation. Ecological Modeling 18:187–207.

    Article  Google Scholar 

  • Krzyzstofowicz, R. (1999). Bayesian theory of probabilistic forecasting via deterministic hydrological model. Water Resources Research 35(9), pp. 2739–2750.

    Article  Google Scholar 

  • Krzyzstofowicz, R. (2001). The case for probabilistic forecasting in hydrology. Journal of Hydrology 249, pp. 2–9.

    Article  Google Scholar 

  • Kumar, S.V., Peters-Lidard, C.D., Tian, Y., Geiger, J., Houser, P.R., Olden, R., Lighty, L., Eastman, J.L., Dirmeyer, P., Doty, P., Adams, P.J., Wood, E.F., and Sheffield, J. (2006). LIS – An Interoperable Framework for High Resolution Land Surface Modeling. Environmental Modeling and Software 21(10), pp. 1402–1415. (doi. 10.1016/j.envsoft.2005.07.004).

    Article  Google Scholar 

  • Lee, K.H., and Anagnostou, E.N. (2004). Investigation of the nonlinear hydrologic response to precipitation forcing in physically based land surface modeling. Canadian Journal of Remote Sensing 30(5), pp. 706–716.

    Google Scholar 

  • Margulis, S., and Entekhabi, D. (2001). Temporal disaggregation of satellite-derived monthly precipitation estimates and resulting propagation of error in partitioning of water at the land surface. Hydrology and Earth System Sciences, 5(1), pp. 27–38.

    Article  Google Scholar 

  • Nijssen, B., and Lettenmaier, D.P. (2004). Effect of precipitation sampling error on simulated hydrological fluxes and states: Anticipating the Global Precipitation Measurement satellites. Journal of Geophysical Research, 109(D02103).

    Google Scholar 

  • North, G.R., and Nakamoto, S. (1989). Formalism for comparing rain estimation designs. Journal of Atmospheric and Oceanic Technology, 6, pp. 985–992.

    Article  Google Scholar 

  • Shiklomanov, A.I., Lammers, R.B., and Vörösmarty, C.J. (2002). Widespread decline in hydrological monitoring threatens pan-arctic research. EOS Transactions 83(2), pp. 16–17.

    Article  Google Scholar 

  • Smith E., Asrar, G., Furuhama, Y., Ginati, A., Kummerow, C., Levizzani, V., Mugnai, A., Nakamura, K., Adler, R. Casse, V., Cleave, M., Debois, M., Durning, J., Entin, J., Houser, P., Iguchi, T., Kakar, R., Kaye, J., Kojima, M., Lettenmaier, D.P., Luther, M., Mehta, A., Morel, P., Nakazawa, T., Neeck, S., Okamoto, K., Oki, R., Raju, G., Shepherd, M., Stocker, E., Testud, J., and Wood, E.F. (2007). The international global precipitation measurement (GPM) program and mission: An overview. In Measuring Precipitation from Space: EURAINSAT and the Future, (Eds.) V. Levizzani and F.J. Turk, Kluwer Academic Publishers (In press; copy available at http://gpm.gsfc.nasa.gov).

  • Steiner, M. (1996). Uncertainty of estimates of monthly areal rainfall for temporally sparse remote observations. Water Resources Research, 32:373–388.

    Article  Google Scholar 

  • Steiner, M., Bell, T.L., Zhang, Y., and Wood, E.F. (2003). Comparison of two methods for estimating the sampling-related uncertainty of satellite rainfall averages based on a large radar dataset. Journal of Climate, 16:3759–3778.

    Article  Google Scholar 

  • Stokstad, E. (1999). Scarcity of rain, stream gages threatens forecasts. Science 285, pp. 1199.

    Article  Google Scholar 

  • Syed, T.H., Lakshmi, V., Paleologos, E., Lohmann, D., Mitchell, K., and Famiglietti, J. 2004. Analysis of process controls in land surface hydrological cycle over the continental United States. Journal of Geophysical Research 109(D22105), doi: 10.1029/2004JD004640.

    Google Scholar 

  • Teo, C.K. (2006). Satellite-based rainfall estimation and its application to crop yield modeling. PhD Thesis (unpublished), Department of Meteorology, University of Reading.

    Google Scholar 

  • Turk, J., Bauer, P., and Ebert, E.E., and Arkin, P.A. (2005). Satellite-Derived Precipitation Verification Activities within the International Precipitation Working Group (IPWG), 14th Conference on Satellite Meteorology and Oceanography, P2.15.

    Google Scholar 

  • Venugopal, V., Foufoula-Georgio, E., and Sapozhinikhov, V. (1999). A space-time downscaling model for rainfall. Journal of Geophysical Research-Atmospheres, 104(D16), pp. 19705–19721.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Tennessee Technological University, Cookeville, TN 38505, USA

    Faisal Hossain & Nitin Katiyar

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  1. Faisal Hossain
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  2. Nitin Katiyar
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Editors and Affiliations

  1. Simula Research Laboratory, 134, 1325, Lysaker, Norway

    Xing Cai

  2. Department of Informatics, University of Oslo, 1080 Blindern, 0316, Oslo, Norway

    Xing Cai

  3. Department of Hydrology and Water Resources, The University of Arizona, Tucson, Arizona, 85721, USA

    T. -C. Jim Yeh

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Hossain, F., Katiyar, N. (2008). Advancing the Use of Satellite Rainfall Datasets for Flood Prediction in Ungauged Basins: The Role of Scale, Hydrologic Process Controls and the Global Precipitation Measurement Mission. In: Cai, X., Yeh, T.C.J. (eds) Quantitative Information Fusion for Hydrological Sciences. Studies in Computational Intelligence, vol 79. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75384-1_7

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  • DOI: https://doi.org/10.1007/978-3-540-75384-1_7

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