182
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
Bjornsson, H. and Venegas, S. A. (1997). A Manual for EOF and SVD Analyses of Climate
Data. McGill University, Montréal, Québec.
Bowman, K. P. (2005). Comparison of TRMM precipitation retrievals with rain gauge data
from ocean buoys. Journal of Climate, 18(1), 178–190.
Chang, N. B., Daranpob, A., Yang, J., and Jin, K. R. (2009). A comparative data mining analysis for information retrieval of MODIS images: Monitoring lake turbidity changes at
Lake Okeechobee, Florida. Journal of Applied Remote Sensing, 3, 033549.
Chen, J. Y. and Adams, B. J. (2006). Integration of artificial neural networks with conceptual
models in rainfall–runoff modeling. Journal of Hydrology, 318(1–4), 232–249.
Chow, V. T., Maidment, D. R., and Mavs, L. W. (1988). Applied Hydrology. McGraw-Hill,
New York, USA.
Easterling, D. R., Meeh, G. A., Parmesan, C., Changnon, S. A., Karl, T. R., and Mearns, L. O.
(2000). Climate extremes: Observations, modeling, and impacts. Science, 289(5487),
2068–2074.
El-Shafie, A., Mukhlisin, M., Najah, A. A., and Taha, M. R. (2011). Performance of artificial
neural network and regression techniques for rainfall–runoff prediction. International
Journal of the Physical Sciences, 6(8), 1997–2003.
El-Shafie, A., Noureldin, A. E., Taha, M. R., and Basri, H. (2008). Neural network model for
Nile River inflow forecasting based on correlation analysis of historical inflow data.
Journal of Applied Science, 8(24), 4487–4499.
Harvey, J. M. and Jiawei, H. (2001). Geographic Data Mining and Knowledge Discovery.
Taylor & Francis, London, UK.
Hou, P., Beeton, R. J. S., Carter, R. W., Dong, X. G., and Li, X. (2007). Response to environmental flows in the lower Tarim River, Xinjiang, China: Ground water. Journal of Arid
Environment, 83, 371–382.
Hsu, K. L., Gupta, H. V., and Sorooshian, S. (1995). Artificial neural-network modeling of the
rainfall–runoff process. Water Resources Research, 31(10), 2517–2530.
Jeong, D. I. and Kim, Y. O. (2005). Rainfall–runoff models using artificial neural networks for
ensemble streamflow prediction. Hydrological Processes, 19, 3819–3835.
Kelly, P. M. and Adger, W. N. (2000). Theory and practice in assessing vulnerability to climate
change and facilitating adaptation. Climatic Change, 47(4), 325–352.
Kite, G. W. and Pietroniro, A. (1996). Remote sensing applications in hydrological modelling.
Hydrological Sciences Journal—Journal des Sciences Hydrologiques, 41(4), 563–591.
Loboda, N. S., Glushkov, A. V., and Khokhlov, V. N. (2005). Using meteorological data
for reconstruction of annual runoff series over an ungauged area: Empirical orthogonal function approach to Moldova–Southwest Ukraine region. Atmospheric Research,
77(1–4), 100–113.
Marks, D. A., Kulie, M. S., Robinson, M., Silberstein, D. S., Wolff, D. B., Ferrier, B. S.,
Amitail, E., Fisher, B., Wang, J., Augustine, D., and Thiele, O. (2000). Climatological
processing and product development for the TRMM Ground Validation Program.
Physics and Chemistry of the Earth—Part B: Hydrology Oceans and Atmosphere,
25(10–12), 871–875.
Mcculloch, J. S. G. (2007). All our yesterdays: A hydrological retrospective. Hydrology and
Earth System Sciences, 11(1), 3–11.
Minns, A. W. and Hall, M. J. (1996). Artificial neural networks as rainfall–runoff models.
Hydrological Sciences Journal—Journal des Sciences Hydrologiques, 41(3), 399–417.
North, G. R., Bell, T. L., Cahalan, R. F., and Moeng, F. J. (1982). Sampling errors in the estimation of empirical orthogonal functions. Monthly Weather Review, 110(7), 699–706.
Puebla, C. R., Encinas, A. H., Nieto, S., and Garmendia, J. (1998). Spatial and temporal patterns of annual precipitation variability over the Iberian Peninsula. International Journal
of Climatology, 18(3), 299–316.
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
Bjornsson, H. and Venegas, S. A. (1997). A Manual for EOF and SVD Analyses of Climate
Data. McGill University, Montréal, Québec.
Bowman, K. P. (2005). Comparison of TRMM precipitation retrievals with rain gauge data
from ocean buoys. Journal of Climate, 18(1), 178–190.
Chang, N. B., Daranpob, A., Yang, J., and Jin, K. R. (2009). A comparative data mining analysis for information retrieval of MODIS images: Monitoring lake turbidity changes at
Lake Okeechobee, Florida. Journal of Applied Remote Sensing, 3, 033549.
Chen, J. Y. and Adams, B. J. (2006). Integration of artificial neural networks with conceptual
models in rainfall–runoff modeling. Journal of Hydrology, 318(1–4), 232–249.
Chow, V. T., Maidment, D. R., and Mavs, L. W. (1988). Applied Hydrology. McGraw-Hill,
New York, USA.
Easterling, D. R., Meeh, G. A., Parmesan, C., Changnon, S. A., Karl, T. R., and Mearns, L. O.
(2000). Climate extremes: Observations, modeling, and impacts. Science, 289(5487),
2068–2074.
El-Shafie, A., Mukhlisin, M., Najah, A. A., and Taha, M. R. (2011). Performance of artificial
neural network and regression techniques for rainfall–runoff prediction. International
Journal of the Physical Sciences, 6(8), 1997–2003.
El-Shafie, A., Noureldin, A. E., Taha, M. R., and Basri, H. (2008). Neural network model for
Nile River inflow forecasting based on correlation analysis of historical inflow data.
Journal of Applied Science, 8(24), 4487–4499.
Harvey, J. M. and Jiawei, H. (2001). Geographic Data Mining and Knowledge Discovery.
Taylor & Francis, London, UK.
Hou, P., Beeton, R. J. S., Carter, R. W., Dong, X. G., and Li, X. (2007). Response to environmental flows in the lower Tarim River, Xinjiang, China: Ground water. Journal of Arid
Environment, 83, 371–382.
Hsu, K. L., Gupta, H. V., and Sorooshian, S. (1995). Artificial neural-network modeling of the
rainfall–runoff process. Water Resources Research, 31(10), 2517–2530.
Jeong, D. I. and Kim, Y. O. (2005). Rainfall–runoff models using artificial neural networks for
ensemble streamflow prediction. Hydrological Processes, 19, 3819–3835.
Kelly, P. M. and Adger, W. N. (2000). Theory and practice in assessing vulnerability to climate
change and facilitating adaptation. Climatic Change, 47(4), 325–352.
Kite, G. W. and Pietroniro, A. (1996). Remote sensing applications in hydrological modelling.
Hydrological Sciences Journal—Journal des Sciences Hydrologiques, 41(4), 563–591.
Loboda, N. S., Glushkov, A. V., and Khokhlov, V. N. (2005). Using meteorological data
for reconstruction of annual runoff series over an ungauged area: Empirical orthogonal function approach to Moldova–Southwest Ukraine region. Atmospheric Research,
77(1–4), 100–113.
Marks, D. A., Kulie, M. S., Robinson, M., Silberstein, D. S., Wolff, D. B., Ferrier, B. S.,
Amitail, E., Fisher, B., Wang, J., Augustine, D., and Thiele, O. (2000). Climatological
processing and product development for the TRMM Ground Validation Program.
Physics and Chemistry of the Earth—Part B: Hydrology Oceans and Atmosphere,
25(10–12), 871–875.
Mcculloch, J. S. G. (2007). All our yesterdays: A hydrological retrospective. Hydrology and
Earth System Sciences, 11(1), 3–11.
Minns, A. W. and Hall, M. J. (1996). Artificial neural networks as rainfall–runoff models.
Hydrological Sciences Journal—Journal des Sciences Hydrologiques, 41(3), 399–417.
North, G. R., Bell, T. L., Cahalan, R. F., and Moeng, F. J. (1982). Sampling errors in the estimation of empirical orthogonal functions. Monthly Weather Review, 110(7), 699–706.
Puebla, C. R., Encinas, A. H., Nieto, S., and Garmendia, J. (1998). Spatial and temporal patterns of annual precipitation variability over the Iberian Peninsula. International Journal
of Climatology, 18(3), 299–316.