Discharge Simulation in a Data-Scarce Basin Using Reanalysis and Global Precipitation Data: A Case Study of the White Volta Basin
Affiliation(s)
1 Faculty of Bioresources and Environmental Sciences, Ishikawa Prefectural University, Nonoichi, Japan. 2 Japan International Research Center for Agricultural Sciences, Tsukuba, Japan. 3 Faculty of Agriculture, Kagoshima University, Kagoshima, Japan.
1 Faculty of Bioresources and Environmental Sciences, Ishikawa Prefectural University, Nonoichi, Japan. 2 Japan International Research Center for Agricultural Sciences, Tsukuba, Japan. 3 Faculty of Agriculture, Kagoshima University, Kagoshima, Japan.
ABSTRACT
Basins in many parts of the world are ungauged or poorly gauged, and in some cases existing measurement networks are declining. The purpose of this study was to examine the utility of reanalysis and global precipitation datasets in the river discharge simulation for a data-scarce basin. The White Volta basin of Ghana which is one of international rivers was selected as a study basin. NCEP1, NCEP2, ERA-Interim, and GPCP datasets were compared with corresponding observed precipitation data. Annual variations were not reproduced in NCEP1, NCEP2, and ERA-Interim. However, GPCP data, which is based on satellite and observed data, had good seasonal accuracy and reproduced annual variations well. Moreover, five datasets were used as input data to a hydrologic model with HYMOD, which is a water balance model, and with WTM, which is a river model; thereafter, the hydrologic model was calibrated for each datum set by a global optimization method, and river discharge were simulated. The results were evaluated by the root mean square error, relative error, and water balance error. As a result, the combination of GPCP precipitation and ERA-Interim evaporation data was the best in terms of most evaluations. The relative errors in the calibration and validation periods were 43.1% and 46.6%, respectively. Moreover, the results for the GPCP precipitation and ERA-Interim evaporation were better than those for the combination of observed precipitation and ERA-Interim evaporation. In conclusion, GPCP precipitation data and ERA-Interim evaporation data are very useful in a data-scarce basin water balance analysis.
Basins in many parts of the world are ungauged or poorly gauged, and in some cases existing measurement networks are declining. The purpose of this study was to examine the utility of reanalysis and global precipitation datasets in the river discharge simulation for a data-scarce basin. The White Volta basin of Ghana which is one of international rivers was selected as a study basin. NCEP1, NCEP2, ERA-Interim, and GPCP datasets were compared with corresponding observed precipitation data. Annual variations were not reproduced in NCEP1, NCEP2, and ERA-Interim. However, GPCP data, which is based on satellite and observed data, had good seasonal accuracy and reproduced annual variations well. Moreover, five datasets were used as input data to a hydrologic model with HYMOD, which is a water balance model, and with WTM, which is a river model; thereafter, the hydrologic model was calibrated for each datum set by a global optimization method, and river discharge were simulated. The results were evaluated by the root mean square error, relative error, and water balance error. As a result, the combination of GPCP precipitation and ERA-Interim evaporation data was the best in terms of most evaluations. The relative errors in the calibration and validation periods were 43.1% and 46.6%, respectively. Moreover, the results for the GPCP precipitation and ERA-Interim evaporation were better than those for the combination of observed precipitation and ERA-Interim evaporation. In conclusion, GPCP precipitation data and ERA-Interim evaporation data are very useful in a data-scarce basin water balance analysis.
KEYWORDS
Reanalysis Data, Global Precipitation Data, Ungauged Basin, Hydrologic Model, Discharge Simulation, Africa
Reanalysis Data, Global Precipitation Data, Ungauged Basin, Hydrologic Model, Discharge Simulation, Africa
Cite this paper
Fujihara, Y. , Yamamoto, Y. , Tsujimoto, Y. and Sakagami, J. (2014) Discharge Simulation in a Data-Scarce Basin Using Reanalysis and Global Precipitation Data: A Case Study of the White Volta Basin. Journal of Water Resource and Protection, 6, 1316-1325. doi: 10.4236/jwarp.2014.614121.
Fujihara, Y. , Yamamoto, Y. , Tsujimoto, Y. and Sakagami, J. (2014) Discharge Simulation in a Data-Scarce Basin Using Reanalysis and Global Precipitation Data: A Case Study of the White Volta Basin. Journal of Water Resource and Protection, 6, 1316-1325. doi: 10.4236/jwarp.2014.614121.
References
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http://dx.doi.org/10.1029/2006JD007859 [10] Biemans, H., Hutjes, R.W.A., Kabat, P., Strengers, B.J., Gerten, D. and Rost, S. (2009) Effects of Precipitation Uncertainty on Discharge Calculations for Main River Basins. Journal of Hydrometeorology, 10, 1011-1025.
http://dx.doi.org/10.1175/2008JHM1067.1 [11] Getirana, A.C.V., Espinoza, J.C.V., Ronchail, J. and Filho, O.C.R. (2011) Assessment of Different Precipitation Datasets and Their Impacts on the Water Balance of the Negro River Basin. Journal of Hydrology, 404, 304-322. [12] Kotsuki, S. and Tanaka, K. (2013) Uncertainties of Precipitation Products and Their Impacts on Runoff Estimates through Hydrological Land Surface Simulation in Southeast Asia. Hydrological Research Letters, 7, 79-84.
http://dx.doi.org/10.3178/hrl.7.79 [13] Wagner, S., Kunstmann, H. and Bardossy, A. (2006) Model Based Distributed Water Balance Monitoring of the White Volta Catchment in West Africa through Coupled Meteorological-Hydrological Simulations. Advances in Geosciences, 9, 39-44.
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http://dx.doi.org/10.1175/BAMS-83-11-1631 [18] Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., et al. (2011) The ERA-Interim Reanalysis: Configuration and Performance of the Data Assimilation System. Quarterly Journal of the Royal Meteorological Society, 137, 553-597. [19] Huffman, G.J., Adler, R.F., Morrissey, M., Bolvin, D.T., Curtis, S., Joyce, R., McGavock, B. and Susskind, J. (2001) Global Precipitation at One-Degree Daily Resolution from Multi-Satellite Observations. Journal of Hydrometeorology, 2, 36-50.
http://dx.doi.org/10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2 [20] Vorosmarty, C.J., Fekete, B.M., Meybeck, M. and Lammers, R. (2000) Global System of Rivers: Its Role in Organizing Continental Land Mass and Defining Land-to-Ocean Linkages. Global Biogeochemical Cycles, 14, 599-621.
http://dx.doi.org/10.1029/1999GB900092 [21] Boyle, D.P., Gupta, H.V. and Sorooshian, S. (2000) Toward Improved Calibration of Hydrological Models: Combining the Strengths of Manual and Automatic Methods. Water Resources Research, 36, 3663-3674.
http://dx.doi.org/10.1029/2000WR900207 [22] Kollat, J.B., Reed, P.M. and Wagener, T. (2012) When Are Multiobjective Calibration Trade-Offs in Hydrologic Models Meaningful? Water Resources Research, 48, Published Online. [23] Vorosmarty, C.J., Moore III, B., Grace, A.L., Gildea, M.P., Melillo, J.M., Peterson, B.J., Rastetter, E.D. and Steudler, P.A. (1989) Continental Scale Models of Water Balance and Fluvial Transport: An Application to South America. Global Biogeochemical Cycles, 3, 241-265.
http://dx.doi.org/10.1029/GB003i003p00241 [24] Fujihara, Y., Oda, M., Horikawa, N. and Ogura, C. (2011) Hydrologic Analysis of Rainfed Rice Areas Using a Simple Semi-Distributed Water Balance Model. Water Resources Management, 25, 2061-2080.
http://dx.doi.org/10.1007/s11269-011-9796-z [25] Fujihara, Y., Tanakamaru, H., Hata, T. and Tada, A. (2003) Calibration of Rainfall-Runoff Models Using the Evolution Strategy. Proceedings of the 1st International Conference on Hydrology and Water Resources in Asia Pacific Region, 2, 885-890. [26] Fujihara, Y., Tanakamaru, H., Hata, T. and Tada, A. (2004) Performance Evaluation of Rainfall-Runoff Models Using Multi-Objective Optimization Approach. Proceedings of the 2nd International Conference on Hydrology and Water Resources in Asia Pacific Region, 2, 575-582.
[1] Haddeland, I., Lettenmaier, D.P. and Skaugen, T. (2006) Effects of Irrigation on the Water and Energy Balances of the Colorado and Mekong River Basins. Journal of Hydrology, 324, 210-223.
http://dx.doi.org/10.1016/j.jhydrol.2005.09.028 [2] Hanasaki, N., Kanae, S. and Oki, T. (2006) A Reservoir Operation Scheme for Global River Routing Models. Journal of Hydrology, 327, 22-41.
http://dx.doi.org/10.1016/j.jhydrol.2005.11.011 [3] Siebert, S. and Doll, P. (2010) Quantifying Blue and Green Virtual Water Contents in Global Crop Production as well as Potential Production Losses without Irrigation. Journal of Hydrology, 384, 198-217.
http://dx.doi.org/10.1016/j.jhydrol.2009.07.031 [4] Arnold, J.G., Srinivasan, R., Muttiah, R.S. and Williams, J.R. (1998) Large Area Hydrologic Modeling and Assessment, Part 1: Model Development. Journal of the American Water Resources Association, 34, 73-89.
http://dx.doi.org/10.1111/j.1752-1688.1998.tb05961.x [5] Sivapalan, M., et al. (2003) IAHS Decade on Predictions in Ungauged Basins (PUB), 2003-2012: Shaping an Exciting Future for the Hydrological Sciences. Hydrological Sciences Journal, 48, 857-880.
http://dx.doi.org/10.1623/hysj.48.6.857.51421 [6] Lenters, J.D., Coe, M.T. and Foley, J.A. (2000) Surface Water Balance of the Continental United States, 1963-1995: Regional Evaluation of a Terrestrial Biosphere Model and the NCEP/NCAR Reanalysis. Journal of Geophysical Research, 105, 22393-22425.
http://dx.doi.org/10.1029/2000JD900277 [7] Betts, A.K., Ball, J.H. and Viterbo, P. (2003) Evaluation of the ERA-40 Surface Water Budget and Surface Temperature for the Mackenzie River Basin. Journal of Hydrometeorology, 4, 1194-1211.
http://dx.doi.org/10.1175/1525-7541(2003)004<1194:EOTESW>2.0.CO;2 [8] Su, F., Adam, J.C., Trenberth, K.E. and Lettenmaier, D.P. (2006) Evaluation of Surface Water Fluxes of the Pan-Arctic Land Region with a Land Surface Model and ERA-40 Reanalysis. Journal of Geophysical Research, 111, D5.
http://dx.doi.org/10.1029/2005JD006387 [9] Bromwich, D.H., Fogt, R.L., Hodges, K.I. and Walsh, J.E. (2007) A Tropospheric Assessment of the ERA-40, NCEP, and JRA-25 Global Reanalyses in the Polar Regions. Journal of Geophysical Research, 112, D10111.
http://dx.doi.org/10.1029/2006JD007859 [10] Biemans, H., Hutjes, R.W.A., Kabat, P., Strengers, B.J., Gerten, D. and Rost, S. (2009) Effects of Precipitation Uncertainty on Discharge Calculations for Main River Basins. Journal of Hydrometeorology, 10, 1011-1025.
http://dx.doi.org/10.1175/2008JHM1067.1 [11] Getirana, A.C.V., Espinoza, J.C.V., Ronchail, J. and Filho, O.C.R. (2011) Assessment of Different Precipitation Datasets and Their Impacts on the Water Balance of the Negro River Basin. Journal of Hydrology, 404, 304-322. [12] Kotsuki, S. and Tanaka, K. (2013) Uncertainties of Precipitation Products and Their Impacts on Runoff Estimates through Hydrological Land Surface Simulation in Southeast Asia. Hydrological Research Letters, 7, 79-84.
http://dx.doi.org/10.3178/hrl.7.79 [13] Wagner, S., Kunstmann, H. and Bardossy, A. (2006) Model Based Distributed Water Balance Monitoring of the White Volta Catchment in West Africa through Coupled Meteorological-Hydrological Simulations. Advances in Geosciences, 9, 39-44.
http://dx.doi.org/10.5194/adgeo-9-39-2006 [14] Shahin, M. (2002) Hydrology and Water Resources of Africa. Water Science and Technology Library, 41, Kluwer Academic Publishers, Dordrecht, Boston, London. [15] Onogi, K., Tsutsui, J., Koide, H., Sakamoto, M., S., Kobayashi, Hatsushika, H., et al. (2007) The JRA-25 Reanalysis. Journal of the Meteorological Society of Japan, 85, 369-432.
http://dx.doi.org/10.2151/jmsj.85.369 [16] Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., et al. (1996) The NCEP/NCAR 40-Year Reanalysis Project. Bulletin of the American Meteorological Society, 77, 437-471.
http://dx.doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2 [17] Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S.K., Hnilo, J.J., Fiorino, M. and Potter, G.L. (2002) NCEP-DEO AMIP-II Reanalysis (R-2). Bulletin of the American Meteorological Society, 83, 1631-1643.
http://dx.doi.org/10.1175/BAMS-83-11-1631 [18] Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., et al. (2011) The ERA-Interim Reanalysis: Configuration and Performance of the Data Assimilation System. Quarterly Journal of the Royal Meteorological Society, 137, 553-597. [19] Huffman, G.J., Adler, R.F., Morrissey, M., Bolvin, D.T., Curtis, S., Joyce, R., McGavock, B. and Susskind, J. (2001) Global Precipitation at One-Degree Daily Resolution from Multi-Satellite Observations. Journal of Hydrometeorology, 2, 36-50.
http://dx.doi.org/10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2 [20] Vorosmarty, C.J., Fekete, B.M., Meybeck, M. and Lammers, R. (2000) Global System of Rivers: Its Role in Organizing Continental Land Mass and Defining Land-to-Ocean Linkages. Global Biogeochemical Cycles, 14, 599-621.
http://dx.doi.org/10.1029/1999GB900092 [21] Boyle, D.P., Gupta, H.V. and Sorooshian, S. (2000) Toward Improved Calibration of Hydrological Models: Combining the Strengths of Manual and Automatic Methods. Water Resources Research, 36, 3663-3674.
http://dx.doi.org/10.1029/2000WR900207 [22] Kollat, J.B., Reed, P.M. and Wagener, T. (2012) When Are Multiobjective Calibration Trade-Offs in Hydrologic Models Meaningful? Water Resources Research, 48, Published Online. [23] Vorosmarty, C.J., Moore III, B., Grace, A.L., Gildea, M.P., Melillo, J.M., Peterson, B.J., Rastetter, E.D. and Steudler, P.A. (1989) Continental Scale Models of Water Balance and Fluvial Transport: An Application to South America. Global Biogeochemical Cycles, 3, 241-265.
http://dx.doi.org/10.1029/GB003i003p00241 [24] Fujihara, Y., Oda, M., Horikawa, N. and Ogura, C. (2011) Hydrologic Analysis of Rainfed Rice Areas Using a Simple Semi-Distributed Water Balance Model. Water Resources Management, 25, 2061-2080.
http://dx.doi.org/10.1007/s11269-011-9796-z [25] Fujihara, Y., Tanakamaru, H., Hata, T. and Tada, A. (2003) Calibration of Rainfall-Runoff Models Using the Evolution Strategy. Proceedings of the 1st International Conference on Hydrology and Water Resources in Asia Pacific Region, 2, 885-890. [26] Fujihara, Y., Tanakamaru, H., Hata, T. and Tada, A. (2004) Performance Evaluation of Rainfall-Runoff Models Using Multi-Objective Optimization Approach. Proceedings of the 2nd International Conference on Hydrology and Water Resources in Asia Pacific Region, 2, 575-582.