Evaluation of various spatial rainfall datasets for streamflow simulation using SWAT model of Wunna basin, India

References

• Abbaspour, K. C., Yang, J., Maximov, I., Siber, R., Bogner, K., Mieleitner, J., Zobrist, J., & Srinivasan, R., (2007). Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. Journal of hydrology, 333(2–4), 413–430.
   Web of Science ®Google Scholar
• Akinsanola, A. A., Ogunjobi, K. O., Ajayi V. O., Adefisan E. A., Omotosho J. A., & Sanogo S. (2017). Comparison of five gridded precipitation products at climatological scales over West Africa. Meteorology and Atmospheric Physics, 129(6), 669–689. https://doi.org/10.1007/s00703-016-0493-6
   Web of Science ®Google Scholar
• Arnold, J. G., & Fohrer, N. (2005). SWAT2000: current capabilities and research opportunities in applied basin modeling. Hydrological Processes, 19(3), 563–572. https://doi.org/10.1002/hyp.5611
   Web of Science ®Google Scholar
• Arnold, J. G., Srinivasan, R., Muttiah, R. S., & Williams, J. R. (1998). Large area hydrologic modeling and assessment part I: Model development 1. Journal of the American Water Resources Association, 34(1), 73–89. https://doi.org/10.1111/j.1752-1688.1998.tb05961.x
   Web of Science ®Google Scholar
• Bae, D. H., Jung, I. W., & Lettenmaier, D. P. (2011). Hydrologic uncertainties in climate change from IPCC AR4 GCM simulations of the Chungju basin, Korea. Journal of Hydrology, 401(1–2), 90–105. https://doi.org/10.1016/j.jhydrol.2011.02.012
   Web of Science ®Google Scholar
• Bergstrom, S. (1976). Development and application of a conceptual runoff model for Scandinavian catchments. SMHI RHO 7. Norrköping. 134.
   Google Scholar
• Bergstrom, S. (1992). The HBV Model: Its Structure and Applications, Swedish Meteorological and Hydrological Institute (SMHI), Hydrology, Norrköping, 35.
   Google Scholar
• Bui, H. T., Ishidaira, H., & Shaowei, N. (2019). Evaluation of the use of global satellite–gauge and satellite-only precipitation products in stream flow simulations. Applied Water Science, 9(3), 53. https://doi.org/10.1007/s13201-019-0931-y
   Web of Science ®Google Scholar
• Cunderlik, M.J., 2003. Hydrologic model selection fort the CFCAS project: Assessment of water resources risk and vulnerability to changing climatic conditions, Project Report I. University of Western Ontario.
   Google Scholar
• Das, J., & Nanduri, U. V. (2018). Assessment and evaluation of potential climate change impact on monsoon flows using machine learning technique over Wainganga River basin, India. Hydrological Sciences Journal, 63(7), 1020–1046.
   Web of Science ®Google Scholar
• Dinku, T., Chidzambwa, S., Ceccato P., Connor S. J., & Ropelewski C. F. (2008). Validation of high-resolution satellite rainfall products over complex terrain. International Journal of Remote Sensing, 29(14), 4097–4110. https://doi.org/10.1080/01431160701772526
   Web of Science ®Google Scholar
• Duncan, J. M., & Biggs, E. M. (2012). Assessing the accuracy and applied use of satellite-derived precipitation estimates over Nepal. Applied Geography, 34, 626–638. https://doi.org/10.1016/j.apgeog.2012.04.001
   Web of Science ®Google Scholar
• Feldman, A. (2000). Hydrologic modeling System HEC-HMS – Technical Reference Manual. US Army Corps of Engineers, Hydrologic Engineering Center.
   Google Scholar
• Freychet, N., Duchez, Wu C.-H., Chen C.-A., Hsu H.-H., Hirschi J., Forryan A., Sinha B., New A. L., Graham T., Andrews M. B., Tu C.-Y., & Lin S.-J. (2017). Variability of hydrological extreme events in East Asia and their dynamical control: A comparison between observations and two high-resolution global climate models. Climate Dynamics, 48(3-4), 745–766. https://doi.org/10.1007/s00382-016-3108-5
   Web of Science ®Google Scholar
• Fu, Y., Xia, J., Yuan W., Xu B., Wu X., Chen Y., & Zhang H. (2016). Assessment of multiple precipitation products over major river basins of China. Theoretical and Applied Climatology, 123(1-2), 11–22. https://doi.org/10.1007/s00704-014-1339-0
   Web of Science ®Google Scholar
• Garee, K., Chen, X., Bao, A., Wang, Y., & Meng, F. (2017). Hydrological modeling of the upper Indus Basin: a case study from a high-altitude glacierized catchment Hunza. Water, 9 (17), 1–20. https://doi.org/10.3390/w9010017
   PubMedGoogle Scholar
• Gu, G., Adler, R. F., Huffman G. J., & Curtis S. (2007). Tropical rainfall variability on interannual-to-interdecadal and longer time scales derived from the GPCP monthly product. Journal of Climate, 20(15), 4033–4046. https://doi.org/10.1175/JCLI4227.1
   Web of Science ®Google Scholar
• Huffman, G. J., Adler, R. F., Morrissey M. M., Bolvin D. T., Curtis S., Joyce R., McGavock B., & Susskind J. (2001). Global precipitation at one-degree daily resolution from multisatellite observations. Journal of Hydrometeorology, 2(1), 36–50.<0036:GPAODD>2.0.CO;2
   Web of Science ®Google Scholar
• Huffman, G. J., Adler, R. F., Bolvin D. T., & Gu G. (2009). Improving the global precipitation record: GPCP version 2.1. Geophysical Research Letters, 36(17), L17808. https://doi.org/10.1029/2009GL040000
   Google Scholar
• Huffman, G. J., Bolvin, D. T., Nelkin E. J., Wolff D. B., Adler R. F., Gu G., Hong Y., Bowman K. P., & Stocker E. F. (2007). The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of Hydrometeorology, 8(1), 38–55. https://doi.org/10.1175/JHM560.1
   Web of Science ®Google Scholar
• Hughes, D. A. (2006). Comparison of satellite rainfall data with observations from gauging station networks. Journal of Hydrology, 327(3-4), 399–410. https://doi.org/10.1016/j.jhydrol.2005.11.041
   Web of Science ®Google Scholar
• Jain, S. K., Tyagi, J., & Singh, V. (2010). Simulation of runoff and sediment yield for a himalayan watershed using SWAT model. Journal of Water Resource and Protection, 2(03), 267. https://doi.org/10.4236/jwarp.2010.23031
   Google Scholar
• Kamiguchi, K., Arakawa, O., Kitoh A., Yatagai A. Hamada A., & Yasutomi N. (2010). Development of APHRO_JP, the first Japanese high-resolution daily precipitation product for more than 100 years. Hydrological Research Letters, 4, 60–64. https://doi.org/10.3178/hrl.4.60
   Google Scholar
• Kavetski, D., Kuczera, G., & Franks, S. W., (2006). Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory. Water resources research, 42(3). https://doi.org/10.1029/2005WR004368
   Web of Science ®Google Scholar
• Kneis, D., Chatterjee, C., & Singh, R. (2014). Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi). Hydrology and Earth System Sciences, 18(7), 2493–2502. https://doi.org/10.5194/hess-18-2493-2014
   Web of Science ®Google Scholar
• Lauri, H., Räsänen, T. A., & Kummu, M. (2014). Using reanalysis and remotely sensed temperature and precipitation data for hydrological modeling in monsoon climate: Mekong River case study. Journal of Hydrometeorology, 15(4), 1532–1545.
   Web of Science ®Google Scholar
• Li, D., Christakos, G., Ding X., & Wu J. (2018). Adequacy of TRMM satellite rainfall data in driving the SWAT modeling of Tiaoxi catchment (Taihu lake basin. China). Journal of Hydrology, 556, 1139–1152. https://doi.org/10.1016/j.jhydrol.2017.01.006
   Web of Science ®Google Scholar
• Marhaento, H., Booij M. J., & Hoekstra A. Y. (2018). Hydrological response to future land-use change and climate change in a tropical catchment. Hydrological Sciences Journal, 63(9), 1368–1385. https://doi.org/10.1080/02626667.2018.1511054
   Web of Science ®Google Scholar
• Markstrom, S. L., Regan, R. S., Hay, L. E., Viger, R. J., Webb, R. M., Payn, R. A., & LaFontaine, J. H. (2015). PRMS-IV, the precipitation-runoff modeling system, version 4. US Geological Survey Techniques and Methods, (6-B7 ).
   Google Scholar
• Masih, I., Maskey, S., Uhlenbrook S., & Smakhtin V. (2011). Assessing the impact of Areal precipitation input on streamflow simulations using the SWAT model 1. JAWRA Journal of the American Water Resources Association, 47(1), 179–195. https://doi.org/10.1111/j.1752-1688.2010.00502.x
   Web of Science ®Google Scholar
• Meng, X., Wang, H., & Chen, J. (2019). Profound Impacts of the China Meteorological Assimilation Dataset for SWAT model (CMADS).
   Google Scholar
• Moriasi, D., Arnold, J. G., M. W. Van Liew, Bingner R. L., Harmel R. D., & Veith T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in basin simulations. Transactions of the ASABE, 50(3), 885–900. https://doi.org/10.13031/2013.23153
   Web of Science ®Google Scholar
• Moulin, L., Gaume, E., Obled C. (2009). Uncertainties on mean areal precipitation: Assessment and impact on streamflow simulations. Hydrology and Earth System Sciences, 13(2), 99–114. https://doi.org/10.5194/hess-13-99-2009
   Web of Science ®Google Scholar
• Muzylo, A., Llorens, P., Valente F., Keizer J.J., Domingo F., & Gash J.H.C. (2009). A review of rainfall interception modelling. Journal of Hydrology, 370(1-4), 191–206. https://doi.org/10.1016/j.jhydrol.2009.02.058
   Web of Science ®Google Scholar
• Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology, 10(3), 282–290. https://doi.org/10.1016/0022-1694(70)90255-6
   Google Scholar
• Neitsch, S. L., Arnold, J. G., Kiniry, J. R., & Williams, J. R. (2011). Soil and water assessment tool theoretical documentation version 2009. Texas Water Resources Institute. Texas AgriLife Research and USDA Agriculural Research Service, Temple, Texas, USA.
   Google Scholar
• Nhi, P. T. T., Khoi, D. N., & Hoan, N. X. (2019). Evaluation of five gridded rainfall datasets in simulating streamflow in the upper Dong Nai river basin, Vietnam. International journal of digital earth, 12(3), 311–327. https://doi.org/10.1080/17538947.2018.1426647
   Web of Science ®Google Scholar
• Prakash, S., Mahesh, C., Gairola R. M., & Pal P. K. (2010). Estimation of Indian summer monsoon rainfall using Kalpana-1 VHRR data and its validation using rain gauge and GPCP data. Meteorology and Atmospheric Physics, 110(1-2), 45–57. https://doi.org/10.1007/s00703-010-0106-8
   Web of Science ®Google Scholar
• Refsgaard, J. C., & Storm, B. (1995). Computer models of basin hydrology. Water Resources Publication, 809–846.
   Google Scholar
• Reshmidevi, T. V., Kumar, D. N., Mehrotra R., & Sharma A. (2018). Estimation of the climate change impact on a catchment water balance using an ensemble of GCMs. Journal of Hydrology, 556, 1192–1204. https://doi.org/10.1016/j.jhydrol.2017.02.016
   Web of Science ®Google Scholar
• Rishma, C., Katpatal, Y. B., & Jasima, P. (2015). Assessment of enso impacts on rainfall and runoff of Venna river basin, Maharashtra using spatial approach. Discovery, 39(178), 100–106.
   Google Scholar
• Saha, S., Moorthi, S., Pan, H. L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R., Gayno, G., Wang, J., Hou, Y. T., Chuang, H-y., Juang, H-M., Sela, J., … . & Goldberg, M. (2010). The NCEP climate forecast system reanalysis. Bulletin of the American Meteorological Society, 91(8), 1015–1058.
   Web of Science ®Google Scholar
• Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer D., Hou Y-T., Chuang H., Iredell M., Ek M., Meng J., Yang R., Mendez M. P., van den Dool H., Zhang Q., Wang W., Chen M., & Becker E. (2014). The NCEP climate forecast system version 2. Journal of Climate, 27(6), 2185–2208. https://doi.org/10.1175/JCLI-D-12-00823.1
   Web of Science ®Google Scholar
• Sehgal, J. L., Mandal, D. K., Mandal, C., & Vadivelu, S. (1992). Agroecological regions of India (2nd Ed.). Tech Bull Publ. 24. 130, NBSS & LUP, Nagpur.
   Google Scholar
• Tan, M., Ibrahim, A., Duan Z., Cracknell A., & Chaplot V. (2015). Evaluation of six high-resolution satellite and ground-based precipitation products over Malaysia. Remote Sensing, 7(2), 1504–1528. https://doi.org/10.3390/rs70201504
   Web of Science ®Google Scholar
• Tang, X., Zhang, J., Wang G., Yang Q., Yang Y., Guan T., Liu C., Jin J., Liu Y., & Bao Z. (2019). Evaluating suitability of multiple precipitation products for the Lancang river basin. Chinese Geographical Science, 29(1), 37–57. https://doi.org/10.1007/s11769-019-1015-5
   Web of Science ®Google Scholar
• Terink, W., Lutz, A. F., Simons G. W. H., Immerzeel W. W., & Droogers P. (2015). SPHY v2. 0: Spatial processes in Hydrology. Geoscientific Model Development, 8(7), 2009–2034. https://doi.org/10.5194/gmd-8-2009-2015
   Web of Science ®Google Scholar
• Thampi, S. G., Raneesh, K. Y., & Surya, T. V. (2010). Influence of scale on SWAT model calibration for streamflow in a river basin in the humid tropics. Water Resources Management, 24(15), 4567–4578. https://doi.org/10.1007/s11269-010-9676-y
   Web of Science ®Google Scholar
• Tiwari, S., Kar, S. C., Bhatla R., & Bansal R. (2018). Temperature index based snowmelt runoff modelling for the Satluj River basin in the western Himalayas. Meteorological Applications, 25(2), 302–313. https://doi.org/10.1002/met.1692
   Web of Science ®Google Scholar
• Tzoraki, O., Kritsotakis, M., & Baltas, E. (2014). Spatial water Use efficiency Index towards resource sustainability: Application in the island of Crete, Greece. International Journal of Water Resources Development, 31(4), 669–681. https://doi.org/10.1080/07900627.2014.949637
   Web of Science ®Google Scholar
• USDA Soil conservation service. (1972). National Engineering Hand Book section 4 Hydrology, chapter 4-10.
   Google Scholar
• Vergara, H., Hong, Y., Gourley J. J., Anagnostou E. N., Maggioni V., Stampoulis D., & Kirstetter P-E. (2014). Effects of resolution of satellite-based rainfall estimates on hydrologic modeling skill at different scales. Journal of Hydrometeorology, 15(2), 593–613. https://doi.org/10.1175/JHM-D-12-0113.1
   Web of Science ®Google Scholar
• Vu, M. T., Raghavan, S. V., & Liong, S. Y. (2012). SWAT use of gridded observations for simulating runoff – a Vietnam river basin study. Hydrology and Earth System Sciences, 16(8), 2801–2811. https://doi.org/10.5194/hess-16-2801-2012
   Web of Science ®Google Scholar
• Wang, J. J., Adler, R. F., & Gu, G. (2008). Tropical rainfall-surface temperature relations using Tropical Rainfall Measuring Mission precipitation data. Journal of Geophysical Research: Atmospheres, 113 (D18). https://doi.org/10.1029/2007JD009540
   Web of Science ®Google Scholar
• Wang, J., Wang, W., Fu, X., & Seo, K. H. (2012). Tropical intraseasonal rainfall variability in the CFSR. Climate Dynamics, 38(11-12), 2191–2207. https://doi.org/10.1007/s00382-011-1087-0
   Web of Science ®Google Scholar
• Wheater, H., Sorooshian, S., & Sharma, K. D. (2007). Hydrological modelling in arid and semi-arid areas. Cambridge University Press.
   Google Scholar
• Worqlul, A. W., Maathuis, B., Adem A. A., Demissie S. S., Langan S., & Steenhuis T. S. (2014). Comparison of rainfall estimations by TRMM 3B42, MPEG and CFSR with ground-observed data for the lake Tana basin in Ethiopia. Hydrology and Earth System Sciences, 18(12), 4871–4881. https://doi.org/10.5194/hess-18-4871-2014
   Web of Science ®Google Scholar
• Worqlul, A. W., Yen, H., Collick, A. S., Tilahun, S. A., Langan, S., & Steenhuis, T. S. (2017). Evaluation of CFSR, TMPA 3B42 and ground-based rainfall data as input for hydrological models, in data-scarce regions: The upper Blue Nile Basin, Ethiopia. Catena, 152, 242–251. https://doi.org/10.1016/j.catena.2017.01.019
   Web of Science ®Google Scholar
• Wu, K., & Johnston, C. A. (2007). Hydrologic response to climatic variability in a great Lakes Watershed: A case study with the SWAT model. Journal of Hydrology, 337(1-2), 187–199. https://doi.org/10.1016/j.jhydrol.2007.01.030
   Web of Science ®Google Scholar
• Xue, Y., Huang, B., Hu Z-Z., Kumar A., Wen C., Behringer D., & Nadiga S. (2011). An assessment of oceanic variability in the NCEP climate forecast system reanalysis. Climate Dynamics, 37(11-12), 2511–2539. https://doi.org/10.1007/s00382-010-0954-4
   Web of Science ®Google Scholar
• Yan, B., Fang, N. F., Zhang, P. C., & Shi, Z. H. (2013). Impacts of land use change on watershed streamflow and sediment yield: An assessment using hydrologic modelling and partial least squares regression. Journal of Hydrology, 484, 26–37. https://doi.org/10.1016/j.jhydrol.2013.01.008
   Web of Science ®Google Scholar
• Yang, Y., Wang, G., Wang L., Yu J., Xu Z., & Hui D. (2014). Evaluation of gridded precipitation data for driving SWAT model in area upstream of three gorges reservoir. PLoS One, 9(11), e112725. https://doi.org/10.1371/journal.pone.0112725
   PubMed Web of Science ®Google Scholar
• Yasutomi, N., Hamada, A., & Yatagai, A. (2011). Development of a long-term daily gridded temperature dataset and its application to rain/snow discrimination of daily precipitation. Global Environmental Research, 15(2), 165–172.
   Google Scholar
• Yatagai, A., Kamiguchi, K., Arakawa, O., Hamada, A., Yasutomi, N., & Kitoh, A. (2012). APHRODITE: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bulletin of the American Meteorological Society, 93(9), 1401–1415. https://doi.org/10.1175/BAMS-D-11-00122.1
   Web of Science ®Google Scholar
• Yoo, C. (2000). On the sampling errors from raingauges and microwave attenuation measurements. Stochastic Environmental Research and Risk Assessment, 14(1), 69–77. https://doi.org/10.1007/s004770050005
   Web of Science ®Google Scholar