References
- Agrawal, R., & Regulwar, D. G. (2016). Flood analysis of Dhudhana river in upper Godavari basin using HEC-RAS. Int J Eng Res, 1, 188–191. https://doi.org/https://doi.org/10.17950/ijer/v5i1/044
- Alaghmand, S., bin Abdullah, R., Abustan, I., & Eslamian, S. (2012). Comparison between capabilities of HEC-RAS and MIKE11 hydraulic models in river flood risk modelling (A case study of Sungai Kayu Ara river basin, Malaysia. International Journal of Hydrology Science and Technology, 2(3), 270–291. https://doi.org/https://doi.org/10.1504/IJHST.2012.049187
- Alvisi, S., Mascellani, G., Franchini, M., & Bárdossy, A. (2006). Water level forecasting through fuzzy logic and artificial neural network approaches. Hydrology and Earth System Sciences, 10(1), 1–17. https://doi.org/https://doi.org/10.5194/hess-10-1-2006
- Barbetta, S., Moramarco, T., & Perumal, M. (2017). A Muskingum-based methodology for river discharge estimation and rating curve development under significant lateral inflow conditions. Journal of Hydrology, 554(October), 216–232. https://doi.org/https://doi.org/10.1016/j.jhydrol.2017.09.022
- Brych, K., Dittrt, F., & Elias, V. (2002). “Development of flood boundary maps of urban areas using HEC-RAS software”. FRIEND 2002- Regional Hydrology: Bridging the Gap Between Research and Practice, 274: 237–242.
- Bulygina, N., & Gupta, H. (2011). Correcting the mathematical structure of a hydrological model via Bayesian data assimilation. Water Resources Research, https://doi.org/https://doi.org/10.1029/2010WR009614
- Chen, W., Hong, H., Li, S., Shahabi, H., Wang, Y., Wang, X., & Ahmad, B. B. (2019). Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles. Journal of Hydrology, 575, 864–873. https://doi.org/https://doi.org/10.1016/j.jhydrol.2019.05.089
- Chen, W., Li, H., Hou, E., Wang, S., Wang, G., Panahi, M., Li, T., Peng, T., Guo, C., Niu, C., Xiao, L., Wang, J., Xie, X., & Ahmad, B. B. (2018). GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models. Science of The Total Environment, 634, 853–867. https://doi.org/https://doi.org/10.1016/j.scitotenv.2018.04.055
- Chen, W., Panahi, M., Tsangaratos, P., Shahabi, H., Ilia, I., Panahi, S., Li, S., Jaafari, A., & Ahmad, B. B. (2019). Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility. Catena, 172, 212–231. https://doi.org/https://doi.org/10.1016/j.catena.2018.08.025
- Chow, V. T. (1959). Open channel hydraulics. McGraw-Hill.
- Clarke, R. T. (1999). Uncertainty in the estimation of mean annual flood due to rating-curve indefinition. Journal of Hydrology, 222(1–4), 185–190. https://doi.org/https://doi.org/10.1016/S0022-1694(99)00097-9
- Di Baldassarre, G., & Claps, P. (2011). A hydraulic study on the applicability of flood rating curves. Hydrology Research, 42(1), 10–19. https://doi.org/https://doi.org/10.2166/nh.2010.098
- Di Baldassarre, G., & Montanari, A. (2009). Uncertainty in river discharge observations: A quantitative analysis. Hydrology and Earth System Sciences, 13(6), 913–921. https://doi.org/https://doi.org/10.5194/hess-13-913-2009
- Di Baldassarre, G., & Uhlenbrook, S. (2012). Is the current flood of data enough? A treatise on research needs for the improvement of flood modelling. Hydrological Processes, 26(1), 153–158. https://doi.org/https://doi.org/10.1002/hyp.8226
- Domeneghetti, A., Castellarin, A., & Brath, A. (2012). Assessing rating-curve uncertainty and its effects on hydraulic model calibration. Hydrology and Earth System Sciences, 16(4), 1191–1202. https://doi.org/https://doi.org/10.5194/hess-16-1191-2012
- GSI. (1999). District Resource Map, issued 1999.
- Hashim, M., & Shahabi, H. (2015). Landslide susceptibility mapping using GIS-based statistical models and remote sensing data in tropical environment. Scientific Reports. https://www.nature.com/articles/srep09899.
- Hayami, S. (1951). “On the Propagation of Flood Waves, Bul”.
- Horton, R. E. (1945). Erosional development of streams and their drainage basins; hydrophysical approach to quantitative morphology. Geological Society of America Bulletin, 56(3), 275–370. https://doi.org/https://doi.org/10.1130/0016-7606(1945)56[275:EDOSAT]2.0.CO;2
- Hulsman, P., Bogaard, T. A., & Savenije, H. H. G. (2018). Rainfall-runoff modelling using river-stage time series in the absence of reliable discharge information: A case study in the Semi-Arid Mara river basin. Hydrology and Earth System Sciences, 22(10), 5081–5095. https://doi.org/https://doi.org/10.5194/hess-22-5081-2018
- Ilja Van Meerveld, H. J., Vis, M. J. P., & Seibert, J. (2017). Information content of stream level class data for hydrological model calibration. Hydrology and Earth System Sciences, 21(9), 4895–4905. https://doi.org/https://doi.org/10.5194/hess-21-4895-2017
- IWD. (2017). Annual flood report, 2017. Government of West Bengal, India Kolkata.
- IWD. (2019). Irrigation and waterways department.” Government of West Bengal, India. https://wbiwd.gov.in/.
- Jian, J., Ryu, D., Costelloe, J. F., & Su, C. H. (2017). Towards hydrological model calibration using river level measurements. Journal of Hydrology: Regional Studies, 10, 95–109. https://doi.org/https://doi.org/10.1016/j.ejrh.2016.12.085
- Karamuz, E., Osuch, M., & Romanowicz, R. J. (2016). The Influence of rating curve uncertainty on flow conditions in the river Vistula in Warsaw. In P. Rowiński & A. Marion (Eds), Hydrodynamic and mass transport at freshwater aquatic interfaces (pp. 153–166). Springer. https://doi.org/https://doi.org/10.1007/978-3-319-27750-9_13
- Kavousizadeh, A., Maghrebi, M. F., & Ahmadi, A. (2019). Stage-discharge estimation in compound open channels with composite roughness. Hydrology Research, 50(3), 809–824. https://doi.org/https://doi.org/10.2166/nh.2019.021
- Knebl, M. R., Yang, Z. L., Hutchison, K., & Maidment, D. R. (2005). Regional scale flood modeling using NEXRAD rainfall, GIS, and HEC-HMS/RAS: A case study for the San Antonio river basin summer 2002 storm event. Journal of Environmental Management, 75(4), 325–336. https://doi.org/https://doi.org/10.1016/j.jenvman.2004.11.024
- Ko, F. W. Y., & Lo, F. L. C. (2018). From landslide susceptibility to landslide frequency: A territory-wide study in Hong Kong. Engineering Geology, 242, 12–22. https://doi.org/https://doi.org/10.1016/j.enggeo.2018.05.001
- Kuczera, G. (1996). Correlated rating curve error in flood frequency inference. Water Resources Research, https://doi.org/https://doi.org/10.1029/96WR00804
- Kute, S., Kakad, S., Bhoye, V., & Walunj, A. (2014). Flood modeling of river Godavari using HEC-RAS. Int J Res Eng Technol, 3((09|9)), 81–87.
- Laio, F., Di Baldassarre, G., & Montanari, A. (2009). Model selection techniques for the frequency analysis of hydrological extremes. Water Resources Research, 45(7), https://doi.org/https://doi.org/10.1029/2007WR006666
- Liu, W. C., & Chung, C. E. (2014). Enhancing the predicting accuracy of the water stage using a physical-based model and an artificial neural network-genetic algorithm in a river system. Water (Switzerland), 6(6), 1642–1661. https://doi.org/https://doi.org/10.3390/w6061642
- McMillan, H., Freer, J., Pappenberger, F., Krueger, T., & Clark, M. (2010). Impacts of uncertain river flow data on rainfall-runoff model calibration and discharge predictions. Hydrological Processes, 24(10), 1270–1284. https://doi.org/https://doi.org/10.1002/hyp.7587
- Mcmillan, H. K., & Westerberg, I. K. (2015). Rating curve estimation under epistemic uncertainty. Hydrological Processes, 29(7), 1873–1882. https://doi.org/https://doi.org/10.1002/hyp.10419
- 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://www.sciencedirect.com/science/article/pii/0022169470902556. https://doi.org/https://doi.org/10.1016/0022-1694(70)90255-6
- Nathanson, M., Kean, J. W., Grabs, T. J., Seibert, J., Laudon, H., & Lyon, S. W. (2012). Modelling rating curves using remotely sensed LiDAR data. Hydrological Processes, 26(9), 1427–1434. https://doi.org/https://doi.org/10.1002/hyp.9225
- NRSC BHUBAN. (2016). India geo-platform of ISRO: Disaster services (NDEM Public) [Online]. Bhuban: Indian Space Research Organization. https://bhuvan-app1.nrsc.gov.in/disaster/disaster.php?id=flood
- Panda, R. K., Pramanik, N., & Bala, B. (2010). Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model. Computers and Geosciences, 36(6), 735–745. https://doi.org/https://doi.org/10.1016/j.cageo.2009.07.012
- Parhi, P. K., Sankhua, R. N., & Roy, G. P. (2012). Calibration of channel roughness for Mahanadi river, (India) using HEC-RAS model. Journal of Water Resource and Protection, 4(10), 847–850. https://doi.org/https://doi.org/10.4236/jwarp.2012.410098
- Pechlivanidis, I. G., Jackson, B., & Mcmillan, H. (2010). “The use of entropy as a model diagnostic in rainfall-runoff modelling”.
- Pelletier, P. M. (1988). Uncertainties in the single determination of river discharge: A literature review. Canadian Journal of Civil Engineering, 15(5), 834–850. https://doi.org/https://doi.org/10.1139/l88-109
- Perumal, M., & Ranga Raju, K. G. (1998). Variable-parameter stage-hydrograph routing method. II: Evaluation. Journal of Hydrologic Engineering, 3(2), 115–121. https://doi.org/https://doi.org/10.1061/(ASCE)1084-0699(1998)3:2(115)
- Quirogaa, V. M., Kurea, S., Udoa, K., & Manoa, A. (2016). Application of 2D numerical simulation for the analysis of the February 2014 Bolivian Amazonia flood: Application of the new HEC-RAS Version 5. Ribagua, 3(1), 25–33. https://doi.org/https://doi.org/10.1016/j.riba.2015.12.001
- Revilla-Romero, B., Beck, H. E., Burek, P., Salamon, P., de Roo, A., & Thielen, J. (2015). Filling the gaps: Calibrating a rainfall-runoff model using satellite-derived surface water extent. Remote Sensing of Environment, 171, 118–131. https://doi.org/https://doi.org/10.1016/j.rse.2015.10.022
- Seibert, J., & Vis, M. J. P. (2016). How informative are stream level observations in different geographic regions? Hydrological Processes, 30(14), 2498–2508. https://doi.org/https://doi.org/10.1002/hyp.10887
- Sellami, H., La Jeunesse, I., Benabdallah, S., & Vanclooster, M. (2013). Parameter and rating curve uncertainty propagation analysis of the SWAT model for two small Mediterranean catchments. Hydrological Sciences Journal, 58(8), 1635–1657. https://doi.org/https://doi.org/10.1080/02626667.2013.837222
- Sikorska, A. E., Scheidegger, A., Banasik, K., & Rieckermann, J. (2013). Considering rating curve uncertainty in water level predictions. Hydrology and Earth System Sciences, 17(11), 4415–4427. https://doi.org/https://doi.org/10.5194/hess-17-4415-2013
- Sun, W., Ishidaira, H., Bastola, S., & Yu, J. (2015). Estimating daily time series of stream flow using hydrological model calibrated based on satellite observations of river water surface width : Toward real world applications. Environmental Research, 139, 36–45. https://doi.org/https://doi.org/10.1016/j.envres.2015.01.002
- Survey of India. (1978). Topographical map. Government of India.
- Thakur, B., Parajuli, R., Kalra, A., Ahmad, S., & Gupta, R. (2017). Coupling HEC-RAS and HEC-HMS in precipitation runoff modeling and evaluating flood plain inundation map. In C. N. Dunn & V. W. Brian (Eds), World Environmental and Water Resources Congress 2017: Hydraulics and waterways and water distribution systems analysis - Selected papers from the World Environmental and Water Resources Congress 2017 (pp. 240–251). American Society of Civil Engineers (ASCE). https://doi.org/https://doi.org/10.1061/9780784480625.022
- Thyer, M., Renard, B., Kavetski, D., Kuczera, G., & Clark, M. (2011). “Improving hydrological model predictions by incorporating rating curve uncertainty.” In Proceedings of the 34th world congress of the International Association for Hydro-Environment Research and Engineering: 33rd hydrology and water resources symposium and 10th conference on hydraulics in water engineering, 1546. Engineers Australia.
- WMO. (2014). “2014 Atlas of mortality and economic losses from weather, climate and water extremes.” https://drive.google.com/a/wmo.int/file/d/0BwdvoC9AeWjUd1RwQW5Ld2hqTDQ/view.
- WMO. (2018). 2018 annual Report: WMO for the twenty-first Century.
- Yang, J., Townsend, R. D., & Daneshfar, B. (2006). Applying the HEC-RAS model and GIS techniques in river network floodplain delineation. Canadian Journal of Civil Engineering, 33(1), 19–28. https://doi.org/https://doi.org/10.1139/l05-102
- Yesilnacar, E., & Topal, T. (2005). Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek Region (Turkey). Engineering Geology, 79(3–4), 251–266. https://doi.org/https://doi.org/10.1016/j.enggeo.2005.02.002