Catchment Yield Prediction in an Ungauged Basin Using PyTOPKAPI
This study extends the use of the Drainage Area Regionalization (DAR) method in generating synthetic data and calibrating PyTOPKAPI stream yield for an ungauged basin at a daily time scale. The generation of runoff in determining a river yield has been subjected to various topographic and spatial meteorological variables, which integers form the Catchment Characteristics Model (CCM). Many of the conventional CCM models adapted in Africa have been challenged with a paucity of adequate, relevance and accurate data to parameterize and validate the potential. The purpose of generating synthetic flow is to test a hydrological model, which will not suffer from the impact of very low flows or very high flows, thus allowing to check whether the model is structurally sound enough or not. The employed physically-based, watershed-scale hydrologic model (PyTOPKAPI) was parameterized with GIS-pre-processing parameters and remote sensing hydro-meteorological variables. The validation with mean annual runoff ratio proposes a decent graphical understanding between observed and the simulated discharge. The Nash-Sutcliffe efficiency and coefficient of determination (R²) values of 0.704 and 0.739 proves strong model efficiency. Given the current climate variability impact, water planner can now assert a tool for flow quantification and sustainable planning purposes.
[1] G. Blöschl, Runoff prediction in ungauged basins: synthesis across processes, places and scales. 2013: Cambridge University Press.
[2] H. Hellebrand, et al. Identification of hydrologically similar basins by means of parameter transfer with a view to regionalization. in EGU General Assembly Conference Abstracts. 2015.
[3] T. Razavi and P. Coulibaly, Streamflow prediction in ungauged basins: review of regionalization methods. Journal of Hydrologic Engineering, 2012. 18(8): p. 958-975.
[4] J. Song, et al., Streamflow prediction in ungauged basins by regressive regionalization: a case study in Huai River Basin, China. Hydrology Research, 2015: p. nh2015155.
[5] I.K. Westerberg, et al., 2016 Uncertainty in hydrological signatures for gauged and ungauged catchments. Water Resources Research, 2016.
[6] Y. Chen, GIS and remote sensing in hydrology, water resources and environment. 2004: International Assn of Hydrological Sciences.
[7] S. Khatami, and B. Khazaei, Benefits of Gis ApplicAtion in HydroloGicAl ModelinG: A Brief Summary. VATTEN–Journal of Water Management and Research, 2014. 70(1): p. 41-49.
[8] C.C. Gianfagna, et al., Watershed area ratio accurately predicts daily streamflow in nested catchments in the Catskills, New York. Journal of Hydrology: Regional Studies, 2015. 4: p. 583-594.
[9] S. Sinclair, and G.G.S. Pegram, A sensitivity assessment of the TOPKAPI model with an added infiltration module. Journal of Hydrology, 2013. 479: p. 100-112.
[10] Z. Liu, M.L. Martina, and E. Todini, Flood forecasting using a fully distributed model: application of the TOPKAPI model to the Upper Xixian Catchment. Hydrology and Earth System Sciences Discussions, 2005. 9(4): p. 347-364.
[11] A. Dahl, D. Emerson, and A. Veccia, Evaluation of Drainage-Area Ratio Method Used to Estimate Streamflow for the Red River of the North Basin, North Dakota and Minnesota. 2006.
[12] K. Ergen, and E. Kentel, An integrated map correlation method and multiple-source sites drainage-area ratio method for estimating streamflows at ungauged catchments: A case study of the Western Black Sea Region, Turkey. Journal of environmental management, 2016. 166: p. 309-320.
[13] Y.M. Mohamoud, Prediction of daily flow duration curves and streamflow for ungauged catchments using regional flow duration curves. Hydrological Sciences Journal, 2008. 53(4): p. 706-724.
[14] S.A. Archfield, and R.M. Vogel, Map correlation method: Selection of a reference streamgage to estimate daily streamflow at ungaged catchments. Water Resources Research, 2010. 46(10).
[15] V.Y. Smakhtin, Generation of natural daily flow time‐series in regulated rivers using a non‐linear spatial interpolation technique. Regulated Rivers: Research & Management, 1999. 15(4): p. 311-323.
[16] D.G.Emerson, A.V. Vecchia, and A.L. Dahl, Evaluation of drainage-area ratio method used to estimate streamflow for the Red River of the North Basin, North Dakota and Minnesota. 2005: US Department of the Interior, US Geological Survey.
[17] R.A. Lawrie,D.D.Stretch, and R. Perissinotto, The effects of wastewater discharges on the functioning of a small temporarily open/closed estuary. Estuarine, Coastal and Shelf Science, 2010. 87(2): p. 237-245.
[18] D. Stretch, and I. Zietsman, Hydrodynamics: Flows, Residence Times, Water Levels and Mouth Dynamics Contributions to Information Requirements for the Implementation Of Resource Directed Measures for Estuaries, in Response of the Biological Communities to Flow Variation and Mouth State in Two KwaZulu-Natal Temporarily Open/closed Estuaries. 2004, JB Adams Pretoria, South Africa.
[19] R.,Schulze, and A.Pike, Development and Evaluation of an Installed Hydrological Modelling System. Water Research Commission, Pretoria, South Africa, 2004.
[20] L. Fry, et al., Identifying streamgage networks for maximizing the effectiveness of regional water balance modeling. Water Resources Research, 2013. 49(5): p. 2689-2700.
[21] K. Nruthya, and V. Srinivas, Evaluating Methods to Predict Streamflow at Ungauged Sites using Regional Flow Duration Curves: A Case Study. Aquatic Procedia, 2015. 4: p. 641-648.
[22] V.T., Chow, Open channel flow. MacGraw-Hill Book Co. Inc.: New York, 1959.
[23] GLCC, Global Land Cover Characteristics. 1997: United States Geological Survey (USGS).Available: http://edcdaac.usgs.gov/glcc. (Accessed 22 may 2016).
[24] F. Nachtergaele, et al., Harmonized World Soil Database (HWSD). Food and Agriculture Organization of the United Nations, Rome, 2009.
[25] W.D. Rawls,. Brakensiek, and K. Saxtonn, Estimation of soil water properties. Transactions of the ASAE, 1982. 25(5): p. 1316-1320.
[26] I. FAO, and I. ISRIC, JRC (2012) Harmonized World Soil Database (version 1.2). FAO, Rome, Italy and IIASA, Laxenburg, Austria, 2014.
[27] K.O., Asante, et al., Technical manual for the geospatial stream flow model (GeoSFM). 2008, U. S. Geological Survey.
[28] The Théo Vischel, et al., Implementation of the TOPKAPI model in South Africa:Initial results from the Liebenbergsvlei catchment.
[29] L. Foglia, et al., Sensitivity analysis, calibration, and testing of a distributed hydrological model using error‐based weighting and one objective function. Water Resources Research, 2009. 45(6).
[30] C.-z. Li, et al., Effect of calibration data series length on performance and optimal parameters of hydrological model. Water Science and Engineering, 2010. 3(4): p. 378-393.
[31] USGS website, 2016. Available: http://earthexplorer.usgs.gov (Accessed 22 may 2016).
[1] G. Blöschl, Runoff prediction in ungauged basins: synthesis across processes, places and scales. 2013: Cambridge University Press.
[2] H. Hellebrand, et al. Identification of hydrologically similar basins by means of parameter transfer with a view to regionalization. in EGU General Assembly Conference Abstracts. 2015.
[3] T. Razavi and P. Coulibaly, Streamflow prediction in ungauged basins: review of regionalization methods. Journal of Hydrologic Engineering, 2012. 18(8): p. 958-975.
[4] J. Song, et al., Streamflow prediction in ungauged basins by regressive regionalization: a case study in Huai River Basin, China. Hydrology Research, 2015: p. nh2015155.
[5] I.K. Westerberg, et al., 2016 Uncertainty in hydrological signatures for gauged and ungauged catchments. Water Resources Research, 2016.
[6] Y. Chen, GIS and remote sensing in hydrology, water resources and environment. 2004: International Assn of Hydrological Sciences.
[7] S. Khatami, and B. Khazaei, Benefits of Gis ApplicAtion in HydroloGicAl ModelinG: A Brief Summary. VATTEN–Journal of Water Management and Research, 2014. 70(1): p. 41-49.
[8] C.C. Gianfagna, et al., Watershed area ratio accurately predicts daily streamflow in nested catchments in the Catskills, New York. Journal of Hydrology: Regional Studies, 2015. 4: p. 583-594.
[9] S. Sinclair, and G.G.S. Pegram, A sensitivity assessment of the TOPKAPI model with an added infiltration module. Journal of Hydrology, 2013. 479: p. 100-112.
[10] Z. Liu, M.L. Martina, and E. Todini, Flood forecasting using a fully distributed model: application of the TOPKAPI model to the Upper Xixian Catchment. Hydrology and Earth System Sciences Discussions, 2005. 9(4): p. 347-364.
[11] A. Dahl, D. Emerson, and A. Veccia, Evaluation of Drainage-Area Ratio Method Used to Estimate Streamflow for the Red River of the North Basin, North Dakota and Minnesota. 2006.
[12] K. Ergen, and E. Kentel, An integrated map correlation method and multiple-source sites drainage-area ratio method for estimating streamflows at ungauged catchments: A case study of the Western Black Sea Region, Turkey. Journal of environmental management, 2016. 166: p. 309-320.
[13] Y.M. Mohamoud, Prediction of daily flow duration curves and streamflow for ungauged catchments using regional flow duration curves. Hydrological Sciences Journal, 2008. 53(4): p. 706-724.
[14] S.A. Archfield, and R.M. Vogel, Map correlation method: Selection of a reference streamgage to estimate daily streamflow at ungaged catchments. Water Resources Research, 2010. 46(10).
[15] V.Y. Smakhtin, Generation of natural daily flow time‐series in regulated rivers using a non‐linear spatial interpolation technique. Regulated Rivers: Research & Management, 1999. 15(4): p. 311-323.
[16] D.G.Emerson, A.V. Vecchia, and A.L. Dahl, Evaluation of drainage-area ratio method used to estimate streamflow for the Red River of the North Basin, North Dakota and Minnesota. 2005: US Department of the Interior, US Geological Survey.
[17] R.A. Lawrie,D.D.Stretch, and R. Perissinotto, The effects of wastewater discharges on the functioning of a small temporarily open/closed estuary. Estuarine, Coastal and Shelf Science, 2010. 87(2): p. 237-245.
[18] D. Stretch, and I. Zietsman, Hydrodynamics: Flows, Residence Times, Water Levels and Mouth Dynamics Contributions to Information Requirements for the Implementation Of Resource Directed Measures for Estuaries, in Response of the Biological Communities to Flow Variation and Mouth State in Two KwaZulu-Natal Temporarily Open/closed Estuaries. 2004, JB Adams Pretoria, South Africa.
[19] R.,Schulze, and A.Pike, Development and Evaluation of an Installed Hydrological Modelling System. Water Research Commission, Pretoria, South Africa, 2004.
[20] L. Fry, et al., Identifying streamgage networks for maximizing the effectiveness of regional water balance modeling. Water Resources Research, 2013. 49(5): p. 2689-2700.
[21] K. Nruthya, and V. Srinivas, Evaluating Methods to Predict Streamflow at Ungauged Sites using Regional Flow Duration Curves: A Case Study. Aquatic Procedia, 2015. 4: p. 641-648.
[22] V.T., Chow, Open channel flow. MacGraw-Hill Book Co. Inc.: New York, 1959.
[23] GLCC, Global Land Cover Characteristics. 1997: United States Geological Survey (USGS).Available: http://edcdaac.usgs.gov/glcc. (Accessed 22 may 2016).
[24] F. Nachtergaele, et al., Harmonized World Soil Database (HWSD). Food and Agriculture Organization of the United Nations, Rome, 2009.
[25] W.D. Rawls,. Brakensiek, and K. Saxtonn, Estimation of soil water properties. Transactions of the ASAE, 1982. 25(5): p. 1316-1320.
[26] I. FAO, and I. ISRIC, JRC (2012) Harmonized World Soil Database (version 1.2). FAO, Rome, Italy and IIASA, Laxenburg, Austria, 2014.
[27] K.O., Asante, et al., Technical manual for the geospatial stream flow model (GeoSFM). 2008, U. S. Geological Survey.
[28] The Théo Vischel, et al., Implementation of the TOPKAPI model in South Africa:Initial results from the Liebenbergsvlei catchment.
[29] L. Foglia, et al., Sensitivity analysis, calibration, and testing of a distributed hydrological model using error‐based weighting and one objective function. Water Resources Research, 2009. 45(6).
[30] C.-z. Li, et al., Effect of calibration data series length on performance and optimal parameters of hydrological model. Water Science and Engineering, 2010. 3(4): p. 378-393.
[31] USGS website, 2016. Available: http://earthexplorer.usgs.gov (Accessed 22 may 2016).
@article{"International Journal of Earth, Energy and Environmental Sciences:75252", author = "B. S. Fatoyinbo and D. Stretch and O. T. Amoo and D. Allopi", title = "Catchment Yield Prediction in an Ungauged Basin Using PyTOPKAPI", abstract = "This study extends the use of the Drainage Area Regionalization (DAR) method in generating synthetic data and calibrating PyTOPKAPI stream yield for an ungauged basin at a daily time scale. The generation of runoff in determining a river yield has been subjected to various topographic and spatial meteorological variables, which integers form the Catchment Characteristics Model (CCM). Many of the conventional CCM models adapted in Africa have been challenged with a paucity of adequate, relevance and accurate data to parameterize and validate the potential. The purpose of generating synthetic flow is to test a hydrological model, which will not suffer from the impact of very low flows or very high flows, thus allowing to check whether the model is structurally sound enough or not. The employed physically-based, watershed-scale hydrologic model (PyTOPKAPI) was parameterized with GIS-pre-processing parameters and remote sensing hydro-meteorological variables. The validation with mean annual runoff ratio proposes a decent graphical understanding between observed and the simulated discharge. The Nash-Sutcliffe efficiency and coefficient of determination (R²) values of 0.704 and 0.739 proves strong model efficiency. Given the current climate variability impact, water planner can now assert a tool for flow quantification and sustainable planning purposes.
", keywords = "Ungauged Basin, Catchment Characteristics Model, Synthetic data, GIS. ", volume = "11", number = "3", pages = "281-8", }
