Design hydrograph estimation in small and ungauged basins: a comparative assessment of event based (EBA4SUB) and continuous (COSMO4SUB) modeling approaches

• Autorzy: Petroselli A. , Grimaldi S. , Piscopia R. , Tauro F.

Warianty tytułu

PL

Ewaluacja hydrografów koncepcyjnych w małych zlewniach niewyposażonych w stacje pomiarowe: ocena porównawcza podejścia bazującego na zdarzeniach (EBA4SUB) z metodą modelowania ciągłego (COSMO4SUB)

• Języki publikacji: EN

Abstrakty

EN

Aim of the study Aim of the study is to provide a comparative assessment of event based (EBA4SUB) and continuous (COSMO4SUB) modeling approaches for rainfall-runoff modeling for small and ungauged basins, focusing on the influence of the Antecedent Moisture Condition (AMC) of the soil on the estimated design peak discharge. Material and methods The event based approach is the EBA4SUB software. It consists in selecting a design rainfall event, estimating the rainfall excess, and transforming it into the direct hydrograph. The continuous approach is the COSMO4SUB framework. It consists in generating a long synthetic rainfall time series at sub-daily resolution that feeds a continuous rainfall–runoff model. Then, a discharge time series is determined, providing the estimation of the runoff and the related peak discharge. Results and conclusions Results show the critical role of antecedent moisture condition (AMC) and how subjective the event-based approach is for determining the design hydrograph and peak discharge. In the event-based approach, AMC is arbitrarily selected by the analyst, while in the continuous modeling it is automatically determined using the synthetic rainfall input. Our findings indicate that the event-based approach systematically leads to a considerable overestimation of floods if AMC III (wet soil) is assumed or to a slight underestimation of floods if AMC II (average condition for soil humidity) is selected.

PL

Cel pracy Praca ma na celu stworzenie oceny porównawczej podejścia bazującego na zdarzeniach (EBA4SUB) z metodą modelowania ciągłego (COSMO4SUB) używanych do tworzenia modelu opad–odpływ w małych zlewniach niewyposażonych w stacje pomiarowe, ze szczególnym uwzględnieniem wpływu uwilgotnienia gleby przed wystąpieniem opadów (ang. Antecedent Moisture Condition, AMC) podczas przewidywanego spływu szczytowego. Materiały i metody Oprogramowanie EBA4SUB korzysta z podejścia bazującego na zdarzeniach. Wprowadza się do niego dane dotyczące zdarzenia opadowego i szacowanej skali opadu w celu wygenerowania bezpośredniego wykresu hydrograficznego. Model COSMO4SUB korzysta z podejścia ciągłego. Model generuje dane na podstawie szeregów czasowych długotrwałych syntetycznych opadów deszczu o częstotliwości wyższej niż codzienna. Dane te są wykorzystywane w modelu opad–odpływ. Następnie określa się szeregi czasowe spływu, co pozwala na oszacowanie odpływu i powiązanego spływu szczytowego. Wyniki i wnioski Wyniki świadczą o kluczowej roli uwilgotnienia gleby przed wystąpieniem opadów (AMC) oraz o tendencyjności podejścia bazującego na zdarzeniach podczas określania koncepcyjnych hydrografów oraz spływu szczytowego. Podczas stosowania podejścia bazującego na zdarzeniach analityk arbitralnie wybiera AMC, natomiast w przypadku podejścia bazującego na modelowaniu ciągłym, AMC jest określane automatycznie przy użyciu danych dotyczących opadów syntetycznych. Z badania wynika, że podejście bazujące na zdarzeniach prowadzi do regularnego i znacznego przeszacowywania skali powodzi, gdy zakłada się, że AMC jest na poziomie III (gleba wilgotna) oraz do nieznacznego niedoszacowywania skali powodzi, gdy zakłada się, że AMC jest na poziomie II (gleba średnio wilgotna).

• Wydawca: -
• Czasopismo: Acta Scientiarum Polonorum. Formatio Circumiectus
• Rocznik: 2019
• Tom: 18
• Numer: 4
• Opis fizyczny: p.113-124,fig.,ref.

Twórcy

autor

Petroselli A.

• Department of Economics, Engineering, Society and Enterprise (DEIM), University of Tuscia, Via San Camillo De Lellis snc, 01100 Viterbo, Italy

autor

Grimaldi S.

• Department for Innovation in Biological, Agro-food and Forest systems (DIBAF), University of Tuscia, Via San Camillo de Lellis snc, 01100 Viterbo, Italy

autor

Piscopia R.

autor

Tauro F.

• Department for Innovation in Biological, Agro-food and Forest systems (DIBAF), University of Tuscia, Via San Camillo de Lellis snc, 01100 Viterbo, Italy

Bibliografia

• Alfieri, L., Laio, F., Claps, P. (2008). A simulation experiment for optimal design hyetograph selection. Hydrological Processes 22, 813–820.
• Bartlett, M.S., Parolari, A.J., McDonnell, J.J., Porporato, A. (2016a). Beyond the SCS-CN method: A theoretical framework for spatially lumped rainfall-runoff response. Water Resour. Res., 52, DOI:10.1002/ 2015WR018439.
• Bartlett, M.S., Parolari, A.J., McDonnell, J.J., Porporato, A. (2016b). Framework for event-based semidistributed modeling that unifies the SCS-CN method, VIC, PDM, and TOPMODEL. Water Resour. Res., 52, DOI:10.1002/2016WR019084.
• Chow, V.T., Maidment, D.R., Mays, L.W. (1988). Applied hydrology. New York: McGraw Hill.
• Di Baldassarre, G., Castellarin, A., Montanari, A., Brath, A. (2009). Probability weighted hazard maps for comparing different flood risk management strategies: a case study. Nat. Hazards 50 (3), 479–496.
• Directive 2007/60/EC. 2007. Of the European Parliament and of the Council of 23 October 2007 on the assessment and management of flood risk. Official Journal of the European Union L288, 27–34.
• Dooge, J.C.I. (1973). Linear theory of hydrologic systems. Technical Bulletin 1468. United States Department of Agriculture.
• Durbude, D.G., Jain, M.K., Mishra, S.K. (2011). Long-term hydrologic simulation using SCS-CN-based improved soil moisture accounting procedure. Hydrological Processes 25(4), 561–579.
• Eli, R.N, Lamont, S.J. (2010). Curve numbers and urban runoff modeling – application limitations. Low Impact Development 2010: Redefining Water in the City, Proceedings of the 2010 International Low Impact Development Conference, 405–418.
• European Commission. (2000). CORINE (Coordination of Information on Environment) Database, a Key Database for European Integrated Environmental Assessment. Programme of the European Commission, European Environmental Agency (EEA).
• FEMA. (2009). Guidelines and Specifications for Flood Hazard Mapping Partners, Appendix C: Guidance for Riverine Flooding Analyses and Mapping. http://www.fema.gov/library/viewRecord.do?id=
• Garen, D.C, Moore, D.S. (2005). Curve number hydrology in water quality modeling: uses, abuses, and future directions. Journal of the American Water Resources Association, 41(2), 377–388.
• Geetha, K., Mishra, S.K., Eldho, T.I., Rastogi, A.K., Pandey, R.P. (2007). Modifications to SCS-CN method for longterm hydrologic simulation. Journal of Irrigation and Drainage Engineering, 133(5), 475–486.
• Giandotti, M. (1934). Previsione delle piene e delle magre dei corsi d’acqua (Estimation of floods and droughts of rivers). Istituto Poligrafico dello Stato, 8, 107–117. In Italian.
• Green, W.H., Ampt, G.A. (1911). Studies on soil physics. J. Agric. Sci. 4, 1–24.
• Grimaldi, S., Petroselli, A., Nardi, F., Alonso G. (2010). Flow time estimation with variable hillslope velocity in ungauged basins. Adv. Water Resour. 33, 1216–23.
• Grimaldi S., Petroselli A., Serinaldi F. (2012a). A continuous simulation model for design hydrograph estimation in ungauged watersheds. Hydrological Sciences Journal, 57(6), 1035–1051.
• Grimaldi, S., Petroselli, A., Serinaldi, F. (2012b). Design hydrograph estimation in small and ungauged watersheds: continuous simulation method versus event-based approach. Hydrol Process, 26(20), 3124–34.
• Grimaldi, S., Petroselli, A., Romano, N. (2013a). GreenAmpt Curve Number mixed procedure as an empirical tool for rainfall-runoff modelling in small and ungauged basins. Hydrological Processes, 27(8), 1253–1264.
• Grimaldi, S., Petroselli, A., Romano, N. (2013b). Curve-Number/Green-Ampt mixed procedure for streamflow predictions in ungauged basins: parameter sensitivity analysis. Hydrological Processes, 27(8), 1265–1275.
• Grimaldi, S., Petroselli A., Arcangeletti, E., Nardi F. (2013c). Flood mapping in ungauged basins using fully continuous hydrologic–hydraulic modeling. Journal of Hydrology, 487, 39–47.
• Grimaldi, S., Petroselli, A. (2015). Do we still need the rational formula? An alternative empirical procedure for peak discharge estimation in small and ungauged basins, Hydrol. Sci. J., 60, 66–7.
• Grimaldi, S., Petroselli, A., Salvadori, G., De Michele, C. (2016). Catchment compatibility via copulas: a non-parametric study of the dependence structures of hydrological responses. Advances in Water Resources, 90, 116–133.
• Hsieh, L.S., Hsu, M.H., Li, M.H. (2006). An assessment of structural measures for flood-prone lowlands with high population density along the Keelung River in Taiwan. Natural Hazards, 37(1–2), 133–152.
• Hoes, O, Nelen, F. (2005). Continuous simulation or eventbased modelling to estimate flood probabilities? In Water Resources Management III, WIT transactions on ecology and the environment, vol. 80, de Conceicao Cunha M, Brebbia CA (eds). WIT press: Southampton, UK; 3–10.2206.
• IGMI (Italian Geographic Military Institute). (2003). Raster (Matrix) Numerical DEM of Italy (Internal Factsheet, in Italian). http://www.igmi.org/pdf/info_matrix2003.pdf.
• Kalyanapu, A., Judi, D., McPherson, T., Burian, S. (2012). Monte Carlo-based flood modelling framework for estimating probability weighted flood risk. J. Flood Risk Manage. 5, 37–48.
• Koutsoyiannis, D., Onof, C. (2001) Rainfall disaggregation using adjusting procedures on a Poisson cluster model. J Hydrol. 246,109–22.
• Leclerc, G., Schaake, J.C. (1972). Derivation of hydrologic frequency curves. Report 142. Mass. Inst. of Technol., Cambridge, MA, USA.
• Merwade, V., Olivera, F., Arabi, M., Edleman, S. (2008). Uncertainty in flood inundation mapping: current issues and future directions. J. Hydrol. Eng., 13(7), 608–620.
• Moretti, G., Montanari, M. (2008). Inferring the flood frequency distribution for an ungauged basin using a spatially distributed rainfall-runoff model. Hydrology and Earth System Sciences, 12, 1141–1152.
• Nishat, S, Guo, Y, Baetz, B.W. (2010). Antecedent soil moisture conditions of different soil types in South-western Ontario, Canada. Hydrological Processes, 24, 2417–2424.
• Nnadi, F.N., Kline, F.X., Wary, H.L., Wanielista, MP. (1999). Comparison of design storm concepts using continuous simulation with short duration storms. Journal of the American Water Resources Association, 31(1), 61–85.
• NRCS (Natural Resources Conservation Service). (2008). National engineering handbook – part 630, Hydrology. U.S. Department of Agriculture, Washington, DC, USA.
• Ogden, F. L., Hawkins R.P., Walter M.T., Goodrich D.C. (2017). Comment on ‘‘Beyond the SCS-CN method: Atheoretical framework for spatially lumped rainfall-runoff response’’ by M. S. Bartlett et al., Water Resour. Res., 53, 6345–6350, DOI:10.1002/2016WR020176.
• Onof, C., Wheater, H.S. (1993) Modelling of British rainfall using a Random Parameter Bartlett–Lewis Rectangular Pulse Model. J Hydrol.149, 67–95.
• Onof, C., Wheater, H.S. (1994) Improvements to the modelling of British rainfall using a Random Parameter Bartlett–Lewis rectangular pulse model. J Hydrol.157, 177–95.
• Oliveira, F., Stolpa, D. (2003). Effect of the storm hyetograph duration and shape on the watershed response. In: Proceedings of the 82nd Annual Meeting of the Transportation Research Board, Washington, USA.
• Papaioannou, G., Efstratiadis, A., Vasiliades, L., Loukas, A., Papalexiou, S. M., Koukouvinos, A., Tsoukalas, I., Kossieris, P. (2018). An operational method for Floods Directive implementation in ungauged urban areas. Hydrology 5 (2), 24.
• Petroselli, A., Fernandez Alvarez, A. (2012). The flat area issue in DEMs and its consequences on the rainfall-runoff modelling. GIScience & Remote Sensing, 49(5), 711–734.
• Petroselli, A., Grimaldi, S. (2018). Design hydrograph estimation in small and fully ungauged basin: a preliminary assessment of the EBA4SUB framework. J. flood risk assessment. 11, 197–210.
• Piscopia, R., Petroselli, A., Grimaldi S. (2015). A software package for the prediction of design flood hydrograph in small and ungauged basins, Journal of Agricultural Engineering, XLVI, 432, 74–84.
• Ponce, V.M, Hawkins R.H. (1996). Runoff Curve Number: has it reached maturity? Journal of Hydrologic Engineering 1(1), 11–19.
• Read, L.K., Vogel R.M. (2015). Reliability, return periods, and risk under nonstationarity. Water Resour. Res., 51, DOI:10.1002/2015WR017089.
• Verhoest, NEC, Vandenberghe, S, Cabus, P, Onof, C, Meca-Figueras, T, Jameleddine S. (2010). Are stochastic point rainfall models able to preserve extreme flood statistics? Hydrological Processes, 24, 3439–3445.
• Viglione, A, Blosch,l G. (2009). On the role of storm duration in the mapping of rainfall to flood return periods. Hydrology and Earth System Sciences, 13, 205–216.
• Wałęga, A. (2016). The importance of calibration parameters on the accuracy of the floods description in the Snyder’s model. Journal of Water and Land Development, 28, 19–25.
• Winter, B., Schneeberger, K., Dung, N.V., Huttenlau M., Achleitner S., Stötter, J., Merz, B., Vorogushyn, S. (2019). A continuous modelling approach for design flood estimation on sub-daily time scale. Hydrological Sciences Journal, 64(5), 539–554.