Articles | Volume 25, issue 10
https://doi.org/10.5194/hess-25-5493-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-5493-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Advanced sensitivity analysis of the impact of the temporal distribution and intensity of rainfall on hydrograph parameters in urban catchments
Department of Materials, Environmental Sciences and Urban Planning,Università Politecnica delle Marche, SIMAU, 60121 Ancona, Italy
Bartosz Szeląg
Faculty of Environmental, Geomatic and Energy Engineering, Kielce University of Technology, 25-314 Kielce, Poland
Adam Kiczko
Faculty of Civil and Environmental Engineering, Warsaw University of Life Sciences (SGGW), 02-797 Warsaw, Poland
Dariusz Majerek
Faculty of Fundamentals of Technology, Lublin University of Technology, 20-618 Lublin, Poland
Monika Majewska
Faculty of Environmental, Geomatic and Energy Engineering, Kielce University of Technology, 25-314 Kielce, Poland
Jakub Drewnowski
Faculty of Civil and Environmental Engineering, Gdańsk University of Technology, 80-233 Gdańsk, Poland
Grzegorz Łagód
Faculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, Poland
Related authors
No articles found.
Adam Kozioł, Adam Kiczko, Marcin Krukowski, Elżbieta Kubrak, Janusz Kubrak, Grzegorz Majewski, and Andrzej Brandyk
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-74, https://doi.org/10.5194/hess-2024-74, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Floodplain trees play a crucial role in increasing flow resistance. Their impact extends beyond floodplains to affect the main channel. The experiments reveal the influence of floodplain trees on the discharge capacity of channels with varying roughness. We determine resistance coefficients for different roughness levels of the main channel bottom. The research contributes to a deeper understanding of open-channel flow dynamics and has practical implications for river engineering.
Francesco Fatone, Bartosz Szeląg, Przemysław Kowal, Arthur McGarity, Adam Kiczko, Grzegorz Wałek, Ewa Wojciechowska, Michał Stachura, and Nicolas Caradot
Hydrol. Earth Syst. Sci., 27, 3329–3349, https://doi.org/10.5194/hess-27-3329-2023, https://doi.org/10.5194/hess-27-3329-2023, 2023
Short summary
Short summary
A novel methodology for the development of a stormwater network performance simulator including advanced risk assessment was proposed. The applied tool enables the analysis of the influence of spatial variability in catchment and stormwater network characteristics on the relation between (SWMM) model parameters and specific flood volume, as an alternative approach to mechanistic models. The proposed method can be used at the stage of catchment model development and spatial planning management.
Monika Barbara Kalinowska, Kaisa Västilä, Michael Nones, Adam Kiczko, Emilia Karamuz, Andrzej Brandyk, Adam Kozioł, and Marcin Krukowski
Hydrol. Earth Syst. Sci., 27, 953–968, https://doi.org/10.5194/hess-27-953-2023, https://doi.org/10.5194/hess-27-953-2023, 2023
Short summary
Short summary
Vegetation is commonly found in rivers and channels. Using field investigations, we evaluated the influence of different vegetation coverages on the flow and mixing in the small naturally vegetated channel. The obtained results are expected to be helpful for practitioners, enlarge our still limited knowledge, and show the further required scientific directions for a better understanding of the influence of vegetation on the flow and mixing of dissolved substances in real natural conditions.
Bartosz Szeląg, Adam Kiczko, Grzegorz Wałek, Ewa Wojciechowska, Michał Stachura, and Francesco Fatone
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-109, https://doi.org/10.5194/hess-2022-109, 2022
Manuscript not accepted for further review
Short summary
Short summary
A methodology for the development of a sewer network performance simulator and risk assesssment is given. The influence of catchment characteristics, sewer network and SWMM parameters on specific flood volume was taken into account in comparison with developed methods. The influence of spatial variability of catchment and sewer network characteristics on the relation between SWMM parameters and sewage flooding was determined, which can be used for spatial planning and urban catchment management.
Adam Kiczko, Kaisa Västilä, Adam Kozioł, Janusz Kubrak, Elżbieta Kubrak, and Marcin Krukowski
Hydrol. Earth Syst. Sci., 24, 4135–4167, https://doi.org/10.5194/hess-24-4135-2020, https://doi.org/10.5194/hess-24-4135-2020, 2020
Short summary
Short summary
The study compares the uncertainty of discharge curves for vegetated channels, calculated using several methods, including the simplest ones, based on the Manning formula and advanced approaches, providing a detailed physical representation of the channel flow processes. Parameters of each method were identified for the same data sets. The outcomes of the study include the widths of confidence intervals, showing which method was the most successful in explaining observations.
Bartosz Szeląg, Roman Suligowski, Jan Studziński, and Francesco De Paola
Hydrol. Earth Syst. Sci., 24, 595–614, https://doi.org/10.5194/hess-24-595-2020, https://doi.org/10.5194/hess-24-595-2020, 2020
Short summary
Short summary
A method for linking releases of a storm overflow with the precipitation-forming mechanism, depending on air circulation, was presented. The logit model was used to simulate overflow releases, and a rainfall generator accounting for a forming mechanism was used for forecasting. It was found that the logit model is universal and can be applied to a catchment with diverse geographical characteristics and that the precipitation-forming mechanism has an impact on the operation of the storm overflow.
A. Kiczko, R. J. Romanowicz, M. Osuch, and E. Karamuz
Nat. Hazards Earth Syst. Sci., 13, 3443–3455, https://doi.org/10.5194/nhess-13-3443-2013, https://doi.org/10.5194/nhess-13-3443-2013, 2013
Related subject area
Subject: Urban Hydrology | Techniques and Approaches: Mathematical applications
Application of logistic regression to simulate the influence of rainfall genesis on storm overflow operations: a probabilistic approach
Singularity-sensitive gauge-based radar rainfall adjustment methods for urban hydrological applications
Bartosz Szeląg, Roman Suligowski, Jan Studziński, and Francesco De Paola
Hydrol. Earth Syst. Sci., 24, 595–614, https://doi.org/10.5194/hess-24-595-2020, https://doi.org/10.5194/hess-24-595-2020, 2020
Short summary
Short summary
A method for linking releases of a storm overflow with the precipitation-forming mechanism, depending on air circulation, was presented. The logit model was used to simulate overflow releases, and a rainfall generator accounting for a forming mechanism was used for forecasting. It was found that the logit model is universal and can be applied to a catchment with diverse geographical characteristics and that the precipitation-forming mechanism has an impact on the operation of the storm overflow.
L.-P. Wang, S. Ochoa-Rodríguez, C. Onof, and P. Willems
Hydrol. Earth Syst. Sci., 19, 4001–4021, https://doi.org/10.5194/hess-19-4001-2015, https://doi.org/10.5194/hess-19-4001-2015, 2015
Short summary
Short summary
A new methodology is proposed in this paper, focusing on improving the applicability of the operational weather radar data to urban hydrology with rain gauge data. The proposed methodology employed a simple yet effective technique to extract additional information (called local singularity structure) from radar data, which was generally ignored in related works. The associated improvement can be particularly seen in capturing storm peak magnitudes, which is critical for urban applications.
Cited articles
Ashley, R. A. and Parmeter, C. F.:
Sensitivity analysis of an OLS multiple regression inference with respect to possible linear endogeneity in the explanatory variables, for both modest and for extremely large samples,
Econometrics,
8, 11, https://doi.org/10.3390/econometrics8010011, 2020.
Ballinas-González, H. A., Alcocer-Yamanaka, V. H., Canto-Rios, J. J., and Simuta-Champo, R.:
Sensitivity Analysis of the Rainfall–Runoff Modeling Parameters in Data-Scarce Urban Catchment,
Hydrology,
7, 73, https://doi.org/10.3390/hydrology7040073, 2020.
Barco, J., Wong, K. M., and Stenstrom, M. K.:
Automatic Calibration of the U.S. EPA SWMM Model for a Large Urban Catchment,
J. Hydraul. Eng.,
134, 466–474, https://doi.org/10.1061/(ASCE)0733-9429(2008)134:4(466), 2008.
Bárdossy, A.: Calibration of hydrological model parameters for ungauged catchments, Hydrol. Earth Syst. Sci., 11, 703–710, https://doi.org/10.5194/hess-11-703-2007, 2007.
Berne, A., Delrieu, G., Creutin, G., and Obled, C.:
Temporal and spatial resolution of rainfall measurements required for urban hydrology,
J. Hydrol.,
299, 166–179, https://doi.org/10.1016/j.jhydrol.2004.08.002, 2004.
Beven, K. and Binley, A.:
The future of distributed models: Model calibration and uncertainty prediction,
Hydrol. Process.,
6, 279–298, https://doi.org/10.1002/hyp.3360060305, 1992.
Buahin, C. A. and Horsburgh, J. S.:
Evaluating the simulation times and mass balance errors of component-based models: An application of OpenMI 2.0 to an urban stormwater system,
Environ. Modell. Softw.,
72, 92–109, https://doi.org/10.1016/j.envsoft.2015.07.003, 2015.
Chan, H.-C., Chen, P.-A., and Lee, J.-T.:
Rainfall-Induced Landslide Susceptibility Using a Rainfall–Runoff Model and Logistic Regression,
Water,
10, 1354, https://doi.org/10.3390/w10101354, 2018.
Chisari, C., Rizzano, G., Amadio, C., and Galdi, V.:
Sensitivity analysis and calibration of phenomenological models for seismic analyses,
Soil Dyn. Earthq. Eng.,
109, 10–22, https://doi.org/10.1016/j.soildyn.2018.02.024, 2018.
Chomicz, K.:
Ulewy i deszcze nawalne w Polsce, Wiadomości Służby Hydrologicznej i Meteorologicznej, 2, 177–260, 1951 (in Polish).
Cristiano, E., ten Veldhuis, M.-C., and van de Giesen, N.: Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a review, Hydrol. Earth Syst. Sci., 21, 3859–3878, https://doi.org/10.5194/hess-21-3859-2017, 2017.
Cristiano, E., ten Veldhuis, M. C., Wright, D. B., Smith, J. A., and van de Giesen, N.:
The Influence of Rainfall and Catchment Critical Scales on Urban Hydrological Response Sensitivity,
Water Resour. Res.,
55, 3375–3390, https://doi.org/10.1029/2018WR024143, 2019.
Crocetti, P., Eusebi, A. L., Bruni, C., Marinelli, E., Darvini, G., Carini, C. B., Bollettini, C., Recanati, V., Akyol, Ç., and Fatone, F.:
Catchment-wide validated assessment of combined sewer overflows (CSOs) in a mediterranean coastal area and possible disinfection methods to mitigate microbial contamination,
Environ. Res., 196, 110367, https://doi.org/10.1016/j.envres.2020.110367, 2020.
De Paola, F. and Ranucci, A.:
Analysis of spatial variability for stormwater capture tank assessment,
Irrig. Drain.,
61, 682–690, https://doi.org/10.1002/ird.1675, 2012.
Dotto, C. B. S., Mannina, G., Kleidorfer, M., Vezzaro, L., Henrichs, M., McCarthy, D. T., Freni, G., Rauch, W., and Deletic, A.:
Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling,
Water Res.,
46, 2545–2558, https://doi.org/10.1016/j.watres.2012.02.009, 2012.
Dunkerley, D.:
Identifying individual rain events from pluviograph records: a review with analysis of data from an Australian dryland site,
Hydrol. Process.,
22, 5024–5036, 2008.
DWA-A 118E:
Hydraulische Bemessung und Nachweis von Entwässerungssystemen, Hennef (Germany): German Association for Water, Wastewater and Waste,
ISBN 3-924063-49-4, 2006.
Fletcher, T. D., Andrieu, H., and Hamel, P.:
Understanding, management and modelling of urban hydrology and its consequences for receiving waters: A state of the art,
Adv. Water Resour.,
51, 261–279, https://doi.org/10.1016/j.advwatres.2012.09.001, 2013.
Fraga, I., Cea, L., Puertas, J., Suárez, J., Jiménez, V., and Jácome, A.:
Global Sensitivity and GLUE-Based Uncertainty Analysis of a 2D-1D Dual Urban Drainage Model,
J. Hydrol. Eng.,
21, 04016004, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001335, 2016.
Fu, G. and Butler, D.:
Copula-based frequency analysis of overflow and flooding in urban drainage systems,
J. Hydrol.,
510, 49–58, https://doi.org/10.1016/j.jhydrol.2013.12.006, 2014.
Fu, G., Butler, D., Khu, S.-T., and Sun, S.:
Imprecise probabilistic evaluation of sewer flooding in urban drainage systems using random set theory,
Water Resour. Res.,
47, 1–13, https://doi.org/10.1029/2009WR008944, 2011.
Garofalo, G., Giordano, A., Piro, P., Spezzano, G., and Vinci, A.:
A distributed real-time approach for mitigating CSO and flooding in urban drainage systems,
J. Netw. Comput. Appl.,
78, 30–42, https://doi.org/10.1016/j.jnca.2016.11.004, 2017.
Gernaey, K. V., Flores-Alsina, X., Rosen, C., Benedetti, L., and Jeppsson, U.:
Dynamic influent pollutant disturbance scenario generation using a phenomenological modelling approach,
Environ. Modell. Softw.,
26, 1255–1267, https://doi.org/10.1016/j.envsoft.2011.06.001, 2011.
Gironás, J., Roesner, L. A., Rossman, L. A., and Davis, J.:
A new applications manual for the Storm Water Management Model (SWMM),
Environ. Modell. Softw.,
25, 813–814, https://doi.org/10.1016/j.envsoft.2009.11.009, 2010.
Grum, M. and Aalderink, R. H.:
Uncertainty in return period analysis of combined sewer overflow effects using embedded Monte Carlo simulations,
Water Sci. Technol.,
39, 233–240, https://doi.org/10.1016/S0273-1223(99)00063-3, 1999.
Guan, M., Sillanpää, N., and Koivusalo, H.:
Modelling and assessment of hydrological changes in a developing urban catchment,
Hydrol. Process.,
29, 2880–2894, https://doi.org/10.1002/hyp.10410, 2015.
Hosmer, D. W. and Lemeshow, S.:
Applied Logistic Regression,
John Wiley & Sons, Inc., Hoboken, NJ, USA, 2000.
Iooss, B. and Lemaître, P.:
A Review on Global Sensitivity Analysis Methods,
Springer, New York, Uncertainty Management in Simulation-Optimization of Complex Systems, https://doi.org/10.1007/978-1-4899-7547-8_5, 101–122, 2015.
Jato-Espino, D., Sillanpää, N., Andrés-Doménech, I., and Rodriguez-Hernandez, J.:
Flood Risk Assessment in Urban Catchments Using Multiple Regression Analysis,
J. Water Res. Pl.,
144, 04017085, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000874, 2018.
Jia, H., Yao, H., Tang, Y., Yu, S. L., Field, R., and Tafuri, A. N.:
LID-BMPs planning for urban runoff control and the case study in China,
J. Environ. Manage.,
149, 65–76, https://doi.org/10.1016/j.jenvman.2014.10.003, 2015.
Joo, J., Lee, J., Kim, J. H., Jun, H., and Jo, D.:
Inter-Event Time Definition Setting Procedure for Urban Drainage Systems,
Water,
6, 45–58, https://doi.org/10.3390/w6010045, 2014.
Kiczko, A., Szeląg, B., Kozioł, A. P., Krukowski, M., Kubrak, E., Kubrak, J., and Romanowicz, R. J.:
Optimal Capacity of a Stormwater Reservoir for Flood Peak Reduction,
J. Hydrol. Eng.,
23, 04018008, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001636, 2018.
Kleidorfer, M., Deletic, A., Fletcher, T. D., and Rauch, W.:
Impact of input data uncertainties on urban stormwater model parameters,
Water Sci. Technol.,
60, 1545–1554, https://doi.org/10.2166/wst.2009.493, 2009.
Kleinbaum, D. G. and Klein, M.:
Logistic Regression,
Springer New York, New York, NY, 2010.
Krebs, G., Kokkonen, T., Valtanen, M., Setälä, H., and Koivusalo, H.:
Spatial resolution considerations for urban hydrological modelling,
J. Hydrol.,
512, 482–497, https://doi.org/10.1016/j.jhydrol.2014.03.013, 2014.
Krvavica, N. and Rubinić, J.:
Evaluation of Design Storms and Critical Rainfall Durations for Flood Prediction in Partially Urbanized Catchments,
Water,
12, 1–20, https://doi.org/10.3390/w12072044, 2020.
Leandro, J. and Martins, R.:
A methodology for linking 2D overland flow models with the sewer network model SWMM 5.1 based on dynamic link libraries,
Water Sci. Technol.,
73, 3017–3026, https://doi.org/10.2166/wst.2016.171, 2016.
Li, C., Wang, W., Xiong, J., and Chen, P.:
Sensitivity Analysis for Urban Drainage Modeling Using Mutual Information,
Entropy,
16, 5738–5752, https://doi.org/10.3390/e16115738, 2014.
Li, X. and Willems, P.:
Probabilistic flood prediction for urban sub-catchments using sewer models combined with logistic regression models,
Urban Water J.,
16, 687–697, https://doi.org/10.1080/1573062X.2020.1726409, 2019.
Link, K. G., Stobb, M. T., Di Paola, J., Neeves, K. B., Fogelson, A. L., Sindi, S. S., and Leiderman, K.:
A local and global sensitivity analysis of a mathematical model of coagulation and platelet deposition under flow,
edited by: Garcia de Frutos, P.,
PLoS One,
13, e0200917, https://doi.org/10.1371/journal.pone.0200917, 2018.
Liu, Y., Liu, J., Li, C., Yu, F., Wang, W., and Qiu, Q.:
Parameter Sensitivity Analysis of the WRF-Hydro Modeling System for Streamflow Simulation: a Case Study in Semi-Humid and Semi-Arid Catchments of Northern China,
Asia-Pac. J. Atmos. Sci., 57, 451–466, https://doi.org/10.1007/s13143-020-00205-2, 2020.
McGarity, A. E.:
Watershed Systems Analysis for Urban Storm-Water Management to Achieve Water Quality Goals,
J. Water Res. Pl.,
139, 464–477, https://doi.org/10.1061/(asce)wr.1943-5452.0000280, 2013.
Meynink, W. J. and Cordery, I.:
Critical duration of rainfall for flood estimation,
Water Resour. Res.,
12, 1209–1214, 1976.
Mirzaei, M., Huang, Y. F., El-Shafie, A., and Shatirah, A.:
Application of the generalized likelihood uncertainty estimation (GLUE) approach for assessing uncertainty in hydrological models: a review,
Stoch. Env. Res. Risk A.,
29, 1–5, https://doi.org/10.1007/s00477-014-1000-6, 2015.
Morio, J.:
Global and local sensitivity analysis methods for a physical system,
Eur. J. Phys.,
32, 1577–1583, https://doi.org/10.1088/0143-0807/32/6/011, 2011.
Mrowiec, M.:
The effective dimensioning and dynamic regulation sewage reservoirs,
Wydawnictwo Politechniki Czȩstochowskiej, Czȩstochowa, 2009.
Muleta, M. K., McMillan, J., Amenu, G. G., and Burian, S. J.: Bayesian approach for uncertainty analysis of an urban storm water model and its application to a heavily urbanized watershed, J. Hydrol. Eng., 1360–1371, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000705, 2013.
Myers, R. H., Montgomery, D. C., Vining, G. G., and Robinson, T. J.:
Generalized Linear Models: With Applications in Engineering and the Sciences, second edn.,
John Wiley & Sons, Inc., Hoboken, NJ, USA, 2010.
Ochoa-Rodriguez, S., Wang, L., Gires, A., Pina, R., Reinoso-Rondinel, R., Bruni, G., Ichiba, A., Gaitan, S., Cristiano, E., Assel, J., Kroll, S., Murlà-Tuyls, D., Tisserand, B., Schertzer, D., Tchiguirinskaia, I., Onof, C., Willems, P., and ten Veldhuis, A. E. J.:
Impact of Spatial and Temporal Resolution of Rainfall Inputs on Urban Hydrodynamic Modelling Outputs: A Multi- Catchment Investigation,
J. Hydrol.,
531, 389–407, 2015.
Pianosi, F., Beven, K., Freer, J., Hall, J. W., Rougier, J., Stephenson, D. B., and Wagener, T.:
Sensitivity analysis of environmental models: A systematic review with practical workflow,
Environ. Modell. Softw.,
79, 214–232, https://doi.org/10.1016/j.envsoft.2016.02.008, 2016.
Rabori, A. M. and Ghazavi, R.:
Urban Flood Estimation and Evaluation of the Performance of an Urban Drainage System in a Semi-Arid Urban Area Using SWMM,
Water Environ. Res.,
90, 2075–2082, https://doi.org/10.2175/106143017X15131012188213, 2018.
Razavi, S. and Gupta, H. V.:
What do we mean by sensitivity analysis? The need for comprehensive characterization of “global” sensitivity in Earth and Environmental systems models,
Water Resour. Res.,
51, 3070–3092, https://doi.org/10.1002/2014WR016527, 2015.
Rossman, L. A.:
Storm water management model user's manual Version 5.1,
Natl. Risk Manag. Lab. Off. Res. Dev., United States Environ. Prot. Agency, Cincinnati, Ohio, (September), 352, 2015.
Romanowicz, R. J. and Beven, K. J.:
Comments on generalised likelihood uncertainty estimation,
Reliab. Eng. Syst. Safe.,
91, 1315–1321, https://doi.org/10.1016/j.ress.2005.11.030, 2006.
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., and Tarantola, S.:
Global Sensitivity Analysis. The Primer,
John Wiley & Sons, Ltd, Chichester, UK, 2007.
Schilling, W.:
Rainfall data for urban hydrology: What do we need?,
Atmos. Res.,
27, 5–21, 1991.
Siekmann, M. and Pinnekamp, J.:
Indicator based strategy to adapt urban drainage systems in regard to the consequences caused by climate change,
in:
12th International Conference on Urban Drainage, Porto Alegre/Brazil, 11–16 September 2011.
Skotnicki, M. and Sowiński, M.:
The influence of depression storage on runoff from impervious surface of urban catchment,
Urban Water J.,
12, 207–218, https://doi.org/10.1080/1573062X.2013.839717, 2015.
Sumner, G.:
Precipitation: Process and Analysis,
Wiley, New York, 1988.
Szelag, B., Kiczko, A., and Dabek, L.:
Sensitivity and uncertainty analysis of hydrodynamic model (SWMM) for storm water runoff forecasting in an urban basin – A case study,
Ochr. Sr.,
38, 15–22, 2016.
Szeląg, B., Bąk, Ł., and Górski, J.:
Wpływ charakterystyk opadowych na parametry hydrogramu odpływu ze zlewni zurbanizowanej,
Woda-Środowisko-Obszary Wiejskie,
2, 103–114, 2014 (in Polish).
Szelag, B., Kiczko, A., and Dabek, L.: Sensitivity and uncertainty analysis of hydrodynamic model (SWMM) for storm water runoff forecasting in an urban basin – A case study, Ochr. Sr., 38, 15–22, 2016.
Thorndahl, S.:
Stochastic long term modelling of a drainage system with estimation of return period uncertainty,
Water Sci. Technol.,
59, 2331–2339, https://doi.org/10.2166/wst.2009.305, 2009.
Todeschini, S., Papiri, S., and Ciaponi, C.:
Performance of stormwater detention tanks for urban drainage systems in northern Italy,
J. Environ. Manage.,
101, 33–45, https://doi.org/10.1016/j.jenvman.2012.02.003, 2012.
Tolley, D., Foglia, L., and Harter, T.:
Sensitivity Analysis and Calibration of an Integrated Hydrologic Model in an Irrigated Agricultural Basin With a Groundwater-Dependent Ecosystem,
Water Resour. Res.,
55, 7876–7901, https://doi.org/10.1029/2018WR024209, 2019.
Touil, S., Degre, A., and Chabaca, M. N.: Sensitivity analysis of point and parametric pedotransfer functions for estimating water retention of soils in Algeria, SOIL, 2, 647–657, https://doi.org/10.5194/soil-2-647-2016, 2016.
Wartalska, K., Kaźmierczak, B., Nowakowska, M., and Kotowski, A.:
Analysis of Hyetographs for Drainage System Modeling,
Water,
12, 149, https://doi.org/10.3390/w12010149, 2020.
Watt, E. and Marsalek, J.:
Critical review of the evolution of the design storm event concept,
Can. J. Civil Eng.,
40, 105–113, https://doi.org/10.1139/cjce-2011-0594, 2013.
Xu, Z., Xiong, L., Li, H., Xu, J., Cai, X., Chen, K., and Wu, J.:
Runoff simulation of two typical urban green land types with the Stormwater Management Model (SWMM): sensitivity analysis and calibration of runoff parameters,
Environ. Monit. Assess.,
191, 343, https://doi.org/10.1007/s10661-019-7445-9, 2019.
Yang, X., Jomaa, S., and Rode, M.:
Sensitivity Analysis of Fully Distributed Parameterization Reveals Insights Into Heterogeneous Catchment Responses for Water Quality Modeling,
Water Resour. Res.,
55, 10935–10953, https://doi.org/10.1029/2019WR025575, 2019.
Yang, Y. and Chui, T. F. M.: Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2020-460, in review, 2020.
Zoppou, C.:
Review of urban storm water models,
Environ. Modell. Softw.,
16, 195–231, https://doi.org/10.1016/S1364-8152(00)00084-0, 2001.
Short summary
A sensitivity analysis based on a simulator of hydrograph parameters (volume, maximum flow) is shown. The method allows us to analyze the impact of calibrated hydrodynamic model parameters, including rainfall distribution and intensity, on the hydrograph. A sensitivity coefficient and the effect of the simulator uncertainty on calculation results are presented. This approach can be used to select hydrographs for calibration and validation of models, which has not been taken into account so far.
A sensitivity analysis based on a simulator of hydrograph parameters (volume, maximum flow) is...