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Contrasting Uncertainties in Estimating Floods and Low Flow Extremes

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Abstract

Evaluation of possible sources of uncertainty and their influence on water resource planning and extreme hydrological characteristics are very important for extreme risk reduction and management. The main objective is to identify and holistically address the uncertainty propagation from the input data to the frequency of hydrological extremes. This novel uncertainty estimation framework has four stages that comprise hydrological models, hydrological parameter sets, and frequency distribution types. The influence of uncertainty on the simulated flow is not uniform across all the selected eight catchments due to different flow regimes and runoff generation mechanisms. The result shows that uncertainty in peak flow frequency simulation mainly comes from the input data quality. Whereas, in the low flow frequency, the main contributor to the total uncertainty is model parameterization. The total uncertainty in the estimation of QT90 (extreme peak flow quantile at 90-year return period) quantile shows the interaction of input data and extreme frequency models has significant influence. In contrast, the hydrological models and hydrological parameters have a substantial impact on the QT10 (extreme low flow quantile at 10-year return period) estimation. This implies that the four factors and their interactions may cause significant risk in water resource management and flood and drought risk management. Therefore, neglecting these factors in disaster risk management, water resource planning, and evaluation of environmental impact assessment is not feasible and may lead to significant impact.

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References

  • Ajami NK, Duan Q, Sorooshian S (2007) An integrated hydrologic Bayesian multimodel combination framework: confronting input, parameter, and model structural uncertainty in hydrologic prediction. Water Resour Res 43(1):1–19. https://doi.org/10.1029/2005WR004745

    Article  Google Scholar 

  • Bae DH, Trinh HL, Nguyen HM (2018) Uncertainty estimation of the SURR model parameters and input data for the Imjin River basin using the GLUE method. J Hydro Environ Res 20(October 2016):52–62. https://doi.org/10.1016/j.jher.2018.05.001

    Article  Google Scholar 

  • Bergström, S. (1976). Development and application of a conceptual runoff model for Scandinavian catchments. Smhi, RHO 7(November), 134. https://doi.org/10.1007/s11069-004-8891-3

  • Beven K (2007) Towards integrated environmental models of everywhere: uncertainty, data and modelling as a learning process. Hydrol Earth Syst Sci 11(1):460–467. https://doi.org/10.5194/hess-11-460-2007

    Article  Google Scholar 

  • Beven K, Binley A (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrol Process 6(3):279–298. https://doi.org/10.1002/hyp.3360060305

    Article  Google Scholar 

  • Beven K, Binley A (2014) GLUE: 20 years on. Hydrol Process 28(24):5897–5918. https://doi.org/10.1002/hyp.10082

    Article  Google Scholar 

  • Blöschl G, Bierkens MFP, Chambel A, Cudennec C, Destouni G, Fiori A, Kirchner JW, McDonnell JJ, Savenije HHG, Sivapalan M, Stumpp C, Toth E, Volpi E, Carr G, Lupton C, Salinas J, Széles B, Viglione A, Aksoy H, Allen ST, Amin A, Andréassian V, Arheimer B, Aryal SK, Baker V, Bardsley E, Barendrecht MH, Bartosova A, Batelaan O, Berghuijs WR, Beven K, Blume T, Bogaard T, Borges de Amorim P, Böttcher ME, Boulet G, Breinl K, Brilly M, Brocca L, Buytaert W, Castellarin A, Castelletti A, Chen X, Chen Y, Chen Y, Chifflard P, Claps P, Clark MP, Collins AL, Croke B, Dathe A, David PC, de Barros FPJ, de Rooij G, di Baldassarre G, Driscoll JM, Duethmann D, Dwivedi R, Eris E, Farmer WH, Feiccabrino J, Ferguson G, Ferrari E, Ferraris S, Fersch B, Finger D, Foglia L, Fowler K, Gartsman B, Gascoin S, Gaume E, Gelfan A, Geris J, Gharari S, Gleeson T, Glendell M, Gonzalez Bevacqua A, González-Dugo MP, Grimaldi S, Gupta AB, Guse B, Han D, Hannah D, Harpold A, Haun S, Heal K, Helfricht K, Herrnegger M, Hipsey M, Hlaváčiková H, Hohmann C, Holko L, Hopkinson C, Hrachowitz M, Illangasekare TH, Inam A, Innocente C, Istanbulluoglu E, Jarihani B, Kalantari Z, Kalvans A, Khanal S, Khatami S, Kiesel J, Kirkby M, Knoben W, Kochanek K, Kohnová S, Kolechkina A, Krause S, Kreamer D, Kreibich H, Kunstmann H, Lange H, Liberato MLR, Lindquist E, Link T, Liu J, Loucks DP, Luce C, Mahé G, Makarieva O, Malard J, Mashtayeva S, Maskey S, Mas-Pla J, Mavrova-Guirguinova M, Mazzoleni M, Mernild S, Misstear BD, Montanari A, Müller-Thomy H, Nabizadeh A, Nardi F, Neale C, Nesterova N, Nurtaev B, Odongo VO, Panda S, Pande S, Pang Z, Papacharalampous G, Perrin C, Pfister L, Pimentel R, Polo MJ, Post D, Prieto Sierra C, Ramos MH, Renner M, Reynolds JE, Ridolfi E, Rigon R, Riva M, Robertson DE, Rosso R, Roy T, Sá JHM, Salvadori G, Sandells M, Schaefli B, Schumann A, Scolobig A, Seibert J, Servat E, Shafiei M, Sharma A, Sidibe M, Sidle RC, Skaugen T, Smith H, Spiessl SM, Stein L, Steinsland I, Strasser U, Su B, Szolgay J, Tarboton D, Tauro F, Thirel G, Tian F, Tong R, Tussupova K, Tyralis H, Uijlenhoet R, van Beek R, van der Ent RJ, van der Ploeg M, van Loon AF, van Meerveld I, van Nooijen R, van Oel PR, Vidal JP, von Freyberg J, Vorogushyn S, Wachniew P, Wade AJ, Ward P, Westerberg IK, White C, Wood EF, Woods R, Xu Z, Yilmaz KK, Zhang Y (2019) Twenty-three unsolved problems in hydrology (UPH)–a community perspective. Hydrol Sci J 64(10):1141–1158. https://doi.org/10.1080/02626667.2019.1620507

    Article  Google Scholar 

  • Breinl, K., Di, G., Girons, M., Hagenlocher, M., Vico, G., & Rutgersson, A. (2017). Can weather generation capture precipitation patterns across different climates , spatial scales and under data scarcity ? June, 1–12. https://doi.org/10.1038/s41598-017-05822-y

  • Chen X, Yang T, Wang X, Xu CY, Yu Z (2013) Uncertainty Intercomparison of different hydrological models in simulating extreme flows. Water Resour Manag 27(5):1393–1409. https://doi.org/10.1007/s11269-012-0244-5

    Article  Google Scholar 

  • Feng X, Cheng W, Fu B, Lü Y (2016) The role of climatic and anthropogenic stresses on long-term runoff reduction from the loess plateau, China. Sci Total Environ 571:688–698. https://doi.org/10.1016/j.scitotenv.2016.07.038

    Article  Google Scholar 

  • Griffis, V. W., & Stedinger, J. R. (2007). Evolution of flood frequency analysis with bulletin 17. June, 283–297

  • Hamon, W. R. (1964). Computation of direct runoff amounts from storm rainfall. In General Assembly of Berkeley, Symposium on Surface Waters: Vol. Extract of (pp. 52–62)

  • Hattermann FF, Vetter T, Breuer L, Su B, Daggupati P, Donnelly C, Fekete B, Florke F, Gosling SN, Hoffmann P, Liersch S, Masaki Y, Motovilov Y, Muller C, Samaniego L, Stacke T, Wada Y, Yang T, Krysnaova V (2018) Sources of uncertainty in hydrological climate impact assessment: a cross-scale study. Environ Res Lett 13(1). https://doi.org/10.1088/1748-9326/aa9938

  • He S, Guo S, Liu Z, Yin J, Chen K, Wu X (2018) Uncertainty analysis of hydrological multi-model ensembles based on CBP-BMA method. Hydrol Res 49(5):1636–1651. https://doi.org/10.2166/nh.2018.160

    Article  Google Scholar 

  • Her Y, Yoo SH, Cho J, Hwang S, Jeong J, Seong C (2019) Uncertainty in hydrological analysis of climate change: multi-parameter vs. multi-GCM ensemble predictions. Sci Rep 9(1):1–22. https://doi.org/10.1038/s41598-019-41334-7

    Article  Google Scholar 

  • Jin X, Xu CY, Zhang Q, Singh VP (2010) Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model. J Hydrol 383(3–4):147–155. https://doi.org/10.1016/j.jhydrol.2009.12.028

    Article  Google Scholar 

  • Kavetski D, Kuczera G, Franks SW (2006) Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory. Water Resour Res 42(3):1–9. https://doi.org/10.1029/2005WR004368

    Article  Google Scholar 

  • Kiang JE, Gazoorian C, McMillan H, Coxon G, Le Coz J, Westerberg IK, Belleville A, Sevrez D, Sikorska AE, Petersen-Øverleir A, Reitan T, Freer J, Renard B, Mansanarez V, Mason R (2018) A comparison of methods for Streamflow uncertainty estimation. Water Resour Res 54(10):7149–7176. https://doi.org/10.1029/2018WR022708

    Article  Google Scholar 

  • Kidd C, Huffman G (2011) Global precipitation measurement. Meteorol Appl 18(3):334–353. https://doi.org/10.1002/met.284

    Article  Google Scholar 

  • Mathevet T, Michel C, Andréassian V, Perrin C (2006) A bounded version of the Nash-Sutcliffe criterion for better model assessment on large sets of basins. IAHS-AISH Publication 307:211–219

    Google Scholar 

  • Meresa H (2019) Modelling of river flow in ungauged catchment using remote sensing data: application of the empirical (SCS-CN), artificial neural network (ANN) and hydrological model (HEC-HMS). Modeling Earth Systems and Environment 5(1):257–273. https://doi.org/10.1007/s40808-018-0532-z

    Article  Google Scholar 

  • Meresa HK, Gatachew MT (2019) Climate change impact on river flow extremes in the upper Blue Nile river basin. Journal of Water and Climate Change 10(4):759–781. https://doi.org/10.2166/wcc.2018.154

    Article  Google Scholar 

  • Meresa HK, Romanowicz RJ (2017) The critical role of uncertainty in projections of hydrological extremes. Hydrol Earth Syst Sci 21(8):4245–4258. https://doi.org/10.5194/hess-21-4245-2017

    Article  Google Scholar 

  • Mockler EM, Chun KP, Sapriza-Azuri G, Bruen M, Wheater HS (2016) Assessing the relative importance of parameter and forcing uncertainty and their interactions in conceptual hydrological model simulations. Adv Water Resour 97:299–313. https://doi.org/10.1016/j.advwatres.2016.10.008

    Article  Google Scholar 

  • Okoli K, Mazzoleni M, Breinl K, Di Baldassarre G (2019) A systematic comparison of statistical and hydrological methods for design flood estimation. Hydrol Res 50(6):1665–1678. https://doi.org/10.2166/nh.2019.188

    Article  Google Scholar 

  • Perrin, C., Michel, C., & Andréassian, V. (2003). Improvement of a parsimonious model for streamflow simulation. J Hydrol, 279(1–4), 275–289. https://doi.org/10.1016/S0022-1694(03)00225-7

  • Prein AF, Pendergrass AG (2019) Can we constrain uncertainty in hydrologic cycle projections? Geophys Res Lett 46(7):3911–3916. https://doi.org/10.1029/2018GL081529

    Article  Google Scholar 

  • Qi W, Zhang C, Fu G, Zhou H, Liu J (2016) Quantifying uncertainties in extreme flood predictions under climate change for a medium-sized basin in northeastern China. J Hydrometeorol 17(12):3099–3112. https://doi.org/10.1175/JHM-D-15-0212.1

    Article  Google Scholar 

  • Refsgaard JC, Drews M, Jeppesen E, Madsen H, Markandya A, Olesen JE, Porter JR, Christensen JH (2013) The role of uncertainty in climate change adaptation strategies — A Danish water management example. 337–359. https://doi.org/10.1007/s11027-012-9366-6

  • Song X, Zhang J, Zhan C, Xuan Y, Ye M, Xu C (2015) Global sensitivity analysis in hydrological modeling: review of concepts, methods, theoretical framework, and applications. J Hydrol 523(225):739–757. https://doi.org/10.1016/j.jhydrol.2015.02.013

    Article  Google Scholar 

  • Sun H, Jiang T, Jing C, Su B, Wang G (2017) Uncertainty analysis of hydrological return period estimation, taking the upper Yangtze River as an example. Hydrology and Earth System Sciences Discussions, February, pp 1–26. https://doi.org/10.5194/hess-2016-566

    Book  Google Scholar 

  • Tian Y, Xu YP, Zhang XJ (2013) Assessment of climate change impacts on river high flows through comparative use of GR4J, HBV and Xinanjiang models. Water Resour Manag 27(8):2871–2888. https://doi.org/10.1007/s11269-013-0321-4

    Article  Google Scholar 

  • Vesely FM, Paleari L, Movedi E, Bellocchi G, Confalonieri R (2019) Quantifying uncertainty due to stochastic weather generators in climate change impact studies. Sci Rep 9(1):1–8. https://doi.org/10.1038/s41598-019-45745-4

    Article  Google Scholar 

  • Vetter T, Reinhardt J, Flörke M, Van Griensven A, Hattermann F, Seidou O, Su B, Vervoort RW (2016) Evaluation of sources of uncertainty in projected hydrological changes under climate change in 12 large-scale river basins. Clim Chang 141:419–433. https://doi.org/10.1007/s10584-016-1794-y

    Article  Google Scholar 

  • Vrugt JA, ter Braak CJF, Clark MP, Hyman JM, Robinson BA (2008) Treatment of input uncertainty in hydrologic modeling: doing hydrology backward with Markov chain Monte Carlo simulation. Water Resour Res 44(12):1–15. https://doi.org/10.1029/2007wr006720

    Article  Google Scholar 

  • Winter B, Schneeberger K, Huttenlau M, Stötter J (2018) Sources of uncertainty in a probabilistic flood risk model. Nat Hazards 91(2):431–446. https://doi.org/10.1007/s11069-017-3135-5

    Article  Google Scholar 

  • Yen H, Wang R, Feng Q, Young C, Chen S, Tseng W, Wolfe JE, White MJ, Arnold G (2018) Input uncertainty on watershed modeling. Evaluation of precipitation and air temperature data by latent variables using SWAT 122(June):16–26. https://doi.org/10.1016/j.ecoleng.2018.07.014

    Article  Google Scholar 

  • Zhang C, Yan H, Takase K, Oue H (2016) Comparison of the soil physical properties and hydrological processes in two different forest type catchments. Water Resour 43(1):225–237. https://doi.org/10.1134/S0097807816120034

    Article  Google Scholar 

  • Zhao RJ (1992) The Xinanjiang model applied in China. J Hydrol 135(1): 371–381. https://doi.org/10.1016/0022-1694(92)90096-E

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Acknowledgments

The authors thank two reviewers for helpful comments in improving the manuscript. This study is supported by the CAS Pioneer Talents Program,the National Natural Science Foundation of China (Grant No. 41971032), and the CAS-CSIRO drought propagation collaboration project. We extend our thanks to Jinkai Luan for kindly providing the hydro-meteorological data of China.

Funding

This study is supported by the CAS Pioneer Talents Program and the National Natural Science Foundation of China (Grant No. 41971032).

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  1. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

    Hadush Meresa & Yongqiang Zhang

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  1. Hadush Meresa
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Contributions

H. M. designed and performed the experiments, computational framework, derived the models, and analyzed the data. Also worked out almost all the technical details and performed the numerical calculations for the suggested experiment. H. M., Y. Z. and J. T. contributed to the design and execution of the research, the analysis of the results, and the manuscript’s writing. Finally reviewed, edited, and approved by Y. Z..

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Correspondence to Yongqiang Zhang.

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Meresa, H., Zhang, Y. Contrasting Uncertainties in Estimating Floods and Low Flow Extremes. Water Resour Manage 35, 1775–1795 (2021). https://doi.org/10.1007/s11269-021-02809-3

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