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Water Resources Management

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01-06-2019

A Multi-Model Nonstationary Rainfall-Runoff Modeling Framework: Analysis and Toolbox

Authors: Mojtaba Sadegh, Amir AghaKouchak, Alejandro Flores, Iman Mallakpour, Mohammad Reza Nikoo

Published in: Water Resources Management | Issue 9/2019

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Abstract

We present a framework and toolbox for multi-model (one at a time) nonstationary modeling of rainfall-runoff (RR) transformation. The designed time-varying nature of the five available conceptual RR models in the toolbox allows for modeling processes that are nonstationary in essence. Nonstationary Rainfall-Runoff Toolbox (NRRT) delivers insights about underlying watershed processes through interactive tuning of model parameters to reflect temporal nonstationarities. The toolbox includes a number of performance metrics, along with visual graphics to evaluate the goodness-of-fit of the model simulations. Our analysis shows that the proposed time-varying RR modeling framework successfully captures the nonstationary behavior of the Wights catchment in Australia. A multi-model analysis of this catchment, that has endured deforestation, provides insights on the functionality of different conceptual modules of RR models, and their representation of the real-world.

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Aghakouchak A, Habib E (2010) Application of a conceptual hydrologic model in teaching hydrologic processes. Int J Eng Educ 26:963–973

AghaKouchak A, Nakhjiri N, Habib E (2013) An educational model for ensemble streamflow simulation and uncertainty analysis. Hydrol Earth Syst Sci 17:445–452CrossRef

Bergström S (1992) The HBV model: its structure and applications. Swedish Meteorological and Hydrological Institute, Report 4, Norrköping, Sweden

Bettenay E, Russel WRG, Hudson DR, Gilkes RJ (1980) A description of experimental catchments in the collie area, Western Australia, tech. Pap. 7. Land Resour. Manage., Perth

Beven K, Freer J (2001) Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. J Hydrol 249:11–29CrossRef

Boyle DP (2001) Multicriteria calibration of hydrologic models, PhD Thesis, Department of Hydrology and Water Resources Engineering, The University of Arizona

Boyle DP, Gupta HV, Sorooshian S (2000) Toward improved calibration of hydrologic models: combining the strengths of manual and automatic methods. Water Resour Res 36:3663–3674CrossRef

Brown AE, Podger P, Davidson A, Dowling T, Zhang L (2006) A methodology to predict the impact of changes in forest cover on flow duration curves. CSIRO Land and Water Science Report 8/06, Canberra

Byrd RH, Gilbert JC, Nocedal J (2000) A trust region method based on interior point techniques for nonlinear programming. Math Program 89:149–185CrossRef

Cheng L, AghaKouchak A, Gilleland E, Katz RW (2014) Non-stationary extreme value analysis in a changing climate. Clim Chang 127:353–369CrossRef

Efstratiadis A, Nalbantis I, Koutsoyiannis D (2015) Hydrological modelling of temporally-varying catchments: facets of change and the value of information. Hydrol Sci J 60:1438–1461CrossRef

Gharari S, Hrachowitz M, Fenicia F, Savenije H (2013) An approach to identify time consistent model parameters: sub-period calibration. Hydrol Earth Syst Sci 17:149–161CrossRef

Grenier Y (1983) Time-dependent ARMA modeling of nonstationary signals. IEEE Trans Acoust Speech Signal Process 31:899–911CrossRef

Koutsoyiannis D (2006) Nonstationarity versus scaling in hydrology. J Hydrol 324:239–254CrossRef

Koutsoyiannis D (2011) Hurst-Kolmogorov dynamics and uncertainty1. J Am Water Resour Assoc 47:481–495CrossRef

Koutsoyiannis D, Montanari A (2015) Negligent killing of scientific concepts: the stationarity case. Hydrol Sci J 60:1174–1183CrossRef

Le Moine N (2008) Le bassin versant de surface vu par le souterrain: une voie d’amélioration des performances et du réalisme des modèles pluie-débit?, Ph.D. thesis, Paris 6

Leclerc M, Ouarda TB (2007) Non-stationary regional flood frequency analysis at ungauged sites. J Hydrol 343:254–265CrossRef

Lins HF, Cohn TA (2011) Stationarity: wanted dead or alive? J Am Water Resour Assoc 47:475–480CrossRef

Marshall L, Sharma A, Nott D (2006) Modeling the catchment via mixtures: issues of model specification and validation. Water Resour Res 42:W11409CrossRef

Milly PCD, Betancourt J, Falkenmark M, Hirsch RM, Kundzewicz ZW, Lettenmaier DP, Stouffer RJ (2008) Stationarity is dead: whither water management? Science 319:573–574. https://doi.org/10.1126/science.1151915, http://science.sciencemag.org/content/319/5863/573 CrossRef

Mohammadpour J, Scherer CW (2012) Control of linear parameter varying systems with applications. Springer Science & Business Media, BostonCrossRef

Mroczkowski M, Raper PG, Kuczera G (1997) The quest for more powerful validation of conceptual catchment models. Water Resour Res 33:2325–2335CrossRef

Nash JE et al (1960) A unit hydrograph study, with particular reference to British catchments. Proc Inst Civ Eng 17(3):249–282

Niedzwiecki M (2000) Identification of time-varying processes. Wiley, New York

Ouarda T, El-Adlouni S (2011) Bayesian nonstationary frequency analysis of hydrological variables. J Am Water Resour Assoc 47:496–505

Pathiraja S, Marshall L, Sharma A, Moradkhani H (2016a) Detecting non-stationary hydrologic model parameters in a paired catchment system using data assimilation. Adv Water Resour 94:103–119CrossRef

Pathiraja S, Marshall L, Sharma A, Moradkhani H (2016b) Hydrologic modeling in dynamic catchments: a data assimilation approach. Water Resour Res 52:3350–3372CrossRef

Perrin C (2000) Vers une amélioration d’un modele pluie-débit au travers d’une approche comparative, Ph.D. thesis, Ph. D. Thesis, INP Grenoble/Cemagref Antony, France

Perrin C, Michel C, Andréassian V (2003) Improvement of a parsimonious model for streamflow simulation. J Hydrol 279:275–289CrossRef

Pushpalatha R, Perrin C, Le Moine N, Mathevet T, Andréassian V (2011) A downward structural sensitivity analysis of hydrological models to improve low-flow simulation. J Hydrol 411:66–76CrossRef

Richards JA (1983) Analysis of periodically time-varying systems. Springer Science & Business Media, New YorkCrossRef

Sadegh M, Vrugt JA, Xu C, Volpi E (2015) The stationarity paradigm revisited: hypothesis testing using diagnostics, summary metrics, and DREAM (ABC). Water Resour Res 51:9207–9231CrossRef

Sadegh M, Ragno E, AghaKouchak A (2017) Multivariate copula analysis toolbox (MvCAT): describing dependence and underlying uncertainty using a Bayesian framework. Water Resour Res 53(6):5166–5183CrossRef

Sadegh M, Majd MS, Hernandez J, Haghighi AT (2018) The quest for hydrological signatures: effects of data transformation on Bayesian inference of watershed models. Water Resour Manag 32(5):1867–1881CrossRef

Salas JD, Obeysekera J (2013) Revisiting the concepts of return period and risk for nonstationary hydrologic extreme events. J Hydrol Eng 19:554–568CrossRef

Schaake JC, Koren VI, Duan Q-Y, Mitchell K, Chen F (1996) Simple water balance model for estimating runoff at different spatial and temporal scales. J Geophys Res Atmos 101:7461–7475CrossRef

Singh VP, Woolhiser DA (2002) Mathematical modeling of watershed hydrology. J Hydrol Eng 7:270–292CrossRef

Waltz RA, Morales JL, Nocedal J, Orban D (2006) An interior algorithm for nonlinear optimization that combines line search and trust region steps. Math Program 107:391–408CrossRef

Westra S, Thyer M, Leonard M, Kavetski D, Lambert M (2014) A strategy for diagnosing and interpreting hydrological model nonstationarity. Water Resour Res 50:5090–5113CrossRef

Title: A Multi-Model Nonstationary Rainfall-Runoff Modeling Framework: Analysis and Toolbox
Authors: Mojtaba Sadegh
Amir AghaKouchak
Alejandro Flores
Iman Mallakpour
Mohammad Reza Nikoo
Publication date: 01-06-2019
Publisher: Springer Netherlands
Published in: Water Resources Management / Issue 9/2019
Print ISSN: 0920-4741
Electronic ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-019-02283-y

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