nach oben

Water Resources Management

Erschienen in:

01.06.2019

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

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

Erschienen in: Water Resources Management | Ausgabe 9/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Vorheriger Artikel A New Integrated Portfolio Based Water-Energy-Environment Nexus in Wetland Catchments

Nächster Artikel Multi-Scenario Model Predictive Control Based on Genetic Algorithms for Level Regulation of Open Water Systems under Ensemble Forecasts

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

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

Titel: A Multi-Model Nonstationary Rainfall-Runoff Modeling Framework: Analysis and Toolbox
verfasst von: Mojtaba Sadegh
Amir AghaKouchak
Alejandro Flores
Iman Mallakpour
Mohammad Reza Nikoo
Publikationsdatum: 01.06.2019
Verlag: Springer Netherlands
Erschienen in: Water Resources Management / Ausgabe 9/2019
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-019-02283-y

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 9/2019

Cost Efficiency of English and Welsh Water Companies: a Meta-Stochastic Frontier Analysis

A GIS-Based Framework to Identify Opportunities to Use Surface Water to Offset Groundwater Withdrawals

Hydrological Uncertainty Processor (HUP) with Estimation of the Marginal Distribution by a Gaussian Mixture Model

Random Bagging Classifier and Shuffled Frog Leaping Based Optimal Sensor Placement for Leakage Detection in WDS

Advanced Evaluation Methodology for Water Quality Assessment Using Artificial Neural Network Approach

A Dynamic Flow Forecast Model for Urban Drainage Using the Coupled Artificial Neural Network