Predicting the temporal transferability of model parameters through a hydrological signature analysis

Dilhani Ishanka JAYATHILAKE , Tyler SMITH

Front. Earth Sci. ›› 2020, Vol. 14 ›› Issue (1) : 110 -123.

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Front. Earth Sci. ›› 2020, Vol. 14 ›› Issue (1) : 110 -123. DOI: 10.1007/s11707-019-0755-y
RESEARCH ARTICLE
RESEARCH ARTICLE

Predicting the temporal transferability of model parameters through a hydrological signature analysis

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Abstract

Attention has recently increased on the use of hydrological signatures as a potential tool for assessing the fidelity of model structures and providing insights into the transfer of model parameters. The utility of hydrological signatures as model performance/reliability indicators in a calibration-validation testing scenario (i.e., the temporal transfer of model parameters) is the focus of this study. The Probability Distributed Model, a flexible conceptual hydrological model, is used to test the approach across a number of catchments included in the MOPEX data set. We explore the change in model performance across calibration and validation time periods and contrast it to the corresponding change in several hydrological signatures to assess signature worth. Results are explored in finer detail by utilizing a moving window approach to calibration and validation time periods. The results of this study indicated that the most informative signature can vary, both spatially and temporally, based on physical and climatic characteristics and their interaction to the model parameterization. Thus, one signature could not adequately illustrate complex watershed behaviors nor predict model performance in new analysis periods.

Keywords

streamflow / hydrological signature / validation testing / model calibration

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Dilhani Ishanka JAYATHILAKE, Tyler SMITH. Predicting the temporal transferability of model parameters through a hydrological signature analysis. Front. Earth Sci., 2020, 14(1): 110-123 DOI:10.1007/s11707-019-0755-y

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References

[1]

Auer A H Jr (1974). The rain versus snow threshold temperatures. Weatherwise, 27(2): 67

[2]

Baker D B, Richards R P, Loftus T T, Kramer J W (2004). A new flashiness index: characteristics and applications to midwestern rivers and streams. JAWRA J Am Water Res, 40(2): 503–522

[3]

Beck H E, van Dijk A I, de Roo A, Miralles D G, McVicar T R, Schellekens J, Bruijnzeel L A (2016). Global—scale regionalization of hydrologic model parameters. Water Resour Res, 52(5): 3599–3622

[4]

Beven K (2002). Towards a coherent philosophy for modelling the environment. P Royal Soc A-Math Phy, 458(2026): 2465–2484

[5]

Blöschl G, Sivapalan M, Savenije H, Wagener T, Viglione A, eds. (2013). Runoff Prediction in Ungauged Basins: Synthesis Across Processes, Places and Scales. Cambridge University Press

[6]

Casper M C, Grigoryan G, Gronz O, Gutjahr O, Heinemann G, Ley R, Rock A (2012). Analysis of projected hydrological behavior of catchments based on signature indices. Hydrol Earth Syst Sci, 16(2): 409–421

[7]

Castiglioni S, Lombardi L, Toth E, Castellarin A, Montanari A (2010). Calibration of rainfall-runoff models in ungauged basins: a regional maximum likelihood approach. Adv Water Resour, 33(10): 1235–1242

[8]

Dai A (2008). Temperature and pressure dependence of the rain-snow phase transition over land and ocean. Geophys Res Lett, 35(12)

[9]

Donnelly C, Andersson J C, Arheimer B (2016). Using flow signatures and catchment similarities to evaluate the E-HYPE multi-basin model across Europe. Hydrol Sci J, 61(2): 255–273

[10]

Duan Q, Schaake J, Andréassian V, Franks S, Goteti G, Gupta H V, Gusev Y M, Habets F, Hall A, Hay L, Hogue T, Huang M, Leavesley G, Liang X, Nasonova O N, Noilhan J, Oudin L, Sorooshian S, Wagener T, Wood E F (2006). Model parameter estimation experiment (MOPEX): an overview of science strategy and major results from the second and third workshops. J Hydrol (Amst), 320(1–2): 3–17

[11]

Euser T, Winsemius H C, Hrachowitz M, Fenicia F, Uhlenbrook S, Savenije H H G (2013). A framework to assess the realism of model structures using hydrological signatures. Hydrol Earth Syst Sci, 17(5): 1893–1912.

[12]

Ewen J (2011). Hydrograph matching method for measuring model performance. J Hydrol (Amst), 408(1–2): 178–187

[13]

Grayson R, Blöschl G (2001). Spatial patterns in catchment hydrology: observations and modelling. CUP Archive

[14]

Hingray B, Schaefli B, Mezghani A, Hamdi Y (2010). Signature-based model calibration for hydrological prediction in mesoscale Alpine catchments. Hydrolog Sci J, 55(6): 1002–1016

[15]

Hrachowitz M, Fovet O, Ruiz L, Euser T, Gharari S, Nijzink R, Freer J, Savenije H H G, Gascuel-Odoux C (2014). Process consistency in models: The importance of system signatures, expert knowledge, and process complexity. Water Resour Res, 50(9): 7445–7469

[16]

Hrachowitz M, Savenije H H G, Blöschl G, McDonnell J J, Sivapalan M, Pomeroy J W, Arheimer B, Blume T, Clark M P, Ehret U, Fenicia F, Freer J E, Gelfan A, Gupta H V, Hughes D A, Hut R W, Montanari A, Pande S, Tetzlaff D, Troch P A, Uhlenbrook S, Wagener T, Winsemius H C, Woods R A, Zehe E, Cudennec C (2013). A decade of predictions in ungauged Basins (PUB)—a review. Hydrol Sci J, 58(6): 1198–1255

[17]

Kay A L, Jones D A, Crooks S M, Kjeldsen T R, Fung C F (2007). An investigation of site-similarity approaches to generalisation of a rainfall-runoff model. Hydrol Earth Syst Sci Discuss, 11(1): 500–515

[18]

Koren V I, Finnerty B D, Schaake J C, Smith M B, Seo D J, Duan Q Y (1999). Scale dependencies of hydrologic models to spatial variability of precipitation. J Hydrol (Amst), 217(3–4): 285–302

[19]

Masih I, Uhlenbrook S, Maskey S, Ahmad M D (2010). Regionalization of a conceptual rainfall–runoff model based on similarity of the flow duration curve: a case study from the semi-arid Karkheh basin, Iran. J Hydrol (Amst), 391(1–2): 188–201

[20]

Merz R, Parajka J, Blöschl G (2011). Time stability of catchment model parameters: implications for climate impact analyses. Water Resour Res, 47(2): W02531

[21]

Montanari A, Toth E (2007). Calibration of hydrological models in the spectral domain: an opportunity for scarcely gauged basins? Water Resour Res, 43(5): W05434

[22]

Moore R J (1985). The probability-distributed principle and runoff production at point and basin scales. Hydrol Sci J, 30(2): 273–297

[23]

Moore R J (2007). The PDM rainfall-runoff model. Hydrol Earth Syst Sci Discuss, 11(1): 483–499

[24]

Nash J, Sutcliffe J V (1970). River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol (Amst), 10(3): 282–290

[25]

Olden J D, Poff N L (2003). Redundancy and the choice of hydrologic indices for characterizing streamflow regimes. River Res Appl, 19(2): 101–121

[26]

Parajka J, Blöschl G, Merz R (2007). Regional calibration of catchment models: potential for ungauged catchments. Water Resour Res, 43(6): W06406

[27]

Patil S, Stieglitz M (2012). Controls on hydrologic similarity: role of nearby gauged catchments for prediction at an ungauged catchment. Hydrol Earth Syst Sci, 16(2): 551–562

[28]

Patil S D, Stieglitz M (2015). Comparing spatial and temporal transferability of hydrological model parameters. J Hydrol (Amst), 525: 409–417

[29]

Prudhomme C, Haxton T, Crooks S, Jackson C, Barkwith A, Williamson J, Kelvin J, Mackay J, Wang L, Young A, Watts G (2013). Future flows hydrology: an ensemble of daily river flow and monthly groundwater levels for use for climate change impact assessment across Great Britain. Earth Syst Sci Data, 5(1): 101–107

[30]

Razavi S, Gupta H V (2016a). A new framework for comprehensive, robust, and efficient global sensitivity analysis: 1. theory. Water Resour Res, 52(1): 423–439

[31]

Razavi S, Gupta H V (2016b). A new framework for comprehensive, robust, and efficient global sensitivity analysis: 2. application. Water Resour Res, 52(1): 440–455

[32]

Samaniego L, Bárdossy A, Kumar R (2010). Streamflow prediction in ungauged catchments using copula-based dissimilarity measures. Water Resour Res, 46(2): W02506

[33]

Samuel J, Coulibaly P, Metcalfe R A (2011). Estimation of continuous streamflow in Ontario ungauged basins: comparison of regionalization methods. J Hydrol Eng, 16(5): 447–459

[34]

Sawicz K, Wagener T, Sivapalan M, Troch P A, Carrillo G (2011). Catchment classification: empirical analysis of hydrologic similarity based on catchment function in the eastern USA. Hydrol Earth Syst Sci, 15(9): 2895–2911

[35]

Seibert J (2003). Reliability of model predictions outside calibration conditions. Nord Hydrol, 34(5): 477–42

[36]

Seibert J, McDonnell J J (2002). On the dialog between experimentalist and modeler in catchment hydrology: use of soft data for multicriteria model calibration. Water Resour Res, 38(11): 23–1

[37]

Tolson B A, Shoemaker C A (2007). Dynamically dimensioned search algorithm for computationally efficient watershed model calibration. Water Resour Res, 43(1): W01413

[38]

Tolson B A, Shoemaker C A (2008). Efficient prediction uncertainty approximation in the calibration of environmental simulation models. Water Resour Res, 44(4): W04411

[39]

Vaze J, Post D A, Chiew F H S, Perraud J M, Viney N R, Teng J (2010). Climate non-stationarity-validity of calibrated rainfall-runoff models for use in climate change studies. J Hydrol (Amst), 394(3–4): 447–457

[40]

Wagener T, McIntyre N, Lees M J, Wheater H S, Gupta H V (2003). Towards reduced uncertainty in conceptual rainfall-runoff modelling: dynamic identifiability analysis. Hydrol Processes, 17(2): 455–476

[41]

Wagener T, Sivapalan M, Troch P, Woods R (2007). Catchment classification and hydrologic similarity. Geogr Compass, 1(4): 901–931

[42]

Westerberg I K, Guerrero J L, Younger P M, Beven K J, Seibert J, Halldin S, Freer J E, Xu C Y (2011). Calibration of hydrological models using flow-duration curves. Hydrol Earth Syst Sci, 15(7): 2205–2227

[43]

Westerberg I K, Wagener T, Coxon G, McMillan H K, Castellarin A, Montanari A, Freer J (2016). Uncertainty in hydrological signatures for gauged and ungauged catchments. Water Resour Res, 52(3): 1847–1865

[44]

Yadav M, Wagener T, Gupta H (2007). Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins. Adv Water Resour, 30(8): 1756–1774

[45]

Zhang Y Q, Viney N R, Chiew F H S, Van Dijk A I J M, Liu Y Y (2011). Improving hydrological and vegetation modelling using regional model calibration schemes together with remote sensing data. In: Proceedings of the 19th International Congress on Modelling and Simulation (MODSIM’11): 3448–3454

[46]

Zhang Y, Zheng H, Chiew F H, Arancibia J P, Zhou X (2016). Evaluating regional and global hydrological models against streamflow and evapotranspiration measurements. J Hydrometeorol, 17(3): 995–1010

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