1. bookVolume 69 (2021): Issue 1 (March 2021)
Journal Details
License
Format
Journal
eISSN
1338-4333
First Published
28 Mar 2009
Publication timeframe
4 times per year
Languages
English
access type Open Access

Stepwise prediction of runoff using proxy data in a small agricultural catchment

Published Online: 26 Jan 2021
Volume & Issue: Volume 69 (2021) - Issue 1 (March 2021)
Page range: 65 - 75
Received: 01 Apr 2020
Accepted: 20 Jul 2020
Journal Details
License
Format
Journal
eISSN
1338-4333
First Published
28 Mar 2009
Publication timeframe
4 times per year
Languages
English
Abstract

In this study, the value of proxy data was explored for calibrating a conceptual hydrologic model for small ungauged basins, i.e. ungauged in terms of runoff. The study site was a 66 ha Austrian experimental catchment dominated by agricultural land use, the Hydrological Open Air Laboratory (HOAL). The three modules of a conceptual, lumped hydrologic model (snow, soil moisture accounting and runoff generation) were calibrated step-by-step using only proxy data, and no runoff observations. Using this stepwise approach, the relative runoff volume errors in the calibration and first and second validation periods were –0.04, 0.19 and 0.17, and the monthly Pearson correlation coefficients were 0.88, 0.71 and 0.64, respectively. By using proxy data, the simulation of state variables improved compared to model calibration in one step using only runoff data. Using snow and soil moisture information for model calibration, the runoff model performance was comparable to the scenario when the model was calibrated using only runoff data. While the runoff simulation performance using only proxy data did not considerably improve compared to a scenario when the model was calibrated on runoff data, the more accurately simulated state variables imply that the process consistency improved.

Keywords

Ardia, D., Ospina Arango, J.D., Giraldo Gomez, N.D., 2010a. Jump-diffusion calibration using differential evolution. Wilmott Magazine, 55, 76–79.10.1002/wilm.10034Search in Google Scholar

Ardia, D., Boudt, K., Carl, P., Mullen, K.M., Peterson, B.G., 2010b. Differential evolution with ‘DEoptim’: An application to non-convex portfolio optimization. The R Journal, 3, 1, 27–34.10.32614/RJ-2011-005Search in Google Scholar

Ardia, D., Mullen, K.M., Peterson, B.G., Ulrich, J., 2016. ‘DE-optim’: Differential evolution in ‘R’. version 2.2-4.Search in Google Scholar

Avanzi, F., Maurer, T., Glaser, S.D., Bales, R.C., Conklin, M.H., 2020. Information content of spatially distributed ground-based measurements for hydrologic-parameter calibration in mixed rain-snow mountain headwaters. Journal of Hydrology, 582, 124478.10.1016/j.jhydrol.2019.124478Search in Google Scholar

Baroni, G., Schalge, B., Rakovec, O., Kumar, R., Schüler, L., Samaniego, L., Simmer, C., Attinger, S., 2019. A comprehensive distributed hydrological modeling intercomparison to support process representation and data collection strategies. Water Resources Research, 55, 990–1010.10.1029/2018WR023941Search in Google Scholar

Bergström, S., 1976. Development and application of a conceptual runoff model for Scandinavian catchments. Department of Water Resources Engineering, Lund Institute of Technology, University of Lund, Bulletin Series A, no. 52.Search in Google Scholar

Bergström, S., Lindström, G., 2015. Interpretation of runoff processes in hydrological modelling – experience from the HBV approach. Hydrological Processes, 29, 3535–3545.10.1002/hyp.10510Search in Google Scholar

Beven, K.J., Freer, J., 2001. Equifinality, data assimilation, and uncertainty estimation in mechanistic modeling of complex environmental systems using the GLUE methodology. Journal of Hydrology, 249, 1–4, 11–29.10.1016/S0022-1694(01)00421-8Search in Google Scholar

Blöschl, G., Sivapalan, M., Wagener, T., Viglione, A., Savenije, H., 2013. Runoff Prediction in Ungauged Basins: Synthesis across Processes, Places and Scales. Cambridge University Press, United Kingdom, 465 p.10.1017/CBO9781139235761Search in Google Scholar

Blöschl, G., Blaschke, A.P., Broer, M., Bucher, C., Carr, G., Chen, X., Eder, A., Exner-Kittridge, M., Farnleitner, A., Flores-Orozco, Haas, P., Hogan, P., Kazemi Amiri, A., Oismüller, M., Parajka, J., Silasari, R., Stadler, P., Strauss, P., Vreugdenhil, M., Wagner, W., Zessner, M., 2016. The Hydrological Open Air Laboratory (HOAL) in Petzenkirchen: a hypothesis-driven observatory. Hydrology and Earth System Sciences, 20, 227–255.10.5194/hess-20-227-2016Search in Google Scholar

Criss, R.E., Winston, W.E., 2008. Do Nash values have value? Discussion and alternate proposals. Hydrological Processes, 22, 2723–2725.10.1002/hyp.7072Search in Google Scholar

Eder, A., Strauss, P., Kreuger, T., Quinton, J., 2010. Comparative calculation of suspended sediment loads with respect to hysteresis effects. Journal of Hydrology, 389, 1–2, 168–176.10.1016/j.jhydrol.2010.05.043Search in Google Scholar

Eder, A., Exner-Kittridge, M., Strauss, P., Blöschl, G., 2014. Re-suspension of bed sediment in a small stream – results from two flushing experiments. Hydrology and Earth System Sciences, 18, 1043–1052.10.5194/hess-18-1043-2014Search in Google Scholar

Esri, DigitalGlobe, GeoEye, i-cubed, USDA FSA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, GIS User Community, 2020. “World Imagery” [basemap]. https://services.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer. (March 31, 2020)Search in Google Scholar

Fenicia, F., Savenije, H.H.G., Matgen, P., Pfister, L., 2007. A comparison of alternative multiobjective calibration strategies for hydrological modeling. Water Resources Research, 43, 3, W03434.10.1029/2006WR005098Search in Google Scholar

Gelleszun, M., Kreye, P., Meon, G., 2017. Representative parameter estimation for hydrological models using a lexicographic calibration strategy. Journal of Hydrology, 553, 722–734.10.1016/j.jhydrol.2017.08.015Search in Google Scholar

Gui, Z., Liu, P., Cheng, L., Guo, S., Wang, H., Zhang, L., 2019. Improving Runoff Prediction Using Remotely Sensed Actual Evapotranspiration during Rainless Periods. Journal of Hydrologic Engineering, 24, 12, 04019050.10.1061/(ASCE)HE.1943-5584.0001856Search in Google Scholar

Hall, D.K., Riggs, G.A., 2016a. MODIS/Terra Snow Cover Daily L3 Global 500m Grid, Version 6. [January 2013 – December 2017]. Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center. DOI: 10.5067/MODIS/MOD10A1.006 [24 January 2018].Search in Google Scholar

Hall, D.K., Riggs, G.A., 2016b. MODIS/Aqua Snow Cover Daily L3 Global 500m Grid, Version 6. [January 2013 – December 2017]. Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center. DOI: 10.5067/MODIS/MYD10A1.006. [24 January 2018].Search in Google Scholar

Hay, L.E., Leavesley, G.H., Clark, M.P., Markstrom, S.L., Viger, R.J., Umemoto, M., 2006. Step wise, multiple objective calibration of a hydrologic model for a snowmelt dominated basin. Journal of the American Water Resources Association, 42, 4, 877–890.10.1111/j.1752-1688.2006.tb04501.xSearch in Google Scholar

Hogue, T.S., Sorooshian, S., Gupta, H., Holz, A., Braatz, D., 2000. A multistep automatic calibration scheme for river forecasting models. Journal of Hydrometeorology, 1, 524–542.10.1175/1525-7541(2000)001<0524:AMACSF>2.0.CO;2Search in Google Scholar

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. Hydrological Sciences Journal, 58, 6, 1198–1255.10.1080/02626667.2013.803183Search in Google Scholar

Kuppel, S., Tetzlaff, D., Maneta, M.P., Soulsby, C., 2018. What can we learn from multi-data calibration of a process-based ecohydrological model? Environmental Modelling & Software, 101, 301–316.10.1016/j.envsoft.2018.01.001Search in Google Scholar

Kuras, P.K., Alila, Y., Weiler, M., Spittlehouse, D., Winkler, R., 2011. Internal catchment process simulation in a snow-dominated basin: performance evaluation with spatiotempo-rally variable runoff generation and groundwater dynamics. Hydrological Processes, 25, 3187–3203.10.1002/hyp.8037Search in Google Scholar

Lindström, G., Johansson, B., Persson, M., Gardelin, M., Bergström, S., 1997. Development and test of the distributed HBV-96 hydrological model. Journal of Hydrology, 201, 1–4, 272–288.10.1016/S0022-1694(97)00041-3Search in Google Scholar

López, L.P., Sutanudjaja, E.H., Schellekens, J., Sterk, G., Bier-kens, M.F.P., 2017. Calibration of a large-scale hydrological model using satellite-based soil moisture and evapotranspi-ration products. Hydrology and Earth System Sciences, 21, 3125–3144.10.5194/hess-21-3125-2017Search in Google Scholar

Lu, M., Li, X., 2015. Strategy to automatically calibrate parameters of a hydrological model: a multi-step optimization scheme and its application to the Xinanjiang model. Hydro-logical Research Letters, 9, 4, 69–74.10.3178/hrl.9.69Search in Google Scholar

Merz, R., Blöschl, G., 2004. Regionalisation of catchment model parameters. Journal of Hydrology, 287, 95–123.10.1016/j.jhydrol.2003.09.028Search in Google Scholar

Merz, R., Parajka, J., Blöschl, G., 2011. Time stability of catchment model parameters: Implications for climate impact analyses. Water Resources Research, 47, 1015–1031.10.1029/2010WR009505Search in Google Scholar

Mullen, K., Ardia, D., Gil, D., Windover, D., Cline, J., 2011. ‘DEoptim’: An R Package for Global Optimization by Differential Evolution. Journal of Statistical Software, 40, 6, 1–26.10.18637/jss.v040.i06Search in Google Scholar

Murer, E., Wagenhofer, J., Aigner, F., Cline, J., 2004. Die nutzbare Feldkapazität der mineralischen Böden der land-wirtschaftlichen Nutzfläche Österreichs. Schriftenreihe BAW, Band 20, 72–78.Search in Google Scholar

Nijzink, R.C., Almeida, S., Pechlivanidis, I.G., Capell, R., Gustafssons, D., Arheimer, B., Parajka, J., Freer, J., Han, D., Wagener, T., van Nooijen, R.R.P., Savenije, H.H.G., Hrachowitz, M., 2018. Constraining conceptual hydrological models with multiple information sources. Water Resources Research, 54, 8332–8362.10.1029/2017WR021895Search in Google Scholar

Ning, S., Ishidaira, H., Wang, J., 2015. Calibrating a hydrologic model by step-wise method using GRACE TWS and discharge data. Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), 71, 4, I_85-I_90.10.2208/jscejhe.71.I_85Search in Google Scholar

Parajka, J., Merz, R., Blöschl, G., 2003. Estimation of daily potential evapotranspiration for regional water balance modeling in Austria. In: 11th International Poster Day and Institute of Hydrology Open Day “Transport of Water, Chemicals and Energy in the Soil - Crop Canopy - Atmosphere System”, Slovak Academy of Sciences, Bratislava, pp. 299–306.Search in Google Scholar

Parajka, J., Merz, R., Blöschl, G., 2007. Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments. Hydrological Processes, 21, 435–446.10.1002/hyp.6253Search in Google Scholar

Parajka, J., Viglione, A., Rogger, M., Salinas, J.L., Sivapalan, M., Blöschl, G., 2013. Comparative assessment of predictions in ungauged basins - Part 1: Runoff hydrograph studies. Hydrology and Earth System Sciences, 17, 1783–1795.10.5194/hess-17-1783-2013Search in Google Scholar

Rogger, M., Kohl, B., Pirkl, H., Viglione, A., Komma, J., Kirn-bauer, R., Merz, R., Blöschl, G., 2012. Runoff models and flood frequency statistics for design flood estimation in Austria - Do they tell a consistent story? Journal of Hydrology, 456–457, 30–43.10.1016/j.jhydrol.2012.05.068Search in Google Scholar

Savenije, H.H.G., 2001. Equifinality, a blessing in disguise? Hydrological Processes, 15, 2835–2838.10.1002/hyp.494Search in Google Scholar

Schrödter, H., 1985. Verdunstung - Anwendungsorientierte Messverfahren und Bestimmungsmethoden. Springer, 186 p. ISBN: 978-3-642-70434-5.Search in Google Scholar

Seibert, J., 2000. Multi-criteria calibration of a conceptual runoff model using a genetic algorithm. Hydrology and Earth System Sciences, 4, 215–224.10.5194/hess-4-215-2000Search in Google Scholar

Silasari, R., Parajka, J., Ressl, C., Strauss, P., Blöschl, G., 2017. Potential of time - lapse photography for identifying saturation area dynamics on agricultural hillslopes. Hydrological Processes, 31, 3610–3627.10.1002/hyp.11272Search in Google Scholar

Silvestro, F., Gabellani, S., Rudari, R., Delogu, F., Laiolo, P., Boni, G., 2015. Uncertainty reduction and parameter estimation of a distributed hydrological model with ground and remote-sensing data. Hydrology and Earth System Sciences, 19, 1727–1751.10.5194/hess-19-1727-2015Search in Google Scholar

Sleziak, P., Szolgay, J., Hlavcova, K., Danko, M., Parajka, J., 2020. The effect of the snow weighting on the temporal stability of hydrologic model efficiency and parameters. Journal of Hydrology, 583, 124639.10.1016/j.jhydrol.2020.124639Search in Google Scholar

Széles, B., Broer, M., Parajka, J., Hogan, P., Eder, A., Strauss, P., Blöschl, G., 2018. Separation of scales in transpiration effects on low flows – A spatial analysis in the Hydrological Open Air Laboratory. Water Resources Research, 54, 9, 6168–6188.10.1029/2017WR022037622101530449909Search in Google Scholar

Széles, B., Parajka, J., Hogan, P., Silasari, R., Pavlin, L., Strauss, P., Blöschl, G., 2020. The added value of different data types for calibrating and testing a hydrologic model in a small catchment. Submitted to Water Resources Research.10.1029/2019WR026153759444733149373Search in Google Scholar

Thyer, M., Beckers, J., Spittlehouse, D., Alila, Y., Winkler, R., 2004. Diagnosing a distributed hydrologic model for two high-elevation forested catchments based on detailed stand-and basin-scale data. Water Resources Research, 40, W01103.10.1029/2003WR002414Search in Google Scholar

Viglione, A., Parajka, J., Rogger, M., Salinas, J.L., Laaha, G., Sivapalan, M., Blöschl, G., 2013. Comparative assessment of predictions in ungauged basins – Part 3: Runoff signatures in Austria. Hydrology and Earth System Sciences, 17, 2263–2279.10.5194/hess-17-2263-2013Search in Google Scholar

Viglione, A., Rogger, M., Pirkl, H., Parajka, J., Blöschl, G., 2018. Conceptual model building inspired by field-mapped runoff generation mechanisms. Journal of Hydrology and Hydromechanics, 66, 3, 303–315.10.2478/johh-2018-0010Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo