1. bookVolume 68 (2020): Issue 2 (June 2020)
Journal Details
First Published
28 Mar 2009
Publication timeframe
4 times per year
access type Open Access

Controls on event runoff coefficients and recession coefficients for different runoff generation mechanisms identified by three regression methods

Published Online: 26 May 2020
Volume & Issue: Volume 68 (2020) - Issue 2 (June 2020)
Page range: 155 - 169
Received: 09 May 2019
Accepted: 30 Jan 2020
Journal Details
First Published
28 Mar 2009
Publication timeframe
4 times per year

The event runoff coefficient (Rc) and the recession coefficient (tc) are of theoretical importance for understanding catchment response and of practical importance in hydrological design. We analyse 57 event periods in the period 2013 to 2015 in the 66 ha Austrian Hydrological Open Air Laboratory (HOAL), where the seven subcatchments are stratified by runoff generation types into wetlands, tile drainage and natural drainage. Three machine learning algorithms (Random forest (RF), Gradient Boost Decision Tree (GBDT) and Support vector machine (SVM)) are used to estimate Rc and tc from 22 event based explanatory variables representing precipitation, soil moisture, groundwater level and season. The model performance of the SVM algorithm in estimating Rc and tc is generally higher than that of the other two methods, measured by the coefficient of determination R2, and the performance for Rc is higher than that for tc. The relative importance of the explanatory variables for the predictions, assessed by a heatmap, suggests that Rc of the tile drainage systems is more strongly controlled by the weather conditions than by the catchment state, while the opposite is true for natural drainage systems. Overall, model performance strongly depends on the runoff generation type.


Asefa, T., Kemblowski, M., McKee, M., Khalil, A., 2006. Multi-time scale stream flow predictions: the support vector machines approach. Journal of Hydrology, 318, 1–4, 7–16.10.1016/j.jhydrol.2005.06.001Search in Google Scholar

Basak, D., Pal, S., Patranabis, D.C., 2007. Support vector regression. Neural Information Processing-Letters and Reviews, 11, 10, 203–224.Search in Google Scholar

Baudron, P., Alonso-Sarría, F., García-Aróstegui, J.L., Canovas-Garcia, F., Martinez-Vicente, D., Moreno-Brotons, J., 2013. Identifying the origin of groundwater samples in a multi-layer aquifer system with Random Forest classification. Journal of Hydrology, 499, 303–315.10.1016/j.jhydrol.2013.07.009Search in Google Scholar

Ben-Hur, A., Weston, J., 2010. A user’s guide to support vector machines. In: Carugo, O., Eisenhaber, F. (Eds.): Data Mining Techniques for the Life Sciences. Methods in Molecular Biology (Methods and Protocols), Vol 609. Humana Press, 2010, pp. 223–239.Search in Google Scholar

Biswal, B., Kumar, D.N., 2014. What mainly controls recession flows in river basins? Advances in water resources, 65, 25–33.10.1016/j.advwatres.2014.01.001Search 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, A., 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, 1, 227.10.5194/hess-20-227-2016Search 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, Cambridge.10.1017/CBO9781139235761Search in Google Scholar

Blume, T., Zehe, E., Bronstert, A., 2007. Rainfall–runoff response, event-based runoff coefficients and hydrograph separation. Hydrological Sciences Journal, 52, 5, 843–862.10.1623/hysj.52.5.843Search in Google Scholar

Breiman, L., 2001. Random forests. Machine Learning, 45, 1, 5–32.10.1023/A:1010933404324Search in Google Scholar

Brutsaert, W., Nieber, J.L., 1977. Regionalized drought flow hydrographs from a mature glaciated plateau. Water Resources Research, 13, 3, 637–643.10.1029/WR013i003p00637Search in Google Scholar

Cánovas-García, F., Alonso-Sarría, F., Gomariz-Castillo, F., Onate-Valdivieso, F., 2017. Modification of the random forest algorithm to avoid statistical dependence problems when classifying remote sensing imagery. Computers & Geosciences, 103, 1–11.10.1016/j.cageo.2017.02.012Search in Google Scholar

Chapelle, O., Vapnik, V., 2000. Model selection for support vector machines. In: NIPS’99 Proceedings of the 12th International Conference on Neural Information Processing Systems, Denver, CO, November 29 - December 4, 1999, pp. 230–236.Search in Google Scholar

Chapman, T.G., Maxwell, A.I., 1996. Baseflow separationcomparison of numerical methods with tracer experiments. In: Hydrology and Water Resources 23rd Symposium, Hobart, 1996. National Conference Publication – Institution of Engineers Australia NCP, 2(5), pp. 539–546.Search in Google Scholar

Chen, B., Krajewski, W.F., Helmers, M.J., Zhang, Z., 2019. Spatial variability and temporal persistence of event runoff coefficients for cropland hillslopes. Water Resources Research, 55, 2, 1583–1597.10.1029/2018WR023576Search in Google Scholar

Cortes, C., Vapnik, V., 1995. Support-vector networks. Machine Learning, 20, 3, 273–297.10.1007/BF00994018Search in Google Scholar

Cortez, P., Embrechts, M.J., 2013. Using sensitivity analysis and visualization techniques to open black box data mining models. Information Sciences, 225, 1–17.10.1016/j.ins.2012.10.039Search in Google Scholar

Deka, P.C., 2014. Support vector machine applications in the field of hydrology: a review. Applied Soft Computing, 19, 372–386.10.1016/j.asoc.2014.02.002Search in Google Scholar

Dietterich, T.G., 1997. Machine-learning research. AI Magazine, 18, 4, 97–97.Search in Google Scholar

Erdal, H.I., Karakurt, O., 2013. Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms. Journal of Hydrology, 477, 119–128.10.1016/j.jhydrol.2012.11.015Search in Google Scholar

Exner-Kittridge, M., Strauss, P., Blöschl, G., Eder, A., Saracevic, E., Zessner, M., 2016. The seasonal dynamics of the stream sources and input flow paths of water and nitrogen of an Austrian headwater agricultural catchment. Science of the Total Environment, 542, 935–945.10.1016/j.scitotenv.2015.10.151Search in Google Scholar

Friedman, J.H., 2001. Greedy function approximation: a gradient boosting machine. Annals of Statistics, 45, 1, 1189–1232.10.1214/aos/1013203451Search in Google Scholar

Friedman, J.H., 2002. Stochastic gradient boosting. Computational Statistics & Data Analysis, 38, 4, 367–378.10.1016/S0167-9473(01)00065-2Search in Google Scholar

Gaál, L., Szolgay, J., Kohnová, S., Parajka, J., Merz, R., Viglione, A., Blöschl, G., 2012. Flood timescales: Understanding the interplay of climate and catchment processes through comparative hydrology. Water Resources Research, 48, W04511.10.1029/2011WR011509Search in Google Scholar

Gottschalk, L., Weingartner, R., 1998. Distribution of peak flow derived from a distribution of rainfall volume and runoff coefficient, and a unit hydrograph. Journal of Hydrology, 208, 3–4, 148–162.10.1016/S0022-1694(98)00152-8Search in Google Scholar

Hayes, D.C., Young, R.L., 2006. Comparison of peak discharge and runoff characteristic estimates from the rational method to field observations for small basins in Central Virginia. U.S. Department of the Interior, U.S. Geological Survey, Scientific Investigation Reports, 2005-5254.10.3133/sir20055254Search in Google Scholar

Ho, T.K., 1995. Random decision forests. In: ICDAR ‘95 Proceedings of the Third International Conference on Document Analysis and Recognition, 1, IEEE Computer Society Washington, DC, USA, 14–15 August 1995, pp. 278–282.Search in Google Scholar

Horn, R.A., Johnson, C.R., 1985. Matrix Analysis. Cambridge University Press, Cambridge.10.1017/CBO9780511810817Search in Google Scholar

Hsu, C.W., Chang, C.C., Lin, C.J., 2003. A practical guide to support vector classification. Technical Report. Department of Computer Science, National Taiwan University, Taipei.Search in Google Scholar

Hwang, S.H., Ham, D.H., Kim, J.H., 2012. Forecasting performance of LS-SVM for nonlinear hydrological time series. KSCE Journal of Civil Engineering, 16, 5, 870–882.10.1007/s12205-012-1519-3Search in Google Scholar

Krakauer, N.Y., Temimi, M., 2011. Stream recession curves and storage variability in small watersheds. Hydrology and Earth System Sciences, 15, 7, 2377–2389.10.5194/hess-15-2377-2011Search in Google Scholar

Liaw, A., Wiener, M., 2002. Classification and regression by random Forest. R News, 2, 3, 18–22.Search in Google Scholar

Longobardi, A., Villani, P., Grayson, R.B., Western, A., 2003. On the relationship between runoff coefficient and catchment initial conditions. In: Proceedings of MODSIM, pp. 867–872.Search in Google Scholar

Maity, R., Bhagwat, P.P., Bhatnagar, A., 2010. Potential of support vector regression for prediction of monthly streamflow using endogenous property. Hydrological Processes, 24, 7, 917–923.10.1002/hyp.7535Search in Google Scholar

Merz, R., Blöschl, G., 2009. A regional analysis of event runoff coefficients with respect to climate and catchment characteristics in Austria. Water Resources Research, 45, 1, W01405.10.1029/2008WR007163Search in Google Scholar

Merz, R., Blöschl, G., Parajka, J., 2006. Spatio-temporal variability of event runoff coefficients. Journal of Hydrology, 331, 3–4, 591–604.10.1016/j.jhydrol.2006.06.008Search in Google Scholar

Naghibi, S.A., Ahmadi, K., Daneshi, A., 2017. Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping. Water Resources Management, 31, 9, 2761–2775.10.1007/s11269-017-1660-3Search in Google Scholar

Naghibi, S.A., Pourghasemi, H.R., Dixon, B., 2016. GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environmental Monitoring and Assessment, 188, 1, 44.10.1007/s10661-015-5049-626687087Search in Google Scholar

Norbiato, D., Borga, M., Merz, R., Blöschl, G., Carton, A., 2009. Controls on event runoff coefficients in the eastern Italian Alps. Journal of Hydrology, 375, 3–4, 312–325.10.1016/j.jhydrol.2009.06.044Search in Google Scholar

Osuna, E.E., 1998. Support vector machines: Training and applications. Diss. Massachusetts Institute of Technology.Search in Google Scholar

Palleiro, L., Rodríguez-Blanco, M.L., Taboada-Castro, M.M., Taboada-Castro, M.T., 2014. Hydrological response of a humid agroforestry catchment at different time scales. Hydrological Processes, 28, 4, 1677–1688.10.1002/hyp.9714Search in Google Scholar

Patnaik, S., Biswal, B., Kumar, D.N., Sivakumar, B., 2015. Effect of catchment characteristics on the relationship between past discharge and the power law recession coefficient. Journal of Hydrology, 528, 321–328.10.1016/j.jhydrol.2015.06.032Search in Google Scholar

Rodríguez-Blanco, M.L., Taboada-Castro, M.M., Taboada-Castro, M.T., 2012. Rainfall–runoff response and eventbased runoff coefficients in a humid area (northwest Spain). Hydrological Sciences Journal, 57, 3, 445–459.10.1080/02626667.2012.666351Search in Google Scholar

Sachdeva, S., Bhatia, T., Verma, A.K., 2018. GIS-based evolutionary optimized Gradient Boosted Decision Trees for forest fire susceptibility mapping. Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer. International Society for the Prevention and Mitigation of Natural Hazards, 92, 3, 1399–1418.10.1007/s11069-018-3256-5Search in Google Scholar

Şen, Z., Altunkaynak, A., 2006. A comparative fuzzy logic approach to runoff coefficient and runoff estimation. Hydrological Processes, 20, 9, 1993–2009.10.1002/hyp.5992Search in Google Scholar

Shen, C., 2018. A transdisciplinary review of deep learning research and its relevance for water resources scientists. Water Resources Research, 54, 8558–8593. https://doi.org/10.1029/2018WR02264310.1029/2018WR022643Search in Google Scholar

Sivapalan, M., 2003. Prediction in ungauged basins: a grand challenge for theoretical hydrology. Hydrological Processes, 17, 15, 3163–3170.10.1002/hyp.5155Search 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, https://doi.org/10.1029/2017WR022037.10.1029/2017WR022037Search in Google Scholar

Tachecí, P., Žlabek, P., Kvitek, T., Peterkova, J., 2013. Analysis of rainfall-runoff events in four subcatchments of the Kopaninský potok (Czech Republic). Bodenkultur, 64, 3–4, 105–111.Search in Google Scholar

Tague, C., Grant, G.E., 2004. A geological framework for interpreting the low-flow regimes of Cascade streams, Willamette River Basin, Oregon. Water Resources Research, 40, 4, W04303.10.1029/2003WR002629Search in Google Scholar

Tallaksen, L.M., 1995. A review of baseflow recession analysis. Journal of Hydrology, 165, 1–4, 349–370.10.1016/0022-1694(94)02540-RSearch in Google Scholar

Tarasova, L., Basso, S., Poncelet, C., Merz, R., 2018a. Exploring controls on rainfall-runoff events: 2. Regional patterns and spatial controls of event characteristics in Germany. Water Resources Research, 54, https://doi.org/10.1029/2018WR02258810.1029/2018WR022588Search in Google Scholar

Tarasova, L., Basso, S., Zink, M., Merz, R. 2018b. Exploring controls on rainfall-runoff events: 1. Time-series-based event separation and temporal dynamics of event runoff response in Germany. Water Resources Research, 54, https://doi.org/10.1029/2018WR02258710.1029/2018WR022587Search in Google Scholar

Tian, F., Li, H., Sivapalan, M., 2012. Model diagnostic analysis of seasonal switching of runoff generation mechanisms in the Blue River basin, Oklahoma. Journal of Hydrology, 418, 136–149.10.1016/j.jhydrol.2010.03.011Search in Google Scholar

Vapnik, V., Golowich, S., Smola, A., 1997. Support vector method for function approximation, regression estimation, and signal processing. In: NIPS’96 Proceedings of the 9th International Conference on Neural Information Processing Systems, Denver, Colorado, 3–5 December 1996, pp. 281–287.Search in Google Scholar

Viglione, A., Merz, R., Blöschl, G., 2009. On the role of the runoff coefficient in the mapping of rainfall to flood return periods. Hydrology and Earth System Sciences, 13, 5, 577–593.10.5194/hess-13-577-2009Search in Google Scholar

Wainwright, J., Parsons, A.J., 2002. The effect of temporal variations in rainfall on scale dependency in runoff coefficients. Water Resources Research, 38, 12, 1271.Search in Google Scholar

Zimmermann, B., Zimmermann, A., Turner, B.L., Francke, T., Elsenbeer, H., 2014. Connectivity of overland flow by drainage network expansion in a rain forest catchment. Water Resources Research, 50, 2, 1457–1473.10.1002/2012WR012660Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo