Water Resources Management

, Volume 29, Issue 10, pp 3519–3533 | Cite as

Fuzzy Logic for Rainfall-Runoff Modelling Considering Soil Moisture

Article

Abstract

This study developed Mamdani-type fuzzy logic model to simulate daily discharge as a function of soil moisture measured at three different depths (10, 20 and 40 cm) and rainfall. The model was applied to 13 km2 size Colorso Basin in central Italy for a period from October 2002 to April 2004. For each variable of soil moisture, rainfall, and discharge, 9 fuzzy subsets were employed while 30 fuzzy rules, relating the input variables (soil moisture and rainfall) to the output variable (discharge), were optimized. The model employed the min inferencing, max composition, and the centroid method. The model application results revealed that Mamdani-type fuzzy logic model can be employed to incorporate soil moisture along with rainfall to simulate discharge. Using soil moisture measured at 40 cm soil depth along with rainfall produced better simulation of discharge with NS= 0.68 and R = 0.82. The performance of the model was also tested against a conceptual rainfall-runoff model of MISDc (Modello Idrologico Semi-Distribuito in continuo). MISDc couples an event-specific component with a module for continuous time soil water balance for taking into account the variable antecedent wetness conditions. The MISDc model requires estimation of seven parameters and the measurements of the hydrometeorological variables such as rainfall and air temperature. The comparative study revealed that fuzzy model performs better in capturing runoff peak rates and overall trend of high and small flooding events.

Keywords

Soil moisture Rainfall Discharge Simulation Fuzzy logic Mamdani Watershed 

References

  1. Anctil F, Michel C, Perrin C, Andreassian V (2004) A soil moisture index as an auxiliary ANN input for stream flow forecasting. J Hydrol 286:155–167CrossRefGoogle Scholar
  2. Anctil F, Lauzon L, Filion M (2008) Added gains of soil moisture content observations for streamflow predictions using neural networks. J Hydrol 359(3–4):225–234CrossRefGoogle Scholar
  3. Aubert D, Loumagne C, Oudin L (2003) Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall runoff model. J Hydrol 280:145–161CrossRefGoogle Scholar
  4. Beck HE, de Jeu RAM, Schellekens J, van Dijk AIJM, Bruijnzeel LA (2010) Improving curve number based storm runoff estimates using soil moisture proxies. IEEE J Sel Top Appl Earth Obse Remote Sens 2(4):1939–1404Google Scholar
  5. Berthet L, Andréassian V, Perrin C, Javelle P (2009) How crucial is it to account for the antecedent moisture conditions in flood forecasting? Comparison of event-based and continuous approaches on 178 catchments. Hydrol Earth Syst Sci 13:819–831CrossRefGoogle Scholar
  6. Brocca L, Melone F, Moramarco T, Singh VP (2009a) Assimilation of observed soil moisture data in storm rainfall-runoff modelling. J Hydrol Eng 14(2):153–165CrossRefGoogle Scholar
  7. Brocca L, Melone F, Moramarco T, Morbidelli R (2009b) Antecedent wetness conditions estimation based on ERS scatterometer data. J Hydrol 364(1–2):73–87CrossRefGoogle Scholar
  8. Brocca L, Melone F, Moramarco T (2011) Distributed rainfall-runoff modelling for flood frequency estimation and flood forecasting. Hydrol Process 25(18):2801–2813CrossRefGoogle Scholar
  9. Brocca L, Tullo T, Melone F, Moramarco T, Morbidelli R (2012a) Catchment scale soil moisture spatial-temporal variability. J Hydrol 422–423:63–75CrossRefGoogle Scholar
  10. Brocca L, Moramarco T, Melone F, Wagner W, Hasenauer S, Hahn S (2012b) Assimilation of surface and root-zone ASCAT soil moisture products into rainfall-runoff modelling. IEEE Trans Geosci Remote Sens 50(7):2542–2555CrossRefGoogle Scholar
  11. Brocca L, Liersch S, Melone F, Moramarco T, Volk M (2013) Application of a model-based rainfall-runoff database as efficient tool for flood risk management. Hydrol Earth Syst Sci 17:3159–3169CrossRefGoogle Scholar
  12. Camici S, Tarpanelli A, Brocca L, Melone F, Moramarco T (2011) Design soil moisture” estimation by comparing continuous and storm-based rainfall-runoff modelling. Water Resour Res 47:W05527CrossRefGoogle Scholar
  13. Casper M, Gemmar P, Gronz O, Johst M, Stüber M (2007) Fuzzy logic-based rainfall–runoff modelling using soil moisture measurements to represent system state. Hydrol Sci J 52(3):478–490CrossRefGoogle Scholar
  14. Chow VT, Maidment DR, Mays LW (1988) Applied hydrology. McGraw-Hill, New York, Chap. 5.5 Google Scholar
  15. Coppala EA, Duckstein L, Davis D (2002) Fuzzy rule based methodology for estimating monthly groundwater rechrage in a temperate watershed. J Hydrol Eng 7(4):326–335CrossRefGoogle Scholar
  16. Di Baldassarre G, Montanari A (2010) Uncertainty in river discharge observations: a quantitative analysis. Hydrol Earth Syst Sci 13:913–921CrossRefGoogle Scholar
  17. Doorenbos J, Pruitt WO (1977) Background and development of methods to predict reference crop evapotranspiration (ETo). In: FAO-ID-24, Appendix II, 108–119Google Scholar
  18. Elshorbagy A, Corzo G, Srinivasulu S, Solomatine DP (2010) Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - part 2: application. Hydrol Earth Syst Sci 14:1943–1961CrossRefGoogle Scholar
  19. Famiglietti JS, Wood EF (1994) Multiscale modeling of spatially variable water and energy balance processes. Water Resour Res 11:3061–3078CrossRefGoogle Scholar
  20. Gautam MR, Watanabe K, Saegusa H (2000) Runoff analysis in humid forest catchment with artificial neural network. J Hydrol 235:117–36CrossRefGoogle Scholar
  21. Goodrich DC, Schmugge TJ, Jackson TJ, Unkrich CL, Keefer TO, Parry R, Bach LB, Amer SA (2004) Runoff simulation sensitivity to remotely sensed initial soil water content. Water Resour Res 30(5):1393–1406CrossRefGoogle Scholar
  22. Grayson RB, Western AW (1998) Towards areal estimation of soil water content from point measurements: time and space stability of mean response. J Hydrol 207:68–82CrossRefGoogle Scholar
  23. Huang M, Gallichand J, Dong C, Wang Z, Shao M (2007) Use of soil moisture data and curve number method for estimating runoff in the Loess Plateau of China. Hydrol Process 21(11):1471–1481CrossRefGoogle Scholar
  24. Jacobs JM, Myers DA, Whitfield BM (2003) Improved rainfall/runoff estimates using remotely sensed soil moisture. J Am Water Resour Assoc 4:313–324CrossRefGoogle Scholar
  25. Jantzen J (1999) Design of fuzzy controllers. Technical report, No:98-E864, Department of Automation, Technical University of DenmarkGoogle Scholar
  26. Komma J, Blöschl G, Reszler C (2008) Soil moisture updating by Ensemble Kalman filtering in real-time flood forecasting. J Hydrol 357(3–4):228–242CrossRefGoogle Scholar
  27. Koren V, Moreda F, Smith M (2008) Use of soil moisture observations to improve parameter consistency in watershed calibration. Phys Chem Earth 33:1068–1080Google Scholar
  28. Kumar ARS, Goyal MK, Ojha CSP, Singh RD, Swamee PK, Nema RK (2013) Application of ANN, fuzzy logic and decision tree algorithms for the development of reservoir operating rules. Water Resour Manag 27(3):911–925CrossRefGoogle Scholar
  29. Mamdani EH (1977) Application of the fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Trans Comput C-26:1182–1191CrossRefGoogle Scholar
  30. Mein RG, Larson CL (1973) Modeling infiltration during a steady rain. Water Resour Res 9(2):384–394CrossRefGoogle Scholar
  31. Merz R, Plate EJ (1997) An analysis of the effects of spatial variability of soil and soil moisture on runoff. Water Resour Res 33(12):2909–2922CrossRefGoogle Scholar
  32. Merz R, Bardossy A (1998) Effects of spatial variability on the rainfall runoff process in a small loess catchment. J Hydrol 212–213:304–317CrossRefGoogle Scholar
  33. Meyles E, Williams A, Ternan L, Dowd J (2003) Runoff generation in relation to soil moisture patterns in a small Dartmoor catchment, Southwest England. Hydrol Process 17:251–264CrossRefGoogle Scholar
  34. Morbidelli R, Corradini C, Saltalippi C, Brocca L (2012) Initial soil water content as input to field-scale infiltration and surface runoff models. Water Resour Manag 26(7):1793–1807CrossRefGoogle Scholar
  35. Panigrahi DP, Mujumdar PP (2000) Reservoir operation modelling with fuzzy logic. Water Resour Manag 14(2):89–109CrossRefGoogle Scholar
  36. Parajka J, Naemi V, Bloschl G, Komma J (2009) Matching ERS scatterometer based soil moisture patterns with simulations of a conceptual dual layer hydrologic model over Austria. Hydrol Earth Syst Sci 13:259–271CrossRefGoogle Scholar
  37. Scipal K, Drusch M, Wagner W (2008) Assimilation of a ERS scatterometer derived soil moisture index in the ECMWF numerical weather prediction system. Adv Water Resour 31:1101–1112CrossRefGoogle Scholar
  38. Sentek Sensor Technologies (1997) Enviroscan: hardware manual, version 3.0. Sentek Pty Ltd, AustraliaGoogle Scholar
  39. Sen Z (1998) Fuzzy algorithm for estimation of solar irradiation from sunshine duration. Solar Energy 63(1):39–49CrossRefGoogle Scholar
  40. Sen Z (2004) Fuzzy logic and system models in water sciences. Turkish Water Foundation, İstanbulGoogle Scholar
  41. Tagaki T, Sugeno M (1985) Fuzzy identification of systems and its applications to modelling and control. IEEE Trans Syst Man Cybern 15:116–132CrossRefGoogle Scholar
  42. Tayfur G, Kavvas ML, Govindaraju RS, Storm DE (1993) Applicability of St.Venant equations for two-dimensional overland flows over rough infiltrating surfaces. J Hydraul Eng 119(1):51–63CrossRefGoogle Scholar
  43. Tayfur G, Ozdemir S, Singh VP (2003) Fuzzy logic algorithm for runoff-induced sediment transport from bare soil surfaces. Adv Water Resour 26(12):1249–1256CrossRefGoogle Scholar
  44. Tayfur G (2006) Fuzzy, ANN, and regression models to predict longitudinal dispersion coefficient in natural streams. Hydrol Res Nordic Hydrol 37(2):143–164Google Scholar
  45. Tayfur G, Singh VP (2006) ANN and fuzzy logic models for simulating event-based rainfall-runoff. J Hydraul Eng 132(12):1321–1330CrossRefGoogle Scholar
  46. Tayfur G, Singh VP (2011) Predicting mean and bankfull discharge from channel cross-sectional area by expert and regression methods. Water Resour Manag 25(5):1253–1267CrossRefGoogle Scholar
  47. Tayfur G (2012) Soft computing in water resources engineering. WIT Press, SouthamptonGoogle Scholar
  48. Tayfur G, Nadiri AA, Moghaddam AA (2014) Supervised ıntelligent committee machine method for hydraulic conductivity estimation. Water Resour Manag 28(4):1173–1184CrossRefGoogle Scholar
  49. Troutman BM, Karlinger MB (1985) Unit hydrograph approximation assuming linear flow trough topologically random channel networks. Water Resour Res 21:743–754CrossRefGoogle Scholar
  50. van Steenbergen N, Willems P (2013) Increasing river flood preparedness by real-time warning based on wetness state conditions. J Hydrol 489:227–237CrossRefGoogle Scholar
  51. Wang XJ, Zhao RH, Hao YW (2011) Flood control operations based on the theory of variable fuzzy sets. Water Resour Manag 25(3):777–792CrossRefGoogle Scholar
  52. Wooldridge SA, Kalma JD, Walker JP (2003) Importance of soil moisture measurements for inferring parameters in hydrologic models of low-yielding ephemeral catchments. Environ Model Software 18(1):35–48CrossRefGoogle Scholar
  53. Zehe E, Graeff T, Morgner M, Bauer A, Bronstert A (2010) Plot and field scale soil moisture dynamics and subsurface wetness control on runoff generation in a headwater in the Ore mountains. Hydrol Earth Syst Sci 14:873–889CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Department of Civil EngineeringIzmir Institute of TechnologyUrlaTurkey
  2. 2.Research Institute for Geo-Hydrological Protection, CNRPerugiaItaly

Personalised recommendations