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A new distributed rainfall-runoff (DR2) model based on soil saturation and runoff cumulative processes

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  • López-Vicente, M.
  • Navas, A.

Abstract

One important issue in agricultural management and hydrological research is the assessment of water stored during a rainfall event. In this study, a new GIS-based rainfall-runoff model is presented to estimate soil moisture status (SMS) for each month of the year after an average rainfall event with maximum intensity. The new model computes the volume of actual available water (Waa) downwards from divides, taking into account the different configurations of the upslope contributing area, infiltration processes and climatic parameters. Results show that the spatial distribution of the different soil types is the main controlling factor in the initiation of runoff and, to a lesser extent, the antecedent topsoil moisture and the volumetric water content of the soil at saturation. Monthly Waa and SMS maps and Palmer Z-indexes present similar spatial patterns, although the values and the extension of the different dry and wet categories varied considerably. Predominant wet conditions occurred in May, September, October, November and December and dry conditions appeared in February, March and July. The wettest conditions took place in gently sloping areas, according to the topographic wetness index. Maps based on Palmer Z-indexes match very closely the SMS patterns predicted by the DR2 model from January to September, but the similarity was poor from October to December. Spatial predictions with the new model identify the different sub-categories of soil wetness for each soil type in greater detail. The DR2 model seems to be of interest to monitor humidity variations and trends in time and space and to provide valuable information for sustainable soil and water resource management.

Suggested Citation

  • López-Vicente, M. & Navas, A., 2012. "A new distributed rainfall-runoff (DR2) model based on soil saturation and runoff cumulative processes," Agricultural Water Management, Elsevier, vol. 104(C), pages 128-141.
    • Handle: RePEc:eee:agiwat:v:104:y:2012:i:c:p:128-141
    DOI: 10.1016/j.agwat.2011.12.007
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    References listed on IDEAS

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    1. Sergio Vicente-Serrano, 2007. "Evaluating the Impact of Drought Using Remote Sensing in a Mediterranean, Semi-arid Region," 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, vol. 40(1), pages 173-208, January.
    2. Ph. Ciais & M. Reichstein & N. Viovy & A. Granier & J. Ogée & V. Allard & M. Aubinet & N. Buchmann & Chr. Bernhofer & A. Carrara & F. Chevallier & N. De Noblet & A. D. Friend & P. Friedlingstein & T. , 2005. "Europe-wide reduction in primary productivity caused by the heat and drought in 2003," Nature, Nature, vol. 437(7058), pages 529-533, September.
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    1. López-Vicente, M. & Navas, A. & Gaspar, L. & Machín, J., 2013. "Advanced modelling of runoff and soil redistribution for agricultural systems: The SERT model," Agricultural Water Management, Elsevier, vol. 125(C), pages 1-12.
    2. Zhang, Baoqing & Wu, Pute & Zhao, Xining & Wang, Yubao & Wang, Jiawen & Shi, Yinguang, 2012. "Drought variation trends in different subregions of the Chinese Loess Plateau over the past four decades," Agricultural Water Management, Elsevier, vol. 115(C), pages 167-177.

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