Abstract
Accurate awareness of how rainfall, land use changes, and soil types control water fluxes in agricultural floodplains remains a crucial challenge in water resource research. This study examines soil moisture conditions, soil texture and rainfall characteristics, together with different artificial drainage network structures covering a time-span of 100 years (1924–2010), as drivers for runoff production in an agricultural floodplain. The research incorporates a multiple-layer generalised Green-Ampt approach to simulate water infiltration into the ground. Once the storage offered by the soil is saturated, a portion of the surface storage provided by the drainage network satisfies the infiltration capacity, thus delaying runoff. The watershed response is defined by the uNSI (updated Network Saturated index, (Sofia and Tarolli, 2017), that indicates the moment the available storage (soil + network) is 100% saturated. The results highlighted how interlocking relations between soil properties, the geometry of the network and temporal variations of precipitation determine runoff generation timing. For short return times, intense rainfalls tend to produce a quicker response in areas with soils prone to saturation, and with decreased network complexity. However, when the event magnitude increases, this combination of soil and network structure produces the fastest response when rainfall is more regular. Intense events in zones with soils with higher permeability produce a quicker response the simpler the network is. When soils are prone to runoff, and the network efficiency increases, runoff production is delayed in time. When soils have elevated permeability, and the network has a reduced efficiency and path heterogeneity, increasing the network simplicity would result in similar outcomes. Moreover, if the path heterogeneity and network efficiency increases, for a given network sinuosity, runoff generation would be delayed. Quantifying these effects is indeed crucial for many environmental problems, including the prediction of impacts of a changing climate and land use and the associated pressures.
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