In the coming years, water management will face new challenges for several reasons. Global warming is reducing water availability, population growth is dramatically increasing global water demand that is generally concentrated in a large metropolis, pollution is impacting on available resources, making the future scenarios regarding water use and availability even more dramatic (Manfreda, 2013). Therefore, hydrological monitoring of river systems is a critical aspect that deserves particular attention for water budget assessment.
Hydrological monitoring relies on two classical instruments, such as 1) the pluviometric and 2) the hydrometric stations. While rainfall is measured with a dense network of stations, the number of hydrometric stations is significant lower all over the world. On one hand, the pluviometer is a simple device whose calibration is straightforward. On the other hand, stream flow gauges only provide an indirect measure of the streamflow represented by the water level stage.
This variable must be related to discharge through a regression function, which is the so-called flow rating curve (FRC). The reliability of these function is given as granted in many hydrological applications, but it may affect the outcomes of any hydrological study, the proper prediction of floods and the correct estimate of low flows. Therefore, their correct calibration is crucial for water resources research.
The FRC is an empirical function fitted to pairs of river water stage and discharge observations. Its definition is strongly influenced by the number of observations available, the range of variability of observations, and their distribution over time. Nevertheless, the number of observations available for a reliable estimation of FRC is not frequently limited and affected by errors. This is due to the fact that traditional techniques require long experimental campaigns and qualified personnel. This makes it difficult in practice to find a well-defined and monitored cross-section where the FRC is fully described with a range of measurements that range from low to high flow. Consequently, it is common to have flow velocity information only for low discharges, while the derivation of high flows is obtained by extrapolation (Dal Sasso et al., 2018)
Therefore, the traditional method produces a significant uncertainty in the discharge estimates especially in data-sparse environments, but such a condition is very common in hydrology producing, as a result, a limitation for all analysis connected to water management and quantification.
It is possible to improve the reliability of FRC by adopting an alternative methodology with respect to traditional approaches. The FRC can be obtained as the product of two functions that represent the relationship between mean flow velocity and the river wetted area as a function of the river stage. The two regression functions can be used to derive river discharge as well, but with the great advantage that the geometrical relationship of the wetted area can be obtained also from the topographic survey of the cross-section. Such a physical information can be exploited to increase model reliability (see Manfreda et al., 2018), but it can also be used to gain useful information to support the FRC extrapolation at higher river stage values. Such a methodology allows us to increase significantly the reliability of FRC and can support water management worldwide.
These findings are described in the article entitled On the derivation of flow rating curves in data-scarce environments, recently published in the Journal of Hydrology. This work was conducted by Salvatore Manfreda from the Department of European and Mediterranean Cultures, Architecture Environment and Cultural Heritage of the University of Basilicata.
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