Run‐of‐river power plants in Alpine regions: Whither optimal capacity?

Abstract

Although run-of-river hydropower represents a key source of renewable energy, it cannot prevent stresses on river ecosystems and human well-being. This is especially true in Alpine regions, where the outflow of a plant is placed several kilometers downstream of the intake, inducing the depletion of river reaches of considerable length. Here multiobjective optimization is used in the design of the capacity of run-of-river plants to identify optimal trade-offs between two contrasting objectives: the maximization of the profitability and the minimization of the hydrologic disturbance between the intake and the outflow. The latter is evaluated considering different flow metrics: mean discharge, temporal autocorrelation, and streamflow variability. Efficient and Pareto-optimal plant sizes are devised for two representative case studies belonging to the Piave river (Italy). Our results show that the optimal design capacity is strongly affected by the flow regime at the plant intake. In persistent regimes with a reduced flow variability, the optimal trade-off between economic exploitation and hydrologic disturbance is obtained for a narrow range of capacities sensibly smaller than the economic optimum. In erratic regimes featured by an enhanced flow variability, instead, the Pareto front is discontinuous and multiple trade-offs can be identified, which imply either smaller or larger plants compared to the economic optimum. In particular, large capacities reduce the impact of the plant on the streamflow variability at seasonal and interannual time scale. Multiobjective analysis could provide a clue for the development of policy actions based on the evaluation of the environmental footprint of run-of-river plants.

Contrasting signatures of distinct human water uses in regulated flow regimes

“…Conversely, in catchments where data are only available for reservoir release, the mean and the coefficient of variation of natural streamflows have been evaluated based on equation (1) (see SI). The impact of dams and reservoirs on the temporal trajectory of downstream releases has been also analyzed across a broad range of time scales (from daily to yearly) [54][55][56][57]. Temporal patterns of streamflows are critical for population dynamics and life cycle stages, and their modifications might impact adaptation strategies by creating adverse conditions for fish movement and reproduction [58][59][60].…”

Impacts of small cascaded hydropower plants on river discharge in a basin in Southern China

“…regime changes in the short term, leading to drastic fluctuations in river discharge within a relatively short time frame (Shiau & Wu, 2013). Comprehensive and complex management strategies may result in different disturbances to the intake and outflow volumes (Lazzaro, Basso, Schirmer, & Botter, 2013;Lazzaro & Botter, 2015;Suen & Eheart, 2006). The peak flow may submerge riparian zones, affecting the shoal zone habitat and promoting hydrophilic plants to become the dominant population (Wang, Li, Huang, Tang, & Zhao, 2017).…”

“…However, the results are different in basins that have large dams and reservoirs. In the Mekong basin, many studies have found a considerable increase in the dry-season discharge and a decrease in the wet-season discharge as a result of large hydropower plants (Räsänen et al, 2017;Lauri et al, 2012). The reservoirs store water during the wet season and release it during the dry season, thus increasing the dry-season flows and reducing the wetseason flows.…”

Stochastic modeling of interannual variation of hydrologic variables

“…For example, simple modeling approaches from stochastic hydrology [Botter et al, 2007;Müller et al, 2014;Basso et al, 2015Basso et al, , 2016 10.1002/2017GL074139 can often skillfully predict daily discharge variability in natural catchments using a constrained set of predictors derived from rainfall climatology, storage capacity of the unsaturated zone, vegetation water use, and the drainage characteristics of the catchment groundwater system. Subsequent extensions of this framework have been used to study run-of-river hydropower generation [Müller et al, 2016;Lazzaro and Botter, 2015], flood risk assessment [Basso et al, 2016], river macroinvertebrate habitat extent [Ceola et al, 2014], and riparian vegetation extent [Doulatyari et al, 2014], among other applications across the geological and ecological sciences. With such a model for daily flows, climatic and catchment-scale properties could be linked to annual measures of flow variability by computing seasonal or annual sums of the (predicted) daily flow variable.…”