Comment on hess-2022-244

Abstract

Reservoir regulation affects various streamflow characteristics from low to high flows with important implications for downstream water users. Still, information on past reservoir operations is rarely publicly available and it is hardly known how reservoir operation signals, i.e. information on when water is stored in and released from reservoirs, vary over a certain region. Here, we develop a statistical model to reconstruct reservoir operation signals from observed streamflow time series that encompass a period before and a period after a known year of reservoir construction. In a first step, a generalized additive model (GAM) regresses streamflow time series from the unregulated pre-reservoir period on four covariates including temperature, precipitation, day of the year, and glacier mass balance changes. In a second step, this GAM, which represents natural conditions, is applied to predict natural streamflow, i.e. streamflow that would be expected in the absence of the reservoir, for the regulated period. The difference between the observed regulated streamflow signal and the predicted natural baseline should correspond to the reservoir operation signal. We apply this approach to reconstruct the seasonality of reservoir regulation, i.e. information on when water is stored in and released from a reservoir, from a dataset of 74 catchments in the Central Alps with a known reservoir construction date. We group these reconstructed regulation seasonalities using functional clustering to identify groups of catchments with similar reservoir operation strategies. We find that reservoir management varies by catchment elevation, with seasonal redistribution from summer to winter being strongest in high-elevation catchments. These elevational differences suggests a clear relationship between reservoir operation and climate and catchment characteristics, which has practical implications. First, these elevational differences in reservoir regulation can and should be considered in hydrological model calibration. Furthermore, the reconstructed reservoir operation signals can be used to study the joint impact of climate change and reservoir operation on different streamflow signatures, including extreme events.