Informing the operations of water reservoirs over multiple temporal scales by direct use of hydro-meteorological data

Abstract

Water reservoir systems may become more adaptive and reliable to external changes by enlarging the information sets used in their operations. Models and forecasts of future hydro-climatic and socio-economic conditions are traditionally used for this purpose. Nevertheless, the identification of skillful forecasts and models might be highly critical when the system comprises several processes with inconsistent dynamics (fast and slow) and disparate levels of predictability. In these contexts, the direct use of observational data, describing the current conditions of the water system, may represent a practicable and zero-cost alternative. This paper contrasts the relative contribution of state observations and perfect forecasts of future water availability in improving multipurpose water reservoirs operation over short- and long-term temporal scales. The approach is demonstrated on the snow-dominated Lake Como system, operated for flood control and water supply. The Information Selection Assessment (ISA) framework is adopted to retrieve the most relevant information to be used for conditioning the operations. By explicitly distinguishing between observational dataset and future forecasts, we quantify the relative contribution of current water system state estimates and perfect streamflow forecasts in improving the lake regulation with respect to both flood control and water supply. Results show that using the available observational data capturing slow dynamic processes, particularly the snow melting process, produces a 10% improvement in the system performance. This latter represents the lower bound of the potential improvement, which may increase to the upper limit of 40% in case skillful (perfect) long-term streamflow forecasts are used

Inferred inflow forecast horizons guiding reservoir release decisions across the United States

“…Forecast reliability, in turn, depends on the available predictive information. An operator might rely on upstream water storage (e.g., soil moisture, snowpack, and lake levels) (Shukla and Lettenmaier, 2011), hydrological regime state (Turner and Galelli, 2016), climate indices and teleconnections (Yang et al, 2017;Libisch-Lehner et al, 2019), weather forecasts (Georgakakos et al, 2005;Shukla et al, 2012;Nayak et al, 2018), current river flow rates (Hejazi et al, 2008), knowledge of planned water releases from upstream dams, and perhaps some or all of these in combination (Denaro et al, 2017). This enormous scope for variability in forecast quality and application across dams means there is no obvious way to identify the actual operationalized forecast, or indeed the model used to assimilate it into decision making, for a given system without insight into individual agencies' models and data preferences.…”

Disentangling human impact from natural controls of sediment dynamics in an Alpine catchment

“…Morgan and Duzant, 2008;Wischmeier and Smith, 1978;Francipane et al, 2012), we adopt a daily timescale due to data availability and the coarse temporal resolution of sediment sampling. This is supported by the results of Costa et al (2018b) which using an iterative input variable selection algorithm (Galelli and Castelletti, 2013;Denaro et al, 2017) to show that total daily catchment-averaged ER explains 75% of the variability of suspended sediment concentration at the Rhône River outlet, including total daily catchment-averaged IM and SM raises the explained variance of suspended sediment concentration up to 90%.…”

Robustness Metrics: How Are They Calculated, When Should They Be Used and Why Do They Give Different Results?

“…To satisfy the summer water demand peak, the current regulation operates the lake to store a large fraction of the snowmelt in order to be, approximately, at full capacity between June and July (Denaro et al, ). The projected anticipation of the snow melt caused by increasing temperature, coupled with the predicted decrease of water availability in the summer period, would require storing additional water and for longer periods, ultimately increasing the flood risk.…”