The Lake Como water system (Adda River Basin) is a typical snow-dominated Alpine basin, heavily exploited for hydropower production in the upper catchment and with a multi-sectoral water use in the lower part.
The system comprises one large regulated lake (active capacity 247 Mm3) and 16 alpine hydropower reservoirs (globally storing 545 Mm3). It is a paradigmatic example of many Alpine watersheds: a large storage capacity distributed in small reservoirs, mainly operated for hydropower production and located in the upper watershed region; a regulated lake in the middle region; and multiple water consumption users, mainly irrigated agriculture, in the lower region. The stakeholders of Lake Como include several hydropower companies and irrigation districts.
A number of physical-based models and decision models were set up to resemble the complexity of this water system. Optimisation and real-time control tools were applied to cope with increasingly high and frequent droughts that are exacerbating the conflict between energy and food production, and threatening the main ecosystem services and flood buffering capacity. A multi-agent modelling framework was used to represent the farmer choices (e.g. crop, fertilizer) in lower agricultural region.
The overall study provided valuable insights on the operational value of improved forecasts to better inform decision-making both by the hydropower reservoir operators and by the farmers, particularly under extreme event conditions.
For the hydropower sector, this case study was used to investigate alternative hydropower reservoir operation strategies under different forecasts and climate projections and ultimately assess the operational value of climate services. Gains from improved management, considering water values and the spatial-temporal dependence of the hydrological time series in the basin, were also analysed.
For the agricultural sector, we evaluated the climate change impact on the agricultural practices in the Lake Como irrigation district, and to assess the operational value of improved weather forecasts and derived information (e.g., drought index predictions) in informing farmers.
Tools and models
In this case study, we applied and further developed the following models:
- TOPKAPI-ETH model: a distributed physical based hydrological model used here to describe the hydrological processes of the upper Lake Como catchment. The model allowed us to translate the forecasted climatic variables into the hydrological variables of interest, and served as a basis for better informing the medium-long term operation of reservoirs, as well as the risks associated to climate change projections.
- DistriLake model: a decisional model of the water infrastructures in the system, including the main hydropower reservoirs, the Lake Como dam and main downstream diversion dams. Optimal control tools were embedded within the model to capture the historical operation of these infrastructures assuming a rational operator’s strategy, and to inform better decisions by introducing new forecast information, or coordination mechanisms.
- MAS-Idragra model: a multi-agent agronomic model reproducing the observed configuration of the agricultural districts in the lower part of the study area, as well as the farmers’ local decision-making processes (e.g. crop choices). The MAS-Idragra model extended the scope of the assessment of the operational value of forecast information by accounting for the farmers’ behavioural context and evaluating the forecast information from the end-users’ perspective.
Seasonal inflow forecasts in the snow-dominated Alpine basin in Italy suggest storing snowmelt volumes and allocating the production in the period January-March, when the price of electricity is high. When using the solution based only on climatology, with no forecast model information, the reservoir management model does not see this opportunity and allocates the production in the fall, resulting in an empty reservoir at the end of the year, with, consequently, low future revenue opportunities.
Read more on this case study.
Our goal is to improve water management by better informing decision-making.