Designing With Information Feedbacks: Forecast Informed Reservoir Sizing and Operation

Abstract

The value of streamflow forecasts to inform water infrastructure operations has been extensively studied. Yet, their value in informing infrastructure design is still unexplored. In this work, we investigate how dam design is shaped by information feedbacks supporting the implementation of flexible operating policies informed by streamflow forecasts to enable the design of less costly reservoirs relative to alternatives that do not rely on forecast information. Our approach initially explores the maximum potential gain attainable by searching and using the most valuable forecast information and lead time. We then analyze the results? sensitivities relative to existing and synthetic biased forecasts. We demonstrate our approach through an ex post analysis of the Kariba Dam in the Zambezi River Basin. Results show that informing dam design with perfect forecasts enables attaining the same hydropower production of the existing dam, while reducing infrastructure size and associated capital costs by 20%. A forecast‐informed operation of the existing system can instead facilitate an annual average increase of 60 GWh in hydropower production. This finding, extrapolated to the new planned dams in the basin, suggests that forecast informed policies could yield power production benefits equal to 75% of the current annual electricity consumption of the Zambian agricultural sector. The use of biased forecasts substantially reduces this gain, showing that the ESP forecasts value is marginal and that informed infrastructure designs are particularly vulnerable to forecast overestimation. Advancing information feedbacks may therefore become a valuable asset for the ongoing hydropower expansion in the basin.

Operations Eclipse Sequencing in Multipurpose Dam Planning

“…Here, we advance a multi-objective framework that jointly optimizes construction timing and operations of multi-purpose reservoirs to better address tradeoffs across environmental, energy, food, and economic objectives in planning how intensively to develop a large river basin (i.e., the pace and number of new dams). And whereas previous work has focused on a single reservoir's sizing and operating policy (Bertoni et al, 2019(Bertoni et al, , 2021 and timing of reservoirs' construction together with their individual operating policies (Geressu & Harou, 2019), our approach optimizes the sequencing of multiple reservoirs and integrates their operations in a fully coordinated manner across the entire reservoir network. Our framework for optimal sequencing and operations differs from other approaches where triggers (Kwakkel et al, 2015) or thresholds (Herman & Giuliani, 2018) activate a sequence of adaptation actions.…”

Leveraging Spatial Patterns in Precipitation Forecasts Using Deep Learning to Support Regional Water Management

“…Finally, this approach may provide information to support adaptive water management in the face of a more variable climate (Bertoni et al., 2021; Cohen & Herman, 2021). The question of how to effectively integrate such binary precipitation forecast information would require further study, combining optimization methods with input from decision makers.…”

Unfolding the relationship between seasonal forecast skill and value in hydropower production: A global analysis

“…For example, untapped hydropower potential (Zhou et al, 2015;Hoes et al, 2017) and seasonal streamflow predictability could be evaluated to derive some first, qualitative, conclusions on expected reservoir characteristics and performance. A case in point are run-of-the-river dams: these systems have a short time-to-fill characteristic and are therefore suitable for implementing forecast-informed reservoir sizing and operations (Bertoni et al, 2021). Conversely, if new dams are constructed in areas known to lack forecast skill or monitoring systems, then a larger storage capacity may be justifiable for dams operating with basic control rules.…”

“…For example, a quantitative relationship between reservoir characteristics, forecast skill, and value could be applied in regional analyses designed to determine the minimum forecast skill required by an existing or planned reservoir network (Bertoni et al, 2021). The expected continuous development of forecast systems will only continue to foster such analyses (Johnson et al, 2019;Crochemore et al, 2020;Troin et al, 2021).…”