Finding the perfect balance

In many basins across the globe, increasing competition for water resources that serve multiple users in a variety of sectors, along with uncertain climatic conditions, are forcing decision makers to rethink the way in which multipurpose water storage facilities are managed. The IIASA Water Program has developed a novel optimization model aimed at improving reservoir operation in the transboundary Rio Grande basin. This model will enable resource managers to robustly, efficiently, and sustainably, allocate water among multiple users.

As the name implies, multipurpose reservoirs are constructed and equipped to provide storage and release of water for multiple purposes such as flood control, power development, irrigation, recreation, and domestic water supply. The management of these facilities often involve complementary objectives, but also conflicting goals [1]. An objective that has to do with supplying water for use in agriculture for example, would require having the water elevation close to its maximum storage capacity to increase water supply reliability. Conversely, flood management objectives would advocate for an empty reservoir with capacity to manage high, possibly catastrophic, inflows to reduce the risk of overtopping. Integrating water supply for environmental restoration as an additional objective may create even more competition. Different stakeholder groups typically pursue one objective over another, which leads to problems on how to minimize trade-offs among competing water management objectives in the presence of various uncertainties [2].

The trade-offs between competing objectives are traditionally managed by reservoir operation rules that dictate the range of water levels in the reservoir at the end of each month. The water level of the reservoir is directly related to its storage capacity, and therefore also to how much water it is able to supply to users. Traditionally, deterministic optimization methods are used to determine the optimal reservoir operation rules. This means that certain variables in real-life systems, such as inflows to the reservoir, water demands, or system losses, become parameters within the model (i.e., average inflow) [3]. Consequently, deterministic optimization models may fail to include the impacts of low probability, but high cost events such as floods or droughts. A major limitation of the deterministic approach is the consideration of a single set of streamflow, that is, the amount of water flowing in a river [4]. In reality, river systems may have high variability over different years, and a deterministic approach is not able to implicitly incorporate extreme floods or droughts in the optimization process.

While working with researchers from the Water Program as a participant of the 2017 IIASA Young Scientists Summer Program, Jose Pablo Ortiz-Partida developed a novel dynamic stochastic optimization model to address this problem [5]. The new model aims to improve reservoir management under uncertain climatic conditions and competition among many water-dependent systems, including water supply for off-stream uses, environmental requirements for healthy ecosystems, downstream delivery commitments, and flood protection. It maximizes regional economic benefits as a function of reservoir deliveries and integrates stochastic inflows into a water allocation system with different users, and physical and institutional constraints. The model derives robust reservoir operation rules that perform well under a wide range of uncertain climatic conditions.

Inflows considered within the stochastic and deterministic approaches in the Big Bend Reach of the Rio Grande.

The new model was applied to the case of the Big Bend Reach of the Rio Grande–a transboundary river basin of high importance for the United States and Mexico–in order to guide the risk-informed design of efficient and sustainable reservoir operation policies. The results suggest that the operation of the considered reservoir can be enhanced in such a way that higher economic benefits can be achieved in both the United States and Mexico, even while increasing environmental water allocation. The study expands global research on optimizing reservoir operations and has the potential to change current thinking where human and environmental objectives are mutually exclusive.

Seasonal distribution of profits under robust and traditional reservoir operation for different periods in the Big Bend Reach of the Rio Grande.


[1] Labadie J (2004). Optimal operation of multireservoir systems: State-of-the-art review. Journal of Water Resources Planning and Management 130: 93-111.

[2] Loucks D & Sigvaldason O (1981). Multiple reservoir operation in North America. Surface Water Impoundments. ASCE, pp. 711-728.

[3] Farmer W & Vogel R (2016). On the deterministic and stochastic use of hydrologic models. Water Resources Research 52(7): 5619-5633.

[4] Philbrick C & Kitanidis P (1999). Limitations of deterministic optimization applied to reservoir operations. Journal of Water Resources Planning and Management 125(3): 135-142.

[5] Ortiz-Partida J (2017), Robust management of multipurpose reservoirs under uncertainty. YSSP Report, IIASA, pp. 66.

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