Modeling Reservoir Release Using Pseudo-Prospective Learning and Physical Simulations to Predict Water Temperature

TL;DR

This paper introduces a novel data-driven approach combining pseudo-prospective learning and physical simulations to predict water temperature in reservoir-influenced stream networks, especially when release data is unavailable.

Contribution

It develops a state-aware graph model and a parallel learning structure that infers reservoir releases and leverages physics-based simulations to enhance temperature predictions.

Findings

Over 10% accuracy improvement in stream temperature prediction without release data.

Enhanced performance when incorporating release data and physical simulations.

Effective modeling of reservoir impacts on downstream water temperature.

Abstract

This paper proposes a new data-driven method for predicting water temperature in stream networks with reservoirs. The water flows released from reservoirs greatly affect the water temperature of downstream river segments. However, the information of released water flow is often not available for many reservoirs, which makes it difficult for data-driven models to capture the impact to downstream river segments. In this paper, we first build a state-aware graph model to represent the interactions amongst streams and reservoirs, and then propose a parallel learning structure to extract the reservoir release information and use it to improve the prediction. In particular, for reservoirs with no available release information, we mimic the water managers' release decision process through a pseudo-prospective learning method, which infers the release information from anticipated water temperature dynamics. For reservoirs with the release information, we leverage a physics-based model to simulate the water release temperature and transfer such information to guide the learning process for other reservoirs. The evaluation for the Delaware River Basin shows that the proposed method brings over 10\% accuracy improvement over existing data-driven models for stream temperature prediction when the release data is not available for any reservoirs. The performance is further improved after we incorporate the release data and physical simulations for a subset of reservoirs.