Synthesizing data products, mathematical models, and observational measurements for lake temperature forecasting

TL;DR

This paper introduces a Gaussian process surrogate-based framework for lake temperature forecasting that improves accuracy and uncertainty quantification over traditional models, especially for short-term predictions.

Contribution

The paper develops a bias-corrected Gaussian process surrogate model that enhances lake temperature forecasts by addressing model bias and high-dimensional data challenges.

Findings

GPBC outperforms climatological and raw GLM models in forecast accuracy.

The approach provides reliable uncertainty quantification up to two weeks ahead.

The method effectively handles large, stochastic, high-dimensional data.

Abstract

We present a novel forecasting framework for lake water temperature, which is crucial for managing lake ecosystems and drinking water resources. The General Lake Model (GLM) has been previously used for this purpose, but, similar to many process-based simulation models, it: requires a large number of inputs, many of which are stochastic; presents challenges for uncertainty quantification (UQ); and can exhibit model bias. To address these issues, we propose a Gaussian process (GP) surrogate-based forecasting approach that efficiently handles large, high-dimensional data and accounts for input-dependent variability and systematic GLM bias. We validate the proposed approach and compare it with other forecasting methods, including a climatological model and raw GLM simulations. Our results demonstrate that our bias-corrected GP surrogate (GPBC) can outperform competing approaches in terms of forecast accuracy and UQ up to two weeks into the future.