Parameter estimation and uncertainty analysis in hydrological modeling - Herrera, Paulo A.; Marazuela, Miguel Angel; Hofmann, Thilo (Wiley interdisciplinary reviews. Water, 2022)
Nowadays, mathematical models of hydrological systems are used routinely to guide decision making in diverse subjects, such as: environmental and risk assessments, design of remediation strategies for contaminated sites, and evaluation of the impact of climate change on water resources. The correct development and use of them is relevant beyond the realm of hydrology. The continuous improvement in computational power and data collection are leading to the development of increasingly complex models, which integrate multiple coupled physical processes to achieve a better representation of the modeled system. Most of the parameters included in models are difficult to measure directly, so they must be estimated from collected data through a calibration procedure. Furthermore, when models are used to make forecasts about future or hypothetical scenarios, it is important to bound the uncertainty of their results. Therefore, the application of systematic approaches for parameter estimation, sensitivity, and uncertainty analysis to integrate data and models and quantify potential errors, is more necessary now than it was in the past. Even though methodological frameworks for these purposes exist, they have had a slow adoption due to their high computational cost and the required technical knowledge to apply them. We analyze existing methodologies, discuss remaining challenges, and present a survey of emerging trends for the application of parameter estimation and uncertainty analysis in hydrological modeling. Thus, the main objective of this overview article is contributing to improving the quality of models and to their correct use as support tools for decision‐making. This article is categorized under: Science of Water Science of Water > Hydrological Processes Science of Water > Methods Running and estimating the parameters of numerical hydrological models requires solving well‐posed forward and ill‐posed inverse problems