Machine learning unravels controls on river water temperature regime dynamics

Wade, Jeffrey; Kelleher, Christa; Hannah, David M.

doi:10.1016/j.jhydrol.2023.129821

Cited by 8 publications

(3 citation statements)

References 71 publications

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“…On the one hand, opaque models do not necessarily guarantee higher accuracy (Jiang et al., 2022). On the other hand, valuable information can be extracted from accurate DL models (Varadharajan et al., 2022; Wade et al., 2023). Although DL models are not suitable for simple interpretation by directly reflecting on individual model parameters, insights can still be gained from DL models through appropriate interpretation methods.…”

Section: Discussionmentioning

confidence: 99%

Explaining the Mechanism of Multiscale Groundwater Drought Events: A New Perspective From Interpretable Deep Learning Model

Cai,

Shi,

Zhou

et al. 2024

Water Resources Research

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This study presents a new approach to understand the causes of groundwater drought events with interpretable deep learning (DL) models. As prerequisites, accurate long short‐term memory (LSTM) models for simulating groundwater are built for 16 regions representing three types of spatial scales in the southeastern United States, and standardized groundwater index is applied to identify 233 groundwater drought events. Two interpretation methods, expected gradients (EG) and additive decomposition (AD), are adopted to decipher the DL‐captured patterns and inner workings of LSTM networks. The EG results show that: (a) temperature‐related features were the primary drivers of large‐scale groundwater droughts, with their importance increasing from 56.1% to 63.1% as the drought events approached from 6 months to 15 days. Conversely, precipitation‐related features were found to be the dominant factors in the formation of groundwater drought in small‐scale catchments, with the overall importance ranging from 59.8% to 53.3%; (b) Seasonal variations in the importance of temperature‐related factors are inversely related between large and small spatial scales, being more significant in summer for larger regions and in winter for catchments; and (c) temperature‐related factors exhibited an overall “trigger effect” on causing groundwater drought events in the studying areas. The AD method unveiled how the LSTM network behaved differently in retaining and discarding information when emulating different groundwater droughts. In summary, this study provides a new perspective for the causes of groundwater drought events and highlights the potential and prospect of interpretable DL in enhancing our understanding of hydrological processes.

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Section: Discussionmentioning

confidence: 99%

Explaining the Mechanism of Multiscale Groundwater Drought Events: A New Perspective From Interpretable Deep Learning Model

Cai,

Shi,

Zhou

et al. 2024

Water Resources Research

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“…However, cool water releases below dams reduce thermal sensitivity values. Wade et al (2023) trained random forest models on monthly thermal sensitivity values for 400 U.S. sites. They found that dam storage overwhelmed 23 other influences on river thermal regimes.…”

Section: Stream Temperature Signatures Are Dominated By Dam Impactsmentioning

confidence: 99%

When good signatures go bad: Applying hydrologic signatures in large sample studies

McMillan,

Coxon,

Araki

et al. 2023

Hydrological Processes

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Hydrologic signatures are quantitative metrics that describe streamflow statistics and dynamics. Signatures have many applications, including assessing habitat suitability and hydrologic alteration, calibrating and evaluating hydrologic models, defining similarity between watersheds and investigating watershed processes. Increasingly, signatures are being used in large sample studies to guide flow management and modelling at continental scales. Using signatures in studies involving 1000s of watersheds brings new challenges as it becomes impractical to examine signature parameters and behaviour in each watershed. For example, we might wish to check that signatures describing flood event characteristics have correctly identified event periods, that signature values have not been biassed by data errors, or that human and natural influences on signature values have been correctly interpreted. In this commentary, we draw from our collective experience to present case studies where naïve application of signatures fails to correctly identify streamflow dynamics. These include unusual precipitation or flow regimes, data quality issues, and signature use in human‐influenced watersheds. We conclude by providing guidance and recommendations on applying signatures in large sample studies.

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“…Feigl et al 2021, Qiu et al 2021) and understanding the controls on river temperature dynamics (e.g. Wade et al 2023).…”

Section: Introductionmentioning

confidence: 99%

Rising summer river water temperature across Canada: spatial patterns and hydroclimatic controls

Shrestha,

Pesklevits,

Bonsal

et al. 2024

Environ. Res. Lett.

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Understanding the spatio-temporal variability of climate-induced river water temperature change is critical for identifying hotspots and assessing the impacts on ecological and socioeconomic systems. Here, we employ the air2stream model reconstructed river temperature records for 106 stations in Canada (Nash Sutcliffe coefficient goodness-of-fit: minimum = 0.79; median = 0.93; maximum = 0.97) to analyze summer temperature changes over the years 1980-2018. Results reveal widespread river temperature increases from June to September, with significantly increasing trends for about 40-60% of stations. Additionally, we find significantly rising 7-day maximum temperature and increasing occurrences over the critical 18 and 20°C thresholds for about 30-65% of stations. Furthermore, by employing the Ward’s agglomerative hierarchical clustering machine learning method, we identify eight regions of spatially coherent variability and change. We find that the south-east, coast and northern prairies are the regions of high vulnerability because of the likely impacts of rising summer water temperatures on cold-water aquatic species. Additionally, by using the random forests machine learning method, we demonstrate that mean air temperature and its trends are the primary drivers of mean water temperature and trends, respectively. Thus, with the projected enhanced air temperature increase across Canada, an amplified future summer river warming can be expected, which could have severe consequences, particularly in already thermally-stressed river systems.

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Machine learning unravels controls on river water temperature regime dynamics

Cited by 8 publications

References 71 publications

Explaining the Mechanism of Multiscale Groundwater Drought Events: A New Perspective From Interpretable Deep Learning Model

Explaining the Mechanism of Multiscale Groundwater Drought Events: A New Perspective From Interpretable Deep Learning Model

When good signatures go bad: Applying hydrologic signatures in large sample studies

Rising summer river water temperature across Canada: spatial patterns and hydroclimatic controls

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