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A two-step downscaling method for high-scale super-resolution of daily temperature — a case study of Wei River Basin, China

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Abstract

Climate data with high spatial and temporal resolution were of great significance for regional environmental management, such as for early response to possible predicted local climate changes and extreme weather. However, the current downscaling targets for CMIP6 climate simulations were mostly medium-resolution (MR) reanalysis data, which were still coarse for local analysis. A two-step downscaling method was proposed for 100 × resolution enhancements of general circulation model (GCM) daily temperature data in this study. First, the historical GCM outputs were 10 × downscaled to a set of dynamically predictable MR data using a deep convolutional neural network (CNN), which included both encode-decode structure and long-short skip connections. Then, using high-resolution (HR) topographic data and MR climate data as auxiliary data, the GCM data were super-resolved to a series of images with spatial resolution of 1 km. A one-step downscaling analysis combined only with HR topographic data was performed as comparison. Seven evaluation metrics were selected to evaluate the prediction accuracy, and the results showed that the overall performance of two-step downscaling method was better than one-step downscaling method. Higher Nash–Sutcliffe efficiency (NSE) and lower mean absolute relative error (MARE) indicated that the two-step method performed better prediction of peak and low values. It was further confirmed by accuracy evaluation on the 10% max and 10% min values of the testing dataset. The introduction of dynamically predictable MR data could provide effective detailed information during the downscaling process and improve the prediction accuracies. Finally, the projected data of four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) during 2015–2050 were downscaled to the study area. The complex temporal and spatial variations indicated that there were great differences in temperature changes in a basin, and differentiated management measures should be proposed in advance.

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Data availability

The data presented in this study are available on request from the corresponding author. Declarations.

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Funding

This work is supported by Open Research Fund Program of State key Laboratory of Hydroscience and Engineering (sklhse-2021-B-07).

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Authors and Affiliations

  1. State Key Laboratory of Hydro-Science and Engineering, Tsinghua University, Beijing, 100084, China

    Xue Li & Man Zhang

  2. Tianjin Key Laboratory of Water Resources and Environment, Tianjin Normal University, Tianjin, 300387, China

    Xue Li, Yingyin Zhou & Jian Sha

  3. Tianjin Key Laboratory of Aquatic Science and Technology, Tianjin Chengjian University, Tianjin, China

    Zhong-Liang Wang

Authors
  1. Xue Li
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  2. Yingyin Zhou
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  3. Man Zhang
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  4. Jian Sha
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  5. Zhong-Liang Wang
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Contributions

All the authors contributed to the study conception and design. Data analysis, methodology, software, and code writing were performed by Xue Li and Man Zhang. Data curation and validation were performed by Sha Jian and Zhou Yingyin. Conceptualization and supervision were performed by Zhang Man and Wang Zhong-Liang. The first draft of the manuscript was written by Xue Li, and all the authors commented on the previous versions of the manuscript. All the authors read and approved the final manuscript.

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Correspondence to Man Zhang.

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Li, X., Zhou, Y., Zhang, M. et al. A two-step downscaling method for high-scale super-resolution of daily temperature — a case study of Wei River Basin, China. Environ Sci Pollut Res 30, 32474–32488 (2023). https://doi.org/10.1007/s11356-022-24422-6

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