Evaluating climate change impacts on streamflow variability based on a multisite multivariate GCM downscaling method in the Jing River of China
Projected hydrological variability is important for future resource and hazard management of water supplies because changes in hydrological variability can cause more disasters than changes in the mean state. However, climate change scenarios downscaled from Earth System Models (ESMs) at single...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-11-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://www.hydrol-earth-syst-sci.net/21/5531/2017/hess-21-5531-2017.pdf |
| _version_ | 1818197093256265728 |
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| author | Z. Li J. Jin J. Jin |
| author_facet | Z. Li J. Jin J. Jin |
| author_sort | Z. Li |
| collection | DOAJ |
| description | Projected hydrological variability is important for future
resource and hazard management of water supplies because changes in
hydrological variability can cause more disasters than changes in the mean
state. However, climate change scenarios downscaled from Earth System Models (ESMs) at
single sites cannot meet the requirements of distributed hydrologic
models for simulating hydrological variability. This study developed
multisite multivariate climate change scenarios via three steps: (i) spatial
downscaling of ESMs using a transfer function method, (ii) temporal
downscaling of ESMs using a single-site weather generator, and (iii) reconstruction
of spatiotemporal correlations using a distribution-free
shuffle procedure. Multisite precipitation and temperature change scenarios
for 2011–2040 were generated from five ESMs under four representative
concentration pathways to project changes in streamflow variability using the
Soil and Water Assessment Tool (SWAT) for the Jing River, China. The
correlation reconstruction method performed realistically for intersite and
intervariable correlation reproduction and hydrological modeling. The SWAT
model was found to be well calibrated with monthly streamflow with a model
efficiency coefficient of 0.78. It was projected that the annual mean
precipitation would not change, while the mean maximum and minimum
temperatures would increase significantly by 1.6 ± 0.3 and
1.3 ± 0.2 °C; the variance ratios of 2011–2040 to 1961–2005 were 1.15 ± 0.13
for precipitation, 1.15 ± 0.14 for mean maximum
temperature, and 1.04 ± 0.10 for mean minimum temperature. A warmer
climate was predicted for the flood season, while the dry season was
projected to become wetter and warmer; the findings indicated that the
intra-annual and interannual variations in the future climate would be
greater than in the current climate. The total annual streamflow was found to
change insignificantly but its variance ratios of 2011–2040 to 1961–2005
increased by 1.25 ± 0.55. Streamflow variability was predicted to
become greater over most months on the seasonal scale because of the
increased monthly maximum streamflow and decreased monthly minimum
streamflow. The increase in streamflow variability was attributed mainly to
larger positive contributions from increased precipitation variances rather
than negative contributions from increased mean temperatures. |
| first_indexed | 2024-12-12T01:44:29Z |
| format | Article |
| id | doaj.art-82b2ea2677754161982f6a66bb0f5d32 |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-12-12T01:44:29Z |
| publishDate | 2017-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-82b2ea2677754161982f6a66bb0f5d322022-12-22T00:42:37ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382017-11-01215531554610.5194/hess-21-5531-2017Evaluating climate change impacts on streamflow variability based on a multisite multivariate GCM downscaling method in the Jing River of ChinaZ. Li0J. Jin1J. Jin2College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi, 712100, ChinaCollege of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, ChinaDepartments of Watershed Sciences, Utah State University, Logan, UT 84322, USAProjected hydrological variability is important for future resource and hazard management of water supplies because changes in hydrological variability can cause more disasters than changes in the mean state. However, climate change scenarios downscaled from Earth System Models (ESMs) at single sites cannot meet the requirements of distributed hydrologic models for simulating hydrological variability. This study developed multisite multivariate climate change scenarios via three steps: (i) spatial downscaling of ESMs using a transfer function method, (ii) temporal downscaling of ESMs using a single-site weather generator, and (iii) reconstruction of spatiotemporal correlations using a distribution-free shuffle procedure. Multisite precipitation and temperature change scenarios for 2011–2040 were generated from five ESMs under four representative concentration pathways to project changes in streamflow variability using the Soil and Water Assessment Tool (SWAT) for the Jing River, China. The correlation reconstruction method performed realistically for intersite and intervariable correlation reproduction and hydrological modeling. The SWAT model was found to be well calibrated with monthly streamflow with a model efficiency coefficient of 0.78. It was projected that the annual mean precipitation would not change, while the mean maximum and minimum temperatures would increase significantly by 1.6 ± 0.3 and 1.3 ± 0.2 °C; the variance ratios of 2011–2040 to 1961–2005 were 1.15 ± 0.13 for precipitation, 1.15 ± 0.14 for mean maximum temperature, and 1.04 ± 0.10 for mean minimum temperature. A warmer climate was predicted for the flood season, while the dry season was projected to become wetter and warmer; the findings indicated that the intra-annual and interannual variations in the future climate would be greater than in the current climate. The total annual streamflow was found to change insignificantly but its variance ratios of 2011–2040 to 1961–2005 increased by 1.25 ± 0.55. Streamflow variability was predicted to become greater over most months on the seasonal scale because of the increased monthly maximum streamflow and decreased monthly minimum streamflow. The increase in streamflow variability was attributed mainly to larger positive contributions from increased precipitation variances rather than negative contributions from increased mean temperatures.https://www.hydrol-earth-syst-sci.net/21/5531/2017/hess-21-5531-2017.pdf |
| spellingShingle | Z. Li J. Jin J. Jin Evaluating climate change impacts on streamflow variability based on a multisite multivariate GCM downscaling method in the Jing River of China Hydrology and Earth System Sciences |
| title | Evaluating climate change impacts on streamflow variability based on a multisite multivariate GCM downscaling method in the Jing River of China |
| title_full | Evaluating climate change impacts on streamflow variability based on a multisite multivariate GCM downscaling method in the Jing River of China |
| title_fullStr | Evaluating climate change impacts on streamflow variability based on a multisite multivariate GCM downscaling method in the Jing River of China |
| title_full_unstemmed | Evaluating climate change impacts on streamflow variability based on a multisite multivariate GCM downscaling method in the Jing River of China |
| title_short | Evaluating climate change impacts on streamflow variability based on a multisite multivariate GCM downscaling method in the Jing River of China |
| title_sort | evaluating climate change impacts on streamflow variability based on a multisite multivariate gcm downscaling method in the jing river of china |
| url | https://www.hydrol-earth-syst-sci.net/21/5531/2017/hess-21-5531-2017.pdf |
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