Rainfall Projection in Yamuna River Basin, India, Using Statistical Downscaling

Chaudhary, Siddharth; Agarwal, Ankit; Nakamura, Tai

doi:10.1007/978-981-13-2038-5_2

“…However, these relationships are always complex and non-linear, making it hard to achieve acceptable downscaling results with linear relationships (Ghosh and Mujumdar, 2008). As a result, in recent years machine learning methods have been gaining popularity given their ability to model non-linear relationships between predictor and predictants (Chaudhary et al, 2019;Yi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

MSCliGAN—A Structure-informed Generative Adversarial Model for Multi-site Statistical Downscaling of Extreme Precipitation Using Multi-model Ensemble

Chaudhuri,

Robertson

2023

Preprint

0

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Although the statistical methods of downscaling climate data have progressed significantly, the development of high-resolution precipitation products continues to be a challenge. This is especially true when interest centres on downscaling value over several study sites. In this paper , we report a new downscaling method termed the multi-site Climate Generative Adversarial Network (MSCliGAN), which can simulate annual maximum precipitation to the regional scale during the 1950-2010 period in different cities in Canada by using different AOGCM's from the Coupled Model Inter-Comparison Project 6 (CMIP6) as input. Auxiliary information provided to the downscaling model included topography and land-cover. The downscaling framework uses a convolution encoder-decoder U-net network to create a generative network and a convolution encoder network to create a critic network. An adversarial training strategy is used to train the model. The critic/discriminator used Wasserstein distance as a loss measure and on the other hand the generator is optimized using a summation of content loss on Nash-Shutcliff Model Efficiency (NS), structural loss on structural similarity index (SSIM), and adversarial loss Wasserstein distance. Downscaling results show that downscaling AOGCMs by incorporating topography and land-use/land-cover can produce spatially coherent fields close to observation over multiple-sites. We believe the model has sufficient downscaling potential in data sparse regions where climate change information is often urgently needed.

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“…However, these relationships are always complex and non-linear, making it hard to achieve acceptable downscaling results with linear relationships (Ghosh and Mujumdar, 2008). As a result, in recent years machine learning methods have been gaining popularity given their ability to model non-linear relationships between predictor and predictants (Chaudhary et al, 2019;Yi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

<strong></strong>Mscligan- A Structure-Informed Generative Adversarial Model for Multi-Site Statistical Downscaling of Extreme Precipitation -Using Multi-Model Ensemble

Chaudhuri¹,

Robertson²

2022

Preprint

1

0

View full text Add to dashboard Cite

Although the statistical methods of downscaling climate data have progressed significantly, the development of high-resolution precipitation products continues to be a challenge. This is especially true when interest centres on downscaling value over several study sites. In this paper , we report a new downscaling method termed the multi-site Climate Generative Adversarial Network (MSCliGAN), which can simulate annual maximum precipitation to the regional scale during the 1950-2010 period in different cities in Canada by using different AOGCM's from the Coupled Model Inter-Comparison Project 6 (CMIP6) as input. Auxiliary information provided to the downscaling model included topography and land-cover. The downscaling framework uses a convolution encoder-decoder U-net network to create a generative network and a convolution encoder network to create a critic network. An adversarial training strategy is used to train the model. The critic/discriminator used Wasserstein distance as a loss measure and on the other hand the generator is optimized using a summation of content loss on Nash-Shutcliff Model Efficiency (NS), structural loss on structural similarity index (SSIM), and adversarial loss Wasserstein distance. Downscaling results show that downscaling AOGCMs by incorporating topography and land-use/land-cover can produce spatially coherent fields close to observation over multiple-sites. We believe the model has sufficient downscaling potential in data sparse regions where climate change information is often urgently needed.

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Sequential downscaling of GRACE products to map groundwater level changes in Krishna River basin

Gorugantula

¹

,

Kambhammettu

²

2022

Hydrological Sciences Journal

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Rainfall Projection in Yamuna River Basin, India, Using Statistical Downscaling

Cited by 3 publications

References 7 publications

MSCliGAN—A Structure-informed Generative Adversarial Model for Multi-site Statistical Downscaling of Extreme Precipitation Using Multi-model Ensemble

MSCliGAN—A Structure-informed Generative Adversarial Model for Multi-site Statistical Downscaling of Extreme Precipitation Using Multi-model Ensemble

<strong></strong>Mscligan- A Structure-Informed Generative Adversarial Model for Multi-Site Statistical Downscaling of Extreme Precipitation -Using Multi-Model Ensemble

Sequential downscaling of GRACE products to map groundwater level changes in Krishna River basin

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