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Comparison of different downscaling schemes for obtaining regional high-resolution soil moisture data

International Journal of Applied Earth Observations and Geoinformation
Obtaining regional fine-scale daily Soil Moisture (SM) data is crucial for better understanding carbon and water cycles. Currently, downscaling from passive microwave SM products has become the most commonly utilized approach for generating regional high-
Yulin Shangguan, Cheng Tong, Zhou Shi, Hongquan Wang, Xiaodong Deng   +4 more
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A downscaling and bias correction method for climate model ensemble simulations of local-scale hourly precipitation

Scientific Reports, 2023
Ensemble simulations of climate models are used to assess the impact of climate change on precipitation, and require downscaling at the local scale. Statistical downscaling methods have been used to estimate daily and monthly precipitation from observed ...
Takao Yoshikane, Kei Yoshimura
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Hybrid precipitation downscaling over coastal watersheds in Japan using WRF and CNN

Journal of Hydrology: Regional Studies, 2021
Study region: Kuma River Watershed in Japan. Study focus: High-quality precipitation information is desirable in hydrological modeling and water resources management.
Tongbi Tu, Kei Ishida, Ali Ercan, Masato Kiyama, Motoki Amagasaki, Tongtiegang Zhao   +5 more
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Analysis of the impact of climate change on groundwater related hydrological fluxes: a multi-model approach including different downscaling methods [PDF]

Hydrology and Earth System Sciences, 2011
Climate change related modifications in the spatio-temporal distribution of precipitation and evapotranspiration will have an impact on groundwater resources.
S. Stoll, H. J. Hendricks Franssen, M. Butts, W. Kinzelbach   +3 more
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A Hybrid Statistical Downscaling Framework Based on Nonstationary Time Series Decomposition and Machine Learning

Earth and Space Science, 2022
Downscaling techniques are effective to bridge the scale gap between global circulation models and regional studies. Statistical downscaling methods are prevalent due to their advantages in high computational efficiency and accuracy. However, an implicit
Xintong Li, Xiaodong Zhang, Shuguang Wang   +2 more
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Downscaling SMAP Soil Moisture Products With Convolutional Neural Network

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Soil moisture (SM) downscaling has been extensively investigated in recent years to improve coarse resolution of SM products. However, available methods for downscaling are generally based on pixel-to-pixel strategy, which ignores the information among ...
Wei Xu, Zhaoxu Zhang, Zehao Long, Qiming Qin   +3 more
doaj   +1 more source

Comparing Regression Techniques for Temperature Downscaling in Different Climate Classifications

Engineering Proceedings, 2023
This study aims to identify the optimal regression techniques for downscaling among ten commonly used methods in climatology, including SVR, LinearSVR, LASSO, LASSOCV, Elastic Net, Bayesian Ridge, RandomForestRegressor, AdaBoost Regressor, KNeighbors ...
Ali Ilghami Kkhosroshahi, Mohammad Bejani, Hadi Pourali, Arman Hosseinpour Salehi   +3 more
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Estimating pros and cons of statistical downscaling based on EQM bias adjustment as a complementary method to dynamical downscaling

Scientific Reports
The increasing availability of coarse-scale climate simulations and the need for ready-to-use high-resolution variables drive the climate community to the challenge of reducing computational resources and time for downscaling purposes.
Alfredo Reder, Giusy Fedele, Ilenia Manco, Paola Mercogliano   +3 more
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How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections?

Earth's Future, 2022
We present an intercomparison of a suite of high‐resolution downscaled climate projections based on a six‐member General Circulation Model (GCM) ensemble from Coupled Models Intercomparison Project (CMIP6).
Deeksha Rastogi, Shih‐Chieh Kao, Moetasim Ashfaq   +2 more
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A Lightweight Terrain‐Constraint Model for Wind Spatial Downscaling

Journal of Geophysical Research: Machine Learning and Computation
High‐resolution wind fields has always been the goal of refined meteorological forecasting. Using advanced deep learning algorithms for wind downscaling is an effective approach to achieve this goal. However, the lack of physical process understanding in
Anboyu Guo, Qiyang Liu, Jiuke Wang, Yuanyong Gao, Lixin Wei, Xiaojiang Song, Chi Zhang, Kai Liu, Shuping Ma, Jiu Jiang   +9 more
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