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ARS Home » Plains Area » El Reno, Oklahoma » Grazinglands Research Laboratory » Agroclimate and Natural Resources Research » Research » Publications at this Location » Publication #337132

Research Project: ADAPTING SOIL AND WATER CONSERVATION TO MEET THE CHALLENGES OF A CHANGING CLIMATE

Location: Agroclimate and Natural Resources Research

Title: A weather generator-based statistical downscaling tool for site-specific assessment of climate change impacts

Author
item Chen, Jie - Wuhan University
item Zhang, Xunchang
item Li, Xiangquan - Wuhan University

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/22/2018
Publication Date: 7/18/2018
Citation: Chen, J., Zhang, X.J., Li, X. 2018. A weather generator-based statistical downscaling tool for site-specific assessment of climate change impacts. Transactions of the ASABE. 61(3): 977-993. https://doi.org/10.13031/trans.12601.
DOI: https://doi.org/10.13031/trans.12601

Interpretive Summary: Resolution of Global Climate Models (GCMs) outputs are too coarse to be used as direct inputs to impact models such as crop growth and hydrologic models for assessing climate change impacts on agricultural production, water resources, and eco-system services at farm or local scales. Statistical downscaling approaches are usually used to bridge the spatial gap between GCMs outputs and data requirements of impact models. Stochastic weather generator (a computer software for generating daily weather) can be used as a downscaling tool for bridging the gap. This study presents a new climate downscaling software (called Generator for Point Climate Change (GPCC)) developed based on the stochastic weather generator called CLIGEN. The GPCC software facilitates the rapid generation of single-site climate change scenarios for local and site-specific climate change impact studies using monthly projections from GCMs or Regional Climate Model (RCM). The downscaled variables include precipitation, maximum and minimum temperature. GPCC reduces the task of spatial-temporal downscaling of these variables into data input, spatial downscaling, temporal downscaling, daily weather generation and results analysis. The detailed downscaling methods, their scientific bases, and advantages of GPCC over other commonly used downscaling methods are reviewed. GPCC is written in MATLAB language and the standalone version can be run on Windows XP or above without MATLAB software. It has a graphical user interface that is simple and easy to use including visualizing downscaled outputs. Each interface page/tab and key buttons/options and their functions are described. The software will be useful for scientists and engineers who are interested in evaluating the impacts of climate changes and variations on natural resources and ecosystems services, especially crop production, soil and water conservation, and hydrology.

Technical Abstract: Resolution of climate model outputs are too coarse to be used as direct inputs to impact models for assessing climate change impacts on agricultural production, water resources, and eco-system services at local or site-specific scales. Statistical downscaling approaches are usually used to bridge the gap between climate model outputs and data requirements of impact models. Stochastic weather generator can be used as a downscaling tool for generating climate change scenarios for impact studies. This study presents a new climate downscaling software developed based on the stochastic weather generator CLIGEN. Generator for Point Climate Change (GPCC) facilitates the rapid generation of single-site climate change scenarios for local and site-specific climate change impact studies using monthly projections from Global Climate Model (GCM) or Regional Climate Model (RCM). The downscaled variables include precipitation, maximum and minimum temperature. GPCC reduces the task of spatial-temporal downscaling of these variables into data input, spatial downscaling, temporal downscaling, daily weather generation and results analysis. The detailed downscaling methods, their scientific bases, and advantages of GPCC over other commonly used downscaling methods are reviewed. GPCC is written in MATLAB language and the standalone version can be run on Windows XP or above without MATLAB software. It has a graphical user interface that is simple and easy to use including visualizing downscaled outputs. Each interface page/tab and key buttons/options and their functions are described.