- Reclamation
- R&D
- Research Projects
- Understanding how Different Versions of Distributed Historical Weather Data Affect Hydrologic Model Calibration and Climate Projection Downscaling
Understanding how Different Versions of Distributed Historical Weather Data Affect Hydrologic Model Calibration and Climate Projection Downscaling
Project ID: 675
Principal Investigator: Subhrendu Gangopadhyay
Research Topic: Water Resource Data Analysis
Priority Area Assignments: 2011 (Climate Change and Variability Research), 2012 (Climate Change and Variability Research)
Funded Fiscal Years:
2011 and
2012
Keywords: None
Research Question
Research questions:
1. How different, in terms of spatial patterns, are the presently available distributed weather datasets in terms of precipitation and temperature?
2. How sensitive is the hydrology model response when calibration is carried out with one of the presently available distributed weather datasets and forced with the other datasets?
3. What are the implications of the climatology differences from the distributed weather datasets for statistical downscaling of general circulation model (GCM) outputs?
Need and Benefit
This proposal is aimed at Reclamation's Safety and Technology (S&T) fiscal year 2011 (FY11) call to respond to the following topics:
2. Obtain Climate Projection Data
* Improved understanding on the strengths and weaknesses of downscaled data and datadevelopment methods (e.g., where and when the stationarity assumption of statistical downscaling techniques may not hold and should be replaced by dynamically downscaled data).
4. Assess Natural Systems Response to Climate Change
* Watershed Hydrology
* Guidance on strengths and weaknesses of available versions of spatially distributed hydrologic weather data that may be used for both watershed hydrologic model development (Topic 4) and in climate model bias-correction (Topic 2).
Water agencies will continue to face decisions about which downscaled data to use and implicitly which downscaling method and spatial resolution to rely upon. As decisions are made, there will be interest in understanding the assessment uncertainties introduced by these choices. However, quantitative information on such uncertainties is limited, setting up the need for evaluations such as this proposed effort. Multiple distributed weather sets are currently available
e.g., the CMIP3 GCM archive was downscaled using the Maurer et al. (2002) distributed weather dataset using a single statistical downscaling algorithm [bias-correction]). The question here is, was Maurer et al. (2002) data the best dataset to be used in this downscaling effort? What would have been the implications of using another distributed weather archive? This proposed effort is aimed to answer these questions. Note that the datasets will be analyzed using two measures: (1) spatial similarity of the distributed precipitation and temperature fields of the study datasets and (2) implications on hydrologic modeling. This would then provide guidance on the choice of datasets for statistical downscaling of GCM outputs used in different types of scale-dependent planning assessments.
Contributing Partners
Contact the Principal Investigator for information about partners.
Research Products
Independent Peer Review
The following documents were reviewed by qualified Bureau of Reclamation employees. The findings were determined to be achieved using valid means.
How Does the Choice of Distributed Meteorological Data Affect Hydrologic Model Calibration and Streamflow Simulations? (final, PDF, 3.5MB)
By Marketa Elsner, Subhrendu Gangopadhyay, Tom Pruitt, Levi Brekke, Naoki Mizukami and Martyn Clark
Publication completed on May 29, 2014
Bureau of Reclamation Review
The following documents were reviewed by experts in fields relating to this project's study and findings. The results were determined to be achieved using valid means.
Choosing the Right Meteorological Dataset for Hydrologic Simulations (final, PDF, 966KB)
By Subhrendu Gangopadhyay
Publication completed on September 30, 2014
