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Climate 2017, 5(2), 26; doi:10.3390/cli5020026

Comparative Study of Different Stochastic Weather Generators for Long-Term Climate Data Simulation

1
Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA
2
USDA-Agricultural Research Service, National Soil Erosion Research Laboratory, and Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Yang Zhang
Received: 12 January 2017 / Revised: 19 March 2017 / Accepted: 20 March 2017 / Published: 26 March 2017

Abstract

Climate is one of the single most important factors affecting watershed ecosystems and water resources. The effect of climate variability and change has been studied extensively in some places; in many places, however, assessments are hampered by limited availability of long-term continuous climate data. Weather generators provide a means of synthesizing long-term climate data that can then be used in natural resource assessments. Given their potential, there is the need to evaluate the performance of the generators; in this study, three commonly used weather generators—CLImate GENerator (CLIGEN), Long Ashton Research Station Weather Generator (LARS-WG), and Weather Generators (WeaGETS) were compared with regard to their ability to capture the essential statistical characteristics of observed data (distribution, occurrence of wet and dry spells, number of snow days, growing season temperatures, and growing degree days). The study was based on observed 1966–2015 weather station data from the Western Lake Erie Basin (WLEB), from which 50 different realizations were generated, each spanning 50 years. Both CLIGEN and LARS-WG performed fairly well with respect to representing the statistical characteristics of observed precipitation and minimum and maximum temperatures, although CLIGEN tended to overestimate values at the extremes. This generator also overestimated dry sequences by 18%–30% and snow-day counts by 12%–19% when considered over the entire WLEB. It (CLIGEN) was, however, well able to simulate parameters specific to crop growth such as growing degree days and had an added advantage over the other generators in that it simulates a larger number of weather variables. LARS-WG overestimated wet sequence counts across the basin by 15%–38%. In addition, the optimal growth period simulated by LARS-WG also exceeded that obtained from observed data by 16%–29% basin-wide. Preliminary results with WeaGETS indicated that additional evaluation is needed to better define its parameters. Results provided insights into the suitability of both CLIGEN and LARS-WG for use with water resource applications. View Full-Text
Keywords: weather generators; CLIGEN; LARS-WG; WeaGETS; great lakes; climate; extreme events weather generators; CLIGEN; LARS-WG; WeaGETS; great lakes; climate; extreme events
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Mehan, S.; Guo, T.; Gitau, M.W.; Flanagan, D.C. Comparative Study of Different Stochastic Weather Generators for Long-Term Climate Data Simulation. Climate 2017, 5, 26.

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