|MEHAN, SUSHANT - Purdue University|
|GUO, TIAN - Purdue University|
|GITAU, MARGARET - Purdue University|
Submitted to: Journal of Climate
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/20/2017
Publication Date: 3/26/2017
Citation: Mehan, S., Guo, T., Gitau, M.W., Flanagan, D.C. 2017. Comparative study of different stochastic weather generators for long-term climate data simulation. Climate. 5(2):26.
Interpretive Summary: Climate, especially temperatures and precipitation, greatly affect the natural environment and landscapes, crop growth, and runoff and erosion. In soil conservation planning or other natural resources evaluations, it is critical to have good representation of climate for a location because temperatures and rainfall/snowfall drive the hydrologic, plant growth, erosion, and other physical and biological processes. Computer simulation programs are commonly used to generate strings of daily predictions of weather, using long-term observed values and statistics, as well as mathematical prediction procedures. These “climate generator” programs can produce sets of possible daily temperature and precipitation values that are intended to have the same average values and variability as weather at the observation stations. In this study, 3 common climate generator programs were studied: LARS-WG, CLIGEN, and WeaGETS. They were applied to locations in the Western Lake Erie Basin (WLEB) located in northeastern Indiana, southern Michigan, and northwestern Ohio. For the 3 generators tested, we found that LARS-WG and CLIGEN could be satisfactorily applied, and in most cases represented the observed station precipitation and temperature values well. Apparent problems in setting up input parameters properly for WeaGETS prevented it from performing adequately. These results impact scientists, students, faculty members and others involved in conducting natural resource modeling. Selection and application of satisfactory climate inputs to models is critical for obtaining the best predictions of runoff, soil erosion, and pollutant losses.
Technical 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 (CLIGEN, LARS-WG, and 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, growing range days simulated by LARS-WG also exceeded values 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.