Application of Raster-Based Plots in Hydrology (Case Study: Gamasiyab Spring)

Document Type : Research Article

https://doi.org/10.22067/jsw.v33i5.80597

Abstract

Introduction: Despite being helpful to explore and analyze large multidimensional datasets, visualization Techniques have been rarely considered in hydrology. One of the techniques is Pixel-Based (Raster-Based) graphs. Pixel-based graph is a graphing technique that maximizes displayed information using a pixel or raster-based approach.
Materials and Methods: This study  two types of raster-based graphs, including Raster-Hydrograph and Raster Hyetograph were evaluated, for Gamasiyab Karstic Spring located in Nahavand. The graphs were drawn by applying discharge and rainfall daily information of gamasiyab spring in 1969-2018. The MATLAB was employed to draw the graphs. To calculate the spring discharge, recorded data from Sang Sorakh and Variane Canal station were used. The data gathered for Sang Sorakh and Variane were recorded from 1969 and 2005, respectively. Thus, the spring discharge was the summation of both stations. The maximum, minimum and average discharge was, respectively, 37.97, 0.3 and 4 m3/s. It is important to note that the basin area is about 60 Km2.  
Results and Discussion: By applying the graphs, six different phenomena were investigated:

Snowmelt: According to the raster hydrograph of the Gamasiyab spring, snowmelt occurs in the first 200 to 300 days of year (e.g. early April to late July). According to this graph, during the recent years, snowmelt period shortened. As of 2004, that the number of snowmelt days showed a considerable reduction as compared to the previous years. This issue has become more intense for the years after 2013 indicating a change in the spring discharge regime.
Drought: According to the raster hydrograph of the Gamasiyab spring, droughts were observed in 1998 and 1999.
Storm Flow: According to the raster hydrograph of the Gamasiyab spring, a storm flow was observed in the middle of April,1986. Storm flows were also observed in late February of 1986 and 2005, and the late March of 2016.
Dry Year: Dry Year is a year that the discharge is less than the average. 2008 and 2009 were the examples of dry years. In addition, 2014 was one-year low water.
Dry Month: Determine dry months are used for baseflow separation. In dry months, discharge is due to baseflow, and rainfall and snowmelt play a very small role in the discharge.
Monthly changes: Monthly changes happen when rapid changes in discharge are observed from one month to another. For example, the discharge regime suddenly changes from a dry to wet condition. According to the raster hydrograph of the Gamasiyab spring, the monthly changes in April and May, 2014 were observed. It was observed that the rainfall was almost equal to 0 in June to September. In the other words, rainfall period is from early November to early June. Maximum rainfall is in April and May.

Better results can be achieved by using both Raster Hydrograph and Raster Hyetograph. Discharge of Gamasiyab spring is affected by snowmelt and groundwater flow since late May to late September, and rainfall has no effect on spring discharge in this period. According to these graphs, it can be also concluded that springtime rainfall was impacted with one-month lag time. According to raster hydrograph, the minimum discharge occurs in October, however, the area receives rainfall during October based on raster Hyetograph. Therefore, the discharge increase in the November can be attributed to the precipitation falling during October.
Conclusion: Main benefits of this graphs are: 1. a way to view large datasets.  2. Quickly review and interpret. 3. Develop new types of products. 4. Cost and time efficiency. This method is able to show systematic error, missing data, outliers, comparison different places, potential new products. Results show that the snowmelt period in Gamasiyab spring decreased from 1969 to 2018. This period shortened from 100 to 30 days per year. The year of 2008 was the driest year during the statistical period of the spring, and a drought was also observed in 1998. According to raster hydrograph, the driest month was found to be October. Determining this month is very useful for base flow separation. One can conclude that these graphs including large amount of information, accelerate the processes of scanning and interpretation.

Keywords

• Gamasiyab spring
• Raster graphs
• Pixel-based graphs
• Raster hydrograph

20.1001.1.20084757.1398.33.5.3.2

References

1- Anonymous. 2005. A Guideline to Prepare Karstic and Consolidated Formation Spring Hydrograph. Publication No. 232-A. (In Persian)

2- Helsel D.R., and Hirsch R.M. 2002. Statistical methods in water resources. Chapter A3 in Book 4, Hydrologic Analysis and Interpretation. Techniques of water-resources investigations of the U.S. Geological Survey.

3- McCulloch E.S. 2013. Harnessing the power of big data in biological research. Bioscience 63 9): 715-716.

4- Michener W.K., and Jones M.B. 2012. Ecoinformatics: Supporting ecology as a data-intensive science. Trends in Ecology and Evolution 27(2): 85-93.

5- Kim D.A. ,and Kriegel H.P. 1996. Visualization Techniques for Mining Large Databases: A Comparison. IEEE Transactions on Knowledge and Data Engineering 8(6): 1-23.

6- Keim D.A. 2000. Designing Pixel-Oriented Visualization Techniques: Theory and Applications. IEEE Transactions on Visualization and Computer Graphics 6(1): 59-78.

7- Koehler R. 2004. Raster-Based Streamflow Analysis Applied to the Upper Snake River. Southern Illinois University Carbondale OpenSIUC, Conference Proceedings. 1-11.

8- Mckay S.K. 2014. Informing Flow Management Decisions in the Middle Oconee River. University of Georgia, Degree Doctor of Philosophy, 169 pp.

9- Koehler R., and Ball G. 1998. A Statistical Analysis of Low Flows on the San Pedro River, Arizona. Presentation to the September Symposium of the Arizona Hydrological Society, Tucson, AZ.

10- Koehler R., Pool D., and Kirchner J. 2002. A Visualization Techniquefor Hydrologic Timeseries Analysis. Presentation to the Flood-plain Management Association annual conference. Monterey, CA.

11- Strandhgan E. 2006. Cartographic Tecniques. Cartographic Perspectives, 55.