Quantitative Assessment of Hydrometry Station Density in the Province of Kermanshah Using the Entropy and Zoning Method in the GIS

Document Type : Research

Authors

1 M.Sc. in Water Resources Management

2 Assistant Professor, Water Engineering Department, Faculty of Agricultural Science and Engineering, Razi University

3 Assistant Professor, Department of Water Engineering, Faculty of Agricultural Science and Engineering, Razi University

Abstract

Evaluating the efficiency of water resources monitoring systems and trying to improve the status of various components of these systems, such as modifying the frequency of quantitative variable sampling and relocating stations, is of particular importance. The main reason for the importance of this issue is the significant monthly cost of maintaining these systems; therefore, reducing the redundant and excess information in such systems may have a significant effect on reducing costs without decreasing the value and accuracy of the resulting information. However, if the number of stations is not optimal, the data of sensitive points may be missed. In the discrete entropy theory was used to investigate the accuracy of station information and their density in the region, and to quantitatively evaluate the network of hydrometry stations in the province of Kermanshah. Fourteen stations with a common statistical period of 30 years was evaluated. The results indicate the presence of three critical stations of Doabmarg, Shah Gozar and Hyderabad in the network, which their continued functioning need to be reconsidered. The Pol-e-Chehr, Pirsalman and Ghorbaghistan are the three most important stations in the basin that should remain active in the network, as they have the highest ranking among other stations. Doabmarg Station has the worst condition in the monitoring network and its condition needs to be carefully reviewed and revised. The indexes S (i) R (i) have almost the same values ​​in most stations; each station tries to collect information from other stations as much as it contributes data to other stations.

Keywords

References

Alfonso L, He L, Lobbrecht A, Price R. 2013. Information theory applied to evaluate the discharge monitoring network of the Magdalena River. Journal of Hydroinformatics, 15(1): 211-228
Bazargan Lari A. 1999. Applied statistics, Shiraz University Press, 730 p. (In Persian).
Chao L, Vijay. 2012. Entropy based criterion for hydrometric network evaluation and design. Water Resources Research, 48(5): W05521, doi: 10. 1029/2011 WR011251.
Harmancioglu NB, Alpaslan N. 1992. Water quality moniroring network design: A Problem of Multi‐Objective Decision Making1. JAWRA Journal of the American Water Resources Association, 28(1): 179-192.
Harmancioglu N, Yevjevich V. 1987. Transfer of hydrologic information among river points. Journal of Hydrology, 91(1-2): 103-118.
Hongjun J, Kyungtak K, Seungjin H, Jungwook K, Hung Soo K. 2019. Optimal stream gauge network design using entropy theory and importance of stream gauge stations, Entropy. 21(10): 2-18.
Karamouz M, Hafez B, Kerachian R. 2005. Water quality monitoring network for river systems: Application of Ordinary Kriging. In Proceedings of ASCE-EWRI World Water and Environmental Resources Congress, Alaska. ASCE, Reston VA, USA.
Karamouz M, Nokhandan AK, Kerachian R, Maksimovic Č. 2009. Design of on-line river water quality monitoring systems using the entropy theory: A case study. Environmental monitoring and assessment, 155(1-4): 63-81.
Karimi goghari SH, Khalife S. 2011. Evaluating the efficiency of hydrometric networks using discrete entropy theory (Case study: Bakhtegan-Maharloo basin). Journal of Watershed Management Research 2(3):34-50. (In Persian).
Keum J, Kornelsen K, Leach J, Coulibaly P. 2017. Entropy Applications to Water Monitoring Network Design: A Review. Entropy, 19(11):613–635. DOI:10.3390/e19110613.
Mahjouri N, Kerachian R. 2011. Revising river water quality monitoring networks using discrete entropy theory: The Jajrood River experience. Environmental Monitoring and Assessment, 175(1-4): 291-302.
Marcus M, Knapp HV, Tasker GD. (2003). Entropy and generalized least square methods in assessment of the regional value of stream gages. Journal of Hydrology, 283(1-4):107-121.
Mishra AK, Coulibaly P. 2009. Developments in hydrometric network design: A review. Reviews of Geophysics, 47(2):1-24.
Mishra AK, Coulibaly P. 2010. Hydrometric network evaluation for Canadian watersheds. Journal of Hydrology, 380(3): 420-437.
Mogheir Y, De Lima JLMP, Singh VP. 2004. Characterizing the spatial variability of groundwater quality using the entropy theory: I. Synthetic data. Hydrological Processes, 18(11):2165–2179.
Mogheir Y, Singh VP. 2002. Application of information theory to groundwater quality monitoring networks. Water Resources Management, 16(1): 37-49.
Mogheir Y, De Lima JLMP, Singh VP. 2003. Assessment of spatial structure of groundwater quality variables based on the entropy theory. Hydrology and Earth System Sciences Discussions, 7(5): 707-721.
Ozkul S, Harmancioglu NB, Singh VP. 2000. Entropy-based assessment of water quality monitoring networks. Journal of Hydrologic Engineering, 5(1): 90-100.
Sarlak N, Sorman A. 2006. Evaluation and selection of streamflow network station using entropy method. Turkish Journal of Engineering and Environmental Sciences, 30: 91-100.
Shannon CE. 1948. A mathematical theory of communiation. Bell System Technical Journal, 27(3): 379-423.
Vivekanandan N. 2012. Evaluation of streamflow using Entropy Measures of Normal and Lognormal Distributions. Bonfring International Journal of Industrial Engineering and Management Science, 2(3): 33-37.
Vivekanandan N. 2014. Entropy based assessment of hydrometric network using normal and log – normal distribution. Applied Mathematics and Sciences: An International Journal (MathSJ). 1 (1): 33-42.
Werstuck C, Coulibaly P. 2017. Hydrometric network design using dual entropy multi-objective optimization in the Ottawa River Basin, Hydrology Research .48 (6): 1639-1651.

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History

  • Receive Date: 13 December 2020
  • Revise Date: 11 January 2021
  • Accept Date: 15 March 2021

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