Document Type : Research Article

Authors

1 Department of Natural Engineering, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, Iran

2 Department of Natural Engineering, Faculty of Natural Resources and Earth Sciences, University of Shahrekord

Abstract

Introduction: Rainfall data are required for planning, designing, developing and managing water resources projects as well as hydrological studies. Some previous studies have suggested increasing the density of the rain gauge network to reduce the estimation error. However, more operational stations require more installation costs and monitoring. Some common techniques including statistical methods, spatial interpolation, information-based theory and combination are used to evaluate and design the network. Chaharmahal va Bakhtiari province is a mountainous region; hence, a denser rainfall network is expected in this mountainous environment. The aim of this study was to evaluate the condition of rain gauge stations in Chaharmahal va Bakhtiari province using two approaches, i.e. geostatistical methods and entropy theory.
Materials and Methods: The main required data set for this study is a time series of rainfall data. These data were collected on a daily scale from the Regional Water Company of Chaharmahal va Bakhtiari. After performing statistical tests, the annual data series was prepared for 46 rain gauge stations. A statistical period of 2000 to 2016 was used. The homogeneity of data was investigated by double mass test and histogram drawing methods using Excel and SPSS software, and the existence of trend in the time series of data was investigated by applying a Spearman test. Then, the adequacy of rain gauges in the gauging network was investigated. Annual rainfall interpolation maps and their standard error maps were prepared using the kriging method. Contribution of each station in reducing or increasing the error in the rain gauge network was investigated by removing each station in a cross validation procedure. The efficiency of the rain gauge network was evaluated using the concept of discrete entropy and the values of entropy indices. The value of keeping the rain gauge stations was determined using the net exchange information index.
Results and Discussion: There was no homogeneity problem and significant trend in the data series. Considering the permissible error percentage of 5%, there is a need to add 15 new rain gauge stations to the network. To apply the geostatistical method, we applied it once without deleting any station; then, the kriging interpolation error was calculated for the precipitation data. Then, only one station was removed at each stage, and both the error and the contribution of each station in increasing or decreasing the error compared to the case without Station deletion were obtained. The results indicated that Ab-Turki, Shahrekord, Borujen and Barez stations were more important than other stations. Two stations namely Chaman-Goli and Ben stations can also be considered as the influential stations in error due to the density of stations in the region and error maps. Similarly, the results of the entropy theory method were found effective in evaluating the design of the rain gauge network. The highest value of H(x) was observed in the data of Armand station (3.26) and the lowest value was observed in Abbasabad station (2.28). Since H(x) shows the uncertainty of measuring data, the maximum and minimum uncertainty were found for Armand and Abbasabad sites, respectively. Based on the Net Exchange Information Index, Bardeh, Bareh Mardeh and Dezkabad stations were ranked 1 to 3, respectively, indicating that they transmit and receive more information than other stations. On the other hand, a number of stations including Dorak anari, Abtorki and Chelo stations had the lowest values.
Conclusion: Due to the vast extent of the area and also considering the permissible error percentage of 5%, the number of the stations in this area was found to be insufficient. Thus, although calculating the kriging error maps showed that some stations do not have a significant share in increasing the error, removing the stations is not recommendable. Regarding the new stations, new 15 rain gauge stations are needed to check out the error maps. According to the field observations, the higher priority should be given to the northwestern area (which had the largest interpolation error) in the first place. For the regions with lower error, such as northeast, east, southeast, west and southwest that do not have rain gauge stations, additional rain gauge stations should be constructed.
 

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  • Adhikary S.K., Yilmaz A.G., and Muttil N. 2015. Optimal design of rain gauge network in the Middle Yarra River catchment, Australia. Hydrological Processes 29(11): 2582-2599.
  • Alizadeh A. Applied Hydrology. Astan Quds Razavi Publication, Mashhad. (In Persian)
  • Asadi A., and Jalali M. 2016. Review and Evaluation of Geostatistical Method of Kriging in the spatial distribution precipitation (Case Study: North West of Iran). Geographic Space 15(52): 187-204. (In Persian)
  • Barca E., Passarella G., and Uricchio V. 2008. Optimal extension of the raingauge monitoring network of the Apulian regional consortium for cropprotection. Environmental Monitoring and Assessment 145: 375-
  • Bechler A., Vrac M., and Bel L. 2015. A spatial hybrid approach for downscaling of extremeprecipitation fiJournal of Geophysical Research: Atmospheres 120(10): 4534-4550.
  • Biau G., Zorita E., Von Storch H., and Wackernagel H. 2010. Estimation of precipitation bykriging in the EOF space of the Sea level pressure fi Journal of Climate 12(12): 1070-1085.
  • Chaharmahal-Bakhtiari Meteorological Administration. Available at http://www.chbmet.ir (visited 27 September 2021).
  • Chebbi A., Bargaoui Z.K., and Cunha M.D.C. 2011. Optimal extension of raingauge monitoring network for rainfall intensity and erosivity indexinterpolation. Journal of Hydrologic Engineering 16: 665-
  • Chen Y.C., Wei C., and Yeh H.C. 2008. Rainfall network design using kriging andentropy. Hydrological Processes 22: 340-
  • Cheng K.S., Lin Y.C., and Liou J.J. 2008. Rain-gauge network evaluation andaugmentation using geostatistics. Hydrological Processes 22: 2554-
  • Jessop A. 1995. Informed Assessments, an Introduction to Information, Entropy and Statistics. Ellis Horwoo, New York.
  • Karimi Hosseini A., Bozorg Haddad O., Hoorfar A.H., and Ebrahimi K. 2010. Rainfall Network Design Using Entropy Approach. Iranian Journal of Watershed Management Science and Engineering 4(11): 1-12. (In Persian with English abstract)
  • Kasaee Roodsari B., Ghahreman B., and Sharifi M.B. 2010. Study of rain-gauge network density using geostatistical methods (Case study: Northern, Eastern and Razavi Khorasan Provinces). Iranian Journal of Watershed Management Science and Engineering 4(10): 35-44. (In Persian with English abstract)
  • Kassim A.H.M., and Kottegoda N.T. 1991. Rainfall network design throughcomparative kriging methods. Hydrological Sciences Journal 36: 223-
  • Kawachi T., Maruyama T., and Singh V.P. 2001. Rainfall entropy fordelineation of water resources zones in Japan. Journal of Hydrology 246: 36-44.
  • Khalifeh S. 2014. Evaluation of monitoring network density using discrete entropy theory. Journal of Water Science Engineering 4(10): 19-36. (In Persian)
  • Kilibarda M., Hengl T., Heuvelink G.B.M., Graler B., Pebesma E., Percec Tadic , and Bajat B. 2014. Spatio-temporal interpolation of daily temperatures for global landareas at 1 km resolution. Journal of Geophysical Research: Atmospheres 119(5): 2294-2313.
  • Manz B., Buytaert W., Zulkafli Z., Lavado W., Willems B., Robles L.A., and Rodriguez-Sanchez J.P. 2016. High-resolution satellite-gauge merged precipitation climatologies of the Tropical Andes. Journal of Geophysical Research: Atmospheres 121(3): 1190-
  • , Knapp H.V., and Tasker G.D. 2003. Entropy and generalized least square methods in assessment of the regional value of stream gages. Journal of Hydrology 283: 107-121.
  • Marofi S., Golmohammadi G., Mohammadi K., and Zare Abyaneh H. 2010. Evaluation of Geostatisical Methods for Estimating Spatial Distribution of Annual Rainfall Using GIS. Water and Soil Science 19.1(2): 147-164. (In Persian)
  • Mishra A.K., and Coulibaly P. 2009. Developments in hydrometric network design: a review.Reviews of Geophysics 47(2): 2415-
  • Mishra A.K., and Coulibaly P. 2010. Hydrometric network evaluation for Canadian watersheds. Journal of Hydrology 380: 420-
  • Mogheir Y., and Singh V.P. 2003. Specification of information needs for groundwater management planning in developing country. Groundwater Hydrology. Balema Publisher, Tokyo.
  • Moss M.E. and Tasker G.D. 1991. An intercomparison of hydrological network-designtechnologies. Hydrological Sciences Journal 36(3): 209-
  • Nazeri Tehrani M., and Ramazani Y. 2018. River drought analysis using time series. Birjand University Press, Birjand. (In Persian)
  • Papamichail D.M., and Metaxa I.G. 1996. Geostatistical analysis of spatialvariability of rainfall and optimal design of rain gauge network. WaterResources Management 10: 107-
  • Pardo-Iguzquiza E. 1998. Optimal selection of number and location of rainfallgauges for areal rainfall estimation using geostatistics and simulated annealing.Journal of Hydrology 210: 206-
  • Ramazani Y., Pourreza-Bilondi M., Yaghoobzadeh M., and Nazeri Tahrudi M.  2018. Qualitative Monitoring of Drinking Water Using Entropy Indices (Case Study: Central Aquifer of Birjand Plain). Iranian Journal of Irrigation & Drainage 12(3): 556-568. (In Persian with English abstract)
  • Shaghaghian M.R., and Abedini M.J. 2013. Rain gauge network design using coupled geostatistical and multivariate techniques. Scientia Iranica 20(2): 259-269.
  • Shahidi A., KhasheiSiuki A., Ramazani Y., and Nazeri-Tahrudi M. 2019. Designing Monitoring Network for Rain Gauge Stations Using IrregularityTheory (Case Study: Urmia Lake Basin). Iranian Journal of Irrigation and Drainage 2(13): 296-308. (In Persian with English abstract)
  • Shannon C.E.1948. A mathematical theory of communication. The Bell System Technical Journal 27: 379-423.
  • Singh V.P. 2013. Entropy theory and its application in environmental and water engineering. John Wiley and Sons, New Jersey.
  • Tsintikidis D., Georgakakos K.P., Sperfslage J.A., Smith D.E., and Carpenter T.M. 2002.Precipitation uncertainty and rain gauge network design within Folsom Lakewatershed. Journal of Hydrologic Engineering 7: 175-
  • Valipoor E., Ghorbani M.A., and Asadi E. 2020. Rainfall Network Optimization using Information Entropy and Fire Fly AlgorithmCase Study: East Basin of Urmia Lake. Journal of Watershed Management Research 11(21): 11-23. (In Persian with English abstract)
  • Xu P., Wang D., Singh V.P., Wang Y., Wu J., Wang L., Zou X., Liu J., Zou Y., and He R. 2018. A kriging and entropy-based approach to raingauge network design. Environmental Research 161: 61-75.
  • Yeh H.C., Chen Y.C., Wei C., and Chen R.H. 2011. Entropy and kriging approach to rainfall network design. Paddy and Water Environment 9(3): 343-355.
  • Yue S., Pilon P., and Cavadias G. 2002. Power of the Mann-Kendall and Spearman’s tests for detecting monotonic trends in hydrological series. Journal of Hydrology 259: 254-271.
  • Zandkarimi A., Mokhtari D., and Zandkarimi S. 2018. The spatial analysis and optimization of rain gauging station network in Kurdistan Province using the Kriging Error Variance. Geographical Data 27(105): 115-126. (In Persian)
  • Zarei A., Asadi E., Ebrahimi A., Jafari M., Malekian A., Mohammadi Nasrabad H., Chemura A., Maskell G. 2020. Prediction of future grassland vegetationcover fluctuation under climate change scenarios. Ecol. Indicat. 119, 106858.
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