GIS-AHP based site selection to identify the optimum number of meteorological stations: Karasu Watershed case study

Abdul Baqi Ahady a,b* , Hakan Uyguçgil a , Ali Arda Sorman a

a Eskisehir Technical University, Eskisehir 26555, Turkey
b Kabul Polytechnic University, Karta-e-Mamorin, 5th District, Kabul, Afghanistan

*Corresponding author e-mail: baqi.ahady@gmail.com

H. Uyguçgil: uygucgil@eskisehir.edu.tr; A. Sorman: asorman@eskisehir.edu.tr

https://doi.org/10.29258/CAJWR/2023-R1.v9-1/89-112.eng

Research article

Abstract

The density of meteorological stations in most watersheds across the globe is far lower than recommended by the World Meteorological Organization (WMO). However, for some basins, including those used as pilot, an adequate quantity of weather stations is crucial for collecting high-accuracy data. This study aimed to 1) estimate the optimum number of meteorological stations and 2) demarcate the most appropriate sites for their installation considering physical and environmental factors directly and indirectly influencing both objectives, i.e. to develop a well-optimized weather station network. The Weighted Overlay method and six (6) environmental factors –- precipitation variance, slope, elevation, proximity of existing stations, land cover and land use, as well as distance from roads –- were applied to delineate the potential locations. All parameters were mapped out separately and then reclassified for scoring (0 to 100 scale) based on their significance. The Analytic Hierarchy Process (AHP) method was applied to determine the impact of each factor. Based on the analysis, the precipitation variance received 38% weight, while the distance from road was computed to reach only 3% weight. The Weighted Overlay map of the Karasu Watershed was delineated into corresponding highly suitable, moderately suitable, suitable, marginally suitable, and not suitable zones. Finally, the recommended station locations were validated using a hypsometric curve to ensure proper coverage of different elevations. The research will improve the climate change and water resource management applications by informing them with sufficient climatic data about the entire target area including all variations, as well as will help addressing the challenge of data shortage and thus increase the quality of future thematic research.

Available in English

Download the article (eng)

For citation: Ahady, A., Uyguçgil, H., Sorman, A., (2023). GIS-AHP based site selection to identify the optimum number of meteorological stations: Karasu Watershed case study. Central Asian Journal of Water Research9(1), 89-112. https://doi.org/10.29258/CAJWR/2023-R1.v9-1/89-112.eng

References

Ahady, A. B., Pekkan, E., Sorman, A. A., & Deliry, S. I. (2022). Evaluating the hydrological performance of gridded precipitation datasets using GR2M for a mountainous watershed in Turkey. Arabian Journal of Geosciences, 15(8), 1-16.

Awadallah, A. G. (2012). Selecting optimum locations of rainfall stations using kriging and entropy. International Journal of Civil & Environmental Engineering IJCEE-IJENS, 12(1), 36-41.

Awadh, S. M., Al-Mimar, H., & Yaseen, Z. M. (2021). Groundwater availability and water demand sustainability over the upper mega aquifers of Arabian Peninsula and west region of Iraq. Environment, Development and Sustainability, 23(1), 1-21. https://doi.org/https://doi. org/10.1007/s10668-019-00578-z

Awawdeh, M. M., ElMughrabi, M. A., & Atallah, M. Y. (2018). Landslide susceptibility mapping using GIS and weighted overlay method: a case study from North Jordan. Environmental Earth Sciences, 77(21), 1-15.

Baltalar, H. (1999 – 2021). Analytical Hierarchy Process (AHP). Hasan Baltalar. Retrieved 14/6/2021 from http://www.hasanbaltalar.com/

Bankanza, J. C. M. (2011). Time variation of the effect of geographical factors on spatial distribution of summer precipitation over the Czech Republic. IDŐJÁRÁS, 115(1-2), 51-70.

Basharat, M., Shah, H. R., & Hameed, N. (2016). Landslide susceptibility mapping using GIS and weighted overlay method: a case study from NW Himalayas, Pakistan. Arabian Journal of Geosciences, 9(4), 1-19.

Basher, R. E., & Zheng, X. (1998). Mapping rainfall fields and their ENSO variation in data‐sparse tropical south‐west Pacific Ocean region. International Journal of Climatology: A Journal of the Royal Meteorological Society, 18(3), 237-251.

Brown, N., Gerard, F., & Fuller, R. (2002). Mapping of land use classes within the CORINE land cover map of Great Britain. The Cartographic Journal, 39(1), 5-14.

Carver, S. J. (1991). Integrating multi-criteria evaluation with geographical information systems. International Journal of Geographical Information System, 5(3), 321-339.

Chang, N.-B., & Makkeasorn, A. (2010). Optimal site selection of watershed hydrological monitoring stations using remote sensing and grey integer programming. Environmental modeling & assessment, 15(6), 469-486.

Chen, Y. C., Wei, C., & Yeh, H. C. (2008). Rainfall network design using kriging and entropy. Hydrological Processes: An International Journal, 22(3), 340-346.

Erdogan, S. A., Šaparauskas, J., & Turskis, Z. (2017). Decision making in construction management: AHP and expert choice approach. Procedia Engineering, 172, 270-276.

Ertas, C., Akkol, B., Coskun, C., Uysal, G., Sorman, A., & Sensoy, A. (2016). Evaluation of Probabilistic Streamflow Forecasts Based on EPS for a Mountainous Basin in Turkey. Procedia Engineering, 154, 490-497.

Eskandari, S. (2017). A new approach for forest fire risk modeling using fuzzy AHP and GIS in Hyrcanian forests of Iran. Arabian Journal of Geosciences, 10(8), 190.

Finklin, A. I. (1990). Weather Station Handbook–: An Interagency Guide for Wildland Managers (Vol. 2140). National Wildfire Coordinating Group.

Hassan, I., Javed, M. A., Asif, M., Luqman, M., Ahmad, S. R., Ahmad, A., Akhtar, S., & Hussain, B. (2020). Weighted overlay based land suitability analysis of agriculture land in Azad Jammu and Kashmir using GIS and AHP. Pakistan Journal of Agricultural Sciences, 57(6).

Hong, N. T., Truc, P. T. T., Liem, N. D., & Loi, N. K. (2016). Optimal selection of number and location of meteo-hydrological monitoring networks on vu gia–thu bon river basin using GIS. International Journal on Advanced Science Engineering Information Technology, 6(3), 324-326.

Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Xie, P., & Yoo, S.-H. (2015). NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG). Algorithm Theoretical Basis Document (ATBD) Version, 4, 26.

Iqbal, A. B., Rahman, M. M., Mondal, D. R., Khandaker, N. R., Khan, H. M., Ahsan, G. U., Jakariya, M., & Hossain, M. M. (2020). Assessment of Bangladesh groundwater for drinking and irrigation using weighted overlay analysis. Groundwater for Sustainable Development, 10, 100312.

Jabbar, F. K., Grote, K., & Tucker, R. E. (2019). A novel approach for assessing watershed susceptibility using weighted overlay and analytical hierarchy process (AHP) methodology: a case study in Eagle Creek Watershed, USA. Environmental Science and Pollution Research, 26(31), 31981-31997.

Johnson, G. L., & Hanson, C. L. (1995). Topographic and atmospheric influences on precipitation variability over a mountainous watershed. Journal of Applied Meteorology and Climatology, 34(1), 68-87.

Joly, D., & Gillet, F. (2017). Interpolation of temperatures under forest cover on a regional scale in the French Jura Mountains. International Journal of Climatology, 37, 659-670.

Joly, D., Nilsen, L., Fury, R., Elvebakk, A., & Brossard, T. (2003). Temperature interpolation at a large scale: test on a small area in Svalbard. International Journal of Climatology: A Journal of the Royal Meteorological Society, 23(13), 1637-1654.

Kaliraj, S., Chandrasekar, N., & Magesh, N. (2015). Evaluation of multiple environmental factors for site-specific groundwater recharge structures in the Vaigai River upper basin, Tamil Nadu, India, using GIS-based weighted overlay analysis. Environmental Earth Sciences, 74(5), 4355-4380.

Krishnamurthy, J., & Srinivas, G. (1995). Role of geological and geomorphological factors in ground water exploration: a study using IRS LISS data. International Journal of Remote Sensing, 16(14), 2595-2618.

Malczewski, J. (2006). GIS‐based multicriteria decision analysis: a survey of the literature. International journal of geographical information science, 20(7), 703-726.

Mandal, S., & Mondal, S. (2019). Weighted overlay analysis (woa) model, certainty factor (cf) model and analytical hierarchy process (ahp) model in landslide susceptibility studies. In Statistical approaches for landslide susceptibility assessment and prediction (pp. 135-162). Springer.

Marg, M. B. a. B. S. Z. (1994). Recommendations for establishing network of raingauge stations. Delhi, India: Bureau of Indian Standards

Nagarajan, M., & Singh, S. (2009). Assessment of groundwater potential zones using GIS technique. Journal of the Indian Society of Remote Sensing, 37(1), 69-77.

Özdağoğlu, A., & Özdağoğlu, G. (2007). Comparison of AHP and fuzzy AHP for the multi-criteria decision making processes with linguistic evaluations.

Ozdemir, S., & Sahin, G. (2018). Multi-criteria decision-making in the location selection for a solar PV power plant using AHP. Measurement, 129, 218-226.

Pani, S., Chakrabarty, A., & Bhadur, S. (2016). Groundwater potential zone Identification by analytical hierarchy process (AHP) weighted overlay in GIS Environment—a case study of Jhargram Block, Paschim Medinipur. Int J Remote Sens Geosci (IJRSG), 5(3), 1-10.

Pardo-Igúzquiza, E. (1998). Optimal selection of number and location of rainfall gauges for areal rainfall estimation using geostatistics and simulated annealing. Journal of hydrology, 210(1-4), 206-220.

Pickett, E., & Whiting, R. (1981). The design of cost-effective air quality monitoring networks. Environmental Monitoring and Assessment, 1(1), 59-74.

Saghafian, B., Davtalab, R., Rafieeinasab, A., & Ghanbarpour, M. R. (2016). An integrated approach for site selection of snow measurement stations. Water, 8(11), 539.

Şensoy, A., Şorman, A. A., Tekeli, A., Şorman, A. Ü., & Garen, D. (2006). Point‐scale energy and mass balance snowpack simulations in the upper Karasu basin, Turkey. Hydrological Processes: An International Journal, 20(4), 899-922.

Shaghaghian, M., & Abedini, M. (2013). Raingauge network design using coupled geostatistical and multivariate techniques. Scientia Iranica, 20(2), 259-269.

Shen, Z., Chen, L., Liao, Q., Liu, R., & Hong, Q. (2012). Impact of spatial rainfall variability on hydrology and nonpoint source pollution modeling. Journal of Hydrology, 472, 205-215.

Shit, P. K., Bhunia, G. S., & Maiti, R. (2016). Potential landslide susceptibility mapping using weighted overlay model (WOM). Modeling Earth Systems and Environment, 2(1), 21.

Şorman, A. A., Uysal, G., & Şensoy, A. (2019). Probabilistic snow cover and ensemble streamflow estimations in the Upper Euphrates Basin. Journal of Hydrology and Hydromechanics, 67(1), 82- 92. https://doi.org/https://doi.org/10.2478/johh-2018-0025

Tan, M. L., & Yang, X. (2020). Effect of rainfall station density, distribution and missing values on SWAT outputs in tropical region. Journal of Hydrology, 584, 124660.

Tzeng, G.-H., & Huang, J.-J. (2011). Multiple attribute decision making: methods and applications. CRC press.

Velasquez, M., & Hester, P. T. (2013). An analysis of multi-criteria decision making methods. International journal of operations research, 10(2), 56-66.

Xu, P., Wang, D., Singh, V. P., Wang, Y., Wu, J., Wang, L., Zou, X., Liu, J., Zou, Y., & 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., & Chen, R.-H. (2011). Entropy and kriging approach to rainfall network design. Paddy and Water Environment, 9(3), 343-355.

GIS and AHP, meteorological stations, optimum number, weighted overlay

Leave a Reply