Improving Flow Estimation Accuracy Through the Integration of Hydrological Methods and Remote Sensing Data: Emphasizing the Role of Soil Texture and Land Use in Unguaged Sites Located Hydrometric Data

Document Type : Research Article

Authors

1 PhD. student of Watershed Management Engineering Faculty of Natural Resources Lorestan University, Khorram Abad, Lorestan, Iran

2 Associate Professor, Department of Range and Watershed Management Engineering, Faculty of Natural Resources, Lorestan University, Khorramabad, Iran

3 Assistant Professor, Department of Range and Watershed Management Engineering, Faculty of Natural Resources, Lorestan University, Khorramabad, Iran

Abstract

Effective water resource management in areas with limited hydrometric data requires the application of innovative and integrated methods to examine hydrological dynamics more accurately. This study investigates and analyzes how flow was estimated in the sub-basins of the Dez in Lorestan Province. Initially, Sentinel-1 and 2 satellite data were used, along with SRCI and BI indices, to extract maps of soil textures, land use, and curve number (CN). Subsequently, Relying on rainfall and discharge data from 1992 to 2023 and statistical analysis, the return period of rainfall and flow for the studied sub-basins was calculated utilizing EasyFit software. The flow for each sub-basin was estimated using the SCS method and multivariate regression. The results indicated that multivariate regression, evaluated using the Durbin-Watson statistic (1.74), the coefficient of determination (0.768), the mean squared error (17.88), and the Nash-Sutcliffe efficiency (0.758) for a 2-year return period, was the most suitable method for estimating flow at ungauged stations within the sub-basins of the Dez River. Overall, this research presents effective approaches for water resource management and the optimization of hydrological in Lorestan Province, To optimize cost and time efficiency, the use of multivariate regression for flow estimation in ungauged hydrometric sub-basins is recommended.

Keywords

Main Subjects


Abed, M. S., Kadhim, F. J., Almusawi, J. K., Imran, H., Bernardo, L. F. A., & Henedy, S. N. (2023). Utilizing multivariate adaptive regression splines (MARS) for precise estimation of soil compaction parameters. Applied Sciences13(21), 11634.
Alizadeh A. (2024). Principles of applied hydrology. Publications of Imam Reza University; 941 p. (in Persian)
Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., ... & Brisco, B. (2020). Google earth engine cloud computing platform for remote sensing big data applications: A comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing13, 5326-5350.
Asfaw, E., Suryabhagavan, K. V., & Argaw, M. (2018). Soil salinity modeling and mapping using remote sensing and GIS: The case of Wonji sugar cane irrigation farm, Ethiopia. Journal of the Saudi Society of Agricultural Sciences17(3), 250-258.
Aziz, M. T., Islam, M. R., Kader, Z., Imran, H. M., Miah, M., Islam, M. R., & Salehin, M. (2023). Runoff assessment in the Padma River Basin, Bangladesh: a GIS and RS platform in the SCS-CN approach. Journal of Sedimentary Environments8(2), 247-260.
Baghel, S., Kothari, M., Tripathi, M. P., Das, S., Kumar, A., & Kuriqi, A. (2023). Water conservation appraisal using surface runoff estimated by an integrated SCS-CN and MCDA-AHP technique. Journal of Earth System Science, 132(3), 127.
Berndt, C., Chi, W. C., Jegen, M., Lebas, E., Crutchley, G., Muff, S., & Feseker, T. (2019). Tectonic controls on gas hydrate distribution off SW Taiwan. Journal of Geophysical Research: Solid Earth124(2), 1164-1184.
Bousbih, S., Zribi, M., Pelletier, C., Gorrab, A., Lili-Chabaane, Z., Baghdadi, N., Ben Aissa, N., & Mougenot, B. (2019). Soil texture estimation using radar and optical data from Sentinel-1 and Sentinel-2. Remote Sensing11(13), 1520.
Caloz, R., Abednego, B., & Collet, C. (1988, April). The normalisation of a soil brightness index for the study of changes in soil conditions. In Spectral Signatures of Objects in Remote Sensing (Vol. 287, p. 363).
Danoedoro, P., & Zukhrufiyati, A. (2015, May). Integrating spectral indices and geostatistics based on Landsat-8 imagery for surface clay content mapping in Gunung Kidul area, Yogyakarta, Indonesia. In Proceeding of the 36th Asian Conference on Remote Sensing, Yogyakarta. https://www. researchgate. net/publication/302580476.
Eisfelder, C., Boemke, B., Gessner, U., Sogno, P., Alemu, G., Hailu, R., Mesmer, C., & Huth, J. (2024). Cropland and Crop Type Classification with Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine for Agricultural Monitoring in Ethiopia. Remote Sensing16(5), 866.
Esfandyari Darabad, F., Pourganji, Z., Mostafazadeh, R., & Aghaie, M. (2022). Comparison of Effective Rainfall Conversion Methods to Surface Runoff in Flood Hydrographic Simulation of Nanehkaran Watershed, Ardabil Province. Hydrogeomorphology9(32), 86-63. (in Persian)
Faqe Ibrahim, G. R., Rasul, A., & Abdullah, H. (2023). Improving crop classification accuracy with integrated Sentinel-1 and Sentinel-2 data: a case study of barley and wheat. Journal of Geovisualization and Spatial Analysis7(2), 22.
Gharakhani. M., Aghamohammadi, H., & Vahidnia, M.H. (2022). Flood hazard zonation using spatial hydrologic analysis in GIS and interpretation of satellite images: A case study on Aharchay catchment. J. Sus. Dev. & Env, 3(2):67-86. (in Persian)
GIS Geography. Sentinel-2 Bands Combinations. Available from: https://gisgeography.com/sentinel-2-bands-combinations/
Haghighizadeh, A,, Mohammadi, M., & Noori, F. (2015). Simulation of rainfall-runoff process using artificial neural network, adaptive neuro-fuzzy system, and multivariate regression (Case study: Khoramabad watershed). Ecohydrology, 2(2):233-243. (in Persian)
Hagras, A. (2023). Runoff modeling using SCS-CN and GIS approach in the Tayiba Valley Basin, Abu Zenima area, South-west Sinai, Egypt. Modeling Earth Systems and Environment, 9(4), 3883-3895.
Hoseini, Y. (2020). Comparison of uniform and SCS unit hydrograph methods to estimate maximum flood discharge of Amughin Basin. Hydrogeomorphology6(21), 87-107. (in Persian)
Kariuki, P. C., Woldai, T., & Van Der Meer, F. (2004). Effectiveness of spectroscopy in identification of swelling indicator clay minerals. International Journal of Remote Sensing25(2), 455-469.
Khan, N. M., Rastoskuev, V. V., Sato, Y., & Shiozawa, S. (2005). Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators. Agricultural Water Management77(1-3), 96-109.
Khedmati, H., Manshouri, M. O. H. A. M. M. A. D., Heydarizadeh, M. A. J. I. D., & Sedghi, H. O. S. S. E. I. N. (2010). Zonation and estimation of flood discharge in unguaged sites located in south-east basins of Iran using a combination of flood index and multi-variable regression methods (Sistan and Baluchistan, Kerman, Yazd and Hormozgan provinces). Water and Soil24(3). (in Persian)
Kim, S., Kim, S., Green, C. H., & Jeong, J. (2022). Multivariate polynomial regression modeling of total dissolved-solids in rangeland stormwater runoff in the Colorado River Basin. Environmental Modelling & Software157, 105523.
KN, J. H., Channavar, V. R., Malappanavar, N., Radder, V. S., Chandrakar, T., & Basavaraj, D. B. (2024). Spatial Analysis of Surface Runoff Using SCS-CN Technique Integrated with GIS and Remote Sensing. International Journal of Environment and Climate Change14(5), 441-454.
Mohammadzadeh, A., & Massoudzadegan, S. (2017). Forecasting Daily Volatility and Value at Risk with High Frequency Data. Development and Transformation Management Quarterly, 8(27):63-74. (in Persian)
Moharrami, M., Attarchi, S., Gloaguen, R., & Alavipanah, S. K. (2024). Integration of Sentinel-1 and Sentinel-2 Data for Ground Truth Sample Migration for Multi-Temporal Land Cover Mapping. Remote Sensing16(9), 1566.
Mullissa, A., Saatchi, S., Dalagnol, R., Erickson, T., Provost, N., Osborn, F., ... & Melling, D. (2024). LUCA: A Sentinel-1 SAR-Based Global Forest Land Use Change Alert. Remote Sensing16(12), 2151.
Nasiry, M. K., Said, S., & Ansari, S. A. (2023). Analysis of surface runoff and sediment yield under simulated rainfall. Modeling Earth Systems and Environment9(1), 157-173.
Noor Mohammadi, P., Haghighizadeh, A., Tahmasbi Poor, N., & Zeinivand, H. (2016). Identifying locations with potential for rainwater harvesting in the Sarab Seyed Ali watershed using two methods: NRCS-CN and GIS-based decision support system (DSS). Ecohydrology, 3(2):279-291. (in Persian)
Raj, R., Kumar, R., Aishwarya, M., Aswini, M., & Cheraku, S. (2024). An artificial neural network and SCS–CN-based model for runoff estimation: a case study of the Peddavagu watershed. Water Practice & Technology19(7), 2734-2743.
Rasti, S., Mahdavifardnh, M., Shaykh Ghaderi, H., Nasiri, A., & Taktaz, N. Z. (2022). Improving Classification accuracy by combining multi-season images of Sentinel 1 and 2 in order to prepare a land use map in the cloud space of Google Earth Engine (Case study: Guilan province). Geography and Human Relationships5(3), 357-373. (in Persian)
Rutledge, D. N., & Barros, A. S. (2002). Durbin–Watson statistic as a morphological estimator of information content. Analytica Chimica Acta454(2), 277-295.
Sabins, F. F. (1999). Remote sensing for mineral exploration. Ore geology reviews14(3-4), 157-183.
Salas, J. D. (1993). Analysis and modelling of hydrological time series. Handbook of hydrology19.
Sepahvand, T., Soleimani-Motlagh, M., Zeinivand, H., & Mirzaei Mosivand, A. (2023). Estimating Flood through the Fractal Theory-Based Precipitation Estimation and the CN Extracted from Sentinel 2 in HEC-HMS Model: A Case Study of Thireh Watershed in Borujerd-Dorud Region. Desert Ecosystem Engineering, 12(38), 87-103. (in Persian)
Setayesh, M. H., & Namazi, N. R. (2015). Investigating the Relationship between Intangible Assets, Profitability and Value of Firms Listed in Tehran Stock Exchange. Accounting and Auditing Research7(26), 4-27. (in Persian)
Soleimani, K,, Shakarian, F., Abdali, S., & Saberi A. (2021). Prioritization of flood risk potential in the Talaar watershed using GIS. Ecohydrology, 8(3):749-762. (in Persian)
Stenberg, B., Rossel, R. A. V., Mouazen, A. M., & Wetterlind, J. (2010). Visible and near infrared spectroscopy in soil science. Advances in agronomy107, 163-215.
Tali-Khoshk, S., Mohseni Saravi, M., Vatakhah, M., & Khalighi-Sigarodi, S. (2015). Comparison of Neuro-fuzzy and SCS methods in sub-watersheds prioritization for watershed measures (Case study: Taleghan watershed). Journal of Range and Watershed Managment68(2), 213-225. doi: 10.22059/jrwm.2015.54922. (in Persian)
Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., & Brisco, B. (2020). Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS journal of photogrammetry and remote sensing164, 152-170.
Teluguntla, P., Thenkabail, P. S., Oliphant, A., Xiong, J., Gumma, M. K., Congalton, R. G., Yadav, K., & Huete, A. (2018). A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform. ISPRS journal of photogrammetry and remote sensing144, 325-340.
Tosan, M., & Beyranvand, Z. (2023). The role of flood analysis in different return periods using empirical relationships for small watersheds in the stability of aquifers. Journal of Auifer and Qanat, 4(1), 169-180. (in Persian)
Vinutha, T. Y., Rakesh, C. J., Lokanath, S., & Kumar, A. K. (2024). Surface Runoff Estimation Using SCS-CN Method for Kurumballi Sub-watershed in Shivamogga District, Karnataka, India. Nature Environment & Pollution Technology23(2).
Vörösmarty, C. J., McIntyre, P. B., Gessner, M. O., Dudgeon, D., Prusevich, A., Green, P., ... & Davies, P. (2010). Global threats to human water security and river biodiversity. Nature, 467(7315), 555-561.
Yazdi-Feyzabadi, V., Bahrampour, M., Rashidian, A., Haghdoost, A. A., Akbari Javar, M., & Mehrolhassani, M. H. (2018). Prevalence and intensity of catastrophic health care expenditures in Iran from 2008 to 2015: a study on Iranian household income and expenditure survey. International journal for equity in health17, 1-13.
Yoothong, K., Moncharoen, L., Vijarnson, P., & Eswaran, H. (1997). Clay mineralogy of Thai soils. Applied Clay Science11(5-6), 357-371.
Zema, D. A., Parhizkar, M., Plaza-Alvarez, P. A., Xu, X., & Lucas-Borja, M. E. (2024). Using random forest and multiple-regression models to predict changes in surface runoff and soil erosion after prescribed fire. Modeling Earth Systems and Environment10(1), 1215-1228.
Zhao, Q., Yu, L., Li, X., Peng, D., Zhang, Y., & Gong, P. (2021). Progress and trends in the application of Google Earth and Google Earth Engine. Remote Sensing13(18), 3778.
Volume 11, Issue 3
October 2024
Pages 337-354
  • Receive Date: 22 July 2024
  • Revise Date: 30 August 2024
  • Accept Date: 12 September 2024
  • First Publish Date: 22 September 2024
  • Publish Date: 22 September 2024