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Fallahi khoshhi, M., Karbalaee Doree, A., Hedjazizadeh, Z., Hamezadeh, P. (2024). Evaluation and Comparison of Satellite Rainfall Products in Mountainous Areas with Lack of Meteorological Data of Lorestan Province. , 38(4), 429-443. doi: 10.22067/jsw.2024.87068.1395
M. Fallahi khoshhi; A.R. Karbalaee Doree; Z. Hedjazizadeh; P. Hamezadeh. "Evaluation and Comparison of Satellite Rainfall Products in Mountainous Areas with Lack of Meteorological Data of Lorestan Province". , 38, 4, 2024, 429-443. doi: 10.22067/jsw.2024.87068.1395
Fallahi khoshhi, M., Karbalaee Doree, A., Hedjazizadeh, Z., Hamezadeh, P. (2024). 'Evaluation and Comparison of Satellite Rainfall Products in Mountainous Areas with Lack of Meteorological Data of Lorestan Province', , 38(4), pp. 429-443. doi: 10.22067/jsw.2024.87068.1395
Fallahi khoshhi, M., Karbalaee Doree, A., Hedjazizadeh, Z., Hamezadeh, P. Evaluation and Comparison of Satellite Rainfall Products in Mountainous Areas with Lack of Meteorological Data of Lorestan Province. , 2024; 38(4): 429-443. doi: 10.22067/jsw.2024.87068.1395

Evaluation and Comparison of Satellite Rainfall Products in Mountainous Areas with Lack of Meteorological Data of Lorestan Province

آب و خاک
Volume 38, Issue 4 - Serial Number 96, September and October 2024, Pages 429-443    PDF (2.27 M)
Document Type: Research Article
DOI: 10.22067/jsw.2024.87068.1395
Authors
M. Fallahi khoshhi* 1; A.R. Karbalaee Doree2; Z. Hedjazizadeh2; P. Hamezadeh3
1Researcher, The Academic Center for Education, Culture and Research (ACECR), Lorestan Branch, Lorestan, Iran
2Department of Climatology Faculty of Geographical Sciences, Kharazmi University, Tehran, Iran
3Graduate in Water Structures, Faculty of Agriculture, University of Lorestan, Lorestan, Iran
Abstract
Introduction
The large temporal and spatial changes of precipitation, especially in mountainous areas, have turned it into a controversial variable in climate models. Measuring precipitation (rain and snow) along with its distribution and changes is very important to improve our understanding of global water cycle and energy, water resources monitoring, hydrological modeling. Lack of reliable data is one of the most important challenges in rainfall analysis. Due to the significant temporal and spatial variability of precipitation in mountainous areas, accurate spatially distributed data is crucial for effective water resource assessment and management. However, many mountainous regions have limited rain gauge stations. Today, satellite products are commonly used to measure precipitation in these areas, but the variability among these products raises concerns about their accuracy in mountainous regions. Additionally, the quality of satellite products differs between various products and across different climatic regions, making it essential to thoroughly evaluate them before use. The purpose of this research was to evaluate the precipitation data of two satellite products (GPM, PERSIAN) and reanalysis data (ECMWF) in the estimation of precipitation in mountainous areas without stations in Lorestan province.
 
Method
This study utilized rainfall data from 24 synoptic and rain gauge stations across Lorestan province. Emphasis was placed on stations situated in or near mountainous regions. The selected stations were chosen based on their suitable spatial distribution and record length. The rainfall data spanned the period from 2015 to 2021 and included daily, monthly, and annual measurements. To evaluate satellite rainfall algorithms and estimate rainfall in regions with limited data, data from the GPM and PERSIAN satellites were employed, along with ECMWF reanalysis data. The PERSIAN rainfall algorithm is a remote sensing-based method that utilizes artificial neural networks. It calibrates infrared data with passive microwave estimates and converts longwave infrared images into rainfall estimates using a three-step process. The spatial resolution of this product is 0.25° x 0.25°, and it offers hourly, daily, and monthly temporal resolution. The PERSIAN rainfall algorithm data can be accessed from https://chrsdata.eng.uci.edu. The GPM mission aims to provide continuous observations of Earth's precipitation. It employs the GPM Microwave Imager (GMI) and Dual-frequency Precipitation Radar (DPR) to observe both snow and rain. The final product, called IMERG, is generated through multiple runs of the algorithm for each observation time. Initial estimates are quickly provided, and subsequent estimates improve as more information becomes available. The spatial resolution of the GPM product is 1° x 1°, and it offers hourly, daily, and monthly temporal resolution. IMERG data can be obtained from https://gpm.nasa.gov/data. CMWF reanalysis data is derived from the combination of short-term simulations of numerical weather prediction models with ground-based observational data. These simulations are controlled with observational data, and the resulting reanalysis database provides global coverage from 1979 with a spatial resolution ranging from 0.125° x 0.125° to 3°. The temporal resolution of ECMWF reanalysis data is hourly, daily, and monthly. More information about ECMWF data can be found at https://www.ecmwf.int/ (Azizi, 2019).  To evaluate the accuracy of the products, R-squared correlation (R2), root mean square error (RMSE), standard deviation (MAD), correlation coefficient (R), error deviation (MBE) and Nash-Sutcliffe coefficient (NS) were used. Also, the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) indices were used to validate the data.
 
Results
The results showed that none of the three products are suitable for estimating daily precipitation in mountainous areas. However, on a monthly scale, these products provide reasonable estimates. Among the three, the GPM satellite product demonstrated better accuracy on a monthly scale, based on error levels and the spatial distribution of estimated precipitation. On an annual scale, GPM also performed best, as indicated by both statistical errors and the spatial patterns of average annual precipitation. According to the MBE index, on daily and monthly scales, the ECMWF product tended to overestimate precipitation, while the PERSIANN and GPM products underestimated it. On an annual scale, GPM and ECMWF products overestimated precipitation, whereas PERSIANN underestimated it.
Keywords
ECMWF; GPM; Mountain; PERSIAN; Precipitation; Satellite
References
  1. Abdulhaipour, A., Ahmadi, H., Aminnejad, B. (2022). Exponential microscale of satellite precipitation considering heterogeneous spatial relationship between precipitation and environmental variables. Natural Geography, 14(54), 109-126. (In Persian)
  2. Adane, G.B., Hirpa, B.A., Gebru, B.M., Song, C., & Lee, W.K. (2021). Integrating satellite rainfall estimates with hydrological water balance model: rainfall-runoff modeling in awash river basin, Ethiopia. Water, 13(6), 800. https://doi.org/10.3390/w13060800
  3. Azizi, J., Rasulzadeh, A., Rahmati, A., Shayghi, A., & Bakhter, A. (2019). Evaluation of the performance of Era-5 reanalyzed data in estimating daily and monthly rainfall in Ardabil province. Iran Water and Soil Research (Agricultural Sciences of Iran), 51(11), 2937-2951. (In Persian). https://doi.org/10.22059/ijswr.2020.302176. 668600
  4. Barros, A.P., & Arulraj, M. (2020). Remote sensing of orographic precipitation. Satellite Precipitation Measurement, 2, 559-582. https://doi.org/10.1007/978-3-030-35798-6_6
  5. Berthomier, L., Perier, L., & Espresso. (2023) A Global deep learning model to estimate precipitation from satellite observations. Meteorology, 2, 421-444. https://doi.org/10.3390/meteorology2040025
  6. Chen, S., Zhang, L., & Dunxian, C. (2019). Spatial downscaling of tropical rainfall measuring Mission (TRMM) annual and monthly precipitation data over the middle and lower reaches of the Yangtze River Basin, China. Water, 11(3), P.568. 12. https://doi.org/10.3390/w11030568
  7. Chen, X., & Huang, G. (2020). Applicability and hydrologic substitutability of TMPA satellite precipitation product in the Feilaixia Catchment, China. Water, 12(6), 1803. https://doi.org/10.3390/w12061803
  8. Dejene, I.N., Wedajo, G.K., & Bayissa, Y.A. (2023). Satellite rainfall performance evaluation and application to monitor meteorological drought: a case of Omo-Gibe basin, Ethiopia. Nat Hazards 119, 167–201. https://doi.org/ 10.1007/s11069-023-06127-2
  9. Dezfooli, D., Hosseini-Moghari, S.M., & Ebrahimi, K. (2016). Comparison of TRMM-3B42 V7 and PERSIANN satellites precipitation data with ground-based data (Case study: Gorganrood Basin, Iran). JWSS, 20(76), 85-98. https://doi.org/10.18869/acadpub.jstnar.20.76.85
  10. Dinku, T., Ceccato, P., & Connor, S.J. (2011). Challenges of satellite rainfall estimation over mountainous and arid parts of east Africa. International Journal of Remote Sensing, 32(21), 5965–5979. https://doi.org/10.1080/ 01431161.2010.499381
  11. Emami, H., Salajegheh, A., Moghaddamnia, A., & Khalighi Sigaroudi, S. (2020). Evaluation of TRMM satellite accuracy and efficiency in estimating monthly rainfall in Gorganroud watershed. Iranian Journal of Ecohydrology, 7(3), 719-729. https://doi.org/10.22059/ije.2020.261641.917
  12. Fatemi, Q.S., Yazdan Panah, H. (2013). evaluation of different interpolation methods in order to estimate the precipitation data of Isfahan province. Geographical Space, 12(40). (In Persian)
  13. Gao, Y., Guan, J., Zhang, F., Wang, X., & Long, Z. (2022). Attention-unet-based near-real-time precipitation estimation from fengyun-4A satellite imageries. Remote Sensing, 14, 2925. https://doi.org/10.3390/rs14122925
  14. Ghairi Sara, F., & Yazdan Panah, H. (2013). Evaluation of different interpolation methods in order to estimate the precipitation data of Isfahan province. Geographical Space, 12(40).
  15. Han, P.D., Long, Z., Han, M., Du, L., Dai & Hao, X. (2019). Improved understanding of snowmelt runoff from the headwaters of China's Yangtze River using remotely sensed snow products and hydrological modeling. Remote Sensing of Environment, 224, 44-59. https://doi.org/10.1016/j.rse.2019.01.041
  16. Hosseini, M., Seyed Mohammad, I., & Shahab, E. (2017). Introduction of global networked precipitation databases. Water and Sustainable Development, 5(2), 153-162. (In Persian). https://doi.org/10.22067/jwsd.v5i2.70826
  17. Hsu, K.L., Gao, X., Sorooshian, S., & Gupta, H.V. (1997). Precipitation estimation from remotely sensed information using artificial neural networks. Journal of Applied Meteorology, 36, 1176-1190. https://doi.org/ 10.1175/1520-0450(1997)036%3C1176:PEFRSI%3E2.0.CO;2
  18. https://chrsdata.eng.uci.edu
  19. https://gpm.nasa.gov/data
  20. https://www.ecmwf.int
  21. Huffman, G., Stocker, E., Bolvin, D., Nelkin, E., & Tan, J.(2019). GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree × 0.1 degree V06. Available online:. https://doi.org/10.5067/GPM/IMERG/3B-HH/06
  22. Jiang, S.L., Ren, B., Yong, Y., Hong, X., Yang & Yuan, F. (2016). Evaluation of latest TMPA and CMORPH precipitation products with independent rain gauge observation networks over high-latitude and low-latitude basins in China. Chinese Geographical Science, 26(4), 439-455. https://doi.org/10.1007/s11769-016-0818-x
  23. Khanmohammadi, Z., Mahjoobi, E., Gharachelou, S., & Banikhedmat, A. (2022). Statistical assessment of satellite rainfall products in daily and monthly gauge spatial scales. Journal of Watershed Engineering and Management, 14(4), 512-527.  https://doi.org/10.22092/ijwmse.2022.355269.1908
  24. Kubota, T., Aonashi, K., Ushio, T., Shige, S., Takayabu, Y.N., Kachi, M., Arai, Y., Tashima, T., Masaki, T., & Kawamoto, N. (2020). Global Satellite Mapping of Precipitation (GSMaP) products in the GPM era. In Satellite Precipitation Measurement; Springer: Cham, Switzerland. https://doi.org/10.1007/978-3-030-24568-9_20
  25. Lu, X., Li, J., Liu, Y., Li, Y., & Huo, H.(2023). Quantitative precipitation estimation in the Tianshan mountains based on machine learning. Remote Sensing, 15, 3962. https://doi.org/10.3390/rs15163962
  26. Ma, Z., He, K., Tan, X., Xu, J., Miri, M., Rahimi, M., & Noroozi, A. (2019). Evaluation and comparison of GPM and TRMM daily precipitation with observed precipitation across Iran. Watershed Engineering and Management, 11(4), 972-983. https://doi.org/10.22092/ijwmse.2018.121397.1469
  27. Ma, Z., Xu, J., He, K., Han, X., Ji, Q., Wang, T., Xiong, W., & Hong, Y. (2020). An updated moving window algorithm for hourly-scale satellite precipitation downscaling: A case study in the southeast coast of China. Journal Of Hydrology, 581, P.124378. https://doi.org/10.1016/j.jhydrol.2019.124378
  28. NASA Global Precipitation Measurement (GPM).( 2020). Integrated Multi-satellitE Retrievals for GPM (IMERG). Algorithm Theoretical Basis Document (ATBD) Version 06.
  29. Ordoni, M., Memarian, H., Akbari, M., & Pourreza, M. (2019). Validation of GPM-IMERG satellite rainfall data in half-hourly and daily time scales (case study: Gorganrood watershed). Journal of Water and Soil Protection Research, 27(4), 149-166. (In Persian). https://doi.org/10.22069/jwsc.2020.17531.3301
  30. Panthi, J.P., Dahal, M.L., Shrestha, S., Aryal, N.Y., Krakauer, S.M., Pradhanang & Karki, R. (2015). Spatial and temporal variability of rainfall in the Gandaki River Basin of Nepal Himalaya. Climate, 3(1), 210-226. https:// doi.org/10.3390/cli3010210
  31. Paolo, F., Luca, B., Raphael, Q., Luca, C., Carla, S., Wolfgang, W., & Angelica, T. (2022). High resolution (1 km) satellite rainfall estimation from SM2RAIN applied to Sentinel-1: Po River Basin as case study. Hydrology Earth System Science, 26, 2481–2497. https://doi.org/10.5194/hess-26-2481-2022
  32. Ruan, H., Zou, S., Yang, D., Wang, Y., Yin, Z., Lu, Z., & Xu, B. (2017). Runoff simulation by SWAT model using high-resolution gridded precipitation in the upper Heihe River Basin, northeastern Tibetan Plateau. Water, 9(11), 866. https://doi.org/10.3390/w9110866
  33. Sharifi, E., Eitzinger, J., Dorigo, W. (2019). Performance of the state-of-the-art gridded precipitation products over mountainous Terrain: A regional study over Austria. Remote Sens., 11, 2018. https://doi.org/10.3390/rs11172018
  34. Shokri, K., Akhund Ali, S., & Sharifi, M. (2019). Evaluating the performance of PERSIANN and PERSIANN-CDR satellite precipitation algorithms and investigating the impact of roughness on it (case study: Helleh watershed). Eco-Hydrology, 7(2), 511-530. (In Persian). https://doi.org/10.22034/jdmal.2020.38472
  35. Tafte, A., Mallah, S., & Ebrahimi, N. (2019). Examining the results of daily, ten-day and monthly data of satellite images in estimating the amount of precipitation using the Google Earth Engine system in Khuzestan province. Protection of water and soil resources (scientific-research), 9(3), 93-104. (In Persian)
  36. Talchabhadel, R., Aryal, A., Kawaike, K., Yamanoi, K., Nakagawa, H., Bhatta, B., Karki, S., & Thapa, B.R. (2021). Evaluation of precipitation elasticity using precipitation data from ground and satellite-based estimates and watershed modeling in Western Nepal. Journal of Hydrology: Regional Studies, 33, 100768. https://doi.org/ 10.1016/j.ejrh.2020.100768
  37. Tan, X., Yong, B., & Ren, L. (2018). Error features of the hourly GSMaP multi-satellite precipitation estimates over nine major basins of China. Hydrology Research, 49(3), 761-779. https://doi.org/10.2166/nh.2017.263
  38. Taye, M., Sahlu, D., Zaitchik, BF., & Neka, M. (2020). Evaluation of satellite rainfall estimates for meteorological drought analysis over the Upper Blue Nile Basin. Ethiopia Geosci 10(9), 352. https://doi.org/10.3390/ geosciences10090352
  39. Vasavi, , Krishna, V.S., Sri P.D., Navena, M., & HariKiran, C. (2022). Rainfall estimation from satellite images using cloud classifications, 2022 IEEE North Karnataka Subsection Flagship International Conference (NKCon), Vijaypur, India, , pp. 1-5. https://doi.org/10.1109/NKCon56289.2022.10126540
  40. Wang, C., Xu, J., Tang, G., Yang, Y., & Hong, Y.(2020). Infrared precipitation estimation using convolutional neural network. IEEE Trans. Geosci. Remote Sens. 8, 8612–8625. https://doi.org/10.1109/TGRS.2020.2989183
  41. Wei, L., Jiang S., Ren, L., Zhang, L., Wang, M., & Duan, Z. (2020). Preliminary utility of the retrospective IMERG precipitation product for large-scale drought monitoring over Mainland China. Remote Sens, 12, 2993. https:// doi.org/10.3390/rs12182993
  42. Wilks, D.S. (2011). Statistical methods in the atmospheric sciences. Academic press.
  43. Yousefi, K., Alireza, N., & Jamei, M. (2021). Combination of interpolation methods and TRMM satellite precipitation products in order to increase the accuracy of rainfall maps in Mazandaran province. Journal of Water and Soil Conservation Research, 28(3), 49-70. (In Persian). https://doi.org/10.22069/jwsc.2022.19286.3477
Zeng, Q.H., Chen., C.Y., Xu. M.X., Jie, J., Chen, S.L., & Liu, J. (2018). The effect of rain gauge density and distribution on runoff simulation using a lumped hydrological modelling approach. Journal of Hydrology, 563, 106-122. https://doi.org/10.1016/j.jhydrol.2018.05.058

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