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Validity, reliability and certainty of PERSIANN and TRMM satellite-derived daily precipitation data in arid and semiarid climates

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Having doubts about the adequacy of reliability level of satellite-derived precipitation products, along with their application in large number of hydrological models, has led to many studies on evaluating the efficiency of such data. In this study, two new procedures were proposed to compute reliability and certainty degrees of PERSIANN and TRMM 3B42RT data sets, and six traditional indicators were used to evaluate their validation. In addition, the cumulative density function (cdf) of the above-mentioned data sets was compared with the ground-based observations in 23 synoptic stations in Fars, Iran. The Kolmogorov–Smirnov test was performed using the data sets at 5% significance level which led to the result of null hypothesis that was not being rejected, suggesting that the satellite-derived daily precipitation data (SDDPD) and ground based observations are drawn from the same distribution. Results indicated that TRMM and PERSIANN follow quite similar probability pattern of ground-based observations in arid and semiarid climate, respectively. However, data probability pattern of TRMM cannot be considered similar to ground-based observations in arid region, neither can PERSIANN in semiarid climate. Among common cross-validating attributes, the values of ME and BIAS, in addition to RMSE and MAE, led to the conclusion that in PERSIANN, the rainfall daily rates are almost underestimated while TRMM overestimates the values mainly in semiarid regions. Moreover, the PERSIANN was found to be significantly correlated with IDM (De Martonne aridity Index), and the values of underestimation increased with growth of the index. The reliability values of SDDPD over the study area, for both TRMM and PERSIANN, show the reverse trend with increasing IDM in almost all acceptable error intervals. Along with effects of climate conditions, the reliability degrees of PERSIANN seem quite more consistent at different acceptable error intervals in comparison with the corresponding values of TRMM. In addition to validity and reliability, the error entropy of SDDPD, as an index for uncertainty degree, increases as the IDM rises, which is theoretically corresponds with reliability concept. However, in comparison with PERSIANN, TRMM data set, overall, has higher degree of uncertainty. In addition, to evaluate effect of daily rainfall intensity on the uncertainty degree of SDDPD, the uncertainty degree slightly increases as daily rainfall intensifies to about 15 mm/day. But for higher daily rainfall intensities, on the other hand, the uncertainty degree seems to gradually decline as the daily rainfall increases.
Czasopismo
Rocznik
Strony
1745--1767
Opis fizyczny
Bibliogr. 88 poz.
Twórcy
  • Department of Civil Engineering, Faculty of Engineering, Estahban Branch, Islamic Azad University, Estahban, Iran
  • Department of Civil Engineering, Faculty of Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
  • Department of Civil Engineering, Faculty of Engineering, Estahban Branch, Islamic Azad University, Estahban, Iran
  • Department of Civil Engineering, Faculty of Engineering, Estahban Branch, Islamic Azad University, Estahban, Iran
Bibliografia
  • 1. Adeyewa ZD, Nakamura K (2003) Validation of TRMM radar rainfall data over major climatic regions in Africa. J Appl Meteorol 42(2):331–347
  • 2. AghaKouchak A, Mehran A, Norouzi H, Behrangi A (2012) Systematic and random error components in satellite precipitation data sets. Geophys Res Lett 39(9)
  • 3. Amorocho J, Espildora B (1973) Entropy in the assessment of uncertainty in hydrologic systems and models. Water Resour Res 9(6):1511–1522
  • 4. Arellano-Valle RB, Contreras-Reyes JE, Genton MG (2013) Shannon entropy and mutual information for multivariate skew-elliptical distributions. Scand J Stat 40(1):42–62
  • 5. Awange JL, Ferreira VG, Forootan E, Khandu, Andam-Akorful SA, Forootan E, Agutu NO, He XF (2016) Uncertainties in remotely sensed precipitation data over Africa. Int J Climatol 36(1):303–323
  • 6. Baltas E (2007) Spatial distribution of climatic indices in northern Greece. Meteorol Appl 14(1):69–78
  • 7. Birolini A (2017) Basic concepts, quality & reliability (RAMS) assurance of complex equipment & systems, reliability engineering: theory and practice. Springer, Berlin, pp 1–24
  • 8. Bityukov SI, Maksimushkina AV, Smirnova VV (2016) Comparison of histograms in physical research. Nucl Energy Technol 2(2):108–113
  • 9. Caloiero T, Veltri S, Caloiero P, Frustaci F (2018) Drought analysis in Europe and in the Mediterranean Basin using the standardized precipitation index. Water 10(8):1043
  • 10. Camici S, Ciabatta L, Massari C, Brocca L (2018) How reliable are satellite precipitation estimates for driving hydrological models: a verification study over the Mediterranean area. J Hydrol 563:950–961
  • 11. Chen S, Hong Y, Cao Q, Gourley JJ, Kirstetter P-E, Yong B, Tian Y, Zhang Z, Shen Y, Hu J, Hardy J (2013a) Similarity and difference of the two successive V6 and V7 TRMM multisatellite precipitation analysis performance over China. J Geophys Res Atmos 118(23):13060–13074
  • 12. Chen S, Hong Y, Gourley JJ, Huffman GJ, Tian Y, Cao Q, Yong B, Kirstetter P-E, Hu J, Hardy J, Li Z, Khan SI, Xue X (2013b) Evaluation of the successive V6 and V7 TRMM multisatellite precipitation analysis over the Continental United States. Water Resour Res 49(12):8174–8186
  • 13. Darand M, Amanollahi J, Zandkarimi S (2017) Evaluation of the performance of TRMM multi-satellite precipitation analysis (TMPA) estimation over Iran. Atmos Res 190:121–127
  • 14. De Martonne E (1926) Une nouvelle fonction climatologique: L’indice d’aridité. La Meteorologie 2:449–458
  • 15. Derin Y, Yilmaz KK (2014) Evaluation of multiple satellite-based precipitation products over complex topography. J Hydrometeorol 15(4):1498–1516
  • 16. Dinku T, Chidzambwa S, Ceccato P, Connor SJ, Ropelewski CF (2008) Validation of high-resolution satellite rainfall products over complex terrain. Int J Remote Sens 29(14):4097–4110
  • 17. Dinku T, Ceccato P, Connor SJ (2011) Challenges of satellite rainfall estimation over mountainous and arid parts of east Africa. Int J Remote Sens 32(21):5965–5979
  • 18. Duan Z, Bastiaanssen WGM, Liu J (2012) Monthly and annual validation of TRMM mulitisatellite precipitation analysis (TMPA) products in the Caspian Sea Region for the period 1999–2003. Paper presented at the 2012 IEEE international geoscience and remote sensing symposium
  • 19. Ebert EE (2007) Methods for verifying satellite precipitation estimates. In: Levizzani V, Bauer P, Turk FJ (eds) Measuring precipitation from space: EURAINSAT and the future. Springer, Dordrecht, pp 345–356
  • 20. Emadodin I, Reinsch T, Taube F (2019) Drought and Desertification in Iran. Hydrology 6(3):66
  • 21. Gadgil S, Narayana Iyengar R (1980) Cluster analysis of rainfall stations of the Indian peninsula. Q J R Meteorol Soc 106(450):873–886
  • 22. Gao X, Zhu Q, Yang Z, Wang H (2018) Evaluation and hydrological application of CMADS against TRMM 3B42V7, PERSIANN-CDR, NCEP-CFSR, and gauge-based datasets in Xiang River Basin of China. Water 10(9):1225
  • 23. Geng Q, Wu P, Zhao X, Wang Y (2014) Comparison of classification methods for the divisions of wet/dry climate regions in Northwest China. Int J Climatol 34(7):2163–2174
  • 24. Gilewski P (2021) Impact of the grid resolution and deterministic interpolation of precipitation on rainfall-runoff modeling in a sparsely gauged mountainous catchment. Water 13(2):230
  • 25. Gilewski P, Nawalany M (2018) Inter-comparison of rain-gauge, radar, and satellite (IMERG GPM) precipitation estimates performance for rainfall-runoff modeling in a mountainous catchment in Poland. Water 10(11):1665
  • 26. Gourley JJ, Hong Y, Flamig ZL, Li L, Wang J (2010) Intercomparison of rainfall estimates from radar, satellite, gauge, and combinations for a season of record rainfall. J Appl Meteorol Climatol 49(3):437–452
  • 27. Gumindoga W, Rientjes THM, Haile AT, Makurira H, Reggiani P (2019) Performance evaluation of CMORPH satellite precipitation product in the Zambezi Basin. Int J Remote Sens 40(20):7730–7749
  • 28. Gupta HV, Wagener T, Liu Y (2008) Reconciling theory with observations: elements of a diagnostic approach to model evaluation. Hydrol Process 22(18):3802–3813
  • 29. Gupta HV, Kling H, Yilmaz KK, Martinez GF (2009) Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. J Hydrol 377(1):80–91
  • 30. Heydarizad M, Raeisi E, Sori R, Gimeno L, Nieto R (2018) The role of moisture sources and climatic teleconnections in Northeastern and South-Central Iran’s hydro-climatology. Water 10(11):1550
  • 31. Heydarizad M, Raeisi E, Sorí R, Gimeno L (2019) Developing meteoric water lines for Iran based on air masses and moisture sources. Water 11(11):2359
  • 32. Hirpa FA, Gebremichael M, Hopson T (2010) Evaluation of high-resolution satellite precipitation products over very complex terrain in Ethiopia. J Appl Meteorol Climatol 49(5):1044–1051
  • 33. Islam MA, Yu B, Cartwright N (2020) Assessment and comparison of five satellite precipitation products in Australia. J Hydrol 590:125474
  • 34. Javanmard S, Yatagai A, Nodzu MI, BodaghJamali J, Kawamoto H (2010) Comparing high-resolution gridded precipitation data with satellite rainfall estimates of TRMM_3B42 over Iran. Adv Geosci 25:119–125
  • 35. Kidd C, Levizzani V (2011) Status of satellite precipitation retrievals. Hydrol Earth Syst Sci 15(4):1109–1116
  • 36. Kidd C, Bauer P, Turk J, Huffman GJ, Joyce R, Hsu KL, Braithwaite D (2012) Intercomparison of high-resolution precipitation products over northwest Europe. J Hydrometeorol 13(1):67–83
  • 37. Krakauer NY, Pradhanang SM, Lakhankar T, Jha AK (2013) Evaluating satellite products for precipitation estimation in mountain regions: a case study for Nepal. Remote Sens 5(8):4107–4123
  • 38. Langat PK, Kumar L, Koech R (2019) Identification of the most suitable probability distribution models for maximum, minimum, and mean streamflow. Water 11(4):734
  • 39. Li C, Tang G, Hong Y (2018) Cross-evaluation of ground-based, multi-satellite and reanalysis precipitation products: applicability of the triple collocation method across Mainland China. J Hydrol 562:71–83
  • 40. Liu J, Duan Z, Jiang J, Zhu AX (2015) Evaluation of three satellite precipitation products TRMM 3B42, CMORPH, and PERSIANN over a subtropical watershed in China. Adv Meteorol 2015:151239
  • 41. Liu S, Kang W, Wang T (2016) Drought variability in Inner Mongolia of northern China during 1960–2013 based on standardized precipitation evapotranspiration index. Environ Earth Sci 75(2):145
  • 42. Lu X, Tang G, Liu X, Wang X, Liu Y, Wei M (2021) The potential and uncertainty of triple collocation in assessing satellite precipitation products in Central Asia. Atmos Res 252:105452
  • 43. Martinez-Villalobos C, Neelin JD (2019) Why do precipitation intensities tend to follow gamma distributions? J Atmos Sci 76(11):3611–3631
  • 44. Masood M, Shakir AS, Azhar AH, Nabi G, Habib u, R. (2020) Assessment of real time, multi-satellite precipitation products under diverse climatic and topographic conditions. Asia-Pac J Atmos Sci 56(4):577–591
  • 45. Melo DCD, Xavier AC, Bianchi T, Oliveira PTS, Scanlon BR, Lucas MC, Wendland E (2015) Performance evaluation of rainfall estimates by TRMM multi-satellite precipitation analysis 3B42V6 and V7 over Brazil. J Geophys Res Atmos 120(18):9426–9436
  • 46. Michaelides S, Levizzani V, Anagnostou E, Bauer P, Kasparis T, Lane JE (2009) Precipitation: measurement, remote sensing, climatology and modeling. Atmos Res 94(4):512–533
  • 47. Moazami S, Golian S, Kavianpour MR, Hong Y (2013) Comparison of PERSIANN and V7 TRMM multi-satellite precipitation analysis (TMPA) products with rain gauge data over Iran. Int J Remote Sens 34(22):8156–8171
  • 48. Moazami S, Golian S, Hong Y, Sheng C, Kavianpour MR (2016) Comprehensive evaluation of four high-resolution satellite precipitation products under diverse climate conditions in Iran. Hydrol Sci J 61(2):420–440
  • 49. Mosaffa H, Shirvani A, Khalili D, Nguyen P, Sorooshian S (2020) Post and near real-time satellite precipitation products skill over Karkheh River Basin in Iran. Int J Remote Sens 41(17):6484–6502
  • 50. Nair S, Srinivasan G, Nemani R (2009) Evaluation of multi-satellite TRMM derived rainfall estimates over a western state of India. J Meteorol Soc Jpn Ser II 87(6):927–939
  • 51. Nastos PT, Kapsomenakis J, Philandras KM (2016) Evaluation of the TRMM 3B43 gridded precipitation estimates over Greece. Atmos Res 169:497–514
  • 52. Nguyen P, Thorstensen A, Sorooshian S, Hsu K, AghaKouchak A (2015) Flood forecasting and inundation mapping using HiResFlood-UCI and near-real-time satellite precipitation data: the 2008 Iowa Flood. J Hydrometeorol 16(3):1171–1183
  • 53. Nicholson SE, Some B, McCollum J, Nelkin E, Klotter D, Berte Y, Diallo BM, Gaye I, Kpabeba G, Ndiaye O, Noukpozounkou JN, Tanu MM, Thiam A, Toure AA, Traore AK (2003a) Validation of TRMM and other rainfall estimates with a high-density gauge dataset for West Africa. Part I: validation of GPCC rainfall product and Pre-TRMM satellite and blended products. J Appl Meteorol 42(10):1337–1354
  • 54. Nicholson SE, Some B, McCollum J, Nelkin E, Klotter D, Berte Y, Diallo BM, Gaye I, Kpabeba G, Ndiaye O, Noukpozounkou JN, Tanu MM, Thiam A, Toure AA, Traore AK (2003b) Validation of TRMM and other rainfall estimates with a high-density gauge dataset for West Africa. Part II: validation of TRMM rainfall products. J Appl Meteorol 42(10):1355–1368
  • 55. Nourani V, Baghanam AH, Adamowski J, Gebremichael M (2013) Using self-organizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network based rainfall–runoff modeling. J Hydrol 476:228–243
  • 56. Ochoa A, Pineda L, Crespo P, Willems P (2014) Evaluation of TRMM 3B42 precipitation estimates and WRF retrospective precipitation simulation over the Pacific-Andean region of Ecuador and Peru. Hydrol Earth Syst Sci 18(8):3179–3193
  • 57. O’Hagan A, Leonard TOM (1976) Bayes estimation subject to uncertainty about parameter constraints. Biometrika 63(1):201–203
  • 58. Paredes Trejo FJ, Alves Barbosa H, Peñaloza-Murillo MA, Moreno MA, Farías A (2016) Intercomparison of improved satellite rainfall estimation with CHIRPS gridded product and rain gauge data over Venezuela. Atmósfera 29:323–342
  • 59. Pellicone G, Caloiero T, Guagliardi I (2019) The De Martonne aridity index in Calabria (Southern Italy). J Maps 15(2):788–796
  • 60. Pipunic RC, Ryu D, Costelloe JF, Su C-H (2015) An evaluation and regional error modeling methodology for near-real-time satellite rainfall data over Australia. J Geophys Res Atmos 120(20):10767–10783
  • 61. Price K, Purucker ST, Kraemer SR, Babendreier JE, Knightes CD (2014) Comparison of radar and gauge precipitation data in watershed models across varying spatial and temporal scales. Hydrol Process 28(9):3505–3520
  • 62. Prigent C (2010) Precipitation retrieval from space: an overview. C R Geosci 342(4):380–389
  • 63. Rahmawati N, Lubczynski MW (2018) Validation of satellite daily rainfall estimates in complex terrain of Bali Island, Indonesia. Theor Appl Climatol 134(1):513–532
  • 64. Robinson DW (2008) Entropy and uncertainty. Entropy 10(4):493–506
  • 65. Salmani-Dehaghi N, Samani N (2019) Spatiotemporal assessment of the PERSIANN family of satellite precipitation data over Fars Province, Iran. Theor Appl Climatol 138(3):1333–1357
  • 66. Sapiano MRP, Janowiak JE, Shi W, Higgins RW, Silva VBS (2010) Regional evaluation through independent precipitation measurements: USA. In: Gebremichael M, Hossain F (eds) Satellite rainfall applications for surface hydrology. Springer, Dordrecht, pp 169–191
  • 67. Scheel MLM, Rohrer M, Huggel C, Santos Villar D, Silvestre E, Huffman GJ (2011) Evaluation of TRMM multi-satellite precipitation analysis (TMPA) performance in the Central Andes region and its dependency on spatial and temporal resolution. Hydrol Earth Syst Sci 15(8):2649–2663
  • 68. Shaghaghian MR, Abedini MJ (2013) Rain gauge network design using coupled geostatistical and multivariate techniques. Sci Iran 20(2):259–269
  • 69. Sohn BJ, Han H-J, Seo E-K (2010) Validation of satellite-based high-resolution rainfall products over the Korean Peninsula using data from a dense rain gauge network. J Appl Meteorol Climatol 49(4):701–714
  • 70. Stamp LD, Wooldridge SW (1951) London essays in geography: Rodwell Jones Memorial Volume. Harvard University Press, Cambridge
  • 71. Su F, Hong Y, Lettenmaier DP (2008) Evaluation of TRMM multisatellite precipitation analysis (TMPA) and its utility in hydrologic prediction in the La Plata Basin. J Hydrometeorol 9(4):622–640
  • 72. Sun Q, Miao C, Duan Q, Ashouri H, Sorooshian S, Hsu K-L (2018) A review of global precipitation data sets: data sources, estimation, and intercomparisons. Rev Geophys 56(1):79–107
  • 73. Tabari H, Hosseinzadeh Talaee P, Mousavi Nadoushani SS, Willems P, Marchetto A (2014) A survey of temperature and precipitation based aridity indices in Iran. Quat Int 345:158–166
  • 74. Tan ML, Santo H (2018) Comparison of GPM IMERG, TMPA 3B42 and PERSIANN-CDR satellite precipitation products over Malaysia. Atmos Res 202:63–76
  • 75. Teegavarapu RSV (2019) Chapter 1—methods for analysis of trends and changes in hydroclimatological time-series. In: Teegavarapu R (ed) Trends and changes in hydroclimatic variables. Elsevier, Amsterdam, pp 1–89
  • 76. Thiemig V, Rojas R, Zambrano-Bigiarini M, Levizzani V, De Roo A (2012) Validation of satellite-based precipitation products over sparsely gauged African River Basins. J Hydrometeorol 13(6):1760–1783
  • 77. Thornthwaite C (1955) Mather, JR. The water balance. Drexel Institute of Technology–Laboratory of Climatology, Centerton, NJ, 104 p. Publications in climatology, vol 8(1)
  • 78. Tian Y, Peters-Lidard CD, Eylander JB, Joyce RJ, Huffman GJ, Adler RF, Hsu K, Turk FJ, Garcia M, Zeng J (2009) Component analysis of errors in satellite-based precipitation estimates. J Geophys Res Atmos 114(D24)
  • 79. Vallejo-Bernal SM, Urrea V, Bedoya-Soto JM, Posada D, Olarte A, Cárdenas-Posso Y, Ruiz-Murcia F, Martínez MT, Petersen WA, Huffman GJ, Poveda G (2021) Ground validation of TRMM 3B43 V7 precipitation estimates over Colombia. Part I: monthly and seasonal timescales. Int J Climatol 41(1):601–624
  • 80. Wilby RL, Wigley TML (2002) Future changes in the distribution of daily precipitation totals across North America. Geophys Res Lett 29(7):39-1-39–4
  • 81. Yang Y, Luo Y (2014) Evaluating the performance of remote sensing precipitation products CMORPH, PERSIANN, and TMPA, in the arid region of northwest China. Theor Appl Climatol 118(3):429–445
  • 82. Yong B, Ren L-L, Hong Y, Wang J-H, Gourley JJ, Jiang S-H, Chen X, Wang W (2010) Hydrologic evaluation of multisatellite precipitation analysis standard precipitation products in basins beyond its inclined latitude band: a case study in Laohahe basin, China. Water Resour Res 46(7)
  • 83. Zambrano-Bigiarini M, Nauditt A, Birkel C, Verbist K, Ribbe L (2017) Temporal and spatial evaluation of satellite-based rainfall estimates across the complex topographical and climatic gradients of Chile. Hydrol Earth Syst Sci 21(2):1295–1320
  • 84. Zarghami M, Abdi A, Babaeian I, Hassanzadeh Y, Kanani R (2011) Impacts of climate change on runoffs in East Azerbaijan, Iran. Glob Planet Change 78(3):137–146
  • 85. Zhang Q, Sun P, Chen X, Jiang T (2011) Hydrological extremes in the Poyang Lake basin, China: changing properties, causes and impacts. Hydrol Process 25(20):3121–3130
  • 86. Zou KH, Tuncali K, Silverman SG (2003) Correlation and simple linear regression. Radiology 227(3):617–628
  • 87. Zubieta R, Getirana A, Espinoza JC, Lavado W (2015) Impacts of satellite-based precipitation datasets on rainfall–runoff modeling of the Western Amazon basin of Peru and Ecuador. J Hydrol 528:599–612
  • 88. Zulkafli Z, Buytaert W, Onof C, Manz B, Tarnavsky E, Lavado W, Guyot J-L (2014) A comparative performance analysis of TRMM 3B42 (TMPA) versions 6 and 7 for hydrological applications over Andean-Amazon River Basins. J Hydrometeorol 15(2):581–592
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