Validation of CHIRPS satellite based precipitation data against the in situ observations using the Copula method: a case study of Kosar Dam basin, Iran

Mokhtari, Shirin; Sharafati, Ahmad; Raziei, Tayeb

doi:10.1007/s11600-021-00682-7

Artykuł - szczegóły

Tytuł artykułu

Validation of CHIRPS satellite based precipitation data against the in situ observations using the Copula method: a case study of Kosar Dam basin, Iran

Autorzy

Mokhtari Shirin , Sharafati Ahmad , Raziei Tayeb

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-021-00682-7

Warianty tytułu

Języki publikacji

Abstrakty

The present study compares the daily and monthly precipitation estimates of the CHIRPS satellite data with the in situ measurements at four stations scattered over the Kosar Dam basin in southwestern Iran. The uncertainty of the satellite precipitation estimates was calculated through simulation with the Copula functions. For this purpose, 55% of the stations, daily and monthly rainfall data relative to the 1987–2012 period were used for training (simulation), and the other 45% were used for testing (validation) the performance of the Copula model. First, the daily, monthly, and annual satellite precipitation estimates were statistically compared with precipitation observed at the stations and the whole basin using the Pearson correlation coefficient (CC), root mean square error (RMSE), and Bias statistics. The computed CC between the areal average of observed and satellite precipitation estimation at the basin is 0.49, 0.82, and 0.33 for daily, monthly, and annual time scales, respectively. The difference (biases) between the satellite estimates and in situ measurements was then calculated for daily, monthly, and annual time scales over the training period. The obtained biases were subsequently fitted with the General Extreme Value distribution function coupled with the Gaussian Copula model to generate a series of similar random biases for all precipitation events. Then, the generated random biases were summed with the original satellite estimates to correct the associated biases. The bias-corrected precipitation for the training period was then compared to the original estimates of the satellite at the stations and the whole basin using the P-factor, R-factor, Bias, RMSE, and CC statistics. The statistics show that the random biases generated by the Copula method for the monthly CHIRPS satellite data relative to the 14-year training period have reduced the error rate of the satellite data by 74 to 95 percent when compared to observations. The satellite precipitation estimates of the 11-year test period were also corrected using the generated random biases in the training period. The results show that the bias correction considerably improved the monthly estimates and reduced the error rate of the satellite estimation by about 76 percent. In general, the simulation of the satellite precipitation with the Gaussian Copula model was performed satisfactorily at the monthly time scale, but it was less efficient at the daily time scale.

Słowa kluczowe

bias correction copula CHIRPS precipitation

korekcja błędu kopuła CHIRPS opad atmosferyczny

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2022

Tom

Vol. 70, no 1

Strony

465--484

Opis fizyczny

Bibliogr. 45 poz.

Twórcy

autor

Mokhtari Shirin

Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

autor

Sharafati Ahmad

asharafati@srbiau.ac.ir

Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

https://orcid.org/0000-0003-0448-2871

autor

Raziei Tayeb

Agricultural Research, Education and Extension Organization (AREO), Soil Conservation and Watershed Management Research Institute (SCWMRI), Tehran, Iran

Bibliografia

1. AghaKouchak A, Bárdossy A, Habib E (2010) Conditional simulation of remotely sensed rainfall data using a non-Gaussian v-transformed copula. Adv Water Resour 33:624–634. https://doi.org/10.1016/j.advwatres.2010.02.010
2. AghaKouchak A, Behrangi A, Sorooshian S et al (2011) Evaluation of satellite-retrieved extreme precipitation rates across the central United States. J Geophys Res 116:D02115. https://doi.org/10.1029/2010JD014741
3. Ahmadi M, Narangifard M (2015) Quality assessment and detection of forest area changes using satellite images (Case study: Rustam, Fars). J RS GIS Nat Resour 6:87–100
4. Alazzy AA, Lü H, Chen R et al (2017) Evaluation of Satellite Precipitation Products and Their Potential Influence on Hydrological Modeling over the Ganzi River Basin of the Tibetan Plateau. Adv Meteorol 2017:1–23. https://doi.org/10.1155/2017/3695285
5. Alijanian M, Rakhshandehroo GR, Mishra AK, Dehghani M (2017) Evaluation of satellite rainfall climatology using CMORPH, PERSIANN-CDR, PERSIANN, TRMM, MSWEP over Iran. Int J Climatol 37:4896–4914. https://doi.org/10.1002/joc.5131
6. Ashouri H, Hsu K-L, Sorooshian S et al (2015) PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies. Bull Am Meteorol Soc 96:69–83. https://doi.org/10.1175/bams-d-13-00068.1
7. Bai L, Shi C, Li L et al (2018) Accuracy of CHIRPS Satellite-Rainfall Products over Mainland China. Remote Sens. https://doi.org/10.3390/rs10030362
8. Behrangi A, Khakbaz B, Jaw TC et al (2011) Hydrologic evaluation of satellite precipitation products over a mid-size basin. J Hydrol 397:225–237. https://doi.org/10.1016/j.jhydrol.2010.11.043
9. Bitew MM, Gebremichael M (2011) Evaluation of satellite rainfall products through hydrologic simulation in a fully distributed hydrologic model. Water Resour Res. https://doi.org/10.1029/2010wr009917
10. Bitew MM, Gebremichael M, Ghebremichael LT, Bayissa YA (2012) Evaluation of high-resolution satellite rainfall products through streamflow simulation in a hydrological modeling of a small mountainous watershed in Ethiopia. J Hydrometeorol 13:338–350
11. Dinku T, Ruiz F, Connor SJ, Ceccato P (2010) Validation and intercomparison of satellite rainfall estimates over Colombia. J Appl Meteorol Climatol 49:1004–1014
12. Dinku T, Funk C, Peterson P et al (2018) Validation of the CHIRPS satellite rainfall estimates over eastern Africa. Q J R Meteorol Soc 144:292–312. https://doi.org/10.1002/qj.3244
13. Duan Z, Liu J, Tuo Y et al (2016) Evaluation of eight high spatial resolution gridded precipitation products in Adige Basin (Italy) at multiple temporal and spatial scales. Sci Total Environ 573:1536–1553
14. Funk CC, Peterson PJ, Landsfeld MF et al (2014) A quasi-global precipitation time series for drought monitoring. US Geol Surv Data Ser 832:1–12
15. Funk C, Peterson P, Landsfeld M et al (2015) The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci Data 2:1–21
16. Gao YC, Liu M (2013) Evaluation of high-resolution satellite precipitation products using rain gauge observations over the Tibetan Plateau. Hydrol Earth Syst Sci 17:837–849
17. Ghozat A, Sharafati A, Hosseini SA (2021) Long-term spatiotemporal evaluation of CHIRPS satellite precipitation product over different climatic regions of Iran. Theor Appl Climatol. https://doi.org/10.1007/s00704-020-03428-5
18. Hou AY, Kakar RK, Neeck S et al (2014) The global precipitation measurement mission. Bull Am Meteorol Soc 95:701–722
19. Huang Y, Bárdossy A, Zhang K (2019) Sensitivity of hydrological models to temporal and spatial resolutions of rainfall data. Hydrol Earth Syst Sci 23:2647–2663
20. Javanmard S, Yatagai A, Nodzu MI et al (2010) Comparing high-resolution gridded precipitation data with satellite rainfall estimates of TRMM-3B42 over Iran. Adv Geosci 25:119–125. https://doi.org/10.5194/adgeo-25-119-2010
21. Jiang S, Zhou M, Ren L et al (2016) Evaluation of latest TMPA and CMORPH satellite precipitation products over Yellow River Basin. Water Sci Eng 9:87–96. https://doi.org/10.1016/j.wse.2016.06.002
22. Katiraie-Boroujerdy P-S, Nasrollahi N, Hsu K, Sorooshian S (2013) Evaluation of satellite-based precipitation estimation over Iran. J Arid Environ 97:205–219
23. Lockhoff M, Zolina O, Simmer C, Schulz J (2014) Evaluation of Satellite-Retrieved Extreme Precipitation over Europe using Gauge Observations. J Clim 27:607–623. https://doi.org/10.1175/jcli-d-13-00194.1
24. Mashingia F, Mtalo F, Bruen M (2014) Validation of remotely sensed rainfall over major climatic regions in Northeast Tanzania. Phys Chem Earth, Parts a/b/c 67–69:55–63. https://doi.org/10.1016/j.pce.2013.09.013
25. Miao C, Ashouri H, Hsu K-L et al (2015) Evaluation of the PERSIANN-CDR daily rainfall estimates in capturing the behavior of extreme precipitation events over China. J Hydrometeorol 16:1387–1396. https://doi.org/10.1175/jhm-d-14-0174.1
26. 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 34:8156–8171. https://doi.org/10.1080/01431161.2013.833360
27. Moazami S, Golian S, Kavianpour MR, Hong Y (2014) Uncertainty analysis of bias from satellite rainfall estimates using copula method. Atmos Res 137:145–166
28. Mohamoud YM, Prieto LM (2012) Effect of temporal and spatial rainfall resolution on HSPF predictive performance and parameter estimation. J Hydrol Eng 17:377–388
29. Peng B, Shi J, Ni-Meister W et al (2014) Evaluation of TRMM multisatellite precipitation analysis (TMPA) products and their potential hydrological application at an arid and semi-arid basin in China. IEEE J Sel Top Appl Earth Obs Remote Sens 7:3915–3930
30. Pratama AW, Buono A, Hidayat R, Harsa H (2018) Estimating parameter of nonlinear bias correction method using NSGA-II in daily precipitation data. Telkomnika (Telecommunication Comput Electron Control 16:241–249. https://doi.org/10.12928/TELKOMNIKA.v16i1.6848
31. Saeidizand R, Sabetghadam S, Tarnavsky E, Pierleoni A (2018) Evaluation of CHIRPS rainfall estimates over Iran. Q J R Meteorol Soc 144:282–291. https://doi.org/10.1002/qj.3342
32. Salvadori G, De Michele C (2010) Multivariate multiparameter extreme value models and return periods: a copula approach. Water Resour Res 46:W10501. https://doi.org/10.1029/2009WR009040
33. Sorooshian S, AghaKouchak A, Arkin P et al (2011) Advancing the Remote Sensing of Precipitation. Bull Am Meteorol Soc 92:1271–1272. https://doi.org/10.1175/bams-d-11-00116.1
34. Su J, Lü H, Zhu Y et al (2019) Evaluating the hydrological utility of latest IMERG products over the Upper Huaihe River Basin, China. Atmos Res 225:17–29
35. Sun R, Yuan H, Liu X, Jiang X (2016) Evaluation of the latest satellite–gauge precipitation products and their hydrologic applications over the Huaihe River basin. J Hydrol 536:302–319
36. Tan M, Ibrahim A, Duan Z et al (2015) Evaluation of Six High-Resolution Satellite and Ground-Based Precipitation Products over Malaysia. Remote Sens 7:1504–1528. https://doi.org/10.3390/rs70201504
37. Tian Y, Peters‐Lidard CD, Eylander JB et al (2009) Component analysis of errors in satellite‐based precipitation estimates. J Geophys Res Atmos 114:D24101. https://doi.org/10.1029/2009JD011949
38. Trejo FJP, Barbosa HA, Peñaloza-Murillo MA et al (2016) Intercomparison of improved satellite rainfall estimation with CHIRPS gridded product and rain gauge data over Venezuela. Atmósfera 29:323–342
39. Vergara H, Hong Y, Gourley JJ et al (2014) Effects of resolution of satellite-based rainfall estimates on hydrologic modeling skill at different scales. J Hydrometeorol 15:593–613
40. Villarini G, Krajewski WF, Smith JA (2009) New paradigm for statistical validation of satellite precipitation estimates: Application to a large sample of the TMPA 0.25° 3‐hourly estimates over Oklahoma. J Geophys Res Atmos 114:D12106. https://doi.org/10.1029/2008JD011475
41. Wang X, Gebremichael M, Yan J (2010) Weighted likelihood copula modeling of extreme rainfall events in Connecticut. J Hydrol 390:108–115. https://doi.org/10.1016/J.JHYDROL.2010.06.039
42. Wang N, Liu W, Sun F et al (2020a) Evaluating satellite-based and reanalysis precipitation datasets with gauge-observed data and hydrological modeling in the Xihe River Basin, China. Atmos Res 234:104746. https://doi.org/10.1016/j.atmosres.2019.104746
43. Wang W, Sun L, Cai Y et al (2020b) Evaluation of multi‐source precipitation data in a watershed with complex topography based on distributed hydrological modeling. River Res Appl. https://doi.org/10.1002/rra.3681
44. Xue X, Hong Y, Limaye AS et al (2013) Statistical and hydrological evaluation of TRMM-based Multi-satellite Precipitation Analysis over the Wangchu Basin of Bhutan: Are the latest satellite precipitation products 3B42V7 ready for use in ungauged basins? J Hydrol 499:91–99
45. Yong B, Liu D, Gourley JJ et al (2015) Global view of real-time TRMM multisatellite precipitation analysis: Implications for its successor global precipitation measurement mission. Bull Am Meteorol Soc 96:283–296

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-e01b523b-65b3-46be-afce-0a2016478fdb