Skip to main content

Advertisement

SpringerLink
Log in

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

  • Research Article - Atmospheric & Space Sciences
  • Published:
Acta Geophysica Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Availability of data and materials

Please contact the corresponding author for data requests.

References

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Hou AY, Kakar RK, Neeck S et al (2014) The global precipitation measurement mission. Bull Am Meteorol Soc 95:701–722

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to reveal their gratitude and appreciation to the data providers, Iranian Meteorological Organization, and Iran Water Resources Management Company.

Author information

Authors and Affiliations

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

    Shirin Mokhtari & Ahmad Sharafati

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

    Tayeb Raziei

Authors
  1. Shirin Mokhtari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ahmad Sharafati
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tayeb Raziei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmad Sharafati.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Code availability

Please contact the corresponding author for code requests.

Additional information

Communicated by Prof. Theodore Karacostas (CO-EDITOR-IN-CHIEF).

The original online version of this article was revised: Wrong category name.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mokhtari, S., Sharafati, A. & Raziei, T. Validation of CHIRPS satellite-based precipitation data against the in situ observations using the Copula method: a case study of Kosar Dam basin, Iran. Acta Geophys. 70, 465–484 (2022). https://doi.org/10.1007/s11600-021-00682-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11600-021-00682-7

Keywords

Search

Navigation