Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Long-term spatiotemporal evaluation of CHIRPS satellite precipitation product over different climatic regions of Iran

  • Original Paper
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

Abstract

Satellite precipitation products are important data sources in different spatial resolutions, time scales, and spatio-temporal coverage. In this study, the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellite precipitation product with a high spatial resolution (0.05°) is evaluated in the period of 1987 to 2017 over different climate regions of Iran. The accuracy of the satellite product is compared with the 68 ground-based meteorological stations over different time scales (i.e., daily, monthly, and annual) and precipitation classes. Results show that the performance of CHIRPS depends on the time scale, precipitation depth, and climate type. The best performance of the product (CC = 0.80, FRMSE = 0.57, NSE = 0.63) across the country is observed in the annual time scale, while the monthly product offers the best performance in the regional scale. The product provides inadequate performance (CC = 0.34, FRMSE = 5.72, NSE = − 0.2) in daily time scale across the country and most of the climatic regions. The product is found to be most accurate in the south and southwest of the country, while the lowest performance is observed over the Caspian coast. The CHIRPS satellite provides the best performance in detection of no/tiny precipitation (POD > 0.90) and the worst performance in light and low, moderate precipitation (POD < 0.10). It is expected that the findings of the current study can be used to manage the water resources and mitigate the disaster at the national level.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

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

References

  • AghaKouchak A, Behrangi A, Sorooshian S, Hsu K, Amitai E (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 

  • Alazzy AA, Lü H, Chen R, Ali AB, Zhu Y, Su J (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 

  • Alexandersson H (1986) A homogeneity test applied to precipitation data. J Climatol 6:661–675

    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, Braithwaite DK, Knapp KR, Cecil LD, Nelson BR, Prat OP (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 

  • Ashouri H, Nguyen P, Thorstensen A, Hsu KL, Sorooshian S, Braithwaite D (2016) Assessing the efficacy of high-resolution satellite-based PERSIANN-CDR precipitation product in simulating streamflow. J Hydrometeorol 17:2061–2076. https://doi.org/10.1175/jhm-d-15-0192.1

    Article  Google Scholar 

  • Bai L, Shi C, Li L, Yang Y, Wu J (2018) Accuracy of CHIRPS satellite-rainfall products over mainland China. Remote Sens 10. https://doi.org/10.3390/rs10030362

  • Beck HE, van Dijk AIJM, Levizzani V, Schellekens J, Miralles DG, Martens B, de Roo A (2017a) MSWEP: 3-hourly 0.25 deg; global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data. Hydrol Earth Syst Sci 21:589–615. https://doi.org/10.5194/hess-21-589-2017

    Article  Google Scholar 

  • Beck HE, Vergopolan N, Pan M, Levizzani V, van Dijk AIJM, Weedon GP, Brocca L, Pappenberger F, Huffman GJ, Wood EF (2017b) Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrol Earth Syst Sci 21:6201–6217. https://doi.org/10.5194/hess-21-6201-2017

    Article  Google Scholar 

  • Behrangi A, Khakbaz B, Jaw TC, AghaKouchak A, Hsu K, Sorooshian S (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:47. https://doi.org/10.1029/2010wr009917

  • Bodian A, Dezetter A, Deme A, Diop L (2016) Hydrological evaluation of TRMM rainfall over the upper Senegal River basin. Hydrology 3. https://doi.org/10.3390/hydrology3020015

  • Chen F-W, Liu C-W (2012) Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan. Paddy Water Environ 10:209–222. https://doi.org/10.1007/s10333-012-0319-1

    Article  Google Scholar 

  • Dinku T, Ceccato P, Grover-Kopec E, Lemma M, Connor SJ, Ropelewski CF (2007) Validation of satellite rainfall products over East Africa’s complex topography. Int J Remote Sens 28:1503–1526. https://doi.org/10.1080/01431160600954688

    Article  Google Scholar 

  • Dinku T, Funk C, Peterson P, Maidment R, Tadesse T, Gadain H, Ceccato P (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 

  • Dirks KN, Hay JE, Stow CD, Harris D (1998) High-resolution studies of rainfall on Norfolk Island: Part II: interpolation of rainfall data. J Hydrol 208:187–193

    Article  Google Scholar 

  • Domroes M, Kaviani M, Schäfer D (1998) An analysis of regional and intra-annual precipitation variability over Iran using multivariate statistical methods. Theor Appl Climatol 61:151–159. https://doi.org/10.1007/s007040050060

  • Funk C, Peterson P, Landsfeld M 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, Pedreros D, Verdin J, Shukla S, Husak G, Rowland J, Harrison L, Hoell A, Michaelsen J (2015) The climate hazards infrared precipitation with stations-a new environmental record for monitoring extremes. Sci Data 2:150066. https://doi.org/10.1038/sdata.2015.66

    Article  Google Scholar 

  • Gao YC, Liu MF (2013) Evaluation of high-resolution satellite precipitation products using rain gauge observations over the Tibetan Plateau. Hydrol Earth Syst Sci 17:837–849. https://doi.org/10.5194/hess-17-837-2013

    Article  Google Scholar 

  • Goovaerts P (2000) Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. J Hydrol 228:113–129

    Article  Google Scholar 

  • Guo R, Liu Y (2016) Evaluation of satellite precipitation products with rain gauge data at different scales: implications for hydrological applications. Water 8. https://doi.org/10.3390/w8070281

  • Guo H, Bao A, Liu T, Ndayisaba F, He D, Kurban A, de Maeyer P (2017) Meteorological drought analysis in the lower mekong basin using satellite-based long-term CHIRPS product. Sustainability:9. https://doi.org/10.3390/su9060901

  • Habib E, Henschke A, Adler RF (2009) Evaluation of TMPA satellite-based research and real-time rainfall estimates during six tropical-related heavy rainfall events over Louisiana, USA. Atmos Res 94:373–388. https://doi.org/10.1016/j.atmosres.2009.06.015

    Article  Google Scholar 

  • Hsu K, Gao X, Sorooshian S, Gupta HV (1997) Precipitation estimation from remotely sensed information using artificial neural networks. J Appl Meteorol 36:1176–1190

    Article  Google Scholar 

  • Huffman GJ, Bolvin DT, Nelkin EJ, Wolff DB, Adler RF, Gu G, Hong Y, Bowman KP, Stocker EF (2007) The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8:38–55

    Article  Google Scholar 

  • Janjai S, Nimnuan P, Nunez M, Buntoung S, Cao J (2015) An assessment of three satellite-based precipitation data sets as applied to the Thailand region. Phys Geogr 36:282–304. https://doi.org/10.1080/02723646.2015.1045286

    Article  Google Scholar 

  • 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. https://doi.org/10.5194/adgeo-25-119-2010

    Article  Google Scholar 

  • Jiang S, Zhou M, Ren L, Cheng XR, Zhang PJ (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 

  • Joyce RJ, Janowiak JE, Arkin PA, Xie P (2004) CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J Hydrometeorol 5:487–503

    Article  Google Scholar 

  • Katiraie-Boroujerdy PS, Nasrollahi N, lin HK, Sorooshian S (2013) Evaluation of satellite-based precipitation estimation over Iran. J Arid Environ 97:205–219. https://doi.org/10.1016/j.jaridenv.2013.05.013

    Article  Google Scholar 

  • Kummerow C, Barnes W, Kozu T, Shiue J, Simpson J (1998) The tropical rainfall measuring mission (TRMM) sensor package. J Atmos Ocean Technol 15:809–817

    Article  Google Scholar 

  • Kurtzman D, Navon S, Morin E (2009) Improving interpolation of daily precipitation for hydrologic modelling: spatial patterns of preferred interpolators. Hydrol Process 23:3281–3291. https://doi.org/10.1002/hyp.7442

    Article  Google Scholar 

  • Lakew HB, Moges SA, Asfaw DH (2017) Hydrological evaluation of satellite and reanalysis precipitation products in the upper Blue Nile Basin: a case study of Gilgel Abbay. Hydrology 4. https://doi.org/10.3390/hydrology4030039

  • Li B, Huang J, Jin Z, Liu Z (2010) Methods for calculating precipitation spatial distribution of Zhejiang Province based on GIS. J Zhejiang Univ (science Ed 37:239–244

  • Li Z, Yang D, Hong Y (2013) Multi-scale evaluation of high-resolution multi-sensor blended global precipitation products over the Yangtze River. J Hydrol 500:157–169. https://doi.org/10.1016/j.jhydrol.2013.07.023

    Article  Google Scholar 

  • Li Z, Yang D, Gao B, Jiao Y, Hong Y, Xu T (2015) Multiscale hydrologic applications of the latest satellite precipitation products in the Yangtze River basin using a distributed hydrologic model. J Hydrometeorol 16:407–426. https://doi.org/10.1175/jhm-d-14-0105.1

    Article  Google Scholar 

  • Lin XS, Yu Q (2008) Study on the spatial interpolation of agroclimatic resources in Chongqing. J Anhui Agric 36:13431–13463

    Google Scholar 

  • Liu X, Pan Y, Zhu X, Yang T, Bai J, Sun Z (2018) Drought evolution and its impact on the crop yield in the North China Plain. J Hydrol 564:984–996. https://doi.org/10.1016/j.jhydrol.2018.07.077

    Article  Google Scholar 

  • Lloyd CD (2005) Assessing the effect of integrating elevation data into the estimation of monthly precipitation in Great Britain. J Hydrol 308:128–150. https://doi.org/10.1016/j.jhydrol.2004.10.026

    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 

  • Mahbod M, Veronesi F, Shirvani A (2019) An evaluative study of TRMM precipitation estimates over multi-day scales in a semi-arid region, Iran. Int J Remote Sens 40:4143–4174. https://doi.org/10.1080/01431161.2018.1562258

    Article  Google Scholar 

  • Malik A, Kumar A, Kim S et al (2020) Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model. Eng Appl Comput Fluid Mech 14:323–338

    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, Sorooshian S, Duan Q (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, 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 61:420–440. https://doi.org/10.1080/02626667.2014.987675

    Article  Google Scholar 

  • Modarres R (2006) Regional precipitation climates of Iran. J Hydrol (New Zealand) 45:13–27

    Google Scholar 

  • Nabaei S, Sharafati A, Yaseen ZM, Shahid S (2019) Copula based assessment of meteorological drought characteristics: regional investigation of Iran. Agric For Meteorol 276:107611

    Article  Google Scholar 

  • Otieno H, Yang J, Liu W, Han D (2014) Influence of rain gauge density on interpolation method selection. J Hydrol Eng 19:04014024. https://doi.org/10.1061/(asce)he.1943-5584.0000964

    Article  Google Scholar 

  • Paredes-Trejoet FJ, Barbosa HA, Lakshmi Kumar TV (2017) Validating CHIRPS-based satellite precipitation estimates in Northeast Brazil. J Arid Environ 139:26–40. https://doi.org/10.1016/j.jaridenv.2016.12.009

  • Ramsauer T, Weiß T, Marzahn P (2018) Comparison of the GPM IMERG final precipitation product to RADOLAN weather radar data over the topographically and climatically diverse Germany. Remote Sens 10. https://doi.org/10.3390/rs10122029

  • 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 

  • Seyyedi H, Anagnostou EN, Beighley E, McCollum J (2015) Hydrologic evaluation of satellite and reanalysis precipitation datasets over a mid-latitude basin. Atmos Res 164–165:37–48. https://doi.org/10.1016/j.atmosres.2015.03.019

    Article  Google Scholar 

  • Sharafati A, Pezeshki E (2020) A strategy to assess the uncertainty of a climate change impact on extreme hydrological events in the semi-arid Dehbar catchment in Iran. Theor Appl Climatol 139:389–402

    Article  Google Scholar 

  • Sharafati A, Tafarojnoruz A, Shourian M, Yaseen ZM (2019) Simulation of the depth scouring downstream sluice gate: the validation of newly developed data-intelligent models. J Hydro-environment Res

    Google Scholar 

  • Sharafati A, Nabaei S, Shahid S (2020) Spatial assessment of meteorological drought features over different climate regions in Iran. Int J Climatol 40:1864–1884

    Article  Google Scholar 

  • Shili ZLW (2003) Spatial interpolation methods of daily weather data in Northeast China [J]. Q J Appl Meteorol 5:605–615

    Google Scholar 

  • Shrestha M, Takara K, Kubota T, Bajracharya S (2011) Verification of GSMaP rainfall estimates over the central Himalayas. J Japan Soc Civ Eng Ser B1 (Hydraulic Eng) 67:I_37–I_42

    Google Scholar 

  • Shrestha NK, Qamer FM, Pedreros D, Murthy MSR, Wahid SM, Shrestha M (2017) Evaluating the accuracy of Climate Hazard Group (CHG) satellite rainfall estimates for precipitation based drought monitoring in Koshi basin, Nepal. J Hydrol Reg Stud 13:138–151. https://doi.org/10.1016/j.ejrh.2017.08.004

    Article  Google Scholar 

  • Sorooshian S, Hsu K-L, Gao X, Gupta HV, Imam B, Braithwaite D (2000) Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull Am Meteorol Soc 81:2035–2046

    Article  Google Scholar 

  • Sorooshian S, AghaKouchak A, Arkin P, Eylander J, Foufoula-Georgiou E, Harmon R, Hendrickx JMH, Imam B, Kuligowski R, Skahill B, Skofronick-Jackson G (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 

  • Tan M, Ibrahim A, Duan Z, Cracknell A, Chaplot V (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 

  • Tan ML, Samat N, Chan NW, Roy R (2018) Hydro-meteorological assessment of three GPM satellite precipitation products in the Kelantan River basin, Malaysia. Remote Sens 10:1011

    Article  Google Scholar 

  • WMO (2014) Guide to meteorological instruments and methods of observation (WMO-8, updated 2017). Geneva

  • Wu L, Wu XJ, Xiao CC, Tian Y (2010) On temporal and spatial error distribution of five precipitation interpolation models. Geogr Geo-Inf Sci 26:19–24

    Google Scholar 

  • Yuan F, Zhang L, Win K, Ren L, Zhao C, Zhu Y, Jiang S, Liu Y (2017) Assessment of GPM and TRMM multi-satellite precipitation products in streamflow simulations in a data-sparse mountainous watershed in Myanmar. Remote Sens:9. https://doi.org/10.3390/rs9030302

  • Zambrano F, Wardlow B, Tadesse T (2016) Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile. Remote Sens Agric Ecosyst Hydrol XVIII 9998:999823. https://doi.org/10.1117/12.2241032

    Article  Google Scholar 

  • Zhao Y, Xie Q, Lu Y, Hu B (2017) Hydrologic evaluation of TRMM multisatellite precipitation analysis for Nanliu River basin in humid southwestern China. Sci Rep 7:2470. https://doi.org/10.1038/s41598-017-02704-1

    Article  Google Scholar 

  • Zhu H, JIA S (2004) Uncertainty in the spatial interpolation of rainfall data. Prog Geogr 23:34–42. https://doi.org/10.11820/dlkxjz.2004.02.005

    Article  Google Scholar 

Download references

Acknowledgments

The first author expresses his sincere acknowledgement for Prof G. Reza Rakhshandehroo (Dept. of Civil and Environmental Engineering, Shiraz University) for their thoughtful comments and valuable advice during this study. The authors acknowledge the Islamic Republic of Iran Meteorological Organization and the original producers of CHIRPS for providing free downloadable precipitation data.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ahmad Sharafati.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghozat, A., Sharafati, A. & Hosseini, S.A. Long-term spatiotemporal evaluation of CHIRPS satellite precipitation product over different climatic regions of Iran. Theor Appl Climatol 143, 211–225 (2021). https://doi.org/10.1007/s00704-020-03428-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-020-03428-5

Keywords