Evaluation of RFE Satellite Precipitation and its Use in Streamflow Simulation in Poorly Gauged Basins

Abstract

The performance of satellite Rainfall Estimates (RFE, version 2.0) at daily resolution was evaluated in comparison with ground-based meteorological datasets (GBD) by applying statistical and hydrological modeling approaches. In-situ daily rainfall observations from 5 stations in and around the periphery of the Nasia river basin in Ghana, covering a period of 15 years (2001–2015), were used in this research. Comparison of the observed rainfall data with satellite-based estimates revealed a strong positive correlation, which yielded the highest correlation coefficient (R2) of 0.74 at the monthly timescale as against the weak positive linear relationship with the highest R2 of 0.41 at the daily timescale. Mean annual precipitation computed from both datasets also showed close correspondence yielding 978.83 mm/annum and 977.12 mm/annum for RFE and GBD, respectively. Calibration at the daily timescale showed that the ground-based data (GBD) performed better in simulating the observed streamflows compared to the satellite-based (RFE) simulations yielding a Nash Sutcliffe Efficiency (NSE) of 0.81 and 0.67 for the GBD and RFE, respectively. At the monthly timescale, the performance of both datasets improved, resulting in an NSE of 0.89 and 0.80 for the GBD and RFE, respectively. Although the RFE-based simulations could not perfectly reproduce the observed discharge, it can be used to supplement traditional in-situ gauge data to address the problem of non-availability of observed rainfall data for hydrological applications such as water resources planning and assessment. Future research into the usability of the RFE in other medium scale river basins could be carried out to compare with these results.

Highlights

• There is a strong correlation between RFE version 2.0 and ground-based monthly rainfall data

• RFE-based simulations appear to underestimate the observed hydrographs compared to ground-based data simulations

• RFE could be used to supplement in-situ observations of rainfall for hydrological analysis

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

References

  1. Adjei KA, Ren L, Appiah-Adjei EK, Kankam-Yeboah K, Agyapong AA (2012) Validation of TRMM data in the black Volta basin of Ghana. J Hydrol Eng 17:647–654. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000487

    Article  Google Scholar 

  2. Adjei KA, Ren L, Appiah-Adjei EK, Odai SN (2015) Application of satellite-derived rainfall for hydrological modelling in the data-scarce black Volta trans-boundary basin. Hydrol Res 46(5):777–790. https://doi.org/10.2166/nh.2014.111

    Article  Google Scholar 

  3. Adler RF, Huffman GJ, Chang A, Ferraro R, Xie P-P, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J, Arkin P, Nelkin E (2003) The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). J Hydrometeorol 4:1147–1167. https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2

    Article  Google Scholar 

  4. Ahmed Abd Elhamid MI, Eltahan Abdelhamid MH, Mohamed LME, Hamouda IA (2020) Assessment of the two satellite-based precipitation products TRMM and RFE rainfall records using ground-based measurements. Alexandria Eng J 59(2):1049–1058. https://doi.org/10.1016/j.aej.2020.03.035

    Article  Google Scholar 

  5. Akinyemi DF, Ayanlade OS, Nwaezeigwe JO, Ayanlade A (2019) A comparison of the accuracy of multi-satellite precipitation estimation and ground meteorological records over southwestern Nigeria. Remote Sens Earth Syst Sci 3:1–12. https://doi.org/10.1007/s41976-019-00029-3

    Article  Google Scholar 

  6. Artan G, Gadain H, Smith JL, Asante K, Bandaragoda CJ, Verdin JP (2007) Adequacy of satellite derived rainfall data for stream flow modeling. Nat Hazards 43:167–185. https://doi.org/10.1007/s11069-007-9121-6

    Article  Google Scholar 

  7. Barry B, Obuobie E, Andreini M, Andah W, Plaquet M (2005) The Volta river basin: a comprehensive assessment of water management in agriculture. International Water Management Institute, 198p. http://www.iwmi.cgiar.org/assessment/files_new/research_projects/river_basin_development_and_management/VoltaRiverBasin_Boubacar.pdf (Accessed 15-03-2020)

  8. Becker A, Finger P, Meyer-Christoffer A, Rudolf B, Schamm K, Schneider U, Ziese M (2013) A description of the global land-surface precipitation data products of the global precipitation climatology Centre with sample applications including centennial (trend) analysis from 1901–present. Earth Syst Sci Data 5:71–99. https://doi.org/10.5194/essd-5-71-2013

    Article  Google Scholar 

  9. Brown ME (2008) Rainfall Estimates. In: Famine early warning systems and remote sensing data. Springer, Berlin, pp 65–77. https://doi.org/10.1007/978-3-540-75369-8_4

    Google Scholar 

  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. https://doi.org/10.1016/j.jhydrol.2018.06.067

    Article  Google Scholar 

  11. De Silva MMGT, Weerakoon SB, Herath S (2014) Modeling of event and continuous flow hydrographs with HEC-HMS: case study in the Kelani River basin, Sri Lanka. J Hydrol Eng 19(4):800–806. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000846

    Article  Google Scholar 

  12. Dembélé M, Zwart SJ (2016) Evaluation and comparison of satellite-based rainfall products in Burkina Faso, West Africa. Int J Remote Sens 37(17):3995–4014. https://doi.org/10.1080/01431161.2016.1207258

    Article  Google Scholar 

  13. Downing DJ, Gardner RH, Hoffman FO (1985) An examination of response-surface methodologies for uncertainty analysis in assessment models. Technometrics 27(2):151–163. https://doi.org/10.2307/1268763

    Article  Google Scholar 

  14. Fallah A, Orth R (2020) Climate-dependent propagation of precipitation uncertainty into the water cycle. Hydrol Earth Syst Sci 24(7):3725–3735. https://doi.org/10.5194/hess-24-3725-2020

    Article  Google Scholar 

  15. Fleming M, Neary V (2004) Continuous hydrologic modeling study with the hydrologic modeling system. J Hydrol Eng 9:175–183. https://doi.org/10.1061/(ASCE)1084-0699(2004)9:3(175)

    Article  Google Scholar 

  16. Gebregiorgis AS, Hossain F (2014) Estimation of satellite rainfall error variance using readily available geophysical features. IEEE Trans Geosci Remote Sens 52(1):288–304. https://doi.org/10.1109/TGRS.2013.2238636

    Article  Google Scholar 

  17. Hapuarachchi P, Inomata H, Fukami K, Kachi M, Oki R (2007) Applicability of satellite-based precipitation data for near real-time flood forecasting. International Centre for Water Hazard and Risk Management (ICHARM) under the auspices of UNESCO, https://www.isac.cnr.it/~ipwg/meetings/geneva-2007/pres/hapuarachchi_pehrpp.pdf (Accessed 24-03-2020)

  18. Harris I, Jones PD, Osborn TJ, Lister DH (2013) Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 dataset. Int J Climatol 34(3):623–642. https://doi.org/10.1002/joc.3711

    Article  Google Scholar 

  19. Hermance JF, Sulieman HM (2013) Comparing satellite RFE data with surface gauges for 2012 extreme storms in African East Sahel. Remote Sens Lett 4(7):696–705. https://doi.org/10.1080/2150704X.2013.787498

    Article  Google Scholar 

  20. Huffman GJ, Adler RF, Bolvin DT, Gu G (2009) Improving the global precipitation record: GPCP version 2.1. Geophys Res Lett 36:L17808. https://doi.org/10.1029/2009GL040000

    Article  Google Scholar 

  21. Islam MN, Uyeda H (2007) Use of TRMM in determining the climatic characteristics of rainfall over Bangladesh. Remote Sens Environ 108:264–276. https://doi.org/10.1016/j.rse.2006.11.011

    Article  Google Scholar 

  22. Kafle T, Hazarika M, Karki S, Shrestha R, Sharma S, Samarakoon L (2007) Basin scale rainfall-runoff modelling for flood forecasts. In: Proceedings of the 5th Annual Mekong Flood Forum, Ho Chi Minh City, Vietnam 17–18pp. https://www.researchgate.net/scientific-contributions/T-P-Kafle-2010315579 (Accessed 20-01-2020)

  23. Kankam-Yeboah K, Obuobie E, Amisigo B, Opoku-Ankomah Y (2013) Impact of climate change on streamflow in selected river basins in Ghana. Hydrol Sci J 58(4):773–788. https://doi.org/10.1080/02626667.2013.782101

    Article  Google Scholar 

  24. Laouacheria F, Mansouri R (2015) Comparison of WBNM and HEC-HMS for runoff hydrograph prediction in a small urban catchment. Water Resour Manag 29(8):2485–2501. https://doi.org/10.1007/s11269-015-0953-7

    Article  Google Scholar 

  25. Liu Z, Ostrenga D, Teng W, Kempler S (2012) Tropical rainfall measuring Mission (TRMM) precipitation data and Services for Research and Applications. Bull Am Meteorol Soc 93(9):1317–1325. https://doi.org/10.1175/bams-d-11-00152.1

    Article  Google Scholar 

  26. Logah FY, Adjei KA, Obuobie E, Gyamfi C, Odai SN (2020) Evaluation and comparison of satellite rainfall products in the black Volta basin. Environ Process. https://doi.org/10.1007/s40710-020-00465-0

  27. MacDonald AM, Bonsor HC, Dochartaigh BÉÓ, Taylor RG (2012) Quantitative maps of groundwater resources in Africa. Environ Res Lett 7(2):024009. https://doi.org/10.1088/1748-9326/7/2/024009

    Article  Google Scholar 

  28. McCollum JR, Gruber A, Ba MB (2000) Discrepancy between gauges and satellite estimates of rainfall in equatorial Africa. J Appl Meteorol 39(5):666–679. https://doi.org/10.1175/1520-0450-39.5.666

    Article  Google Scholar 

  29. Meng J, Li L, Hao Z, Wang J, Shao Q (2014) Suitability of TRMM satellite rainfall in driving a distributed hydrological model in the source region of Yellow River. J Hydrol 509:320–332. https://doi.org/10.1016/j.jhydrol.2013.11.049

    Article  Google Scholar 

  30. Mensah FO, Alo C, Yidana SM (2014) Evaluation of groundwater recharge estimates in a partially metamorphosed sedimentary basin in a tropical environment: application of natural tracers. Sci World J 2014:8. https://doi.org/10.1155/2014/419508

    Article  Google Scholar 

  31. Mishra AK, Coulibaly P (2009) Developments in hydrometric network design: a review. Rev Geophys 47:2–24. https://doi.org/10.1029/2007RG000243

    Article  Google Scholar 

  32. Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885–900. https://doi.org/10.13031/2013.23153

    Article  Google Scholar 

  33. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—A discussion of principles. J Hydrol 10(3):282–290. https://doi.org/10.1016/0022-1694(70)90255-6

    Article  Google Scholar 

  34. National Oceanic and Atmospheric Administration (NOAA) (2018) Climate Prediction Center (CPC) https://edcintl.cr.usgs.gov/downloads/sciweb1/shared//fews/web/africa/daily/rfe/downloads/yearly/ (Accessed 10-11-2018)

  35. New M, Lister D, Hulme M, Makin I (2002) A high-resolution data set of surface climate over global land areas. Clim Res 21:1–25. https://doi.org/10.3354/cr021001

    Article  Google Scholar 

  36. Nikolopoulos EI, Anagnostou EN, Hossain F, Gebremichael M, Borga M (2010) Understanding the scale relationships of uncertainty propagation of satellite rainfall through a distributed hydrologic model. J Hydrometeorol 11(2):520–532. https://doi.org/10.1175/2009JHM1169.1

    Article  Google Scholar 

  37. Oguntunde PG (2004) Evapotranspiration and complimentarity relations in the water balance of the Volta Basin: field measurements and GIS-based regional estimates, PhD thesis. Ecol Dev Ser 22:1–6 https://cuvillier.de/de/shop/publications/2840

    Google Scholar 

  38. Ouédraogo WAA, Raude JM, Gathenya JM (2018) Continuous modeling of the Mkurumudzi river catchment in Kenya using the HEC-HMS conceptual model: calibration, validation, model performance evaluation and sensitivity analysis. Hydrology 5(3):44. https://doi.org/10.3390/hydrology5030044

    Article  Google Scholar 

  39. Poméon T, Jackisch D, Diekkrüger B (2017) Evaluating the performance of remotely sensed and reanalysed precipitation data over West Africa using HBV light. J Hydrol 547:222–235. https://doi.org/10.1016/j.jhydrol.2017.01.055

    Article  Google Scholar 

  40. Prigent C (2010) Precipitation retrieval from space: an overview. Compt Rendus Geosci 342:380–389. https://doi.org/10.1016/j.crte.2010.01.004

    Article  Google Scholar 

  41. Rahman NA, Taher S, Alahamdi F, Nasir KAM (2016) Arid hydrological modeling at Wadi Alaqiq, Madinah, Saudi Arabia. J Teknol 78(7):51–58. https://doi.org/10.11113/jt.v78.4516

    Article  Google Scholar 

  42. Ren P, Li J, Feng P, Guo Y, Ma Q (2018) Evaluation of multiple satellite precipitation products and their use in hydrological modelling over the Luanhe river basin, China. Water 10(6):677. https://doi.org/10.3390/w10060677

    Article  Google Scholar 

  43. Sampath DS, Weerakoon SB, Herath S (2015) HEC-HMS model for runoff simulation in a tropical catchment with intra-basin diversions – case study of the Deduru Oya river basin, Sri Lanka. Eng J Inst Eng Sri Lanka 48(1):1–9. https://doi.org/10.4038/engineer.v48i1.6843

    Article  Google Scholar 

  44. Saouabe T, El Khalki EM, Saidi MEM, Najmi A, Hadri A, Rachidi S, Tramblay Y (2020) Evaluation of the GPM-IMERG precipitation product for flood modeling in a semi-arid mountainous basin in Morocco. Water 12(9):2516. https://doi.org/10.3390/w12092516

    Article  Google Scholar 

  45. Sharannya TM, Al-Ansari N, Deb Barma S, Mahesha A (2020) Evaluation of satellite precipitation products in simulating streamflow in a humid tropical catchment of India using a semi-distributed hydrological model. Water 12(9):2400. https://doi.org/10.3390/w12092400

    Article  Google Scholar 

  46. Sivapalan M, Takeuchi K, Franks SW, Gupta VK, Karambiri H, Lakshmi V, O’Connell PE (2003) IAHS decade on predictions in ungauged basins (PUB), 2003–2012: shaping an exciting future for the hydrological sciences. Hydrol Sci J 48(6):857–880. https://doi.org/10.1623/hysj.48.6.857.51421

    Article  Google Scholar 

  47. Sorooshian S, Hsu KL, Gao X, Gupta HV, Imam B, Braithwaite D (2000) Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull Am Meteorol Soc 81(9):2035–2046. https://doi.org/10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2

    Article  Google Scholar 

  48. 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. https://doi.org/10.1175/JHM-D-12-032.1

    Article  Google Scholar 

  49. Thiemig V, Rojas R, Zambrano-Bigiarini M, De Roo A (2013) Hydrological evaluation of satellite-based rainfall estimates over the Volta and Baro-Akobo Basin. J Hydrol 499:324–338. https://doi.org/10.1016/j.jhydrol.2013.07.012

    Article  Google Scholar 

  50. Tolson BA, Shoemaker CA (2007) Dynamically dimensioned search algorithm for computationally efficient watershed model calibration. Water Resour Res 43(1):1–16. https://doi.org/10.1029/2005WR004723

    Article  Google Scholar 

  51. Try S, Tanaka S, Tanaka K, Sayama T, Oeurng C, Uk S, Takara K, Hu MC, Han DW (2020) Comparison of gridded precipitation datasets for rainfall-runoff and inundation modeling in the Mekong river basin. PLoS One 15(1):e0226814. https://doi.org/10.1371/journal.pone.0226814

    Article  Google Scholar 

  52. United States Army Corps of Engineers (2000) HEC-HMS Hydrologic Modeling System. Technical Reference Manual - Version 3.5 CPD-74B, 1–134. Accessed 25-06-2018

  53. United States Army Corps of Engineers (2015) HEC-HMS Hydrologic Modeling System. User’s Manual - Version 4.1 CPD-74A, 1–600. Accessed 10-06-2018

  54. Willmott CJ, Matsuura K (1995) Smart interpolation of annually averaged air temperature in the United States. J Appl Meteorol 34:2577–2586. https://doi.org/10.1175/1520-0450(1995)034<2577:SIOAAA>2.0.CO;2

    Article  Google Scholar 

  55. Xie H, Zhou X, Hendrickx JMH, Vivoni ER, Guan H, Tian YQ, Small EE (2006) Evaluation of NEXRAD stage III precipitation data over a semiarid region. J Am Water Resour Assoc 42(1):237–256. https://doi.org/10.1111/j.1752-1688.2006.tb03837.x

    Article  Google Scholar 

  56. Xie S-P, Deser C, Vecchi GA, Ma J, Teng H, Wittenberg AT (2010) Global warming pattern formation: sea surface temperature and rainfall. J Clim 25(4):966–986. https://doi.org/10.1175/2009JCLI3329.1

    Article  Google Scholar 

  57. Xu H, Taylor RG, Kingston DG, Jiang T, Thompson JR, Todd MC (2010) Hydrological modeling of river Xiangxi using SWAT2005: a comparison of model parameterizations using station and gridded meteorological observations. Quat Int 226:54–59. https://doi.org/10.1016/j.quaint.2009.11.037

    Article  Google Scholar 

  58. Zhang Y, Li Y, Ji X, Luo X, Li X (2018) Evaluation and hydrologic validation of three satellite-basedprecipitation products in the upper catchment of the Red River Basin, China. Remote Sens 10(12):1–22. https://doi.org/10.3390/rs10121881

    Article  Google Scholar 

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Acknowledgements

This research was funded by the Regional Water and Environmental Sanitation Centre Kumasi (RWESCK) at the Kwame Nkrumah University of Science and Technology (KNUST), College of Engineering, Kumasi-Ghana with funding from Ghana Government through the World Bank under the Africa Centers of Excellence Project. The views expressed in this study do not reflect those of the World Bank, Ghana Government and KNUST. The authors also thank Ghana Meteorological Agency and Ghana Hydrological Services Department for making available all the relevant hydro- meteorological datasets.

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All authors contributed to the research conceptualization and design. Material preparation and data collection were performed by Sylvester Darko. Methodology was written by Kwaku Amaning Adjei. Data analysis was undertaken by Charles Gyamfi. Supervision and review of draft manuscript was done by Samuel Nii Odai. Hubert Osei-Wusuansa facilitated the acquisition of funding and related resources. All authors read and approved the final manuscript.

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Correspondence to Sylvester Darko.

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Darko, S., Adjei, K.A., Gyamfi, C. et al. Evaluation of RFE Satellite Precipitation and its Use in Streamflow Simulation in Poorly Gauged Basins. Environ. Process. (2021). https://doi.org/10.1007/s40710-021-00495-2

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Keywords

  • Nasia river basin
  • White Volta river basin
  • Streamflow
  • RFE
  • Ground-based data
  • HEC-HMS modeling
  • Calibration-validation