Abstract
The impacts of climate change are one of the challenges that the world is facing. This study evaluates the behavior of streamflows using various rainfall data in the Qinhuai River basin (China) through the probability distributed model (PDM) rainfall–runoff model. The methodology consisted of (i) assessing the hydrological model capability to reproduce the hydrological processes of the basin using multi-source rainfall and (ii) estimating present (2010:2015) and future (2020:2099) runoff using the regional climate model (RCM) under the representative concentration pathway’s (RCP) scenarios 4.5 and 8.5; it must be noted that the downscaling and bias corrections are done by the China Meteorological Administration. The results are used for these works, (iii) trend analysis based on the Mann–Kendall methods. The results showed a decent performance of the model simulating streamflow over the Qinhuai River basin with 0.95 of R2 for calibration and 0.77 for validation and a root-mean-square error (RMSE), respectively, of 29.7 and 86.25. The performance criteria of this model are determined through R2 statistic and the RMSE. Rainfall data (rain gauge, C-band radar, S-band radar), Climate Prediction Center (CPC) morphing technique (CMORPH), and global precipitation measurement (GPM) satellite rainfall indicated fair adequation between the actual and simulated flows with statistic coefficient greater than 0.95 for calibration. A significant change trend at 0.05 level was found for the future runoff simulated under both RCP’s scenarios at annual time scales.
Similar content being viewed by others
References
Abro MI et al (2019) Hydrological appraisal of rainfall estimates from radar, satellite, rain gauge and satellite–gauge combination on the Qinhuai River Basin, China. Hydrol Sci J 64(16):1957–1971. https://doi.org/10.1080/02626667.2018.1557335
Abro MI et al (2020a) Hydrological evaluation of satellite and reanalysis precipitation products in the glacier-fed river basin (Gilgit). Arab J Geosci 13(14):631. https://doi.org/10.1007/s12517-020-05621-2
Abro MI et al (2020b) Statistical and qualitative evaluation of multi-sources for hydrological suitability in flood-prone areas of Pakistan’. Journal of Hydrology 588(April):125117. https://doi.org/10.1016/j.jhydrol.2020.125117 (Elsevier)
Abro MI, Zhu D, Elahi E, Majidano AA, Solangi BK (2021) Hydrological simulation using multi-sources precipitation estimates in the Huaihe River Basin. Arabian Journal of Geosciences 2021(14):1–12. https://doi.org/10.1007/s12517-021-08254-1
Adediran GA (2015) Hydrological Forecasting with Radar and the Probability Distributed Hydrological Model (PDM). Dissertation.Com Boca Raton, Florida USA. https://doi.org/10.13140/RG.2.1.2556.7840
Ali RO, Chunju Z, Azam MI (2018) The effects of human activities, climatic conditions and land-use factors on water resources development in Huai river basin Northeast China. International Journal of Hydrology, 2(2):107–114. https://doi.org/10.15406/ijh.2018.02.00059
Amorim J da S, Viola MR, Junqueira R, de Oliveira VA, de Mello CR (2020) Evaluation of satellite precipitation products for hydrological modeling in the Brazilian cerrado biome. Water (Switzerland) 12(9). https://doi.org/10.3390/W12092571
Bengal W (2014) Application of sequential Mann-k\Kendall test for detection of approximate significant change point in surface air temperature for Kolkata weather observatory, west Bengal, India. International journal of current research 6(02):5319–5324
Bian GD, Du JK, Song MM, Xu YP, Xie SP, Zheng WL, Xu CY (2017) A procedure for quantifying runoff response to spatial and temporal changes of impervious surface in Qinhuai River basin of southeastern China. Catena 157(July 2016):268–278. https://doi.org/10.1016/j.catena.2017.05.023
Du J, Rui H, Zuo T, Li Q, Zheng D, Chen A, Xu CY (2013) Hydrological simulation by SWAT model with fixed and varied parameterization approaches under land use change. Water Resour Manage 27(8):2823–2838. https://doi.org/10.1007/s11269-013-0317-0
Fang GH et al (2015) Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China, pp 2547–2559. https://doi.org/10.5194/hess-19-2547-2015
Gill PE et al (1981) Practical optimization, Practical Optimization: Algorithms and Engineering Applications, pp 1–669. https://doi.org/10.1007/978-0-387-71107-2
Gu H et al (2014) Impact of climate change on hydrological extremes in the Yangtze River impact of climate change on hydrological extremes in the Yangtze River Basin, China, (September). https://doi.org/10.1007/s00477-014-0957-5
Hao L, Sun G, Liu Y, Wan J, Qin M, Qian H, Chen J (2015) Urbanization dramatically altered the water balances of a paddy field-dominated basin in southern China. Hydrol Earth Syst Sci 19(7):3319–3331. https://doi.org/10.5194/hess-19-3319-2015
Huang A et al (2016) Journal of Geophysical Research : Atmospheres, pp 654–675. https://doi.org/10.1002/2016JD025456.Received
Kendall MG (1975) Rank Correlation Methods. 4th Edition, Charles Griffin, London
Mann HB (1945) Non-parametric test against trend. Econometrica 13:245-259. https://doi.org/10.2307/1907187
Maraun D (2016) Bias correcting climate change simulations - a critical review. Curr Clim Change Rep 2:211–220. https://doi.org/10.1007/s40641-016-0050-x
Mendez M, Maathuis B, Hein-Griggs D, Alvarado-Gamboa L-F (2020) Performance evaluation of bias correction methods for climate change monthly precipitation projections. Water 12(482). https://doi.org/10.3390/w12020482
Moore RJ (1985) The probability-distributed principle and runoff production at point and basin scales. Hydrol Sci J 30(2):273–297. https://doi.org/10.1080/02626668509490989
Moore RJ (1999) ‘Real-Time Flood Forecasting Systems: Perspectives and Prospects’, Floods and Landslides: Integrated Risk Assessment, pp. 147–189. https://doi.org/10.1007/978-3-642-58609-5_11
Moore RJ (2007) The PDM rainfall-runoff model. Hydrol Earth Syst Sci 11(1):483–499. https://doi.org/10.5194/hess-11-483-2007
Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7(4):308–313. https://doi.org/10.1093/comjnl/7.4.308
Niyongendako M et al (2020) Trend and variability analysis of rainfall and extreme temperatures in Burundi, 10(6):36–51. https://doi.org/10.9734/IJECC/2020/v10i630203
Nkunzimana A et al (2019) Spatiotemporal variation of rainfall and occurrence of extreme events over Burundi during 1960 to 2010. Arab J Geosci 12(5):176. https://doi.org/10.1007/s12517-019-4335-y
Oki T, Kanae S (2006) Global hydrological cycles and word water resources. Science 313(5790):1068–1072
O’Connor KM (1982) Derivation of discretely coincident forms of continuous linear time-invariant models using the transfer function approach. Journal of Hydrology 59(1–2):1–48. https://doi.org/10.1016/0022-1694(82)90002-6
Oleyiblo JO, Li Z (2010) Application of HEC-HMS for flood forecasting in Misai and Wan’ an catchments in China. Water Sci Eng 3(1):14–22. https://doi.org/10.3882/j.issn.1674-2370.2010.01.002
Rica C (2020) Performance evaluation of bias correction methods for climate change monthly precipitation projections
Smith JM (1977) Mathematical modelling and digital simulation for engineers and scientists, Wiley, Chichester, UK. 332 pp
Sneyres R (1990) Technical note no. 143 on the statistical analysis of time series of observation. World Meteorological Organisation. Geneva, Switzerland
Song S, Xu YP, Yang L (2015) The effects of urbanization on catchment storage capacity – a conceptual model in plain catchment in Yangtz River Delta. Proceedings of the 14th International Conference on Environmental Science and Technology Rhodes, Greece, 3-5, September
Sridhar S, Raviraj A (2017) Statistical trend analysis of rainfall in Amaravathi River Basin using Mann-Kendall test. Curr World Environ 12(1):89–96. https://doi.org/10.12944/CWE.12.1.11
Tolika K (2019) Bias correction of climate model’s precipitation using the copula method and its application in river basin simulation. https://doi.org/10.3390/w11030600
Vrac M, Noël T, Vautard R (2016) Bias correction of precipitation through singularity stochastic removal : because occurrences matter, (1), pp 5237–5258. https://doi.org/10.1002/2015JD024511.Received
Zhu D, Das S, Ren Q (2017) Hydrological appraisal of climate change impacts on the water resources of the Xijiang basin, South China. Water (Switzerland) 9(10):793. https://doi.org/10.3390/w9100793
Zhu D et al (2020) Hydrological evaluation of hourly merged satellite–station precipitation product in the mountainous basin of China using a distributed hydrological model. Meteorol Appl 27(2):1–16. https://doi.org/10.1002/met.1909
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Additional information
Responsible Editor: Broder J. Merkel
Muhammad Ilyas Abro is a co-first author.
Highlights
• Assessing the hydrological model capability to reproduce the hydrological processes of the basin using multi-source rainfall.
• Estimating present (2010:2015) and future (2020: 2099) runoff using the RCMs under the RCP’s scenarios 4.5 and 8.5.
• The correlation coefficient is 0.95 for calibration and 0.77 for validation and has a RMSE, respectively, of 29.7 and 86.25.
• A significant change trends at 0.05 level found for the future runoff simulated under both RCP’s scenarios at annual time scales.
Rights and permissions
About this article
Cite this article
Akpovi, B.A., Zhu, D., Abro, M.I. et al. Hydrological appraisal using multi-source rainfall data in PDM model over the Qinhuai River basin in China. Arab J Geosci 15, 236 (2022). https://doi.org/10.1007/s12517-022-09545-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12517-022-09545-x