Evaluation of the applicability of climate forecast system reanalysis weather data for hydrologic simulation: A case study in the Bahe River Basin of the Qinling Mountains, China

doi:10.1007/s11442-017-1392-6

Abstract

Abstract:

In recent years, global reanalysis weather data has been widely used in hydrological modeling around the world, but the results of simulations vary greatly. To consider the applicability of Climate Forecast System Reanalysis (CFSR) data in the hydrologic simulation of watersheds, the Bahe River Basin was used as a case study. Two types of weather data (conventional weather data and CFSR weather data) were considered to establish a Soil and Water Assessment Tool (SWAT) model, which was used to simulate runoff from 2001 to 2012 in the basin at annual and monthly scales. The effect of both datasets on the simulation was assessed using regression analysis, Nash-Sutcliffe Efficiency (NSE), and Percent Bias (PBIAS). A CFSR weather data correction method was proposed. The main results were as follows. (1) The CFSR climate data was applicable for hydrologic simulation in the Bahe River Basin (R² of the simulated results above 0.50, NSE above 0.33, and |PBIAS| below 14.8. Although the quality of the CFSR weather data is not perfect, it achieved a satisfactory hydrological simulation after rainfall data correction. (2) The simulated streamflow using the CFSR data was higher than the observed streamflow, which was likely because the estimation of daily rainfall data by CFSR weather data resulted in more rainy days and stronger rainfall intensity than was actually observed. Therefore, the data simulated a higher base flow and flood peak discharge in terms of the water balance, except for some individual years. (3) The relation between the CFSR rainfall data (x) and the observed rainfall data (y) could be represented by a power exponent equation: y=1.4789x^0.8875 (R²=0.98,P<0.001). There was a slight variation between the fitted equations for each station. The equation provides a theoretical basis for the correction of CFSR rainfall data.

Key words: CFSR, weather data, hydrologic simulation, applicability evaluation, SWAT model, Bahe River Basin

Sheng HU, Haijun QIU, Dongdong YANG, Mingming CAO, Jinxi SONG, Jiang WU, Chenlu HUANG, Yu GAO. Evaluation of the applicability of climate forecast system reanalysis weather data for hydrologic simulation: A case study in the Bahe River Basin of the Qinling Mountains, China[J].Journal of Geographical Sciences, 2017, 27(5): 546-564.

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References 28

[1]	Abbaspour K C, Rouholahnejad E, Vaghefi Set al., 2015. A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model.Journal of Hydrology, 524: 733-752. doi: 10.1016/j.jhydrol.2015.03.027
[2]	Barrett C B, 1993. The development of the Nile hydrometeorological forecast system.Journal of the American Water Resources Association, 29: 933-938. doi: 10.1111/j.1752-1688.1993.tb03254.x
[3]	Blacutt L A, Herdies D L, Gonçalves L G G Det al., 2015. Precipitation comparison for the CFSR, MERRA, TRMM3B42 and Combined Scheme datasets in Bolivia.Atmospheric Research, 163: 117-131. doi: 10.1016/j.atmosres.2015.02.002
[4]	Dile Y T, Srinivasan R, 2014. Evaluation of CFSR climate data for hydrologic prediction in data-scarce watersheds: An application in the Blue Nile River Basin.Journal of the American Water Resources Association, 50(5): 1226-1241. doi: 10.1111/jawr.12182
[5]	Fuka D R, Walter M T, MacAlister Cet al., 2014. Using the Climate Forecast System Reanalysis as weather input data for watershed models.Hydrological Processes, 28(22): 5613-5623. doi: 10.1002/hyp.10073
[6]	Gupta H V, Sorooshian S, Yapo P O, 1999. Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration.Journal of Hydrologic Engineering, 4(2): 135-143. doi: 10.1061/(ASCE)1084-0699(1999)4:2(135)
[7]	Hu S, 2015. Simulation and prediction research of the ecohydrological process based on SWAT model in Beiluo River Basin [D]. Xi’an: Northwest University. (in Chinese)
[8]	Hu Y Z, Ni Y Y, Shao Het al., 2013. Applicability study of CFSR, ERA-Interim and MERRA precipitation estimates in Central Asia.Arid Land Geography, 36(4): 700-708. (in Chinese)
[9]	Lai Z Q, Li S, Li C Get al., 2013. Improvement and applications of SWAT model in the upper-middle Heihe River Basin.Journal of Natural Resources, 28(8): 1404-1413. (in Chinese) doi: 10.11849/zrzyxb.2013.08.013
[10]	Liu C M, Xia J, Guo S Let al., 2004. Advances in distributed hydrological modeling in the Yellow River basin.Advances in Water Science, 15(4): 495-500. (in Chinese) doi: 10.3321/j.issn:1001-6791.2004.04.017
[11]	Liu G H, Luan Z Q, Yan B Xet al., 2014. Runoff simulation for marsh rivers in Sanjiang Plain based on SWAT model.Journal of China Hydrology, 34(1): 46-51. (in Chinese)
[12]	Moriasi D N, Arnold J G, Liew M W Vet al., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations.Transactions of the ASABE, 50(3): 885-900. doi: 10.13031/2013.23153
[13]	Nash J E, Sutcliffe J V, 1970. River flow forecasting through conceptual models (Part 1): A discussion of principles.Journal of Hydrology, 10(3): 282-290. doi: 10.1016/0022-1694(70)90255-6
[14]	Rouholahnejad E, Abbaspour K C, Srinivasan Ret al., 2014. Water resources of the Black Sea Basin at high spatial and temporal resolution.Water Resources Research, 50(7): 5866-5885. doi: 10.1002/2013WR014132
[15]	Sharp E, Dodds P, Barrett Met al., 2015. Evaluating the accuracy of CFSR reanalysis hourly wind speed forecasts for the UK, using in situ measurements and geographical information.Renewable Energy, 77: 527-538. doi: 10.1016/j.renene.2014.12.025
[16]	Tang W, Lin Z H, Yang C Get al., 2014. Evaluation of a hydrological simulation over the Huaihe River basin using the coupled land surface and hydrologic model system and its uncertainty analysis.Climatic and Environmental Research, 19(4): 463-476. (in Chinese) doi: 10.3878/j.issn.1006-9585.2013.13066
[17]	Troin M, Caya D, Velázquez J Aet al., 2015. Hydrological response to dynamical downscaling of climate model outputs: A case study for western and eastern snowmelt-dominated Canada catchments.Journal of Hydrology Regional Studies, 4: 595-610. doi: 10.1016/j.ejrh.2015.09.003
[18]	Wang L, Chen X W, 2008. Simulation of hydrological effects on vegetation restoration of degraded mountain ecosystem with SWAT model.Journal of Mountain Science, 26(1): 71-75. (in Chinese) doi: 10.3969/j.issn.1008-2786.2008.01.012
[19]	Wang Z G, Liu C M, Huang Y B, 2003. The theory of SWAT model and its application in Heihe Basin.Progress in Geography, 22(1): 79-86. (in Chinese) doi: 10.3969/j.issn.1007-6301.2003.01.010
[20]	Ward E, Buytaert W, Peaver Let al., 2011. Evaluation of precipitation products over complex mountainous terrain: A water resources perspective.Advances in Water Resources, 34(10): 1222-1231. doi: 10.1016/j.advwatres.2011.05.007
[21]	Worqlul A W, Maathuis B, Adem A Aet al., 2014. Comparison of rainfall estimations by TRMM 3B42, MPEG and CFSR with ground-observed data for the Lake Tana basin in Ethiopia.Hydrology & Earth System Sciences, 18(18): 4871-4881. doi: 10.5194/hess-18-4871-2014
[22]	Xiang H, Zhang F, Jiang Jet al., 2014. Analysis of global cloud amount over the past 30 years based on CFSR data.Meteorological Monthly, 40(5): 555-561. (in Chinese) doi: 10.7519/j.issn.1000-0526.2014.05.005
[23]	Xu S Q, Chen J, Song R G, 2010. The application study of NCEP reanalysis data. Journal of Qingdao University (Natural Science Edition), 23(3): 38-41. (in Chinese)
[24]	Ye B S, Yang D Q, Ding Y Jet al., 2007. A bias-corrected precipitation climatology for China.Acta Geographica Sinica, 62(1): 3-13. (in Chinese) doi: 10.3321/j.issn:0375-5444.2007.01.001
[25]	Yu Y M, Mu Z X, 2015. Applicability of CFSR data in runoff simulation of cold highland area.Journal of Irrigation and Drainage, 34(11): 93-97. (in Chinese) doi: 10.13522/j.cnki.ggps.2015.11.020
[26]	Zhao H G,Yang S T,Wang Z Wet al., 2015. Evaluating the suitability of TRMM satellite rainfall data for hydrological simulation using a distributed hydrological model in the Weihe River catchment in China.Journal of Geographical Sciences, 25(2): 177-195. doi: 10.1007/s11442-015-1161-3
[27]	Zhao T B, Fu C B, Ke Z Jet al., 2010. Global atmosphere reanalysis datasets: Current status and recent advances. Advances in Earth Science, 25(3): 242-254. (in Chinese) doi: 10.3724/SP.J.1037.2010.00186
[28]	Zhou Z B, Wang H, Jia Y Wet al., 2005. Temporal downscaling daily precipitation in lack-data watershed: A case study in Yellow River.Resources Science, 27(1): 92-96. (in Chinese) doi: 10.3321/j.issn:1007-7588.2005.01.016

Stations	Id	Average annual rainfall (mm)	Elevation (m)	Controlling sub-basins
Bayuan^a	P01	773.9	1144	Sub 13-14, 17
Mujiayan^c	P02	896.3	794	Sub 3-4, 7-8, 19, 21
Muhuguan^b	P03	649.9	1200	Sub 25-28
Lanqiao^b	P04	676.7	1768	Sub 22-23
Luolicun^c,e	P05	830.4	544	Sub 12, 15-16, 18, 20
Gepaizhen^a	P06	853.6	1145	Sub 32-33
Yuchuan^a	P07	891.2	1117	Sub 29-31
Longwangmiao^c	P08	806.2	1352	Sub 34-35
Wangchuan^a	P09	904.2	985	Sub 24
Pantaowan^a	P10	655.5	495	Sub 2, 5-6, 9-11
Maduwang^c,e	P11	637.2	431	Sub 1
Xiqu^c	P12	582.8	402	Sub 1
CFSR1^d	p3391094	1223.5	1590	Sub 27, 29-31, 33-35
CFSR2^d	p3391097	1271.0	1142	Sub 28, 32
CFSR3^d	p3421091	437.0	470	Sub 1, 5
CFSR4^d	p3421094	645.2	680	Sub 2, 6-12, 15-16, 18-20, 22-24
CFSR5^d	p3421097	983.7	1385	Sub 3-4, 13-14, 17, 25-26

Time scales	Hydro-gauging stations	Conventional		CFSR
Time scales	Hydro-gauging stations	NSE	PBIAS	NSE	PBIAS
Monthly	Luolicun	0.708	31.841	0.428	-4.260
Monthly	Maduwang	0.718	28.640	0.372	-14.401
Annual	Luolicun	0.151	31.840	0.339	-4.523
Annual	Maduwang	0.182	28.587	0.370	-14.783

Researcher	Year	Revising data or not	Revisal method	Notes
Dile et al.	2014	No	-	They introduced Climate Forecast System Reanalysis (CFSR) data into a Soil Water and Assessment Tool (SWAT) model, but did not undertake a revision.
Fuka et al.	2014	No	-
Worqlul et al.	2014	No	-	They found that the estimates of Multi-Sensor Precipitation Estimate-Geostationary (MPEG) and CFSR data conformed to the actual value, but CFSR overestimated or underestimated precipitation at some stations.
Blacutt et al.	2015	No	-	They focused on the contrast between the statistical characteristics of CFSR and conventional weather data, ignoring their relevance at a monthly scale.
Zhao et al.	2015	No	-	They used a monadic linear regression to analyze Tropical Rainfall Measuring Mission (TRMM) satellite data and observed precipitation data and found that the degree of fitting was relatively high. However, they did not give the fitting equation for the stations investigated.
Yu et al.	2015	Yes	Error ratio method	They defined a correction coefficient (measured annual precipitation/CFSR annual precipitation), but the modified scale was large.