An optimized baseflow separation method for assessment of seasonal and spatial variability of baseflow and the driving factors

doi:10.1007/s11442-021-1927-8

Abstract

Abstract:

Baseflow is an important component of river or streamflow. It plays a vital role in water utilization and management. An improved Eckhardt recursive digital filter (IERDF) is proposed in this study. The key filter parameter and maximum baseflow index (BFI_max) were estimated using the minimum smoothing method to improve baseflow estimation accuracy. The generally considered BFI_max of 0.80, 0.50 and 0.25 according to the drainage basin’s predominant geological characteristics often leads to significant errors in the regions that have complex subsurface and hydrologic conditions. The IERDF improved baseflow estimation accuracy by avoiding arbitrary parameter values. The proposed method was applied for baseflow separation in the upstream of Yitong River, a tributary of the Second Songhua River, and its performance was evaluated by comparing the results obtained using isotope-tracer data. The performance of IERDF was also compared with nine baseflow separation techniques belonging to filter, BFI and HYSEP methods. The IERDF was also applied for baseflow separation and calculation of rainfall infiltration recharge coefficient at different locations along the Second Songhua River’s mainstream for the period 2000-2016. The results showed that the minimum smoothing method significantly improved BFI_max estimation accuracy. The baseflow process line obtained using IEDRF method was consistent with that obtained using isotope ¹⁸O. The IERDF estimated baseflow also showed stability and reliability when applied in the mainstream of the Second Songhua River. The BFI alone in the river showed an increase from the upstream to the downstream. The proportion of baseflow to total flow showed a decrease with time. The intra-annual variability of BFI was different at different locations of the river due to varying climatic conditions and subsurface characteristics. The highest BFI was observed at the middle reaches of the river in summer due to a water surplus from power generation. The research provided valuable information on baseflow characteristics and runoff mode determination, which can be used for water resources assessment and optimization of economic activity distribution in the region.

Key words: Improved Eckhardt recursive digital filtering, baseflow separation, rainfall infiltration coefficient, the Second Songhua River Basin

SUN Jiaqi, WANG Xiaojun, Shamsuddin SHAHID, LI Hongyan. An optimized baseflow separation method for assessment of seasonal and spatial variability of baseflow and the driving factors[J].Journal of Geographical Sciences, 2021, 31(12): 1873-1894.

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Years	BFI	Years	BFI
2003	0.15	2010	0.09
2004	0.32	2011	0.72
2005	0.21	2012	0.47
2006	0.07	2013	0.22
2007	0.19	2014	0.61
2008	0.28	2015	0.76
2009	0.41	2016	0.31

Year	HYSEP (Fixed)	HYSEP (Slide)	HYSEP (Local min)	BFI (F)	BFI (K)	F1	F2	F3	F4	IERDF
2003	0.13	0.13	0.15	0.15	0.15	0.24	0.21	0.22	0.33	0.26
2004	0.28	0.28	0.29	0.32	0.32	0.39	0.33	0.34	0.49	0.41
2005	0.20	0.17	0.10	0.21	0.22	0.32	0.30	0.31	0.44	0.35
2006	0.01	0.01	0.10	0.07	0.07	0.20	0.18	0.20	0.32	0.23
2007	0.15	0.15	0.14	0.19	0.19	0.30	0.27	0.28	0.41	0.32
2008	0.27	0.26	0.29	0.28	0.30	0.38	0.34	0.36	0.48	0.40
2009	0.39	0.37	0.36	0.41	0.41	0.45	0.37	0.40	0.51	0.45
2010	0.07	0.08	0.07	0.09	0.09	0.21	0.19	0.21	0.33	0.23
2011	0.61	0.62	0.73	0.72	0.73	0.69	0.48	0.57	0.69	0.67
2012	0.48	0.46	0.45	0.47	0.55	0.57	0.46	0.47	0.62	0.57
2013	0.17	0.18	0.17	0.22	0.22	0.32	0.27	0.29	0.45	0.35
2014	0.55	0.54	0.56	0.61	0.61	0.64	0.46	0.50	0.66	0.61
2015	0.67	0.67	0.60	0.76	0.77	0.66	0.44	0.48	0.68	0.62
2016	0.24	0.23	0.20	0.31	0.31	0.34	0.29	0.31	0.43	0.36
Average	0.30	0.30	0.30	0.34	0.35	0.41	0.33	0.35	0.49	0.42
Maximum	0.67	0.67	0.73	0.76	0.77	0.69	0.48	0.57	0.69	0.67
Minimum	0.01	0.01	0.07	0.07	0.07	0.20	0.18	0.20	0.32	0.23
Extremum	67	67	10.28	11.08	11.32	3.45	2.67	2.85	2.16	2.96
Variance	0.04	0.04	0.04	0.05	0.05	0.03	0.01	0.01	0.02	0.02
CV	0.69	0.70	0.70	0.65	0.65	0.41	0.31	0.33	0.26	0.35

Year	River runoff (m³/s)	Direct runoff (m³/s)	Underground runoff (m³/s)	BFI
2000	212.12	87.05	125.08	0.59
2001	221.24	85.70	135.54	0.61
2002	147.64	63.20	84.44	0.57
2003	142.57	59.84	82.73	0.58
2004	202.75	86.72	116.03	0.57
2005	296.11	137.73	158.38	0.53
2006	156.60	73.85	82.75	0.53
2007	190.82	101.37	89.45	0.47
2008	140.72	75.43	65.29	0.46
2009	155.20	79.26	75.94	0.49
2010	373.55	174.17	199.38	0.53
2011	177.43	84.45	92.98	0.52
2012	228.51	108.85	119.66	0.52
2013	358.62	172.61	186.01	0.52
2014	160.14	81.48	78.66	0.49
2015	147.36	61.89	85.47	0.58
2016	262.21	111.76	150.45	0.57

Year	River runoff (m³/s)	Direct runoff (m³/s)	Underground runoff (m³/s)	BFI
2000	284.55	172.28	112.27	0.39
2001	398.51	208.49	190.02	0.48
2002	245.58	136.75	108.83	0.44
2003	213.15	118.43	94.72	0.44
2004	343.99	210.92	133.08	0.39
2005	549.18	349.21	199.98	0.36
2006	274.16	176.16	98.00	0.36
2007	281.44	168.43	113.01	0.40
2008	254.71	151.23	103.48	0.41
2009	265.89	154.38	111.51	0.42
2010	766.54	447.31	319.23	0.42
2011	301.06	185.50	115.56	0.38
2012	363.10	194.37	168.73	0.46
2013	738.79	450.25	288.54	0.39
2014	275.52	200.50	75.02	0.27
2015	243.12	134.52	108.60	0.45
2016	409.15	249.05	160.10	0.39

Year	River runoff (m³/s)	Direct runoff (m³/s)	Underground runoff (m³/s)	BFI
2000	357.85	64.27	293.58	0.82
2001	388.99	69.45	319.54	0.82
2002	332.28	53.96	278.32	0.84
2003	241.76	41.97	199.79	0.83
2004	373.34	64.65	308.69	0.83
2005	641.87	124.66	517.21	0.81
2006	419.45	78.95	340.50	0.81
2007	343.32	68.38	274.94	0.80
2008	329.22	64.11	265.11	0.81
2009	338.13	61.01	277.12	0.82
2010	745.48	142.37	603.11	0.81
2011	478.52	86.57	391.95	0.82
2012	325.96	65.96	260.00	0.80
2013	885.01	166.49	718.52	0.81
2014	441.38	89.71	351.67	0.80
2015	217.09	38.60	178.49	0.82
2016	542.17	111.12	431.05	0.80