What controls the partitioning between baseflow and mountain block ...

What Controls the Partitioning between Baseflow and Mountain Block Recharge in the Qinghai-Tibet Plateau?

Yingying Yao1,2, Chunmiao Zheng1,3,4,*, Charles Andrews3 , Yi Zheng3, Aijing Zhang1, Jie Liu1

1

Institute of Water Sciences, Peking University, Beijing, China. Department of Civil and Environmental Engineering, National University of Singapore, Engineering Drive 2, Singapore (Current address). 3 South University of Science and Technology of China, Shenzhen, China. 4 Department of Geological Sciences, University of Alabama, Tuscaloosa, Alabama. 2

*Corresponding author: Chunmiao Zheng (South University of Science and Technology of China; [email protected])

Key points 

Regional groundwater flow is modeled and analyzed for the mountainous region of the Qinghai-Tibet Plateau



18 to 20% of precipitation recharges the groundwater system and 65% of the groundwater discharges as stream baseflow while 35% becomes mountain block recharge



The depth-dependency of hydraulic conductivity controls the partitioning of groundwater recharge between baseflow and mountain block recharge

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/2017GL074344 © 2017 American Geophysical Union. All rights reserved.

Abstract Mountainous areas are referred to as “water towers” since they are the source of water for many low-lying communities. The hydrologic budgets of these areas, which are particularly susceptible to climate change, are typically poorly constrained. To address this, we analyzed the partitioning between baseflow and mountain block recharge (MBR) using a regional groundwater model of the northern Qinghai-Tibet Plateau run with multiple scenarios. We found that ~19% of precipitation is recharged, approximately 35% of which becomes MBR, while 65% discharges as baseflow. This partitioning is relatively independent of the recharge rate but is sensitive to exponential depth-decrease of hydraulic conductivity (K). The MBR is more sensitive to this exponential decrease in K than baseflow. The proportion of MBR varies from twice to half of baseflow as the decay exponent increases by more than five-fold. Thus, the depth-dependence of K is critical for quantifying hydrologic partitioning in these sensitive areas. Key words: Qinghai-Tibet Plateau, baseflow, mountain block recharge (MBR), depthdependent hydraulic conductivity, Heihe River Basin

1 Introduction Mountainous terrain comprises about 25% of the land surface of the earth and produces about 32% of the surface runoff [Chow et al., 2012]. With the increasing demand on limited water supplies, particularly in arid and semiarid regions, the potential effects of climate change on water resources in mountainous regions have received considerable attention in recent years [Brooks et al., 2015; Evans et al., 2015; Viviroli et al., 2011]. In many large watersheds, surface water and inter-basin groundwater flows, which originate from mountainous areas, provide the major sources of water for densely populated downstream alluvial plains. Thus, the water resources in the mountainous regions are important when evaluating the overall water balance of a watershed with mountainous headwater regions. However, groundwater flow in mountainous regions is poorly constrained due to the complexity and heterogeneity of the groundwater systems, limited data availability, and the highly uncertain nature of groundwater recharge under a changing climate. Groundwater systems in mountains terrain control two important contributions to the water resources of downstream alluvial plains: the baseflow of the mountain streams and mountain block recharge (MBR) [Welch and Allen, 2012]. MBR is the groundwater discharge from the mountain block to the aquifers of downgradient alluvium [Manning and Solomon, 2003; Welch and Allen, 2012; Wilson and Guan, 2004]. There is typically high uncertainty in estimating the partitioning of recharge between baseflow and MBR due to multiple controlling factors. Theoretical modeling studies have demonstrated that typically 5 to 20% of recharge becomes MBR, and that it is plausible for up to 40% to become MBR [Gleeson and Manning, 2008; Welch and Allen, 2012]. The significant controls on MBR include specific characteristics of mountain topography, particularly depth of stream incision which governs the groundwater flow path, the ratio of recharge to hydraulic conductivity which governs water table elevation, and the vertical distribution of hydraulic conductivity in the mountain block which governs the depth of groundwater circulation [Gleeson and Manning, 2008; Jiang et al., 2009; Welch and Allen, 2012]. Other studies using realistic models of specific mountainous regions have found that roughly 20% to 30% of recharge becomes MBR and also demonstrate the theoretical controls on MBR/baseflow partitioning identified in the generic model studies generally apply in more realistic mountain settings as well [Ball et al., 2014; Manning and Solomon, 2005]. However, previous modelling works were © 2017 American Geophysical Union. All rights reserved.

performed on small-scale systems. For larger regional mountainous areas (>20,000 km2), especially like the Himalayas with complex combinations of topography, stream networks and geologic structure, the extent to which MBR/baseflow partitioning follows the theoretical demonstrations are poorly understood. The north Qinghai-Tibet Plateau – Qilian Mountains is one of the most important “water towers” for the west inland arid and semiarid regions in China. The hydrologic dynamics of such “water towers”, especially their subsurface component, is mostly unknown. This basic but missing insight is necessary for regional-scale water resources management. One key gap is the extent to which baseflow and mountain block recharge are partitioned and what factors control this. To address this gap, multiple groundwater flow modeling scenarios with varying groundwater recharge and depth-dependent hydraulic conductivity distributions were performed to investigate the magnitude and variability of baseflow and MBR to downstream alluvial plains. The depth-decay exponent model used for aquifer hydraulic conductivity in this study has been described in theoretical modeling studies and in field observations, but its performance and function in controlling baseflow and MBR has not been tested before in a large regional mountainous area. The results of this regional investigation are critical not only for the water resources of the study region but also provides broad knowledge on the hydrogeologic factors that control water partitioning and hydrologic budgets of mountainous regions which serve as water towers to many communities.

2 Description of Study Area The Qilian Mountains, on the northern margin of the Tibetan Plateau with elevations ranging from about 1,700 m above mean sea level in the mountain valleys to over 5,600 m at the high peaks, is critical for supplying water to vast arid northwestern China. This study focuses on the North Qilian and Central Qilian regions, which are the headwater areas of the Heihe River, covering an area of over 25,000 km2 (Figure 1), with 187 mountainous drainage sub-basins. The North Qilian Mountains, which abut the middle portion of the Heihe River alluvial valley, are a geosyncline structure zone characterized by ophiolite belts, Ordovician arc volcanic rocks and Carboniferous metamorphic rocks (Supporting Information) [Song et al., 2013]. The southern boundary of the North Qilian Mountains is the Heihe Fault. South of the fault is the steep mountain range of Central Qilian that is the result of a continued uplifting of the region. These mountains are underlain by metacrystalline rocks [Li et al., 1991]. Precipitation increases with elevation from approximately 190 mm per year in the middle reaches of the HRB to about 730 mm per year on the Tibetan Plateau. The mean annual temperature decreases with elevation from about 6.2℃ in the middle alluvial plain of HRB to about -9.6℃ on the plateau [Z Zhou et al., 1989]. Runoff, which originates from the Qilian Mountains, maintains a vibrant agricultural economy in the vast middle reach and a fragile ecological balance in the lower reach of the Heihe River.

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3 Methodology 3.1 Groundwater model A three-dimensional numerical groundwater flow model, with a series of alternative parameter combinations, was developed for the Qilian Mountains to represent the pattern of groundwater flow and assess variations in stream baseflow and MBR in the Qilian Mountains. The groundwater model was created using MODFLOW-NWT [Niswonger et al., 2011] in steady state mode, which assumes that the groundwater flow system is at long-term equilibrium conditions. The 90 m STRM DEM data for the HRB was used to define the land surface topography and hydrostratigraphy. In the vertical direction, the model domain was discretized into seven layers, and the total depth of the model is approximately 2.8 km. Details of the model are described in the Supporting Information. Lateral boundary conditions for model layers one through seven are specified no-flow conditions on the south, east and west sides of the model domain. The northern boundary is a head-dependent boundary that represents flow from the mountain block to the alluvial plains of the middle HRB. The flux across this boundary, which is referred to as MBR, depends on the hydraulic-head differences between the mountains and the water table in the middle HRB. The top boundary is a flux boundary representing recharge from the infiltration of precipitation (P). Groundwater recharge (R) represents the portion of precipitation that actually infiltrates to the water table, which is commonly referred to as recharge. The ratio of R to P (R/P) for arid and semi-arid mountainous basins has been reported to vary between 0.03 to 0.4 [Scanlon et al., 2006], and is assumed as spatially uniform in this model. Distributed P was computed through the spatial interpolation of a precipitation dataset from eight meteorological stations and 16 precipitation stations covering the period of 1951 to 2010 (Figure S4a). The spatial distribution of evaporation rates (ET) was also obtained through interpolation of meteorological datasets (Figure S4b). Evaporation was represented in the model in the valley areas where the water table is shallower than 10 m below the ground surface. Initial estimates of hydraulic conductivity were based on values of permeability for the lithology in each surface rock type [Gleeson et al., 2011], and adjusted during the calibration. On a regional scale, hydraulic conductivity is a function of mapped lithology and depth below land surface [Gleeson et al., 2011; Jiang et al., 2010; Kuang and Jiao, 2014]. A general exponential decay model was used for the mountainous groundwater system to represent the depth-dependency of hydraulic conductivity (K):

logKz = logK0 - Az

(1)

where A is the decay exponent (m-1) of vertical K (m/s), K0 (m/s) is the K at the ground surface, Kz (m/s) is the permeability at depth of z (m). Based on the value of A tested in theoretical numerical models [Cardenas and Jiang, 2010; Jiang et al., 2009], we use A values between 0.001m-1 and 0.006 m-1 to test variability of baseflow and MBR to changes in K with depth.

3.2 Baseflow estimates Stream baseflow is a good indicator of average groundwater discharge and provides reliable sets of constraints for calibrating a numerical groundwater model [Ge et al., 2008]. Digital filter-based algorithms are widely used as effective methods to estimate baseflow for model calibration [Alansi et al., 2009; F Zhou et al., 2013]. In this study, we used two digital © 2017 American Geophysical Union. All rights reserved.

filter algorithms for estimating baseflow, the algorithm of Lyne-Hollick [Jie et al., 1996] and the algorithm of Eckhardt [Jiang et al., 2010] (Supporting Information). These two algorithms have been demonstrated to produce reasonable estimates of baseflow in the arid regions of northwest China [Fan et al., 2013; Luo et al., 2012]. The baseflow was estimated using the observed streamflow from 1946 to 2012 at seven hydrological stations within the model domain.

4 Results and Discussion 4.1 Model performance The filtered-baseflow estimates from observed streamflow were used to constrain and calibrate the groundwater model. Alternative parameter combinations were tested to evaluate parameter sensitivity and the reasonableness of the model. Good agreement was achieved between model-calculated baseflow and estimated baseflow from monitoring data, as shown in Figure S6. The parameter-combinations of 0.2 for R/P and the calibrated average hydraulic conductivity values as listed in Figure 3(a) provided the best model calibration. For the two large streams in the model domain, the Yingluoxia and the Binggou, with estimated baseflow of 21.1 m3/s and 12.4 m3/s, respectively, the mean error between model-calculated baseflow and estimated baseflow from monitoring data is 0.61 m3/s. For the five small streams in the model domain with baseflows of less than 5 m3/s, the mean error is 0.1 m3/s. The nondimensional normalized root mean square error of fit (Fit-NRMSE) was used to evaluate the goodness of the calibrated model (Supporting Information). The Fit-NRMSE of the modeled results was 0.96 for all stream tributaries, the distribution of which control the entire model area. Therefore, good agreement was achieved with baseflow estimates, and the groundwater flow model is an appropriate, simplified proxy to explore the groundwater flow patterns, to estimate stream baseflows and to estimate MBR.

4.2 Groundwater flow patterns Hydraulic heads and groundwater flow paths in the Qilian Mountains in the calibrated model are shown in a plan view (a), stereo view (b) and cross-section profile (c) on Figure 2. Hydraulic heads decrease from south to north in the direction of decreasing land surface elevation. Flow paths (red lines) were calculated with a particle tracking algorithm [Pollock, 2012]. The longest tracking distance was 54 km, and the shortest was 0.26 km. Approximately 64% of groundwater flow was in local circulation for which the particle tracking distances were smaller than 10 km within local catchments (sub-basin) terminated by discharge to stream. Approximately 31% was in inter-catchment circulation that traveled across adjacent tributaries and the particle tracking distances were between 10 km and 30 km. Approximately 95% of groundwater flow circulation is within the upper 1,000 m at the local and inter-catchment scales. The regional flow pattern shows topography-controlled nested flow systems at both the local and inter-catchment scales. 4.3 Factors controlling hydrologic partitioning

The variability of baseflow and MBR with changing groundwater recharge was evaluated under alternative mountain R/P scenarios with the ratio varying between 0.05 and 0.3 (Table S6). Reasonable estimates of the R/P ratio are between 0.18 and 0.2 based on the Fit-NRMSD, and the averaged water table is about 3750 m for entire model domain (Figure © 2017 American Geophysical Union. All rights reserved.

3). Baseflow and MBR are correlated positively with groundwater recharge and account for over 99% of the total subsurface outflow, however, the partitioning between them remains relatively constant with R/P ratios between 0.1 and 0.25. Lower recharge and relatively greater K in the top aquifers, namely the lower R/K (i.e., roughly 0.01) tested to control the water table [Gleeson and Manning, 2008], are possible reasons which make the water table is not high sufficiently within the mountain ridges to increase baseflow fraction.

Reasonable decay exponents for hydraulic conductivity in the Qilian Mountains are between 0.004 and 0.005 m-1, based on the Fit-NRMSE in Table S7. The MBR is correlated negatively and the baseflow is correlated positively with the decay exponent as shown in Figure 3(c). The depth-dependent change in K controls the partition of the baseflow, MBR and modeled valley evaporation as shown in Figure 3(d). This result is consistent with the finding that the MBR fraction increases with an increasing active layer depth based on theoretical models [Gleeson and Manning, 2008]. The depth decay exponent A acts as a controlling factor to infer depth of active groundwater circulation in the bedrock. The “active layers”, based on the reasonable decay exponents, almost extend to the depth of 1.8 km, and lower permeability bedrock dominates the lithology of Qilian Mountains deeper than 1.8 km. The variability of baseflow and MBR with vertical hydraulic conductivity was evaluated under alternative anisotropy scenarios (Supporting Information), the possibility of verticallyoriented flow caused by fractures in regional scale are not observed by our model. A dimensionless elasticity index (EI) (Supporting Information) was used to evaluate the sensitivity of baseflow and MBR to groundwater recharge and depth-dependent K. A positive EI means that when groundwater recharge or depth-dependent K increases (decreases), the MBR or baseflow increases (decreases). When the reverse is the case, EI is negative. Within the reasonable range of 0.18 and 0.2 for the R/P ratio, and the range of 0.004 and 0.005 for the A decay exponent, the change in the baseflow, MBR and the EI are calculated and presented in Table 1. An increase in the R/P of 0.01, increases the groundwater recharge by 5.55% of the original value, and baseflow and MBR increase by 4.56% and 5.54% of their original values, respectively. When the A decay exponent is increased by 0.001, the baseflow increases by 5.87% of its original value and the MBR decreases by 11.31% of its original value. Based on the absolute EI value, MBR is more sensitive to both groundwater recharge and depth-dependent K than baseflow. Table 1 Summary of response of mountainous groundwater system to a change in the ratio R/P and decay exponent of hydraulic conductivity (A). ΔBaseflow ΔMBR 8 3 b % 10 m /yr EI % 108m3/yr 0.82 ↑4.56 ↑0.56 ↑5.54 ↑0.37 R/P ↑0.01 0.23 ↑5.87 ↑0.74 ↓-11.31 ↓-0.95 A ↑0.001 a Incremental change in R/P and A; 0.01 and 0.001 m-1, respectively, b EI: elasticity index Factor

Δ

a

EIb 0.99 -0.45

5 Conclusions A regional three-dimensional numerical groundwater flow model was developed for the Qilian Mountains region on the Qinghai-Tibet Plateau. The model was calibrated using mountain stream baseflows estimated from monitoring data as the primary calibration target.

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Based on the calibrated model, a sensitivity analysis under alternative scenarios of groundwater recharge and depth-dependency of hydraulic conductivity was conducted to evaluate the processes and controlling factors that impact baseflow and MBR. Major study findings include the following: (1) Local and intermediate scale flow dominate the mountainous groundwater system. About 64% of the total flow occurs in local flow systems within individual stream catchments, while 31% of the groundwater flow is in intermediate flow systems with flow across multiple catchments. These groundwater flows (95% of the total) circulate to a maximum depth of 1,000 m. Approximately 5% of groundwater flow occurs in a deeper, regional flow system, extending to a depth of approximately 1.8 km. (2) In the mountainous region, 18% to 20% of precipitation reaches the water table as groundwater recharge. Approximately 65% of the recharge flows toward and discharges to the mountain streams as baseflow, and approximately 35% flows from the mountainous region directly to the downstream groundwater aquifer. (3) The partitioning of recharge between MBR and baseflow is more dependent on the depthdependency of the hydraulic conductivity than on the recharge rate. Characterizing the depth dependency of hydraulic conductivity with an exponential decay function, we estimated that the proportion of MBR varies from being twice that of baseflow to being half that of baseflow when the decay exponent increases by more than five-fold. Our results suggest the need to reconsider the lateral groundwater boundary conditions for all alluvial plain groundwater system, as MBR is more sensitive to climate change than baseflow. Our results also indicate the estimation of depth-dependent hydraulic conductivity in the mountainous region has a great effect on global evaluations of hydrological processes. The findings in this study provide valuable information for other basin-scale ecohydrological studies. Acknowledgments The authors thank M. Bayani Cardenas, John L Wilson, Huad Guan and two anonymous reviewers for their helpful comments. The authors thank Lyndsay B. Ball for her suggestions on model development. This research was supported by the National Natural Science Foundation of China (grants no. 91225301, 91425303 and 2014490511). All data reported in this paper can be accessed in supporting information and in the cited references. References Alansi, A., M. Amin, G. Abdul Halim, H. Shafri, and W. Aimrun (2009), Validation of SWAT model for stream flow simulation and forecasting in Upper Bernam humid tropical river basin, Malaysia, Hydrology and Earth System Sciences Discussions, 6(6), 7581-7609. Ball, L. B., J. S. Caine, and S. Ge (2014), Controls on groundwater flow in a semiarid folded and faulted intermountain basin, Water Resources Research, 50(8), 6788-6809. Brooks, P. D., J. Chorover, Y. Fan, S. E. Godsey, R. M. Maxwell, J. P. McNamara, and C. Tague (2015), Hydrological partitioning in the critical zone: Recent advances and opportunities for developing transferable understanding of water cycle dynamics, Water Resources Research, 51(9), 6973-6987. Cardenas, M. B., and X.-W. Jiang (2010), Groundwater flow, transport, and residence times through topography-driven basins with exponentially decreasing permeability and porosity, Water Resources Research, 46(11), n/a-n/a. Chow, F. K., S. F. De Wekker, and B. J. Snyder (2012), Mountain weather research and forecasting: recent progress and current challenges, Springer Science & Business Media. Evans, S. G., S. Ge, and S. Liang (2015), Analysis of groundwater flow in mountainous, headwater

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catchments with permafrost, Water Resources Research. Fan, Y., Y. Chen, Y. Liu, and W. Li (2013), Variation of baseflows in the headstreams of the Tarim River Basin during 1960–2007, Journal of Hydrology, 487, 98-108. Ge, S., Q. B. Wu, N. Lu, G. L. Jiang, and L. Ball (2008), Groundwater in the Tibet Plateau, western China, Geophysical Research Letters, 35(18). Gleeson, T., and A. H. Manning (2008), Regional groundwater flow in mountainous terrain: Threedimensional simulations of topographic and hydrogeologic controls, Water Resources Research, 44(10), n/a-n/a. Gleeson, T., L. Smith, N. Moosdorf, J. Hartmann, H. H. Dürr, A. H. Manning, L. P. H. van Beek, and A. M. Jellinek (2011), Mapping permeability over the surface of the Earth, Geophysical Research Letters, 38(2), n/a-n/a. Jiang, X.-W., X.-S. Wang, and L. Wan (2010), Semi-empirical equations for the systematic decrease in permeability with depth in porous and fractured media, Hydrogeology Journal, 18(4), 839850. Jiang, X.-W., L. Wan, X.-S. Wang, S. Ge, and J. Liu (2009), Effect of exponential decay in hydraulic conductivity with depth on regional groundwater flow, Geophysical Research Letters, 36(24). Jie, C., L. Yanchou, and D. Guoyu (1996), Stages of Quaternary tectonic movement in west Qilianshan Mountain and Jiuxi Basin, Quaternary Sciences, 3, 263-271. Kuang, X., and J. J. Jiao (2014), An integrated permeability-depth model for Earth's crust, Geophysical Research Letters, 41(21), 7539-7545. Li, D., Z. Lun, J. Guo, and Y. Zhu (1991), Regional geology of Qinghai Province, edited, Geology Publishing House, Beijing. Luo, Y., J. Arnold, P. Allen, and X. Chen (2012), Baseflow simulation using SWAT model in an inland river basin in Tianshan Mountains, Northwest China, Hydrol. Earth Syst. Sci., 16(4), 1259-1267. Manning, A. H., and D. K. Solomon (2003), Using noble gases to investigate mountain-front recharge, Journal of Hydrology, 275(3-4), 194-207. Manning, A. H., and D. K. Solomon (2005), An integrated environmental tracer approach to characterizing groundwater circulation in a mountain block, Water Resources Research, 41(12), n/a-n/a. Niswonger, R. G., S. Panday, and M. Ibaraki (2011), MODFLOW-NWT, a Newton formulation for MODFLOW-2005, US Geological Survey Techniques and Methods, 6, A37. Pollock, D. W. (2012), User guide for MODPATH version 6: a particle tracking model for MODFLOW, US Department of the Interior, US Geological Survey. Scanlon, B. R., K. E. Keese, A. L. Flint, L. E. Flint, C. B. Gaye, W. M. Edmunds, and I. Simmers (2006), Global synthesis of groundwater recharge in semiarid and arid regions, Hydrological Processes, 20(15), 3335-3370. Song, S., Y. Niu, L. Su, and X. Xia (2013), Tectonics of the North Qilian orogen, NW China, Gondwana Research, 23(4), 1378-1401. Viviroli, D., et al. (2011), Climate change and mountain water resources: overview and recommendations for research, management and policy, Hydrology and Earth System Sciences, 15(2), 471-504. Welch, L. A., and D. M. Allen (2012), Consistency of groundwater flow patterns in mountainous topography: Implications for valley bottom water replenishment and for defining groundwater flow boundaries, Water Resources Research, 48(5), n/a-n/a. Wilson, J. L., and H. Guan (2004), Mountain‐Block Hydrology and Mountain‐Front Recharge, Groundwater recharge in a desert environment: The Southwestern United States, 113-137. Zhou, F., Y. Xu, Y. Chen, C. Y. Xu, Y. Gao, and J. Du (2013), Hydrological response to urbanization at different spatio-temporal scales simulated by coupling of CLUE-S and the SWAT model in the Yangtze River Delta region, Journal of Hydrology, 485, 113-125. Zhou, Z., R. Zhao, and J. Mao (1989), Regional geology of Gansu ProvinceRep., Geology Publishing House, Beijing.

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Figure 1. Study area-Qilian Mountains

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(a)

(m)

(b) A’

R/P=0.2 Stream Flow path

A Generalized flow pattern Model grid

K (m/s) Layer 1 Layer 2 Layer 3 Layer 4 Layer 5 Layer 6 Layer 7

9.697e-6 3.472e-6 1.157e-6 9.259e-7 5.787e-8 1.754e-10 9.259e-13

Head (m) 5320.0 4450.0 3580.0 2710.0 1840.0 970.0

(c)

Figure 2. (a) Plan view, (b) stereo view and (c) cross section view of modeled hydraulic head distribution and flow paths.

© 2017 American Geophysical Union. All rights reserved.

MBR

Mean Baseflow

GW Table

5500

80

5000

70

4500

15

4000 3500

10

3000 5

2000 0

0.05

0.1

0.12

0.15

0.18

0.2

0.25

0.3

(b)

0.35

0.4

60 50

0.3

40 0.2

30 20

0.1

10

Baseflow%

0 0.05

0.1

0.12 0.15 0.18

80

18

4400

Proportion of Total Outflow (Baseflow% and MBR%)

70

(d)

0

16

4200

14 12

4000

10

3800

8

3600

6

3400

(c) 0 0

0.001

0.002

Baseflow GW Table 0.003

0.004

MBR

0.005

Decay exponent, A (m-1)

0.006

3200

3000 0.007

Water Table Elevation (m)

Total Flow (108×m3/year)

4600

Baseflow Range Mean Baseflow

0.2

0.25

0.3

R/P

R/P

2

ET%

0

20

4

MBR%

0.3 0.25

60 0.2

50 40

0.15

30

0.1

20 10

Baseflow%

MBR%

ET%

0.05 0

0.001

0.002

0.003

0.004

0.005

Decay exponent, A (m-1)

0.006

Figure 3. (a) Total baseflow, total MBR and average water table elevation as a function of R/P with calibrated K; (b) outflow percentages as a function of R/P with calibrated K; (c) total baseflow, total MBR and average water table elevation as a function of decay exponent A (R/P = 0.2); and (d) outflow percentages as a function of decay exponent A (R/P = 0.2). The shaded area on (a) and (c) are the baseflow range computed by the digital filter-based algorithm.

© 2017 American Geophysical Union. All rights reserved.

Proportion of Total Outflow (ET%)

(a) 0

2500

0.5

Proportion of Total Outflow (ET%)

Baseflow

Proportion of Total Outflow (Baseflow% and MBR%)

20

Baseflow Range

Water Table Elevation (m)

Total Flow (108×m3/year)

25