Quantifying the Effect of Driving Factors on Spring Discharge in an Industrialized Karst Watershed
Publication: Journal of Hydrologic Engineering
Volume 28, Issue 7
Abstract
Identifying the footprint of controlling factors on the spring discharge is challenging under the impacts of human activities and climate change. The challenge can be addressed by (1) exploring the temporal scaling features of spring discharge; and (2) disentangling and quantifying the effects of latent factors on the spring discharge. Taking an example of the Niangziguan Spring in China, the study explores the spring discharge over a 49-year (1959–2007) time window. The results show that the spring discharge is controlled by the positive feedback mechanism before the beginning of groundwater exploitation, while human activities cause variability in spring discharge. The effects of latent factors on the spring discharge characterize the obvious temporal scale-dependent. The quantitative analysis indicates that the local recharge of groundwater possesses the largest contribution (43%) to spring discharge on the temporal scale of 4–16 months. The western North Pacific monsoon (WNPM) and Indian Summer monsoon (ISM) cause the smallest effect (3%) on spring discharge. Human activities have become one of the most important factors (27%) in controlling spring discharge. The results are useful to predict or simulate the groundwater dynamic processes in the study reported here considering that spring discharge is a natural outlet of groundwater.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
The data of the spring discharge and precipitation can be obtained from the corresponding author or Jia Zhong Qian ([email protected]). Information on ISM and the WNPM are available from the Monsoon Monitoring: http://apdrc.soest.hawaii.edu/projects/monsoon/.
Acknowledgments
The work was supported by the National Natural Science Foundation of China (Grant Nos. 41831289 and 11972148), the Fundamental Research Funds for the Central Universities (Grant No. B210202092), China Scholarship Council (Grant No. 201906710070).
References
Aghabozorgi, S., A. S. Shirkhorshidi, and T. Y. Wah. 2015. “Time-series clustering—A decade review.” Inf. Syst. 53 (6): 16–38. https://doi.org/10.1016/j.is.2015.04.007.
Berendrecht, W. L., and F. C. van Geer. 2016. “A dynamic factor modeling framework for analyzing multiple groundwater head series simultaneously.” J. Hydrol. 536 (May): 50–60. https://doi.org/10.1016/j.jhydrol.2016.02.028.
Bloomfield, J. P., B. P. Marchant, and A. A. McKenzie. 2019. “Changes in groundwater drought associated with anthropogenic warming.” Hydrol. Earth Syst. Sci. 23 (3): 1393–1408. https://doi.org/10.5194/hess-23-1393-2019.
Chaovalit, P., A. Gangopadhyay, G. Karabatis, and Z. Chen. 2011. “Discrete wavelet transform-based time series analysis and mining.” ACM Comput. Surv. 43 (2): 1–37. https://doi.org/10.1145/1883612.1883613.
Charlier, J.-B., B. Ladouche, and J.-C. Maréchal. 2015. “Identifying the impact of climate and anthropic pressures on karst aquifers using wavelet analysis.” J. Hydrol. 523 (5): 610–623. https://doi.org/10.1016/j.jhydrol.2015.02.003.
Daubechies, I. 1988. “Orthonormal bases of compactly supported wavelets.” Commun. Pure Appl. Math. 41 (7): 909–996. https://doi.org/10.1002/cpa.3160410705.
de Artigas, M. Z., A. G. Elias, and P. F. de Campra. 2006. “Discrete wavelet analysis to assess long-term trends in geomagnetic activity.” Phys. Chem. Earth 31 (1): 77–80. https://doi.org/10.1016/j.pce.2005.03.009.
Giacofci, M., S. Lambert-Lacroix, G. Marot, and F. Picard. 2013. “Wavelet-based clustering for mixed-effects functional models in high dimension.” Biometrics 69 (1): 31–40. https://doi.org/10.1111/j.1541-0420.2012.01828.x.
Gu, X., H. Sun, G. R. Tick, Y. Lu, Y. Zhang, Y. Zhang, and K. Schilling. 2020. “Identification and scaling behavior assessment of the dominant hydrological factors of nitrate concentrations in streamflow.” J. Hydrol. Eng. 25 (6): 06020002. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001934.
Guo, Q., Y. Wang, T. Ma, and L. Li. 2005. “Variation of karst spring discharge in the recent five decades as an indicator of global climate change: A case study at Shanxi, northern China.” Sci. China Ser. D 48 (11): 2001–2010. https://doi.org/10.1360/04yd0113.
Hao, Y., G. Liu, H. Li, Z. Li, J. Zhao, and T.-C. J. Yeh. 2012. “Investigation of karstic hydrological processes of Niangziguan Springs (North China) using wavelet analysis.” Hydrol. Processes 26 (20): 3062–3069. https://doi.org/10.1002/hyp.8265.
Hao, Y., Y. Wang, Y. Zhu, Y. Lin, and T.-C. J. Yeh. 2009a. “Response of karst springs to climate change and anthropogenic activities: The Niangziguan Springs, China.” Prog. Phys. Geogr. 33 (5): 634–649. https://doi.org/10.1177/0309133309346651.
Hao, Y., J. Zhang, J. Wang, R. Li, P. Hao, and H. Zhan. 2016. “How does the anthropogenic activity affect the spring discharge?” J. Hydrol. 540 (Jun): 1053–1065. https://doi.org/10.1016/j.jhydrol.2016.07.024.
Hao, Y., Y. Zhu, Y. Zhao, W. Wang, X. Du, and T.-C. J. Yeh. 2009b. “The role of climate and human influences in the dry-up of the Jinci Springs, China.” J. Am. Water Resour. Assoc. 45 (5): 1228–1237. https://doi.org/10.1111/j.1752-1688.2009.00356.x.
Holmes, E., E. Ward, and M. Scheuerell. 2014. Analysis of multivariate time-series using the MARSS package. Seattle: Northwest Fisheries Science Center.
Hu, C., Y. Hao, T.-C. J. Yeh, B. Pang, and Z. Wu. 2008. “Simulation of spring flows from a karst aquifer with an artificial neural network.” Hydrol. Processes 22 (5): 596–604. https://doi.org/10.1002/hyp.6625.
Huo, X., Z. Liu, Q. Duan, P. Hao, Y. Zhang, Y. Hao, and H. Zhan. 2016. “Linkages between large-scale climate patterns and karst spring discharge in Northern China.” J. Hydrometeorol. 17 (2): 713–724. https://doi.org/10.1175/JHM-D-15-0085.1.
Ihlen, E. A. F. 2012. “Introduction to multifractal detrended fluctuation analysis in MATLAB.” Front. Physiol. 3 (Mar): 141. https://doi.org/10.3389/fphys.2012.00141.
Kaplan, D., R. Muñoz-Carpena, and A. Ritter. 2010. “Untangling complex shallow groundwater dynamics in the floodplain wetlands of a southeastern U.S. coastal river.” Water Resour. Res. 46 (8): W08528. https://doi.org/10.1029/2009WR009038.
Kovács, J., L. Márkus, J. Szalai, and I. S. Kovács. 2015. “Detection and evaluation of changes induced by the diversion of River Danube in the territorial appearance of latent effects governing shallow-groundwater fluctuations.” J. Hydrol. 520 (Jun): 314–325. https://doi.org/10.1016/j.jhydrol.2014.11.052.
Kuss, A. J. M., and J. J. Gurdak. 2014. “Groundwater level response in U.S. principal aquifers to ENSO, NAO, PDO, and AMO.” J. Hydrol. 519 (Sep): 1939–1952. https://doi.org/10.1016/j.jhydrol.2014.09.069.
Labat, D., J. Ronchail, and J. L. Guyot. 2005. “Recent advances in wavelet analyses: Part 2—Amazon, Parana, Orinoco and Congo discharges time scale variability.” J. Hydrol. 314 (1–4): 289–311. https://doi.org/10.1016/j.jhydrol.2005.04.004.
Larocque, M., A. Mangin, M. Razack, and O. Banton. 1998. “Contribution of correlation and spectral analyses to the regional study of a large karst aquifer (Charente, France).” J. Hydrol. 205 (3–4): 217–231. https://doi.org/10.1016/S0022-1694(97)00155-8.
Liu, Y., B. Wang, H. Zhan, Y. Fan, Y. Zha, and Y. Hao. 2017. “Simulation of nonstationary spring discharge using time series models.” Water Resour. Manage. 31 (15): 4875–4890. https://doi.org/10.1007/s11269-017-1783-6.
Mallat, S. 1999. A wavelet tour of signal processing. New York: Elsevier.
Márkus, L., O. Berke, J. Kovács, and W. Urfer. 1999. “Spatial prediction of the intensity of latent effects governing hydrogeological phenomena.” Environmetrics 10 (5): 633–654. https://doi.org/10.1002/(SICI)1099-095X(199909/10)10:5%3C633::AID-ENV378%3E3.0.CO;2-8.
Molino-Minero-Re, E., F. García-Nocetti, and H. Benítez-Pérez. 2015. “Application of a time-scale local Hurst exponent analysis to time series.” Digital Signal Process. 37 (11): 92–99. https://doi.org/10.1016/j.dsp.2014.11.007.
Monsoon Monitoring web service. 2016. “Day-to-day monsoon circulation and indices.” Accessed March 18, 2016. http://apdrc.soest.hawaii.edu/projects/monsoon/.
Moosavi, V., M. Vafakhah, B. Shirmohammadi, and N. Behnia. 2013. “A wavelet-ANFIS hybrid model for groundwater level forecasting for different prediction periods.” Water Resour. Manage. 27 (5): 1301–1321. https://doi.org/10.1007/s11269-012-0239-2.
Muñoz-Carpena, R., A. Ritter, and Y. C. Li. 2005. “Dynamic factor analysis of groundwater quality trends in an agricultural area adjacent to Everglades National Park.” J. Contam. Hydrol. 80 (1–2): 49–70. https://doi.org/10.1016/j.jconhyd.2005.07.003.
Nalley, D., J. Adamowski, and B. Khalil. 2012. “Using discrete wavelet transforms to analyze trends in streamflow and precipitation in Quebec and Ontario (1954–2008).” J. Hydrol. 475 (Jan): 204–228. https://doi.org/10.1016/j.jhydrol.2012.09.049.
Negi, G. C. S., and V. Joshi. 2004. “Rainfall and spring discharge patterns in two small drainage catchments in the Western Himalayan Mountains, India.” Environmentalist 24 (1): 19–28. https://doi.org/10.1023/B:ENVR.0000046343.45118.78.
Nourani, V., M. T. Alami, and F. D. Vousoughi. 2016. “Hybrid of SOM-clustering method and wavelet-ANFIS approach to model and infill missing groundwater level data.” J. Hydrol. Eng. 21 (9): 05016018. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001398.
Nourani, V., and G. Andalib. 2015. “Daily and monthly suspended sediment load predictions using wavelet based artificial intelligence approaches.” J. Mountain Sci. 12 (1): 85–100. https://doi.org/10.1007/s11629-014-3121-2.
Nourani, V., G. Andalib, and D. Dąbrowska. 2017. “Conjunction of wavelet transform and SOM-mutual information data pre-processing approach for AI-based multi-station nitrate modeling of watersheds.” J. Hydrol. 548 (3): 170–183. https://doi.org/10.1016/j.jhydrol.2017.03.002.
Oh, Y.-Y., S.-T. Yun, S. Yu, and S.-Y. Hamm. 2017. “The combined use of dynamic factor analysis and wavelet analysis to evaluate latent factors controlling complex groundwater level fluctuations in a riverside alluvial aquifer.” J. Hydrol. 555 (10): 938–955. https://doi.org/10.1016/j.jhydrol.2017.10.070.
Peng, C. K., S. V. Buldyrev, A. L. Goldberger, S. Havlin, F. Sciortino, M. Simons, and H. E. Stanley. 1992. “Long-range correlations in nucleotide sequences.” Nature 356 (6365): 168–170. https://doi.org/10.1038/356168a0.
Peng, C. K., S. Havlin, H. E. Stanley, and A. L. Goldberger. 1995. “Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series.” Chaos 5 (1): 82–87. https://doi.org/10.1063/1.166141.
Post, V. E. A., and J. R. von Asmuth. 2013. “Hydraulic head measurements—New technologies, classic pitfalls.” Hydrogeol. J. 21 (4): 737–750. https://doi.org/10.1007/s10040-013-0969-0.
Ritter, A., and R. Muñoz-Carpena. 2006. “Dynamic factor modeling of ground and surface water levels in an agricultural area adjacent to Everglades National Park.” J. Hydrol. 317 (3–4): 340–354. https://doi.org/10.1016/j.jhydrol.2005.05.025.
Sang, Y.-F. 2012. “A practical guide to discrete wavelet decomposition of hydrologic time series.” Water Resour. Manage. 26 (11): 3345–3365. https://doi.org/10.1007/s11269-012-0075-4.
Schuler, P., L. Duran, P. Johnston, and L. Gill. 2020. “Quantifying and numerically representing recharge and flow components in a Karstified carbonate aquifer.” Water Resour. Res. 56 (11): e2020WR027717. https://doi.org/10.1029/2020WR027717.
Sivarajan, N. A., A. K. Mishra, M. Rafiq, V. Nagraju, and S. Chandra. 2019. “Examining climate change impact on the variability of ground water level: A case study of Ahmednagar district, India.” J. Earth Syst. Sci. 128 (5): 122. https://doi.org/10.1007/s12040-019-1172-z.
Sun, H., X. Gu, J. Zhu, Z. Yu, and Y. Zhang. 2019. “Fractal nature of groundwater level fluctuations affected by riparian zone vegetation water use and river stage variations.” Sci. Rep. 9 (1): 15383. https://doi.org/10.1038/s41598-019-51657-0.
Suryanarayana, C., C. Sudheer, V. Mahammood, and B. K. Panigrahi. 2014. “An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India.” Neurocomputing 145 (5): 324–335. https://doi.org/10.1016/j.neucom.2014.05.026.
Taylor, R. G., et al. 2013. “Ground water and climate change.” Nat. Clim. Change 3 (4): 322–329. https://doi.org/10.1038/nclimate1744.
Trček, B., M. Veselič, and J. Pezdič. 2006. “The vulnerability of karst springs—A case study of the Hubelj spring (SW Slovenia).” Environ. Geol. 49 (6): 865–874. https://doi.org/10.1007/s00254-006-0182-8.
Trenberth, K. E. 2011. “Changes in precipitation with climate change.” Clim. Res. 47 (1): 123–138. https://doi.org/10.3354/cr00953.
Yuan, L., H. Sun, Y. Zhang, Y. Zhang, X. Gu, and S. Dawley. 2019. “Temporal scaling analytical method to identify multi-fractionality in groundwater head fluctuations.” Ground Water 57 (3): 485–491. https://doi.org/10.1111/gwat.12831.
Zhu, J., M. H. Young, and J. Osterberg. 2012. “Impacts of riparian zone plant water use on temporal scaling of groundwater systems.” Hydrol. Processes 26 (9): 1352–1360. https://doi.org/10.1002/hyp.8241.
Zuur, A. F., R. J. Fryer, I. T. Jolliffe, R. Dekker, and J. J. Beukema. 2003. “Estimating common trends in multivariate time series using dynamic factor analysis.” Environmetrics 14 (7): 665–685. https://doi.org/10.1002/env.611.
Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 28 • Issue 7 • July 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Aug 7, 2022
Accepted: Jan 18, 2023
Published online: Apr 29, 2023
Published in print: Jul 1, 2023
Discussion open until: Sep 29, 2023
ASCE Technical Topics:
- Analysis (by type)
- Business management
- Climates
- Dynamic analysis
- Engineering fundamentals
- Environmental engineering
- Geohazards
- Geotechnical engineering
- Groundwater
- Human and behavioral factors
- Hydrologic engineering
- Industries
- Karst
- Meteorology
- Monsoons
- Organizations
- Practice and Profession
- Precipitation
- River engineering
- River systems
- Storms
- Water (by type)
- Water and water resources
- Water discharge
- Water management
- Watersheds
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.