# Temporal Effects of Groundwater on Physical and Biotic Components of a Karst Stream

^{1}

^{2}

^{3}

^{4}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Region

#### 2.2. Data Collection and Preparation

^{3}s

^{−1}) was calculated using the velocity-area method for each cross-section [36]. Air and water temperature were measured and averaged from the same three cross-sections with an YSI Pro Plus multimeter (YSI Inc., Yellow Springs, OH, USA). Although we did not sample all the reaches at the same time on each occasion, fieldwork in all reaches was completed within a 2 to 3-hour period to minimize variation due to time of sampling.

^{−2}). Chl. a concentration for each reach was averaged from the three cross-section replicates.

#### 2.3. Statistical Analysis

^{3}s

^{−1}which represents approximately the 25th and 75th percentiles of the distribution of discharge across all reaches. For the timing metrics, we identified specific months when the maximum (M_mean_max) and minimum (M_mean_min) monthly mean water temperature or discharge was reached. We also calculated all the aforementioned nine metrics for Chl. a, because algal biomass also varies temporally [42].

## 3. Results

#### 3.1. Effects of Groundwater on Stream Water Temperature and Discharge Regimes

^{3}s

^{−1}(Table 3). Discharge displayed a significant 12-month periodicity in the all reaches across the study duration with the peak in May and trough in January or February (Table 4, Figure 3). However, significance of such periodicity (indicated by black line circles) varied among years and among reaches (Figure 3). Besides the annual periodicity, discharge also displayed a significant 2–6 months periodicity in 2013 across all reaches (Figure 3). During the low flow period, mean discharge in HSZ was 0.03 m

^{3}s

^{−1}, which was lower than the sum of mean discharge of the two upstream reaches (SNY: 0.03 m

^{3}s

^{−1}, DLT: 0.02 m

^{3}s

^{−1}). In contrast, this pattern was not evident during the high flow period (Appendix A Figure A1).

#### 3.2. Effects of Groundwater on Associations between Chl. a Dynamics, Water Temperature, and Discharge

^{−2}increased as elevation decreased (Table 3). The temporal dynamics of Chl. a concentrations in HSZ was, however, largely different than in the reaches less affected by groundwater (Table 3). Specifically, Chl. a magnitude was lower in HSZ when compared with the smaller upstream reach, SNY (Table 3). The CV of Chl. a concentrations in HSZ was similar to that in DLT (Table 3). The 12-month periodicity in Chl. a concentrations was significant for all reaches except for HSZ (Table 4, Figure 4). Moreover, HSZ had the highest mean Chl. a concentrations among the four reaches during the high flow period (Appendix A Figure A1); however, differences were not significant in this test (Kruskal–Wallis chi-squared = 5.092, df = 3, p-value = 0.165). During the low flow period, the mean Chl. a concentration in HSZ (7.17 mg m

^{−2}) was slightly higher than that in the tributary reach of DLT (5.59 mg m

^{−2}), and was lower than the other two mainstream reaches. These differences were also not significant (Appendix A Figure A1).

## 4. Discussion

^{3}s

^{−1}) was a little smaller than the sum of flows in the two upstream reaches of SNY and DLT (0.225 m

^{3}s

^{−1}) in the whole study duration, and this difference was even more evident during the low flow period. It is likely that a proportion of downwelling flow had longer flow paths than the distance of the study reaches, longer subsurface residence times than we could detect in this study, or had lateral path(s) beyond the watershed boundary [57]. Therefore, although discharge magnitude increased longitudinally, karst topographic features likely induced a higher variability of flow dynamics in HSZ than in other reaches. Overall, the different regime metrics of discharge in HSZ compared with other reaches provides evidence that this reach is significantly influenced by groundwater discharge.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Appendix A

**Table A1.**Convergent cross-mapping detecting the best cross-map skill (ρ) and time lag indicating the causal effects of stream water temperature (WT) or discharge on dynamics of benthic chlorophyll a concentration (Chl. a) from July 2011 to June 2017 for each reach. -: No causality is detected. SNY = Sheng Nong Yaun; DLT = Da Long Tan; HSZ = Hong Shi Zi; JZG = Ji Zi Gou.

Stream Reach | WT Causing Chl. a | Discharge Causing Chl. a | ||
---|---|---|---|---|

ρ | Time Lag (Month) | ρ | Time Lag (Month) | |

SNY | 0.267 | 0 | 0.101 | 2 |

DLT | 0.309 | 2 | 0.103 | 2 |

HSZ | 0.135 | 0 | - | |

JZG | 0.230 | 0 | 0.089 | 2 |

**Figure A1.**Plots demonstrating among-reach differences in the mean (± standard deviation) for water temperature, discharge, and chlorophyll a concentration (Chl. a) during the high-flow period (a, May to July) and the low-flow period (b, December to February), respectively. The χ

^{2}value and the p-value are the results of Kruskal–Wallis tests among the four reaches. The lower-case letters above whiskers are the results of pairwise comparisons, with the same letters indicating no difference at p ≤ 0.05. SNY = Sheng Nong Yaun; DLT = Da Long Tan; HSZ = Hong Shi Zi; JZG = Ji Zi Gou.

**Figure A2.**Extended convergent cross-mapping detecting causality and time lags between chlorophyll a concentrations (Chl. a) and water temperature (left) or discharge (right) using time series from July 2011 to June 2017 for each study reach. SNY = Sheng Nong Yaun; DLT = Da Long Tan; HSZ = Hong Shi Zi; JZG = Ji Zi Gou.

## References

- Hayashi, M.; Rosenberry, D.O. Effects of ground water exchange on the hydrology and ecology of surface water. Groundwater
**2002**, 40, 309–316. [Google Scholar] [CrossRef] - Wondzell, S.M. Groundwater–surface-water interactions: Perspectives on the development of the science over the last 20 years. Freshw. Sci.
**2015**, 34, 368–376. [Google Scholar] [CrossRef] - Boulton, A.J.; Hancock, P.J. Rivers as groundwater-dependent ecosystems: A review of degrees of dependency, riverine processes and management implications. Aust. J. Bot.
**2006**, 54, 133–144. [Google Scholar] [CrossRef] - Pipan, T. Epikarst—A Promising Habitat: Copepod Fauna, Its Diversity and Ecology: A Case Study From Slovenia (Europe); ZRC Publishing: Postojna–Ljubljana, Slovenia, 2005. [Google Scholar]
- Boulton, A.J.; Findlay, S.; Marmonier, P.; Stanley, E.H.; Valett, H.M. The functional significance of the hyporheic zone in streams and rivers. Annu. Rev. Ecol. Syst.
**1998**, 29, 59–81. [Google Scholar] [CrossRef] - Burrows, R.M.; Rutlidge, H.; Valdez, D.G.; Venarsky, M.; Bond, N.R.; Andersen, M.S.; Fry, B.; Eberhard, S.M.; Kennard, M.J. Groundwater supports intermittent-stream food webs. Freshw. Sci.
**2018**, 37, 42–53. [Google Scholar] [CrossRef] [Green Version] - Robinson, C.T.; Doering, M. Spatial patterns in macroinvertebrate assemblages in surface-flowing waters of a glacially-influenced floodplain. Aquat. Sci.
**2013**, 75, 373–384. [Google Scholar] [CrossRef] - Malard, F.; Tockner, K.; Dole-Olivier, M.J.; Ward, J. A landscape perspective of surface-subsurface hydrological exchanges in river corridors. Freshw. Biol.
**2002**, 47, 621–640. [Google Scholar] [CrossRef] - Findlay, S. Importance of surface-subsurface exchange in stream ecosystems: The hyporheic zone. Limnol. Oceanogr.
**1995**, 40, 159–164. [Google Scholar] [CrossRef] - Robertson, A.; Wood, P. Ecology of the hyporheic zone: Origins, current knowledge and future directions. Fund. Appl. Limnol.
**2010**, 176, 279–289. [Google Scholar] [CrossRef] - Boulton, A.J.; Datry, T.; Kasahara, T.; Mutz, M.; Stanford, J.A. Ecology and management of the hyporheic zone: Stream-groundwater interactions of running waters and their floodplains. J. N. Am. Benthol. Soc.
**2010**, 29, 26–40. [Google Scholar] [CrossRef] - Mathers, K.L.; Hill, M.J.; Wood, P.J. Benthic and hyporheic macroinvertebrate distribution within the heads and tails of riffles during baseflow conditions. Hydrobiologia
**2017**, 794, 17–30. [Google Scholar] [CrossRef] [Green Version] - Dole-Olivier, M.-J. The hyporheic refuge hypothesis reconsidered: A review of hydrological aspects. Mar. Freshw. Res.
**2011**, 62, 1281–1302. [Google Scholar] [CrossRef] - Kawanishi, R.; Inoue, M.; Dohi, R.; Fujii, A.; Miyake, Y. The role of the hyporheic zone for a benthic fish in an intermittent river: A refuge, not a graveyard. Aquat. Sci.
**2013**, 75, 425–431. [Google Scholar] [CrossRef] - Mejia, F.H.; Baxter, C.V.; Berntsen, E.K.; Fremier, A.K. Linking groundwater-surface water exchange to food production and salmonid growth. Can. J. Fish. Aquat. Sci.
**2016**, 73, 1650–1660. [Google Scholar] [CrossRef] - Olsen, D.A.; Young, R.G. Significance of river-aquifer interactions for reach-scale thermal patterns and trout growth potential in the Motueka River, New Zealand. Hydrogeol. J.
**2009**, 17, 175–183. [Google Scholar] [CrossRef] - Torgersen, C.E.; Price, D.M.; Li, H.W.; McIntosh, B.A. Multiscale thermal refugia and stream habitat associations of chinook salmon in northeastern Oregon. Ecol. Appl.
**1999**, 9, 301–319. [Google Scholar] [CrossRef] - Pepin, D.M.; Hauer, F.R. Benthic responses to groundwater–surface water exchange in 2 alluvial rivers in northwestern Montana. J. N. Am. Benthol. Soc.
**2002**, 21, 370–383. [Google Scholar] [CrossRef] - Wright, K.K.; Baxter, C.V.; Li, J.L. Restricted hyporheic exchange in an alluvial river system: Implications for theory and management. J. N. Am. Benthol. Soc.
**2005**, 24, 447–460. [Google Scholar] [CrossRef] - Arrigoni, A.S.; Poole, G.C.; Mertes, L.A.K.; O’Daniel, S.J.; Woessner, W.W.; Thomas, S.A. Buffered, lagged, or cooled? Disentangling hyporheic influences on temperature cycles in stream channels. Water Resour. Res.
**2008**, 44, W09418. [Google Scholar] [CrossRef] - Kath, J.; Harrison, E.; Kefford, B.J.; Moore, L.; Wood, P.J.; Schäfer, R.B.; Dyer, F. Looking beneath the surface: Using hydrogeology and traits to explain flow variability effects on stream macroinvertebrates. Ecohydrology
**2016**, 9, 1480–1495. [Google Scholar] [CrossRef] - Stubbington, R. The hyporheic zone as an invertebrate refuge: A review of variability in space, time, taxa and behaviour. Mar. Freshw. Res.
**2012**, 63, 293–311. [Google Scholar] [CrossRef] - Kløve, B.; Ala-aho, P.; Bertrand, G.; Boukalova, Z.; Ertürk, A.; Goldscheider, N.; Ilmonen, J.; Karakaya, N.; Kupfersberger, H.; Kvœrner, J.; et al. Groundwater dependent ecosystems. Part I: Hydroecological status and trends. Environ. Sci. Policy
**2011**, 14, 770–781. [Google Scholar] [CrossRef] - Coulter, A.A.; Galarowicz, T.L. Fish assemblage and environmental differences upstream and downstream of a cave: A potential reset mechanism. Environ. Biol. Fishes
**2015**, 98, 1223–1231. [Google Scholar] [CrossRef] - Stubbington, R.; Wood, P.J.; Reid, I.; Gunn, J. Benthic and hyporheic invertebrate community responses to seasonal flow recession in a groundwater-dominated stream. Ecohydrology
**2011**, 4, 500–511. [Google Scholar] [CrossRef] - Arismendi, I.; Johnson, S.L.; Dunham, J.B.; Haggerty, R. Descriptors of natural thermal regimes in streams and their responsiveness to change in the Pacific Northwest of North America. Freshw. Biol.
**2013**, 58, 880–894. [Google Scholar] [CrossRef] - Olden, J.D.; Naiman, R.J. Incorporating thermal regimes into environmental flows assessments: Modifying dam operations to restore freshwater ecosystem integrity. Freshw. Biol.
**2010**, 55, 86–107. [Google Scholar] [CrossRef] - Caissie, D. The thermal regime of rivers: A review. Freshw. Biol.
**2006**, 51, 1389–1406. [Google Scholar] [CrossRef] - Woodward, G.; Bonada, N.; Brown, L.E.; Death, R.G.; Durance, I.; Gray, C.; Hladyz, S.; Ledger, M.E.; Milner, A.M.; Ormerod, S.J. The effects of climatic fluctuations and extreme events on running water ecosystems. Philos. Trans. R. Soc. B
**2016**, 371, 20150274. [Google Scholar] [CrossRef] [Green Version] - Lau, D.C.P.; Goedkoop, W.; Vrede, T. Cross-ecosystem differences in lipid composition and growth limitation of a benthic generalist consumer. Limnol. Oceanogr.
**2013**, 58, 1149–1164. [Google Scholar] [CrossRef] - Brett, M.T.; Bunn, S.E.; Chandra, S.; Galloway, A.W.E.; Guo, F.; Kainz, M.J.; Kankaala, P.; Lau, D.C.P.; Moulton, T.P.; Power, M.E.; et al. How important are terrestrial organic carbon inputs for secondary production in freshwater ecosystems? Freshw. Biol.
**2017**, 62, 833–853. [Google Scholar] [CrossRef] - DeNicola, D. Periphyton responses to temperature at different ecological levels. In Algal Ecology: Freshwater Benthic Ecosystems; Stevenson, R.J., Bothwell, M.L., Lowe, R.L., Thorp, J.H., Eds.; Academic Press: San Diego, CA, USA, 1996; pp. 149–181. ISBN 0-12-668450-2. [Google Scholar]
- Biggs, B.J.F.; Nikora, V.I.; Snelder, T.H. Linking scales of flow variability to lotic ecosystem structure and function. River Res. Appl.
**2005**, 21, 283–298. [Google Scholar] [CrossRef] - Zhu, Z.; Song, C. Scientific Survey of Shennongjia Nature Reserve; China Forestry Publishing House: Beijing, China, 1999; ISBN 7-5038-2335-8. (In Chinese) [Google Scholar]
- Sweeting, M.M. Karst in China: Its Geomorphology and Environment; Springer-Verlag: Berlin/Heidelberg, Germany, 1995; 265p, ISBN 3642795226. [Google Scholar]
- Gore, J.A.; Banning, J. Chapter 3—Discharge measurements and streamflow analysis. In Methods in Stream Ecology, 3rd ed.; Hauer, F.R., Lamberti, G.A., Eds.; Academic Press: Boston, MA, USA, 2017; Volume 1, pp. 49–70. [Google Scholar]
- Tang, T.; Jia, X.; Jiang, W.; Cai, Q. Multi-scale temporal dynamics of epilithic algal assemblages: Evidence from a Chinese subtropical mountain river network. Hydrobiologia
**2016**, 770, 289–299. [Google Scholar] [CrossRef] - Bureau, C.E.P. Standard Methods for Examination of Water and Wastewater, 4th ed.; Chinese Environmental Science Press: Beijing, China, 2004; ISBN 7-80163-400-4. (In Chinese) [Google Scholar]
- O’Driscoll, M.A.; DeWalle, D.R. Stream-air temperature relations to classify stream-ground water interactions in a karst setting, central Pennsylvania, USA. J. Hydrol.
**2006**, 329, 140–153. [Google Scholar] [CrossRef] - Harig, A.L.; Fausch, K.D. Minimum habitat requirements for establishing translocated cutthroat trout populations. Ecol. Appl.
**2002**, 12, 535–551. [Google Scholar] [CrossRef] - Rodman, A.R.; Scott, J.T. Comparing two periphyton collection methods commonly used for stream bioassessment and the development of numeric nutrient standards. Environ. Monit. Assess.
**2017**, 189, 360. [Google Scholar] [CrossRef] - Winder, M.; Cloern, J.E. The annual cycles of phytoplankton biomass. Philos. Trans. R. Soc. B
**2010**, 365, 3215–3226. [Google Scholar] [CrossRef] [PubMed] - Cazelles, B.; Chavez, M.; Berteaux, D.; Ménard, F.; Vik, J.O.; Jenouvrier, S.; Stenseth, N.C. Wavelet analysis of ecological time series. Oecologia
**2008**, 156, 287–304. [Google Scholar] [CrossRef] - Keitt, T.H.; Fischer, J. Detection of scale-specific community dynamics using wavelets. Ecology
**2006**, 87, 2895–2904. [Google Scholar] [CrossRef] - Torrence, C.; Compo, G.P. A practical guide to wavelet analysis. Bull. Am. Meteorol. Soc.
**1998**, 79, 61–78. [Google Scholar] [CrossRef] - Vasseur, D.A.; Yodzis, P. The color of environmental noise. Ecology
**2004**, 85, 1146–1152. [Google Scholar] [CrossRef] - Cloern, J.E.; Jassby, A.D. Patterns and scales of phytoplankton variability in estuarine–coastal ecosystems. Estuaries coasts
**2010**, 33, 230–241. [Google Scholar] [CrossRef] - Chang, C.-W.; Ushio, M.; Hsieh, C. Empirical dynamic modeling for beginners. Ecol. Res.
**2017**, 32, 785–796. [Google Scholar] [CrossRef] - Deyle, E.R.; Fogarty, M.; Hsieh, C.; Kaufman, L.; MacCall, A.D.; Munch, S.B.; Perretti, C.T.; Ye, H.; Sugihara, G. Predicting climate effects on Pacific sardine. Proc. Natl. Acad. Sci. USA
**2013**, 110, 6430–6435. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Sugihara, G.; May, R.; Ye, H.; Hsieh, C.; Deyle, E.; Fogarty, M.; Munch, S. Detecting causality in complex ecosystems. Science
**2012**, 338, 496–500. [Google Scholar] [CrossRef] [PubMed] - Ye, H.; Sugihara, G. Information leverage in interconnected ecosystems: Overcoming the curse of dimensionality. Science
**2016**, 353, 922–925. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Van Nes, E.H.; Scheffer, M.; Brovkin, V.; Lenton, T.M.; Ye, H.; Deyle, E.; Sugihara, G. Causal feedbacks in climate change. Nat. Clim. Chang.
**2015**, 5, 445–448. [Google Scholar] [CrossRef] - Ye, H.; Deyle, E.R.; Gilarranz, L.J.; Sugihara, G. Distinguishing time-delayed causal interactions using convergent cross mapping. Sci. Rep.
**2015**, 5, 14750. [Google Scholar] [CrossRef] [Green Version] - Burkholder, B.K.; Grant, G.E.; Haggerty, R.; Khangaonkar, T.; Wampler, P.J. Influence of hyporheic flow and geomorphology on temperature of a large, gravel-bed river, Clackamas River, Oregon, USA. Hydrol. Process.
**2008**, 22, 941–953. [Google Scholar] [CrossRef] - Hannah, D.M.; Malcolm, I.A.; Bradley, C. Seasonal hyporheic temperature dynamics over riffle bedforms. Hydrol. Process.
**2009**, 23, 2178–2194. [Google Scholar] [CrossRef] - Story, A.; Moore, R.; Macdonald, J. Stream temperatures in two shaded reaches below cutblocks and logging roads: Downstream cooling linked to subsurface hydrology. Can. J. For. Res.
**2003**, 33, 1383–1396. [Google Scholar] [CrossRef] - Ford, D.; Williams, P.D. Karst Hydrogeology and Geomorphology; John Wiley & Sons Ltd.: Chichester, UK, 2007; ISBN 978-0-470-84996-5. [Google Scholar]
- Wyatt, K.H.; Hauer, F.R.; Pessoney, G.F. Benthic algal response to hyporheic-surface water exchange in an alluvial river. Hydrobiologia
**2008**, 607, 151–161. [Google Scholar] [CrossRef] - Álvarez, M.; Pardo, I. Factors controlling epilithon biomass in a temporary, karstic stream: The interaction between substratum and grazing. J. N. Am. Benthol. Soc.
**2007**, 26, 207–220. [Google Scholar] [CrossRef] - Godwin, C.M.; Carrick, H.J. Spatio-temporal variation of periphyton biomass and accumulation in a temperate spring-fed stream. Aquat. Ecol.
**2008**, 42, 583–595. [Google Scholar] [CrossRef]

**Figure 1.**Locations of the 4 stream reaches in the headwaters of Nan River. The ellipse circles illustrate stream channel where discrete swallow holes and springs were observed. SNY = Sheng Nong Yaun; DLT = Da Long Tan; HSZ = Hong Shi Zi; JZG = Ji Zi Gou.

**Figure 2.**Continuous wavelet power spectra showing the strength of the periodicities of stream water temperature changes between July 2011 and June 2017 for each stream reach. The colors code for power intensity from dark blue (low intensity) to dark red (high intensity), and solid black line circles indicate significant (p < 0.05) coherent time–frequency regions. The shaded area is the time–frequency region affected by edge-effects and was not included in the analyses. The original time series are demonstrated above each wavelet plot. SNY = Sheng Nong Yaun; DLT = Da Long Tan; HSZ = Hong Shi Zi; JZG = Ji Zi Gou.

**Figure 3.**Continuous wavelet power spectra showing the periodicity of discharge from July 2011 to June 2017 for each stream reach. Further information on wavelet spectra are described in Figure 2. The original time series are demonstrated above each wavelet plot, with different value ranges to illustrate reach-specific variation. SNY = Sheng Nong Yaun; DLT = Da Long Tan; HSZ = Hong Shi Zi; JZG = Ji Zi Gou.

**Figure 4.**Continuous wavelet power spectra showing the periodicity of benthic algal chlorophyll a concentrations (Chl. a) from July 2011 to June 2017 for each study reach. Further information on wavelet spectra are described in Figure 2. The original time series are demonstrated above each wavelet plot. SNY = Sheng Nong Yaun; DLT = Da Long Tan; HSZ = Hong Shi Zi; JZG = Ji Zi Gou.

**Figure 5.**Convergent cross-mapping (CCM) between benthic chlorophyll a concentration (Chl. a) versus stream water temperature (WT) (left) and discharge (right) using time series from July 2011 to June 2017 for each study reach. The y-axis represents the Cross Map Skill (rho or Pearson’s correlation coefficient) and the x-axis represents the library size (different lengths of data subsampled randomly from the time series) used for modeling. The time lags having the highest cross map skill in CCM were used in the final CCM for significance testing. Shaded areas are the 95% confidence intervals (5th to 95th percentiles) of 100 surrogate time series from the null model (light red area = 95% confidence intervals of the null model for the red line; light blue area = 95% confidence intervals of the null model for the blue line; overlap=light purple). rho improving with increasing library size indicates causality, and rho beyond the 95% confidence intervals of the null model implies significant causality. For JZG, the red line is beyond the light shaded area, indicating that WT has significant effects on Chl. a. SNY = Sheng Nong Yaun; DLT = Da Long Tan; HSZ = Hong Shi Zi; JZG = Ji Zi Gou.

Sheng Nong Yuan | Da Long Tan | Hong Shi Zi | Ji Zi Gou | |
---|---|---|---|---|

Abbreviation | SNY | DLT | HSZ | JZG |

Latitude (°N) | 31.4722 | 31.4935 | 31.4997 | 31.5215 |

Longitude (°E) | 110.2983 | 110.2967 | 110.3222 | 110.3361 |

Elevation (m a.s.l.) | 2320 | 2220 | 1950 | 1820 |

Stream order | 1 | 1 | 2 | 2 |

Wetted width (m, mean ± SD) | 3.03 ± 2.34 | 2.81 ± 0.87 | 5.82 ± 3.63 | 6.05 ± 3.07 |

Water depth (cm, mean± SD) | 16.5 ± 7.4 | 11.5 ± 3.8 | 18.1 ± 7.8 | 20.5 ± 5.0 |

**Table 2.**The Y intercept, slope, and adjusted R

^{2}(R

^{2}

_{adj}) for the regression between stream water temperature and air temperature from July 2011 to June 2017 for each study reach. All regression models are significant with p < 0.001. SNY = Sheng Nong Yaun; DLT = Da Long Tan; HSZ = Hong Shi Zi; JZG = Ji Zi Gou.

Stream Reach | Regression Coefficient | R^{2}_{adj} | |
---|---|---|---|

Y Intercept | Slope | ||

SNY | 2.296 | 0.373 | 0.807 |

DLT | 1.834 | 0.453 | 0.830 |

HSZ | 4.162 | 0.352 | 0.861 |

JZG | 3.327 | 0.476 | 0.850 |

**Table 3.**Values of stream water temperature, discharge, and benthic chlorophyll a concentration (Chl. a) regime metrics for each study reach during the study. Mean_max = the maximum monthly mean value; Mean_min = the minimum monthly mean value; M_mean_max = the month with the maximum monthly mean value; M_mean_min = the month with the minimum monthly mean value. CV: coefficient of variation. SNY = Sheng Nong Yaun; DLT = Da Long Tan; HSZ = Hong Shi Zi; JZG = Ji Zi Gou.

Regime | Descriptors | Metrics | Stream Reach | |||
---|---|---|---|---|---|---|

SNY | DLT | HSZ | JZG | |||

Water temperature | Magnitude | Mean (°C) | 5.9 | 6.7 | 7.6 | 9.1 |

Mean_max (°C) | 9.6 | 12.0 | 11.4 | 14.7 | ||

Mean_min (°C) | 1.2 | 0.9 | 3.7 | 2.7 | ||

Variability | Range (°C) | 10.3 | 13.4 | 11.9 | 17.3 | |

CV (%) | 52.4 | 59.5 | 37.9 | 48.6 | ||

Frequency | Months < 3 °C | 19 | 19 | 2 | 8 | |

Months > 10 °C | 1 | 17 | 19 | 33 | ||

Timing | M_mean_max | Aug | Aug | Aug | Aug | |

M_mean_min | Jan | Jan | Jan | Jan | ||

Discharge | Magnitude | Mean (m^{3} s^{−1}) | 0.145 | 0.080 | 0.196 | 0.482 |

Mean_max (m^{3} s^{−1}) | 0.527 | 0.227 | 0.700 | 1.274 | ||

Mean_min (m^{3} s^{−1}) | 0.012 | 0.016 | 0.013 | 0.096 | ||

Variability | Range (m^{3} s^{−1}) | 1.717 | 0.553 | 3.251 | 4.329 | |

CV (%) | 163.3 | 108.0 | 234.4 | 155.8 | ||

Frequency | Months < 0.02 m^{3} s^{−1} | 15 | 14 | 19 | 0 | |

Months > 0.20 m^{3} s^{−1} | 12 | 5 | 14 | 38 | ||

Timing | M_mean_max | May | May | May | May | |

Mmean_min | Feb | Jan | Jan | Jan | ||

Chl. a | Magnitude | Mean (mg m^{−2}) | 5.60 | 3.56 | 5.28 | 7.58 |

Mean_max (mg m^{−2}) | 9.46 | 6.70 | 7.98 | 20.42 | ||

Mean_min (mg m^{−2}) | 2.68 | 1.44 | 2.43 | 2.24 | ||

Variability | Range (mg m^{−2}) | 16.83 | 13.81 | 18.38 | 33.82 | |

CV (%) | 66.0 | 83.8 | 82.7 | 112.4 | ||

Frequency | Months < 5 mg m^{−2} | 32 | 54 | 43 | 38 | |

Months > 10 mg m^{−2} | 8 | 3 | 11 | 19 | ||

Timing | M_mean_max | Dec | Feb | Dec | Feb | |

M_mean_min | July | July | May | Apr |

**Table 4.**Significant (p < 0.05) periodicities (Unit: month) in stream water temperature (WT), discharge, and benthic chlorophyll a concentration (Chl. a) across July 2011 to June 2017 identified by comparing time-averaged wavelet spectrum to a null red-noise power spectrum by wavelet analysis for each reach. Numbers in parentheses represent the periods having the highest average wavelet power. -: no significant period is detected. SNY = Sheng Nong Yaun; DLT = Da Long Tan; HSZ = Hong Shi Zi; JZG = Ji Zi Gou.

SNY | DLT | HSZ | JZG | |
---|---|---|---|---|

WT | 10–14 (12) | 10–14 (12) | 10–14 (12) | 10–14 (12) |

Discharge | 12 | 11–13 (12) | 12 | 12 |

Chl. a | 10–13 (12) | 11–13 (12) | - | 10–13 (12) |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Tang, T.; Guo, S.; Tan, L.; Li, T.; Burrows, R.M.; Cai, Q.
Temporal Effects of Groundwater on Physical and Biotic Components of a Karst Stream. *Water* **2019**, *11*, 1299.
https://doi.org/10.3390/w11061299

**AMA Style**

Tang T, Guo S, Tan L, Li T, Burrows RM, Cai Q.
Temporal Effects of Groundwater on Physical and Biotic Components of a Karst Stream. *Water*. 2019; 11(6):1299.
https://doi.org/10.3390/w11061299

**Chicago/Turabian Style**

Tang, Tao, Shuhan Guo, Lu Tan, Tao Li, Ryan M. Burrows, and Qinghua Cai.
2019. "Temporal Effects of Groundwater on Physical and Biotic Components of a Karst Stream" *Water* 11, no. 6: 1299.
https://doi.org/10.3390/w11061299