Climate change is projected to increase the intensity and
frequency of extreme climatic events such as tropical cyclones. However, few
studies have examined the responses of hydrochemical processes to climate
extremes. To fill this knowledge gap, we compared the relationship between
stream discharge and ion input–output budget during typhoon and non-typhoon
periods in four subtropical mountain watersheds with different levels of
agricultural land cover in northern Taiwan. The results indicated that the
high predictability of ion input–output budgets using stream discharge
during the non-typhoon period largely disappeared during the typhoon periods. For ions
such as Na
One of the major concerns of global climate change is increases in extreme climatic events such as flooding, droughts, and tropical cyclones (Phillips, 2017). Mounting evidence suggests that such events have strong effects on ecosystem function such as biodiversity, productivity, phenology, nutrient cycling, and community resistance to invasion (Holmgren et al., 2006; Fay et al., 2008; Jentsch and Beierkuhnlein, 2008; Smith, 2011; Chang et al., 2017a; Sinha et al., 2017). Predicting ecological effects of climate extremes is challenging because their effects on ecosystems could be dramatically different from “typical” or “normal” climatic variability (Smith, 2011).
Land use change has been considered a potential environmental threat at both local and global scales (Foley et al., 2005; Tang et al., 2005). A large number of studies have reported that replacing natural forests with agricultural lands causes large increases in surface runoff, sediment yield and nutrient export (Kosmas et al., 1997; Hill et al., 1998; Gessesse et al., 2015). Locally, in a study of nutrient cycling in upstream watersheds of northern Taiwan, the replacement of 22 % of the natural forests by tea plantations reduced the nitrogen retention ratio by 50 % (Lin et al., 2015). The consequences of land use change on nutrient retention is likely most dramatic during extreme events such as tropical cyclones when precipitation exceeds soil infiltration capacity. A study on paired watersheds in Taiwan indicated that sediment yield was 1 order of magnitude lower in plantations with gentler slopes than natural forests with steeper slopes during base flow (Tsai et al., 2009). However, during the peak flow of a typhoon event, the sediment yield was 1 order of magnitude greater in the plantations than the natural forests (Tsai et al., 2009).
Studies of nutrient input and output in both temperate and subtropical regions reported that hydrological control of the net nutrient input–output budget could override the effect of plant growth, leading to greater nutrient export in the growing season when biological demand is high (Likens and Bormann, 1995; Chang et al., 2017a). Although rarely examined, it can be expected that differences in nutrient export between disturbed and undisturbed watersheds are most dramatic during extreme storm events, relative to less extreme, typical periods.
With the projected increases in climate extremes in many parts of the world (Elsner et al., 2010; Donat et al., 2016; Borodina et al., 2017; Pfahl et al., 2017), the relationship between precipitation or stream discharge and nutrient export could shift to a new phase, which cannot be extrapolated from relationships that are mostly driven by “typical” storms. In a previous study, we illustrated differences in monthly nutrient input and output among four mountain watersheds differing in levels of tea plantation cover in northern Taiwan (Lin et al., 2015). Here, we report the differences in the ion input–output budget between “regular” flow periods and typhoon periods in the four watersheds. The objectives of this study are to (1) test if typhoon storms will cause distinct alternation in nutrient input–output budget due to the nonlinear nature of many ecological processes in response to disturbance (Burkett et al., 2005; Jentsch, 2007) and (2) to examine differences in the relationship between stream discharge and input–output budget among ions and among watersheds with different levels of agricultural land cover.
This study was conducted at the 303 km
Location and land uses of the studied watersheds at the Feitsui
Reservoir Watershed
We sampled stream water at four subwatersheds (A1, A2, F1, and F2) and precipitation water at two of the four subwatersheds (A1 and F2) within FRW on a weekly basis between September 2012 and August 2015 (Fig. 1a). Natural forest is the major land cover type of all watersheds (> 68 %); however, agricultural lands are also important at A1 (22 %) and A2 (17 %). A1, A2, and F2 are small watersheds (< 3 ha) drained by first-order streams, while the F1 watershed (86 ha) is drained by a third-order stream that drains through A1 and A2 (Fig. 1).
Weekly samples were collected with a 20 cm diameter polyethylene (PE)
bucket. Weekly stream water samples were collected by immersing a PE bucket
into the stream. For both precipitation and stream water, a 600 mL subsample
was taken using a PE bottle and transported to the laboratory with
conductivity and pH being measured the same day of collection. After the
measurement of pH and conductivity, samples were filtered (0.45
Precipitation in mountainous area is quite dynamic due to the interaction between orography and circulation. Following Huang et al. (2011), we used 10 rainfall stations to simulate the discharges of the four sites via the Hydrologiska Byråns Vattenbalansavdelning (HBV) model. The areal rainfall from a Thiessen polygon was applied, and thus the rainfall spatial heterogeneity has been considered partially. Precipitation of each of the four watersheds was then obtained from the spatial distribution of precipitation. Stream discharge of the four ungauged watersheds was also simulated by the HBV model processed through TUWmodel (ver. 0.1-8) (Parajka et al., 2013). Five daily rain gauges, maintained by Water Resource Agency (WRA), and five metrological stations, maintained by the Central Weather Bureau (CWB) of Taiwan with hourly observed rainfall, temperature, wind speed, and solar radiation, were used to estimate daily rainfall and potential evapotranspiration. The daily evapotranspiration is also observed by Taipei Feitsui Reservoir Administration (TFRA, Taiwan) at the Feitsui meteorological station. The observed rainfall, temperature and evapotranspiration were applied into 20 sub-catchments with the Thiessen polygon method. Daily discharge was monitored in three main tributaries of Baishi Creek by TFRA. In the calibration against the observed values, parameters were generated by the package DEoptim (ver. 2.2-4) (Mullen et al., 2011). Three objective functions – Nash–Sutcliffe efficiency (NSE), its power of 2, and log scale – were used to adjust the model to suit normal, extreme, and low flow conditions. The total runoff derived from the HBV model was further separated into three components: surface runoff, subsurface runoff and groundwater. The validation gauge is located in the inflow of dam of reservoir. The modeled daily discharge was aggregated into weekly discharge. Although the HBV model has been successfully applied in northern Taiwan (Chang et al., 2017a), due to the lack of in situ measurements of discharge, the estimates are subject to some uncertainty. The paired weekly ion concentrations and water volume of precipitation and streamflow were used for the ion input–output budget calculations (i.e., output via stream discharge–input through precipitation).
Mean weekly precipitation, discharge and runoff ratio
Because we did not sample precipitation and stream water on a storm-by-storm basis, we separated the weekly samples into typhoon samples and non-typhoon samples to examine the effects of typhoon storms on hydrochemistry. Following Chang et al. (2013), weekly samples collected between the first and last typhoon warnings issued by the CWB of Taiwan are considered typhoon samples, and such a week was referred as a typhoon-affected week. Although there is a time lag between precipitation and streamflow, this lag was typically only a few hours in mountain watersheds of Taiwan (Huang et al., 2012), so this short lag has only limited effects on the division of typhoon and non-typhoon samples. This definition may overestimate the total quantity of precipitation and stream discharge associated with typhoon storms because typhoons rarely lasted for a week; thus, part of the weekly samples classified as typhoon samples included water before or after the typhoon storm periods. In contrast, this definition diluted the extreme nature of typhoon storms, as the weekly samples included some water from small storms or base flow. Although a storm-based sampling would better capture the effects of typhoon storms on hydrochemistry, it is dangerous to collect samples during typhoons, and it would also miss the base flow hydrochemistry. To compare discharge–ion budget relationship between typhoon periods and periods with precipitation comparable to typhoon weeks, we identified 7 weeks that had precipitation greater than precipitation of the minimal typhoon storms (160 mm) and categorized them as large non-typhoon precipitation weeks.
Runoff ratio of the four watersheds during typhoon and non-typhoon periods.
During the sampling period, weekly precipitation ranged from 1 to 507 mm,
while weekly streamflow ranged from 10 to 446 mm (Fig. 2a and Table S1 in
the Supplement). The weekly runoff ratio was negatively related to
precipitation quantity and was highly variable during the non-typhoon period
but varied much less during the typhoon period (Fig. 2b). The ratio of total
runoff to precipitation was not different between non-typhoon period
(0.69–0.81) and the typhoon period (0.64–0.78), but the ratio of surface
runoff to precipitation was smaller in the non-typhoon period (0.06–0.15)
than the typhoon period (0.27–0.33) (Table 1) because surface
runoff was proportionally greater during the typhoon period than the non-typhoon period
(Fig. 3). There was a total of 11 typhoon-affected weeks based on our
definition. The 11 typhoon-affected weeks contributed 2862 mm or 26 % of
total precipitation (10 845 mm) and 1991 mm or 22 % of total stream
discharge (9067 mm) for the three sampling years (Fig. 2 and Table S1). The
quantity of precipitation and discharge of typhoon-affected weeks ranged from
168 and 122 mm for typhoon Goni (21–24 August 2015) to 507 and 446 mm for
typhoon Soudelor (7–9 August 2015), respectively (Table S1). Typhoons
contributed 87–98 % of the weekly precipitation and 80–93 % of the
weekly discharge, respectively, of the 11 typhoon-affected weeks (Table S1).
The mean weekly precipitation (
The weekly maximal hourly, 6, 12, and 24 h precipitation of the
typhoon-affected weeks were generally considerably greater than those of the
non-typhoon weeks and the differences were greater with greater time
intervals. The greatest value of maximal hourly, 6, 12, and 24 h
precipitation during the typhoon period reached 54, 43, 33, and
19 mm h
One striking pattern during the typhoon period (i.e., the 11 typhoon-affected
weeks) is the lack of predictability of stream discharge on input–output
budget for many ions in most watersheds. This lack of predictability is in
contrast to the high level of predictability during the non-typhoon period
(Figs. 5, 6, and Table S2). During non-typhoon periods, stream discharge is a
good predictor of net export of all ions except NH
In addition to the lack of predictability of stream discharge for
input–output budgets during typhoon periods, there were distinct differences
in the discharge–budget relationship between typhoon and non-typhoon periods
for many ions. There was a positive relationship between stream discharge and
the Na
The average weekly precipitation and estimated streamflow composition during typhoon and non-typhoon period among the four watersheds. The grey bars indicate 1 standard deviation.
Weekly maximum 1, 6, 12, and 24 h precipitation of the rain gauge station COA530 (referring to the location in Fig. 1) used in this study. The black dots are non-typhoon storms with precipitation greater than that of the minimal typhoon storms (160 mm).
Relationship between stream discharge and nutrient budget
(stream output–precipitation input) of cations (Na
In addition to the opposite directions of the relationship between discharge
and ion budget between typhoon and non-typhoon periods, the 11
typhoon-affected weeks also affected the overall relationship between
discharge and ion budget. The positive relationship between discharge and
Cl
Relationship between stream discharge and nutrient budget
(stream output–precipitation input) of anions (Cl
Nitrate exhibited a unique pattern in the relationship between stream
discharge and input–output budget. Stream discharge was an excellent
predictor of net NO
The input–output budget of PO
Mean weekly budget for non-typhoon weeks, large
non-typhoon precipitation weeks and typhoon weeks.
For K
The striking differences in the discharge–budget patterns between typhoon and non-typhoon periods should be related to changes in the relative proportion of sources of stream discharge. Stream discharge originates from three sources, surface runoff, subsurface runoff and groundwater. Among the three sources, groundwater was more important during low than high flow periods, whereas the contribution from surface runoff should be more important during heavy storms than small storms. The contribution from subsurface flow probably dominated the discharge at our study site, especially in F1 and F2 because a study at a natural forest 12 km southeast from our study site indicated that even during a heavy typhoon storm, with precipitation near 700 mm in 2 days, there was no observable surface runoff (Lin et al., 2011). The contribution from subsurface runoff and groundwater to total discharge likely resulted in the very high runoff ratios for weeks with small amount of precipitation. For example, on 28 January 2014, the weekly precipitation and discharge were 1.5 and 13 mm, respectively, which led to the highest runoff ratio, 8.7, for the entire study period (Fig. 2). The effect of subsurface runoff and groundwater on disrupting the precipitation–runoff relationship is evident from the greater ratio of surface runoff to precipitation during the typhoon period than the non-typhoon period, while the ratio of total runoff to precipitation was not different between the two periods (Table 1 and Fig. 3). Our results indicate that, under certain circumstances, contributions from baseflow need to be removed in order to detect and meaningfully assess the precipitation–runoff relationship (Table 1). However, it is noted that without direct measurements of streamflow in two of the watersheds, it is difficult to confidently validate the estimation of streamflow separation in this case.
Changes in relatively contributions of different sources of water (or old
water relative to new water) on stream discharge play a key role in
regulating ion concentrations during a storm and between periods of different
flow rates (Elwood and Turner, 1989; Giusti and Neal, 1993; Bishop et al.,
2004). Among the three sources, groundwater is enriched with ions derived
from rock weathering such as K
The large differences in weekly precipitation, stream discharge and weekly maximal hourly, 6, 12, and 24 h precipitation between the typhoon and non-typhoon periods (Table S1) clearly illustrate the extreme effects of typhoon storms. The lack of predictability of stream discharge on ion input–output budgets for the typhoon period is attributable to the high variability of ion budgets associated with typhoon storms (Figs. 5, 6 and Table S2). High variability associated with typhoons is not only limited to ion budgets but also to water resources. In a study of long-term biogeochemistry in a natural hardwood forest in northeastern Taiwan, the 20-year average annual precipitation was 3840 mm, but was 3240 mm when precipitation associated with typhoon storms was excluded, with annual contributions from typhoon storms varying from 0 % (0/2770 mm) in 1995 to 42 % (1711/4033 mm) in 2008 (Chang et al., 2017b).
The lack of predictability of stream discharge on the budget of several ions is possibly due to damages to the forests and farms by the typhoons. Damages to trees may affect the level of foliar nutrient leaching and nutrient uptake by roots and thus the nutrient export (Lin et al., 2011). The poor correlation between maximum wind velocity and precipitation quantity reported by Lin et al. (2011) suggests that precipitation quantity is not a good predictor of the magnitude of typhoon influences on nutrient input–output budget and likely contributed to the low predictability of discharge on ion budget during typhoon period.
Many hydrological models are constructed primarily based on non-extreme
conditions or on a combination of both extreme and non-extreme conditions
(Wade et al., 2006; Shih et al., 2016; Lu et al., 2017). However, our results
showed that in many cases such models would not perform well during extreme
conditions such as during typhoons. There are at least three ways that
extreme events could lead to model failure. First, in many cases the pattern
seen during the more regular period does not exist during extreme conditions,
such as a loss of predictability of the budgets of many ions when using
stream discharge during the typhoon period (Figs. 5 and 6). Second, in some
cases such as the budget of Na
Climate change will increase the frequency and intensity of extreme climate events such as flooding, drought, and tropical cyclones (Emanuel, 2005; Elsner et al., 2010; Hirabayashi et al., 2013; Cook et al., 2015; Pfahl et al., 2017). Many studies report increases in extreme precipitation events and flooding from observations over the past half century, particularly in the tropics and subtropics (Hirabayashi et al., 2013; Fischer and Knutti, 2016). Furthermore, the upward trends in frequency and intensity of tropical cyclones due to warming climate are expected to lead to the development of more destructive cyclones (Emanuel, 2005; Elsner et al., 2010). A recent analysis indicated that there was a manifest westward shift of tropical cyclones in the northwest Pacific (Wu et al., 2015), such that risks of extreme precipitation and flooding events are expected to rise in this region, which includes Taiwan. Our results show that hydrological consequences of extreme events can not be directly extrapolated from non-extreme conditions. Because rare but extreme events can cause abrupt changes (Müller et al., 2014), separation of hydrochemical processes into more regular and extreme conditions is more likely to capture the whole spectrum of hydrochemical responses to a variety of climate conditions. In addition, regime shifts could invalidate future predictions calibrated on past trends (Müller et al., 2014). Thus, hydrological models must recognize and incorporate the unpredictability and even chaotic nature of extreme storms to make model predictions more reliable.
The differences in the input–output budgets of NO
The close relationship between stream discharge and NO
The increases in net retention of NH
However, the greater retention of NH
Our analysis of ion input–output budget illustrates that hydrochemistry
during typhoon storms is highly variable, and models built from regular
periods have low predictability of ion budgets during extreme storm periods.
Hydrochemical responses to typhoon storms are distinctly different from those
of regular storms and have the potential to dominate the long-term
hydrochemical patterns. Much greater increases in NO
Raw data are available in the Supplement.
The supplement related to this article is available online at:
Teng-Chiu Lin designed and performed the research. Chung-Te Chang, Jr-Chuan Huang, Yu-Ting Shih, and Teng-Chiu Lin conducted the field and laboratory work. Chung-Te Chang, Yu-Ting Shih, and Teng-Chiu Lin analyzed the data. Chung-Te Chang, Jr-Chuan Huang, Lixin Wang, and Teng-Chiu Lin contributed to the discussion and interpretation of the results. Chung-Te Chang and Teng-Chiu Lin wrote the first draft and all authors contributed substantial edits.
The authors declare that they have no conflict of interest.
This study was supported by grants from the Ministry of Science and Technology (MOST 101-2116-M-003-003, 102-2116-M-003-007 to Teng-Chiu Lin; 105-2410-H-002-218-MY3, 105-2811-H-002-024, 106-2811-H-002-027 to Chung-Te Chang), Taiwan. Lixin Wang acknowledges the support from the National Natural Science Foundation (EAR-1562055). The authors thank Craig E. Martin of the University of Kansas for thoroughly editing the manuscript. Edited by: Paul Stoy Reviewed by: three anonymous referees