Intra-Event Isotopic Changes in Water Vapor and Precipitation in South China

Abstract

Synchronous observations of the isotopic composition of water vapor and precipitation for 24 rain events were performed. Rain events driven by low-level jets exhibited similar isotopic changes in precipitation and water vapor. The vertical activity of water vapor in convection causes the isotopic variation in precipitation to be opposite to that of water vapor. Isotopic changes of precipitation in low-pressure systems were partially synchronized with that of water vapor at high but not low water vapor concentrations. Changes in microphysical meteorological properties in stratiform precipitation give rise to different patterns of isotopic changes in water. The re-evaporation of raindrops can be determined by the enrichment ratio of heavy isotopes in the water under the cloud base, which is closely related to the raindrop radius. Stratiform precipitation, with small raindrop sizes, was prone to kinetic fractionation under the cloud base. The raindrop radius of low-level jets was small, favoring exchange with surrounding air and re-evaporation. The moist air mass in convection facilitates isotopic exchange of raindrops with surrounding water vapor, leading to low enrichment ratios. The lowest enrichment ratios in low-pressure systems were due to environments characterized by large-scale water vapor convergence.

1. Introduction

The stable isotopic composition (δ¹⁸O/δ¹⁶O, δ²H/δ¹H) of water has been considered in a variety of hydrological and meteorological studies, with applications including the classification of runoff [1,2], division of precipitation inputs [3,4], partition of evapotranspiration [5,6] and Global Circulation Models (GCMs) [7,8,9]. The conventional stable isotopic composition of water is divided into the composition of precipitation isotopes (δ_p) and water vapor (δ_v), where observations of δ_v have been greatly improved in recent years due to the development of laser spectroscopy technology. Without the need for condensing devices, in situ measurements with laser spectroscopy techniques save time, reduce costs and improve accuracy.

At the interannual scale, both δ_p and δ_v are associated with climate change, such as the El Niño–Southern Oscillation (ENSO) [10,11]. Seasonal variations in δ_p and δ_v are influenced by water vapor sources [11,12] and atmospheric circulation [13,14]. At the event scale, δ_p and δ_v are sensitive to condensation [15,16,17,18], precipitation regimes [19,20] and hydrological processes within and outside clouds [9]. Equilibrium fractionation and kinetic fractionation play a key role in changes of δ_p and δ_v [21,22,23].

Rayleigh fractionation is typically used to simulate isotopic fractionation in a closed system. Specifically, the stable isotopic composition of water will change according to Rayleigh fractionation: δ = δ_of^{(a − 1)}, where δ_o is the initial isotopic composition of water, δ is the isotopic composition of water when the ratio of remaining water is f and a is the equilibrium fractionation coefficient related to temperature [21]. The isotopic compositions of water vapor in a mixing system under conditions where the residual material remains in equilibrium after precipitation removal (called the composition of the equilibrium water vapor isotope, δ_e = (1 + δ_p) a − 1) can be calculated from the equilibrium fractionation coefficient. The Rayleigh process occurs under pseudo-adiabatic conditions because it is impossible to obtain a small amount of energy from a completely closed system [24] and Rayleigh fractionation lacks consideration of isotopic fractionation in both the vertical and meridional directions. Kinetic fractionation includes processes such as re-evaporation of raindrops under the cloud base and diffusive exchange with surrounding air, where re-evaporation is often cited as a possible reason for inconsistency between observations and Rayleigh predictions [11,25]. However, methods to quantify re-evaporation are still under development.

Many have carried out studies of isotopic changes in precipitation during single rain events. In previous studies, three variations of δ_p are most commonly reported: sinking shape, “V” shape and “W” shape. A sinking shape exhibits a downward curve, in which the air temperature and δ_p gradually decrease, reflecting the gradual adiabatic condensation process of atmospheric water vapor following a Rayleigh fractionation model [24]. The mechanistic explanation for the “V” shape is that δ_p is relatively depleted in isotopically lighter water, with heavier isotopes preferentially condensing out [21]. As precipitation decreases, δ_p tends to be enriched in the light isotopes due to exchange with surrounding air [26]. The “W” shape is considered a repetition of the “V” type process due to the passage of the front in cyclic rain events [27]. The value of δ_p during rain events changes little when the interface between cold and warm fronts is stable [28]. The value of δ_p can increase in an upslope [29], or an “L” shape [30], or even a “∧” shape for typhoons in southeastern China [31].

The changes of δ_p and δ_v typically observed for different rain events are summarized in Figure 1. The arrow pointing upwards from the surface refers to soil evaporation and water evaporation into the air. When evaporation occurs, the heavy isotopes in the water are retained by mass and the light isotopes are added to the water vapor and raindrops, thus contributing to the lower isotope ratios of the water vapor and raindrops. For a warm and humid low-level jet with high concentrations of rising water vapor, the heavy isotopes will be enriched in precipitation and water vapor; the opposite holds for a dry and cold low-level jet (Figure 1a) [32]. Changes in δ_p in convection generally follow Rayleigh fractionation, with values tending to be more negative as the rain continues. High-altitude subsidence brings water vapor with relatively negative isotopes from the upper troposphere to the surface, promoting the depletion of δ_v in convection [33]. If the raindrops are sufficiently large to not exchange with the surrounding air during the falling process [34], the initial δ_p of the rain event will initially be more negative (Figure 1b). As convection weakens, the amount of precipitation as well as the raindrop radius will decrease. Exchange with surrounding air promotes the enrichment of light isotopes while evaporation under the cloud base leads to the enrichment of heavy isotopes in precipitation. Changes in δ_p and δ_v maintain the same trends during the movement of low-pressure systems (Figure 1c) with high water vapor concentrations [35]. Steady stratiform precipitation does not cause a change in water vapor state and as such, the δ_p and δ_v in stratiform precipitation remain steady (Figure 1d); this can be attributed to the homogeneous moisture of stratiform clouds [36,37].

Researchers have conducted studies on the δ_p and δ_v in single rain events using different methods. However, there is a lack of holistic and systematic knowledge of the isotopic changes in precipitation and water vapor for different precipitation regimes, which requires multi-field rain event observations. The function of raindrop re-evaporation under the cloud base could contribute most to changes in δ_p and δ_v due to different precipitation regimes and local environmental factors during precipitation [38,39]. The calculation of heavy isotope enrichment in water under the cloud bases of different precipitation regimes enables a quantitative evaluation of the proportion of water redistribution by re-evaporation, emphasizing the importance of kinetic isotope exchange between raindrops and water vapor under clouds [40].

Simultaneous observations of δ_p and δ_v during rain events are reported less often. In this paper, we reported the variations in δ_p and δ_v in 24 rain events that occurred in Guangzhou, China, from April 2016 to January 2018, to understand the differences in isotopic variation patterns of δ_p and δ_v in different precipitation regimes. We analyzed the variation in δ_p and δ_v based on water vapor sources and Rayleigh fractionation and attempted to establish a link between patterns of variation of δ_p and δ_v and precipitation regimes. We also aimed to evaluate the degree of the effect of isotopic kinetic fractionation under different precipitation regimes by calculating the enrichment ratios of heavy isotopes in water under the cloud bases and evaluating the extent of the effect of isotope power fractionation. Further applications of isotope techniques in hydrological and meteorological observations can be accomplished if rain events can be characterized based on the isotopic behavior of precipitation and water vapor.

2. Method

2.1. Sample Collection and Measurement

Rainwater sampling was carried out using a self-made rainwater collector installed on the roof of the School of Geography and Planning, Sun Yat-sen University, China (113. 32° E, 23.13° N). The equipment was comprised of a cylinder with three main parts for collection, transfer downstream and storage, respectively (Figure S1). A layer of light pure paraffin oil floated on the water sample to prevent evaporation. Rainwater samples were collected hourly in 500 mL bottles, transferred at 8:00 am each morning and were sealed in a 4 °C refrigerator until subjected to measurements. Determination of precipitation regimes (rain density and radar data) was based on the tropical cyclone data center of the China Meteorological Administration (CMA), available online: http://tcdata.typhoon.org.cn (accessed on 21 July 2018).

A shielded air inlet was installed on the roof of the building (30 m a.g.l.) connected to an ultra-high precision isotope analyzer (Picarro L2130-i, Picarro Inc., Sunnyvale, CA, USA). The air was sampled continually (once per second) at a flow rate of 34 L/min by a vacuum pump. The hydrogen and oxygen isotopic compositions of liquid water and water vapor were expressed as the δ value relative to the reference on the Vienna Standard Mean Ocean Water scale (V-SMOW): δ $=({\mathrm{R}}_{\mathrm{S}}/{\mathrm{R}}_{\mathrm{V}-\mathrm{SMOW}}-1)\times 1000$ [41], where R is the ratio of the abundance of the heavy to light isotope ans S denotes the sample. The analytical precision for liquid water was 0.09‰ for δ¹⁸O and 0.43‰ for δ²H and for water vapor was 0.22‰/60 s for δ¹⁸O and 2.16‰/60 s for δ²H (refer to the Supplementary File).

2.2. HYSPLIT

The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to characterize water vapor transport [42]. Meteorological data from the Global Data Assimilation System (GDAS) with a horizontal resolution of 1° × 1° were used as input data. HYSPLIT can output various weather fields, including hourly pressure, altitude, temperature and specific humidity along each trajectory. The back time was set to 72 h because the minimum duration of the global water vapor cycle is about 3 to 10 d [43]. The air mass trajectories arriving at the observation point at 18:00 UTC each day were calculated separately. We set four heights (500, 1000, 1500 and 3000 m) to understand differences in water vapor sources from different altitudes in rain events. In South China, the average cloud base height of convective precipitation in summer is about 400–1000 m, while the average cloud base height with predominant multilayer cloud precipitation in winter is above 1000 m [44]. The range of the lower vertical section of typhoons is typically within 3000 m. By rejecting the hourly position and humidity changes of the air parcel with humidity values <0.05 g/kg in the sinking area, the source of the water vapor transport leading to the terminal precipitation could be obtained. A cluster analysis was then carried out to represent the air mass trajectories of a single rain event.

2.3. Calculation of Re-Evaporation

Kinetic effects, such as the re-evaporation of raindrops, can partially compensates for the decrease in δ values caused by Rayleigh fractionation [45,46,47]. The relative influence of re-evaporation is defined as the enrichment ratio of heavy isotopes (E) under the cloud base, which can be estimated according to [48]:

$$E_v=\left({\delta }_c-{\delta }_v\right)/{\delta }_v$$

(1)

$$E_p=\left({\delta }_c-{\delta }_p\right)/{\delta }_p$$

(2)

$$\mathrm{D}-{\mathrm{D}}_{\mathrm{c}}={\delta }^2-8\times {\delta }^{18}$$

(3)

where E_v and E_p refer to the enrichment ratio of heavy isotopes of water vapor and precipitation under the cloud base, respectively and ${\delta }_c$ is the isotopic change of water under the cloud base. The change of the d-excess was calculated by the measured d-excess and the d-excess under the cloud base (D_c).

The isotopic change of water under the cloud base, δ_c, can be determined by [49]:

$${\delta }_c=\left(1-\frac{{\gamma }^i}{a^i}\right)\left(f_e{}^{\beta i}-1\right)$$

(4)

where i refers to mass numbers of the heavier isotope and α is the equilibrium fractionation coefficient. f_e is the remaining proportion after evaporation. β and γ can be estimated using the following equations:

$${\beta }^i=\frac{1-a^i{\left(D/D^{\prime }\right)}^n\left(1-h\right)}{a^i{\left(D/D^{\prime }\right)}^n\left(1-h\right)}$$

(5)

$${\gamma }^i=\frac{a^{i}h}{1-a^i{\left(D/D^{\prime }\right)}^n\left(1-h\right)}$$

(6)

where h is the relative humidity, D and D’ are the diffusion of ¹H²H¹⁶O (¹H¹H¹⁸O) and ¹H¹H¹⁶O in the air, respectively, D/D’ is 1.024/1.0289 and n is 0.58 [50]. The remaining proportion after evaporation is calculated from precipitation (P) and evaporation:

$$f_e=\frac{P}{P+V_{evp}t}$$

(7)

$$V_{evp}=4\pi r{D}_r\left(1+\frac{F_a}{s^{\prime }}\right)\left({\rho }_a-{\rho }_b\right)$$

(8)

where V_evp is the evaporation rate, s′ represents the effective thickness of the shell around the raindrop, D_r is the diffusion coefficient, r is the radius of the falling raindrop, ${\rho }_a$ and ${\rho }_b$ are the densities of the surface of the falling raindrop and the surrounding air, respectively and $F_a$ is a dimensionless value for measuring the actual heat exchange heat. D_r(${\rho }_a-{\rho }_b$) can be determined form the humidity and temperature (g cm⁻¹ s⁻¹) and $4\mathsf{\pi }r\left(1+\frac{F_a}{s^{\prime }}\right)$ (cm) is primarily determined by the raindrop size and ambient temperature. The evaporation rate of falling raindrops can be determined by multiplying the relative humidity, temperature and raindrop size [51].

The raindrop fall time can be estimated from the fall velocity (v) and the distance between the ground and cloud base, with the fall velocity calculated according to the following relationship [52]:

$$\mathrm{v}=9.58\left\{1-exp\left[-{\left(\frac{\mathrm{r}}{0.885}\right)}^{1.147}\right]\right\}$$

(9)

Precipitation is assumed to form near average cloud base levels (1500 m) [53] and raindrops with a radius of less than 0.035 mm are considered not to evaporate or to evaporate so completely that they do not reach the ground because they fall most slowly and have the highest surface area to volume ratio.

Raindrop diameter (2r) is estimated from the Weibull distribution [54], which provides results in good agreement with measurements [55,56] compared to other distributions:

$$N\left(D\right)=N_o\frac{c}{b}{(\frac{D}{b})}^{c-1}{e}^{-{\left(\frac{D}{b}\right)}^c}$$

(10)

where D is the diameter of the raindrops and N_o, b and c are parameters with the common values of 1000 m⁻³, 0.26R^0.44 mm and 0.95R^0.14, respectively, where R is the precipitation rate (mm/h).

3. Results

3.1. Isotopic Changes in Precipitation

A total of 142 rainwater samples were collected from April 2016 to February 2018 (

Table 1. Weighted average isotopic composition of water for each rain event.

Event Number	Date	Duration (h)	# of Samples	Precipitation (mm)	Precipitation Isotopes (‰)				Water Vapor Isotopes (‰)				Equilibrium Vapor Isotopes (‰)
					δ18O	std	δ2H	std	δ18O	std	δ2H	std	δ18O	δ2H
N1	18 April 2016	6	8	26.9	−0.82	0.60	−4.09	3.56	−13.37	0.43	−93.92	2.93	−10.21	−79.91
N2	20 April 2016	4	4	8.7	−1.49	1.25	−6.38	5.64	−11.66	0.26	−85.20	1.70	−10.95	−81.55
N3	22 April 2016	3	3	1.6	−1.99	0.17	−10.40	3.79	−12.52	0.08	−91.20	0.53	−11.15	−85.31
N4	24 April 2016	7	7	3.4	−0.53	1.13	−1.86	4.24	−12.66	0.35	−89.27	1.71	−10.26	−78.00
N5	30 April 2016	6	6	17.6	−0.54	1.02	2.83	3.06	−12.56	0.33	−84.38	2.08	−10.36	−75.06
N6	3 May 2016	7	7	32.5	−0.57	1.04	0.00	3.72	−12.54	0.56	−87.02	3.49	−10.40	−76.08
N7	6 May 2016	4	4	9	−1.94	1.12	−9.62	2.67	−13.20	0.64	−91.98	2.90	−11.25	−82.82
N8	10 May 2016	17	17	153.5	−2.64	0.89	−11.56	3.99	−13.26	0.73	−90.62	4.47	−11.65	−84.35
N9	20 May 2016	13	6	37.1	−2.89	0.11	−10.17	2.46	−12.56	0.80	−88.05	4.86	−12.18	−87.28
N10	27 May 2016	16	13	19.1	−6.01	1.46	−41.17	11.19	−18.00	1.32	−126.55	8.48	−12.30	−92.44
N11	4 June2016	7	5	28.5	−5.81	1.16	−36.08	7.71	−15.25	1.23	−107.73	9.01	−14.76	−108.01
N12	5 June 2016	2	2	22.4	−7.27	1.60	−46.39	12.09	−15.39	0.07	−108.19	0.51	−15.85	−112.27
N13	21 October 2016	2	2	0.3	−3.01	2.38	−26.18	7.06	−15.09	0.37	−108.08	2.38	−12.71	−96.47
N14	8 May 2017	4	4	48.3	−3.70	0.63	−13.53	3.39	−12.42	1.08	−89.51	6.09	−12.86	−86.95
N15	2 June 2017	8	6	3.01	−5.47	2.25	−34.32	18.46	−12.98	0.39	−93.95	2.53	−14.69	−103.18
N16	14 June 2017	4	4	6.6	−8.30	0.59	−58.20	4.44	−12.92	0.80	−95.27	6.02	−17.48	−126.88
N17	16 June 2017	3	3	20.6	−5.93	0.77	−40.34	4.55	−21.03	0.20	−151.05	1.48	−14.81	−107.89
N18	17 June 2017	9	9	34	−9.23	2.76	−63.21	20.42	−20.62	0.60	−147.50	4.27	−16.45	−117.91
N19	22 June 2017	2	2	0.6	−3.45	0.28	−17.23	2.53	−14.35	0.72	−102.51	4.71	−12.16	−83.56
N20	13 July 2017	2	2	22.3	−5.45	0.01	−36.38	0.91	−13.51	0.09	−95.88	0.80	−14.02	−99.60
N21	15 July 2017	2	2	1.9	−4.43	0.90	−22.75	6.20	−13.51	0.85	−94.71	5.16	−13.68	−96.90
N22	14 September 2017	2	2	40.6	−10.61	0.38	−73.90	1.61	−16.63	0.30	−120.47	2.11	−19.42	−139.64
N23	13 November 2017	15	15	12.2	−6.70	0.62	−41.69	3.99	−16.70	0.25	−121.24	1.44	−15.85	−113.79
N24	6 January 2018	13	9	67.5	−5.96	1.25	−32.35	10.73	−15.50	1.13	−111.78	8.44	−12.80	−96.77

). The total amount of precipitation in an event ranged from 0.3 mm (N13) to 153.5 mm (N8) with a mean of 25.76 mm. The precipitation of nine rain events was greater than the mean. The weighted average of δ¹⁸O_p for the 24 rain events was −3.70‰. The event with the lowest δ¹⁸O_p value was N22 (a typhoon event) and the highest was N4. The δ¹⁸O_p values of N4, N5 and N6 were close to those of seawater (100 km away). The variations in δ¹⁸O_p are shown in Figure 2. All panels have the same y-axis range to simplify comparisons. The range of the x-axis varies due to different durations of each rain event.

Based on the change in δ¹⁸O_p in 24 rain events, the δ¹⁸O_p can be divided into five shapes: “—” (unchanged), “\” (sinking), “/” (rising), “V” and “∧”. The number of rain events for each δ¹⁸O_p shape was 3, 7, 5, 3 and 6, respectively. N3, N9 and N20 were the unchanged shape rain events, during which δ¹⁸O_p values changed very little. The standard deviation of δ¹⁸O_p was 0.17‰ for N3, 0.11‰ for N9 and 0.014‰ for N20. N2, N5, N8, N12, N17, N21 and N24 were the declining shape rain events during which the δ¹⁸O_p became gradually more negative. The drop of δ¹⁸O_p in events N2, N12 and N21 was 2.64, 2.52 and 1.28‰, respectively. N13, N15, N16, N19 and N22 were rising shape rain events in which the value of δ¹⁸O_p increased over time. The increase in δ¹⁸O_p value was 2.38‰ for event N13 and 5.64‰ for event N15. The “V” and “∧” shaped rain events were characterized by long durations and featured systematic and complex isotopic changes of δ¹⁸O_p, or example N10 with only 19.1mm in 16 h. The variation in δ¹⁸O_p was a “W+V” shape change, which means more factors were involved that need to be considered.

3.2. Isotopic Changes in Water Vapor

The event values of δ¹⁸O_v ranged from −21.03‰ (N17) to −11.66‰ (N2) with a mean value of −14.51‰. The variations in δ_v sometimes differed from those for δ_p (Figure 2). For example, variations of δ_v resulted in an “L” shape in event N11. N3, N12, N20 and N23 were unchanged shape rain events during which δ¹⁸O_v changed little, e.g., 0.08‰ for event N3. Event N2, N4, N17 and N24 are declining shape rain events. The δ¹⁸O_v value of events N2, N4, N17 and N24 decreased by 0.71, 0.35, 0.20 and 0.25‰, respectively. By the end of rising shape rain events N5, N13, N16 and N21, the δ¹⁸O_v had increased by 1.03, 0.37, 0.81 and 0.85‰, respectively. Similar to the δ¹⁸O_p, the value of δ¹⁸O_v could take a “V” or “∧” shape. The δ¹⁸O_v in events N1 and N22 was 0.43 and 0.25‰ higher, respectively, during an intermittent precipitation. The variations in δ¹⁸O_v for event N10 were complex, more so than a simple W-shape can describe. It is worth noting that an “L” shape was also evident in the changes of δ¹⁸O_v. The remaining events were classified as “V” shape rain events.

3.3. The Enrichment of Heavy Isotopes by Re-Evaporation

The isotopic composition of equilibrium water vapor was calculated based on the δ_p (Table 1), with the mean value of δ¹⁸O_e found to be 0.95‰ higher than the mean δ¹⁸O_v. The isotopic change under the cloud base was −7.79~−9.36‰ for δ¹⁸O and −60.6~−81.51‰ for δD, which were generally consistent with the model results found [57]. E_p was notably higher than E_v. The minimum, maximum and mean values of $E_p^{18}$ were −0.17% (N22), 15.29% (N4) and 3.40%, respectively. The value of $E_v^{18}$ for each rain event was so small that the difference between maximum and minimum values was only 0.63%, indicating little isotopic fractionation in the vapor profile from the cloud base to the ground surface. The estimated raindrop radius for the 24 rain events ranged from 0.22 to 1.17 mm. Figure 3a depicts how the evaporation, evaporation rate and proportion remaining after evaporation all increased with increasing raindrop radius. However, variations of E_p did not follow the raindrop size (

Table 2. Enrichment rate of heavy isotopes and the effective fractionation factor for different styles of rain events.

Style	Number	Temperature (°C)	RH (%)	Raindrop Radius (mm)	Evaporation Rate	Evaporation	Remaining Proportion after Evaporation	Isotopic Change of Water under the Cloud Base (‰)		Enrichment Rate (Precipitation)		Enrichment Rate (Vapor)		Effective Fractionation Factor
								δ18O	δ2H	δ18O	δ2H	δ18O	δ2H	δ18O	δ2H
S1	N1	22.21	95.10	0.76	6.22	0.37	87.81	−8.49	−72.20	9.38	16.66	−0.23	−0.15	16.35	22.97
	N2	22.80	94.17	0.61	4.44	0.18	83.05	−8.57	−71.07	4.77	10.14	−0.06	−0.04	7.85	13.35
	N16	26.50	90.83	0.55	4.21	0.17	79.67	−8.84	−67.38	0.07	0.16	0.35	0.32	1.56	1.64
	N20	33.61	76.64	1.00	14.17	0.28	88.72	−9.36	−60.60	0.72	0.67	0.04	0.04	2.48	2.64
	N24	14.02	98.36	0.80	5.14	0.67	91.00	−7.79	−81.51	0.31	1.52	0.04	0.02	2.60	3.46
S2	N5	21.43	91.25	0.66	4.80	0.29	85.93	−8.40	−73.24	14.48	−26.90	−0.19	−0.12	23.13	−29.84
	N6	23.87	87.78	0.77	6.72	0.47	87.35	−8.65	−69.98	14.27		−0.21	−0.14	22.14
	N8	23.57	95.60	0.94	9.35	1.59	90.62	−8.66	−70.10	2.29	5.06	−0.09	−0.06	5.03	7.84
	N11	25.32	89.89	0.74	6.55	0.46	86.14	−8.73	−68.97	0.50	0.91	−0.02	−0.01	2.63	2.99
	N14	22.68	90.83	1.03	10.21	0.41	92.20	−8.53	−71.67	1.30	4.30	0.06	−0.01	3.35	6.62
	N15	29.83	98.90	0.35	2.25	0.18	62.62	−9.09	−63.82	0.66	0.86	0.10	0.08	2.37	2.74
	N18	26.49	92.18	0.72	6.62	0.60	85.09	−8.85	−67.44	−0.04	0.07	−0.11	−0.11	2.23	2.33
S3	N7	24.58	93.33	0.61	4.66	0.19	82.83	−8.69	−69.48	3.47	6.22	−0.15	−0.10	6.79	9.56
	N10	26.47	88.78	0.50	3.62	0.58	76.76	−8.84	−67.33	0.47	0.64	−0.16	−0.13	2.99	3.07
	N13	26.43	89.50	0.22	0.93	0.02	61.68	−8.77	−67.63	1.91	1.17	−0.19	−0.06	5.01	3.47
	N17	25.15	97.25	0.87	8.50	0.26	88.98	−8.74	−69.05	0.47	0.71	−0.28	−0.27	3.55	3.74
	N19	30.48	90.27	0.31	1.96	0.04	60.52	−9.11	−63.20	1.64	2.67	−0.14	−0.18	4.16	5.95
	N22	26.04	91.60	1.17	14.17	0.28	93.48	−8.82	−68.02	−0.17	−0.08	0.19	0.17	1.57	1.63
S4	N3	22.14	96.60	0.37	1.95	0.06	73.25	−8.47	−72.06	3.26	5.93	−0.08	−0.06	6.29	8.77
	N4	24.17	83.89	0.37	2.08	0.15	69.98	−8.61	−69.71	15.29	36.43	−0.22	−0.15	23.95	47.94
	N9	25.09	94.07	0.66	5.38	0.70	84.14	−8.73	−68.98	2.02	5.78	−0.03	−0.06	4.35	8.66
	N12	24.15	97.75	1.01	10.35	0.21	91.54	−8.66	−70.12	0.19	0.51	0.08	0.08	2.12	2.33
	N21	27.70	84.50	0.45	3.20	0.06	74.77	−8.91	−66.27	1.01	1.91	0.00	−0.03	3.05	4.16
	N23	21.71	95.94	0.43	2.51	0.38	76.43	−8.39	−73.20	0.25	0.76	−0.03	−0.05	2.49	2.91

and Figure 3b). The range of d_p (d-excess of precipitation) was 2.38~16.07‰, with a mean value of 9.02‰. The corrected d-excess rose by an average of 2.75‰ (Figure 3c). Most of dp decreased in rain events due to re-evaporation, but there were rain events such as N13 and N17 where d_p raised by 2.53‰ and 0.87‰, respectively.

4. Discussion

4.1. Effects of Water Vapor Sources and Rayleigh Fractionation

The water vapor trajectories driven by water vapor sources were a critical factor controlling the characteristics of δ_p [58,59,60] and the measurements of δ_v were markedly different depending on the source of water vapor [61,62]. The vapor trajectories calculated by HYSPLIT were used to analyze the relationships between the water vapor source and changes in δ¹⁸O_v and δ¹⁸O_p (Figure 4). The source of water vapor for events N2 and N5 was similar, resulting in similar changes in δ¹⁸O_p and δ¹⁸O_v. Events N12 and N17 behaved consistently in terms of vapor trajectories, δ¹⁸O_p and δ¹⁸O_v. In general, values of δ¹⁸O_p and δ¹⁸O_v for events under similar meteorological conditions (Table 1) and with similar water vapor sources, varied comparably. A single vapor source also resulted in similar changes in δ¹⁸O_p and δ¹⁸O_v; for example, the vapor trajectories of N20 mainly originated from the Pacific Ocean. However, in the case of multiple sources of water vapor, δ¹⁸O_p and δ¹⁸O_v did not demonstrate the same variation even when some of water vapor sources were the same. Moreover, sources of water vapor from opposite directions, such as southeast and southwest, did not result in opposite changes in δ¹⁸O_p and δ¹⁸O_v. The vapor trajectories of events N6 and N7 partially coincided and the variations of δ¹⁸O_v were the same, but the change of δ¹⁸O_p in N6 was a “∧” shape and in N7 trended upward; the same held true for events N9, N10, N14 and N23. In addition, the same water vapor source could also correspond to different variations in δ¹⁸O_p and δ¹⁸O_v, as noted for events N11 and N16. Different sources of water vapor were also capable of generating similar variations in δ¹⁸O_p and δ¹⁸O_v. For example, the water vapor for event N7 came from the Pacific Ocean and the Indian Ocean, while the vapor trajectories of N8 originated from the northern mainland. Yet, the changes in δ¹⁸O_p and δ¹⁸O_v in these two events were similar. Thus, the relationship between changes in the isotopic composition of water and the source of water vapor in rain events remains unclear. Changes of δ¹⁸O_p and δ¹⁸O_v could not be rationalized in cases where the water vapor sources were partially the same or inconsistent and may be related to the mechanism of precipitation formation and micrometeorological physical processes in the rain event [11,63]. Event N7 was formed by the convergence of southeast and southwest winds during the summer monsoon transition period, while event N8 was caused by a cold front and local warm and humid airflow, resulting in opposite changes in δ¹⁸O_p and δ¹⁸O_v.

Rayleigh fractionation was used to evaluate the isotopic change of each rain event (Figure 5). Rain event temperatures were typically ~24 °C, with only event N24 occurring at a notably cooler 15 °C. We assumed that the isotope ratios of water satisfy the equilibrium fractionation during condensation (Rayleigh fractionation) before it reaches Guangzhou. Condensation temperature remained constant throughout a rain event. Although the condensation temperature varied for each rain event, the change of equilibrium fractionation coefficient from the condensation temperature was small. The starting point of δ_p from seawater for the 24 rain events is 0‰. As the temperature in the first hour increases, the initial value of δ¹⁸O_v becomes more positive. Precipitation coming directly from the ocean satisfied Rayleigh fractionation, with an initial ratio greater than 0.9 [64]. The initial ratios of 24 rain events were between 0.35 and 1, with a mean value of 0.68, indicating the local initial precipitation had a degree of heavy isotope separation. The events with the highest ratio, such as N2 (0.95), N5 (1) and N8 (0.9), showed little loss of heavy isotopes during the moisture transportation process from the ocean to the local area and during the precipitation formation process. Rain events whose initial ratio was less than 0.5 had more than half of the water from the ocean consumed by the rainout process. The events that fit the Rayleigh fractionation curve were N2, N5, N7, N8, N10, N12, N17, N20 and N24; however, their different initial ratios suggested variations of δ_p and δ_v are independent of the initial ratios. The initial points of events N1, N4, N5, N11, N13 and N16 satisfied the Rayleigh fractionation prediction, but the subsequent change of δ¹⁸O_p did not; this demonstrates that even if the early values of δ_p matched Rayleigh fractionation, it may not be able to capture later changes in δ_p. Initial values of δ¹⁸O_p and δ¹⁸O_v were determined by the source of precipitation and how much is lost to rainout during the process of water transport.

The variations in δ_p and δ_v were different from Rayleigh’s prediction due to its idealized assumptions and neglect of kinetic effects [13]. Events that satisfy the predicted isotopic variations mostly reflected pseudo-Rayleigh conditions [61]. The traditional Rayleigh fractionation framework is not sufficient to explain isotopic variation in the subtropics and tropics, where most precipitation comes from deep convection and vertical motion dominates horizontal transport [11]. Those rain events that did not align with Rayleigh’s prediction were most likely affected by extra-systemic processes such as re-evaporation [8,9,20,23], surface evapotranspiration [65,66] and exchange with surrounding air [67,68,69]. If a raindrop experiences re-evaporation, isotopically light molecules increase in the water vapor and δ_v will be more negative than δ_e, as in most of our observed cases, (except N12, N18, N22 and N24). Additionally, the data that deviate from the Rayleigh curve may reflect the advection of remaining water vapor from the precedent clouds with a relatively low isotopic composition [9]. Rayleigh fractionation as a system mechanism can generally indicate which process may affect the variations in δ¹⁸O_p and δ¹⁸O_v in rain events, but it cannot help to classify rain events, as the changes of δ¹⁸O_p and δ¹⁸O_v may be closely related to precipitation mechanisms.

4.2. Rain Event Styles Based on Formation Mechanism

Temperature did not affect the local value of δ_p, but the isotopic composition of water declined with increasing precipitation (Figure 6a), implying a precipitation amount effect [69,70]. The precipitation regimes were used as an indicator to link various types of rain events. Four rain event styles were based on the variations in δ_p and δ_v. The first style (S1) featured simultaneous changes in δ_p and δ_v and the second style (S2) featured opposite changes in δ_p and δ_v. S1 included events N1, N2, N16, N20 and N24 and S2 included events N5, N6, N8, N11, N14, N15 and N18. The third style (S3) featured the partial coincidence of δ_p and δ_v and the fourth (S4) was characterized by uncorrelated changes in δ_p and δ_v. S3 included events N7, N10, N13, N17, N19 and N22 while S4 included events N3, N4, N9, N12, N21 and N23.

The precipitation in S1 was characterized by moderate rain events, except for rainstorm event N24. Most rain events of this style were caused by low-level jets [16], with similar variations in δ_p and δ_v but different shapes. Intra-cloud processes in the dynamical system (e.g., cloud thickness, cloud top height), sub-cloud processes (e.g., temperature, density of precipitation) were captured by δ_v and affected the δ_p at any time. For event N24, dry cold air from the north mainland at high altitude moved to South China and mixed with a warm wet air mass from the Pacific Ocean to develop a low-level jet. The values of δ_p and δ_v had been decreased, even in the late precipitation period, but did not match the predictions of Rayleigh fraction. A reasonable explanation is that residual precipitation forms at lower altitudes after precipitation stops, when the rainfall intensity decreases due to the dissolution of the cold front [15].

S2 was characterized by heavy rain events, formed by convection, with opposing changes in δ_p and δ_v. Strong convection can be either well-organized or unorganized. Well-organized convective results in opposing shapes of δ_p and δ_v, e.g., event N5. The δ¹⁸O_p of event N5 began with a decreasing trend, at a rate of −0.41‰ per hour, while the δ¹⁸O_v increased from −13.27‰ to −12.25‰. In contrast, unorganized convection is a three-dimensional system that covers multiple precipitation processes [39] and provides conditions for changes in raindrop radius and, thus, δ_p and δ_v [65]. This was evident in the enrichment of heavy isotopes in events N6, N11 and N14. Water vapor supplementation in unorganized convection may allow for light isotopes accumulation before the precipitation stops [15], which accounts for the late changes in δ_p in events N15 and N18, with high water vapor concentrations of 33.33 and 34.41 mmol/mol, respectively. In unorganized convection, the values of δ_p and δ_v show complex and different variations due to sufficient time for processes such as evolution of raindrop size and water vapor replenishment to occur.

S3 mostly occurs in tropical low-pressure systems. Events N7 and N10 were caused by the tropical low depressions in the South China Sea and events N13 and N22 were formed by the super-strong Typhoons Haima and Doksuri, respectively. Variation in water vapor concentration may be responsible for controlling the variation of δ_p and δ_v in S3. The changes in δ_p during event N10 were synchronous with changes in δ_v when the water vapor concentration peaked at 33.35 mmol/mol from 14:00. However, at lower water vapor concentration levels, the variations of δ_p and δ_v began to differ. After 23:00, the water vapor concentration began to decrease to less than 30 mmol/mol, with a decrease in δ_p and a continued increase in δ_v. The same was true for the δ_p changes of tropical cyclone rains, as the water vapor concentration provided by the front and tail ends is related to the system structure [71], Pacific storms [16] and eight typhoons in Fuzhou from 2013 to 2017 [31]. The changes in δ_p and δ_v in low-pressure systems with high water concentration were consistent, while those with low water concentration differed.

Compared to other styles of rain events, S4 was characterized by a low number of thunderstorms and mainly consisted of stratiform precipitation. The total amount of precipitation was only 78.6 mm. With the exception of N9, a common characteristic of the other five rain events of this style was a small variation in δ_v. The δ¹⁸O_v ranged from 0.07 to 0.85‰, with a mean of 0.40‰. In such rain events, changes in δ_p are most likely related to the kinetic effects and thermal structures under the cloud base [16]. The δ¹⁸O_p value of events N12 and N21 continued to decrease, similar to the ideal isotopic change pattern of raindrops. However, there is a feedback mechanism by re-evaporation that limits the ideal isotopic change pattern of raindrops [72]. The re-evaporation causes a decrease in the size of a raindrop that, in turn, reduces its rate of descent and increases the time to exchange with the surrounding air. Therefore, the δ¹⁸O_p value of events N4 and N23 initially increased followed by a decrease. Event N9 was a rain event during which δ¹⁸O_p did not noticeably change but δ¹⁸O_v slightly fluctuated. A small percentage of re-evaporation of raindrops can contribute to the depletion of heavy isotopes in water vapor [40,73], which provides an explanation for the steady δ_p and decreasing δ_v during event N9. Both δ_p and δ_v for S4 were affected by kinetic effects or micrometeorological physical properties, resulting in a less-enriched heavy isotope in δ_p than expected and a less-depleted light isotope in δ_v than predicted.

4.3. Re-Evaporation under the Cloud Base

Raindrops that fall into unsaturated air will partially re-evaporate and become enriched in heavy isotopes, depleting the surrounding water vapor [21,50]. Values of E_p are influenced by evaporation, which involves both the evaporation rate and falling time of raindrops. The evaporation rate and fall time are in turn determined by the standard height for precipitation clouds and the radius of the raindrops. The estimated raindrop radius was 0.74 mm (S2) > 0.73 mm (S1) > 0.61 mm (S3) > 0.55 mm (S4), similar to the global drop size distributions for different precipitation mechanisms [74]. The higher the liquid water content and the deeper the warm cloud, the stronger the convection and the larger the raindrop. The mean value of E_p in the four precipitation regimes was 6.11% (S4) > 4.44% (S1) > 2.29% (S2) > 1.59% (S3), which means greater liquid water content results in larger raindrops and a smaller enrichment ratio when precipitation is generated at the same altitude.

In low-pressure systems, with increased air mass convergence, more water vapor comes from the surrounding air column, resulting in a lower value of δ¹⁸O_v [21,67,68,73] and a smallest value of E_p. The second smallest value of E_p is for convection because the moist air mass carried by strong convection facilitates isotopic diffusion exchange between raindrops and water vapor, which in turn limits the re-evaporation of raindrops [8,9,34]. Although raindrops are enriched in heavy isotopes, the isotopic compositions of evaporated water vapor are depleted relative to the surrounding water vapor [75]. Light isotopes in subsequent precipitation from this air mass may be depleted, resulting in a lower δ¹⁸O_p [7,8]. Such dynamic microphysical processes can cause the E_p for δ¹⁸O in convection to be as high as 14.48% (N5) or as low as −0.04% (N18). The slightly smaller raindrop radius of low-level jets than convection can explain the larger E_p of low-level jets. In addition, the high heavy isotope enrichment of the low-level jets may be due to the fact that raindrops with the same radius in a low-level jet would have a shorter falling time than in convection (Figure 6b). Compared to convection, with a smaller raindrop size, the raindrop feedback mechanism would be weaker [72], leading to stronger re-evaporation in the low-level jets. Stratiform precipitation had the lowest precipitation rates, the smallest raindrops radius and the greatest E_p.

Convection and stratiform are single cloud systems and multi-cloud systems (low level jet and low pressure) have a wide range of cloud heights, but the height of local precipitation cloud base where condensation occurs in multi-cloud systems is fixed within a certain range. Re-evaporation is a post-condensation process and from this perspective, estimates of E_p can be applied to multi-cloud systems. Falling time is a key element for determining the accuracy of the E_p, as the standard height for precipitation clouds was set to 1500 m, which is not reflective of reality. The difference in falling time between 1500 and 500 m for raindrops with a radius of 0.5 mm is 258 s (Figure 6b). At 25 °C and 95% relative humidity, the evaporation of a raindrop with a radius of 0.5 mm from an altitude of 1500 m is 0.13 mm and from an altitude of 500 m is 0.04 mm. An evaporation difference of 0.09 mm between the two altitudes can lead to an enrichment ratio bias of 13.32% (not shown).

The isotopic effective fractionation factor (E_f = δ_v/δ_p) was used to determine the fractionation effect between water vapor and precipitation during a rain event. Changes in E_f are likely caused by potential precipitation and rainout processes (including equilibrium fractionation and kinetic fractionation). The greater the E_f, the greater the degree of isotope effective fractionation. The mean value of E_f for the four precipitation regimes was 9.75 (S4) > 7.49 (S1) > 5.41 (S2) > 4.29 (S3), the same as the mean value order of E_p. The mean equilibrium fractionation factors of local rain events are 1.01 for δ¹⁸O and 1.09 for δ²H. The conversion of water vapor to precipitation results in an isotopic fractionation of −1‰, showing that isotopic composition of water under the cloud base changed little and potential precipitation had a minimal effect on E_f. Kinetic isotopic fractionation accounts for most of the fractionation effect. Assuming a weaker to negligible fractionation with respect to isotope exchange, E_f can be used to evaluate the results of re-evaporation. There was a good linear fit between E_f and E_p (Figure 7), which provides a reasonable mechanical reference for E_p. E_p decreased linearly as E_f decreased, which is consistent with the general law of isotopic fractionation of water in rain events. Our future work will use satellite data (such as TRMM data, radar data) to accurately determine the cloud base height and improve the enrichment ratio calculations of heavy isotopes in water under the cloud base.

5. Conclusions

This paper summarized and classified hourly changes in δ_v and δ_p for 24 rain events in South China. Characteristics of δ_v and δ_p and precipitation regimes were analyzed. The importance of re-evaporation in the distribution of water during precipitation can be understood based on calculations of isotopic evaporation enrichment ratios under the cloud base.

The initial values of δ_p and δ_v were determined by the source of water vapor and how much rainout was lost during moisture transport. Rain events with the same water vapor sources were likely to have the same, but not absolute, changes in δ_p and δ_v. Variations in of δ_p and δ_v were related to precipitation mechanism. Changes in microphysical meteorological properties, including the size of the raindrop radius, exchange of surrounding water vapor and re-evaporation caused isotopic changes in water of different precipitation regimes to have different patterns.

Overall, the findings of this paper show the following:

(1)

Synchronous changes of δ_p and δ_v are found in low-level jets. Residual precipitation forms at low altitudes after the end of precipitation, resulting in opposing changes in δ_p and δ_v.

(2)

Large-scale convective activity, characterized by opposing changes in δ_p and δ_v, brings in water vapor depleted in heavy isotopes from the upper troposphere, while rainout causes enrichment of heavy isotopes in precipitation. A decrease in precipitation amount and raindrop size at the end of the rain event and possible re-evaporation under the cloud base, leads to the depletion of heavy isotopes in precipitation.

(3)

The precipitation environment of low-pressure systems creates a high water vapor concentration, replenishes water vapor and synchronizes variations in δ_p and δ_v. Once the water vapor concentration decreases, the changes in δ_p and δ_v no longer coincide.

(4)

For small-scale events and small precipitation amounts, precipitation may occur within stable clouds, with less variable δ_v.

The re-evaporation of raindrops is one of the kinetic fractionation processes that can be determined by the enrichment ratios of heavy isotopes (E_p). The value of E_p varies for different precipitation regimes, with the lowest value in low-pressure systems being determined by large-scale air mass convergence. High precipitation rates and large raindrop radii in strong convective activities creates the second largest values of E_p. The raindrop radii of low-level jets are slightly smaller than for convection, prompting the exchange of raindrops with surrounding air and re-evaporation and a value of E_p that is smaller than for convective activity. The largest values of E_p are found for stratiform precipitation and high isotope fractionation for precipitation regimes with high E_p indicates the role of re-evaporation is not negligible during precipitation processes, especially for stratiform precipitation.

Different patterns of changes in δ_p and δ_v can reflect different precipitation regimes. The drawback is that E_p can be biased by the assumption of precipitation cloud height, increasing its uncertainty. However, this paper provides new possible directions for meteorological system discrimination by observing changes in δ_p and δ_v and offers some new information for the characterization and application of δ_p and δ_v in subtropical and tropical regions.