Holocene precipitation hydrogen isotopic values on Nilgiri Plateau (southern India) suggest a combined effect of precipitation amount and transport paths

Abstract

Paleoclimate investigations of the peat deposits in the Nilgiri Plateau, an important paleoclimate archive of India, are mainly restricted to the carbon isotope composition ((δ¹³C) of plant-derived materials and pollen studies. However, it is unclear whether these proxies reflect past variability in temperature or hydrology. Here, we report the hydrogen and carbon isotopic variability of n-alkanoic acid of chain length 28 (δD_C28 and δ¹³C_C28, respectively) and demonstrate that the peatland leaf wax hydrogen isotopes provide a sensitive record of past hydrology. The decoupling of δ¹³C_C28 and δD of vegetation-corrected rain during the Holocene indicate that δD of the leaf wax compounds mainly respond to past hydrological variability whereas δ¹³C variations might reflect the temperature-controlled variability of C3 and C4 vegetation. Conforming with the other paleoclimate records from the region, the δD_C28 variations showed a reducing precipitation trend since the early Holocene. However, a large amplitude of reconstructed δD of rain (~44‰) during the Holocene indicated changes in the moisture source and trajectory could be an additional factor contributing to the orbital-scale δD variability of proxies from the Indian region.

Keywords

Holocene insolation monsoon paleoclimate peat tropics

Introduction

The Holocene paleomonsoon records provide an opportunity to assess the causal link between well-established climate forcing and monsoon (Banerji et al., 2020; Kaushal et al., 2018; Mayewski et al., 2004; Misra et al., 2019; Morrill et al., 2003; Zhang et al., 2011, 2019). For understanding continental rainfall variability and underlying forcing, it is preferable to use terrestrial rainfall proxy archives such as lake sediments, peat bogs and speleothems. The lake sediments (Misra et al., 2019) and speleothem (Kaushal et al., 2018) records have been used to reconstruct paleomonsoon variability in the Indian region. However, due to the limited availability of appropriate sampling sites, the terrestrial records that offer sampling resolution and cover a period enough to reveal a trend in the Holocene precipitation are conspicuously absent from India.

The Nilgiri Plateau, southern India, hosts paleoclimate archives such as peat deposits that are ideal for the preservation of pollen (Sutra, 1997; Sutra et al., 1997; Vasanthy, 1988) and other organic matter (Ramya Bala et al., 2022; Sukumar et al., 1993), soil profiles (Caner et al., 2007), and lake sediments (Raja et al., 2018). In contrast to the other archives in the Indian region, peat deposits offer a continuous record of >40 kyr (Rajagopalan et al., 1997; Ramya Bala et al., 2022) and are important to reveal the orbital-scale climate variability and the factors influencing it. The pollen records from the Nilgiri Plateau have established not only the long-term stability of the Shola-Grassland mosaic but also the heterogeneity of landcover change, going back >30,000 years (Sutra, 1997). The carbon isotopic composition (δ¹³C) of peat (Rajagopalan et al., 1997; Ramya Bala et al., 2022; Sukumar et al., 1993) and soil profiles (Caner et al., 2007) from the Nilgiri Plateau, reflecting the relative proportion of C3 and C4 plants in the catchment area, has been used to reconstruct the past climate variability. However, as the trees to grass ratio in the Nilgiri Plateau is controlled by the winter frost (Joshi et al., 2020), δ¹³C and pollen-based proxies might not reflect past precipitation variability alone. Further, short-term disturbances such as fire and local attributes led to divergence in the δ¹³C records of peat cores (Ramya Bala et al., 2022). Thus the potential of peat deposits from the Nilgiri Plateau in reconstructing past rainfall variability is yet to be fully realized.

The deuterium to hydrogen ratio (δD) of the leaf waxes is known to preserve δD of concurrent environmental water or precipitation (Hou et al., 2008; Huang et al., 2004; Sachse et al., 2012). δD of n-alkanoic acid has been extensively used to reconstruct past precipitation isotope variability (Konecky et al., 2011; Niedermeyer et al., 2010; Pagani et al., 2006; Shuman et al., 2006; Tierney et al., 2008). Although several studies have used δD studies of leaf wax compounds in the peat bogs from higher latitudes (e.g. Daniels et al., 2017; Seki et al., 2011), its potential in reconstructing past rainfall variability in the tropics is not adequately demonstrated. Here, we report the results of δD and δ¹³C-based investigations of n-alkanoic acid fractions of the leaf wax in the peat deposits from the Nilgiri Plateau. The influence of change in the vegetation composition and seasonality in rainfall on the δD of reconstructed rain was assessed. The amplitude of the Holocene-scale changes in δD of reconstructed rain was used to infer the past changes in precipitation amount and associated atmospheric circulation.

Materials and methods

Sampling site

The Nilgiri Plateau in southern India (Figure 1) receives rainfall during the southwest monsoon (SWM) and northeast monsoon (NEM) from June to September and October to December, respectively. The Western and Central Indian Oceans are the major sources of moisture during the SWM (Pathak et al., 2017) (Figure 1a). The rainfall and its seasonality are spatially variable (inset in Figure 1b): the western part (Region I) is dominated by the SWM and receives 2500–5000 mm of rain; the central region (Region II) receives both the monsoons but with a lower annual total (900–1200 mm) and the eastern and southern parts (Region III) receive a higher contribution from the NEM and a lower annual total (1500–2000 mm) (Caner et al., 2007). The sampling site Avalanche–B (76°35′58.40″E, 11°18′06.03″N, ~2000 m amsl, Figure 1) is located in Region I. The contribution of the SWM rainfall to the annual total at Terrace Factory (Region I), Avalanche (Region I), Ootacamund (Region II) and Coonoor (Region III) are 75%, 71%, 42%, and 21%, respectively. The mean monthly temperature variations over the plateau are shown in Figure 1b. Regions I and II are more prone to frost formation which can occur up to 50 days from December to March with its frequency the highest during January (Von Lengerke, 1977). The most important factors controlling vegetation dynamics between trees and grasses here are the daily thermal differences and the extreme temperatures (Joshi et al., 2020; Legris and Blasco, 1969; Legris and Meher-Homji, 1977).

Figure 1.

(a) Major sources of moisture to Indian summer monsoon rainfall and vertically integrated moisture flux divergence over the region (after Pathak et al., 2017). WIO: Western Indian Ocean; UIO: Upper Indian Ocean; CIO: Central Indian Ocean; SIO: Southern Indian Ocean. 1: Lonar Lake (Sarkar et al., 2015), 2: site U1446 (McGrath et al., 2021). Thick black and red arrows schematically indicate proximal and distal transport of moisture during the SWM, respectively. The thin blue arrow indicates the direction of the NEM. The study site is indicated by a star. (b) The Nilgiri Plateau. The three climatic regions (separated by the dashed lines) are as in Caner et al. (2007). Data for the plots in the inset (Von Lengerke, 1977) indicate mean monthly precipitation and temperature in the region. The sampling location Avalanche-B is shown by a star.

The vegetation in the Nilgiri Plateau is characterized by a mosaic of grassland, sedgeland and shola (i.e. stunted evergreen forest) (Blasco, 1971, 1987; Blasco and Tassy, 1975; Meher-Homji, 1967; Ramya Bala et al., 2022; Ranganathan, 1938). The grassland occurs on the hilltops; shola, in the sheltered valleys; and sedgeland, on the waterlogged valley floors (Ranganathan, 1938). While shola vegetation is mainly C3, grassland and sedgeland comprise both C3 and C4 vegetation (Ramya Bala et al., 2022). At the sampling site, the shola vegetation is in proximity in the south, east and west; and the grassland, in the north and northwest (Supplemental Figure S1, available online).

The isotopic characteristics of the modern-day rainfall

Seasonal isotopic variability

As the study area receives SWM and NEM, it is important to characterize their isotopic composition. Four Global Network of Isotopes in Precipitation (GNIP) (IAEA/WMO, 2022) stations and a few year-long investigations (Supplemental Table S1, available online; Deshpande et al., 2003; Lekshmy et al., 2015; Warrier et al., 2010; Yadava et al., 2007) reveal that the NEM is relatively more depleted in the heavy isotopes than the SWM. The differences in the δD of SWM and NEM at GNIP stations varied from 9‰ to 26‰. The δD of stream water at Ootacamund during July (−32‰) and October (−59‰) (Parthasarathy et al., 2021), gave an estimate of the δD of rainfall during the SWM and NEM, respectively.

Isotopic variability in the Nilgiri Plateau

Monthly or seasonal rainwater isotopic data are not available for the Nilgiri Plateau. The δD of surface and groundwater samples collected in the Nilgiri Plateau (Tripti et al., 2019) suggested −32‰, −45‰, and −53‰ as the mean annual δD_rain for Region I, II, and III, respectively (See Supplemental Discussion, available online). The expected δD of rain at the sampling location (~2000 m a.s.l.) considering an elevation effect of −0.8‰/100 m and data given by Resmi et al. (2016) (Supplemental Table S1 available online) is −38‰.

The amount effect in the rainfall and the response of the proxy records

An inverse relationship between the amount of precipitation and its isotopic composition, the “amount effect” (Araguás-Araguás et al., 2000; Dansgaard, 1964), forms a basis for interpreting proxies, such as δD_C28, that inherit the isotopic composition of rains. Based on the monthly rainwater isotopic data Lekshmy et al. (2015) showed that the amount effect is apparent only in the region where the amount of rain received during the NEM and SWM were comparable. Further, the daily rain samples revealed that the lower δ¹⁸O values reflect large-scale organized convection rather than the amount of local rainfall (Lekshmy et al., 2014).

Although the daily and monthly isotopic data are useful in understanding the “amount effect” in terms of atmospheric processes, their utility in interpreting the isotopic records of proxies that are more likely to reflect the isotopic signal of rainfall integrated over a year (e.g. in tree rings) or longer (e.g. in speleothem) is limited. Inter-annual variability in the isotopic composition of rain, more useful while interpreting the isotope-based proxies, at GNIP station Kozhikode showed an inverse relationship between the weighted δ¹⁸O of the annual precipitation and its amount (r = −0.62, N = 11, p < 0.05). Further, the inter-annual δ¹⁸O record of a teak tree (1943–1988) from southern India showed a negative correlation with the amount of concurrent rainfall (Managave et al., 2011) suggesting the utility of the isotopic composition of plant-derived material as a proxy for rainfall.

Sample and radiometric dates

The peat core analyzed was taken using a modified Russian Peat corer (semi-circular) during a field campaign carried out in 1995 by French Institute of Pondicherry (IFP) in the framework of the PhD thesis of Sutra (1997). The core of 7.5 m depth was sub-sampled at 1 cm resolution for pollen analyses and a part of the remaining samples stored at the Laboratory of Palynology and Paleoecology (IFP) was used in this study. Here we report the results of the analysis of a part of the core to a depth of 529 cm (corresponding to 9429 BP). The dates associated with samples from various depths and an age-depth chronology built using Clam (version 2.2) (Blaauw, 2010) were shown in Supplemental Table S2 and Figure S3, available online. The peat accumulation rate was 116 cm/kyr prior to 6.7 ka and 26 cm/kyr after this time.

The samples were selected to get a uniform temporal sampling during the Holocene; an average of ~270 years between the two samples. Further, as the thrust of the present study was hydroclimatic reconstruction using δD, the number of samples analyzed for δD (N = 35) was more than those analyzed for δ¹³C (N = 15).

Lipid extraction and the isotopic measurements

Peat samples were freeze-dried and labile organic fractions from them were extracted using DCM:MeOH in a 9:1 ratio in an Accelerated Solvent Extractor (Dionex ASE 200). Neutral and acid fractions were separated from the total lipid extract using aminopropylsilyl as a solid phase extractor. The neutral fraction was extracted first using DCM and Isopropanol (2:1, v/v). This was followed by the extraction of acid fraction using acetic acid in ether (4%, v/v). The acid fraction was later methylated using MeOH with acetyl chloride to produce fatty acid methyl esters (FAME) following Huang et al. (2004).

δ¹³C and δD of FAME were analyzed in Huang’s lab at Brown University by gas chromatography stable isotope mass spectrometry (GC-IRMS). Peaks associated with different chain lengths of FAMEs were identified from their retention times. For δ¹³C and δD, each of the samples was analyzed twice and thrice, respectively. The precision (1-sigma) of these analyses was better than 0.15 ‰ (for δ¹³C) and 2‰ (for δD). Laboratory isotopic standards of alkane and FAME were routinely measured to check the accuracy of the analysis which showed similar standard deviations for δ¹³C (±0.15‰) and $δ D$ (±2‰). δ¹³C and $δ D$ values are reported with respect to VPDB and VSMOW, respectively.

$δ D_{C 28}$ was estimated from $δ D$ of the corresponding FAME ( $δ D_{C 28}^{FAME}$ ) using the following mass balance approximation

δ D_{C 28} = \frac{n \times δ D_{C 28}^{FAME} - (3 \times δ D_{MeOH})}{n - 3}

(1)

where $δ D_{MeOH}$ is $δ D$ of MeOH and $n$ represents the number of hydrogen atoms in the specific FAME (Huang et al., 2004).

Similarly, $δ^{13} C$ of n-alkanoic acid of chain length 28 ( $δ^{13} C_{C 28}$ ) was estimated from $δ^{13} C$ of corresponding FAME ( $δ^{13} C_{C 28}^{FAME}$ ) using the following mass balance approximation

δ^{13} C_{C 28} = \frac{n \times δ^{13} C_{C 28}^{FAME} - (δ^{13} C_{MeOH})}{n - 1}

(2)

where $δ^{13} C_{MeOH}$ is $δ^{13} C$ of MeOH and $n$ represents the number of carbon atoms in the specific FAME (Huang et al., 2004).

The effect of change in vegetation on δD of n-alkanoic acid of chain length 28 (δD_C28)

An approach adopted by Konecky et al. (2016) was used to assess the effect of changes in the relative contribution of trees and grasses on δD_C28. We first estimated the fraction of C4 plants using $δ^{13} C_{C 28}$ as

{f_{C}}_{4} = (δ^{13} C_{C 28} - δ^{13} C_{C_{28}}^{C_{3}}) / (δ^{13} C_{C_{28}}^{C_{4}} - δ^{13} C_{C_{28}}^{C_{3}})

(3)

where $δ^{13} C_{C_{28}}^{C_{3}}$ and $δ^{13} C_{C_{28}}^{C_{4}}$ are the carbon isotopic compositions of n-alkanoic acid (C₂₈) of C3 and C4 plants, respectively. These values ( $δ^{13} C_{C_{28}}^{C_{3}} = - 36.9$ $δ^{13} C_{C_{28}}^{C_{4}}$ ‰)=−22‰) were from Chikaraishi et al. (2004). $δ^{13} C_{C 28}$ values lower than −36.9‰ and higher than −22‰ were considered to represent 100% C3 and C4 vegetation, respectively. A fewer number of the samples were analyzed for δ¹³C_C28 than for δD_C28 (Figure 2a and b); $f_{C} 4$ values of such samples were estimated based on the interpolated δ¹³C_C28 values (Figure 2b).

Figure 2.

(a) δD_C28 variability in the peat core. Two horizontal gray lines indicate the mean of δD_C28 during the early Holocene (shown in a blue rectangle) and the Late-Holocene (shown in a pink rectangle); associated dotted lines indicate 1-σ standard deviations. (b) δ¹³C_C28 variability (the left axis) and estimated ƒ $C_{4}$ in the catchment area calculated using equation (3) (the right axis). The filled and hollow circles in (b) indicate measured and interpolated values, respectively. (c) δD_Rain estimated after the vegetation correction ( $δ D_{R a i n}^{v e g_c o r})$ and a constant Ɛ_app of −130 ‰ (Feakins et al., 2016). The “Δ” values indicate the range in δD−values in each case. “Δ” values of 52‰, 48‰, 54‰, and 56‰ in (c) correspond to the end-member values given by Chikaraishi and Naraoka (2003), Konecky et al. (2016), Sachse et al. (2012), and Roy and Sanyal (2022), respectively (see Supplemental Table S3, available online). The star shows the estimated δD of the present-day rain (−38‰) at the sampling site.

The vegetation corrected apparent fractionation between $δ D_{C 28}$ and δD of rain ( $ε_{C_{28} - P}^{v e g_{_} c o r}$ ) was calculated as

ε_{C_{28} - P}^{v e g_{_} c o r} = f_{C} 4 \times ε_{C_{28} - P}^{C_{4}} + (1 - f_{C} 4) \times ε_{C_{28} - P}^{C_{3}}

(4)

where $ε_{C_{28} - P}^{C_{3}}$ and $ε_{C_{28} - P}^{C_{4}}$ are, respectively, the apparent fractionation between the δD of n-alkanoic acid of chain length 28 and rainfall. There is an average offset of 25‰ between the δD of alkane and acid (Chikaraishi and Naraoka, 2007).

The vegetation corrected δD of rainfall ( $δ D_{R a i n}^{v e g_c o r}$ ) was then calculated using the following equation

δ D_{R a i n}^{v e g_c o r} = [(δ D_{C 28} + 1000) / (10^{- 3} \times ε_{C_{28} - P}^{v e g_{_} c o r} + 1)] - 1000 .

(5)

To assess the effect of the choice end-member $ε_{C_{28} - P}^{C_{3}}$ and $ε_{C_{28} - P}^{C_{4}}$ values on the $δ D_{R a i n}^{v e g_c o r}$ during the Holocene, we considered different end-member values reported in the literature (Supplemental Table S3, available online). In addition, we also considered the $ε_{a p p}$ of −130‰, recommended for the reconstruction of δD_rain in tropical forests and moist forest environments (Feakins et al., 2016) ( $δ D_{R a i n}^{ε_{a p p} = - 130}$ ).

Results and discussion

δD and δ¹³C variations of n-alkanoic acid and factors controlling them

δD of different homologs of n-alkanoic acid revealed an overall increasing trend during the Holocene (Supplemental Figure S4, available online). δD_C28 variations showed a range of 44‰: the heaviest (−166‰) in the most recent times (0.6 kyr BP) and the lightest (−210‰) at ~8.6 ky BP (Figure 2a). δ¹³C of homologs of higher chain lengths showed the lowest δ¹³C values for a period from 7.7 to 9.4 ky BP. It showed the highest δ¹³C values for a period between 4.4 and 6.6 ky (Supplemental Figure S4, available online and Figure 2b).

δD_C28 and δ¹³C_C28 variability suggest some similarities (Figure 2). For example, around ~ 3 ka BP, and from 7.7 to 9.4 ky BP lower values of δD_C28 and δ¹³C_C28 can be seen. However, the Holocene-scale variations of both are different. The highest δ¹³C_C28 values were seen during 4.4–6.1 ky BP whereas the highest δD_C28 values were associated with the most recent sample. The decreasing trend in δD_C28 observed during the Holocene is absent in the δ¹³C_C28 variations.

Effect of vegetation changes

The $f_{C} 4$ in the samples, estimated using equation (3), varied from 0.5 to 1 (Figure 2b). The lowest $f_{C} 4$ values were observed from 7.7 to 9.4 ky BP, a period of the highest expansion of woody plants in the region during the Holocene. The period between 4.4 and 7.2 ky BP showed a higher abundance of grasses in the region ( $f_{C} 4$ ranging from 0.9 to 1.0) with the highest $f_{C} 4$ of 1 at 4.4 ky BP.

Figure 2c shows $δ D_{R a i n}$ variability estimated using equations (4) and (5) after considering various end-member $ε_{C_{28} - P}^{C_{4}}$ and $ε_{C_{28} - P}^{C_{3}}$ values (Supplemental Table S3, available online). Irrespective of the choice of end-member values, $δ D_{R a i n}$ showed similar trends which suggested a minor control of the tree/grass ratio on the decreasing trend in δD_C28 during the Holocene. The period of pronounced abundances of grasses (4.4–7.2 ky BP) was not associated with anomalous δD_C28 (Figure 2a) and $δ D_{R a i n}$ (Figure 2c) values further support this claim.

Reconstructing δD of rainfall

The absolute $δ D_{R a i n}^{v e g_c o r}$ values and the amount of its increase ( $Δ δ D_{R a i n}^{v e g_c o r})$ during the Holocene (Figure 2c) depended on the choice of the end-member $ε_{C_{28} - P}^{C_{4}}$ and $ε_{C_{28} - P}^{C_{3}}$ values. The $δ D_{R a i n}^{v e g_c o r}$ for the latest sample were significantly depleted than −38‰, the δD of the present-day rain at the sampling site (Figure 2) (Supplemental Table S3, available online). It has been shown that the δD of n-alkanoic acid of trees is higher by 30–50‰ than that of grasses (Chikaraishi et al., 2004; Hou et al., 2007; Krull et al., 2006; Roy et al., 2020). The difference between the $ε_{C_{28} - P}^{C_{3}}$ and $ε_{C_{28} - P}^{C_{4}}$ in Supplemental Table S3, available online vary from − 3.5 to +32‰. It thus appeared that the end-member $ε_{a p p}$ values in the Nilgiri Plateau are likely to be different than those given in Supplemental Table S3, available online.

The $ε_{a p p}$ between the δD of mean annual precipitation and δD of the wax compounds in humid areas globally showed similar values (Feakins et al., 2016; Hou et al., 2007). The $ε_{a p p}$ of −130‰ was suggested for δD_C28 records from moist forest environments and tropical forests (Feakins et al., 2016) which is similar to that reported by Gao et al. (2014) for a humid environment. The δD_rain estimated after considering a constant $ε_{a p p}$ of −130 ‰ ( $δ D_{R a i n}^{ε a p p = - 130}$ ) yielded −36‰ for the most recent sample, a value compatible with the δD_rain at the sampling site that is, −38‰.

Effect of varying contributions from the NEM

The relative contributions from the SWM and NEM could influence δD of the annual rainfall. The pre-Monsoon, SWM and NEM contribute about 13%, 71%, and 16%, respectively, to the annual rainfall at Avalanche (the data from Von Lengerke, 1977). If Avalanche was to receive the same amount of NEM rainfall as at Coonoor, these contributions would change to 10%, 59%, and 31%, respectively. As long as the differences among δD of the pre-Monsoon, SWM and NEM remain the same, the increase in the contribution of the NEM from 16 to 31% decreases δD of the annual rainfall by ~4‰ (Supplemental Table S4, available online), which is much less than 44‰ range in δD₂₈ observed in the present study. It appears that the variation in the contribution of the NEM might not have significantly influenced δD_C28.

Comparison with other rainfall reconstructions

δD_C28 trend revealed in this study follows the solar insolation (Figure 3a and b). The coherence of the Holocene-scale monsoonal reconstruction of the present study with that from other reconstructions from monsoonal Asia (Figure 3) validates the utility of δD of n-alkanoic acid of peat from Nilgiri Plateau as a proxy for rainfall.

Figure 3.

The Holocene precipitation proxy records from the Asian region. (a) Solar insolation at 30⁰N. (b) δD_C28 variability in the peat deposit of Nilgiri plateau (present study). (c) Hydroclimatic changes inferred from a multi-proxy study at Lonar Lake (Prasad et al., 2014). In (c), darker shades of blue and brown represent, respectively, wetter and drier climates. (d) δ¹³C variations of n-alkanoic acids from a sediment core that have a contribution from the Godavari Basin (Ponton et al., 2012). (e) δ¹⁸O variability speleothem from Qunf cave, southern Oman (Fleitmann et al., 2003). (f) δ¹⁸O record of a speleothem from Sahiya cave, India (Kathayat et al., 2017). Precipitation reconstruction based on the core retrieved from (g) Tianchi lake and (h) Gonghai lake (Chen et al., 2017). (i) δ¹³C record of Mayinghai lake sediments (Cheng et al., 2020). (j) δD record of Qinghai lake sediments (Thomas et al., 2016). δ18O record of a speleothem from (k) Dongge cave (Dykoski et al., 2005), (l) Heshang cave (Hu et al., 2008), (m) Jinfo cave (Yang et al., 2019), and (n) Sanbo cave (Wang et al., 2008). (o) Humification index in Hongyuan bog (Yu et al., 2006). (p) δ¹⁸O of bulk carbonate in Seling Co. (Gu et al., 1993). (q) a composite δ¹⁸O record of a speleothem from Kesang cave (Cheng et al., 2012). Note that the values of δD and δ¹⁸O are expressed in permil (‰) and are reversed. The blue rectangle indicates a wetter phase observed around 3 ka.

Synthesis of paleomonsoon variability reconstructed using various terrestrial and oceanic proxies from the Indian region show an overall drying trend during the Holocene (Misra et al., 2019; Banerji et al., 2020). The pollen records from the Nilgiri Plateau have suggested a particularly very wet early Holocene as compared to recent times (Sutra, 1997). The Holocene rainfall trend and the wetter and drier phases revealed in a sediment core from Lonar Lake (Prasad et al., 2014) match well with that reported here (Figure 3b and c). Increasing aridification in the Godavari basin during the Holocene (Ponton et al., 2012) (Figure 3d) corroborates the rainfall variability inferred here. The oxygen isotope variability of speleothem from Qunf cave (Fleitmann et al., 2003) (Figure 3e) also revealed a drying trend during the Holocene. The monsoon variability inferred from various ocean sediment cores from the Arabian Sea and Bay of Bengal also suggested decreasing monsoonal rainfall during the Holocene. These studies are based on (i) percent variations in Globigerina bulloides abundance (Gupta et al., 2003) in the western Arabian sea, (ii) high-resolution terrigenous proxy studies (Thamban et al., 2007), and oxygen isotopic variations (δ¹⁸O) of G. ruber (Saraswat et al., 2013; Tiwari et al., 2015) at the eastern Arabian sea, (iii) sea surface salinity records based on δ¹⁸O variability of G. ruber from the western Bay of Bengal (Rashid et al., 2011). The locations of the ocean sediment cores from the eastern Arabian sea in Saraswat et al. (2013) and Tiwari et al. (2015) are significantly influenced by the discharge of rivers originating in the Western Ghats. As the present study site also lies in the Western Ghats, the decrease in the rainfall during the Holocene inferred at these sites mutually corroborates each other.

The decreasing trend in precipitation during the Holocene has also been reported for various sites in China (Zhang et al., 2017, 2019; Zhao et al., 2021). δ¹⁸O records of the speleothems from Dongee (Figure 3k) (Dykoski et al., 2005), Heshang (Figure 3l) (Hu et al., 2008) and Sanbao caves (Figure 3n) (Wang et al., 2008) demonstrate the decreasing trend in rainfall during the Holocene. δ¹³C studies of peat bogs at Dahu (Zhong et al., 2010), Dajiuhu peat (Ma et al., 2008), Hongyuan peat (Hong et al., 2003) and Hani Peat (Hong et al., 2005) also exhibit weakening of the monsoon. A compilation of eight lake records from various parts of China also suggests the weakening of the East Asian Summer Monsoon during the Holocene (Zhang et al., 2011).

Our δD_C28 record indicated a wetter phase during ~2.4–3.6 kyr BP; the degree of wetness, however, was less than that during the early Holocene (Figure 2a). Interestingly, a coeval wet phase was observed in multiple paleoclimate reconstructions from the Asian region (Figure 3c, f–j and m–q). Additionally, the abundance of G. bulloides in the eastern Arabain Sea (Saravanan et al., 2019) and δ¹⁸O records from Xianglong (Tan et al., 2018) and Dongshiya (Zhang et al., 2018) caves also exhibit a concurrent wetter phase.

Factors influencing δD of the reconstructed rainfall

Even though the extreme values of δD_rain during the Holocene suggested a change of ~44 ‰, the mean isotopic change was of lesser extent. When the mean δD_rain during the early (older than 8.2 ky) and the late (younger than 4.2 ky) Holocene are considered, this range reduces to 16 ‰ (Figure 2a). If the amount effect used by McGrath et al. (2021) (−28.19 mm/1‰ δD) was considered, 16‰ translated to ~22% more rainfall during the early Holocene than during the Late-Holocene. The relationship between annual rainfall and δ¹⁸O of tree-ring cellulose from Kerala (Managave et al., 2011) yielded a −24.7 mm/1‰ increase in δD which, when used, suggested ~20% higher rainfall during the early Holocene.

A modeling study suggested that annual δ⁸Ο of precipitation varied in the precession band (i.e. during high and low summer insolation scenarios) by 2.4–2.8‰ in the Nilgiri region (from Figure 6 in Tabor et al., 2018). This amplitude translated to a 19–22‰ increase in δD_rain (i.e. 8 times δ¹⁸Ο signal). The modeled difference in annual δ⁸Ο of precipitation during high insolation (such as during the early Holocene) and modern conditions suggested ~16‰ increase in δD of precipitation (i.e. 8 times 2‰) in southern India (from Figure 7 in Battisti et al., 2014). It thus appears that the average change in δD_rain during the Holocene is consistent with the model predictions.

The range of extreme values in δD_rain during the Holocene, however, was ~44 ‰ which translated to ~60% and ~55% higher rainfall during the wettest phase of the early Holocene, considering the amount effect of −28.19 mm/1‰ and −24.7 mm/1‰, respectively. Further, the range of ~44‰ was 2 and 2.75 times higher than that reported by Tabor et al. (2018) and Battisti et al. (2014), respectively, for the region and was comparable to the glacial-interglacial range of 50‰ in δD_rain reported by McGrath et al. (2021). It is also much higher than the range of 12‰ observed in the Holocene-level δD variability of n-alkane in a sediment core from the upper Bay of Bengal (Contreras-Rosales et al., 2014). The large amplitude of δD_rain observed in the present study thus indicated a role played by factors other than the amount of precipitation in controlling δD_rain during the wettest phase of the early Holocene.

The emerging view (Battisti et al., 2014; Hu et al., 2019; McGrath et al., 2021; Sarkar et al., 2015; Tabor et al., 2018) suggests that δ⁸Ο_rain (or δD_rain) on an orbital scale in Asia is influenced by factors such as precipitation amount, moisture source and variable rainout during transport of vapor. The insolation-only isotope-enabled climate model indicated changes in the wind strength and directions could affect the relative contributions of vapor from different vapor source regions (Tabor et al., 2018). During precession minima, a higher contribution from a more distal moisture source, associated with more rainout during longer transport, led to the lighter isotopic composition of rainfall in the Indian region (Tabor et al., 2018). It is speculated that δD_rain during the wettest part of the early Holocene reported here plausibly indicates changes in the circulation pattern. WIO is a major source of moisture for rainfall over the Western Ghats followed by the CIO (Pathak et al., 2017) (Figure 1a). The latter constitutes a more distal source than the former (Figure 1a). A stronger monsoon during the higher summer insolation was associated with moisture from more distal sources (Tabor et al., 2018). Therefore, during the wettest phase of the early Holocene the study area might have received a higher contribution of moisture from CIO (Figure 1a), and the rainout during the longer transport path led to a lower δD of the associated rain.

The implication to the peat-based past climate variability in the Nilgiri Plateau

The C3 and C4 plants on the Nilgiri Plateau have δ¹³C values of −26.6 ± 2.3‰ and −10.9 ± 0.97‰, respectively (Rajagopalan et al., 1999). However, considerable ambiguity exists regarding the interpretation of the relative abundance of C3-C4 vegetation inferred from δ¹³C-based studies. Initial investigations of δ¹³C records in the peat (Sukumar et al., 1993) were interpreted in terms of precipitation variability whereas Caner et al. (2007) stressed the role of temperature in controlling δ¹³C in the soil profiles. The recent experimental evidence (Joshi et al., 2020) suggests that winter frosting in the open grasslands hinders the establishment of native trees in the grassland. This suggests that the ratio of grass to trees in the region is mainly controlled by temperature. The role of local disturbance (fire) and topography-controlled hydrology was also invoked to explain the vegetation composition in the area (Ramya Bala et al., 2022).

Sukumar et al. (1993) and Ramya Bala et al. (2022) have carried out δ¹³C-based investigations in peat cores from Sandynallah, from Region II. In the latter, the δ¹³C variability varied depending upon whether the core is from an ecotone between shola and sedgeland (in Core 1, Figure 4c) or the ecoregion of stable sedgeland (in Core 2, Figure 4d).

Figure 4.

Peat-based paleoclimate investigation in the Nilgiri. (a) δD_C28 and (b) δ¹³C_C28 variability in the peat core from Avalanche (this study). δ¹³C variations in (c) Core 1 and (d) Core 2 from Sandynallah (Ramya Bala et al., 2022).

In contrast to the distinct seasonality in rainfall, the seasonality in temperature between Region I and II is not pronounced (the inset in Figure 1). The climate records from Region I and II show overlapping average monthly temperature and the monthly average of daily minimum temperature (Supplementary Figure S5). If the frost-mediated temperature effect controls the C3 and C4 composition in the region, then the similar ecotones from Region I and II are likely to show coherent variability in δ¹³C. A period from ~7.5 to ~4 ka was characterized by the complete dominance of C4-type sedgeland vegetation (shown by a horizontal arrow) followed by the appearance of shola (shown by an inclined arrow) (Figure 4c). These trends can be observed in our δ¹³C_C28 record as well (Figure 4c). The similarity in the δ¹³C variability in our study (Figure 4c) and in Core 1 (Figure 4c), both representing variations in the ecotone between shola and sedgeland, perhaps attests to the frost-mediated temperature control of δ¹³C.

At this stage, it is not clear whether the Holocene scale drying in Region I reported in this study occurred in Region II as well. If it had occurred, then this study can help in resolving the uncertain interpretations in Ramya Bala et al. (2022). The δ¹³C variability in the Core 1 was attributed either to local disturbance (such as a fire) or changes in the climate-initiated hydrological conditions. The hydrological reasons invoked by Ramya Bala et al. (2022) for explaining the changes in the vegetation composition in the Core 1 are at odds with the rainfall variability reconstructed based on δD_C28 in this study (Figure 4a). The higher δ¹³C values during a period from 7.4 to 4 ka in the Core 1 were interpreted to reflect wetter conditions but our δD_C28 record showed a relatively drier phase for the same period. An arid phase was suggested for explaining the lowering of δ¹³C values post 4 ka (shown by an inclined arrow in Figure 4c) but the concurrent phase was wetter in our study. It thus appears that C3-C4 variability in Core 1 may not represent the climate-initiated hydrological changes. Further, the role played by temperature and precipitation in controlling the relative abundances of C3 and C4 sedges in Core 2 was also not fully resolved. Interestingly, the δ¹³C trend in Core 2 and δD_C28 variability show similar variations during the Holocene (Figure 4) plausibly suggesting the role of rainfall in controlling δ¹³C of Core 2. At this stage, it appears that the δ¹³C variability in the ecotone between trees and grassland is controlled by temperature whereas precipitation controls δ¹³C of stable sedgeland ecoregion.

To optimally utilize the peat-based proxies for reconstructing past climate variability, characterization of the isotope-based proxies from the Nilgiri Plateau is necessary. The end-member characterization of δD and δ¹³C of various leaf wax compounds in grasses, shola and sedges needs to be estimated. Based on the δD of the reconstructed rain of the latest sample, it appears that the plant community-averaged $ε_{a p p}$ in the Niligiri region should be close to −130 ‰, the $ε_{a p p}$ value recommended for tropical forests (Feakins et al., 2016). Further, it is important to test the role temperature and rainfall play in controlling the C3 and C4 variability in the sedgeland. The δD-based rainfall reconstructions from Regions II and III would not only reveal the spatial variability in the past rainfall but would also shed light on how temperature and rainfall control the vegetation in this ecologically sensitive zone. As the high-elevation sites show a heightened sensitivity of precipitation isotopes to climate change (Cai et al., 2012), it is necessary to assess the effect of change in the precipitation isotope lapse rate during the Holocene in the study area. Given the complex mosaic landscape of the Nilgiri and other montane forests in south India, further refinement of the spatio-temporal vegetation dynamics requires a proxy like pollen in combination with chemical/molecular isotope proxies to better delineate the landscape-scale drivers of these dynamics in the past (Sutra, 1997).

Conclusion

The Nilgiri Plateau hosts important paleoclimate archives having the potential for reconstructing climate since the last glacial maxima. Its location in a monsoonal environment and its higher altitude, however, result in both rainfall and temperature controlling proxy response in the region. This work demonstrates that the leaf wax δD record of peat deposit provides sensitive and continuous records of past hydrology. It appears to be a better proxy for the reconstruction of past hydrological variability whereas δ¹³C-based proxies reflect the temperature-controlled variability in the composition of vegetation. This is the first report to show the precipitation over the Nilgiri Plateau followed the solar insolation during the Holocene and also the first to show the utility of δD studies of tropical peat in reconstructing past rainfall. However, the large amplitude of the reconstructed δD_rain suggested rainout associated with a longer monsoon transport path could have also contributed to lower δD_rain during the early Holocene.

Supplemental Material

sj-docx-1-hol-10.1177_09596836231183110 – Supplemental material for Holocene precipitation hydrogen isotopic values on Nilgiri Plateau (southern India) suggest a combined effect of precipitation amount and transport paths

Supplemental material, sj-docx-1-hol-10.1177_09596836231183110 for Holocene precipitation hydrogen isotopic values on Nilgiri Plateau (southern India) suggest a combined effect of precipitation amount and transport paths by Shreyas Managave, Yongsong Huang, Jean-Pierre Sutra, Krishnamurthy Anupama and Srinivasan Prasad in The Holocene

Footnotes

Acknowledgements

We thank Lekshmy PR for providing rainwater δD data. Sarah M. McGrath is acknowledged for her critical suggestions. Devi Maheshwori’s help during the preparation of Figure 1 is acknowledged. We thank the Tamil Nadu Forest Department for permission to collect the sediment core analyzed in this study. T. Gopal (IFP) took an active role in extracting the core on the field.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is supported by funding from UGC, New Delhi under Raman Fellowship (Grant No. F. 5-104/2014(IC)).

ORCID iD

Shreyas Managave

Supplemental material

Supplemental material for this article is available online.

References

Araguás-Araguás

Froehlich

Rozanski

(2000) Deuterium and oxygen-18 isotope composition of precipitation and atmospheric moisture. Hydrological Processes 14: 1341–1355.

Banerji

Arulbalaji

Padmalal

(2020) Holocene climate variability and Indian Summer Monsoon: An overview. The Holocene 30: 744–773.

Battisti

Ding

Roe

(2014) Coherent pan-Asian climatic and isotopic response to orbital forcing of tropical insolation. Journal of Geophysical Research Atmospheres 119(21): 11997–12020.

Blaauw

(2010) Methods and code for ‘classical’ age-modelling of radiocarbon sequences. Quaternary Geochronology 5(5): 512–518.

Blasco

(1971) Montagnes du Sud de l'inde; forêts, savanes, écologie. Institut français de Pondichéry, travaux de la section scientifique et technique 10(1): 1–436.

Blasco

(1987) Réflexions sur le déterminisme des formations végétales tropicales. Ecole pratique des hautes études. Mémoires et travaux de l'Institut de Montpellier 17: 3–13.

Blasco

Tassy

(1975) Etude d'un écosystème forestier montagnard du sud de l'Inde. Bulletin d' Ecologie 6: 525–539.

Cai

Zhang

Cheng

, et al. (2012) The Holocene Indian monsoon variability over the southern Tibetan Plateau and its teleconnections. Earth and Planetary Science Letters 335-336: 135–144.

Caner

Seen

Gunnell

, et al. (2007) Spatial heterogeneity of land cover response to climatic change in the Nilgiri highlands (Southern India) since the last glacial maximum. The Holocene 17: 195–205.

10.

Cheng

Liu

Chen

, et al. (2020) Impact of abrupt Late Holocene monsoon climate change on the status of an alpine lake in North China. Journal of Geophysical Research Atmospheres 125: e2019JD031877.

11.

Cheng

Zhang

Spötl

, et al. (2012) The climatic cyclicity in semiarid-arid central Asia over the past 500,000 years. Geophysical Research Letters 39: L01705.

12.

Chen

Huang

, et al. (2017) A novel procedure for pollen-based quantitative paleoclimate reconstructions and its application in China. Science China Earth Sciences 60(11): 2059–2066.

13.

Chikaraishi

Naraoka

(2003) Compound-specific δD-δ¹³C analysis of n-alkanes extracted from terrestrial and aquatic plants. Phytochemistry 63: 361–371.

14.

Chikaraishi

Naraoka

(2007) δ¹³C and δD relationships among three n-alkyl compound classes (n-alkanoic acid, n-alkane and n-alkanol) of terrestrial higher plants. Organic Geochemistry 38(2): 198–215.

15.

Chikaraishi

Naraoka

Poulson

(2004) Hydrogen and carbon isotopic fractionations of lipid biosynthesis among terrestrial (C3, C4 and CAM) and aquatic plants. Phytochemistry 65: 1369–1381.

16.

Contreras-Rosales

Jennerjahn

Tharammal

, et al. (2014) Evolution of the Indian summer monsoon and terrestrial vegetation in the Bengal region during the past 18 ka. Quaternary Science Reviews 102: 133–148.

17.

Daniels

Russell

Giblin

, et al. (2017) Hydrogen isotope fractionation in leaf waxes in the Alaskan Arctic tundra. Geochimica et Cosmochimica Acta 213: 216–236.

18.

Dansgaard

(1964) Stable isotopes in precipitation. Tellus 16: 436–468.

19.

Deshpande

Bhattacharya

Jani

, et al. (2003) Distribution of oxygen and hydrogen isotopes in shallow groundwaters from southern India: Influence of a dual monsoon system. Journal of Hydrology 271: 226–239.

20.

Dykoski

Edwards

Cheng

, et al. (2005) A high-resolution, absolute-dated Holocene and deglacial Asian monsoon record from Dongge Cave, China. Earth and Planetary Science Letters 233: 71–86.

21.

Feakins

Bentley

Salinas

, et al. (2016) Plant leaf wax biomarkers capture gradients in hydrogen isotopes of precipitation from the Andes and Amazon. Geochimica et Cosmochimica Acta 182: 155–172.

22.

Fleitmann

Burns

Mudelsee

, et al. (2003) Holocene forcing of the Indian monsoon recorded in a stalagmite from southern Oman. Science 300: 1737–1739.

23.

Gao

Zheng

Fraser

Huang

(2014) Comparable hydrogen isotopic fractionation of plant leaf wax n-alkanoic acids in arid and humid subtropical ecosystems. Geochemistry Geophysics Geosystemes 15: 361–373.

24.

Gupta

Anderson

Overpeck

(2003) Abrupt changes in the Asian southwest monsoon during the Holocene and their links to the North Atlantic Ocean. Nature 421: 354–357.

25.

Liu

Yuan

, et al. (1993) The changes in monsoon influence in the Qinghai Tibet plateau during the past 12,000 years. Geochemical evidence from the lake Seling sediments. Chinese Science Bulletin 38: 61–64.

26.

Hong

Lin

, et al. (2003) Correlation between Indian Ocean summer monsoon and North Atlantic climate during the Holocene. Earth and Planetary Science Letters 211(3-4): 371–380.

27.

Hong

Lin

, et al. (2005) Inverse phase oscillations between the East Asian and Indian Ocean summer monsoons during the last 12000 years and paleo-El Niño. Earth and Planetary Science Letters 231: 337–346.

28.

Hou

D'Andrea

Huang

(2008) Can sedimentary leaf waxes record D/H ratios of continental precipitation? Field, model, and experimental assessments. Geochimica et Cosmochimica Acta 72: 3503–3517.

29.

Hou

D’Andrea

MacDonald

, et al. (2007) Hydrogen isotopic variability in leaf waxes among terrestrial and aquatic plants around Blood pond, Massachusetts (USA). Organic Geochemistry 38: 977–984.

30.

Huang

Shuman

Wang

, et al. (2004) Hydrogen isotope ratios of individual lipids in lake sediments as novel tracers of climatic and environmental change: A surface sediment test. Journal of Paleolimnology 31: 363–375.

31.

Henderson

Huang

, et al. (2008) Quantification of Holocene Asian monsoon rainfall from spatially separated cave records. Earth and Planetary Science Letters 266: 221–232.

32.

Emile-Geay

Tabor

, et al. (2019) Deciphering oxygen isotope records from Chinese speleothems with an Isotope-Enabled Climate Model. Paleoceanography and Paleoclimatology 34: 2098–2112.

33.

IAEA/WMO (2022) Global Network of Isotopes in Precipitation. The GNIP Database. Accessible at: https://nucleus.iaea.org/wiser/index.aspx

34.

Joshi

Ratnam

Sankaran

(2020) Frost maintains forests and grasslands as alternate states in a montane tropical forest–grassland mosaic; but alien tree invasion and warming can disrupt this balance. Journal of Ecology 108: 122–132.

35.

Kathayat

Cheng

Sinha

, et al. (2017) The Indian monsoon variability and civilization changes in the Indian subcontinent. Science Advances 3: e1701296.

36.

Kaushal

Breitenbach

SFM

Lechleitner

, et al. (2018) The Indian summer monsoon from a Speleothem δ¹⁸O Perspective—A review. Quaternary 1: 29.

37.

Konecky

Russell

Bijaksana

(2016) Glacial aridity in central Indonesia coeval with intensified monsoon circulation. Earth and Planetary Science Letters 437: 15–24.

38.

Konecky

Russell

Johnson

, et al. (2011) Atmospheric circulation patterns during late Pleistocene climate changes at Lake Malawi, Africa. Earth and Planetary Science Letters 312(3-4): 318–326.

39.

Krull

Sachse

Mügler

, et al. (2006) Compound-specific δ¹³C and δ²H analyses of plant and soil organic matter: A preliminary assessment of the effects of vegetation change on ecosystem hydrology. Soil Biology and Biochemistry 38: 3211–3221.

40.

Legris

Blasco

(1969) Variabilité des facteurs du climat: Cas des montagnes du Sud de l'Inde et de Ceylan. Institut français de Pondichéry, travaux de la section scientifique et technique 8(1): 1–94.

41.

Legris

Meher-Homji

(1977) Phytogeographic outlines of the hill ranges of peninsular India. Tropical Ecology 18(1): 10–24.

42.

Lekshmy

Midhun

Ramesh

, et al. (2014) ¹⁸O depletion in monsoon rain relates to large scale organized convection rather than the amount of rainfall. Scientific Reports 4(1): 5.

43.

Lekshmy

Midhun

Ramesh

(2015) Spatial variation of amount effect over peninsular India and Sri Lanka: Role of seasonality. Geophysical Research Letters 42: 5500–5507.

44.

Zhu

Zheng

, et al. (2008) High-resolution geochemistry records of climate changes since late-glacial from Dajiuhu peat in Shennongjia mountains, central China. Chinese Science Bulletin 53: 28–41.

45.

Managave

Sheshshayee

Ramesh

, et al. (2011) Response of cellulose oxygen isotope values of teak trees in differing monsoon environments to monsoon rainfall. Dendrochronologia 29: 89–97.

46.

Mayewski

Rohling

Curt Stager

, et al. (2004) Holocene climate variability. Quaternary Research 62(3): 243–255.

47.

McGrath

Clemens

Huang

, et al. (2021) Greenhouse gas and ice volume drive Pleistocene Indian summer monsoon precipitation isotope variability. Geophysical Research Letters 48: e2020GL092249.

48.

Meher-Homji

(1967) Phytogeography of the South Indian Hill stations. Bulletin of the Torrey Botanical Club 94: 230.

49.

Misra

Tandon

Sinha

(2019) Holocene climate records from lake sediments in India: Assessment of coherence across climate zones. Earth-Science Reviews 190: 370–397.

50.

Morrill

Overpeck

Cole

(2003) A synthesis of abrupt changes in the Asian summer monsoon since the last deglaciation. The Holocene 13(4): 465–476.

51.

Niedermeyer

Schefuß

Sessions

, et al. (2010) Orbital- and millennial-scale changes in the hydrologic cycle and vegetation in the western African Sahel: Insights from individual plant wax δD and δ¹³C. Quaternary Science Reviews 29: 2996–3005.

52.

Pagani

Pedentchouk

Huber

, et al. (2006) Arctic hydrology during global warming at the Palaeocene/Eocene thermal maximum. Nature 442: 671–675.

53.

Parthasarathy

Swain

Balu

(2021) Hydrochemical and stable isotopic characteristics of waters in and around an eutrophic lake—Ooty, Tamil Nadu, India. Arabian Journal of Geosciences 14: 561.

54.

Pathak

Ghosh

Martinez

, et al. (2017) Role of oceanic and land moisture sources and transport in the seasonal and interannual variability of summer monsoon in India. Journal of Climate 30(5): 1839–1859.

55.

Ponton

Giosan

Eglinton

, et al. (2012) Holocene aridification of India. Geophysical Research Letters 39: L03704.

56.

Prasad

Anoop

Riedel

, et al. (2014) Prolonged monsoon droughts and links to indo-Pacific warm pool: A Holocene record from Lonar Lake, Central India. Earth and Planetary Science Letters 391: 171–182.

57.

Rajagopalan

Ramesh

Sukumar

(1999) Climatic implications of δ¹³C and δ¹⁸O ratios from C3 and C4 plants growing in a tropical montane habitat in southern India. Journal of Biosciences 24: 491–498.

58.

Rajagopalan

Sukumar

Ramesh

, et al. (1997) Late quaternary vegetation and climatic changes from tropical peat in southern India - an extended record up to 40,000 years BP. Current Science 73: 60–63.

59.

Raja

Achyuthan

Geethanjali

, et al. (2018) Late pleistocene paleoflood deposits identified by grain size signatures, Parsons Valley Lake, Nilgiris, Tamil Nadu. Journal of the Geological Society of India 91: 547–553.

60.

Ramya Bala

Pullyottum Kavil

Tayasu

, et al. (2022) Paleovegetation dynamics in an alternative stable states landscape in the montane Western Ghats, India. The Holocene 32: 297–307.

61.

Ranganathan

(1938) Studies in the ecology of the shola grassland vegetation of the Nilgiri Plateau. Indian Forester 64(9): 523–541.

62.

Rashid

England

Thompson

, et al. (2011) Late glacial to Holocene Indian summer monsoon variability based upon sediment records taken from the Bay of Bengal. Terrestrial Atmospheric and Oceanic Sciences 22: 215–228.

63.

Resmi

Sudharma

Hameed

(2016) Stable isotope characteristics of precipitation of Pamba River basin, Kerala, India. Journal of Earth System Science 125(7): 1481–1493.

64.

Roy

Ghosh

Sanyal

(2020) Morpho-tectonic control on the distribution of C3-C4 plants in the central Himalayan Siwaliks during late Plio-Pleistocene. Earth and Planetary Science Letters 535: 116119.

65.

Roy

Sanyal

(2022) Isotopic and molecular distribution of leaf-wax in plant-soil system of the Gangetic floodplain and its implication for paleorecords. Quaternary International 607: 89–99.

66.

Sachse

Billault

Bowen

, et al. (2012) Molecular paleohydrology: Interpreting the hydrogen-isotopic composition of lipid biomarkers from photosynthesizing organisms. Annual Review of Earth and Planetary Sciences 40: 221–249.

67.

Saraswat

Lea

Nigam

, et al. (2013) Deglaciation in the tropical Indian Ocean driven by interplay between the regional monsoon and global teleconnections. Earth and Planetary Science Letters 375: 166–175.

68.

Saravanan

Gupta

Zheng

, et al. (2019) Late Holocene long arid phase in the Indian subcontinent as seen in shallow sediments of the eastern Arabian Sea. Journal of Asian Earth Sciences 181: 103915.

69.

Sarkar

Prasad

Wilkes

, et al. (2015) Monsoon source shifts during the drying mid-Holocene: Biomarker isotope based evidence from the core ‘monsoon zone’ (CMZ) of India. Quaternary Science Reviews 123: 144–157.

70.

Seki

Meyers

Yamamoto

, et al. (2011) Plant-wax hydrogen isotopic evidence for postglacial variations in delivery of precipitation in the monsoon domain of China. Geology 39: 875–878.

71.

Shuman

Huang

Newby

, et al. (2006) Compound-specific isotopic analyses track changes in seasonal precipitation regimes in the Northeastern United States at ca 8200calyrBP. Quaternary Science Reviews 25: 2992–3002.

72.

Sukumar

Ramesh

Pant

, et al. (1993) A δ¹³C record of late Quaternary climate change from tropical peats in southern India. Nature 364: 703–706.

73.

Sutra

(1997) Contribution palynologique a la connaissance de l'histoire de la vegetation et du climat au cours des 30 derniers millénaires dans le massif des Nilgiri, Inde du Sud. Unpublished PhD Thesis, Universite d'Aix-Marseille III, Marseille, p.174.

74.

Sutra

Bonnefille

Fontugne

(1997) Étude palynologique d'un nouveau sondage dans les marais de Sandynallah (Massif des Nilgiri, Sud-ouest de l'Inde). Géographie Physique et Quaternaire 51(3): 415–426.

75.

Tabor

Otto-Bliesner

Brady

, et al. (2018) Interpreting precession-driven δ ¹⁸O variability in the south Asian monsoon region. Journal of Geophysical Research Atmospheres 123(11): 5927–5946.

76.

Tan

Cai

Cheng

, et al. (2018) Centennial- to decadal-scale monsoon precipitation variations in the upper Hanjiang River region, China over the past 6650 years. Earth and Planetary Science Letters 482: 580–590.

77.

Thamban

Kawahata

Rao

(2007) Indian summer monsoon variability during the Holocene as recorded in sediments of the Arabian Sea: Timing and implications. Journal of Oceanography 63(6): 1009–1020.

78.

Thomas

Huang

Clemens

, et al. (2016) Changes in dominant moisture sources and the consequences for hydroclimate on the northeastern Tibetan Plateau during the past 32 kyr. Quaternary Science Reviews 131: 157–167.

79.

Tierney

Russell

Huang

, et al. (2008) Northern Hemisphere controls on tropical southeast African climate during the past 60,000 years. Science 322: 252–255.

80.

Tiwari

Nagoji

Ganeshram

(2015) Multi-centennial scale SST and Indian summer monsoon precipitation variability since the Mid-Holocene and its nonlinear response to solar activity. The Holocene 25: 1415–1424.

81.

Tripti

Lambs

Moussa

, et al. (2019) Evidence of elevation effect on stable isotopes of water along highlands of a humid tropical mountain belt (Western Ghats, India) experiencing monsoonal climate. Journal of Hydrology 573: 469–485.

82.

Vasanthy

(1988) Pollen analysis of late Quaternary sediments: Evolution of upland savanna in Sandynallah (Nilgiris, south India). Review of Palaeobotany and Palynology 55: 175–192.

83.

Von Lengerke

(1977) The Nilgiris: Weather and Climate of a Mountain Area in South India. Stuttgart: Franz Steiner Verlag.

84.

Wang

Cheng

Edwards

, et al. (2008) Millennial- and orbital-scale changes in the East Asian monsoon over the past 224,000 years. Nature 451: 1090–1093.

85.

Warrier

Babu

Manjula

, et al. (2010) Isotopic characterization of dual monsoon precipitation-evidence from Kerala, India. Current Science 98: 1487–1495.

86.

Yadava

Ramesh

Pandarinath

(2007) A positive ‘amount effect’ in the Sahyadri (Western Ghats) rainfall. Current Science 93(2): 560–564.

87.

Yang

Wang

, et al. (2019) Early-Holocene monsoon instability and climatic optimum recorded by Chinese stalagmites. The Holocene 29(6): 1059–1067.

88.

Zhou

Franzen

, et al. (2006) High-resolution peat records for Holocene monsoon history in the eastern Tibetan Plateau. Science in China Series D 49(6): 615–621.

89.

Zhang

Zhao

Xue

, et al. (2017) Millennial-scale hydroclimate variations in southwest China linked to tropical Indian Ocean since the Last Glacial Maximum. Geology 45(5): 435–438.

90.

Zhang

Brahim

, et al. (2019) The Asian Summer Monsoon: Teleconnections and forcing Mechanisms—A review from Chinese Speleothem δ¹⁸O Records. Quaternary 2(3): 26.

91.

Zhang

Chen

Holmes

, et al. (2011) Holocene monsoon climate documented by oxygen and carbon isotopes from lake sediments and peat bogs in China: A review and synthesis. Quaternary Science Reviews 30: 1973–1987.

92.

Zhang

Yang

Cheng

, et al. (2018) Timing and duration of the East Asian summer monsoon maximum during the Holocene based on stalagmite data from North China. The Holocene 28: 1631–1641.

93.

Zhao

Cheng

Wang

, et al. (2021) Paleoclimate significance of reconstructed rainfall isotope changes in Asian monsoon region. Geophysical Research Letters 48: e2021GL092460.

94.

Zhong

Xue

Zheng

, et al. (2010) Climatic changes since the last deglaciation inferred from a lacustrine sedimentary sequence in the eastern Nanling mountains, south China. Journal of Quaternary Science 25: 975–984.

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Holocene precipitation hydrogen isotopic values on Nilgiri Plateau (southern India) suggest a combined effect of precipitation amount and transport paths

Abstract

Keywords

Introduction

Materials and methods

Sampling site

The isotopic characteristics of the modern-day rainfall

Seasonal isotopic variability

Isotopic variability in the Nilgiri Plateau

The amount effect in the rainfall and the response of the proxy records

Sample and radiometric dates

Lipid extraction and the isotopic measurements

The effect of change in vegetation on δD of n-alkanoic acid of chain length 28 (δDC28)

Results and discussion

δD and δ13C variations of n-alkanoic acid and factors controlling them

Effect of vegetation changes

Reconstructing δD of rainfall

Effect of varying contributions from the NEM

Comparison with other rainfall reconstructions

Factors influencing δD of the reconstructed rainfall

The implication to the peat-based past climate variability in the Nilgiri Plateau

Conclusion

Supplemental Material

sj-docx-1-hol-10.1177_09596836231183110 – Supplemental material for Holocene precipitation hydrogen isotopic values on Nilgiri Plateau (southern India) suggest a combined effect of precipitation amount and transport paths

Footnotes

Acknowledgements

Funding

ORCID iD

Supplemental material

References

Supplementary Material

The effect of change in vegetation on δD of n-alkanoic acid of chain length 28 (δD_C28)

δD and δ¹³C variations of n-alkanoic acid and factors controlling them