Evaluation of Temperature and Precipitation Since 4.3 ka Using Palynological Data from Kundala Lake Sediments, Kerala, India

Abstract

A comprehensive database of paleo vegetation from Kundala Lake, Kerala, was used for a palynological study in a 120 cm sedimentary profile from Kundala Lake (1700 mamsl), Palni Hills, to understand the climate and vegetation equilibrium during the last four millennia. On the basis of pollen assemblage and cluster analysis, a relatively high percentage of evergreen vegetation between 4.3 and 3.4 ka (phase I) was inferred to represent the relicts of middle Holocene vegetation during a warmer climate. Subsequently, in the periods of 3.4–2.3 ka (phase II) and 2.3–0.87 ka (phase III), herbs/shrubs dominated. A relative increase in the percentage of arboreals along with herbaceous taxa was again observed from 0.87 to 0.12 ka (phase IV). Later, in phase V (from 1820 AD to present), few new plant taxa were recorded. On the basis of the ‘coexistence approach’, the Mean Annual Temperature (MAT) was inferred to be 22 °C, 15 °C, 15 °C, 20 °C and 22 °C during phases I to V, respectively. The Mean Annual Precipitation (MAP)was 2660 ± 3700 mm from ~4.3 to 0.12 ka; however, it decreased to ~1750 mm between 3.4 and 2.3 ka. However, pollen evidence reveals short-term cooler spells during the 16/17th century AD, which is in concordance with the globally recorded cooler and arid climate that began sometime from ~5.0 to 4.0 ka. A thematic digital elevation map of vegetation reconstructed for the years 2005 and 2018 shows a reduction in evergreen plants and water bodies in the vicinity of Kundala Lake, which was correlated with the results of palynological studies and Indian meteorological data for the last ~100 years in the region.

1. Introduction

Excess or deficit monsoonal rainfall during a year is an abnormality or variability that has an impact on vegetation and also affects the agriculture economy. Variability in climate and its periodicity in the past has been well established globally [1,2,3,4] and is a serious factor in predicting whether the future trend in global warming is likely to impact biodiversity [5]. The chronological observation of the palynological spectrum in the sedimentary archives has been useful for understanding the behavior of vegetation in response to climatic variability in the past. The pattern of circum-global teleconnection and climate is linked to the Indian Summer Monsoon (ISM), which has recently been discussed in detail [6]. The spatial distribution of ISM precipitation is related to the position and strength of the ‘Monsoon Trough’, extending in the Indian sub-continent from the northwestern part to the Ganga plains and the Bay of Bengal [7]. Widespread global climatic anomalies [8] and reduced ISM precipitation since ~4.0 ka, observed through biotic and abiotic proxy records, have been reported by several researchers [9,10,11,12,13,14]. The intensity, duration and magnitude of the ISM had moderate to severe impacts on vegetation in the past, particularly during the Holocene, which is attributed to northward and southward shifts in the intertropical convergence zone [15,16]. In the past, the dynamics of significant climate-induced ecological changes affected the structure of a forest community [17]. In addition to observations made on climate variability through proxy records from sedimentary archives, a multi-decadal anomaly in recent rainfall has been recorded across the different parts of world using surface meteorological data [18,19,20,21,22]. In view of past records, intensive research work has been carried out in the last 4–5 decades to understand the interannual variability in monsoon rainfall from different regions of the Indian subcontinent in order to predict future trends [23,24,25]. A decreasing trend was observed in the southwest monsoon (SWM—June to August) with an increase in post-monsoon rainfall (October to December) by 6% in the years 1900 to 2015 [26,27]. The present-day anomalous pattern of rainfall is attributed to global warming due to an increase in atmospheric CO₂ to ~400 ppm and temperature by ~1.2 °C [28].

The decrease in ISM rainfall is more prominent at higher altitudes in montane regions of Western Ghats and is related to the decreasing strength of the tropical easterly jet stream during the last 5 decades [29,30,31]. These regions are highly sensitive to the consequences of global warming trends and considered sentinels of climate change. The diversity of evergreen and deciduous plant species in southern Western Ghats depends on low and high seasonality, which is determined by the number of rainy months in a year [17,32]. The vegetation here is commonly called Shola forest (very prone to forest fires) and is unique in terms of its endemic biodiversity. The interplay of pollen assemblages between shrubby/herbaceous grasslands and the Shola forest community has been chronologically documented from lake sedimentary archives since the late Pleistocene to occur at different altitudes in the Annamalai, Palni and Nilgiri Hills (Figure 1 in southern Western Ghats, India [33,34,35,36]. However, the implications of using the palynological database in climatic interpretations are still not well understood and vary with respect to the depth, chronology and altitude of the sedimentary profiles from Western Ghats, India.

Hence, the climate change studies from southern Western Ghats are still at a synoptic scale. In this work, we also summarize the surface meteorological data (since ~1820 AD) and a digital elevation map of vegetation reconstructed for the years 2005 and 2018 in the study area in order to evaluate the recent decadal trend in the climate–vegetation relationship. Following this, we examine palynological assemblages in the sedimentary archive from Kundala Lake, bringing forth records of climatic variability since ~4.8 ka to understand the variability in MAT and MAP using the coexistence approach through palynological assemblages in the sedimentary profile, in addition to Indian meteorological data records for the last 100 years in the study area.

2. Study Area

Kundala Lake, situated at Latitude 10.01.6062 N and Longitude 77.03.271 E, is approximately 15 km from Munnar on the way to Top Station, Kerala (Figure 1), at an altitude of 1700 m above mean sea level (a.m.s.l). The Muthirapuzha River, a tributary of the Periyar River, flows in the low-lying region amidst the hilly area surrounded by thick forest cover at present. A Muttupetty Dam was built for a Hydroelectric Power project. The sediment collection was performed in Kundala Lake, which is about 8 km away from the ‘echo point’ tourist spot (Muttupetty Dam). Residential and agricultural land is more common between the Kundala Lake and Muttupetty Dam. Water from the Kundala Lake flows through the Palar River into the Muttupetty Dam. A dendritic drainage pattern from the hilly slopes surrounding the lake is fed by monsoon water during summers and winters. A detailed study has been conducted for the Muthirapuzha River watershed indicating the different orders of the tributaries from the hilly slopes around the water bodies in the low-lying areas [37].

The humidity is at maximum from October to January, ranging between 55 and 90 percent. The MAP is between 600 and 2500 mm in the region spanning 7–9 months of rainfall. The MAT is ~18 °C, with March being the warmest month (33 °C). The study of floral diversity in southern Western Ghats is of great phytogeographical significance, being a part of Gondwanaland. Hence, it is often called the ‘living fossil’ [38,39,40]. Commonly known as Shola forest, the vegetation in the montane region consists of evergreen to semi-evergreen and moist deciduous taxa. Southern Western Ghats occupies ~5% of the area of the Indian peninsula and consist of heterogenous (tropical/temperate) plant diversity depending on the altitude [41,42]. More than ~5000 flowering plants are present here, out of which 58 are endemic to this region [43]. Three major plant communities are categorized from west to east in the region on the basis of altitude, temperature and precipitation. Firstly, evergreen forests occur in the west, experiencing high rainfall (2500–5000 mm/yr). Secondly, moist deciduous forests occupy the eastward plateau, which receives intermediate rainfall of ~1500–2000 mm/yr. Thirdly, dry deciduous forests occupy the eastward and low-altitude areas, receiving less than 1500 mm/yr during the SWM [33].

3. Materials and Methods

3.1. Chronology of Sediment Core (Kundala Lake)

A radiocarbon (¹⁴C) age estimate of the sediments from Kundala Lake was carried out at the Birbal Sahni Institute of Palaeosciences (BSIP), Lucknow. Initially, the sediment was manually sieved and treated with hydrochloric acid for the removal of carbonates. Using standard procedures and catalysts, the resulting carbon dioxide was collected and converted to acetylene and then to benzene. A Liquid Scintillation Counter (Quantulus3, 1220) was used for the counting process. The calibrated ages (cal yr. BP) are the weighted average of 2σ ranges (

Table 1. Sediment depth and Radiocarbon Age of Kundala core profile.

Depth in cm	Birbal Sahni (BS) Institute Laboratory	Radiocarbon Age (Years BP: Before Present) *	Calibrated Calendar Years
0–10 (Phase V)		0–124
20–25	BS–4027	310 ± 60 *	1557 AD
10–35 (Phase IV)		124–868
35–60 (Phase III)		868–2263
70–75	BS–3780	3100 ± 190 *	1110 BC
60–85 (Phase II)		2263–3367
85–120 (Phase I)		3367–4300
115–120	BS–3779	4300 ± 310 *	2350 BC

* Radiocarbon Age.

) obtained using IntCal20 [44,45]. An age–depth model (Figure 2) was produced using the ‘CLAM’ extension of R studio version 2.3.2 [46]. The time period for palynological phases I to V in the Kundala Lake profile (Table 1) were calculated using the extrapolated radiocarbon age and depth, bracketing in calendar years (AD and BC) in order to understand and correlate the surface meteorological data between 1871 and 2011 AD, and thematic spectral quantification of vegetation in 2005 and 2018 AD.

3.2. Palynological Study

A 120 cm (from top to bottom) sedimentary core from the moist periphery of the Kundala Lake (Figure 1) was collected using a manual auger (hand auger set bayonet connection standard 0.1.11.SO; www.royaleijelkamp.com, accessed on 30 September 2024, distributer in India sales@daskumars.com). The entire sedimentary profile of 120 cm was fine sandy clay. For the palynological study, ten grams of air-dried sediment was dissolved in distilled water. About 5–10 pellets of potassium hydroxide (KOH) were added and boiled for 5 min. After cooling, the solution was passed through a sieve with a mesh size of 150 (105 µm pore size). The filtrate was retained to settle overnight in a cool place. The supernatant was discarded, and the rest of the sediment aliquot was treated with 40% hydrofluoric acid (HF) to remove sand particles (silica). Later, after decanting the HF, the aliquot was centrifuged to remove the remaining HF. The sediments were then treated in series with a glacial acetic acid and acid mixture (anhydrous acetic acid and sulphuric acid (9:1)) with alternate centrifuging to decant the acid in use during this process following Erdtman [47]. The processed samples were then passed through a sieve with a mesh size of 650 (<10 µm pore size). The residue was collected in 10 mL of a distilled water and glycerin mixture (1:1) to prevent drying and the coagulation of fine particles. A drop of sample from this homogenized aliquot was mounted on the glass slides in glycerin jelly medium, and ~150–300pollen/spores were counted, except in a few samples with a low yield of pollen-spores (Figure 3). The pollen sum consisted of evergreen–semi-evergreen vegetation, moist and dry deciduous vegetation and herbs/shrubs. The percentages of Poaceae and trilete/monolete spores were calculated against the pollen sum. The palynological spectrum (Figure 3) was obtained using the Tilia 2.6.1 software [48]. However, a cluster analysis (CONISS) conducted using this software helped to demarcate the five phases of vegetation changes (I–V: from the bottom to the top of the core, respectively).

3.3. Modern Vegetation Cover

A digital elevation map (DEM) was constructed using remote sensing, topographic maps and a land survey (http://opendemdata.info/data/srtm_contour, accessed on 30 September 2024). For a comparison of temporal changes in the vegetation cover during 2005 and2018, a Land Use–Land Cover (LULC) map as a raster WMS layer (LULC250K_1819, LULC250K_0506; thematic map/bhuvan) was processed in QGIS (https://bhuvan-ras2.nrsc.gov.in/cgi-bin/LULC250K.exe, accessed on 30 September 2024) for Kundala Lake and the adjoining area, Kerala (Figure 4A–D). Digital data were processed to obtain the relative percentage of the total area of different spectral quantities of evergreen vegetation, deciduous vegetation, wasteland, crops, shrubs and waterbodies. The obtained variation was recorded as the area of the relative percentage for different land cover types utilizing a variety of spectral indices like the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Built-up Index (NDBI).

3.4. Estimation of Mean Annual Temperature (MAT) and Mean Annual Precipitation (MAP)

The pollen assemblage in each phase was documented (Figure 3) along with the range of temperature and precipitation to which it is acclimatized in the present day climate (

Table 2. Southern WesternGhat flora acclimatized to a range of modern temperature and precipitation levels (Modified after Climatological Tables of Observatories in India [58]).

Mean Annual Temperature (°C)	Mean Annual Precipitation (mm)	Evergreen, Semi-Evergreen/Moist Deciduous Taxa (Shola Forest and Plant Associates)
15–30	2000–3000	Aglaia
20–35	Up to 3000	Anacardium
15–25	Up to 2500	Bignoniaceae
15–25	1500–2500	Chrysophyllum
15–30	1000–2500	Acacia
15–25	1200–2500	Agrostistachys
20–35	1000–3000	Anacardiacea
15–25	1500–3000	Annonaceae
15–25	1200–2500	Arecaceae
15–25	1000–2000	Baccaurea
10–32	1100–3000	Bombax ceiba
15–25	1000–2500	Basella keralensis
15–25	~2500	Caesalpiniaceae
15–25	~2200	Canarium
10–20	~2200	Casuarina
10–20	1500–2500	Celastraceae
10–25	1000–2000	Dillenia
22–35	1500–2500	Dipterocarpaceae
22–33	1500–2500	Dodonaea
5–20	1000–2000	Duabanga
10–20	1500–3000	Dysoxylum
15–30	1500–3000	Elaeocarpus
10–20	1500–3000	Eucalyptus
25–31	2500–4500	Euonymous
5–28	1500–3000	Eurya
10–20	Up to 3000	Garcinia
25–35	Up to 3000	Garuga
10–20	1500–3000	Gentianaceae
10–25	~3000	Gluta
10–25	1500–2500	Hopea
5–25	1500–3000	Humboldtia,
10–25	500–4000	Ilex
5–20	Up to 3000	Knema
10–25	500–1600	Ligustrum
5–20	1000–2000	Limonia
10–20	1000–2000	Lophopetalum
10–20	500–2500	Luvunga
16–28	500–2000	Mallotus
10–40	500–2500	Mangifera indica
6–35	500–3500	Meliaceae
5–25	500–3000	Mesua
13–36	500–4000	Moraceae
10–25	1500–2500	Murraya
10–25	500–3000	Myrtaceae
10–25	500–2600	Nothapodytes
10–25	500–2500	Nothopegia
5–25	500–2500	Oleaceae
6–31	2500–4500	Osbeckia
10–25	1000–2500	Ongoeckia gore
10–25	~3000	Palaquim
5–20	1000–2500	Pinus
10–25	1000–2500	Psychotria
10–25	~3500	Reinwardtiodendron
12–25	1500–2500	Rhododendron
20–35	1000–2000	Sapotacea
25–40	1000–3000	Schleichera
10–25	Up to 3000	Scolopia
10–25	Up to 2500	Semecarpus
5–20	1500–2500	Shorea
5–20	1000–3000	Sterculiaceae
10–25	1500–2500	Striga augustifolia
20–30	2000–3000	Symplocos
13–28	1500–3000	Syzygium
10–25	1000–2000	Terminalia
10–25	1000–2500	Tiliaceae
5–20	1000–2500	Trema
5–20	1000–2500	Turpinia
		Dry deciduous taxa
18–30	500–1500	Fabaceae
15–28	1000–2000	Lagerstoemia
5–15	1000–2000	Madhuca
5–15	Up to 1500	Ricinus
		Herbaceous and woody shrubs
		Rutaceae,
10–20	1500–2500	Ericaceae
10–25	2000–3000	Ixora
10–20	1000–2000	Apiaceae
17–35	1000–2000	Euphorbia
10–25	1000–2000	Hibiscus
12–27	500–2000	Hypericum
10–20	500–2000	Lythraceae
10–20	500–1500	Neonotis
10–20	1000–2000	Senecio
13–28	500–2000	Launea
5–25	500–1500	Myristica
5–15	1000–2000	Blumea
12–20	1000–2000	Campanula
15–35	Up to 2000	Centratherum
5–15	500–1500	Chenopodiaceae
5–25	500–2000	Cnicus
15–40	800–2000	Combretacea
18–30	500–1500	Eriocaulon
5–15	500–1500	Erythrina
5–15	500–2000	Pedicularis
9–22	1000–2000	Pimpinella
10–20	500–1500	Ranunculaceae
5–20	500–1500	Rosaceae
15–28	800–2500	Rubiaceae
22–32	1000–3000	Strobilanthes
15–25	850–2200	Vernonia
12–40	800–1500	Boerhavia
5–20	850–2200	Heracleum
10–20	500–1500	Tabernaemontana
5–20	1000–2000	Impatiens
15–30	1000–2000	Jasminuim
5–15	1500–2500	Lamiaceae
5–15	1500–3500	Liliaceae,
10–20	1500–3500	Clerodendrum
5–20	1500–2500	Cucurbitaceae
20–30	1500–3500	Derris
5–20	500–2000	Caryophyllaceae,
10–15	1000–2000	Chlorophytum
17–35	500–2500	Justicia
10–20	500–2500	Solanaceae
15–30	1500–3000	Tinospora
20–30	1500–3000	Urticaceae,
10–25	500–2500	Xanthium
5–15	500–3500	Cyperaceae
5–25	1000–4000	Poaceae
	500–2500	Aquatic
8–12		Polygala
5–18		Myriophyllum
5–25		Nuphar
15–35		Polygonum
10–25		Potamogeton
10–30		Typha
10–26		Lemna

). An analysis using the coexistence approach was published previously for correlating the morphological resemblance of fossil pollen with modern flora and its existence in specific ranges of temperature and precipitation in order to evaluate the climate of the past [49,50]. We applied this analysis to the pollen assemblage recovered since 4.3 ka in Kundala sediments, relating it to living taxa present in Western Ghats and climatic data from meteorological stations for the study region, which provided the present day MAT and MAP. This was determined from climatologically normal data over 30 years taken from 235 climate stations in India (Table 2). The acclimatization of living taxa in ranges of temperature and precipitation documented earlier by Champion and Seth [51] was followed. According to these findings, five major groups have been identified on the basis of temperature and precipitation moisture content. This is further divided into 16 groups and 200 sub-groups. Logically, the changes in climate–vegetation equilibrium throughout a geological time period or in recent millennia has provided ample justifications for inferring the climate, ecosystem and overall environment with the use of palynological records in sedimentary archives [52,53]. The coarse resolution of potential climate change using climatic data and forests occurring in India has been assessed to outline the regional climate model of the Hadley Centre (HadRM3) and the dynamic global vegetation model for estimating the Integrated Biosphere Simulator (IBIS) 2.5 Model [54,55]. In addition to data on soil texture and nitrogen and carbon sequestration used in the IBIS model, the monthly records on precipitation, relative humidity and minimum/maximum temperature, along with wind speed, are also integrated for evaluating present day climate changes in accordance with the vegetation groups that were documented 5–6 decades earlier by Champion and Seth [51]. Some limitations have been discussed by Cramer et al. [56] and McGuire et al. [57] regarding the uncertainties of this model, which are downscaled at regional levels [54]. Therefore, in the present study, plant communities and their climatic range are grouped following Champion and Seth [51] and downscaled at regional levels following Chaturvedi et al. [54].

4. Results

4.1. Chronology of Kundala Lake Sedimentary Profile

Radiocarbon dates (¹⁴C) were obtained in the sediment core at 20–25 cm, 70–75 cm and 115–120 cm depths, and the ages were 380calyr.BP (~1570 AD), 3280cal yr. BP (~1330 BC) and 4830 cal yr. BP (~2880 cal BC) from top to bottom, respectively (Table 1). No significant lithological changes were observed in the sedimentary deposits. However, the net rates of sedimentation at 0–25 cm, 50–75 cm and 75–120 cm depths were 0.03, 0.02 and 0.08, cm/yr, respectively. The interpolated ages demarcated the duration of palynological phases in equilibrium with climatic conditions since ~4.8 Ka. The oldest phase, phase I, covers a span from ~4.8 to 3.4 Ka. Phases II, III, IV and V represent the time span of~3.4–2.3 Ka, ~2.3–0.9 Ka, ~0.9–0.12 Ka and ~0.12 Ka, respectively (Table 1).

4.2. Palynology (Kundala Lake)

The number of pollen taxa and the percentage occurrences of pollen/spores in a 120 cm sedimentary profile from Kundala Lake show significant succession in vegetation assemblages since ~4.3 ka (Figure 3). The statistically clustered (CONISS) relative percentages of pollen/spores demarcate five phases (I–V) in the core, as given below.

4.2.1. Phase I (4.3–3.4 ka)

The ratio of AP and NAP is 1.67. Arboreal plant taxa account for ~49% of the pollen sum. Out of these, the evergreen–semi–evergreen taxa account for ~40%, and moist and dry deciduous taxa account to ~9.0% (Figure 3). The dominance of evergreen taxa, ranging between 3 and 6%, comprise Aglaia, Icacinaceae, Combretaceae, Eurya, Dodonaea and Moraceae (Figure 3). Others, such as Elaeocarpus, Osbeckia, Bhesa and Trema, account for between 1 and 2% of the pollen sum. Moist/dry deciduous vegetation, such as Myrtaceae, Bombax, Rhododendron, Symplocos and Ilex, account for between 3 and 6%. Others, such as Gomphandra, Ligustrum, Euonymus, Grewia, Schleichera, Wrightia, Pterospermum, Mallotus and Loranthaceae, comprise 1–3% of the pollen sum. Anacardiaceae, Azadirachta, Gordonia, Michelia, Pittosporum, Schefflera, Buchanania, Pterocarpus and Gmelina constitute less than 1%. Herbs and shrubs account for ~29%. Out of total pollen sum, Poaceae and trilete/monolete spores account for 13.7 and ~9 percent, respectively.

4.2.2. Phase II (3.4–2.3 ka)

The ratio of AP and NAP is ~1.0. Evergreen vegetation comprise taxa like Aglaia, which account for more than 1%, but taxa like Osbeckia, Eurya Trema, Dodonaea and Moraceae represent less than 1% of the pollen sum. Moist/dry deciduous vegetation accounts for ~2.6%. Pollen belonging to Anacardiaceae, Bombax, Caesalpinia, Bauhinia, Azadirachta, Loranthaceae, Myrtaceae, Rhododendron and Ilex represent ≥1%. Shrubs and herbs constitute 35.6% (Figure 3). The highest percentage is for Pimpinella (4%). Euphorbiaceae, Rosaceae, Ranunculus, Rubiaceae and Polygala each constitute ≥1%, but Vernonia and Strobilanthes each represent less than 1%. Out of total pollen percentage, Poaceae accounts for ~17%, and trilete and monolete spores account for ~15%. This phase shows a reduction in both the number of taxa and percentage of occurrences of evergreen and moist/dry deciduous taxa, but with an increasing trend in herbs and shrubs along with Poaceae, suggesting an expansion of grass land over the forest area.

4.2.3. Phase III (2.3–0.9 ka)

The ratio of AP and NAP is ~0.5. The evergreen vegetation comprises pollen of Oscbeckia, Icacinaceae, Combretaceae and Elaeocarpus, which each account for less than 1% of the pollen sum, except Aglaia, which increases to ~5%. Pollen belonging to Anacardiaceae, Bombax, Myrtaceae, Rhododendron, Ilex, Gomphandra, Michelia, Ligustrum and Grewia each constitute less than 1% of the pollen sum (Figure 3). Shrubs and herbs account for ~52%, constituting pollen of Acanthaceae, Euphorbiaceae and Asteraceae, each representing ≥3%. Pimpinella and Ranunculus increase by ~9% and 5%, respectively. Rosaceae, Rubiaceae, Polygala, Geranium and Vernonia each constitute ≤3% of the pollen. Out of the total pollen sum, Poaceae accounts for ~13%, and trilete and monolete spores account for ~8%.

4.2.4. Phase IV (0.9–0.12 ka)

The ratio of AP and NAP is ~0.5. Compared to phase III, a similarity in vegetation cover is observed in this phase. The evergreen taxa constitute pollen of Aglaia and Moraceae, each representing more than 3% of the pollen sum. Combretaceae, Elaeocarpus, Bhesa, Eurya and Dodonaea are each represented by ≤1%. Moist/dry deciduous taxa constitute ~5%. Out of these, Anacardiaceae, Bombax and Myrtaceae each constitute ≥2% of the pollen. Loranthaceae, Rhododendron and Ilex each constitute ≤1% of the pollen. Others, such as shrubs and herbs, constitute ~51%. Acanthaceae, Euphorbiaceae, Ranunculus, Pimpinella, Vernonia and Strobilanthes each constitute ≥5% of the pollen. Others, such as Asteraceae, Rosaceae, Rubiaceae, Geranium, Pimpinella, Clematis, Polygala and Jasminum, each constitute ≥1%. Out of total pollen percentage, Poaceae accounts for ~9%, and trilete and monolete spores account for ~12%.

4.2.5. Phase V (Since ~0.12 ka)

The ratio of AP and NAP increases to 1.6. Osbeckia, Moraceae and Elaeocarpus each constitute ≤4% of the pollen. Aglaia constitutes the highest percentage (7%). Combretaceae, Icacinaceae, Bhesa, Eurya, Trema and Dodonaea each constitute ≤3% of the pollen (Figure 3). Moist/dry deciduous vegetation comprises Bombax and Anacardiaceae, accounting for 11% and 9%, respectively. Loranthaceae, Myrtaceae, Caesalpinia, Azadirachta, Grewia and Schleichera each constitute ≤4% of the pollen sum. Hence, herbs and shrubs constitute ~35% of the pollen sum. A reduction in Poaceae (~2%) and fern spores (~5.5%) is observed.

4.3. MAT and MAP

On the basis of the plant community, the MAT showed temperature variability between ~10 °C and ~26 °C in the number of sediments analyzed depth–wise in phase I. In phase II, it was 16 °C to 23 °C. In phase III, it was 13 °C to~20 °C, and in phase IV, it was 16 °C to 20 °C. In phase V, the temperature ranged between 15 °C and 26 °C. Hence, the depth–wise average variability values in MAT were 18, 19, 16, 18 and 20 °C for the periods of 4.3–3.4 ka, 3.4–2.3 ka, 2.3–0.9 ka, 0.9–0.12 ka and ~0.12–present, respectively, showing the highest value in recent years compared to the early part of the late Holocene (Figure 3).The estimated MAP at each depth in phase I (4.3–2.3 ka)was ~1500 mm to3000 mm, and it was 1700–2000 mm between ~2.3 and 0.12 ka (Figure 3). Between 0.9 and the present day, it was 2300–2400 mm, thereby showing less variability. The overall MAP evaluated using the palynological database in Kundala Lake sediments showed high variability in rainfall until 0.12 ka, however, with differences in the range in each of phases I and II. A significant reduction (~1750 mm) in the MAP in the early part of phase II between 3.4 and 2.3 ka was recorded. Although the MAT has increased by ~2°C from ~4.3 ka to present, the MAP has also reduced in recent decades, although with very low variability compared to the larger range in phases I and II.

4.4. DEM Analysis of Kundala Lake Site

The vegetation cover in the year 2005 and 2018 shows a relative percentage of total area of different spectral quantities of evergreen plants, deciduous plants, wastelands, crops, shrubs and waterbodies (Figure 4). Thus, the land cover thematic map reveals a reduction in evergreen plants from 61 to 40 percent in 2018 compared to 2005. However, an increase in deciduous taxa along with crop land and wasteland was observed in 2018. The cropland increased by 4 times with a 50% reduction in water bodies in 2018 compared to 2005.

4.5. Temperature and Precipitation—Instrumental Record Post 1820AD

The extrapolated age in phase V is from~1820 AD (~0.12 Ka) in the top 10 cm of the sediment of the Kundala Lake profile. Following the MAT and MAP analysis using palynological data until~0.12 ka (1820 AD), the temperature and rainfall data documented from Kerala from ~1871 to 2011 were plotted (Figure 5). The interannual instrumental record shows the temporal variation in annual rainfall during this period, indicating a decreasing trend in summer monsoon rainfall by ~10.9 cm during the last 100 years, but an increasing trend of 7.5 cm was recorded in northeast monsoon (NEM) rainfall [59]. According to the above records, the annual rainfall over Kerala is 1.0%, 13.8% and 17.1% during winter, pre–monsoon and post–monsoon, respectively. Between 1871 and 2011, the MAP was ~2800 mm over Kerala, and records from the entire Indian sub–continent show that there is no similarity in the rainfall trends at the regional level [59].

5. Discussion

5.1. Climate–Vegetation Relationship Since Middle Holocene, Southern Western Ghats

Forests and their response to climate variability have been widely observed using simulation models constructed in past studies [60,61]. The Kundala Lake sedimentary profile shows high AP/NAP, indicating high forest cover by arboreals. There has been a decline in the arboreal AP/NAP ratio from 1.5 to 1, and later on, 0.5, since ~3.4 ka onwards (Figure 3). This downfall of arboreals has been succeeded by a relative increase in herbs/shrubs, suggesting an increase in open land. Depth–wise pollen assemblages in phase I show variation in temperature from 10 to 26 °C, indicating a cooler and more arid climate, perhaps hinting at the globally recorded 4.2 ka event. Further, until 0.9 ka, the temperature remained low on the basis of the plant community that existed from the 3rd century BC to the 16th century AD. A fall in temperature by 2–3 °C since the middle Holocene, therefore, affected forest cover. However, from ~0.12 ka to the present (i.e., since ~1820AD) the MAT has increased to a static 20–22 °C (Figure 3), suggesting amelioration of the climate towards warmer conditions, which favoured an increase in both evergreen and moist/dry deciduous vegetation along with the rejuvenation of herbs/shrubs. The vegetation assemblages recorded between~4.3 and 3.4 ka were thus the continued effect of a warmer mid–Holocene climate that favoured evergreen taxa along with mixed moist and dry deciduous vegetation. The late Holocene, in general, remained stable, and the MAT and MAP were very close to the surficial meteorological data, although an increase in post–monsoon rainfall is documented in the last decade [59].

The results from Kundala Lake correlate with the palynological records from other parts of the Indian subcontinent, showing an increasing trend in aridity during the late Holocene [9,34,62,63,64,65,66,67,68,69,70]. Hence, in the Indian subcontinent, the aridity began sometime around 4 ka, with slight differences in different parts. Climate variability and changes in hydroecology monitored through palynological assemblages also reveal cold and arid conditions since ~4.5 ka in the northern part of India, whereby changes in drainage patterns were induced in response to the weakened summer monsoon [53]. The arid climate and the downfall of Harappan Civilization are also attributed to the weakening of the Indian Summer Monsoon [71,72,73].

A recent comprehensive compilation of multi–proxy paleoclimate records from about 679 sites, mostly from North America and Europe, provides a better understanding of climatic variability since the Holocene, identifying the characteristic changes that have occurred since ~4.0 ka at a resolution of 400 years [74]. Cooler and drier climates have been recorded since ~3 ka from lake sedimentary archives in southwestern China, and the temperature was lowest between 0.9 and 0.5 ka [75]. A comprehensive multi–proxy record from different sites in the Indian subcontinent during the last 20 years has provided an understanding of a similar trend in climate change, which varied from a moist mid–Holocene to a drier late Holocene with slight variations and short spells of deviation attributed to external forcings. The varied intensities of temperature and precipitation responded to variability in the landscape, altitudinal/latitudinal changes and the overall geographical settings of the Indian subcontinent, where a distinct climate–vegetation niche is observed in the modern day as well based on the ranges of temperature and precipitation [62,67]. On the basis of palynological assemblages, late Holocene climate records have been similarly documented from sedimentary archives in other parts of the world as well [76,77,78,79].

5.2. Climate–Vegetation Relationship Since ~1820 AD, Kerala

Long–term surface meteorological data between 1871 and 2011 show a decreasing trend in SWM rainfall and an increasing trend in NEM over Kerala [59]. The interannual variability in monsoon rainfall has been tabulated for the last 4 to 5 decades by several researchers [24,25]. According to the above data, a significant decline in SWM rainfall at a rate of 1.7 mm per year has been recorded, with an increase in post–monsoon rainfall (September–October) by 93.9 mm observed since the year 1961 [80]. Between 1900 and 1980 and over the last decade, relatively wetter periods were recorded, with an increase in precipitation of ~5.8 mm in NEM (October–December) compared the normal value of 23.82 mm, and the trendline analysis shows an overall increase of ~75 mm (@~7 mm) at the decadal scale since 1960 during the last 141 years compared to the normal value of ~430 mm over Kerala [59].

Due to several anthropogenic interventions, a change in biophysical resources has been observed, which indirectly influences the distribution of local abrupt rainfall during winters and pre–monsoon season, resulting in a two–fold increase in the frequency of cyclones over the Bay of Bengal in the past 122 years during November [81]. Thus, the cyclones that develop during the post–monsoon season significantly contribute the amount of rainfall during this period in the region. An increasing trend of monsoon rainfall is statistically significant in east Rajasthan, Saurashtra, Kutch and Diu [26,27]. The influence of the Bay of Bengal is one of the peculiar climatic features over the southern peninsula that tends to increase post–monsoon rainfall in the region. The prolonged months of rainfall are beneficial to plants, which supports the length of the growing season [82,83], favouring exceptionally high biodiversity in tropical rainforests controlled by climate, soil–moisture availability and several other biotic and abiotic factors [84]. Heterogeneity in local temperature in both space and time apparently influences vegetation productivity by altering photosynthesis efficiency [85,86,87].

The global atmospheric temperature is expected to increase in the future, and consequently, the magnitude of anomalous patterns in rainfall is expected to increase [26,27,28,88]. The watershed around the Kundala Lake and the present day satellite map (Figure 4A,B) shows the abundance of river water channels. However, thematic vegetation maps of 2005 and 2018 (Figure 4C,D) show a reduction in evergreen plants from 61 to 40%, and an increase in deciduous plants and crop plants from 14 to 40% and from 2 to 8%, respectively, indicating unfavourable climatic stresses for evergreen plants due to enhanced anthropogenic activities, particularly agriculture and arboriculture, in the region. The Kundala Lake area shrank from 13 to 7% with the increase in wasteland during these years. A trend of increased temperature in recent years has been observed in sedimentary profiles as well as with IMD data from the area (Figure 5).

6. Conclusions

The palynological results obtained from the Kundala Lake sedimentary profile and Indian meteorological data for the last 100 years were analyzed to determine the vegetation–climate relationship in the region. Quantitative and qualitative pollen data were used for estimating temperature and precipitation using the coexistence approach. The Mean Annual Temperature (MAT) documented from each sample in a 120 cm core shows average values of 18, 19, 17, 17.5 and 21.5 °C in the periods of 4.3–3.4 ka, 3.4–2.3 ka, 2.3–0.9 ka, 0.9–0.12 ka and ~0.12 to present, respectively. The overall MAP was ~2660 ± 370 mm from ~4.3 to 0.12 ka (phases I–IV); however, a reduction in rainfall to ~1750 mm was observed between 3.4 and 2.3 ka. Upon obtaining the palynological results (until ~1820 AD), we analyzed the surface meteorological data to conduct a further correlation of temperature and precipitation records over the last ~141 years (1871–2011) in Kerala. In addition, regarding the thematic map of vegetation reconstructed for the years 2005 and 2018 in the vicinity of Kundala Lake, the spectral indices show an increase in dry deciduous vegetation and cropland with a reduction in waterbodies and evergreen vegetation.