Keywords

1 Introduction

The indicators of climate change are evidential all over the globe (Cox et al. 2000). Evidences suggest warming attributable to human activities (IPCC 2001, 2007; Alley et al. 2003). Many components of the climate system – including temperatures of the atmosphere, land and ocean, the extent of sea ice and mountain glaciers, the sea level, the distribution of precipitation, and the length of seasons are now changing at rates and in patterns that are not natural and are best explained by the increased atmospheric abundance of greenhouse gases and aerosols generated by human activity during the twentieth century (American Geophysical Union 2008).

The potential consequences of the climate change have been established beyond any doubt at the global level (Rosenzweig and Solecki 2001). Climate exhibits a dominant control over the natural distribution of ecosystems. Fossil records (Davis and Botkin 1985; Woodward 1987) and the observed trends (Hughes 2000; Walther et al. 2002) show that changing climate has a profound influence on the vegetation distribution (Suárez et al. 2002; Beniston 2003). It is, therefore, to be expected that the projected climate change (IPCC 2001) will have a significant impact on the vegetation distribution (Peters and Darling 1985; Parmesan and Yohe 2003; Nautiyal et al. 2004). The expected impacts of climate change in mountainous regions will result in the loss of cooler climatic zones at the peaks of the mountains and the shifting of tree line upslope (Beniston 2003; Dullinger 2012; Gottfried et al. 2012). Huntley (1991) advocated that climate change might result in shifts in the distribution of species, biological invasions, and even species extinctions. Adaptation pathways in the face of changing climate include the replacement of the currently dominant species by more thermophilous species (Thuiller et al. 2005; McMahon et al. 2011; Gottfried et al. 2012).

The indicators of climate change are quite loud and clear in the Himalaya (Scherler et al. 2011; Immerzeel et al. 2012; Romshoo and Rashid 2014; Romshoo et al. 2015). Several studies suggest that Himalaya is experiencing temperature increase that is higher than the global mean of about 0.7 °C for the last century (Bhutiyani et al. 2007). In particular, a strong increase in the mean temperature of about 1.7 °C was recorded in the Himalaya, potentially inducing strong impacts on high-altitude ecosystems, especially changes in the vegetation structure and biodiversity of high-altitude environments (Shrestha et al. 1999; Aryal et al. 2014). With vast areas covered by the natural vegetation, there is a large dependence of communities on forest products and services (Roy et al. 2013). It, hence, becomes imperative to assess the likely impacts of climate change on vegetation distribution and composition.

The impact of climate change is seen everywhere in the Himalaya, as it affects key sectors like snow and glaciers, agriculture, biodiversity, and energy. Significant progress in the modelling of vegetation-climate interactions have been witnessed with the development of dynamic global vegetation models (DGVMs), which include mechanistic representations of the physiological, biophysical, and biogeochemical processes (Woodward and Beerling 1997; Cramer et al. 2001). DVGMs endow with the most comprehensive and flexible approach for generating probabilistic projections of changes in vegetation under changing climate scenarios (Bachelet et al. 2000; Lenihan et al. 2003). The application of DVGMs at the regional scale, indeed, has increased the knowledge base pertaining to the climate change impacts on vegetation (Sykes et al. 2001; Pearson and Dawson 2003). DVGMs have demonstrated that the changing climate can affect the distribution of vegetation types, and there could be a potential vegetation dieback (Solomon 1986; King and Neilson 1992; Grime 1997; Solomon et al. 2007). Recent studies in the Himalaya reveal that most of the areas are very likely to experience shift in the vegetation types as a consequence of changing climate (Brandt et al. 2013; Khan et al. 2013).

This chapter maps the current vegetation distribution in the Kashmir Himalaya and projects its evolution under the changing climate. The studies conducted so far across India have certain limitations as far as the validation of vegetation as simulated by DVGMs is concerned. This chapter also aims to map the current vegetation distribution in the region using remotely sensed data, together with extensive field validations, analyse the hydro-meteorological indicators for changing climatic signals over the region, and project the distribution and composition of potential vegetation in the region as simulated by IBIS and its validation.

2 Materials and Methods

2.1 Vegetation Mapping and Its Validation

A medium-resolution vegetation map from NOAA AVHRR, with a spatial resolution of 1 km, was generated for the entire Kashmir Himalaya. The map was reclassified into broader vegetation types. Stratified random sampling approach, involving the sampling of different vegetation types based on their spatial extent, was adopted to validate the vegetation types. Although ground samples should have been ideally chosen in proportion to the spatial extent of the vegetation types, inaccessibility and challenging topography of the region did not allow it to be done. Overall accuracy of the vegetation-type map was calculated using the following formula:

$$ \rho =\left(n/N\right)\times 100 $$

  • where ρ is classification accuracy, n is number of points correctly classified on image, and N is number of points checked in the field.

Species composition of vegetation types was determined through fieldwork, following a nested-quadrant approach (Rashid et al. 2013). Published literature was also consulted to know the species composition of vegetation types and look for any possible signal of climate-driven ecosystem change in the region.

2.2 Observed and Projected Climate Data

Time series meteorological data from 1980 to 2010 for two observation stations, Pahalgam and Gulmarg, were procured from the Indian Meteorological Department (IMD) to look into the signals of changing climate in the study area and to validate the projected temperature and precipitation predictions in the region. The projected climate data from 1960 to 2099 under A1B scenario were extracted from a PRECIS (Predicting Regional Climates for Impact Studies) run simulated over the study area with a spatial resolution of 0.5° × 0.5°. A time series of meteorological projections, ending 2099, from PRECIS under A1B scenario comprising average annual maximum and minimum temperature, and total annual precipitation from 2011 to 2098, was analysed for the region, which centred at CO2 concentrations of 437 and 630 ppm for the years 2025 and 2075, respectively.

Projected temperatures from PRECIS have a spatial resolution of 50 × 50 km, whereas the observed data from IMD are based on local weather stations. IMD temperature data were, thus, up-scaled to 50 × 50 km using GTOPO (1 km resolution) and applying a standard atmospheric lapse rate of −6.5 km−1. This was done by computing the elevation difference between a PRECIS grid and IMD observation station. In case of PRECIS (with a 50 × 50 km dimension), average elevation value from 2500 pixels was taken as a representative of the whole grid. Lapse rate was applied to the IMD data taking elevation difference between PRECIS and IMD station into consideration.

2.3 Trend Analysis of Climate Data

A time series of observed streamflow (1971–2010), meteorological data – temperature and precipitation (1979–2010) – and projected climate data (2010–2099) were used for trend analysis, and the interpretation of the significance of observed trends is based on a non-parametric Mann-Kendall test. The use of non-parametric tests ensures independent use of a series of assumptions about the population values and is well suited for analysing trends in time series data over time (Gilbert 1987). Thus, Mann-Kendall statistical test was used for monotonic and piecewise trend analyses of the time series of hydro-meteorological data. The test does not assume any special form for the distribution function of the data, including missing data (Yue and Pilon 2004). Man-Kendall static is denoted by S and varies between −1 and +1. Rank 1 is assigned to the highest value in the set. The ‘n’ time series value (X1, X2, X3… Xn) are replaced by their relative ranks (R1, R2, R3…Rn). The test statistic S is given as:

$$ S=\sum \limits_{i\kern0.375em =\kern0.375em 1}^{n-1}\left[\sum \limits_{j\kern0.375em =\kern0.375em i+1}^n\mathit{\operatorname{sgn}}\left( Ri- Rj\right)\right] $$
(40.1)

where sgn (x) = 1 for x > 0, Sgn (x) = 0 for x = 0, and Sgn (x) = −1 for x < 0

If the null hypothesis H0 is true, then S is normally distributed, and its positive value is an indicator of an increasing trend.

2.4 Impact of Changing Climate on Vegetation Distribution

Impact of climate change on vegetation distribution was assessed on the basis of changes observed in distribution of different forest types using future climate change scenario. For this, the global vegetation dynamics model – IBIS (Integrated Biosphere Simulator) – was used to assess the spatial distribution of current and future climate projected by the high-resolution regional climate model, PRECIS, for A1B scenario. Simulations were generated for two future time-frames: (i) Time-frame of 2021–2050, labelled as ‘2035’ (median of the period) and (ii) Time-frame of 2071–2100, labelled as ‘2085’. The simulated results of future scenarios were compared with the ‘baseline’ scenario, which was generated using 1960–1990 observed climate data.

2.4.1 Vegetation Dynamics Model

Designed around a hierarchical, modular structure, the dynamic vegetation model IBIS comprises four modules: the land surface module, vegetation phenology module, carbon balance module, and the vegetation dynamics module (Foley et al. 1996; Kucharik et al. 2000). Despite operating at different time steps, these modules are integrated into a single physically consistent model that may be directly incorporated within AGCMs (atmospheric general circulation models). IBIS is currently coupled with two AGCMs, namely, GENESIS-IBIS (Foley et al. 2000) and CCM3-IBIS (Winter et al. 2009). The state description of the model allows trees and grasses experience different light and water regimes, competition for sunlight and soil moisture which determines the geographic distribution of plant functional types, and the relative dominance of trees and grasses, evergreen and deciduous phenologies, broad-leaf and conifer-leaf forms, and C3 and C4 photosynthetic pathways.

2.4.2 Input Data

IBIS requires a range of input parameters, including climatic as well as pedologic parameters. The main climatic parameters required are monthly mean cloudiness (%), monthly mean precipitation rate (mm/day), monthly mean relative humidity (%), monthly minimum, maximum, and mean temperature (°C), and wind speed (m/s). The main soil parameter is the texture of soil (i. e. % age of sand, silt, and clay). The model also requires topography information. The available soil and topographic information was re-gridded to a 0.5° × 0.5° resolution and used for the run. The observed climatic variables were obtained from CRU (New et al. 1999), while the soil data were obtained from IGBP (IGBP 2000). For climate change projections, RCM outputs from PRECIS were used (Rupakumar et al. 2006). The climate variables for future scenarios were obtained using the method of anomalies (Bretherton et al. 1992). This involved computing the difference between the projected values for a scenario and the control run of the PRECIS model and adding this difference to the value corresponding to the current climate as obtained from the CRU climatology.

2.4.3 Simulating Vegetation Distribution and Composition

The spatial resolution of vegetation simulated with IBIS is 0.5°. For validation, baseline vegetation of the region simulated with IBIS was compared with the vegetation map at 50 × 50 km grid resolution, developed after modifying vegetation map from NOAA AVHRR, ISRO (2010), and Champion and Seth (1968). A digital vegetation-type map of the study area (ISRO 2010) at 1:50,000 scale was used for checking and validation purposes. Since the highest resolution data available for running IBIS had 0.5° resolution, the study area was divided into 0.5° grids, and the vegetation map was reclassified to assess the accuracy of vegetation types and distribution simulated with IBIS. The validation of simulated vegetation was done using kappa coefficient, which is a robust indicator for accuracy assessment, and was calculated using the following formula:

$$ k=\frac{N\sum \limits_{i=1}^r{X}_{ii}-\sum \limits_{i=1}^r\left({X}_{i+}.{X}_{+i}\right)}{N^2-\sum \limits_{i=1}^r\left({X}_{i+}.{X}_{+i}\right)} $$

  • where r is number of rows in error matrix

  • xii is number of observations in row i and column i (on the major diagonal)

  • xi+ is total of observations in row i (shown as marginal total to right of the matrix)

  • x+i total of observations in column i (shown as marginal total at bottom of the matrix)

  • and N is total number of observations included in the matrix

In addition, vegetation distribution for two future time periods, centred at 2035 and 2085, was simulated using IBIS.

3 Results and Discussion

3.1 Vegetation Mapping

The study area showing Jammu and Kashmir state is depicted in Fig. 40.1.

Fig. 40.1
figure 1

Study area showing Jammu and Kashmir state

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Ten classes of vegetation were delineated in this chapter (Table 40.1; Fig. 40.2). It is evident from the figure that 60% of the area is covered by vegetation, whereas the non-vegetated areas (snow-covered areas, barren lands, water-bodies, and built-up areas) cover ~4 0%. Major vegetation types include shrublands (33.62%), forests (16.69%), grasslands (5.89%), and croplands (3.89%). Vegetation types were checked and validated with extensive ground truthing at 450 samples spread over various geographic and vegetation belts in the study area. However, ground truthing could not be carried out in the Chinese- and Pakistan-administered territories within the study area. The dominant shrubland species observed in the areas with altitude less than 3000 m include Berberis lycium, Viburnum grandiflorum, Indigofera heterantha, Parrotiopsis jacquemontiana, Betula utilis, while the prevailing vegetation in alpine shrublands includes Juniperus squamata, Rhododendron campanulatum, and Rosa webbiana. Forest species included Pinus wallichiana, Pinus roxburghii, Pinus gerardiana, Cedrus deodara, Abies pindrow, Quercus semecarpifolia, Q. leucotrichophora, Olea cuspidata, and Ulmus wallichiana. Grasslands were dominated by Cynodon dactylon, Stipa sibirica, Poa alpina, and P. annua (Rashid et al. 2010; Rashid et al. 2013). It is observed that climate change has resulted in the proliferation of alien invasive species in the region (McCarty 2001; Chen et al. 2003; Williams et al. 2007). Many of these alien species are reported to have invaded forest, grassland, and wetland ecosystems across the Kashmir Himalaya (Zutshi 1975; Khuroo et al. 2007; Masoodi et al. 2013).

Table 40.1 Land cover statistics for Jammu and Kashmir state
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Fig. 40.2
figure 2

Major land cover/vegetation types in Jammu and Kashmir state

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Another aspect of this chapter was to compare the land cover types as delineated from AVHRR with land cover map from ISRO, developed in 2010 for part of the Indian-administered Jammu and Kashmir (~1,05,102 km2). For this purpose, land cover classes were generalised into five categories (Petit and Lambin 2002; Buyantuyev and Wu 2007). This is because of the fact that the classification schemes used in AVHRR and ISRO land-cover maps are different, while AVHRR adopts IGBP land-cover nomenclature, and ISRO adopts nomenclature as per Champion and Seth (1968). There is a good agreement as far as cropland and forested area delineation is concerned, but differences occur in spatial extents of grasslands, shrublands, and non-vegetated areas. These variations result partly because of differences in the spatial resolution and mapping scale adopted (Ardizzone et al. 1999; Achard et al. 2001; Shao and Wu 2008). As the spatial resolution of AVHRR is very coarse, grasslands and non-vegetated areas get classified as shrublands.

3.2 Historical Hydro-meteorological Data

Analysis of the time series of average annual minimum and maximum temperatures at Pahalgam meteorological observation station in the study area from 1980 to 2010 depicts a significant rising trend (99%) over the years with R2 of 0.45 and 0.38, respectively. The mean annual precipitation at Pahalgam shows a slightly decreasing but insignificant trend with a very weak R2 of 0.02 (Fig. 40.3a, b, c). Average annual minimum and maximum temperatures at Gulmarg station from 1980 to 2010 also depict a rising trend over the years with R2 of 0.33 and 0.10, with significance values of 90% and 99%, respectively (Fig. 40.3d, e, f). Statistically insignificant trends were observed in case of observed and projected precipitation patterns over the region (Table 40.2).

Fig. 40.3
figure 3

Observed trends in temperature and precipitation at Pahalgam (a, b, c) and Gulmarg (d, e, f)

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Table 40.2 Mann-Kendall statistics for significance of observed and projected climate data
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As is evident from the analysis, the minimum temperatures are rising faster than maximum temperatures over the region. This may bring about significant habitat alteration in the alpine zone (Hamann and Wang 2006; Telwala et al. 2013; Ernakovich et al. 2014). The rise in temperature, together with decreasing precipitation regimes in the region, can have serious impact on forest distribution (Joshi et al. 2012; Cong et al. 2013), snow and glacier resources (Murtaza and Romshoo 2017; Romshoo et al. 2015), water availability (Immerzeel et al. 2010; Jeuland et al. 2013; Sharif et al. 2013), and recreation (Dar et al. 2014).

As far as the climate projections for the area are concerned, both average minimum and maximum projected temperatures show an increasing trend at both Pahalgam (with R2 values of 0.90 and 0.77, respectively) (Fig. 40.4a, b) and Gulmarg stations (with R2 values of 0.91 and 0.81, respectively) (Fig. 40.4d, e). Precipitation projections (Fig. 40.4c, f) show a very weak increasing trend with R2 values of 0.04 and 0.005 for Pahalgam and Gulmarg, with significant interannual variations across the time series. The overall average annual maximum and minimum temperatures over Pahalgam are projected to increase, respectively, by 7.23 °C (+1.84 °C) and 4.89 (+1.51) from 2011 to 2098 under the A1B SRES scenario. Likewise, the average annual maximum and minimum temperatures over Gulmarg are projected to increase by 7.68 (+2.01) and 5.88 (+1.51), respectively. Mann-Kendall analysis of time series for observed and projected climate data (Table 40.2) reveals that temperatures are going to rise very significantly (99% significance value). Similar increases are being simulated over other areas in the region. The trend analysis of the average yearly discharge using Man-Kendall tests at Aru (Pahalgam) hydrological station is given in Table 40.2. A significantly decreasing trend (with significance value of 99%) is observed in case of streamflow observations from 1980 to 2010 at this station.

Fig. 40.4
figure 4

Projected climate trends at Pahalgam (a, b, c) and Gulmarg (d, e, f)

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From the analysis of climatic data for the study area, it can, hence, be inferred that there will be a significant increase in the temperatures, decrease in the streamflow, and comparatively insignificant change in the precipitation pattern by the end of this century. Upon comparison of the upscaled IMD data with the PRECIS projected temperatures during the baseline period, it is found that the two datasets agree reasonably well (Fig. 40.5a, b). Thus, the model is considered credible and cogent for simulating future temperatures over the region. Similar findings have been reported about the use of PRECIS in Himalaya (Akhtar et al. 2008; Kulkarni et al. 2013) and other parts (Tadross et al. 2005; Xu et al. 2006 Giannakopoulos et al. 2013).

Fig. 40.5
figure 5

Validation of PRECIS with IMD upscaled data at Pahalgam. (a) Average annual minimum temperature (b) Average annual maximum temperature

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3.3 Impact of Changing Climate on Vegetation Distribution

Before going for the impact assessment of climate change on vegetation distribution, it is important to assess the accuracy of the baseline vegetation scenario, simulated by IBIS, with the actual vegetation on ground. For this purpose, an actual vegetation map (Fig. 40.6a) was developed at a resolution of 50 × 50 km by modifying the vegetation maps prepared by NOAA AVHRR, Champion and Seth (1968), and ISRO (2010). Some categories of the vegetation were reclassified as per the IBIS vegetation nomenclature. The baseline map, simulated by IBIS for the region (Fig. 40.6b), was compared with the vegetation map developed at 50 × 50 km. A Kappa coefficient of 87.15% (Table 40.3) indicates that the IBIS simulated the baseline vegetation very well (Yuan et al. 2011; Cunha et al. 2013) and, hence, could be relied upon for effectively projecting the future vegetation types ending the current century. IBIS simulates temperate evergreen forest and tropical deciduous forest very well during the baseline period as is evident from 100% agreement between the observed and IBIS simulated vegetation. It slightly over-predicts grasslands (only 66.66% correspondence with the vegetation scenario on ground) and tundra (only 72.42% similarity) and slightly under-predicts dense shrublands (by 33.33%), open shrublands (by 14.28%), and polar desert/rock/ice areas (by 13.63%). These dissimilarities in the observed and simulated baseline vegetation distribution and type are insignificant at the spatial resolution of 50 × 50 km.

Fig. 40.6
figure 6

(a) Baseline vegetation simulated with IBIS. (b) Actual vegetation after AVHRR, Champion and Seth (1968), ISRO (2010). (c) Projected vegetation types by 2035. (d) Projected vegetation types by 2085

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Table 40.3 Matrix showing the comparison of IBIS-simulated baseline vegetation with respect to satellite-derived vegetation
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For the projected vegetation distribution by 2035 (Table 40.4, Fig. 40.6c), most of the area under polar desert/rock/ice is taken over by boreal evergreen forests, and some part of it may be covered by tundra vegetation. Moreover, a substantial part currently under the temperate evergreen forests is colonized by temperate deciduous forests. Areas under open shrublands are taken over by temperate evergreen broadleaf forests. In addition, mixed forests and savannahs appear due to the increase in the projected temperatures observed over the region.

Table 40.4 Changes in vegetation composition across different scenarios from 1960 to 2099
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Vegetation simulated with IBIS for the period towards the end of this century (Table 40.4, Fig. 40.6d) shows expansion of savannahs, reappearance of temperate evergreen coniferous forests, and open shrublands in the Karakoram belt. Dense shrublands will overtake boreal evergreen forests in north-western and central part of the region. There remains only one pixel/grid that comes under rock/ice by the end of the century. As such, it is predicted that snow and glacier resources in the region shall recede due to the rising temperatures discerned over here (Bajracharya et al. 2007; Archer et al. 2010; Immerzeel et al. 2010; Romshoo et al. 2015).

4 Concluding Remarks

This chapter indicates that climate change is evident in the Kashmir Himalaya and affects significantly the naturally occurring vegetation. Northwest Himalaya covers a substantial part of the Himalayan biodiversity hotspot, which signifies the importance of this chapter. A close agreement of the regional climate model projections with the upscaled meteorological data gives credence to the projected vegetation distribution under changing climate, as generated for the region using IBIS. The study demonstrated that the climate change is going to bring about significant changes in the vegetation types and their distribution in the region and might, therefore, adversely affect the products and services available from the forests that currently support a number of livelihoods here. Subalpine and alpine pasture and scrubland vegetation in the upper reaches (>2800 m), which are habitat to many endemic and medicinally important species, are very sensitive to any subtle variation in climate. Hence, in light of the changing climate over the region, these species may get extirpated on altering the fragile ecology of the region. Moreover, the introduction of alien invasive species in terrestrial and aquatic ecosystems in the region can seriously alter the biogeochemical cycling, affecting the biodiversity richness and composition in the Kashmir Himalayan region.