Precipitation changes and their relationships with vegetation responses during 1982 – 2015 in Kunhar River basin, Pakistan

Precipitation is a major determinant of vegetation growth. The impact of precipitation variability is more pronounced in ecosystems where sensitive vegetation is apparent. Therefore, understanding the relationship between precipitation and vegetation is vital to guide appropriate measures towards fragile biomes. We investigated the trends and correlations between precipitation and normalized difference vegetation index (NDVI) for 1982 – 2015 over the Kunhar River basin, Pakistan, using satellite-derived NDVI and globally available interpolated precipitation datasets. Subsequently, we attempt to identify vegetation types that are in ﬂ uenced by precipitation changes. Results show a general decreasing trend in vegetation activity as we go from southern to northern portions of the basin. This decrease is also accompanied by the similarly decreasing precipitation trend in the same direction. The similarity of spatial patterns between the two variables can indicate that, in general, precipitation is playing a guiding role in determining vegetation distribution in the basin. Our lagged correlation analysis revealed that strong precipitation – vegetation correlations ( r > 0.75) are rare in the basin. Agricultural and forested areas show moderate correlations (0.5 < r < 0.75) when NDVI is correlated with the previous month ’ s precipitation values (lag1). In simultaneous month correlation (lag0) and the rest of the lagged correlations (lag2 and lag3), a weak association between precipitation and NDVI is observed. The moderate and weak correlations over the basin might indicate that precipitation is not the only factor in ﬂ uencing vegetation growth in the Kunhar River basin. Other climatic and biogeographic factors such as temperature, solar radiation, topography and soil characteristics also play additional roles in vegetation activities. The results can provide a technical basis and valuable reference to ecological management strategies in the Kunhar River basin for concerned decision-makers and stakeholders.


INTRODUCTION
Kunhar River basin of Pakistan is one of the basins experiencing rapid conversion of forested areas into shrublands and farmlands. The conversion is amplified in recent years due to high population pressure demanding fuel woods and farming spaces (Star Hydropower Limited ). Given the sloping and rugged mountainous nature of the basin, the clearance of forested areas and encroachment of slope farming destabilizes the natural ecosystem.
As a result, loss of biodiversity, landslides and environmental degradation are becoming common in the basin.
The ever-increasing global temperature (IPCC ) is also expected to affect the basin's upland cold region biomes and leave them more vulnerable. Therefore, quantifying the possible impacts of climate change on these fragile ecosystems of the basin helps understand the problem's extent and direct management options accordingly. Although the basin is undergoing these alarming situations, limited researches have been conducted to address the issues.
Using satellite-derived NDVI and globally available interpolated precipitation time series, this study aims to (1) characterize the spatiotemporal patterns of precipitationvegetation trends over the Kunhar River basin, and (2) identify the temporal extent and spatial patterns of vegetation response to precipitation by considering the time-lag effect.

Study area
The Kunhar River is a 171 km long river originating from northwestern Himalaya and flowing southwards to join the They are being lost at a substantially greater rate than regeneration. As a result, the forested areas are now being progressively replaced by a mixed grassland and shrubland community (Star Hydropower Limited ).
In recent years, slope agriculture is also becoming a common agricultural practice in which sloping uplands are being used for farming. The rapid changes in land cover coupled with climatic variabilities and changes will ultimately affect the basin's flora and fauna's well-being.
Due to the steep slopes, landslides, erosion and other environmental disturbances are waiting to happen unless a well-informed decision has been made and wise ecosystem management has been put in place to preserve the natural environment.

Precipitation data
Spatiotemporal studies of climatic variation usually require accurate precipitation datasets. In most cases, these precipitation observations come from in situ rain gauges. However, the data obtained from rain gauges lack the required quality and spatial density (Rana et     In this study, we used GIMMS3 g.v1 (https://ecocast.

Correlation analysis
Nonparametric correlation analyses also provide information on the distribution-free relationship between variables. In this study, we applied one such nonparametric correlation method called Spearman's rank correlation (Best & Roberts ). The Spearman's rank correlation coefficient is a nonparametric measure of correlation strength between two variables. In that sense, an arbitrary monotonic function is set to describe the relationship between the two variables without making assumptions about the underlying distribution they possess (Maritz ). The Spearman's rank correlation coefficient is calculated similarly as the usual parametric Pearson correlation coefficient, but the computations are based on the ranks rather than the actual data values.
The null hypothesis (Ho) of Spearman's rank test assumes 'no correlation', meaning one variable's rank is not covarying with the other variable's rank. In other words, as the rank of one variable increases, the rank of the other variable is not more likely to increase (or decrease). The alternative hypothesis (Ha) considers a positive or negative correlation between the two variables. The optimum value for Spearman's correlation is unity with a correlation value of zero, indicating the absence of correlation.
This study applied Spearman's rank correlation analysis at pixel scale to examine the relationship between precipitation and NDVI. We chose the Spearman's rank correlation against the other competitive Pearson's correlation mainly because of two reasons. The Spearman's correlation evaluates monotonic relationships in which the changes between the variables are not required to be constant. A constant change between the correlating variables is mandatory to apply Pearson's correlation. On the other hand, Spearman's rank correlation is a nonparametric method that is suitable for variables with possible outliers. We performed four correlation analyses between them to identify simultaneous and lagged relationships. We considered the lagged correlation analysis because the precipitation-vegetation relationship is better inferred from lagged rather than the simultaneous correlations due to strong atmospheric variability (Svoray & Karnieli ).  decreasing trend from the southern to the northern portion of the basin (Figure 3(b)). In the downstream part, the forested and agricultural areas show NDVI values ranging from 0.5 to 0.7. In the central portion, however, a more considerable variation in NDVI is observed. In this region, the typical NDVI values range from 0.3 to 0.6. In the basin's northern highlands, growing season NDVI falls as low as  For the rest of the lags (lag2 and lag3), the correlation further diminishes, implying the absence of additional delayed vegetation response to precipitation changes.
An example scatter plot is given in Figure 6 to illustrate the relationship between monthly NDVI and the corresponding precipitation values for lag1 condition. Slope agriculture has shown a better correlation (with r ¼ 0.673).
Irrigated agriculture and coniferous forests exhibited a similar correlation (with r ≈ 0.6). Alpine meadows, on the other hand, showed the least correlation (with r ¼ 0.448).
The general absence of a higher correlation coefficient (r > 0.75) over the basin might suggest that precipitation is not the only determining factor for vegetation activities.
Other factors such as temperature, solar radiation, topography and soil properties might also play an essential role. The   On the other lags (lag2 and lag3), a weaker precipitation-NDVI correlation is observed, suggesting either vegetation in the Kunhar River basin responds quickly with a maximum of 1-month precipitation delay or environmental factors other than precipitation play a significant role in vegetation activities.