Impact of Climate Change on Rainfall Pattern in Upper Kumaon Himalaya Region


 Every single aspect of environment is affected by climate change. Change in rainfall pattern is an immense important research area in climate-change based study. Rainfall pattern has direct impacts on food production and frequencies natural disasters (landslide, cloudburst, flood, drought etc.). Consequently, that appropriate and systemic consideration since it distresses the most of the human life. Situation in Himalayan region is worst. High altitude, less agricultural area, harsh climate with high fragility makes mountain region more vulnerable in term of climate change. The objective of this study is to identify yearly, seasonal, and monthly rainfall trends in the Upper Kumaon region (UKR). Long-term gridded daily rainfall data (1950–2018) were used. Rainfall data was processed and analyzed for a period of 68 years (1950–2018) at four places (four in each Kumaon division) in the surrounding area of Almora, Bageshwar, Pithoragarh, and Champawat. The regression analysis (parametric) method and variability analysis were used to examine historical trends in daily rainfall. The rising and falling trends in rainfall, as well as anomalies, have been studied using regression.The result shows that rainfall demonstrate statistically significant changes occurred in last 34 years. Rainfall variability is higher in low altitude region than high altitude region of Upper Kumaon region.


Introduction
Climate change has become a hot and global subject of research (Preenu et al. 2017). Climate change, its in uences and exposures to mankind, are increasing anxiety worldwide. A soothing and adjustable environment is accountable for life on earth. Minor change in climatic pattern and distribution can create hazardous events for wealth and life. Climate change is truth and it is a biggest crisis of present era (Singh and Gurjar 2011).This climate change crisis is originated from emission of high amount of greenhouses gases by mainly anthropogenic activities and some natural activities (Revadekar et al. 2012). Natural and human activities, entitled "forces" in the climate-based study modify the movement of energy in the atmosphere, cooling and warming Earth by disturbing its heat steadiness. In some area, climate change is forcing warm the earth, while in some area cool it. Greenhouse gases are the primary cause of climate change. As a result of their high e ciency at trapping heat in the atmosphere, the greenhouse effect occurs (Stocker et al. 2013). Climate change is responsible for change in global and local environment. All environmental activities system is being perturbed by climate change. Due to disturbance natural phenomena of an area, anthropogenic activities also get affected. And change in major climatic variable like rainfall, solar radiation and water availability. It is a great deal to the researcher community to understand the climatic change situation and help the mankind to decrease the adverse impact on environment. The varying pattern of rainfall need insistent and appropriate interest, as it will affect whole environmental and human life circle in any geographical region (Dore 2005). Precipitation is main dynamic force of land surface and any type of variability or change in its pattern and seasonality could have direct uctuation on water resource. Variability in rainfall distribution and amount would stimulate change in spatial-temporal distribution of soil moisture, runoff, and groundwater and could change the frequencies of ood and drought. When, we debate over climate change, the mountain regions stand on the forefront.
Mountains occupy around 25 per cent of the land surface of the earth. About 26 percent of the world's population (about 1.5 billion) lives in montane ecology (Kapos et al.2000;Meybeck et al. 2001). These are important source of fresh water as they named as water tower. Mountains provide most critical resources water and diverse ecosystem services. Life and livelihood of more than forty per cent population directly and indirectly lie on the mountain resources.
Mountains are one of the most vulnerable climate change ecosystems and are more rapidly affected than other planetary habitats (Beniston 2006). Climate change poses a signi cant threat to mountain ecosystem services and the populations that rely on them, as well as having signi cant implications for water resources. Many glaciers are receding as a result of escalating temperatures, making them important climate change indicators. Himalayan region is so fragile. The factors that make these hilly regions most vulnerable are its fast-growing population, depletion of resources, unplanned road construction activities, deforestation, high dependency of population on climate-sensitive sectors like agriculture and forestry, lack of agricultural land (Macchi 2010), livelihood insecurity, high malnutrition population and unskilled population. Change in rainfall pattern, variability and seasonal distribution; create unfavourably modi cations in water availability (surface and ground water) (Sharma et al. 2013; Singh and Kumar 2014) frequencies of disaster, cropping pattern, agricultural productivity, vegetation cover (Singh and Mal 2012) and hydropower (Field et al. 2014). Now studies show that Himalayas facing frequently climate change and its induced disaster. Drought, cloudburst, rainfall extremes, landslides etc. are common disaster in mountain region.
This study has been assessed the trends of precipitation in the Upper Kumaon region, India. The Upper Kumaon region is situated in the West Himalayan and it has been selected for the study. This mountain region is highly vulnerable in term of slope, terrain, inaccessibility and scattered settlement and it is important provider of ecological service like water, energy, food, medicine, livelihood etc. except these ecological services, Upper Kumaon region is strategically important for the India. In this region, more than 75 per cent population depends on agriculture and forestry for their livelihood, is likely to be severely affected by climate change. Most of the agriculture activities solely depend on rainfall here. Minor changes in rainfall can devastate life and wealth of the people. Hence, it is very vital to understand the in uence of climate change on rainfall pattern and seasonality in Himalayan region for proper forecasting and management of agricultural production and livelihood of the local population. The foremost aim of the study was to analyze the trend of rainfall in the area over the past 68 years. Linear regression method and has been used to analysis trend and moving average have been used.

Data Sources
Mostly IITM Pune data of the observatories, suffer from large data gaps. Therefore, rainfall records of study area (Pithoragarh) were downloaded from Climate

Trend Analysis
The trend analysis has been done yearly, seasonal and Monthly. It is done by linear regression analysis. Linear trend line of yearly rainfall, monthly rainfall and seasonal rainfall has been computed by the slope value from the linear regression analysis performed (Dash and Hunt 2007;Kothawaleand Rupa 2005). A detailed representation of the outcomes was shown using trend line on each graph (Siraj et al. 2013; Addisuet al.2015) (Rainfall is considered as the dependent variable and the time period is considered as an independent variable such as: Rainfall = t (time), Y = (α + βX + µ). For Y = rainfall, X = periods of time and µ = mistake [21]. For Y = rainfall,X = time period and µ = error (Singh et al. 2013). Furthermore, a non-linear trend had also been established using 6-point moving average lter yearly. Further, the data sets were divided into two halves and trends for the rst and second half was also computed accordingly. The ow of trend has been depicted by graphs and tables individually respectively. The con dence level had also been evaluated at 95 per cent. Generally, a 90 per cent con dence level is considered as a standard one. For the low number of data set achieving a higher con dence level is unusual likewise in the study carried out for seasonal data sets have 4 data sets. Thereafter, the 95 per cent con dence level has been computed as it seems to be more feasible throughout the study.

Seasonality Analysis
Seasonality of rainfall had been computed using the ratio-to-moving average method. Implementing the following process had been applied to compute the seasonality (Dash and Hunt 2007). Firstly, the moving average had been calculated. Since the moving average was calculated for the even set of numbers thereafter the centred moving average had been computed and the ratio of the original data set throughout the season had been calculated for the years. Comparatively maximum and minimum values of monthly rainfall were causing the irregularity. Hence, all monthly data values of the entire period had been averaged using the median. Finally, the seasonal indices were computed by multiplying the monthly average ratio to each month.

Variability Analysis
The seasonal and monthly variability has been evaluated using the coe cient of the variance process (Guhathakutaet al. 2020). This denotes the nature of data sets of rainfall and temperature during the period Further the data sets were divided into two halves and variability for the rst and second half was also computed accordingly.
The co-e cient computed for variability is calculated as follows:
Trans-Himalaya is the northern boundary of the region and is also the international border with China (Tibet) and Nepal by most eastern side (Fig. 1).In south side of study area is bound by Nainital district. This area spreads from foothill up to snow cover peaks of Himalayas, constructing the Indo-Tibetan boundary. (1.55 per cent) occurred over the change of 69 years. The moving average is also computed which also indicates the rapidly decreasing trend among all the four districts (Fig. 2).
On the basis of trends of seasonal rainfall of Upper Kumaon region, in Almora district, the trend analysis of seasonal rainfall demonstrates ( Table 1) that monsoon season i.e. June to August had the most decreasing trend of -3.28 mm/year. But from the observations of the trend analysis of the two-half (1950-1984 and 1985-2018), it has been observed that there is a huge decrease occurred in the monsoon season in last 34 years (1985-2018) with the rate of -2.37 mm/year. One study found that number of rainy days decreased in Almora district during 1992-2003 time spans. Even the seasonal deviation is the highest in monsoon season with the deviation value of 476.42 mm (Joshi et al. 2016). In the district of Champawat, the seasonal analysis reveals that monsoon season i.e, June to August had the most decreasing trend of 0.78 mm/year. But from the observation of the trends of the two-half, it has been observed that there is a huge upsurge happed in the monsoon season in last 34 years (1985-2018) with the rate of 8.16 mm/year. Even the deviation of seasonal rainfall is highest in monsoon season with the deviation value of 343 mm. This increase is due to the heavy rain during monsoon season in the recent past. In the district of Bageshwar, the seasonal analysis shows that monsoon season i.e. June to August had the most decreasing trend of 2.27 mm/year. But from the observation of the trends of the two-half, it has been observed that there is an increase occurred in the monsoon season in last 34 years  with the rate of 2.63 mm/year. The seasonal deviation is the highest in the monsoon season with a deviation of 232.41 mm from 1950 to 2018. In the district of Pithoragarh, the seasonal analysis shows that post-monsoon season i.e. September to November had the most decreasing trend of 1.41 mm/year (Table 1). But from the observation of the trend analysis of the two-half, it has been observed that there is an increase occurred in the monsoon season in last 34 years  with the rate of 5.96 mm/year. The seasonal deviation is the highest in the monsoon.
season with a deviation of 247.28 mm from 1950 to 2018. According to trend of monthly trends the district of Almora, month of August had the decreasing monthly rainfall trend of 2.49 mm/year. And total change from 1950 to 2018 is highest in July (256.11 mm). Moreover, the deviation is highest in July (133 mm). Although month of May exhibits a maximum increasing trend of 0.825 mm/year likewise the total change and the deviation had also exhibit slightly increase, but this increasing nature is very small. In the district of Champawat, month of October had the maximum decreasing monthly trend (0.427 mm) but total change during 1950 to 2018 is high in month of August (234.86 mm). Moreover, the deviation is highest in August (155.44 mm) ( Table 2). Although April and May demonstrate a slightly increasing trend likewise the total change and the deviation had also exhibit slightly increase. In the district of Bageshwar, it has been observed that month of August had the decreasing trend of monthly rainfall (1.45 mm/month). And total change from 1950 to 2018 is highest in August (256 mm). Moreover, the deviation is highest in month of July (142.2 mm). Although April and May exhibit a slightly increasing trend likewise the total change and the deviation had also exhibit slightly increase. For the Pithoragarh district, month of October had the decreasing monthly trend of 0.94 mm/month. And the total change from 1950 to 2018 is highest in July (242.72 mm). Moreover, the deviation is highest in month of July (133.19 mm). Although April and May exhibit a slightly increasing trend, likewise the total change and the deviation had also exhibit slightly increase (Table 2).

Rainfall Variability Analysis of Upper Kumaon Region
The seasonal rainfall analysis reveals in-depth district-wise variability aspect of the rainfall in the Upper Kumaon region. The district-wise analysis of variability in the three consecutive periods (1950-1984, 1985-2018 and 1950-2018) elaborates more insights into the seasonal variability of rainfall over the years. It clearly demonstrated that monsoon season i.e. June to August has least variability throughout. Although in the last 34 years (1985-2018) the variability is highest of in the monsoon season (65.51 per cent). Similarly, the post-monsoon season (i.e. September to November) exhibits the most variability in all three periods. Moreover, the variability is in uenced by the variation occurred in the last 35 years . Variability of seasonal Rainfall is comparatively higher in second half (1985-2018) duration than rst duration  in all the districts (Table 3). It re ects that rainfall variability has been increased in recent past in Upper Kumaon region which further in uences the human life.
As per variability analysis of monthly rainfall it has been found that all the districts are representing that higher variability in month of November and December and lowest variability has been observed in month of July and August. Variability of monthly Rainfall is comparatively higher in second half (1985-2018) duration than duration rst duration  in all the districts (Table 4 and Fig. 3). It re ects that rainfall variability has been increased in recent past in Upper Kumaon region which further in uence human life. Champawat district is facing highest monthly rainfall variability than other districts.
Pithoragarh has least Rainfall variability. It means low altitude district (Champawat) is more prone to rainfall variability than high altitude district (Pithoragarh) in Upper Kumaon region (Table 4 and Fig. 3).

Conclusion
The seasonality, trends and variability analysis conclude the effect of climate change in the Upper Kumaon region. Most of the parts in the world experience an abrupt change in the seasonal phenomenon due to climate change over the years. Upper Kumaon region also faces similar circumstances with the variations in the rainfall and temperature yearly, seasonally and monthly. The mean monthly rainfall seems to be higher during the month of July and August and least during November-December. Although there has been a signi cant decreasing trend in total yearly rainfall over the Years with the changing rate of about 2.5 mm/year in the region. The seasonal trend of rainfall also appears to be decreasing in the region the monsoon season had the highest decreasing rate of almost 1.8 mm/year and lowest decreasing rate of nearly 0.6 mm/year during the winter season. The monthly trends of rainfall ware decreasing rapidly at the highest rate of almost 1.2 mm/year in August. It has been observed that in the recent past the uctuation in the rainfall was very extreme, cause the water scarcity or ood conditions (Negi and Joshi 2004). Although the highest seasonal variability in rainfall computed in the region is nearly 73 per cent during the post-monsoon season and the lowest seasonal variability in rainfall computed in the region is nearly 36 per cent during the monsoon season. The highest monthly variability in rainfall computed in the region is nearly 169 per cent mm/year in month of November and the lowest variability in rainfall computed in the region is nearly 42 per cent in month of August. The ndings of this study are an incentive to study the impact Tables   The Study Area